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Zhao Y, Yu ZM, Cui T, Li LD, Li YY, Qian FC, Zhou LW, Li Y, Fang QL, Huang XM, Zhang QY, Cai FH, Dong FJ, Shang DS, Li CQ, Wang QY. scBlood: A comprehensive single-cell accessible chromatin database of blood cells. Comput Struct Biotechnol J 2024; 23:2746-2753. [PMID: 39050785 PMCID: PMC11266868 DOI: 10.1016/j.csbj.2024.06.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 06/17/2024] [Accepted: 06/18/2024] [Indexed: 07/27/2024] Open
Abstract
The advent of single cell transposase-accessible chromatin sequencing (scATAC-seq) technology enables us to explore the genomic characteristics and chromatin accessibility of blood cells at the single-cell level. To fully make sense of the roles and regulatory complexities of blood cells, it is critical to collect and analyze these rapidly accumulating scATAC-seq datasets at a system level. Here, we present scBlood (https://bio.liclab.net/scBlood/), a comprehensive single-cell accessible chromatin database of blood cells. The current version of scBlood catalogs 770,907 blood cells and 452,247 non-blood cells from ∼400 high-quality scATAC-seq samples covering 30 tissues and 21 disease types. All data hosted on scBlood have undergone preprocessing from raw fastq files and multiple standards of quality control. Furthermore, we conducted comprehensive downstream analyses, including multi-sample integration analysis, cell clustering and annotation, differential chromatin accessibility analysis, functional enrichment analysis, co-accessibility analysis, gene activity score calculation, and transcription factor (TF) enrichment analysis. In summary, scBlood provides a user-friendly interface for searching, browsing, analyzing, visualizing, and downloading scATAC-seq data of interest. This platform facilitates insights into the functions and regulatory mechanisms of blood cells, as well as their involvement in blood-related diseases.
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Affiliation(s)
- Yu Zhao
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
| | - Zheng-Min Yu
- School of Computer, University of South China, Hengyang, Hunan 421001, China
| | - Ting Cui
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Li-Dong Li
- School of Computer, University of South China, Hengyang, Hunan 421001, China
| | - Yan-Yu Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Feng-Cui Qian
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Li-Wei Zhou
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Ye Li
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Qiao-Li Fang
- School of Computer, University of South China, Hengyang, Hunan 421001, China
| | - Xue-Mei Huang
- School of Computer, University of South China, Hengyang, Hunan 421001, China
| | - Qin-Yi Zhang
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Fu-Hong Cai
- School of Computer, University of South China, Hengyang, Hunan 421001, China
| | - Fu-Juan Dong
- School of Computer, University of South China, Hengyang, Hunan 421001, China
| | - De-Si Shang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Chun-Quan Li
- The First Affiliated Hospital & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Maternal and Child Health Care Hospital, National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Qiu-Yu Wang
- School of Computer, University of South China, Hengyang, Hunan 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
- Hunan Provincial Maternal and Child Health Care Hospital, National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
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2
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Alayoubi Y, Bentsen M, Looso M. Scanpro is a tool for robust proportion analysis of single-cell resolution data. Sci Rep 2024; 14:15581. [PMID: 38971877 PMCID: PMC11227528 DOI: 10.1038/s41598-024-66381-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 07/01/2024] [Indexed: 07/08/2024] Open
Abstract
In higher organisms, individual cells respond to signals and perturbations by epigenetic regulation and transcriptional adaptation. However, in addition to shifting the expression level of individual genes, the adaptive response of cells can also lead to shifts in the proportions of different cell types. Recent methods such as scRNA-seq allow for the interrogation of expression on the single-cell level, and can quantify individual cell type clusters within complex tissue samples. In order to identify clusters showing differential composition between different biological conditions, differential proportion analysis has recently been introduced. However, bioinformatics tools for robust proportion analysis of both replicated and unreplicated single-cell datasets are critically missing. In this manuscript, we present Scanpro, a modular tool for proportion analysis, seamlessly integrating into widely accepted frameworks in the Python environment. Scanpro is fast, accurate, supports datasets without replicates, and is intended to be used by bioinformatics experts and beginners alike.
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Affiliation(s)
- Yousef Alayoubi
- Bioinformatics Core Unit (BCU), Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
| | - Mette Bentsen
- Bioinformatics Core Unit (BCU), Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
| | - Mario Looso
- Bioinformatics Core Unit (BCU), Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany.
- Cardio-Pulmonary Institute (CPI), Bad Nauheim, Germany.
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Fu X, Mo S, Buendia A, Laurent A, Shao A, del Mar Alvarez-Torres M, Yu T, Tan J, Su J, Sagatelian R, Ferrando AA, Ciccia A, Lan Y, Owens DM, Palomero T, Xing EP, Rabadan R. GET: a foundation model of transcription across human cell types. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.24.559168. [PMID: 39005360 PMCID: PMC11244937 DOI: 10.1101/2023.09.24.559168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Transcriptional regulation, involving the complex interplay between regulatory sequences and proteins, directs all biological processes. Computational models of transcription lack generalizability to accurately extrapolate in unseen cell types and conditions. Here, we introduce GET, an interpretable foundation model designed to uncover regulatory grammars across 213 human fetal and adult cell types. Relying exclusively on chromatin accessibility data and sequence information, GET achieves experimental-level accuracy in predicting gene expression even in previously unseen cell types. GET showcases remarkable adaptability across new sequencing platforms and assays, enabling regulatory inference across a broad range of cell types and conditions, and uncovering universal and cell type specific transcription factor interaction networks. We evaluated its performance on prediction of regulatory activity, inference of regulatory elements and regulators, and identification of physical interactions between transcription factors. Specifically, we show GET outperforms current models in predicting lentivirus-based massive parallel reporter assay readout with reduced input data. In fetal erythroblasts, we identify distal (>1Mbp) regulatory regions that were missed by previous models. In B cells, we identified a lymphocyte-specific transcription factor-transcription factor interaction that explains the functional significance of a leukemia-risk predisposing germline mutation. In sum, we provide a generalizable and accurate model for transcription together with catalogs of gene regulation and transcription factor interactions, all with cell type specificity.
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Affiliation(s)
- Xi Fu
- Department of Systems Biology, Columbia University, New York, NY, USA
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Shentong Mo
- Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA
- Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE
| | - Alejandro Buendia
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Anouchka Laurent
- Institute for Cancer Genetics, Columbia University, New York, NY, USA
| | - Anqi Shao
- Department of Dermatology, Columbia University, New York, NY, USA
| | | | - Tianji Yu
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Jimin Tan
- Regeneron Genetics Center, Regeneron, Tarrytown, NY, USA
| | - Jiayu Su
- Department of Systems Biology, Columbia University, New York, NY, USA
| | | | - Adolfo A. Ferrando
- Department of Dermatology, Columbia University, New York, NY, USA
- Regeneron Genetics Center, Regeneron, Tarrytown, NY, USA
| | - Alberto Ciccia
- Department of Genetics and Development, Columbia University, New York, NY, USA
| | - Yanyan Lan
- Institute for AI Industry Research, Tsinghua University, Beijing, China
| | - David M. Owens
- Institute for Cancer Genetics, Columbia University, New York, NY, USA
- Department of Pathology & Cell Biology, Columbia University, New York, NY, USA
| | - Teresa Palomero
- Institute for Cancer Genetics, Columbia University, New York, NY, USA
- Department of Pathology & Cell Biology, Columbia University, New York, NY, USA
| | - Eric P. Xing
- Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA
- Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE
| | - Raul Rabadan
- Department of Systems Biology, Columbia University, New York, NY, USA
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
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Zhang X, Luo Z, Marand AP, Yan H, Jang H, Bang S, Mendieta JP, Minow MA, Schmitz RJ. A spatially resolved multiomic single-cell atlas of soybean development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.03.601616. [PMID: 39005400 PMCID: PMC11244997 DOI: 10.1101/2024.07.03.601616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Cis-regulatory elements (CREs) precisely control spatiotemporal gene expression in cells. Using a spatially resolved single-cell atlas of gene expression with chromatin accessibility across ten soybean tissues, we identified 103 distinct cell types and 303,199 accessible chromatin regions (ACRs). Nearly 40% of the ACRs showed cell-type-specific patterns and were enriched for transcription factor (TF) motifs defining diverse cell identities. We identified de novo enriched TF motifs and explored conservation of gene regulatory networks underpinning legume symbiotic nitrogen fixation. With comprehensive developmental trajectories for endosperm and embryo, we uncovered the functional transition of the three sub-cell types of endosperm, identified 13 sucrose transporters sharing the DOF11 motif that were co-up-regulated in late peripheral endosperm and identified key embryo cell-type specification regulators during embryogenesis, including a homeobox TF that promotes cotyledon parenchyma identity. This resource provides a valuable foundation for analyzing gene regulatory programs in soybean cell types across tissues and life stages.
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Affiliation(s)
- Xuan Zhang
- Department of Genetics, University of Georgia, Athens, GA, USA
- These authors contributed equally: Xuan Zhang, Ziliang Luo, Alexandre P. Marand
| | - Ziliang Luo
- Department of Genetics, University of Georgia, Athens, GA, USA
- These authors contributed equally: Xuan Zhang, Ziliang Luo, Alexandre P. Marand
| | - Alexandre P. Marand
- Department of Molecular, Cellular, and Development Biology, University of Michigan, Ann Arbor, MI, USA
- These authors contributed equally: Xuan Zhang, Ziliang Luo, Alexandre P. Marand
| | - Haidong Yan
- Department of Genetics, University of Georgia, Athens, GA, USA
- Current address: College of Grassland Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Hosung Jang
- Department of Genetics, University of Georgia, Athens, GA, USA
| | - Sohyun Bang
- Institute of Bioinformatics, University of Georgia, Athens, GA, USA
| | | | - Mark A.A. Minow
- Department of Genetics, University of Georgia, Athens, GA, USA
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5
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Li J, Fu L, Li Y, Sun W, Yi Y, Jia W, Li H, Liu H, Guo P, Wang Y, Shen Y, Zhang X, Lv Y, Qin B, Li W, Liu C, Liu L, Mazid MA, Lai Y, Esteban MA, Jiang Y, Wu L. A single-cell chromatin accessibility dataset of human primed and naïve pluripotent stem cell-derived teratoma. Sci Data 2024; 11:725. [PMID: 38956385 PMCID: PMC11220047 DOI: 10.1038/s41597-024-03558-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 06/20/2024] [Indexed: 07/04/2024] Open
Abstract
Teratoma, due to its remarkable ability to differentiate into multiple cell lineages, is a valuable model for studying human embryonic development. The similarity of the gene expression and chromatin accessibility patterns in these cells to those observed in vivo further underscores its potential as a research tool. Notably, teratomas derived from human naïve (pre-implantation epiblast-like) pluripotent stem cells (PSCs) have larger embryonic cell diversity and contain extraembryonic lineages, making them more suitable to study developmental processes. However, the cell type-specific epigenetic profiles of naïve PSC teratomas have not been yet characterized. Using single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq), we analyzed 66,384 cell profiles from five teratomas derived from human naïve PSCs and their post-implantation epiblast-like (primed) counterparts. We observed 17 distinct cell types from both embryonic and extraembryonic lineages, resembling the corresponding cell types in human fetal tissues. Additionally, we identified key transcription factors specific to different cell types. Our dataset provides a resource for investigating gene regulatory programs in a relevant model of human embryonic development.
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Affiliation(s)
- Jinxiu Li
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI Research, Shenzhen, 518083, China
- BGI Research, Hangzhou, 310030, China
| | - Lixin Fu
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI Research, Shenzhen, 518083, China
- BGI Research, Hangzhou, 310030, China
| | - Yunpan Li
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Wei Sun
- BGI Research, Shenzhen, 518083, China
- BGI Research, Hangzhou, 310030, China
- College of Life Sciences, Nankai University, Tianjin, 300071, China
| | - Yao Yi
- MRC Metabolic Diseases Unit, Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Wenqi Jia
- BGI Research, Shenzhen, 518083, China
- BGI Research, Hangzhou, 310030, China
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Haiwei Li
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health and Guangzhou Medical University, Guangzhou, Guangdong, 510530, China
| | - Hao Liu
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Pengcheng Guo
- BGI Research, Shenzhen, 518083, China
- BGI Research, Hangzhou, 310030, China
| | - Yang Wang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI Research, Hangzhou, 310030, China
| | - Yue Shen
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI Research, Shenzhen, 518083, China
- BGI Research, Changzhou, 213299, China
| | - Xiuqing Zhang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI Research, Shenzhen, 518083, China
| | - Yuan Lv
- BGI Research, Shenzhen, 518083, China
- BGI Research, Hangzhou, 310030, China
| | - Baoming Qin
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Wenjuan Li
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Chuanyu Liu
- BGI Research, Shenzhen, 518083, China
- BGI Research, Hangzhou, 310030, China
| | - Longqi Liu
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- BGI Research, Shenzhen, 518083, China
- BGI Research, Hangzhou, 310030, China
| | - Md Abdul Mazid
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
| | - Yiwei Lai
- BGI Research, Shenzhen, 518083, China
- BGI Research, Hangzhou, 310030, China
- 3DCStar lab, BGI, Shenzhen, 518083, China
| | - Miguel A Esteban
- BGI Research, Shenzhen, 518083, China
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China
- 3DCStar lab, BGI, Shenzhen, 518083, China
| | - Yu Jiang
- BGI Research, Shenzhen, 518083, China.
- BGI Research, Hangzhou, 310030, China.
- State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Institute of Zoonosis, and College of Veterinary Medicine, Jilin University, Changchun, 130062, China.
| | - Liang Wu
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, 510530, China.
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Trapnell C. Revealing gene function with statistical inference at single-cell resolution. Nat Rev Genet 2024:10.1038/s41576-024-00750-w. [PMID: 38951690 DOI: 10.1038/s41576-024-00750-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/21/2024] [Indexed: 07/03/2024]
Abstract
Single-cell and spatial molecular profiling assays have shown large gains in sensitivity, resolution and throughput. Applying these technologies to specimens from human and model organisms promises to comprehensively catalogue cell types, reveal their lineage origins in development and discern their contributions to disease pathogenesis. Moreover, rapidly dropping costs have made well-controlled perturbation experiments and cohort studies widely accessible, illuminating mechanisms that give rise to phenotypes at the scale of the cell, the tissue and the whole organism. Interpreting the coming flood of single-cell data, much of which will be spatially resolved, will place a tremendous burden on existing computational pipelines. However, statistical concepts, models, tools and algorithms can be repurposed to solve problems now arising in genetic and molecular biology studies of development and disease. Here, I review how the questions that recent technological innovations promise to answer can be addressed by the major classes of statistical tools.
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Affiliation(s)
- Cole Trapnell
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Brotman Baty Institute for Precision Medicine, University of Washington, Seattle, WA, USA.
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA.
- Seattle Hub for Synthetic Biology, Seattle, WA, USA.
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7
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Derisoud E, Jiang H, Zhao A, Chavatte-Palmer P, Deng Q. Revealing the molecular landscape of human placenta: a systematic review and meta-analysis of single-cell RNA sequencing studies. Hum Reprod Update 2024; 30:410-441. [PMID: 38478759 PMCID: PMC11215163 DOI: 10.1093/humupd/dmae006] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 02/12/2024] [Indexed: 07/02/2024] Open
Abstract
BACKGROUND With increasing significance of developmental programming effects associated with placental dysfunction, more investigations are devoted to improving the characterization and understanding of placental signatures in health and disease. The placenta is a transitory but dynamic organ adapting to the shifting demands of fetal development and available resources of the maternal supply throughout pregnancy. Trophoblasts (cytotrophoblasts, syncytiotrophoblasts, and extravillous trophoblasts) are placental-specific cell types responsible for the main placental exchanges and adaptations. Transcriptomic studies with single-cell resolution have led to advances in understanding the placenta's role in health and disease. These studies, however, often show discrepancies in characterization of the different placental cell types. OBJECTIVE AND RATIONALE We aim to review the knowledge regarding placental structure and function gained from the use of single-cell RNA sequencing (scRNAseq), followed by comparing cell-type-specific genes, highlighting their similarities and differences. Moreover, we intend to identify consensus marker genes for the various trophoblast cell types across studies. Finally, we will discuss the contributions and potential applications of scRNAseq in studying pregnancy-related diseases. SEARCH METHODS We conducted a comprehensive systematic literature review to identify different cell types and their functions at the human maternal-fetal interface, focusing on all original scRNAseq studies on placentas published before March 2023 and published reviews (total of 28 studies identified) using PubMed search. Our approach involved curating cell types and subtypes that had previously been defined using scRNAseq and comparing the genes used as markers or identified as potential new markers. Next, we reanalyzed expression matrices from the six available scRNAseq raw datasets with cell annotations (four from first trimester and two at term), using Wilcoxon rank-sum tests to compare gene expression among studies and annotate trophoblast cell markers in both first trimester and term placentas. Furthermore, we integrated scRNAseq raw data available from 18 healthy first trimester and nine term placentas, and performed clustering and differential gene expression analysis. We further compared markers obtained with the analysis of annotated and raw datasets with the literature to obtain a common signature gene list for major placental cell types. OUTCOMES Variations in the sampling site, gestational age, fetal sex, and subsequent sequencing and analysis methods were observed between the studies. Although their proportions varied, the three trophoblast types were consistently identified across all scRNAseq studies, unlike other non-trophoblast cell types. Notably, no marker genes were shared by all studies for any of the investigated cell types. Moreover, most of the newly defined markers in one study were not observed in other studies. These discrepancies were confirmed by our analysis on trophoblast cell types, where hundreds of potential marker genes were identified in each study but with little overlap across studies. From 35 461 and 23 378 cells of high quality in the first trimester and term placentas, respectively, we obtained major placental cell types, including perivascular cells that previously had not been identified in the first trimester. Importantly, our meta-analysis provides marker genes for major placental cell types based on our extensive curation. WIDER IMPLICATIONS This review and meta-analysis emphasizes the need for establishing a consensus for annotating placental cell types from scRNAseq data. The marker genes identified here can be deployed for defining human placental cell types, thereby facilitating and improving the reproducibility of trophoblast cell annotation.
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Affiliation(s)
- Emilie Derisoud
- Department of Physiology and Pharmacology, Karolinska Institutet, Solna, Stockholm, Sweden
| | - Hong Jiang
- Department of Physiology and Pharmacology, Karolinska Institutet, Solna, Stockholm, Sweden
| | - Allan Zhao
- Department of Physiology and Pharmacology, Karolinska Institutet, Solna, Stockholm, Sweden
| | - Pascale Chavatte-Palmer
- INRAE, BREED, Université Paris-Saclay, UVSQ, Jouy-en-Josas, France
- Ecole Nationale Vétérinaire d’Alfort, BREED, Maisons-Alfort, France
| | - Qiaolin Deng
- Department of Physiology and Pharmacology, Karolinska Institutet, Solna, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska University Hospital, Solna, Stockholm, Sweden
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8
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Bilous M, Hérault L, Gabriel AA, Teleman M, Gfeller D. Building and analyzing metacells in single-cell genomics data. Mol Syst Biol 2024; 20:744-766. [PMID: 38811801 PMCID: PMC11220014 DOI: 10.1038/s44320-024-00045-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/03/2024] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).
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Affiliation(s)
- Mariia Bilous
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Léonard Hérault
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Aurélie Ag Gabriel
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Matei Teleman
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland.
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland.
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland.
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Bai D, Ellington CN, Mo S, Song L, Xing EP. AttentionPert: accurately modeling multiplexed genetic perturbations with multi-scale effects. Bioinformatics 2024; 40:i453-i461. [PMID: 38940174 PMCID: PMC11211811 DOI: 10.1093/bioinformatics/btae244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION Genetic perturbations (e.g. knockouts, variants) have laid the foundation for our understanding of many diseases, implicating pathogenic mechanisms and indicating therapeutic targets. However, experimental assays are fundamentally limited by the number of measurable perturbations. Computational methods can fill this gap by predicting perturbation effects under novel conditions, but accurately predicting the transcriptional responses of cells to unseen perturbations remains a significant challenge. RESULTS We address this by developing a novel attention-based neural network, AttentionPert, which accurately predicts gene expression under multiplexed perturbations and generalizes to unseen conditions. AttentionPert integrates global and local effects in a multi-scale model, representing both the nonuniform system-wide impact of the genetic perturbation and the localized disturbance in a network of gene-gene similarities, enhancing its ability to predict nuanced transcriptional responses to both single and multi-gene perturbations. In comprehensive experiments, AttentionPert demonstrates superior performance across multiple datasets outperforming the state-of-the-art method in predicting differential gene expressions and revealing novel gene regulations. AttentionPert marks a significant improvement over current methods, particularly in handling the diversity of gene perturbations and in predicting out-of-distribution scenarios. AVAILABILITY AND IMPLEMENTATION Code is available at https://github.com/BaiDing1234/AttentionPert.
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Affiliation(s)
- Ding Bai
- Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, 00000, United Arabic Emirates
| | - Caleb N Ellington
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, 15213, United States
| | - Shentong Mo
- Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, 00000, United Arabic Emirates
| | - Le Song
- Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, 00000, United Arabic Emirates
| | - Eric P Xing
- Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, 00000, United Arabic Emirates
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, 15213, United States
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10
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Patrick R, Naval-Sanchez M, Deshpande N, Huang Y, Zhang J, Chen X, Yang Y, Tiwari K, Esmaeili M, Tran M, Mohamed AR, Wang B, Xia D, Ma J, Bayliss J, Wong K, Hun ML, Sun X, Cao B, Cottle DL, Catterall T, Barzilai-Tutsch H, Troskie RL, Chen Z, Wise AF, Saini S, Soe YM, Kumari S, Sweet MJ, Thomas HE, Smyth IM, Fletcher AL, Knoblich K, Watt MJ, Alhomrani M, Alsanie W, Quinn KM, Merson TD, Chidgey AP, Ricardo SD, Yu D, Jardé T, Cheetham SW, Marcelle C, Nilsson SK, Nguyen Q, White MD, Nefzger CM. The activity of early-life gene regulatory elements is hijacked in aging through pervasive AP-1-linked chromatin opening. Cell Metab 2024:S1550-4131(24)00231-6. [PMID: 38959897 DOI: 10.1016/j.cmet.2024.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 03/28/2024] [Accepted: 06/06/2024] [Indexed: 07/05/2024]
Abstract
A mechanistic connection between aging and development is largely unexplored. Through profiling age-related chromatin and transcriptional changes across 22 murine cell types, analyzed alongside previous mouse and human organismal maturation datasets, we uncovered a transcription factor binding site (TFBS) signature common to both processes. Early-life candidate cis-regulatory elements (cCREs), progressively losing accessibility during maturation and aging, are enriched for cell-type identity TFBSs. Conversely, cCREs gaining accessibility throughout life have a lower abundance of cell identity TFBSs but elevated activator protein 1 (AP-1) levels. We implicate TF redistribution toward these AP-1 TFBS-rich cCREs, in synergy with mild downregulation of cell identity TFs, as driving early-life cCRE accessibility loss and altering developmental and metabolic gene expression. Such remodeling can be triggered by elevating AP-1 or depleting repressive H3K27me3. We propose that AP-1-linked chromatin opening drives organismal maturation by disrupting cell identity TFBS-rich cCREs, thereby reprogramming transcriptome and cell function, a mechanism hijacked in aging through ongoing chromatin opening.
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Affiliation(s)
- Ralph Patrick
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Marina Naval-Sanchez
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Nikita Deshpande
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia; WHO Collaborating Centre for Reference and Research on Influenza, The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC 3000, Australia
| | - Yifei Huang
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Jingyu Zhang
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Xiaoli Chen
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Ying Yang
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Kanupriya Tiwari
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Mohammadhossein Esmaeili
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Minh Tran
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Amin R Mohamed
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Binxu Wang
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Di Xia
- Genome Innovation Hub, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Jun Ma
- Genome Innovation Hub, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Jacqueline Bayliss
- Department of Anatomy and Physiology, Faculty of Medicine Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Kahlia Wong
- Department of Anatomy and Developmental Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Michael L Hun
- Department of Anatomy and Developmental Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Xuan Sun
- Biomedical Manufacturing, Commonwealth Scientific and Industrial Research Organization, Melbourne, VIC, Australia; Australian Regenerative Medicine Institute, Monash University, Clayton, VIC 3800, Australia
| | - Benjamin Cao
- Biomedical Manufacturing, Commonwealth Scientific and Industrial Research Organization, Melbourne, VIC, Australia; Australian Regenerative Medicine Institute, Monash University, Clayton, VIC 3800, Australia
| | - Denny L Cottle
- Department of Anatomy and Developmental Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Tara Catterall
- St. Vincent's Institute of Medical Research, Fitzroy, VIC 3065, Australia
| | - Hila Barzilai-Tutsch
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC 3800, Australia; Institut NeuroMyoGène, University Claude Bernard Lyon 1, 69008 Lyon, France
| | - Robin-Lee Troskie
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Zhian Chen
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4102, Australia
| | - Andrea F Wise
- Department of Pharmacology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Sheetal Saini
- Department of Pharmacology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Ye Mon Soe
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4102, Australia
| | - Snehlata Kumari
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4102, Australia
| | - Matthew J Sweet
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia; Australian Infectious Diseases Research Centre, The University of Queensland, Brisbane, QLD, Australia
| | - Helen E Thomas
- St. Vincent's Institute of Medical Research, Fitzroy, VIC 3065, Australia
| | - Ian M Smyth
- Department of Anatomy and Developmental Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Anne L Fletcher
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Konstantin Knoblich
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Matthew J Watt
- Department of Anatomy and Physiology, Faculty of Medicine Dentistry and Health Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Majid Alhomrani
- Department of Clinical Laboratories Sciences, Faculty of Applied Medical Sciences, Taif University, Taif, Saudi Arabia; Research Centre for Health Sciences, Taif University, Taif, Saudi Arabia
| | - Walaa Alsanie
- Department of Clinical Laboratories Sciences, Faculty of Applied Medical Sciences, Taif University, Taif, Saudi Arabia; Research Centre for Health Sciences, Taif University, Taif, Saudi Arabia
| | - Kylie M Quinn
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia; School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC 3083, Australia
| | - Tobias D Merson
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC 3800, Australia; National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ann P Chidgey
- Department of Anatomy and Developmental Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Sharon D Ricardo
- Department of Pharmacology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia
| | - Di Yu
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4102, Australia; Ian Frazer Centre for Children's Immunotherapy Research, Child Health Research Centre, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4102, Australia
| | - Thierry Jardé
- Department of Anatomy and Developmental Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia; Development and Stem Cells Program, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia; Cancer Program, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia; Department of Surgery, Cabrini Monash University, Malvern, VIC 3144, Australia
| | - Seth W Cheetham
- Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Christophe Marcelle
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC 3800, Australia; Institut NeuroMyoGène, University Claude Bernard Lyon 1, 69008 Lyon, France
| | - Susan K Nilsson
- Biomedical Manufacturing, Commonwealth Scientific and Industrial Research Organization, Melbourne, VIC, Australia; Australian Regenerative Medicine Institute, Monash University, Clayton, VIC 3800, Australia
| | - Quan Nguyen
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia; School of Biomedical Sciences, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Melanie D White
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia; School of Biomedical Sciences, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia
| | - Christian M Nefzger
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia; Department of Anatomy and Developmental Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia; Development and Stem Cells Program, Monash Biomedicine Discovery Institute, Monash University, Clayton, VIC 3800, Australia; School of Chemistry and Molecular Biosciences, The University of Queensland, St. Lucia, Brisbane, QLD 4072, Australia.
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11
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Yu J, Yan Y, Li S, Xu Y, Parolia A, Rizvi S, Wang W, Zhai Y, Xiao R, Li X, Liao P, Zhou J, Okla K, Lin H, Lin X, Grove S, Wei S, Vatan L, Hu J, Szumilo J, Kotarski J, Freeman ZT, Skala S, Wicha M, Cho KR, Chinnaiyan AM, Schon S, Wen F, Kryczek I, Wang S, Chen L, Zou W. Progestogen-driven B7-H4 contributes to onco-fetal immune tolerance. Cell 2024:S0092-8674(24)00652-4. [PMID: 38968937 DOI: 10.1016/j.cell.2024.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 02/09/2024] [Accepted: 06/09/2024] [Indexed: 07/07/2024]
Abstract
Immune tolerance mechanisms are shared in cancer and pregnancy. Through cross-analyzing single-cell RNA-sequencing data from multiple human cancer types and the maternal-fetal interface, we found B7-H4 (VTCN1) is an onco-fetal immune tolerance checkpoint. We showed that genetic deficiency of B7-H4 resulted in immune activation and fetal resorption in allogeneic pregnancy models. Analogously, B7-H4 contributed to MPA/DMBA-induced breast cancer progression, accompanied by CD8+ T cell exhaustion. Female hormone screening revealed that progesterone stimulated B7-H4 expression in placental and breast cancer cells. Mechanistically, progesterone receptor (PR) bound to a newly identified -58 kb enhancer, thereby mediating B7-H4 transcription via the PR-P300-BRD4 axis. PR antagonist or BRD4 degrader potentiated immunotherapy in a murine B7-H4+ breast cancer model. Thus, our work unravels a mechanistic and biological connection of a female sex hormone (progesterone) to onco-fetal immune tolerance via B7-H4 and suggests that the PR-P300-BRD4 axis is targetable for treating B7-H4+ cancer.
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Affiliation(s)
- Jiali Yu
- Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA; Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, Ann Arbor, MI, USA
| | - Yijian Yan
- Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA; Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, Ann Arbor, MI, USA
| | - Shasha Li
- Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA; Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, Ann Arbor, MI, USA
| | - Ying Xu
- Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA; Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, Ann Arbor, MI, USA
| | - Abhijit Parolia
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Syed Rizvi
- Department of Chemical Engineering, University of Michigan School of Engineering, Ann Arbor, MI, USA
| | - Weichao Wang
- Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA; Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, Ann Arbor, MI, USA
| | - Yiwen Zhai
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Rongxin Xiao
- Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA; Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, Ann Arbor, MI, USA
| | - Xiong Li
- Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA; Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, Ann Arbor, MI, USA
| | - Peng Liao
- Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA; Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, Ann Arbor, MI, USA
| | - Jiajia Zhou
- Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA; Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, Ann Arbor, MI, USA
| | - Karolina Okla
- Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA; Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, Ann Arbor, MI, USA; Department of Oncological Gynecology and Gynecology, Medical University of Lublin, Lublin, Poland
| | - Heng Lin
- Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA; Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, Ann Arbor, MI, USA
| | - Xun Lin
- Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA; Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, Ann Arbor, MI, USA
| | - Sara Grove
- Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA; Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, Ann Arbor, MI, USA
| | - Shuang Wei
- Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA; Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, Ann Arbor, MI, USA
| | - Linda Vatan
- Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA; Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, Ann Arbor, MI, USA
| | - Jiantao Hu
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Justyna Szumilo
- Department of Clinical Pathomorphology, Medical University of Lublin, Lublin, Poland
| | - Jan Kotarski
- Department of Oncological Gynecology and Gynecology, Medical University of Lublin, Lublin, Poland
| | - Zachary T Freeman
- Unit for Laboratory Animal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Stephanie Skala
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Max Wicha
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Kathleen R Cho
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Arul M Chinnaiyan
- Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Michigan Center for Translational Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Howard Hughes Medical Institute, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Samantha Schon
- Department of Obstetrics and Gynecology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Fei Wen
- Department of Chemical Engineering, University of Michigan School of Engineering, Ann Arbor, MI, USA
| | - Ilona Kryczek
- Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA; Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, Ann Arbor, MI, USA
| | - Shaomeng Wang
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA; Department of Pharmacology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Lieping Chen
- Department of Immunobiology, Yale University School of Medicine, New Haven, CT, USA
| | - Weiping Zou
- Department of Surgery, University of Michigan Medical School, Ann Arbor, MI, USA; Center of Excellence for Cancer Immunology and Immunotherapy, University of Michigan Rogel Cancer Center, Ann Arbor, MI, USA; Department of Pathology, University of Michigan Medical School, Ann Arbor, MI, USA; Graduate Program in Immunology, University of Michigan, Ann Arbor, MI, USA; Graduate Program in Cancer Biology, University of Michigan, Ann Arbor, MI, USA.
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12
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Katznelson A, Hernandez B, Fahning H, Zhang J, Burton A, Torres-Padilla ME, Plachta N, Zaret KS, McCarthy RL. Heterochromatin protein ERH represses alternative cell fates during early mammalian differentiation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.06.597604. [PMID: 38895478 PMCID: PMC11185749 DOI: 10.1101/2024.06.06.597604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
During development, H3K9me3 heterochromatin is dynamically rearranged, silencing repeat elements and protein coding genes to restrict cell identity. Enhancer of Rudimentary Homolog (ERH) is an evolutionarily conserved protein originally characterized in fission yeast and recently shown to be required for H3K9me3 maintenance in human fibroblasts, but its function during development remains unknown. Here, we show that ERH is required for proper segregation of the inner cell mass and trophectoderm cell lineages during mouse development by repressing totipotent and alternative lineage programs. During human embryonic stem cell (hESC) differentiation into germ layer lineages, ERH is crucial for silencing naïve and pluripotency genes, transposable elements, and alternative lineage genes. Strikingly, ERH depletion in somatic cells reverts the H3K9me3 landscape to an hESC state and enables naïve and pluripotency gene and transposable element activation during iPSC reprogramming. Our findings reveal a role for ERH in initiation and maintenance of developmentally established gene repression.
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13
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Takeuchi F, Kato N. Ploidy inference from single-cell data: application to human and mouse cell atlases. Genetics 2024; 227:iyae061. [PMID: 38651869 PMCID: PMC11151930 DOI: 10.1093/genetics/iyae061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 03/18/2024] [Accepted: 04/09/2024] [Indexed: 04/25/2024] Open
Abstract
Ploidy is relevant to numerous biological phenomena, including development, metabolism, and tissue regeneration. Single-cell RNA-seq and other omics studies are revolutionizing our understanding of biology, yet they have largely overlooked ploidy. This is likely due to the additional assay step required for ploidy measurement. Here, we developed a statistical method to infer ploidy from single-cell ATAC-seq data, addressing this gap. When applied to data from human and mouse cell atlases, our method enabled systematic detection of polyploidy across diverse cell types. This method allows for the integration of ploidy analysis into single-cell studies. Additionally, this method can be adapted to detect the proliferating stage in the cell cycle and copy number variations in cancer cells. The software is implemented as the scPloidy package of the R software and is freely available from CRAN.
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Affiliation(s)
- Fumihiko Takeuchi
- Baker Department of Cardiometabolic Health, Melbourne Medical School, The University of Melbourne, Melbourne, VIC 3010, Australia
- Systems Genomics Laboratory, Baker Heart and Diabetes Institute, Melbourne, VIC 3004, Australia
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo 162-8655, Japan
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo 162-8655, Japan
- Department of Clinical Genome Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo 113-0033, Japan
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14
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Lindhout FW, Krienen FM, Pollard KS, Lancaster MA. A molecular and cellular perspective on human brain evolution and tempo. Nature 2024; 630:596-608. [PMID: 38898293 DOI: 10.1038/s41586-024-07521-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 04/29/2024] [Indexed: 06/21/2024]
Abstract
The evolution of the modern human brain was accompanied by distinct molecular and cellular specializations, which underpin our diverse cognitive abilities but also increase our susceptibility to neurological diseases. These features, some specific to humans and others shared with related species, manifest during different stages of brain development. In this multi-stage process, neural stem cells proliferate to produce a large and diverse progenitor pool, giving rise to excitatory or inhibitory neurons that integrate into circuits during further maturation. This process unfolds over varying time scales across species and has progressively become slower in the human lineage, with differences in tempo correlating with differences in brain size, cell number and diversity, and connectivity. Here we introduce the terms 'bradychrony' and 'tachycrony' to describe slowed and accelerated developmental tempos, respectively. We review how recent technical advances across disciplines, including advanced engineering of in vitro models, functional comparative genetics and high-throughput single-cell profiling, are leading to a deeper understanding of how specializations of the human brain arise during bradychronic neurodevelopment. Emerging insights point to a central role for genetics, gene-regulatory networks, cellular innovations and developmental tempo, which together contribute to the establishment of human specializations during various stages of neurodevelopment and at different points in evolution.
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Affiliation(s)
- Feline W Lindhout
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge, UK.
| | - Fenna M Krienen
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Katherine S Pollard
- Gladstone Institutes, San Francisco, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
- Department of Epidemiology & Biostatistics, Institute for Computational Health Sciences, and Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Madeline A Lancaster
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge, UK.
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15
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Benchmarking of single-cell ATAC sequencing tools. Nat Biotechnol 2024; 42:856-857. [PMID: 37537503 DOI: 10.1038/s41587-023-01897-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
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16
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De Rop FV, Hulselmans G, Flerin C, Soler-Vila P, Rafels A, Christiaens V, González-Blas CB, Marchese D, Caratù G, Poovathingal S, Rozenblatt-Rosen O, Slyper M, Luo W, Muus C, Duarte F, Shrestha R, Bagdatli ST, Corces MR, Mamanova L, Knights A, Meyer KB, Mulqueen R, Taherinasab A, Maschmeyer P, Pezoldt J, Lambert CLG, Iglesias M, Najle SR, Dossani ZY, Martelotto LG, Burkett Z, Lebofsky R, Martin-Subero JI, Pillai S, Sebé-Pedrós A, Deplancke B, Teichmann SA, Ludwig LS, Braun TP, Adey AC, Greenleaf WJ, Buenrostro JD, Regev A, Aerts S, Heyn H. Systematic benchmarking of single-cell ATAC-sequencing protocols. Nat Biotechnol 2024; 42:916-926. [PMID: 37537502 PMCID: PMC11180611 DOI: 10.1038/s41587-023-01881-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 06/22/2023] [Indexed: 08/05/2023]
Abstract
Single-cell assay for transposase-accessible chromatin by sequencing (scATAC-seq) has emerged as a powerful tool for dissecting regulatory landscapes and cellular heterogeneity. However, an exploration of systemic biases among scATAC-seq technologies has remained absent. In this study, we benchmark the performance of eight scATAC-seq methods across 47 experiments using human peripheral blood mononuclear cells (PBMCs) as a reference sample and develop PUMATAC, a universal preprocessing pipeline, to handle the various sequencing data formats. Our analyses reveal significant differences in sequencing library complexity and tagmentation specificity, which impact cell-type annotation, genotype demultiplexing, peak calling, differential region accessibility and transcription factor motif enrichment. Our findings underscore the importance of sample extraction, method selection, data processing and total cost of experiments, offering valuable guidance for future research. Finally, our data and analysis pipeline encompasses 169,000 PBMC scATAC-seq profiles and a best practices code repository for scATAC-seq data analysis, which are freely available to extend this benchmarking effort to future protocols.
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Grants
- R01 DA047237 NIDA NIH HHS
- R00 AG059918 NIA NIH HHS
- U19 AI057266 NIAID NIH HHS
- G0B5619N Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)
- RF1 MH128842 NIMH NIH HHS
- UM1 HG009436 NHGRI NIH HHS
- 1S80920N Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)
- UM1 HG012076 NHGRI NIH HHS
- RM1 HG007735 NHGRI NIH HHS
- G094121N Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)
- R35 GM124704 NIGMS NIH HHS
- UM1 HG009442 NHGRI NIH HHS
- Wellcome Trust
- H.H. received support for the project PID2020-115439GB-I00- funded by MCIN/AEI/ 10.13039/501100011033. This publication is also supported as part of a project (BCLLATLAS and ESPACE) that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement No 810287 and 874710).
- M.R.C. is supported by the National Institutes on Aging K99/R00AG059918.
- K.B.M. is supported by Wellcome (WT211276/Z/18/Z and Sanger core grant WT206194).
- S.A.T. is supported by Wellcome (WT211276/Z/18/Z and Sanger core grant WT206194).
- This work was supported by funding from the Rita Allen Foundation (W.J.G.), the Human Frontiers Science (RGY006S) (W.J.G.). W.J.G. is a Chan Zuckerberg Biohub investigator and acknowledges grants 2017-174468 and 2018-182817 from the Chan Zuckerberg Initiative, and the National Institutes of Health grants RM1-HG007735, UM1-HG009442, UM1-HG009436, R01- HG00990901, and U19- AI057266 (to W.J.G.). W.J.G. acknowledges funding from Emerson Collective.
- This work was supported by an ERC Consolidator Grant to S.A. (no. 724226_cis- CONTROL), KU Leuven (grant no. C14/22/125 to S.A.), Foundation Against Cancer (grant no, F/2020/1396 to S.A.), F.W.O. (grants G0I2722N, G0B5619N and G094121N to S.A.), Aligning Science Across Parkinson’s (ASAP, grant no. ASAP-000430 to S.A.)
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Affiliation(s)
- Florian V De Rop
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Gert Hulselmans
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Chris Flerin
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Paula Soler-Vila
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Albert Rafels
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Valerie Christiaens
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Carmen Bravo González-Blas
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Domenica Marchese
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Ginevra Caratù
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | | | | | | | - Wendy Luo
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Fabiana Duarte
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Rojesh Shrestha
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - M Ryan Corces
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
| | | | | | | | - Ryan Mulqueen
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Akram Taherinasab
- Division of Hematology & Medical Oncology, Knight Cancer Institute, Oregon Health & Sciences University, Portland, OR, USA
- Division of Oncologic Sciences, Knight Cancer Institute, Oregon Health & Sciences University, Portland, OR, USA
| | - Patrick Maschmeyer
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
| | - Jörn Pezoldt
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Camille Lucie Germaine Lambert
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Marta Iglesias
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Sebastián R Najle
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Zain Y Dossani
- Vitalant Research Institute, San Francisco, CA, USA
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
| | - Luciano G Martelotto
- Adelaide Centre for Epigenetics and the South Australian Immunogenomics Cancer Institute, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- University of Melbourne Centre for Cancer Research, Victoria Comprehensive Cancer Centre, Melbourne, Victoria, Australia
| | - Zach Burkett
- Digital Biology Group, Bio-Rad, Pleasanton, CA, USA
| | | | - José Ignacio Martin-Subero
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Departament de Fonaments Clínics, Facultat de Medicina, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Satish Pillai
- Vitalant Research Institute, San Francisco, CA, USA
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
| | - Arnau Sebé-Pedrós
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- ICREA, Barcelona, Spain
| | - Bart Deplancke
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Cambridge, UK
- Department of Physics/Cavendish Laboratory, University of Cambridge, Cambridge, UK
| | - Leif S Ludwig
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
| | - Theodore P Braun
- Division of Hematology & Medical Oncology, Knight Cancer Institute, Oregon Health & Sciences University, Portland, OR, USA
- Division of Oncologic Sciences, Knight Cancer Institute, Oregon Health & Sciences University, Portland, OR, USA
| | - Andrew C Adey
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - William J Greenleaf
- Department of Genetics, Stanford University, Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Jason D Buenrostro
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Koch Institute of Integrative Cancer Research, Cambridge, MA, USA
- Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Stein Aerts
- VIB Center for Brain and Disease Research, Leuven, Belgium.
- Department of Human Genetics, KU Leuven, Leuven, Belgium.
| | - Holger Heyn
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, Spain.
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17
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Tayyebi Z, Pine AR, Leslie CS. Scalable and unbiased sequence-informed embedding of single-cell ATAC-seq data with CellSpace. Nat Methods 2024; 21:1014-1022. [PMID: 38724693 PMCID: PMC11166566 DOI: 10.1038/s41592-024-02274-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 04/11/2024] [Indexed: 06/13/2024]
Abstract
Standard scATAC sequencing (scATAC-seq) analysis pipelines represent cells as sparse numeric vectors relative to an atlas of peaks or genomic tiles and consequently ignore genomic sequence information at accessible loci. Here we present CellSpace, an efficient and scalable sequence-informed embedding algorithm for scATAC-seq that learns a mapping of DNA k-mers and cells to the same space, to address this limitation. We show that CellSpace captures meaningful latent structure in scATAC-seq datasets, including cell subpopulations and developmental hierarchies, and can score transcription factor activities in single cells based on proximity to binding motifs embedded in the same space. Importantly, CellSpace implicitly mitigates batch effects arising from multiple samples, donors or assays, even when individual datasets are processed relative to different peak atlases. Thus, CellSpace provides a powerful tool for integrating and interpreting large-scale scATAC-seq compendia.
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Affiliation(s)
- Zakieh Tayyebi
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, USA
| | - Allison R Pine
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY, USA
| | - Christina S Leslie
- Computational and Systems Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
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18
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Wang G, Wu H, Zhai X, Zhang L, Zhang C, Cheng C, Xu X, Gao E, Xiong X, Zhang J, Liu Z. Kidney Organoid Modeling of WT1 Mutations Reveals Key Regulatory Paths Underlying Podocyte Development. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2308556. [PMID: 38810140 DOI: 10.1002/advs.202308556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 04/16/2024] [Indexed: 05/31/2024]
Abstract
Wilms tumor-1(WT1) is a crucial transcription factor that regulates podocyte development. However, the epigenomic mechanism underlying the function of WT1 during podocyte development has yet to be fully elucidated. Here, single-cell chromatin accessibility and gene expression maps of foetal kidneys and kidney organoids are generated. Functional implications of WT1-targeted genes, which are crucial for the development of podocytes and the maintenance of their structure, including BMPER/PAX2/MAGI2 that regulates WNT signaling pathway, MYH9 that maintains actin filament organization and NPHS1 that modulates cell junction assembly are identified. To further illustrate the functional importance of WT1-mediated transcriptional regulation during podocyte development, cultured and implanted patient-derived kidney organoids derived from the Induced Pluripotent Stem Cell (iPSCs) of a patient with a heterozygous missense mutation in WT1 are generated. Results from single-cell RNA sequencing (scRNA-seq) and functional assays confirm that the WT1 mutation leads to delays in podocyte development and causes damage to cell structures, due to its failure to activate the targeting genes MAGI2, MYH9, and NPHS1. Notably, correcting the mutation in the patient iPSCs using CRISPR-Cas9 gene editing rescues the podocyte phenotype. Collectively, this work elucidates the WT1-related epigenomic landscape with respect to human podocyte development and identifies the disease-causing role of a WT1 mutation.
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Affiliation(s)
- Gang Wang
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu, 210002, China
| | - Hangdi Wu
- Department of Basic Medical Sciences, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China
| | - Xiuwen Zhai
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu, 210002, China
| | - Li Zhang
- Department of Basic Medical Sciences, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, 311121, China
- Center for Stem Cell and Regenerative Medicine, Department of Basic Medical Sciences & The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China
| | - Changming Zhang
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu, 210002, China
| | - Chen Cheng
- Department of Basic Medical Sciences, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, 311121, China
- Center for Stem Cell and Regenerative Medicine, Department of Basic Medical Sciences & The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China
| | - Xiaodong Xu
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu, 210002, China
| | - Erzhi Gao
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu, 210002, China
| | - Xushen Xiong
- Department of Basic Medical Sciences, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, 311121, China
- State Key Laboratory of Transvascular Implantation Devices, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 311121, China
| | - Jin Zhang
- Department of Basic Medical Sciences, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, 311121, China
- Center for Stem Cell and Regenerative Medicine, Department of Basic Medical Sciences & The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China
- Hematology Institute, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Zhihong Liu
- National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu, 210002, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, 311121, China
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19
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Pratt HE, Andrews G, Shedd N, Phalke N, Li T, Pampari A, Jensen M, Wen C, Consortium P, Gandal MJ, Geschwind DH, Gerstein M, Moore J, Kundaje A, Colubri A, Weng Z. Using a comprehensive atlas and predictive models to reveal the complexity and evolution of brain-active regulatory elements. SCIENCE ADVANCES 2024; 10:eadj4452. [PMID: 38781344 PMCID: PMC11114231 DOI: 10.1126/sciadv.adj4452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 04/25/2024] [Indexed: 05/25/2024]
Abstract
Most genetic variants associated with psychiatric disorders are located in noncoding regions of the genome. To investigate their functional implications, we integrate epigenetic data from the PsychENCODE Consortium and other published sources to construct a comprehensive atlas of candidate brain cis-regulatory elements. Using deep learning, we model these elements' sequence syntax and predict how binding sites for lineage-specific transcription factors contribute to cell type-specific gene regulation in various types of glia and neurons. The elements' evolutionary history suggests that new regulatory information in the brain emerges primarily via smaller sequence mutations within conserved mammalian elements rather than entirely new human- or primate-specific sequences. However, primate-specific candidate elements, particularly those active during fetal brain development and in excitatory neurons and astrocytes, are implicated in the heritability of brain-related human traits. Additionally, we introduce PsychSCREEN, a web-based platform offering interactive visualization of PsychENCODE-generated genetic and epigenetic data from diverse brain cell types in individuals with psychiatric disorders and healthy controls.
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Affiliation(s)
- Henry E. Pratt
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Gregory Andrews
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Nicole Shedd
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Nishigandha Phalke
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Tongxin Li
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
- Khoury College of Computer Science, Northeastern University, Boston, MA 02115, USA
| | - Anusri Pampari
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Matthew Jensen
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Cindy Wen
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | | | - Michael J. Gandal
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Daniel H. Geschwind
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Institute of Precision Health, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Mark Gerstein
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, New Haven, CT 06520, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA
| | - Jill Moore
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Anshul Kundaje
- Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
| | - Andrés Colubri
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Zhiping Weng
- Department of Genomics and Computational Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
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20
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Mendieta JP, Tu X, Jiang D, Yan H, Zhang X, Marand AP, Zhong S, Schmitz RJ. Investigating the cis-Regulatory Basis of C 3 and C 4 Photosynthesis in Grasses at Single-Cell Resolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.05.574340. [PMID: 38405933 PMCID: PMC10888913 DOI: 10.1101/2024.01.05.574340] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
While considerable knowledge exists about the enzymes pivotal for C4 photosynthesis, much less is known about the cis-regulation important for specifying their expression in distinct cell types. Here, we use single-cell-indexed ATAC-seq to identify cell-type-specific accessible chromatin regions (ACRs) associated with C4 enzymes for five different grass species. This study spans four C4 species, covering three distinct photosynthetic subtypes: Zea mays and Sorghum bicolor (NADP-ME), Panicum miliaceum (NAD-ME), Urochloa fusca (PEPCK), along with the C3 outgroup Oryza sativa. We studied the cis-regulatory landscape of enzymes essential across all C4 species and those unique to C4 subtypes, measuring cell-type-specific biases for C4 enzymes using chromatin accessibility data. Integrating these data with phylogenetics revealed diverse co-option of gene family members between species, showcasing the various paths of C4 evolution. Besides promoter proximal ACRs, we found that, on average, C4 genes have two to three distal cell-type-specific ACRs, highlighting the complexity and divergent nature of C4 evolution. Examining the evolutionary history of these cell-type-specific ACRs revealed a spectrum of conserved and novel ACRs, even among closely related species, indicating ongoing evolution of cis-regulation at these C4 loci. This study illuminates the dynamic and complex nature of CRE evolution in C4 photosynthesis, particularly highlighting the intricate cis-regulatory evolution of key loci. Our findings offer a valuable resource for future investigations, potentially aiding in the optimization of C3 crop performance under changing climatic conditions.
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Affiliation(s)
| | - Xiaoyu Tu
- Joint Center for Single Cell Biology, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Daiquan Jiang
- State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong
| | - Haidong Yan
- Department of Genetics, University of Georgia
| | - Xuan Zhang
- Department of Genetics, University of Georgia
| | - Alexandre P Marand
- Department of Genetics, University of Georgia
- Department of Molecular, Cellular, and Development Biology, University of Michigan
| | - Silin Zhong
- State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong
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21
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Siraj L, Castro RI, Dewey H, Kales S, Nguyen TTL, Kanai M, Berenzy D, Mouri K, Wang QS, McCaw ZR, Gosai SJ, Aguet F, Cui R, Vockley CM, Lareau CA, Okada Y, Gusev A, Jones TR, Lander ES, Sabeti PC, Finucane HK, Reilly SK, Ulirsch JC, Tewhey R. Functional dissection of complex and molecular trait variants at single nucleotide resolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.05.592437. [PMID: 38766054 PMCID: PMC11100724 DOI: 10.1101/2024.05.05.592437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Identifying the causal variants and mechanisms that drive complex traits and diseases remains a core problem in human genetics. The majority of these variants have individually weak effects and lie in non-coding gene-regulatory elements where we lack a complete understanding of how single nucleotide alterations modulate transcriptional processes to affect human phenotypes. To address this, we measured the activity of 221,412 trait-associated variants that had been statistically fine-mapped using a Massively Parallel Reporter Assay (MPRA) in 5 diverse cell-types. We show that MPRA is able to discriminate between likely causal variants and controls, identifying 12,025 regulatory variants with high precision. Although the effects of these variants largely agree with orthogonal measures of function, only 69% can plausibly be explained by the disruption of a known transcription factor (TF) binding motif. We dissect the mechanisms of 136 variants using saturation mutagenesis and assign impacted TFs for 91% of variants without a clear canonical mechanism. Finally, we provide evidence that epistasis is prevalent for variants in close proximity and identify multiple functional variants on the same haplotype at a small, but important, subset of trait-associated loci. Overall, our study provides a systematic functional characterization of likely causal common variants underlying complex and molecular human traits, enabling new insights into the regulatory grammar underlying disease risk.
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Affiliation(s)
- Layla Siraj
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Program in Biophysics, Harvard Graduate School of Arts and Sciences, Boston, MA, USA
- Harvard-Massachusetts Institute of Technology MD/PhD Program, Harvard Medical School, Boston, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | | | | | | | | | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA
| | | | | | - Qingbo S. Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
| | | | - Sager J. Gosai
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - François Aguet
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
| | - Ran Cui
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Caleb A. Lareau
- Program in Computational and Systems Biology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Alexander Gusev
- Harvard Medical School and Dana-Farber Cancer Institute, Boston, MA, USA
| | - Thouis R. Jones
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Eric S. Lander
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biology, MIT, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Pardis C. Sabeti
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Hilary K. Finucane
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
| | - Steven K. Reilly
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - Jacob C. Ulirsch
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA USA
- Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA, USA
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
| | - Ryan Tewhey
- The Jackson Laboratory, Bar Harbor, ME, USA
- Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME, USA
- Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA, USA
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22
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Mannens CCA, Hu L, Lönnerberg P, Schipper M, Reagor CC, Li X, He X, Barker RA, Sundström E, Posthuma D, Linnarsson S. Chromatin accessibility during human first-trimester neurodevelopment. Nature 2024:10.1038/s41586-024-07234-1. [PMID: 38693260 DOI: 10.1038/s41586-024-07234-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 02/02/2024] [Indexed: 05/03/2024]
Abstract
The human brain develops through a tightly organized cascade of patterning events, induced by transcription factor expression and changes in chromatin accessibility. Although gene expression across the developing brain has been described at single-cell resolution1, similar atlases of chromatin accessibility have been primarily focused on the forebrain2-4. Here we describe chromatin accessibility and paired gene expression across the entire developing human brain during the first trimester (6-13 weeks after conception). We defined 135 clusters and used multiomic measurements to link candidate cis-regulatory elements to gene expression. The number of accessible regions increased both with age and along neuronal differentiation. Using a convolutional neural network, we identified putative functional transcription factor-binding sites in enhancers characterizing neuronal subtypes. We applied this model to cis-regulatory elements linked to ESRRB to elucidate its activation mechanism in the Purkinje cell lineage. Finally, by linking disease-associated single nucleotide polymorphisms to cis-regulatory elements, we validated putative pathogenic mechanisms in several diseases and identified midbrain-derived GABAergic neurons as being the most vulnerable to major depressive disorder-related mutations. Our findings provide a more detailed view of key gene regulatory mechanisms underlying the emergence of brain cell types during the first trimester and a comprehensive reference for future studies related to human neurodevelopment.
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Affiliation(s)
- Camiel C A Mannens
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden
| | - Lijuan Hu
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden
| | - Peter Lönnerberg
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden
| | - Marijn Schipper
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Caleb C Reagor
- Howard Hughes Medical Institute and Laboratory of Sensory Neuroscience, The Rockefeller University, New York, NY, USA
| | - Xiaofei Li
- Division of Neurodegeneration, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden
| | - Xiaoling He
- John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Roger A Barker
- John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Erik Sundström
- Division of Neurodegeneration, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sten Linnarsson
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden.
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23
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Cui X, Chen X, Li Z, Gao Z, Chen S, Jiang R. Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity. NATURE COMPUTATIONAL SCIENCE 2024; 4:346-359. [PMID: 38730185 DOI: 10.1038/s43588-024-00625-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 04/05/2024] [Indexed: 05/12/2024]
Abstract
Single-cell epigenomic data has been growing continuously at an unprecedented pace, but their characteristics such as high dimensionality and sparsity pose substantial challenges to downstream analysis. Although deep learning models-especially variational autoencoders-have been widely used to capture low-dimensional feature embeddings, the prevalent Gaussian assumption somewhat disagrees with real data, and these models tend to struggle to incorporate reference information from abundant cell atlases. Here we propose CASTLE, a deep generative model based on the vector-quantized variational autoencoder framework to extract discrete latent embeddings that interpretably characterize single-cell chromatin accessibility sequencing data. We validate the performance and robustness of CASTLE for accurate cell-type identification and reasonable visualization compared with state-of-the-art methods. We demonstrate the advantages of CASTLE for effective incorporation of existing massive reference datasets in a weakly supervised or supervised manner. We further demonstrate CASTLE's capacity for intuitively distilling cell-type-specific feature spectra that unveil cell heterogeneity and biological implications quantitatively.
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Affiliation(s)
- Xuejian Cui
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, China
| | - Xiaoyang Chen
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, China
| | - Zhen Li
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, China
| | - Zijing Gao
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, China
| | - Shengquan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China.
| | - Rui Jiang
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, China.
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24
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Kosicki M, Cintrón DL, Page NF, Georgakopoulos-Soares I, Akiyama JA, Plajzer-Frick I, Novak CS, Kato M, Hunter RD, von Maydell K, Barton S, Godfrey P, Beckman E, Sanders SJ, Pennacchio LA, Ahituv N. Massively parallel reporter assays and mouse transgenic assays provide complementary information about neuronal enhancer activity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590634. [PMID: 38712228 PMCID: PMC11071441 DOI: 10.1101/2024.04.22.590634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Genetic studies find hundreds of thousands of noncoding variants associated with psychiatric disorders. Massively parallel reporter assays (MPRAs) and in vivo transgenic mouse assays can be used to assay the impact of these variants. However, the relevance of MPRAs to in vivo function is unknown and transgenic assays suffer from low throughput. Here, we studied the utility of combining the two assays to study the impact of non-coding variants. We carried out an MPRA on over 50,000 sequences derived from enhancers validated in transgenic mouse assays and from multiple fetal neuronal ATAC-seq datasets. We also tested over 20,000 variants, including synthetic mutations in highly active neuronal enhancers and 177 common variants associated with psychiatric disorders. Variants with a high impact on MPRA activity were further tested in mice. We found a strong and specific correlation between MPRA and mouse neuronal enhancer activity including changes in neuronal enhancer activity in mouse embryos for variants with strong MPRA effects. Mouse assays also revealed pleiotropic variant effects that could not be observed in MPRA. Our work provides a large catalog of functional neuronal enhancers and variant effects and highlights the effectiveness of combining MPRAs and mouse transgenic assays.
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Affiliation(s)
- Michael Kosicki
- Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Dianne Laboy Cintrón
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94158, USA
| | - Nicholas F. Page
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Psychiatry and Behavioral Sciences, Kavli Institute for Fundamental Neuroscience, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Ilias Georgakopoulos-Soares
- Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA 17033, USA
| | - Jennifer A. Akiyama
- Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Ingrid Plajzer-Frick
- Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Catherine S. Novak
- Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Momoe Kato
- Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Riana D. Hunter
- Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Kianna von Maydell
- Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Sarah Barton
- Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Patrick Godfrey
- Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Erik Beckman
- Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Stephan J. Sanders
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94158, USA
- Department of Psychiatry and Behavioral Sciences, Kavli Institute for Fundamental Neuroscience, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Institute of Developmental and Regenerative Medicine, Department of Paediatrics, University of Oxford, Oxford, OX3 16 7TY, UK
| | - Len A. Pennacchio
- Environmental Genomics & System Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Nadav Ahituv
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA 94158, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94158, USA
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25
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Choi J, Lee SM, Norwitz ER, Kim JH, Jung YM, Park CW, Jun JK, Lee D, Jin Y, Kim S, Cha B, Park JS, Kim JI. Placental expression quantitative trait loci in an East Asian population. HGG ADVANCES 2024; 5:100276. [PMID: 38310352 PMCID: PMC10883826 DOI: 10.1016/j.xhgg.2024.100276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 01/29/2024] [Accepted: 01/29/2024] [Indexed: 02/05/2024] Open
Abstract
Expression quantitative trait loci (eQTL) analysis measures the contribution of genetic variation in gene expression on complex traits. Although this methodology has been used to examine gene regulation in numerous human tissues, eQTL research in solid tissues is relatively lacking. We conducted eQTL analysis on placentas collected from an East Asian population in an effort to identify gene regulatory mechanisms in this tissue. Placentas (n = 102) were collected at the time of cesarean delivery. mRNA was extracted, sequenced with NGS, and compared with matched maternal and fetal DNA arrays performed using maternal and neonatal cord blood. Linear regression modeling was performed using tensorQTL. Fine-mapping along with epigenomic annotation was used to select putative functional variants. We identified 2,703 coding genes that contained at least one eQTL with statistical significance (false discovery rate <0.05). After fine-mapping, we found 108 previously unreported eQTL variants with posterior inclusion probability >0.1. Of these, 19% were located in genomic regions with evidence from public placental epigenome suggesting that they may be functionally relevant. For example, variant rs28379289 located in the placenta-specific regulatory region changes the binding affinity of transcription factor leading to higher expression of LGALS3, which is known to affect placental function. This study expands the knowledge base of regulatory elements within the human placenta and identifies 108 previously unreported placenta eQTL signals, which are listed in our publicly available GMI eQTL database. Further studies are needed to identify and characterize genetic regulatory mechanisms that affect placental function in normal pregnancy and placenta-related diseases.
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Affiliation(s)
- Jaeyong Choi
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea; Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Korea
| | - Seung Mi Lee
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea
| | | | - Ji Hoi Kim
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea
| | - Young Mi Jung
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea
| | - Chan-Wook Park
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea
| | - Jong Kwan Jun
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea
| | - Dakyung Lee
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea; Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Korea
| | - Yongjoon Jin
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea; Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Korea
| | - Sookyung Kim
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Korea
| | - Bukyoung Cha
- Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Korea
| | - Joong Shin Park
- Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul, Korea.
| | - Jong-Il Kim
- Department of Biomedical Sciences, Seoul National University College of Medicine, Seoul, Korea; Genomic Medicine Institute, Medical Research Center, Seoul National University, Seoul, Korea.
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26
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Zhang X, Marand AP, Yan H, Schmitz RJ. scifi-ATAC-seq: massive-scale single-cell chromatin accessibility sequencing using combinatorial fluidic indexing. Genome Biol 2024; 25:90. [PMID: 38589969 PMCID: PMC11003106 DOI: 10.1186/s13059-024-03235-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 04/01/2024] [Indexed: 04/10/2024] Open
Abstract
Single-cell ATAC-seq has emerged as a powerful approach for revealing candidate cis-regulatory elements genome-wide at cell-type resolution. However, current single-cell methods suffer from limited throughput and high costs. Here, we present a novel technique called scifi-ATAC-seq, single-cell combinatorial fluidic indexing ATAC-sequencing, which combines a barcoded Tn5 pre-indexing step with droplet-based single-cell ATAC-seq using the 10X Genomics platform. With scifi-ATAC-seq, up to 200,000 nuclei across multiple samples can be indexed in a single emulsion reaction, representing an approximately 20-fold increase in throughput compared to the standard 10X Genomics workflow.
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Affiliation(s)
- Xuan Zhang
- Department of Genetics, University of Georgia, Athens, GA, USA
| | - Alexandre P Marand
- Department of Genetics, University of Georgia, Athens, GA, USA
- Current address: Department of Molecular, Cellular, and Development Biology, University of Michigan, Ann Arbor, MI, USA
| | - Haidong Yan
- Department of Genetics, University of Georgia, Athens, GA, USA
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27
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Cao Y, Zhao X, Tang S, Jiang Q, Li S, Li S, Chen S. scButterfly: a versatile single-cell cross-modality translation method via dual-aligned variational autoencoders. Nat Commun 2024; 15:2973. [PMID: 38582890 PMCID: PMC10998864 DOI: 10.1038/s41467-024-47418-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Accepted: 03/28/2024] [Indexed: 04/08/2024] Open
Abstract
Recent advancements for simultaneously profiling multi-omics modalities within individual cells have enabled the interrogation of cellular heterogeneity and molecular hierarchy. However, technical limitations lead to highly noisy multi-modal data and substantial costs. Although computational methods have been proposed to translate single-cell data across modalities, broad applications of the methods still remain impeded by formidable challenges. Here, we propose scButterfly, a versatile single-cell cross-modality translation method based on dual-aligned variational autoencoders and data augmentation schemes. With comprehensive experiments on multiple datasets, we provide compelling evidence of scButterfly's superiority over baseline methods in preserving cellular heterogeneity while translating datasets of various contexts and in revealing cell type-specific biological insights. Besides, we demonstrate the extensive applications of scButterfly for integrative multi-omics analysis of single-modality data, data enhancement of poor-quality single-cell multi-omics, and automatic cell type annotation of scATAC-seq data. Moreover, scButterfly can be generalized to unpaired data training, perturbation-response analysis, and consecutive translation.
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Affiliation(s)
- Yichuan Cao
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China
| | - Xiamiao Zhao
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China
| | - Songming Tang
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China
| | - Qun Jiang
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, 100084, Beijing, China
| | - Sijie Li
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China
| | - Siyu Li
- School of Statistics and Data Science, Nankai University, Tianjin, 300071, China
| | - Shengquan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, 300071, China.
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28
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Ye F, Zhang S, Fu Y, Yang L, Zhang G, Wu Y, Pan J, Chen H, Wang X, Ma L, Niu H, Jiang M, Zhang T, Jia D, Wang J, Wang Y, Han X, Guo G. Fast and flexible profiling of chromatin accessibility and total RNA expression in single nuclei using Microwell-seq3. Cell Discov 2024; 10:33. [PMID: 38531851 DOI: 10.1038/s41421-023-00642-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 12/21/2023] [Indexed: 03/28/2024] Open
Abstract
Single cell chromatin accessibility profiling and transcriptome sequencing are the most widely used technologies for single-cell genomics. Here, we present Microwell-seq3, a high-throughput and facile platform for high-sensitivity single-nucleus chromatin accessibility or full-length transcriptome profiling. The method combines a preindexing strategy and a penetrable chip-in-a-tube for single nucleus loading and DNA amplification and therefore does not require specialized equipment. We used Microwell-seq3 to profile chromatin accessibility in more than 200,000 single nuclei and the full-length transcriptome in ~50,000 nuclei from multiple adult mouse tissues. Compared with the existing polyadenylated transcript capture methods, integrative analysis of cell type-specific regulatory elements and total RNA expression uncovered comprehensive cell type heterogeneity in the brain. Gene regulatory networks based on chromatin accessibility profiling provided an improved cell type communication model. Finally, we demonstrated that Microwell-seq3 can identify malignant cells and their specific regulons in spontaneous lung tumors of aged mice. We envision a broad application of Microwell-seq3 in many areas of research.
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Affiliation(s)
- Fang Ye
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Shuang Zhang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yuting Fu
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lei Yang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Guodong Zhang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yijun Wu
- Department of Thyroid Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jun Pan
- Department of Thyroid Surgery, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Haide Chen
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xinru Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lifeng Ma
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Haofu Niu
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Mengmeng Jiang
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Tingyue Zhang
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Danmei Jia
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Jingjing Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yongcheng Wang
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaoping Han
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Guoji Guo
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
- Liangzhu Laboratory, Zhejiang University, Hangzhou, Zhejiang, China.
- Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
- Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou, Zhejiang, China.
- Institute of Hematology, Zhejiang University, Hangzhou, Zhejiang, China.
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29
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Zhang G, Fu Y, Yang L, Ye F, Zhang P, Zhang S, Ma L, Li J, Wu H, Han X, Wang J, Guo G. Construction of single-cell cross-species chromatin accessibility landscapes with combinatorial-hybridization-based ATAC-seq. Dev Cell 2024; 59:793-811.e8. [PMID: 38330939 DOI: 10.1016/j.devcel.2024.01.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 11/03/2023] [Accepted: 01/18/2024] [Indexed: 02/10/2024]
Abstract
Despite recent advances in single-cell genomics, the lack of maps for single-cell candidate cis-regulatory elements (cCREs) in non-mammal species has limited our exploration of conserved regulatory programs across vertebrates and invertebrates. Here, we developed a combinatorial-hybridization-based method for single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) named CH-ATAC-seq, enabling the construction of single-cell accessible chromatin landscapes for zebrafish, Drosophila, and earthworms (Eisenia andrei). By integrating scATAC censuses of humans, monkeys, and mice, we systematically identified 152 distinct main cell types and around 0.8 million cell-type-specific cCREs. Our analysis provided insights into the conservation of neural, muscle, and immune lineages across species, while epithelial cells exhibited a higher organ-origin heterogeneity. Additionally, a large-scale gene regulatory network (GRN) was constructed in four vertebrates by integrating scRNA-seq censuses. Overall, our study provides a valuable resource for comparative epigenomics, identifying the evolutionary conservation and divergence of gene regulation across different species.
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Affiliation(s)
- Guodong Zhang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China; Liangzhu Laboratory, Zhejiang University, Hangzhou 311121, China
| | - Yuting Fu
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Lei Yang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Fang Ye
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China; Liangzhu Laboratory, Zhejiang University, Hangzhou 311121, China
| | - Peijing Zhang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Shuang Zhang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Lifeng Ma
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Jiaqi Li
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Hanyu Wu
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China
| | - Xiaoping Han
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China; Zhejiang Provincial Key Laboratory for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou 310058, China.
| | - Jingjing Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China; Liangzhu Laboratory, Zhejiang University, Hangzhou 311121, China.
| | - Guoji Guo
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou 310000, China; Liangzhu Laboratory, Zhejiang University, Hangzhou 311121, China; Zhejiang Provincial Key Laboratory for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou 310058, China; Institute of Hematology, Zhejiang University, Hangzhou, China.
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30
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Zhang H, Mulqueen RM, Iannuzo N, Farrera DO, Polverino F, Galligan JJ, Ledford JG, Adey AC, Cusanovich DA. txci-ATAC-seq: a massive-scale single-cell technique to profile chromatin accessibility. Genome Biol 2024; 25:78. [PMID: 38519979 PMCID: PMC10958877 DOI: 10.1186/s13059-023-03150-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 12/20/2023] [Indexed: 03/25/2024] Open
Abstract
We develop a large-scale single-cell ATAC-seq method by combining Tn5-based pre-indexing with 10× Genomics barcoding, enabling the indexing of up to 200,000 nuclei across multiple samples in a single reaction. We profile 449,953 nuclei across diverse tissues, including the human cortex, mouse brain, human lung, mouse lung, mouse liver, and lung tissue from a club cell secretory protein knockout (CC16-/-) model. Our study of CC16-/- nuclei uncovers previously underappreciated technical artifacts derived from remnant 129 mouse strain genetic material, which cause profound cell-type-specific changes in regulatory elements near many genes, thereby confounding the interpretation of this commonly referenced mouse model.
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Affiliation(s)
- Hao Zhang
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA
| | - Ryan M Mulqueen
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Natalie Iannuzo
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA
| | - Dominique O Farrera
- Department of Pharmacology and Toxicology, University of Arizona, Tucson, AZ, USA
| | - Francesca Polverino
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Arizona, Tucson, AZ, USA
- Banner - University Medicine North, Pulmonary - Clinic F, Tucson, AZ, USA
| | - James J Galligan
- Department of Pharmacology and Toxicology, University of Arizona, Tucson, AZ, USA
| | - Julie G Ledford
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA
| | - Andrew C Adey
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA.
- Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR, USA.
- Oregon Health & Science University, Knight Cancer Institute, Portland, OR, USA.
- Oregon Health & Science University, Knight Cardiovascular Institute, Portland, OR, USA.
| | - Darren A Cusanovich
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA.
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA.
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Wagner C, Guilliams M. Time and place: mapping human prenatal macrophages across tissues. Cell Res 2024; 34:191-192. [PMID: 37964002 PMCID: PMC10907690 DOI: 10.1038/s41422-023-00895-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023] Open
Affiliation(s)
- Camille Wagner
- Laboratory of Myeloid Cell Biology in Tissue Homeostasis and Regeneration, VIB Center for Inflammation Research, Technologiepark-Zwijnaarde 71, Ghent, Belgium.
- Department of Biomedical Molecular Biology, Faculty of Science, Ghent University, Ghent, Belgium.
| | - Martin Guilliams
- Laboratory of Myeloid Cell Biology in Tissue Homeostasis and Regeneration, VIB Center for Inflammation Research, Technologiepark-Zwijnaarde 71, Ghent, Belgium.
- Department of Biomedical Molecular Biology, Faculty of Science, Ghent University, Ghent, Belgium.
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32
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Pahl MC, Liu L, Pippin JA, Wagley Y, Boehm K, Hankenson KD, Wells AD, Yang W, Grant SFA. Variant to gene mapping for carpal tunnel syndrome risk loci implicates skeletal muscle regulatory elements. EBioMedicine 2024; 101:105038. [PMID: 38417377 PMCID: PMC10909706 DOI: 10.1016/j.ebiom.2024.105038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 02/13/2024] [Accepted: 02/14/2024] [Indexed: 03/01/2024] Open
Abstract
BACKGROUND Carpal tunnel syndrome (CTS) is a common disorder caused by compression of the median nerve in the wrist, resulting in pain and numbness throughout the hand and forearm. While multiple behavioural and physiological factors influence CTS risk, a growing body of evidence supports a strong genetic contribution. Recent genome-wide association study (GWAS) efforts have reported 53 independent signals associated with CTS. While GWAS can identify genetic loci conferring risk, it does not determine which cell types drive the genetic aetiology of the trait, which variants are "causal" at a given signal, and which effector genes correspond to these non-coding variants. These obstacles limit interpretation of potential disease mechanisms. METHODS We analysed CTS GWAS findings in the context of chromatin conformation between gene promoters and accessible chromatin regions across cellular models of bone, skeletal muscle, adipocytes and neurons. We identified proxy variants in high LD with the lead CTS sentinel SNPs residing in promoter connected open chromatin in the skeletal muscle and bone contexts. FINDINGS We detected significant enrichment for heritability in skeletal muscle myotubes, as well as a weaker correlation in human mesenchymal stem cell-derived osteoblasts. In myotubes, our approach implicated 117 genes contacting 60 proxy variants corresponding to 20 of the 53 GWAS signals. In the osteoblast context we implicated 30 genes contacting 24 proxy variants coinciding with 12 signals, of which 19 genes shared. We subsequently prioritized BZW2 as a candidate effector gene in CTS and implicated it as novel gene that perturbs myocyte differentiation in vitro. INTERPRETATION Taken together our results suggest that the CTS genetic component influences the size, integrity, and organization of multiple tissues surrounding the carpal tunnel, in particular muscle and bone, to predispose the nerve to being compressed in this disease setting. FUNDING This work was supported by NIH Grant UM1 DK126194 (SFAG and WY), R01AG072705 (SFAG & KDH) and the Center for Spatial and Functional Genomics at CHOP (SFAG & ADW). SFAG is supported by the Daniel B. Burke Endowed Chair for Diabetes Research. WY is supported by the Perelman School of Medicine of the University of Pennsylvania.
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Affiliation(s)
- Matthew C Pahl
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Lin Liu
- Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Cell and Developmental Biology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Institute for Diabetes, Obesity & Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia PA19104, USA
| | - James A Pippin
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Yadav Wagley
- Orthopaedic Research Laboratories, Department of Orthopaedic Surgery, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Keith Boehm
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Kurt D Hankenson
- Orthopaedic Research Laboratories, Department of Orthopaedic Surgery, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Andrew D Wells
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, 3615 Civic Center Boulevard, Philadelphia, PA, USA; Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, 3615 Civic Center Boulevard, Philadelphia, PA, USA
| | - Wenli Yang
- Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Cell and Developmental Biology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Institute for Diabetes, Obesity & Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia PA19104, USA.
| | - Struan F A Grant
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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33
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Suzuki K, Hatzikotoulas K, Southam L, Taylor HJ, Yin X, Lorenz KM, Mandla R, Huerta-Chagoya A, Melloni GEM, Kanoni S, Rayner NW, Bocher O, Arruda AL, Sonehara K, Namba S, Lee SSK, Preuss MH, Petty LE, Schroeder P, Vanderwerff B, Kals M, Bragg F, Lin K, Guo X, Zhang W, Yao J, Kim YJ, Graff M, Takeuchi F, Nano J, Lamri A, Nakatochi M, Moon S, Scott RA, Cook JP, Lee JJ, Pan I, Taliun D, Parra EJ, Chai JF, Bielak LF, Tabara Y, Hai Y, Thorleifsson G, Grarup N, Sofer T, Wuttke M, Sarnowski C, Gieger C, Nousome D, Trompet S, Kwak SH, Long J, Sun M, Tong L, Chen WM, Nongmaithem SS, Noordam R, Lim VJY, Tam CHT, Joo YY, Chen CH, Raffield LM, Prins BP, Nicolas A, Yanek LR, Chen G, Brody JA, Kabagambe E, An P, Xiang AH, Choi HS, Cade BE, Tan J, Broadaway KA, Williamson A, Kamali Z, Cui J, Thangam M, Adair LS, Adeyemo A, Aguilar-Salinas CA, Ahluwalia TS, Anand SS, Bertoni A, Bork-Jensen J, Brandslund I, Buchanan TA, Burant CF, Butterworth AS, Canouil M, Chan JCN, Chang LC, Chee ML, Chen J, Chen SH, Chen YT, Chen Z, Chuang LM, Cushman M, Danesh J, Das SK, de Silva HJ, Dedoussis G, Dimitrov L, Doumatey AP, Du S, Duan Q, Eckardt KU, Emery LS, Evans DS, Evans MK, Fischer K, Floyd JS, Ford I, Franco OH, Frayling TM, Freedman BI, Genter P, Gerstein HC, Giedraitis V, González-Villalpando C, González-Villalpando ME, Gordon-Larsen P, Gross M, Guare LA, Hackinger S, Hakaste L, Han S, Hattersley AT, Herder C, Horikoshi M, Howard AG, Hsueh W, Huang M, Huang W, Hung YJ, Hwang MY, Hwu CM, Ichihara S, Ikram MA, Ingelsson M, Islam MT, Isono M, Jang HM, Jasmine F, Jiang G, Jonas JB, Jørgensen T, Kamanu FK, Kandeel FR, Kasturiratne A, Katsuya T, Kaur V, Kawaguchi T, Keaton JM, Kho AN, Khor CC, Kibriya MG, Kim DH, Kronenberg F, Kuusisto J, Läll K, Lange LA, Lee KM, Lee MS, Lee NR, Leong A, Li L, Li Y, Li-Gao R, Ligthart S, Lindgren CM, Linneberg A, Liu CT, Liu J, Locke AE, Louie T, Luan J, Luk AO, Luo X, Lv J, Lynch JA, Lyssenko V, Maeda S, Mamakou V, Mansuri SR, Matsuda K, Meitinger T, Melander O, Metspalu A, Mo H, Morris AD, Moura FA, Nadler JL, Nalls MA, Nayak U, Ntalla I, Okada Y, Orozco L, Patel SR, Patil S, Pei P, Pereira MA, Peters A, Pirie FJ, Polikowsky HG, Porneala B, Prasad G, Rasmussen-Torvik LJ, Reiner AP, Roden M, Rohde R, Roll K, Sabanayagam C, Sandow K, Sankareswaran A, Sattar N, Schönherr S, Shahriar M, Shen B, Shi J, Shin DM, Shojima N, Smith JA, So WY, Stančáková A, Steinthorsdottir V, Stilp AM, Strauch K, Taylor KD, Thorand B, Thorsteinsdottir U, Tomlinson B, Tran TC, Tsai FJ, Tuomilehto J, Tusie-Luna T, Udler MS, Valladares-Salgado A, van Dam RM, van Klinken JB, Varma R, Wacher-Rodarte N, Wheeler E, Wickremasinghe AR, van Dijk KW, Witte DR, Yajnik CS, Yamamoto K, Yamamoto K, Yoon K, Yu C, Yuan JM, Yusuf S, Zawistowski M, Zhang L, Zheng W, Raffel LJ, Igase M, Ipp E, Redline S, Cho YS, Lind L, Province MA, Fornage M, Hanis CL, Ingelsson E, Zonderman AB, Psaty BM, Wang YX, Rotimi CN, Becker DM, Matsuda F, Liu Y, Yokota M, Kardia SLR, Peyser PA, Pankow JS, Engert JC, Bonnefond A, Froguel P, Wilson JG, Sheu WHH, Wu JY, Hayes MG, Ma RCW, Wong TY, Mook-Kanamori DO, Tuomi T, Chandak GR, Collins FS, Bharadwaj D, Paré G, Sale MM, Ahsan H, Motala AA, Shu XO, Park KS, Jukema JW, Cruz M, Chen YDI, Rich SS, McKean-Cowdin R, Grallert H, Cheng CY, Ghanbari M, Tai ES, Dupuis J, Kato N, Laakso M, Köttgen A, Koh WP, Bowden DW, Palmer CNA, Kooner JS, Kooperberg C, Liu S, North KE, Saleheen D, Hansen T, Pedersen O, Wareham NJ, Lee J, Kim BJ, Millwood IY, Walters RG, Stefansson K, Ahlqvist E, Goodarzi MO, Mohlke KL, Langenberg C, Haiman CA, Loos RJF, Florez JC, Rader DJ, Ritchie MD, Zöllner S, Mägi R, Marston NA, Ruff CT, van Heel DA, Finer S, Denny JC, Yamauchi T, Kadowaki T, Chambers JC, Ng MCY, Sim X, Below JE, Tsao PS, Chang KM, McCarthy MI, Meigs JB, Mahajan A, Spracklen CN, Mercader JM, Boehnke M, Rotter JI, Vujkovic M, Voight BF, Morris AP, Zeggini E. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature 2024; 627:347-357. [PMID: 38374256 PMCID: PMC10937372 DOI: 10.1038/s41586-024-07019-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 01/03/2024] [Indexed: 02/21/2024]
Abstract
Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10-8) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores5 in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.
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Affiliation(s)
- Ken Suzuki
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Konstantinos Hatzikotoulas
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
| | - Lorraine Southam
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Henry J Taylor
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Xianyong Yin
- Department of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Kim M Lorenz
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Ravi Mandla
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Alicia Huerta-Chagoya
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Giorgio E M Melloni
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Stavroula Kanoni
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Nigel W Rayner
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ozvan Bocher
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Ana Luiza Arruda
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Graduate School of Experimental Medicine, Technical University of Munich, Munich, Germany
- Munich School for Data Science, Helmholtz Munich, Neuherberg, Germany
| | - Kyuto Sonehara
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Shinichi Namba
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Simon S K Lee
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael H Preuss
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lauren E Petty
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Philip Schroeder
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Brett Vanderwerff
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Mart Kals
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Fiona Bragg
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Kuang Lin
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Xiuqing Guo
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London NorthWest Healthcare NHS Trust, London, UK
| | - Jie Yao
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Young Jin Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Fumihiko Takeuchi
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Jana Nano
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Amel Lamri
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
| | - Masahiro Nakatochi
- Public Health Informatics Unit, Department of Integrated Health Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Sanghoon Moon
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | - Robert A Scott
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - James P Cook
- Department of Health Data Science, University of Liverpool, Liverpool, UK
| | - Jung-Jin Lee
- Division of Translational Medicine and Human Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Ian Pan
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Daniel Taliun
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Esteban J Parra
- Department of Anthropology, University of Toronto at Mississauga, Mississauga, Ontario, Canada
| | - Jin-Fang Chai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Lawrence F Bielak
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yang Hai
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | | | - Niels Grarup
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tamar Sofer
- Department of Biostatistics, Harvard University, Boston, MA, USA
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Department of Medicine, Harvard University, Boston, MA, USA
| | - Matthias Wuttke
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Chloé Sarnowski
- Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Darryl Nousome
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Soo-Heon Kwak
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Jirong Long
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Meng Sun
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Lin Tong
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, University of Chicago, Chicago, IL, USA
| | - Wei-Min Chen
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Suraj S Nongmaithem
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
| | - Raymond Noordam
- Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Victor J Y Lim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Claudia H T Tam
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Chinese University of Hong Kong, Hong Kong, China
| | - Yoonjung Yoonie Joo
- Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Samsung Medical Center, Seoul, South Korea
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Chien-Hsiun Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Bram Peter Prins
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Aude Nicolas
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
| | - Lisa R Yanek
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jennifer A Brody
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Edmond Kabagambe
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Division of Academics, Ochsner Health, New Orleans, LA, USA
| | - Ping An
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA
| | - Anny H Xiang
- Department of Research and Evaluation, Division of Biostatistics Research, Kaiser Permanente of Southern California, Pasadena, CA, USA
| | - Hyeok Sun Choi
- Department of Biomedical Science, Hallym University, Chuncheon, South Korea
| | - Brian E Cade
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Jingyi Tan
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - K Alaine Broadaway
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alice Williamson
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Metabolic Research Laboratories, Wellcome Trust-Medical Research Council Institute of Metabolic Science, Department of Clinical Biochemistry, University of Cambridge, Cambridge, UK
| | - Zoha Kamali
- Department of Epidemiology, University of Groningen, University Medical Centre Groningen, Groningen, The Netherlands
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Jinrui Cui
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Manonanthini Thangam
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Linda S Adair
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Adebowale Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Carlos A Aguilar-Salinas
- Unidad de Investigación en Enfermedades Metabólicas and Departamento de Endocrinología y Metabolismo, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
| | - Tarunveer S Ahluwalia
- Steno Diabetes Center Copenhagen, Herlev, Denmark
- Bioinformatics Center, Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Sonia S Anand
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Alain Bertoni
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Jette Bork-Jensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ivan Brandslund
- Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark
- Department of Clinical Biochemistry, Vejle Hospital, Vejle, Denmark
| | - Thomas A Buchanan
- Department of Medicine, Division of Endocrinology and Diabetes, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Charles F Burant
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus, University of Cambridge, Hinxton, UK
- National Institute for Health and Care Research (NIHR) Blood and Transplant Unit (BTRU) in Donor Health and Behaviour, Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Mickaël Canouil
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
- University of Lille, Lille, France
| | - Juliana C N Chan
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, Chinese University of Hong Kong, Hong Kong, China
| | - Li-Ching Chang
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Miao-Li Chee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Ji Chen
- Exeter Centre of Excellence in Diabetes (ExCEeD), Exeter Medical School, University of Exeter, Exeter, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Shyh-Huei Chen
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Yuan-Tsong Chen
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - Zhengming Chen
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Lee-Ming Chuang
- Division of Endocrinology and Metabolism, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Mary Cushman
- Department of Medicine, University of Vermont, Colchester, VT, USA
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus, University of Cambridge, Hinxton, UK
- National Institute for Health and Care Research (NIHR) Blood and Transplant Unit (BTRU) in Donor Health and Behaviour, Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Swapan K Das
- Section of Endocrinology and Metabolism, Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - H Janaka de Silva
- Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama, Sri Lanka
| | - George Dedoussis
- Department of Nutrition and Dietetics, Harokopio University of Athens, Athens, Greece
| | - Latchezar Dimitrov
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Ayo P Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Shufa Du
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Qing Duan
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kai-Uwe Eckardt
- Department of Nephrology and Medical Intensive Care Medicine, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Department of Nephrology and Hypertension, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Leslie S Emery
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Daniel S Evans
- California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Krista Fischer
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - James S Floyd
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Ian Ford
- Robertson Centre for Biostatistics, University of Glasgow, Glasgow, UK
| | - Oscar H Franco
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Timothy M Frayling
- Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Barry I Freedman
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Pauline Genter
- Department of Medicine, Division of Endocrinology and Metabolism, Lundquist Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Hertzel C Gerstein
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Clicerio González-Villalpando
- Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Publica, Mexico City, Mexico
| | - Maria Elena González-Villalpando
- Centro de Estudios en Diabetes, Unidad de Investigacion en Diabetes y Riesgo Cardiovascular, Centro de Investigacion en Salud Poblacional, Instituto Nacional de Salud Publica, Mexico City, Mexico
| | - Penny Gordon-Larsen
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Myron Gross
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Lindsay A Guare
- Genomics and Computational Biology Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sophie Hackinger
- Department of Human Genetics, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Liisa Hakaste
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
| | - Sohee Han
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, South Korea
| | | | - Christian Herder
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Momoko Horikoshi
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Annie-Green Howard
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Willa Hsueh
- Department of Internal Medicine, Diabetes and Metabolism Research Center, Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Mengna Huang
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
- Center for Global Cardiometabolic Health, Brown University, Providence, RI, USA
| | - Wei Huang
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Yi-Jen Hung
- Division of Endocrine and Metabolism, Tri-Service General Hospital Songshan Branch, Taipei, Taiwan
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
| | - Mi Yeong Hwang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Chii-Min Hwu
- Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Sahoko Ichihara
- Department of Environmental and Preventive Medicine, Jichi Medical University School of Medicine, Shimotsuke, Japan
| | - Mohammad Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Martin Ingelsson
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | | | - Masato Isono
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Hye-Mi Jang
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Farzana Jasmine
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, University of Chicago, Chicago, IL, USA
| | - Guozhi Jiang
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Chinese University of Hong Kong, Hong Kong, China
| | - Jost B Jonas
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
| | - Torben Jørgensen
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Faculty of Medicine, Aalborg University, Aalborg, Denmark
| | - Frederick K Kamanu
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Fouad R Kandeel
- Department of Clinical Diabetes, Endocrinology and Metabolism, Department of Translational Research and Cellular Therapeutics, City of Hope, Duarte, CA, USA
| | | | - Tomohiro Katsuya
- Department of Clinical Gene Therapy, Osaka University Graduate School of Medicine, Osaka, Japan
- Department of Geriatric and General Medicine, Graduate School of Medicine, Osaka University, Osaka, Japan
| | - Varinderpal Kaur
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Takahisa Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Jacob M Keaton
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Abel N Kho
- Division of General Internal Medicine and Geriatrics, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Health Information Partnerships, Institute for Public Health and Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Chiea-Chuen Khor
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
| | - Muhammad G Kibriya
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, University of Chicago, Chicago, IL, USA
| | - Duk-Hwan Kim
- Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Suwon, South Korea
| | - Florian Kronenberg
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Leslie A Lange
- Department of Medicine, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO, USA
| | - Kyung Min Lee
- VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Myung-Shik Lee
- Soochunhyang Institute of Medi-bio Science and Division of Endocrinology, Department of Internal Medicine, Soochunhyang University College of Medicine, Cheonan, South Korea
- Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Nanette R Lee
- USC-Office of Population Studies Foundation, University of San Carlos, Cebu City, Philippines
| | - Aaron Leong
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Yun Li
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ruifang Li-Gao
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Symen Ligthart
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Cecilia M Lindgren
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Allan Linneberg
- Center for Clinical Research and Prevention, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ching-Ti Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jianjun Liu
- Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Adam E Locke
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
- Department of Medicine, Division of Genomics and Bioinformatics, Washington University School of Medicine, St Louis, MO, USA
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Tin Louie
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Andrea O Luk
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Chinese University of Hong Kong, Hong Kong, China
| | - Xi Luo
- Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Julie A Lynch
- VA Salt Lake City Health Care System, Salt Lake City, UT, USA
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Valeriya Lyssenko
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University Diabetes Centre, Malmö, Sweden
- Department of Clinical Science, Center for Diabetes Research, University of Bergen, Bergen, Norway
| | - Shiro Maeda
- Laboratory for Genomics of Diabetes and Metabolism, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Advanced Genomic and Laboratory Medicine, Graduate School of Medicine, University of the Ryukyus, Nishihara, Japan
- Division of Clinical Laboratory and Blood Transfusion, University of the Ryukyus Hospital, Nishihara, Japan
| | - Vasiliki Mamakou
- Dromokaiteio Psychiatric Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Sohail Rafik Mansuri
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Koichi Matsuda
- Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan
| | - Thomas Meitinger
- Institute of Human Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Institute of Human Genetics, Technical University Munich, Munich, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Olle Melander
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Huan Mo
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Andrew D Morris
- Usher Institute to the Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Filipe A Moura
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jerry L Nadler
- Department of Medicine and Pharmacology, New York Medical College, Valhalla, NY, USA
| | - Michael A Nalls
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA
- Data Tecnica International, Glen Echo, MD, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Uma Nayak
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Ioanna Ntalla
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Genome Informatics, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Premium Research Institute for Human Metaverse Medicine (WPI-PRIMe), Osaka University, Suita, Japan
| | - Lorena Orozco
- Instituto Nacional de Medicina Genómica, Mexico City, Mexico
| | - Sanjay R Patel
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Snehal Patil
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Mark A Pereira
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- German Centre for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Fraser J Pirie
- Department of Diabetes and Endocrinology, Nelson R. Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Hannah G Polikowsky
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bianca Porneala
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Gauri Prasad
- Academy of Scientific and Innovative Research, CSIR-Human Resource Development Campus, Ghaziabad, India
- Genomics and Molecular Medicine Unit, CSIR-Institute of Genomics and Integrative Biology, New Delhi, India
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Michael Roden
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Rebecca Rohde
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Katheryn Roll
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Charumathi Sabanayagam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Kevin Sandow
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Alagu Sankareswaran
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India
| | - Naveed Sattar
- School of Cardiovascular and Metabolic Health, University of Glasgow, Glasgow, UK
| | - Sebastian Schönherr
- Institute of Genetic Epidemiology, Medical University of Innsbruck, Innsbruck, Austria
| | - Mohammad Shahriar
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, University of Chicago, Chicago, IL, USA
| | - Botong Shen
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Jinxiu Shi
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai, China
| | - Dong Mun Shin
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Nobuhiro Shojima
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Jennifer A Smith
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Wing Yee So
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, Chinese University of Hong Kong, Hong Kong, China
| | - Alena Stančáková
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | | | - Adrienne M Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Konstantin Strauch
- Institute of Genetic Epidemiology, Helmholtz Zentrum Munchen, German Research Center for Environmental Health, Neuherberg, Germany
- Institute for Medical Biostatistics, Epidemiology, and Informatics (IMBEI), University Medical Center, Johannes Gutenberg University, Mainz, Germany
- Chair of Genetic Epidemiology, Institute of Medical Information Processing, Biometry, and Epidemiology, Faculty of Medicine, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Kent D Taylor
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Unnur Thorsteinsdottir
- deCODE Genetics, Amgen, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Brian Tomlinson
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
- Faculty of Medicine, Macau University of Science and Technology, Macau, China
| | - Tam C Tran
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Fuu-Jen Tsai
- Department of Medical Genetics and Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Jaakko Tuomilehto
- Population Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland
- National School of Public Health, Madrid, Spain
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Diabetes Research Group, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Teresa Tusie-Luna
- Unidad de Biología Molecular y Medicina Genómica, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico
- Departamento de Medicina Genómica y Toxiología Ambiental, Instituto de Investigaciones Biomédicas, UNAM, Mexico City, Mexico
| | - Miriam S Udler
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Adan Valladares-Salgado
- Unidad de Investigacion Medica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Rob M van Dam
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Jan B van Klinken
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Clinical Chemistry, Laboratory of Genetic Metabolic Disease, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Rohit Varma
- Southern California Eye Institute, CHA Hollywood Presbyterian Hospital, Los Angeles, CA, USA
| | - Niels Wacher-Rodarte
- Unidad de Investigación Médica en Epidemiologia Clinica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Eleanor Wheeler
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | | | - Ko Willems van Dijk
- Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Internal Medicine, Division of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands
| | - Daniel R Witte
- Department of Public Health, Aarhus University, Aarhus, Denmark
- Danish Diabetes Academy, Odense, Denmark
| | - Chittaranjan S Yajnik
- Diabetology Research Centre, King Edward Memorial Hospital and Research Centre, Pune, India
| | - Ken Yamamoto
- Department of Medical Biochemistry, Kurume University School of Medicine, Kurume, Japan
| | - Kenichi Yamamoto
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan
- Department of Pediatrics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Kyungheon Yoon
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China
| | - Jian-Min Yuan
- Division of Cancer Control and Population Sciences, UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Salim Yusuf
- Department of Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Matthew Zawistowski
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Liang Zhang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Leslie J Raffel
- Department of Pediatrics, Division of Genetic and Genomic Medicine, UCI Irvine School of Medicine, Irvine, CA, USA
| | - Michiya Igase
- Department of Anti-Aging Medicine, Ehime University Graduate School of Medicine, Touon, Japan
| | - Eli Ipp
- Department of Medicine, Division of Endocrinology and Metabolism, Lundquist Research Institute at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Division of Pulmonary, Critical Care, and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Yoon Shin Cho
- Department of Biomedical Science, Hallym University, Chuncheon, South Korea
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Michael A Province
- Division of Statistical Genomics, Washington University School of Medicine, St Louis, MO, USA
| | - Myriam Fornage
- Institute of Molecular Medicine, University of Texas Health Science Center at Houston School of Public Health, Houston, TX, USA
| | - Craig L Hanis
- Human Genetics Center, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Erik Ingelsson
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
- Department of Health Systems and Population Health, University of Washington, Seattle, WA, USA
| | - Ya-Xing Wang
- Beijing Institute of Ophthalmology, Ophthalmology and Visual Sciences Key Laboratory, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Diane M Becker
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Medicine, Division of Cardiology, Duke University School of Medicine, Durham, NC, USA
| | | | - Sharon L R Kardia
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Patricia A Peyser
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - James C Engert
- Department of Medicine, McGill University, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Amélie Bonnefond
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
- University of Lille, Lille, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Philippe Froguel
- Inserm U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, Lille University Hospital, Lille, France
- University of Lille, Lille, France
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - James G Wilson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Wayne H H Sheu
- School of Medicine, National Defense Medical Center, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Division of Endocrinology and Metabolism, Department of Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Jer-Yuarn Wu
- Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
| | - M Geoffrey Hayes
- Division of Endocrinology, Metabolism and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Anthropology, Northwestern University, Evanston, IL, USA
| | - Ronald C W Ma
- Department of Medicine and Therapeutics, Chinese University of Hong Kong, Hong Kong, China
- Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, Chinese University of Hong Kong, Hong Kong, China
- Hong Kong Institute of Diabetes and Obesity, Chinese University of Hong Kong, Hong Kong, China
| | - Tien-Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Dennis O Mook-Kanamori
- Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Tiinamaija Tuomi
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
- Department of Endocrinology, Helsinki University Hospital, Helsinki, Finland
| | - Giriraj R Chandak
- Genomic Research on Complex Diseases (GRC-Group), CSIR-Centre for Cellular and Molecular Biology (CSIR-CCMB), Hyderabad, India
- Science and Engineering Research Board (SERB), Department of Science and Technology, Ministry of Science and Technology, Government of India, New Delhi, India
| | - Francis S Collins
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Dwaipayan Bharadwaj
- Systems Genomics Laboratory, School of Biotechnology, Jawaharlal Nehru University, New Delhi, India
| | - Guillaume Paré
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, Ontario, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Michèle M Sale
- Department of Public Health Sciences and Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Habibul Ahsan
- Institute for Population and Precision Health (IPPH), Biological Sciences Division, University of Chicago, Chicago, IL, USA
| | - Ayesha A Motala
- Department of Diabetes and Endocrinology, Nelson R. Mandela School of Medicine, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kyong-Soo Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
| | - Miguel Cruz
- Unidad de Investigacion Medica en Bioquimica, Hospital de Especialidades, Centro Medico Nacional Siglo XXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Yii-Der Ida Chen
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Roberta McKean-Cowdin
- Department of Population and Public Health Sciences, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Harald Grallert
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - E-Shyong Tai
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Josee Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Norihiro Kato
- Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland and Kuopio University Hospital, Kuopio, Finland
| | - Anna Köttgen
- Institute of Genetic Epidemiology, Department of Data Driven Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Woon-Puay Koh
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Singapore
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Donald W Bowden
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Colin N A Palmer
- Pat Macpherson Centre for Pharmacogenetics and Pharmacogenomics, University of Dundee, Dundee, UK
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London NorthWest Healthcare NHS Trust, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | | | - Simin Liu
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
- Center for Global Cardiometabolic Health, Brown University, Providence, RI, USA
- Department of Medicine, Brown University Alpert School of Medicine, Providence, RI, USA
| | - Kari E North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Danish Saleheen
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Department of Cardiology, Columbia University Irving Medical Center, New York, NY, USA
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Oluf Pedersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Juyoung Lee
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Bong-Jo Kim
- Division of Genome Science, Department of Precision Medicine, National Institute of Health, Cheongju-si, Korea
| | - Iona Y Millwood
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Robin G Walters
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, UK
| | - Kari Stefansson
- deCODE Genetics, Amgen, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Emma Ahlqvist
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Mark O Goodarzi
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité-Universitätsmedizin, Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
| | - Christopher A Haiman
- Department of Preventive Medicine, Keck School of Medicine of USC, Los Angeles, CA, USA
| | - Ruth J F Loos
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Mindich Child Health and Development Institute, Ichan School of Medicine at Mount Sinai, New York, NY, USA
| | - Jose C Florez
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Daniel J Rader
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Division of Translational Medicine and Therapeutics, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Center for Precision Medicine, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Sebastian Zöllner
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Nicholas A Marston
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Christian T Ruff
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Sarah Finer
- Institute for Population Health Sciences, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Joshua C Denny
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
- All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Toshimasa Yamauchi
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
| | - Takashi Kadowaki
- Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, University of Tokyo, Tokyo, Japan
- Toranomon Hospital, Tokyo, Japan
| | - John C Chambers
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Department of Cardiology, Ealing Hospital, London NorthWest Healthcare NHS Trust, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Maggie C Y Ng
- Center for Genomics and Personalized Medicine Research, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xueling Sim
- Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore
| | - Jennifer E Below
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Philip S Tsao
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- VA Palo Alto Health Care System, Palo Alto, CA, USA
- Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Kyong-Mi Chang
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - James B Meigs
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
- Genentech, South San Francisco, CA, USA
| | - Cassandra N Spracklen
- Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, MA, USA
| | - Josep M Mercader
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, Department of Pediatrics, Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Marijana Vujkovic
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Benjamin F Voight
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
| | - Andrew P Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Division of Musculoskeletal and Dermatological Sciences, University of Manchester, Manchester, UK.
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
| | - Eleftheria Zeggini
- Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
- TUM School of Medicine and Health, Technical University of Munich and Klinikum Rechts der Isar, Munich, Germany.
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Arthur TD, Nguyen JP, D'Antonio-Chronowska A, Matsui H, Silva NS, Joshua IN, Luchessi AD, Greenwald WWY, D'Antonio M, Pera MF, Frazer KA. Complex regulatory networks influence pluripotent cell state transitions in human iPSCs. Nat Commun 2024; 15:1664. [PMID: 38395976 PMCID: PMC10891157 DOI: 10.1038/s41467-024-45506-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 01/26/2024] [Indexed: 02/25/2024] Open
Abstract
Stem cells exist in vitro in a spectrum of interconvertible pluripotent states. Analyzing hundreds of hiPSCs derived from different individuals, we show the proportions of these pluripotent states vary considerably across lines. We discover 13 gene network modules (GNMs) and 13 regulatory network modules (RNMs), which are highly correlated with each other suggesting that the coordinated co-accessibility of regulatory elements in the RNMs likely underlie the coordinated expression of genes in the GNMs. Epigenetic analyses reveal that regulatory networks underlying self-renewal and pluripotency are more complex than previously realized. Genetic analyses identify thousands of regulatory variants that overlapped predicted transcription factor binding sites and are associated with chromatin accessibility in the hiPSCs. We show that the master regulator of pluripotency, the NANOG-OCT4 Complex, and its associated network are significantly enriched for regulatory variants with large effects, suggesting that they play a role in the varying cellular proportions of pluripotency states between hiPSCs. Our work bins tens of thousands of regulatory elements in hiPSCs into discrete regulatory networks, shows that pluripotency and self-renewal processes have a surprising level of regulatory complexity, and suggests that genetic factors may contribute to cell state transitions in human iPSC lines.
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Affiliation(s)
- Timothy D Arthur
- Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Jennifer P Nguyen
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
| | | | - Hiroko Matsui
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - Nayara S Silva
- Northeast Biotechnology Network (RENORBIO), Graduate Program in Biotechnology, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Isaac N Joshua
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | - André D Luchessi
- Northeast Biotechnology Network (RENORBIO), Graduate Program in Biotechnology, Federal University of Rio Grande do Norte, Natal, Brazil
- Department of Clinical and Toxicological Analysis, Federal University of Rio Grande do Norte, Natal, Brazil
| | - William W Young Greenwald
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, 92093, USA
| | - Matteo D'Antonio
- Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, 92093, USA
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA
| | | | - Kelly A Frazer
- Department of Pediatrics, University of California San Diego, La Jolla, CA, 92093, USA.
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA, 92093, USA.
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Zhang X, Marand AP, Yan H, Schmitz RJ. Massive-scale single-cell chromatin accessibility sequencing using combinatorial fluidic indexing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.17.558155. [PMID: 37786710 PMCID: PMC10541611 DOI: 10.1101/2023.09.17.558155] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Single-cell ATAC-seq has emerged as a powerful approach for revealing candidate cis-regulatory elements genome-wide at cell-type resolution. However, current single-cell methods suffer from limited throughput and high costs. Here, we present a novel technique called single-cell combinatorial fluidic indexing ATAC-sequencing ("scifi-ATAC-seq"), which combines a barcoded Tn5 pre-indexing step with droplet-based single-cell ATAC-seq using a widely commercialized microfluidics platform (10X Genomics). With scifi-ATAC-seq, up to 200,000 nuclei across multiple samples in a single emulsion reaction can be indexed, representing a ~20-fold increase in throughput compared to the standard 10X Genomics workflow.
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Affiliation(s)
- Xuan Zhang
- Department of Genetics, University of Georgia, Athens, GA, USA
| | - Alexandre P Marand
- Department of Genetics, University of Georgia, Athens, GA, USA
- Current address: Department of Molecular, Cellular, and Development Biology, University of Michigan, Ann Arbor, MI, USA
| | - Haidong Yan
- Department of Genetics, University of Georgia, Athens, GA, USA
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36
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Yan H, Mendieta JP, Zhang X, Marand AP, Liang Y, Luo Z, Minow MAA, Roulé T, Wagner D, Tu X, Wang Y, Zhong S, Wessler SR, Schmitz RJ. Evolution of plant cell-type-specific cis -regulatory elements. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.08.574753. [PMID: 38260561 PMCID: PMC10802394 DOI: 10.1101/2024.01.08.574753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Cis -regulatory elements (CREs) are critical in regulating gene expression, and yet our understanding of CRE evolution remains a challenge. Here, we constructed a comprehensive single-cell atlas of chromatin accessibility in Oryza sativa , integrating data from 104,029 nuclei representing 128 discrete cell states across nine distinct organs. We used comparative genomics to compare cell-type resolved chromatin accessibility between O. sativa and 57,552 nuclei from four additional grass species ( Zea mays, Sorghum bicolor, Panicum miliaceum , and Urochloa fusca ). Accessible chromatin regions (ACRs) had different levels of conservation depending on the degree of cell-type specificity. We found a complex relationship between ACRs with conserved noncoding sequences, cell-type specificity, conservation, and tissue-specific switching. Additionally, we found that epidermal ACRs were less conserved compared to other cell types, potentially indicating that more rapid regulatory evolution has occurred in the L1 epidermal layer of these species. Finally, we identified and characterized a conserved subset of ACRs that overlapped the repressive histone modification H3K27me3, implicating them as potentially critical silencer CREs maintained by evolution. Collectively, this comparative genomics approach highlights the dynamics of cell-type-specific CRE evolution in plants.
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37
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Wang X, Wu X, Hong N, Jin W. Progress in single-cell multimodal sequencing and multi-omics data integration. Biophys Rev 2024; 16:13-28. [PMID: 38495443 PMCID: PMC10937857 DOI: 10.1007/s12551-023-01092-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 06/27/2023] [Indexed: 03/19/2024] Open
Abstract
With the rapid advance of single-cell sequencing technology, cell heterogeneity in various biological processes was dissected at different omics levels. However, single-cell mono-omics results in fragmentation of information and could not provide complete cell states. In the past several years, a variety of single-cell multimodal omics technologies have been developed to jointly profile multiple molecular modalities, including genome, transcriptome, epigenome, and proteome, from the same single cell. With the availability of single-cell multimodal omics data, we can simultaneously investigate the effects of genomic mutation or epigenetic modification on transcription and translation, and reveal the potential mechanisms underlying disease pathogenesis. Driven by the massive single-cell omics data, the integration method of single-cell multi-omics data has rapidly developed. Integration of the massive multi-omics single-cell data in public databases in the future will make it possible to construct a cell atlas of multi-omics, enabling us to comprehensively understand cell state and gene regulation at single-cell resolution. In this review, we summarized the experimental methods for single-cell multimodal omics data and computational methods for multi-omics data integration. We also discussed the future development of this field.
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Affiliation(s)
- Xuefei Wang
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Xinchao Wu
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Ni Hong
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Wenfei Jin
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
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38
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Ye F, Wang J, Li J, Mei Y, Guo G. Mapping Cell Atlases at the Single-Cell Level. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2305449. [PMID: 38145338 PMCID: PMC10885669 DOI: 10.1002/advs.202305449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 12/01/2023] [Indexed: 12/26/2023]
Abstract
Recent advancements in single-cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential to provide detailed information about every cell type in different organisms, enabling the characterization of cellular diversity at the single-cell level. Global efforts in developing comprehensive cell atlases have profound implications for both basic research and clinical applications. This review provides a broad overview of the cellular diversity and dynamics across various biological systems. In addition, the incorporation of machine learning techniques into cell atlas analyses opens up exciting prospects for the field of integrative biology.
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Affiliation(s)
- Fang Ye
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
| | - Jingjing Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
| | - Jiaqi Li
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
| | - Yuqing Mei
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
| | - Guoji Guo
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
- Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative MedicineDr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative MedicineHangzhouZhejiang310058China
- Institute of HematologyZhejiang UniversityHangzhouZhejiang310000China
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de Almeida BP, Schaub C, Pagani M, Secchia S, Furlong EEM, Stark A. Targeted design of synthetic enhancers for selected tissues in the Drosophila embryo. Nature 2024; 626:207-211. [PMID: 38086418 PMCID: PMC10830412 DOI: 10.1038/s41586-023-06905-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 11/28/2023] [Indexed: 01/19/2024]
Abstract
Enhancers control gene expression and have crucial roles in development and homeostasis1-3. However, the targeted de novo design of enhancers with tissue-specific activities has remained challenging. Here we combine deep learning and transfer learning to design tissue-specific enhancers for five tissues in the Drosophila melanogaster embryo: the central nervous system, epidermis, gut, muscle and brain. We first train convolutional neural networks using genome-wide single-cell assay for transposase-accessible chromatin with sequencing (ATAC-seq) datasets and then fine-tune the convolutional neural networks with smaller-scale data from in vivo enhancer activity assays, yielding models with 13% to 76% positive predictive value according to cross-validation. We designed and experimentally assessed 40 synthetic enhancers (8 per tissue) in vivo, of which 31 (78%) were active and 27 (68%) functioned in the target tissue (100% for central nervous system and muscle). The strategy of combining genome-wide and small-scale functional datasets by transfer learning is generally applicable and should enable the design of tissue-, cell type- and cell state-specific enhancers in any system.
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Affiliation(s)
- Bernardo P de Almeida
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria
- Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, Vienna, Austria
- InstaDeep, Paris, France
| | - Christoph Schaub
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Michaela Pagani
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria
| | - Stefano Secchia
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Eileen E M Furlong
- European Molecular Biology Laboratory (EMBL), Genome Biology Unit, Heidelberg, Germany
| | - Alexander Stark
- Research Institute of Molecular Pathology (IMP), Vienna BioCenter (VBC), Vienna, Austria.
- Medical University of Vienna, Vienna BioCenter (VBC), Vienna, Austria.
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40
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Duan Z, Xu S, Sai Srinivasan S, Hwang A, Lee CY, Yue F, Gerstein M, Luan Y, Girgenti M, Zhang J. scENCORE: leveraging single-cell epigenetic data to predict chromatin conformation using graph embedding. Brief Bioinform 2024; 25:bbae096. [PMID: 38493342 PMCID: PMC10944576 DOI: 10.1093/bib/bbae096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 02/01/2024] [Accepted: 02/20/2024] [Indexed: 03/18/2024] Open
Abstract
Dynamic compartmentalization of eukaryotic DNA into active and repressed states enables diverse transcriptional programs to arise from a single genetic blueprint, whereas its dysregulation can be strongly linked to a broad spectrum of diseases. While single-cell Hi-C experiments allow for chromosome conformation profiling across many cells, they are still expensive and not widely available for most labs. Here, we propose an alternate approach, scENCORE, to computationally reconstruct chromatin compartments from the more affordable and widely accessible single-cell epigenetic data. First, scENCORE constructs a long-range epigenetic correlation graph to mimic chromatin interaction frequencies, where nodes and edges represent genome bins and their correlations. Then, it learns the node embeddings to cluster genome regions into A/B compartments and aligns different graphs to quantify chromatin conformation changes across conditions. Benchmarking using cell-type-matched Hi-C experiments demonstrates that scENCORE can robustly reconstruct A/B compartments in a cell-type-specific manner. Furthermore, our chromatin confirmation switching studies highlight substantial compartment-switching events that may introduce substantial regulatory and transcriptional changes in psychiatric disease. In summary, scENCORE allows accurate and cost-effective A/B compartment reconstruction to delineate higher-order chromatin structure heterogeneity in complex tissues.
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Affiliation(s)
- Ziheng Duan
- Department of Computer Science, University of California, Irvine, 92697 CA, USA
| | - Siwei Xu
- Department of Computer Science, University of California, Irvine, 92697 CA, USA
| | | | - Ahyeon Hwang
- Department of Computer Science, University of California, Irvine, 92697 CA, USA
| | - Che Yu Lee
- Department of Computer Science, University of California, Irvine, 92697 CA, USA
| | - Feng Yue
- Department of Pathology, Northwestern University, 60611 IL, USA
| | - Mark Gerstein
- Molecular Biophysics & Biochemistry, Yale, 06519 CT, USA
| | - Yu Luan
- Department of Cell Systems and Anatomy, UT Health San Antonio, 78229 TX, USA
| | - Matthew Girgenti
- Department of Psychiatry, School of Medicine, Yale, 06519 CT, USA
- Clinical Neurosciences Division, National Center for PTSD, U.S. Department of Veterans Affairs, 06477 CT, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, 92697 CA, USA
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Kim SS, Truong B, Jagadeesh K, Dey KK, Shen AZ, Raychaudhuri S, Kellis M, Price AL. Leveraging single-cell ATAC-seq and RNA-seq to identify disease-critical fetal and adult brain cell types. Nat Commun 2024; 15:563. [PMID: 38233398 PMCID: PMC10794712 DOI: 10.1038/s41467-024-44742-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 01/02/2024] [Indexed: 01/19/2024] Open
Abstract
Prioritizing disease-critical cell types by integrating genome-wide association studies (GWAS) with functional data is a fundamental goal. Single-cell chromatin accessibility (scATAC-seq) and gene expression (scRNA-seq) have characterized cell types at high resolution, and studies integrating GWAS with scRNA-seq have shown promise, but studies integrating GWAS with scATAC-seq have been limited. Here, we identify disease-critical fetal and adult brain cell types by integrating GWAS summary statistics from 28 brain-related diseases/traits (average N = 298 K) with 3.2 million scATAC-seq and scRNA-seq profiles from 83 cell types. We identified disease-critical fetal (respectively adult) brain cell types for 22 (respectively 23) of 28 traits using scATAC-seq, and for 8 (respectively 17) of 28 traits using scRNA-seq. Significant scATAC-seq enrichments included fetal photoreceptor cells for major depressive disorder, fetal ganglion cells for BMI, fetal astrocytes for ADHD, and adult VGLUT2 excitatory neurons for schizophrenia. Our findings improve our understanding of brain-related diseases/traits and inform future analyses.
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Affiliation(s)
- Samuel S Kim
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, UK.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, UK.
| | - Buu Truong
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, UK.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, UK.
| | - Karthik Jagadeesh
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, UK
| | - Kushal K Dey
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, UK
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Amber Z Shen
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Soumya Raychaudhuri
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Manolis Kellis
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, UK
| | - Alkes L Price
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, UK.
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, UK.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, UK.
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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42
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Deng Y, Lu Y, Li M, Shen J, Qin S, Zhang W, Zhang Q, Shen Z, Li C, Jia T, Chen P, Peng L, Chen Y, Zhang W, Liu H, Zhang L, Rong L, Wang X, Chen D. SCAN: Spatiotemporal Cloud Atlas for Neural cells. Nucleic Acids Res 2024; 52:D998-D1009. [PMID: 37930842 PMCID: PMC10767991 DOI: 10.1093/nar/gkad895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/20/2023] [Accepted: 10/05/2023] [Indexed: 11/08/2023] Open
Abstract
The nervous system is one of the most complicated and enigmatic systems within the animal kingdom. Recently, the emergence and development of spatial transcriptomics (ST) and single-cell RNA sequencing (scRNA-seq) technologies have provided an unprecedented ability to systematically decipher the cellular heterogeneity and spatial locations of the nervous system from multiple unbiased aspects. However, efficiently integrating, presenting and analyzing massive multiomic data remains a huge challenge. Here, we manually collected and comprehensively analyzed high-quality scRNA-seq and ST data from the nervous system, covering 10 679 684 cells. In addition, multi-omic datasets from more than 900 species were included for extensive data mining from an evolutionary perspective. Furthermore, over 100 neurological diseases (e.g. Alzheimer's disease, Parkinson's disease, Down syndrome) were systematically analyzed for high-throughput screening of putative biomarkers. Differential expression patterns across developmental time points, cell types and ST spots were discerned and subsequently subjected to extensive interpretation. To provide researchers with efficient data exploration, we created a new database with interactive interfaces and integrated functions called the Spatiotemporal Cloud Atlas for Neural cells (SCAN), freely accessible at http://47.98.139.124:8799 or http://scanatlas.net. SCAN will benefit the neuroscience research community to better exploit the spatiotemporal atlas of the neural system and promote the development of diagnostic strategies for various neurological disorders.
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Affiliation(s)
- Yushan Deng
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Yubao Lu
- Department of Spine Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
| | - Mengrou Li
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
- Institutes of Biology and Medical Sciences (IBMS), Soochow University, Suzhou 215123, China
| | - Jiayi Shen
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
- Peninsula Cancer Research Center, School of Basic Medical Sciences, Binzhou Medical University, Yantai 264003, China
| | - Siying Qin
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Wei Zhang
- Department of Spine Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
| | - Qiang Zhang
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Zhaoyang Shen
- Life Sciences and Technology College, China Pharmaceutical University, Nanjing 211198, China
| | - Changxiao Li
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Tengfei Jia
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
- Institutes of Biology and Medical Sciences (IBMS), Soochow University, Suzhou 215123, China
| | - Peixin Chen
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
- Cam-Su Genomic Resource Center, Medical College of Soochow University, Suzhou 215123, China
| | - Lingmin Peng
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Yangfeng Chen
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
| | - Wensheng Zhang
- Peninsula Cancer Research Center, School of Basic Medical Sciences, Binzhou Medical University, Yantai 264003, China
- Cam-Su Genomic Resource Center, Medical College of Soochow University, Suzhou 215123, China
| | - Hebin Liu
- Institutes of Biology and Medical Sciences (IBMS), Soochow University, Suzhou 215123, China
| | - Liangming Zhang
- Department of Spine Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
| | - Limin Rong
- Department of Spine Surgery, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou 510630, China
| | - Xiangdong Wang
- Zhongshan Hospital, Department of Pulmonary and Critical Care Medicine, Institute for Clinical Science, Shanghai Institute of Clinical Bioinformatics, Shanghai 200000, China
| | - Dongsheng Chen
- State Key Laboratory of Common Mechanism Research for Major Diseases, Suzhou Institute of Systems Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Suzhou 215123, China
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Qian FC, Zhou LW, Zhu YB, Li YY, Yu ZM, Feng CC, Fang QL, Zhao Y, Cai FH, Wang QY, Tang HF, Li CQ. scATAC-Ref: a reference of scATAC-seq with known cell labels in multiple species. Nucleic Acids Res 2024; 52:D285-D292. [PMID: 37897340 PMCID: PMC10767920 DOI: 10.1093/nar/gkad924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/14/2023] [Accepted: 10/16/2023] [Indexed: 10/30/2023] Open
Abstract
Chromatin accessibility profiles at single cell resolution can reveal cell type-specific regulatory programs, help dissect highly specialized cell functions and trace cell origin and evolution. Accurate cell type assignment is critical for effectively gaining biological and pathological insights, but is difficult in scATAC-seq. Hence, by extensively reviewing the literature, we designed scATAC-Ref (https://bio.liclab.net/scATAC-Ref/), a manually curated scATAC-seq database aimed at providing a comprehensive, high-quality source of chromatin accessibility profiles with known cell labels across broad cell types. Currently, scATAC-Ref comprises 1 694 372 cells with known cell labels, across various biological conditions, >400 cell/tissue types and five species. We used uniform system environment and software parameters to perform comprehensive downstream analysis on these chromatin accessibility profiles with known labels, including gene activity score, TF enrichment score, differential chromatin accessibility regions, pathway/GO term enrichment analysis and co-accessibility interactions. The scATAC-Ref also provided a user-friendly interface to query, browse and visualize cell types of interest, thereby providing a valuable resource for exploring epigenetic regulation in different tissues and cell types.
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Affiliation(s)
- Feng-Cui Qian
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Li-Wei Zhou
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Yan-Bing Zhu
- Beijing Clinical Research Institute, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Yan-Yu Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Zheng-Min Yu
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
| | - Chen-Chen Feng
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Qiao-Li Fang
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
| | - Yu Zhao
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
| | - Fu-Hong Cai
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
| | - Qiu-Yu Wang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Hui-Fang Tang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Chun-Quan Li
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Maternal and Child Health Care Hospital, National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
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44
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Lin S, Wu S, Zhao W, Fang Z, Kang H, Liu X, Pan S, Yu F, Bao Y, Jia P. TargetGene: a comprehensive database of cell-type-specific target genes for genetic variants. Nucleic Acids Res 2024; 52:D1072-D1081. [PMID: 37870478 PMCID: PMC10767789 DOI: 10.1093/nar/gkad901] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/27/2023] [Accepted: 10/09/2023] [Indexed: 10/24/2023] Open
Abstract
Annotating genetic variants to their target genes is of great importance in unraveling the causal variants and genetic mechanisms that underlie complex diseases. However, disease-associated genetic variants are often located in non-coding regions and manifest context-specific effects, making it challenging to accurately identify the target genes and regulatory mechanisms. Here, we present TargetGene (https://ngdc.cncb.ac.cn/targetgene/), a comprehensive database reporting target genes for human genetic variants from various aspects. Specifically, we collected a comprehensive catalog of multi-omics data at the single-cell and bulk levels and from various human tissues, cell types and developmental stages. To facilitate the identification of Single Nucleotide Polymorphism (SNP)-to-gene connections, we have implemented multiple analytical tools based on chromatin co-accessibility, 3D interaction, enhancer activities and quantitative trait loci, among others. We applied the pipeline to evaluate variants from nearly 1300 Genome-wide association studies (GWAS) and assembled a comprehensive atlas of multiscale regulation of genetic variants. TargetGene is equipped with user-friendly web interfaces that enable intuitive searching, navigation and browsing through the results. Overall, TargetGene provides a unique resource to empower researchers to study the regulatory mechanisms of genetic variants in complex human traits.
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Affiliation(s)
- Shiqi Lin
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Song Wu
- University of Chinese Academy of Sciences, Beijing 100049, China
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Wei Zhao
- University of Chinese Academy of Sciences, Beijing 100049, China
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Zhanjie Fang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hongen Kang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xinxuan Liu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Siyu Pan
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fudong Yu
- Shanghai-MOST Key Laboratory of Health and Disease Genomics, NHC Key Lab of Reproduction Regulation, Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200237, China
| | - Yiming Bao
- University of Chinese Academy of Sciences, Beijing 100049, China
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Peilin Jia
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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45
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Boddupally K, Rani Thuraka E. Artificial intelligence for prenatal chromosome analysis. Clin Chim Acta 2024; 552:117669. [PMID: 38007058 DOI: 10.1016/j.cca.2023.117669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 11/13/2023] [Accepted: 11/15/2023] [Indexed: 11/27/2023]
Abstract
This review article delves into the rapidly advancing domain of prenatal diagnostics, with a primary focus on the detection and management of chromosomal abnormalities such as trisomy 13 ("Patau syndrome)", "trisomy 18 (Edwards syndrome)", and "trisomy 21 (Down syndrome)". The objective of the study is to examine the utilization and effectiveness of novel computational methodologies, such as "machine learning (ML)", "deep learning (DL)", and data analysis, in enhancing the detection rates and accuracy of these prenatal conditions. The contribution of the article lies in its comprehensive examination of advancements in "Non-Invasive Prenatal Testing (NIPT)", prenatal screening, genomics, and medical imaging. It highlights the potential of these techniques for prenatal diagnosis and the contributions of ML and DL to these advancements. It highlights the application of ensemble models and transfer learning to improving model performance, especially with limited datasets. This also delves into optimal feature selection and fusion of high-dimensional features, underscoring the need for future research in these areas. The review finds that ML and DL have substantially improved the detection and management of prenatal conditions, despite limitations such as small sample sizes and issues related to model generalizability. It recognizes the promising results achieved through the use of ensemble models and transfer learning in prenatal diagnostics. The review also notes the increased importance of feature selection and high-dimensional feature fusion in the development and training of predictive models. The findings underline the crucial role of AI and machine learning techniques in early detection and improved therapeutic strategies in prenatal diagnostics, highlighting a pressing need for further research in this area.
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Affiliation(s)
- Kavitha Boddupally
- JNTUH University, India; CVR College of Engineering, ECE, Hyderabad, India.
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46
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Martens LD, Fischer DS, Yépez VA, Theis FJ, Gagneur J. Modeling fragment counts improves single-cell ATAC-seq analysis. Nat Methods 2024; 21:28-31. [PMID: 38049697 PMCID: PMC10776385 DOI: 10.1038/s41592-023-02112-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 10/25/2023] [Indexed: 12/06/2023]
Abstract
Single-cell ATAC sequencing coverage in regulatory regions is typically binarized as an indicator of open chromatin. Here we show that binarization is an unnecessary step that neither improves goodness of fit, clustering, cell type identification nor batch integration. Fragment counts, but not read counts, should instead be modeled, which preserves quantitative regulatory information. These results have immediate implications for single-cell ATAC sequencing analysis.
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Affiliation(s)
- Laura D Martens
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany
- Helmholtz Association, Munich School for Data Science (MUDS), Munich, Germany
| | - David S Fischer
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Vicente A Yépez
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Fabian J Theis
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany.
- Helmholtz Association, Munich School for Data Science (MUDS), Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
| | - Julien Gagneur
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany.
- Helmholtz Association, Munich School for Data Science (MUDS), Munich, Germany.
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany.
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Lee AS, Ayers LJ, Kosicki M, Chan WM, Fozo LN, Pratt BM, Collins TE, Zhao B, Rose MF, Sanchis-Juan A, Fu JM, Wong I, Zhao X, Tenney AP, Lee C, Laricchia KM, Barry BJ, Bradford VR, Lek M, MacArthur DG, Lee EA, Talkowski ME, Brand H, Pennacchio LA, Engle EC. A cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.22.23300468. [PMID: 38234731 PMCID: PMC10793524 DOI: 10.1101/2023.12.22.23300468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Unsolved Mendelian cases often lack obvious pathogenic coding variants, suggesting potential non-coding etiologies. Here, we present a single cell multi-omic framework integrating embryonic mouse chromatin accessibility, histone modification, and gene expression assays to discover cranial motor neuron (cMN) cis-regulatory elements and subsequently nominate candidate non-coding variants in the congenital cranial dysinnervation disorders (CCDDs), a set of Mendelian disorders altering cMN development. We generated single cell epigenomic profiles for ~86,000 cMNs and related cell types, identifying ~250,000 accessible regulatory elements with cognate gene predictions for ~145,000 putative enhancers. Seventy-five percent of elements (44 of 59) validated in an in vivo transgenic reporter assay, demonstrating that single cell accessibility is a strong predictor of enhancer activity. Applying our cMN atlas to 899 whole genome sequences from 270 genetically unsolved CCDD pedigrees, we achieved significant reduction in our variant search space and nominated candidate variants predicted to regulate known CCDD disease genes MAFB, PHOX2A, CHN1, and EBF3 - as well as new candidates in recurrently mutated enhancers through peak- and gene-centric allelic aggregation. This work provides novel non-coding variant discoveries of relevance to CCDDs and a generalizable framework for nominating non-coding variants of potentially high functional impact in other Mendelian disorders.
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Affiliation(s)
- Arthur S Lee
- Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA
- Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA
- Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, MA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Lauren J Ayers
- Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Michael Kosicki
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA
| | - Wai-Man Chan
- Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA
- Howard Hughes Medical Institute, Chevy Chase, MD
| | - Lydia N Fozo
- Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Brandon M Pratt
- Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Thomas E Collins
- Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Boxun Zhao
- Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, MA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA
| | - Matthew F Rose
- Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA
- Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Pathology, Boston Children's Hospital, Boston, MA
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
- Medical Genetics Training Program, Harvard Medical School, Boston, MA
| | - Alba Sanchis-Juan
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
| | - Jack M Fu
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Isaac Wong
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
| | - Xuefang Zhao
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Alan P Tenney
- Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA
- Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Cassia Lee
- Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA
- Harvard College, Cambridge, MA
| | - Kristen M Laricchia
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Brenda J Barry
- Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA
- Howard Hughes Medical Institute, Chevy Chase, MD
| | - Victoria R Bradford
- Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Monkol Lek
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Daniel G MacArthur
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Centre for Population Genomics, Garvan Institute of Medical Research and UNSW Sydney, Sydney, NSW, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Eunjung Alice Lee
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA
- Department of Genetics, Harvard Medical School, Boston, MA
| | - Michael E Talkowski
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Harrison Brand
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA
- Pediatric Surgical Research Laboratories, Massachusetts General Hospital, Boston, MA
| | - Len A Pennacchio
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA
| | - Elizabeth C Engle
- Department of Neurology, Boston Children's Hospital and Harvard Medical School, Boston, MA
- Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA
- Manton Center for Orphan Disease Research, Boston Children's Hospital, Boston, MA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA
- Howard Hughes Medical Institute, Chevy Chase, MD
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA
- Medical Genetics Training Program, Harvard Medical School, Boston, MA
- Department of Ophthalmology, Boston Children's Hospital and Harvard Medical School, Boston, MA
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48
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Yoshihara M, Nakayama T, Takahashi S. Chromatin accessibility analysis suggested vascular induction of the biliary epithelium via the Notch signaling pathway in the human liver. BMC Res Notes 2023; 16:379. [PMID: 38129911 PMCID: PMC10734141 DOI: 10.1186/s13104-023-06674-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 12/15/2023] [Indexed: 12/23/2023] Open
Abstract
The biliary epithelial cells (cholangiocytes) in the liver originate from undifferentiated liver parenchymal cells (hepatoblasts) that are located adjacent to the portal vein. This differentiation process is driven by Notch signaling, which is recognized for generating salt-and-pepper (fine-grained) patterns, in contrast to one- or two-cell layer (spatially confined) patterning in cholangiocyte differentiation. It is unclear how Notch signaling acts and localizes only in cholangiocytes. A computer simulation study suggested that low production rates of the ligands or receptors of Notch signaling are crucial for the spatially confined patterning, although biochemical examination is lacking. Here, we analyzed a publicly available single-cell ATAC-sequencing dataset from human fetal liver samples. We showed high chromatin accessibility for the ligands only in vascular cells, while that for the receptor is limited to a small population of hepatoblasts. This finding strengthens the previously proposed idea that low production rates of the ligands or receptors of Notch signaling enable vascular induction of cholangiocytes.
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Affiliation(s)
- Masaharu Yoshihara
- Department of Primary Care and Medical Education, Institute of Medicine, University of Tsukuba, Tsukuba, Japan.
- Laboratory Animal Resource Center, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan.
| | - Takahiro Nakayama
- College of Medicine, School of Medicine and Health Sciences, University of Tsukuba, Tsukuba, Japan
| | - Satoru Takahashi
- Department of Anatomy and Embryology, Institute of Medicine, University of Tsukuba, Tsukuba, Japan
- Transborder Medical Research Center, Institute of Medicine, University of Tsukuba, Tsukuba, Japan
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Booth GT, Daza RM, Srivatsan SR, McFaline-Figueroa JL, Gladden RG, Mullen AC, Furlan SN, Shendure J, Trapnell C. High-capacity sample multiplexing for single cell chromatin accessibility profiling. BMC Genomics 2023; 24:737. [PMID: 38049719 PMCID: PMC10696879 DOI: 10.1186/s12864-023-09832-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 11/22/2023] [Indexed: 12/06/2023] Open
Abstract
Single-cell chromatin accessibility has emerged as a powerful means of understanding the epigenetic landscape of diverse tissues and cell types, but profiling cells from many independent specimens is challenging and costly. Here we describe a novel approach, sciPlex-ATAC-seq, which uses unmodified DNA oligos as sample-specific nuclear labels, enabling the concurrent profiling of chromatin accessibility within single nuclei from virtually unlimited specimens or experimental conditions. We first demonstrate our method with a chemical epigenomics screen, in which we identify drug-altered distal regulatory sites predictive of compound- and dose-dependent effects on transcription. We then analyze cell type-specific chromatin changes in PBMCs from multiple donors responding to synthetic and allogeneic immune stimulation. We quantify stimulation-altered immune cell compositions and isolate the unique effects of allogeneic stimulation on chromatin accessibility specific to T-lymphocytes. Finally, we observe that impaired global chromatin decondensation often coincides with chemical inhibition of allogeneic T-cell activation.
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Affiliation(s)
- Gregory T Booth
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Riza M Daza
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Sanjay R Srivatsan
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - José L McFaline-Figueroa
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Department of Biomedical Engineering, Columbia University, New York City, NY, USA
| | - Rula Green Gladden
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Andrew C Mullen
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Scott N Furlan
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
- Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
| | - Cole Trapnell
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA.
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA.
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50
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Sziraki A, Lu Z, Lee J, Banyai G, Anderson S, Abdulraouf A, Metzner E, Liao A, Banfelder J, Epstein A, Schaefer C, Xu Z, Zhang Z, Gan L, Nelson PT, Zhou W, Cao J. A global view of aging and Alzheimer's pathogenesis-associated cell population dynamics and molecular signatures in human and mouse brains. Nat Genet 2023; 55:2104-2116. [PMID: 38036784 PMCID: PMC10703679 DOI: 10.1038/s41588-023-01572-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 10/17/2023] [Indexed: 12/02/2023]
Abstract
Conventional methods fall short in unraveling the dynamics of rare cell types related to aging and diseases. Here we introduce EasySci, an advanced single-cell combinatorial indexing strategy for exploring age-dependent cellular dynamics in the mammalian brain. Profiling approximately 1.5 million single-cell transcriptomes and 400,000 chromatin accessibility profiles across diverse mouse brains, we identified over 300 cell subtypes, uncovering their molecular characteristics and spatial locations. This comprehensive view elucidates rare cell types expanded or depleted upon aging. We also investigated cell-type-specific responses to genetic alterations linked to Alzheimer's disease, identifying associated rare cell types. Additionally, by profiling 118,240 human brain single-cell transcriptomes, we discerned cell- and region-specific transcriptomic changes tied to Alzheimer's pathogenesis. In conclusion, this research offers a valuable resource for probing cell-type-specific dynamics in both normal and pathological aging.
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Affiliation(s)
- Andras Sziraki
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
- The David Rockefeller Graduate Program in Bioscience, The Rockefeller University, New York, NY, USA
| | - Ziyu Lu
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
- The David Rockefeller Graduate Program in Bioscience, The Rockefeller University, New York, NY, USA
| | - Jasper Lee
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
| | - Gabor Banyai
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
| | - Sonya Anderson
- Department of Pathology and Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
| | - Abdulraouf Abdulraouf
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
- The Tri-Institutional MD-PhD Program, New York, NY, USA
| | - Eli Metzner
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
- The Tri-Institutional PhD Program in Computational Biology and Medicine, New York, NY, USA
| | - Andrew Liao
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
- The Tri-Institutional MD-PhD Program, New York, NY, USA
| | - Jason Banfelder
- High Performance Computing Resource Center, The Rockefeller University, New York, NY, USA
| | - Alexander Epstein
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
- The David Rockefeller Graduate Program in Bioscience, The Rockefeller University, New York, NY, USA
| | - Chloe Schaefer
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
| | - Zihan Xu
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
- The David Rockefeller Graduate Program in Bioscience, The Rockefeller University, New York, NY, USA
| | - Zehao Zhang
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
- The David Rockefeller Graduate Program in Bioscience, The Rockefeller University, New York, NY, USA
| | - Li Gan
- Helen and Robert Appel Alzheimer's Disease Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Peter T Nelson
- Department of Pathology and Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
| | - Wei Zhou
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA.
| | - Junyue Cao
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA.
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