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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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Beamish JA, Watts JA, Dressler GR. Gene regulation in regeneration after acute kidney injury. J Biol Chem 2024; 300:107520. [PMID: 38950862 PMCID: PMC11325799 DOI: 10.1016/j.jbc.2024.107520] [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: 06/03/2024] [Revised: 06/19/2024] [Accepted: 06/21/2024] [Indexed: 07/03/2024] Open
Abstract
Acute kidney injury (AKI) is a common condition associated with significant morbidity, mortality, and cost. Injured kidney tissue can regenerate after many forms of AKI. However, there are no treatments in routine clinical practice to encourage recovery. In part, this shortcoming is due to an incomplete understanding of the genetic mechanisms that orchestrate kidney recovery. The advent of high-throughput sequencing technologies and genetic mouse models has opened an unprecedented window into the transcriptional dynamics that accompany both successful and maladaptive repair. AKI recovery shares similar cell-state transformations with kidney development, which can suggest common mechanisms of gene regulation. Several powerful bioinformatic strategies have been developed to infer the activity of gene regulatory networks by combining multiple forms of sequencing data at single-cell resolution. These studies highlight not only shared stress responses but also key changes in gene regulatory networks controlling metabolism. Furthermore, chromatin immunoprecipitation studies in injured kidneys have revealed dynamic epigenetic modifications at enhancer elements near target genes. This review will highlight how these studies have enhanced our understanding of gene regulation in injury response and regeneration.
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Affiliation(s)
- Jeffrey A Beamish
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
| | - Jason A Watts
- Epigenetics and Stem Cell Laboratory, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA
| | - Gregory R Dressler
- Department of Pathology, University of Michigan, Ann Arbor, Michigan, USA.
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Yang Y, Pe’er D. REUNION: transcription factor binding prediction and regulatory association inference from single-cell multi-omics data. Bioinformatics 2024; 40:i567-i575. [PMID: 38940155 PMCID: PMC11211829 DOI: 10.1093/bioinformatics/btae234] [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 Profiling of gene expression and chromatin accessibility by single-cell multi-omics approaches can help to systematically decipher how transcription factors (TFs) regulate target gene expression via cis-region interactions. However, integrating information from different modalities to discover regulatory associations is challenging, in part because motif scanning approaches miss many likely TF binding sites. RESULTS We develop REUNION, a framework for predicting genome-wide TF binding and cis-region-TF-gene "triplet" regulatory associations using single-cell multi-omics data. The first component of REUNION, Unify, utilizes information theory-inspired complementary score functions that incorporate TF expression, chromatin accessibility, and target gene expression to identify regulatory associations. The second component, Rediscover, takes Unify estimates as input for pseudo semi-supervised learning to predict TF binding in accessible genomic regions that may or may not include detected TF motifs. Rediscover leverages latent chromatin accessibility and sequence feature spaces of the genomic regions, without requiring chromatin immunoprecipitation data for model training. Applied to peripheral blood mononuclear cell data, REUNION outperforms alternative methods in TF binding prediction on average performance. In particular, it recovers missing region-TF associations from regions lacking detected motifs, which circumvents the reliance on motif scanning and facilitates discovery of novel associations involving potential co-binding transcriptional regulators. Newly identified region-TF associations, even in regions lacking a detected motif, improve the prediction of target gene expression in regulatory triplets, and are thus likely to genuinely participate in the regulation. AVAILABILITY AND IMPLEMENTATION All source code is available at https://github.com/yangymargaret/REUNION.
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Affiliation(s)
- Yang Yang
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
- Howard Hughes Medical Institute, Chevy Chase, MD 20815, United States
| | - Dana Pe’er
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States
- Howard Hughes Medical Institute, Chevy Chase, MD 20815, United States
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Liu S, Dai W, Jin B, Jiang F, Huang H, Hou W, Lan J, Jin Y, Peng W, Pan J. Effects of super-enhancers in cancer metastasis: mechanisms and therapeutic targets. Mol Cancer 2024; 23:122. [PMID: 38844984 PMCID: PMC11157854 DOI: 10.1186/s12943-024-02033-8] [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: 04/19/2024] [Accepted: 05/28/2024] [Indexed: 06/09/2024] Open
Abstract
Metastasis remains the principal cause of cancer-related lethality despite advancements in cancer treatment. Dysfunctional epigenetic alterations are crucial in the metastatic cascade. Among these, super-enhancers (SEs), emerging as new epigenetic regulators, consist of large clusters of regulatory elements that drive the high-level expression of genes essential for the oncogenic process, upon which cancer cells develop a profound dependency. These SE-driven oncogenes play an important role in regulating various facets of metastasis, including the promotion of tumor proliferation in primary and distal metastatic organs, facilitating cellular migration and invasion into the vasculature, triggering epithelial-mesenchymal transition, enhancing cancer stem cell-like properties, circumventing immune detection, and adapting to the heterogeneity of metastatic niches. This heavy reliance on SE-mediated transcription delineates a vulnerable target for therapeutic intervention in cancer cells. In this article, we review current insights into the characteristics, identification methodologies, formation, and activation mechanisms of SEs. We also elaborate the oncogenic roles and regulatory functions of SEs in the context of cancer metastasis. Ultimately, we discuss the potential of SEs as novel therapeutic targets and their implications in clinical oncology, offering insights into future directions for innovative cancer treatment strategies.
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Affiliation(s)
- Shenglan Liu
- Key Laboratory of Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases, Ministry of Education, Jiangxi Provincal Key Laboratory of Tissue Engineering, School of Pharmacy, Gannan Medical University, Ganzhou, 314000, China
| | - Wei Dai
- Key Laboratory of Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases, Ministry of Education, Jiangxi Provincal Key Laboratory of Tissue Engineering, School of Pharmacy, Gannan Medical University, Ganzhou, 314000, China
| | - Bei Jin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China
| | - Feng Jiang
- Key Laboratory of Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases, Ministry of Education, Jiangxi Provincal Key Laboratory of Tissue Engineering, School of Pharmacy, Gannan Medical University, Ganzhou, 314000, China
| | - Hao Huang
- Key Laboratory of Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases, Ministry of Education, Jiangxi Provincal Key Laboratory of Tissue Engineering, School of Pharmacy, Gannan Medical University, Ganzhou, 314000, China
| | - Wen Hou
- Key Laboratory of Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases, Ministry of Education, Jiangxi Provincal Key Laboratory of Tissue Engineering, School of Pharmacy, Gannan Medical University, Ganzhou, 314000, China
| | - Jinxia Lan
- College of Public Health and Health Management, Gannan Medical University, Ganzhou, 341000, China
| | - Yanli Jin
- College of Pharmacy, Jinan University Institute of Tumor Pharmacology, Jinan University, Guangzhou, 510632, China
| | - Weijie Peng
- Key Laboratory of Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases, Ministry of Education, Jiangxi Provincal Key Laboratory of Tissue Engineering, School of Pharmacy, Gannan Medical University, Ganzhou, 314000, China.
| | - Jingxuan Pan
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China.
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Huo Q, Song R, Ma Z. Recent advances in exploring transcriptional regulatory landscape of crops. FRONTIERS IN PLANT SCIENCE 2024; 15:1421503. [PMID: 38903438 PMCID: PMC11188431 DOI: 10.3389/fpls.2024.1421503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 05/23/2024] [Indexed: 06/22/2024]
Abstract
Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of plant phenotype by altering the expression of particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend the transcriptional regulatory mechanisms that underpin these traits. In the multi-omics era, a large amount of omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, and single-cell omics. The abundant data resources and the emergence of advanced computational tools offer unprecedented opportunities for obtaining a holistic view and profound understanding of the regulatory processes linked to desirable traits. This review focuses on integrated network approaches that utilize multi-omics data to investigate gene expression regulation. Various types of regulatory networks and their inference methods are discussed, focusing on recent advancements in crop plants. The integration of multi-omics data has been proven to be crucial for the construction of high-confidence regulatory networks. With the refinement of these methodologies, they will significantly enhance crop breeding efforts and contribute to global food security.
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Affiliation(s)
| | | | - Zeyang Ma
- State Key Laboratory of Maize Bio-breeding, Frontiers Science Center for Molecular Design Breeding, Joint International Research Laboratory of Crop Molecular Breeding, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
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6
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Trimbour R, Deutschmann IM, Cantini L. Molecular mechanisms reconstruction from single-cell multi-omics data with HuMMuS. Bioinformatics 2024; 40:btae143. [PMID: 38460192 PMCID: PMC11065476 DOI: 10.1093/bioinformatics/btae143] [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: 08/28/2023] [Revised: 12/20/2023] [Accepted: 03/07/2024] [Indexed: 03/11/2024] Open
Abstract
MOTIVATION The molecular identity of a cell results from a complex interplay between heterogeneous molecular layers. Recent advances in single-cell sequencing technologies have opened the possibility to measure such molecular layers of regulation. RESULTS Here, we present HuMMuS, a new method for inferring regulatory mechanisms from single-cell multi-omics data. Differently from the state-of-the-art, HuMMuS captures cooperation between biological macromolecules and can easily include additional layers of molecular regulation. We benchmarked HuMMuS with respect to the state-of-the-art on both paired and unpaired multi-omics datasets. Our results proved the improvements provided by HuMMuS in terms of transcription factor (TF) targets, TF binding motifs and regulatory regions prediction. Finally, once applied to snmC-seq, scATAC-seq and scRNA-seq data from mouse brain cortex, HuMMuS enabled to accurately cluster scRNA profiles and to identify potential driver TFs. AVAILABILITY AND IMPLEMENTATION HuMMuS is available at https://github.com/cantinilab/HuMMuS.
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Affiliation(s)
- Remi Trimbour
- Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics Group, F-75015 Paris, France
- Institut de Biologie de l’Ecole Normale Supérieure, CNRS, INSERM, Ecole Normale Supérieure, Université PSL, 75005 Paris, France
| | - Ina Maria Deutschmann
- Institut de Biologie de l’Ecole Normale Supérieure, CNRS, INSERM, Ecole Normale Supérieure, Université PSL, 75005 Paris, France
| | - Laura Cantini
- Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics Group, F-75015 Paris, France
- Institut de Biologie de l’Ecole Normale Supérieure, CNRS, INSERM, Ecole Normale Supérieure, Université PSL, 75005 Paris, France
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7
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Lin Y, Wu TY, Chen X, Wan S, Chao B, Xin J, Yang JYH, Wong WH, Wang YXR. Data integration and inference of gene regulation using single-cell temporal multimodal data with scTIE. Genome Res 2024; 34:119-133. [PMID: 38190633 PMCID: PMC10903952 DOI: 10.1101/gr.277960.123] [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: 04/06/2023] [Accepted: 12/13/2023] [Indexed: 01/10/2024]
Abstract
Single-cell technologies offer unprecedented opportunities to dissect gene regulatory mechanisms in context-specific ways. Although there are computational methods for extracting gene regulatory relationships from scRNA-seq and scATAC-seq data, the data integration problem, essential for accurate cell type identification, has been mostly treated as a standalone challenge. Here we present scTIE, a unified method that integrates temporal multimodal data and infers regulatory relationships predictive of cellular state changes. scTIE uses an autoencoder to embed cells from all time points into a common space by using iterative optimal transport, followed by extracting interpretable information to predict cell trajectories. Using a variety of synthetic and real temporal multimodal data sets, we show scTIE achieves effective data integration while preserving more biological signals than existing methods, particularly in the presence of batch effects and noise. Furthermore, on the exemplar multiome data set we generated from differentiating mouse embryonic stem cells over time, we show scTIE captures regulatory elements highly predictive of cell transition probabilities, providing new potentials to understand the regulatory landscape driving developmental processes.
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Affiliation(s)
- Yingxin Lin
- School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, NSW 2006, Australia
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR 999077, China
| | - Tung-Yu Wu
- Department of Statistics, Stanford University, Stanford, California 94305-4020, USA
| | - Xi Chen
- Department of Statistics, Stanford University, Stanford, California 94305-4020, USA
| | - Sheng Wan
- Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
| | - Brian Chao
- Department of Electrical Engineering, Stanford University, Stanford, California 94305-9505, USA
| | - Jingxue Xin
- Department of Statistics, Stanford University, Stanford, California 94305-4020, USA
| | - Jean Y H Yang
- School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, NSW 2006, Australia
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR 999077, China
| | - Wing H Wong
- Department of Statistics, Stanford University, Stanford, California 94305-4020, USA;
- Department of Biomedical Data Science, Stanford University, Stanford, California 94305-5464, USA
- Bio-X Program, Stanford University, Stanford, California 94305, USA
| | - Y X Rachel Wang
- School of Mathematics and Statistics, The University of Sydney, NSW 2006, Australia;
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8
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Zhang Z, Ruf-Zamojski F, Zamojski M, Bernard D, Chen X, Troyanskaya O, Sealfon S. Peak-agnostic high-resolution cis-regulatory circuitry mapping using single cell multiome data. Nucleic Acids Res 2024; 52:572-582. [PMID: 38084892 PMCID: PMC10810203 DOI: 10.1093/nar/gkad1166] [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: 07/07/2023] [Revised: 11/15/2023] [Accepted: 11/27/2023] [Indexed: 01/26/2024] Open
Abstract
Single same cell RNAseq/ATACseq multiome data provide unparalleled potential to develop high resolution maps of the cell-type specific transcriptional regulatory circuitry underlying gene expression. We present CREMA, a framework that recovers the full cis-regulatory circuitry by modeling gene expression and chromatin activity in individual cells without peak-calling or cell type labeling constraints. We demonstrate that CREMA overcomes the limitations of existing methods that fail to identify about half of functional regulatory elements which are outside the called chromatin 'peaks'. These circuit sites outside called peaks are shown to be important cell type specific functional regulatory loci, sufficient to distinguish individual cell types. Analysis of mouse pituitary data identifies a Gata2-circuit for the gonadotrope-enriched disease-associated Pcsk1 gene, which is experimentally validated by reduced gonadotrope expression in a gonadotrope conditional Gata2-knockout model. We present a web accessible human immune cell regulatory circuit resource, and provide CREMA as an R package.
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Affiliation(s)
- Zidong Zhang
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Frederique Ruf-Zamojski
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Michel Zamojski
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Daniel J Bernard
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC H3G 1Y6, Canada
| | - Xi Chen
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Olga G Troyanskaya
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Stuart C Sealfon
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
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9
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Gisch DL, Brennan M, Lake BB, Basta J, Keller MS, Melo Ferreira R, Akilesh S, Ghag R, Lu C, Cheng YH, Collins KS, Parikh SV, Rovin BH, Robbins L, Stout L, Conklin KY, Diep D, Zhang B, Knoten A, Barwinska D, Asghari M, Sabo AR, Ferkowicz MJ, Sutton TA, Kelly KJ, De Boer IH, Rosas SE, Kiryluk K, Hodgin JB, Alakwaa F, Winfree S, Jefferson N, Türkmen A, Gaut JP, Gehlenborg N, Phillips CL, El-Achkar TM, Dagher PC, Hato T, Zhang K, Himmelfarb J, Kretzler M, Mollah S, Jain S, Rauchman M, Eadon MT. The chromatin landscape of healthy and injured cell types in the human kidney. Nat Commun 2024; 15:433. [PMID: 38199997 PMCID: PMC10781985 DOI: 10.1038/s41467-023-44467-6] [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: 06/15/2023] [Accepted: 12/14/2023] [Indexed: 01/12/2024] Open
Abstract
There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. Comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measure dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We establish a spatially-anchored epigenomic atlas to define the kidney's active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we note distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3, KLF6, and KLF10 regulates the transition between health and injury, while in thick ascending limb cells this transition is regulated by NR2F1. Further, combined perturbation of ELF3, KLF6, and KLF10 distinguishes two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks.
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Affiliation(s)
- Debora L Gisch
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | | | - Blue B Lake
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- San Diego Institute of Science, Altos Labs, San Diego, CA, USA
| | - Jeannine Basta
- Washington University in Saint Louis, St. Louis, MO, 63103, USA
| | | | | | | | - Reetika Ghag
- Washington University in Saint Louis, St. Louis, MO, 63103, USA
| | - Charles Lu
- Washington University in Saint Louis, St. Louis, MO, 63103, USA
| | - Ying-Hua Cheng
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | | | - Samir V Parikh
- Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA
| | - Brad H Rovin
- Ohio State University Wexner Medical Center, Columbus, OH, 43210, USA
| | - Lynn Robbins
- St. Louis Veteran Affairs Medical Center, St. Louis, MO, 63106, USA
| | - Lisa Stout
- Washington University in Saint Louis, St. Louis, MO, 63103, USA
| | - Kimberly Y Conklin
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Dinh Diep
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Bo Zhang
- Washington University in Saint Louis, St. Louis, MO, 63103, USA
| | - Amanda Knoten
- Washington University in Saint Louis, St. Louis, MO, 63103, USA
| | - Daria Barwinska
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Mahla Asghari
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Angela R Sabo
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | | | - Timothy A Sutton
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | | | | | - Sylvia E Rosas
- Joslin Diabetes Center, Harvard Medical School, Boston, MA, 02215, USA
| | | | | | | | - Seth Winfree
- University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Nichole Jefferson
- Kidney Precision Medicine Project Community Engagement Committee, Dallas, TX, USA
| | - Aydın Türkmen
- Istanbul School of Medicine, Division of Nephrology, Istanbul, Turkey
| | - Joseph P Gaut
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | | | | | | | - Pierre C Dagher
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Takashi Hato
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Kun Zhang
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | | | | | - Shamim Mollah
- Washington University in Saint Louis, St. Louis, MO, 63103, USA
| | - Sanjay Jain
- Washington University in Saint Louis, St. Louis, MO, 63103, USA.
| | - Michael Rauchman
- Washington University in Saint Louis, St. Louis, MO, 63103, USA.
| | - Michael T Eadon
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
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10
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Louarn M, Collet G, Barré È, Fest T, Dameron O, Siegel A, Chatonnet F. Regulus infers signed regulatory relations from few samples' information using discretization and likelihood constraints. PLoS Comput Biol 2024; 20:e1011816. [PMID: 38252636 PMCID: PMC10833539 DOI: 10.1371/journal.pcbi.1011816] [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: 12/16/2022] [Revised: 02/01/2024] [Accepted: 01/08/2024] [Indexed: 01/24/2024] Open
Abstract
MOTIVATION Transcriptional regulation is performed by transcription factors (TF) binding to DNA in context-dependent regulatory regions and determines the activation or inhibition of gene expression. Current methods of transcriptional regulatory circuits inference, based on one or all of TF, regions and genes activity measurements require a large number of samples for ranking the candidate TF-gene regulation relations and rarely predict whether they are activations or inhibitions. We hypothesize that transcriptional regulatory circuits can be inferred from fewer samples by (1) fully integrating information on TF binding, gene expression and regulatory regions accessibility, (2) reducing data complexity and (3) using biology-based likelihood constraints to determine the global consistency between a candidate TF-gene relation and patterns of genes expressions and region activations, as well as qualify regulations as activations or inhibitions. RESULTS We introduce Regulus, a method which computes TF-gene relations from gene expressions, regulatory region activities and TF binding sites data, together with the genomic locations of all entities. After aggregating gene expressions and region activities into patterns, data are integrated into a RDF (Resource Description Framework) endpoint. A dedicated SPARQL (SPARQL Protocol and RDF Query Language) query retrieves all potential relations between expressed TF and genes involving active regulatory regions. These TF-region-gene relations are then filtered using biological likelihood constraints allowing to qualify them as activation or inhibition. Regulus provides signed relations consistent with public databases and, when applied to biological data, identifies both known and potential new regulators. Regulus is devoted to context-specific transcriptional circuits inference in human settings where samples are scarce and cell populations are closely related, using discretization into patterns and likelihood reasoning to decipher the most robust regulatory relations.
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Affiliation(s)
- Marine Louarn
- Univ Rennes, CNRS, Inria, IRISA - UMR 6074, Rennes, France
- UMR_S 1236, Université Rennes 1, INSERM, Etablissement Français du Sang, Rennes, France
| | | | - Ève Barré
- Univ Rennes, CNRS, Inria, IRISA - UMR 6074, Rennes, France
| | - Thierry Fest
- UMR_S 1236, Université Rennes 1, INSERM, Etablissement Français du Sang, Rennes, France
- Laboratoire d’Hématologie, Pôle de Biologie, CHU de Rennes, Rennes, France
| | | | - Anne Siegel
- Univ Rennes, CNRS, Inria, IRISA - UMR 6074, Rennes, France
| | - Fabrice Chatonnet
- UMR_S 1236, Université Rennes 1, INSERM, Etablissement Français du Sang, Rennes, France
- Laboratoire d’Hématologie, Pôle de Biologie, CHU de Rennes, Rennes, France
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11
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Miao Z, Kim J. Uniform quantification of single-nucleus ATAC-seq data with Paired-Insertion Counting (PIC) and a model-based insertion rate estimator. Nat Methods 2024; 21:32-36. [PMID: 38049698 PMCID: PMC10776405 DOI: 10.1038/s41592-023-02103-7] [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: 05/13/2022] [Accepted: 10/25/2023] [Indexed: 12/06/2023]
Abstract
Existing approaches to scoring single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq) feature matrices from sequencing reads are inconsistent, affecting downstream analyses and displaying artifacts. We show that, even with sparse single-cell data, quantitative counts are informative for estimating the regulatory state of a cell, which calls for a consistent treatment. We propose Paired-Insertion Counting as a uniform method for snATAC-seq feature characterization and provide a probability model for inferring latent insertion dynamics from snATAC-seq count matrices.
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Affiliation(s)
- Zhen Miao
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Junhyong Kim
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA.
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12
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Badia-I-Mompel P, Wessels L, Müller-Dott S, Trimbour R, Ramirez Flores RO, Argelaguet R, Saez-Rodriguez J. Gene regulatory network inference in the era of single-cell multi-omics. Nat Rev Genet 2023; 24:739-754. [PMID: 37365273 DOI: 10.1038/s41576-023-00618-5] [Citation(s) in RCA: 48] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/12/2023] [Indexed: 06/28/2023]
Abstract
The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory networks (GRNs). The study of GRNs is useful to understand how cellular identity is established, maintained and disrupted in disease. GRNs can be inferred from experimental data - historically, bulk omics data - and/or from the literature. The advent of single-cell multi-omics technologies has led to the development of novel computational methods that leverage genomic, transcriptomic and chromatin accessibility information to infer GRNs at an unprecedented resolution. Here, we review the key principles of inferring GRNs that encompass transcription factor-gene interactions from transcriptomics and chromatin accessibility data. We focus on the comparison and classification of methods that use single-cell multimodal data. We highlight challenges in GRN inference, in particular with respect to benchmarking, and potential further developments using additional data modalities.
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Affiliation(s)
- Pau Badia-I-Mompel
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Lorna Wessels
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
- Department of Vascular Biology and Tumor Angiogenesis, European Center for Angioscience, Medical Faculty, MannHeim Heidelberg University, Mannheim, Germany
| | - Sophia Müller-Dott
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Rémi Trimbour
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
- Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics Group, Paris, France
| | - Ricardo O Ramirez Flores
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | | | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany.
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13
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Kim D, Tran A, Kim HJ, Lin Y, Yang JYH, Yang P. Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data. NPJ Syst Biol Appl 2023; 9:51. [PMID: 37857632 PMCID: PMC10587078 DOI: 10.1038/s41540-023-00312-6] [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: 08/14/2023] [Accepted: 10/02/2023] [Indexed: 10/21/2023] Open
Abstract
Inferring gene regulatory networks (GRNs) is a fundamental challenge in biology that aims to unravel the complex relationships between genes and their regulators. Deciphering these networks plays a critical role in understanding the underlying regulatory crosstalk that drives many cellular processes and diseases. Recent advances in sequencing technology have led to the development of state-of-the-art GRN inference methods that exploit matched single-cell multi-omic data. By employing diverse mathematical and statistical methodologies, these methods aim to reconstruct more comprehensive and precise gene regulatory networks. In this review, we give a brief overview on the statistical and methodological foundations commonly used in GRN inference methods. We then compare and contrast the latest state-of-the-art GRN inference methods for single-cell matched multi-omics data, and discuss their assumptions, limitations and opportunities. Finally, we discuss the challenges and future directions that hold promise for further advancements in this rapidly developing field.
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Affiliation(s)
- Daniel Kim
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia
- Computational Systems Biology Unit, Children's Medical Research Institute, University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia
| | - Andy Tran
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia
- Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia
| | - Hani Jieun Kim
- Computational Systems Biology Unit, Children's Medical Research Institute, University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia
| | - Yingxin Lin
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia
- Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia
| | - Jean Yee Hwa Yang
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia.
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia.
- Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia.
| | - Pengyi Yang
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia.
- Computational Systems Biology Unit, Children's Medical Research Institute, University of Sydney, Camperdown, NSW, Australia.
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia.
- Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia.
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14
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Wang L, Trasanidis N, Wu T, Dong G, Hu M, Bauer DE, Pinello L. Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics. Nat Methods 2023; 20:1368-1378. [PMID: 37537351 DOI: 10.1038/s41592-023-01971-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 07/05/2023] [Indexed: 08/05/2023]
Abstract
Gene regulatory networks (GRNs) are key determinants of cell function and identity and are dynamically rewired during development and disease. Despite decades of advancement, challenges remain in GRN inference, including dynamic rewiring, causal inference, feedback loop modeling and context specificity. To address these challenges, we develop Dictys, a dynamic GRN inference and analysis method that leverages multiomic single-cell assays of chromatin accessibility and gene expression, context-specific transcription factor footprinting, stochastic process network and efficient probabilistic modeling of single-cell RNA-sequencing read counts. Dictys improves GRN reconstruction accuracy and reproducibility and enables the inference and comparative analysis of context-specific and dynamic GRNs across developmental contexts. Dictys' network analyses recover unique insights in human blood and mouse skin development with cell-type-specific and dynamic GRNs. Its dynamic network visualizations enable time-resolved discovery and investigation of developmental driver transcription factors and their regulated targets. Dictys is available as a free, open-source and user-friendly Python package.
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Affiliation(s)
- Lingfei Wang
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Department of Pathology, Harvard Medical School, Boston, MA, USA
- Gene Regulation Observatory, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nikolaos Trasanidis
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Department of Pathology, Harvard Medical School, Boston, MA, USA
- Hugh and Josseline Langmuir Centre for Myeloma Research, Centre for Haematology, Department of Immunology and Inflammation, Imperial College London, London, UK
| | - Ting Wu
- Division of Hematology/Oncology, Boston Children's Hospital, Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Stem Cell Institute, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Guanlan Dong
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Department of Pathology, Harvard Medical School, Boston, MA, USA
- Division of Genetics and Genomics, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Bioinformatics and Integrative Genomics PhD Program, Harvard Medical School, Boston, MA, USA
| | - Michael Hu
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Daniel E Bauer
- Gene Regulation Observatory, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of Hematology/Oncology, Boston Children's Hospital, Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Stem Cell Institute, Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Luca Pinello
- Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Department of Pathology, Harvard Medical School, Boston, MA, USA.
- Gene Regulation Observatory, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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15
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Chen X, Wang Y, Cappuccio A, Cheng WS, Zamojski FR, Nair VD, Miller CM, Rubenstein AB, Nudelman G, Tadych A, Theesfeld CL, Vornholt A, George MC, Ruffin F, Dagher M, Chawla DG, Soares-Schanoski A, Spurbeck RR, Ndhlovu LC, Sebra R, Kleinstein SH, Letizia AG, Ramos I, Fowler VG, Woods CW, Zaslavsky E, Troyanskaya OG, Sealfon SC. Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data. NATURE COMPUTATIONAL SCIENCE 2023; 3:644-657. [PMID: 37974651 PMCID: PMC10653299 DOI: 10.1038/s43588-023-00476-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 06/06/2023] [Indexed: 11/19/2023]
Abstract
Resolving chromatin-remodeling-linked gene expression changes at cell-type resolution is important for understanding disease states. Here we describe MAGICAL (Multiome Accessibility Gene Integration Calling and Looping), a hierarchical Bayesian approach that leverages paired single-cell RNA sequencing and single-cell transposase-accessible chromatin sequencing from different conditions to map disease-associated transcription factors, chromatin sites, and genes as regulatory circuits. By simultaneously modeling signal variation across cells and conditions in both omics data types, MAGICAL achieved high accuracy on circuit inference. We applied MAGICAL to study Staphylococcus aureus sepsis from peripheral blood mononuclear single-cell data that we generated from subjects with bloodstream infection and uninfected controls. MAGICAL identified sepsis-associated regulatory circuits predominantly in CD14 monocytes, known to be activated by bacterial sepsis. We addressed the challenging problem of distinguishing host regulatory circuit responses to methicillin-resistant and methicillin-susceptible S. aureus infections. Although differential expression analysis failed to show predictive value, MAGICAL identified epigenetic circuit biomarkers that distinguished methicillin-resistant from methicillin-susceptible S. aureus infections.
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Affiliation(s)
- Xi Chen
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Yuan Wang
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Antonio Cappuccio
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Wan-Sze Cheng
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Venugopalan D. Nair
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Clare M. Miller
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Aliza B. Rubenstein
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - German Nudelman
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alicja Tadych
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Chandra L. Theesfeld
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Alexandria Vornholt
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Felicia Ruffin
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Michael Dagher
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Daniel G. Chawla
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | | | | | - Lishomwa C. Ndhlovu
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Robert Sebra
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Steven H. Kleinstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Department of Pathology and Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | | | - Irene Ramos
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Vance G. Fowler
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Christopher W. Woods
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- These authors jointly supervised this work: Elena Zaslavsky, Olga G. Troyanskaya, Stuart C. Sealfon
| | - Olga G. Troyanskaya
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- These authors jointly supervised this work: Elena Zaslavsky, Olga G. Troyanskaya, Stuart C. Sealfon
| | - Stuart C. Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- These authors jointly supervised this work: Elena Zaslavsky, Olga G. Troyanskaya, Stuart C. Sealfon
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16
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Gisch DL, Brennan M, Lake BB, Basta J, Keller M, Ferreira RM, Akilesh S, Ghag R, Lu C, Cheng YH, Collins KS, Parikh SV, Rovin BH, Robbins L, Conklin KY, Diep D, Zhang B, Knoten A, Barwinska D, Asghari M, Sabo AR, Ferkowicz MJ, Sutton TA, Kelly KJ, Boer IHD, Rosas SE, Kiryluk K, Hodgin JB, Alakwaa F, Jefferson N, Gaut JP, Gehlenborg N, Phillips CL, El-Achkar TM, Dagher PC, Hato T, Zhang K, Himmelfarb J, Kretzler M, Mollah S, Jain S, Rauchman M, Eadon MT. The chromatin landscape of healthy and injured cell types in the human kidney. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.07.543965. [PMID: 37333123 PMCID: PMC10274789 DOI: 10.1101/2023.06.07.543965] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
There is a need to define regions of gene activation or repression that control human kidney cells in states of health, injury, and repair to understand the molecular pathogenesis of kidney disease and design therapeutic strategies. However, comprehensive integration of gene expression with epigenetic features that define regulatory elements remains a significant challenge. We measured dual single nucleus RNA expression and chromatin accessibility, DNA methylation, and H3K27ac, H3K4me1, H3K4me3, and H3K27me3 histone modifications to decipher the chromatin landscape and gene regulation of the kidney in reference and adaptive injury states. We established a comprehensive and spatially-anchored epigenomic atlas to define the kidney's active, silent, and regulatory accessible chromatin regions across the genome. Using this atlas, we noted distinct control of adaptive injury in different epithelial cell types. A proximal tubule cell transcription factor network of ELF3 , KLF6 , and KLF10 regulated the transition between health and injury, while in thick ascending limb cells this transition was regulated by NR2F1 . Further, combined perturbation of ELF3 , KLF6 , and KLF10 distinguished two adaptive proximal tubular cell subtypes, one of which manifested a repair trajectory after knockout. This atlas will serve as a foundation to facilitate targeted cell-specific therapeutics by reprogramming gene regulatory networks.
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17
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Lin Y, Wu TY, Chen X, Wan S, Chao B, Xin J, Yang JY, Wong WH, Wang YXR. scTIE: data integration and inference of gene regulation using single-cell temporal multimodal data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.18.541381. [PMID: 37292801 PMCID: PMC10245711 DOI: 10.1101/2023.05.18.541381] [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: 06/10/2023]
Abstract
Single-cell technologies offer unprecedented opportunities to dissect gene regulatory mechanisms in context-specific ways. Although there are computational methods for extracting gene regulatory relationships from scRNA-seq and scATAC-seq data, the data integration problem, essential for accurate cell type identification, has been mostly treated as a standalone challenge. Here we present scTIE, a unified method that integrates temporal multimodal data and infers regulatory relationships predictive of cellular state changes. scTIE uses an autoencoder to embed cells from all time points into a common space using iterative optimal transport, followed by extracting interpretable information to predict cell trajectories. Using a variety of synthetic and real temporal multimodal datasets, we demonstrate scTIE achieves effective data integration while preserving more biological signals than existing methods, particularly in the presence of batch effects and noise. Furthermore, on the exemplar multiome dataset we generated from differentiating mouse embryonic stem cells over time, we demonstrate scTIE captures regulatory elements highly predictive of cell transition probabilities, providing new potentials to understand the regulatory landscape driving developmental processes.
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Affiliation(s)
- Yingxin Lin
- School of Mathematics and Statistics, The University of Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, NSW, Australia
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
| | - Tung-Yu Wu
- Department of Statistics, Stanford University, CA, USA
| | - Xi Chen
- Department of Statistics, Stanford University, CA, USA
| | - Sheng Wan
- Institute of Electronics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Brian Chao
- Department of Electrical Engineering, Stanford University, CA, USA
| | - Jingxue Xin
- Department of Statistics, Stanford University, CA, USA
| | - Jean Y.H. Yang
- School of Mathematics and Statistics, The University of Sydney, NSW, Australia
- Charles Perkins Centre, The University of Sydney, NSW, Australia
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
| | - Wing H. Wong
- Department of Statistics, Stanford University, CA, USA
- Department of Biomedical Data Science, Stanford University, CA, USA
- Bio-X Program, Stanford University, CA, USA
| | - Y. X. Rachel Wang
- School of Mathematics and Statistics, The University of Sydney, NSW, Australia
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18
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Guan PY, Lee JS, Wang L, Lin KZ, Mei W, Chen L, Jiang Y. Destin2: Integrative and cross-modality analysis of single-cell chromatin accessibility data. Front Genet 2023; 14:1089936. [PMID: 36873935 PMCID: PMC9981783 DOI: 10.3389/fgene.2023.1089936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 02/06/2023] [Indexed: 02/19/2023] Open
Abstract
We propose Destin2, a novel statistical and computational method for cross-modality dimension reduction, clustering, and trajectory reconstruction for single-cell ATAC-seq data. The framework integrates cellular-level epigenomic profiles from peak accessibility, motif deviation score, and pseudo-gene activity and learns a shared manifold using the multimodal input, followed by clustering and/or trajectory inference. We apply Destin2 to real scATAC-seq datasets with both discretized cell types and transient cell states and carry out benchmarking studies against existing methods based on unimodal analyses. Using cell-type labels transferred with high confidence from unmatched single-cell RNA sequencing data, we adopt four performance assessment metrics and demonstrate how Destin2 corroborates and improves upon existing methods. Using single-cell RNA and ATAC multiomic data, we further exemplify how Destin2's cross-modality integrative analyses preserve true cell-cell similarities using the matched cell pairs as ground truths. Destin2 is compiled as a freely available R package available at https://github.com/yuchaojiang/Destin2.
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Affiliation(s)
- Peter Y Guan
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, Unites States
| | - Jin Seok Lee
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, Unites States
| | - Lihao Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, Unites States
| | - Kevin Z Lin
- Department of Statistics, University of Pennsylvania, Philadelphia, PA, Unites States
| | - Wenwen Mei
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, Unites States
| | - Li Chen
- Department of Biostatistics, University of Florida, Gainesville, FL, Unites States
| | - Yuchao Jiang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, Unites States.,Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, Unites States.,Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, Unites States
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