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Jia Y, Dong H, Li L, Wang F, Juan L, Wang Y, Guo H, Zhao T. xQTLatlas: a comprehensive resource for human cellular-resolution multi-omics genetic regulatory landscape. Nucleic Acids Res 2024:gkae837. [PMID: 39351883 DOI: 10.1093/nar/gkae837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Revised: 08/26/2024] [Accepted: 09/13/2024] [Indexed: 10/03/2024] Open
Abstract
Understanding how genetic variants influence molecular phenotypes in different cellular contexts is crucial for elucidating the molecular and cellular mechanisms behind complex traits, which in turn has spurred significant advances in research into molecular quantitative trait locus (xQTL) at the cellular level. With the rapid proliferation of data, there is a critical need for a comprehensive and accessible platform to integrate this information. To meet this need, we developed xQTLatlas (http://www.hitxqtl.org.cn/), a database that provides a multi-omics genetic regulatory landscape at cellular resolution. xQTLatlas compiles xQTL summary statistics from 151 cell types and 339 cell states across 55 human tissues. It organizes these data into 20 xQTL types, based on four distinct discovery strategies, and spans 13 molecular phenotypes. Each entry in xQTLatlas is meticulously annotated with comprehensive metadata, including the origin of the tissue, cell type, cell state and the QTL discovery strategies utilized. Additionally, xQTLatlas features multiscale data exploration tools and a suite of interactive visualizations, facilitating in-depth analysis of cell-level xQTL. xQTLatlas provides a valuable resource for deepening our understanding of the impact of functional variants on molecular phenotypes in different cellular environments, thereby facilitating extensive research efforts.
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Affiliation(s)
- Yuran Jia
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Hongchao Dong
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Linhao Li
- School of Medicine and Health, Harbin Institute of Technology, Harbin 150001, China
| | - Fang Wang
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
| | - Liran Juan
- School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Yadong Wang
- School of Medicine and Health, Harbin Institute of Technology, Harbin 150001, China
- Zhengzhou Research Institute, Harbin Institute of Technology, Harbin 450000, China
| | - Hongzhe Guo
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
- Zhengzhou Research Institute, Harbin Institute of Technology, Harbin 450000, China
| | - Tianyi Zhao
- Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
- School of Medicine and Health, Harbin Institute of Technology, Harbin 150001, China
- Zhengzhou Research Institute, Harbin Institute of Technology, Harbin 450000, China
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2
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Kim SK, Seo S, Stein-O'Brien G, Jaishankar A, Ogawa K, Micali N, Luria V, Karger A, Wang Y, Kim H, Hyde TM, Kleinman JE, Voss T, Fertig EJ, Shin JH, Bürli R, Cross AJ, Brandon NJ, Weinberger DR, Chenoweth JG, Hoeppner DJ, Sestan N, Colantuoni C, McKay RD. Individual variation in the emergence of anterior-to-posterior neural fates from human pluripotent stem cells. Stem Cell Reports 2024; 19:1336-1350. [PMID: 39151428 PMCID: PMC11411333 DOI: 10.1016/j.stemcr.2024.07.004] [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: 09/18/2023] [Revised: 07/16/2024] [Accepted: 07/16/2024] [Indexed: 08/19/2024] Open
Abstract
Variability between human pluripotent stem cell (hPSC) lines remains a challenge and opportunity in biomedicine. In this study, hPSC lines from multiple donors were differentiated toward neuroectoderm and mesendoderm lineages. We revealed dynamic transcriptomic patterns that delineate the emergence of these lineages, which were conserved across lines, along with individual line-specific transcriptional signatures that were invariant throughout differentiation. These transcriptomic signatures predicted an antagonism between SOX21-driven forebrain fates and retinoic acid-induced hindbrain fates. Replicate lines and paired adult tissue demonstrated the stability of these line-specific transcriptomic traits. We show that this transcriptomic variation in lineage bias had both genetic and epigenetic origins, aligned with the anterior-to-posterior structure of early mammalian development, and was present across a large collection of hPSC lines. These findings contribute to developing systematic analyses of PSCs to define the origin and consequences of variation in the early events orchestrating individual human development.
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Affiliation(s)
- Suel-Kee Kim
- Lieber Institute for Brain Development, 855 North Wolfe Street, Baltimore, MD 21205, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | - Seungmae Seo
- Lieber Institute for Brain Development, 855 North Wolfe Street, Baltimore, MD 21205, USA
| | | | - Amritha Jaishankar
- Lieber Institute for Brain Development, 855 North Wolfe Street, Baltimore, MD 21205, USA
| | - Kazuya Ogawa
- Lieber Institute for Brain Development, 855 North Wolfe Street, Baltimore, MD 21205, USA
| | - Nicola Micali
- Lieber Institute for Brain Development, 855 North Wolfe Street, Baltimore, MD 21205, USA; Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | - Victor Luria
- Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA; Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Division of Genetics and Genomics, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Amir Karger
- IT-Research Computing, Harvard Medical School, Boston, MA 02115, USA
| | - Yanhong Wang
- Lieber Institute for Brain Development, 855 North Wolfe Street, Baltimore, MD 21205, USA
| | - Hyojin Kim
- Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, 855 North Wolfe Street, Baltimore, MD 21205, USA; Departments of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA; Departments of Psychiatry, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, 855 North Wolfe Street, Baltimore, MD 21205, USA; Departments of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Ty Voss
- Division of Preclinical Innovation, Nation Center for Advancing Translational Science, NIH, Bethesda, MD 20892, USA
| | - Elana J Fertig
- Departments of Oncology, Biomedical Engineering, and Applied Mathematics and Statistics, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Joo-Heon Shin
- Lieber Institute for Brain Development, 855 North Wolfe Street, Baltimore, MD 21205, USA
| | - Roland Bürli
- Astra-Zeneca Neuroscience iMED., 141 Portland Street, Cambridge, MA 01239, USA
| | - Alan J Cross
- Astra-Zeneca Neuroscience iMED., 141 Portland Street, Cambridge, MA 01239, USA
| | - Nicholas J Brandon
- Astra-Zeneca Neuroscience iMED., 141 Portland Street, Cambridge, MA 01239, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, 855 North Wolfe Street, Baltimore, MD 21205, USA; Departments of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA; Departments of Psychiatry, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA; Departments of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA; McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Joshua G Chenoweth
- Lieber Institute for Brain Development, 855 North Wolfe Street, Baltimore, MD 21205, USA
| | - Daniel J Hoeppner
- Lieber Institute for Brain Development, 855 North Wolfe Street, Baltimore, MD 21205, USA
| | - Nenad Sestan
- Department of Neuroscience, Yale School of Medicine, New Haven, CT 06510, USA; Departments of Genetics, Psychiatry, and Comparative Medicine, Kavli Institute for Neuroscience, Program in Cellular Neuroscience, Neurodegeneration and Repair, Child Study Center, Yale School of Medicine, New Haven, CT 06510, USA.
| | - Carlo Colantuoni
- Lieber Institute for Brain Development, 855 North Wolfe Street, Baltimore, MD 21205, USA; Departments of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA; Departments of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA; Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
| | - Ronald D McKay
- Lieber Institute for Brain Development, 855 North Wolfe Street, Baltimore, MD 21205, USA; Departments of Cell Biology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA.
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3
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Boocock J, Alexander N, Tapia LA, Walter-McNeill L, Munugala C, Bloom JS, Kruglyak L. Single-cell eQTL mapping in yeast reveals a tradeoff between growth and reproduction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.07.570640. [PMID: 38106186 PMCID: PMC10723400 DOI: 10.1101/2023.12.07.570640] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Expression quantitative trait loci (eQTLs) provide a key bridge between noncoding DNA sequence variants and organismal traits. The effects of eQTLs can differ among tissues, cell types, and cellular states, but these differences are obscured by gene expression measurements in bulk populations. We developed a one-pot approach to map eQTLs in Saccharomyces cerevisiae by single-cell RNA sequencing (scRNA-seq) and applied it to over 100,000 single cells from three crosses. We used scRNA-seq data to genotype each cell, measure gene expression, and classify the cells by cell-cycle stage. We mapped thousands of local and distant eQTLs and identified interactions between eQTL effects and cell-cycle stages. We took advantage of single-cell expression information to identify hundreds of genes with allele-specific effects on expression noise. We used cell-cycle stage classification to map 20 loci that influence cell-cycle progression. One of these loci influenced the expression of genes involved in the mating response. We showed that the effects of this locus arise from a common variant (W82R) in the gene GPA1 , which encodes a signaling protein that negatively regulates the mating pathway. The 82R allele increases mating efficiency at the cost of slower cell-cycle progression and is associated with a higher rate of outcrossing in nature. Our results provide a more granular picture of the effects of genetic variants on gene expression and downstream traits.
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4
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Qi G, Battle A. Computational methods for allele-specific expression in single cells. Trends Genet 2024:S0168-9525(24)00169-0. [PMID: 39127549 DOI: 10.1016/j.tig.2024.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 07/16/2024] [Accepted: 07/17/2024] [Indexed: 08/12/2024]
Abstract
Allele-specific expression (ASE) is a powerful signal that can be used to investigate multiple molecular mechanisms, such as cis-regulatory effects and imprinting. Single-cell RNA-sequencing (scRNA-seq) enables ASE characterization at the resolution of individual cells. In this review, we highlight the computational methods for processing and analyzing single-cell ASE data. We first describe a bioinformatics pipeline to obtain ASE counts from raw reads synthesized from previous literature. We then discuss statistical methods for detecting allelic imbalance and its variability across conditions using scRNA-seq data. In addition, we describe other methods that use single-cell ASE to address specific biological questions. Finally, we discuss future directions and emphasize the need for an integrated, optimized bioinformatics pipeline, and further development of statistical methods for different technologies.
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Affiliation(s)
- Guanghao Qi
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA; Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
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5
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Gordon MG, Kathail P, Choy B, Kim MC, Mazumder T, Gearing M, Ye CJ. Population Diversity at the Single-Cell Level. Annu Rev Genomics Hum Genet 2024; 25:27-49. [PMID: 38382493 DOI: 10.1146/annurev-genom-021623-083207] [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] [Indexed: 02/23/2024]
Abstract
Population-scale single-cell genomics is a transformative approach for unraveling the intricate links between genetic and cellular variation. This approach is facilitated by cutting-edge experimental methodologies, including the development of high-throughput single-cell multiomics and advances in multiplexed environmental and genetic perturbations. Examining the effects of natural or synthetic genetic variants across cellular contexts provides insights into the mutual influence of genetics and the environment in shaping cellular heterogeneity. The development of computational methodologies further enables detailed quantitative analysis of molecular variation, offering an opportunity to examine the respective roles of stochastic, intercellular, and interindividual variation. Future opportunities lie in leveraging long-read sequencing, refining disease-relevant cellular models, and embracing predictive and generative machine learning models. These advancements hold the potential for a deeper understanding of the genetic architecture of human molecular traits, which in turn has important implications for understanding the genetic causes of human disease.
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Affiliation(s)
| | - Pooja Kathail
- Center for Computational Biology, University of California, Berkeley, California, USA
| | - Bryson Choy
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, California, USA
- Institute for Human Genetics, University of California, San Francisco, California, USA
| | - Min Cheol Kim
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, California, USA
- Institute for Human Genetics, University of California, San Francisco, California, USA
| | - Thomas Mazumder
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, California, USA
- Institute for Human Genetics, University of California, San Francisco, California, USA
| | - Melissa Gearing
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, California, USA
- Institute for Human Genetics, University of California, San Francisco, California, USA
| | - Chun Jimmie Ye
- Arc Institute, Palo Alto, California, USA
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, California, USA
- Institute for Human Genetics, University of California, San Francisco, California, USA
- Bakar Computational Health Sciences Institute, Gladstone-UCSF Institute of Genomic Immunology, Parker Institute for Cancer Immunotherapy, Department of Epidemiology and Biostatistics, Department of Microbiology and Immunology, and Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, California, USA;
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6
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Qi T, Song L, Guo Y, Chen C, Yang J. From genetic associations to genes: methods, applications, and challenges. Trends Genet 2024; 40:642-667. [PMID: 38734482 DOI: 10.1016/j.tig.2024.04.008] [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: 11/08/2023] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/13/2024]
Abstract
Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.
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Affiliation(s)
- Ting Qi
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
| | - Liyang Song
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Yazhou Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Chang Chen
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Jian Yang
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
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7
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Vattulainen M, Smits JGA, Arts JA, Lima Cunha D, Ilmarinen T, Skottman H, Zhou H. Deciphering the heterogeneity of differentiating hPSC-derived corneal limbal stem cells through single-cell RNA sequencing. Stem Cell Reports 2024; 19:1010-1023. [PMID: 38942029 PMCID: PMC11252539 DOI: 10.1016/j.stemcr.2024.06.001] [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: 12/15/2023] [Revised: 05/31/2024] [Accepted: 06/03/2024] [Indexed: 06/30/2024] Open
Abstract
A comprehensive understanding of the human pluripotent stem cell (hPSC) differentiation process stands as a prerequisite for the development of hPSC-based therapeutics. In this study, single-cell RNA sequencing (scRNA-seq) was performed to decipher the heterogeneity during differentiation of three hPSC lines toward corneal limbal stem cells (LSCs). The scRNA-seq data revealed nine clusters encompassing the entire differentiation process, among which five followed the anticipated differentiation path of LSCs. The remaining four clusters were previously undescribed cell states that were annotated as either mesodermal-like or undifferentiated subpopulations, and their prevalence was hPSC line dependent. Distinct cluster-specific marker genes identified in this study were confirmed by immunofluorescence analysis and employed to purify hPSC-derived LSCs, which effectively minimized the variation in the line-dependent differentiation efficiency. In summary, scRNA-seq offered molecular insights into the heterogeneity of hPSC-LSC differentiation, allowing a data-driven strategy for consistent and robust generation of LSCs, essential for future advancement toward clinical translation.
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Affiliation(s)
- Meri Vattulainen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Jos G A Smits
- Department of Molecular Developmental Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, the Netherlands; Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Julian A Arts
- Department of Molecular Developmental Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, the Netherlands
| | - Dulce Lima Cunha
- Department of Molecular Developmental Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, the Netherlands
| | - Tanja Ilmarinen
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
| | - Heli Skottman
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
| | - Huiqing Zhou
- Department of Molecular Developmental Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, the Netherlands; Department of Human Genetics, Radboud University Medical Center, Nijmegen, the Netherlands.
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8
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Antón-Bolaños N, Faravelli I, Faits T, Andreadis S, Kastli R, Trattaro S, Adiconis X, Wei A, Sampath Kumar A, Di Bella DJ, Tegtmeyer M, Nehme R, Levin JZ, Regev A, Arlotta P. Brain Chimeroids reveal individual susceptibility to neurotoxic triggers. Nature 2024; 631:142-149. [PMID: 38926573 PMCID: PMC11338177 DOI: 10.1038/s41586-024-07578-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 05/17/2024] [Indexed: 06/28/2024]
Abstract
Interindividual genetic variation affects the susceptibility to and progression of many diseases1,2. However, efforts to study how individual human brains differ in normal development and disease phenotypes are limited by the paucity of faithful cellular human models, and the difficulty of scaling current systems to represent multiple people. Here we present human brain Chimeroids, a highly reproducible, multidonor human brain cortical organoid model generated by the co-development of cells from a panel of individual donors in a single organoid. By reaggregating cells from multiple single-donor organoids at the neural stem cell or neural progenitor cell stage, we generate Chimeroids in which each donor produces all cell lineages of the cerebral cortex, even when using pluripotent stem cell lines with notable growth biases. We used Chimeroids to investigate interindividual variation in the susceptibility to neurotoxic triggers that exhibit high clinical phenotypic variability: ethanol and the antiepileptic drug valproic acid. Individual donors varied in both the penetrance of the effect on target cell types, and the molecular phenotype within each affected cell type. Our results suggest that human genetic background may be an important mediator of neurotoxin susceptibility and introduce Chimeroids as a scalable system for high-throughput investigation of interindividual variation in processes of brain development and disease.
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Affiliation(s)
- Noelia Antón-Bolaños
- Department of Stem Cell & Regenerative Biology, Harvard University, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Irene Faravelli
- Department of Stem Cell & Regenerative Biology, Harvard University, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Tyler Faits
- Department of Stem Cell & Regenerative Biology, Harvard University, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sophia Andreadis
- Department of Stem Cell & Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Rahel Kastli
- Department of Stem Cell & Regenerative Biology, Harvard University, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sebastiano Trattaro
- Department of Stem Cell & Regenerative Biology, Harvard University, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Xian Adiconis
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Anqi Wei
- Department of Stem Cell & Regenerative Biology, Harvard University, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Abhishek Sampath Kumar
- Department of Stem Cell & Regenerative Biology, Harvard University, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniela J Di Bella
- Department of Stem Cell & Regenerative Biology, Harvard University, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matthew Tegtmeyer
- Department of Stem Cell & Regenerative Biology, Harvard University, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ralda Nehme
- Department of Stem Cell & Regenerative Biology, Harvard University, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joshua Z Levin
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aviv Regev
- Broad Institute of MIT and Harvard, Boston, MA, USA
- Genentech, San Francisco, CA, USA
| | - Paola Arlotta
- Department of Stem Cell & Regenerative Biology, Harvard University, Cambridge, MA, USA.
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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9
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Zaragoza MV, Bui TA, Widyastuti HP, Mehrabi M, Cang Z, Sha Y, Grosberg A, Nie Q. LMNA -Related Dilated Cardiomyopathy: Single-Cell Transcriptomics during Patient-derived iPSC Differentiation Support Cell type and Lineage-specific Dysregulation of Gene Expression and Development for Cardiomyocytes and Epicardium-Derived Cells with Lamin A/C Haploinsufficiency. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.12.598335. [PMID: 38915555 PMCID: PMC11195187 DOI: 10.1101/2024.06.12.598335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
LMNA -Related Dilated Cardiomyopathy (DCM) is an autosomal-dominant genetic condition with cardiomyocyte and conduction system dysfunction often resulting in heart failure or sudden death. The condition is caused by mutation in the Lamin A/C ( LMNA ) gene encoding Type-A nuclear lamin proteins involved in nuclear integrity, epigenetic regulation of gene expression, and differentiation. Molecular mechanisms of disease are not completely understood, and there are no definitive treatments to reverse progression or prevent mortality. We investigated possible mechanisms of LMNA -Related DCM using induced pluripotent stem cells derived from a family with a heterozygous LMNA c.357-2A>G splice-site mutation. We differentiated one LMNA mutant iPSC line derived from an affected female (Patient) and two non-mutant iPSC lines derived from her unaffected sister (Control) and conducted single-cell RNA sequencing for 12 samples (4 Patient and 8 Control) across seven time points: Day 0, 2, 4, 9, 16, 19, and 30. Our bioinformatics workflow identified 125,554 cells in raw data and 110,521 (88%) high-quality cells in sequentially processed data. Unsupervised clustering, cell annotation, and trajectory inference found complex heterogeneity: ten main cell types; many possible subtypes; and lineage bifurcation for Cardiac Progenitors to Cardiomyocytes (CM) and Epicardium-Derived Cells (EPDC). Data integration and comparative analyses of Patient and Control cells found cell type and lineage differentially expressed genes (DEG) with enrichment to support pathway dysregulation. Top DEG and enriched pathways included: 10 ZNF genes and RNA polymerase II transcription in Pluripotent cells (PP); BMP4 and TGF Beta/BMP signaling, sarcomere gene subsets and cardiogenesis, CDH2 and EMT in CM; LMNA and epigenetic regulation and DDIT4 and mTORC1 signaling in EPDC. Top DEG also included: XIST and other X-linked genes, six imprinted genes: SNRPN , PWAR6 , NDN , PEG10 , MEG3 , MEG8 , and enriched gene sets in metabolism, proliferation, and homeostasis. We confirmed Lamin A/C haploinsufficiency by allelic expression and Western blot. Our complex Patient-derived iPSC model for Lamin A/C haploinsufficiency in PP, CM, and EPDC provided support for dysregulation of genes and pathways, many previously associated with Lamin A/C defects, such as epigenetic gene expression, signaling, and differentiation. Our findings support disruption of epigenomic developmental programs as proposed in other LMNA disease models. We recognized other factors influencing epigenetics and differentiation; thus, our approach needs improvement to further investigate this mechanism in an iPSC-derived model.
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10
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Glenn RA, Do SC, Guruvayurappan K, Corrigan EK, Santini L, Medina-Cano D, Singer S, Cho H, Liu J, Broman K, Czechanski A, Reinholdt L, Koche R, Furuta Y, Kunz M, Vierbuchen T. A PLURIPOTENT STEM CELL PLATFORM FOR IN VITRO SYSTEMS GENETICS STUDIES OF MOUSE DEVELOPMENT. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.06.597758. [PMID: 38895226 PMCID: PMC11185710 DOI: 10.1101/2024.06.06.597758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
The directed differentiation of pluripotent stem cells (PSCs) from panels of genetically diverse individuals is emerging as a powerful experimental system for characterizing the impact of natural genetic variation on developing cell types and tissues. Here, we establish new PSC lines and experimental approaches for modeling embryonic development in a genetically diverse, outbred mouse stock (Diversity Outbred mice). We show that a range of inbred and outbred PSC lines can be stably maintained in the primed pluripotent state (epiblast stem cells -- EpiSCs) and establish the contribution of genetic variation to phenotypic differences in gene regulation and directed differentiation. Using pooled in vitro fertilization, we generate and characterize a genetic reference panel of Diversity Outbred PSCs (n = 230). Finally, we demonstrate the feasibility of pooled culture of Diversity Outbred EpiSCs as "cell villages", which can facilitate the differentiation of large numbers of EpiSC lines for forward genetic screens. These data can complement and inform similar efforts within the stem cell biology and human genetics communities to model the impact of natural genetic variation on phenotypic variation and disease-risk.
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Affiliation(s)
- Rachel A. Glenn
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Center for Stem Cell Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Cell and Developmental Biology Program, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY, USA
| | - Stephanie C. Do
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Center for Stem Cell Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Emily K. Corrigan
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Center for Stem Cell Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Present address: Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA and Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Laura Santini
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Center for Stem Cell Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Daniel Medina-Cano
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Center for Stem Cell Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sarah Singer
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hyein Cho
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Center for Stem Cell Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jing Liu
- Mouse Genetics Core Facility, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Karl Broman
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI USA
| | | | | | - Richard Koche
- Center for Epigenetics Research, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yasuhide Furuta
- Mouse Genetics Core Facility, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meik Kunz
- The Bioinformatics CRO, Sanford Florida, 32771 USA
| | - Thomas Vierbuchen
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Center for Stem Cell Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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11
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Sawada T, Barbosa AR, Araujo B, McCord AE, D’Ignazio L, Benjamin KJM, Sheehan B, Zabolocki M, Feltrin A, Arora R, Brandtjen AC, Kleinman JE, Hyde TM, Bardy C, Weinberger DR, Paquola ACM, Erwin JA. Recapitulation of Perturbed Striatal Gene Expression Dynamics of Donors' Brains With Ventral Forebrain Organoids Derived From the Same Individuals With Schizophrenia. Am J Psychiatry 2024; 181:493-511. [PMID: 37915216 PMCID: PMC11209846 DOI: 10.1176/appi.ajp.20220723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2023]
Abstract
OBJECTIVE Schizophrenia is a brain disorder that originates during neurodevelopment and has complex genetic and environmental etiologies. Despite decades of clinical evidence of altered striatal function in affected patients, studies examining its cellular and molecular mechanisms in humans are limited. To explore neurodevelopmental alterations in the striatum associated with schizophrenia, the authors established a method for the differentiation of induced pluripotent stem cells (iPSCs) into ventral forebrain organoids (VFOs). METHODS VFOs were generated from postmortem dural fibroblast-derived iPSCs of four individuals with schizophrenia and four neurotypical control individuals for whom postmortem caudate genotypes and transcriptomic data were profiled in the BrainSeq neurogenomics consortium. Individuals were selected such that the two groups had nonoverlapping schizophrenia polygenic risk scores (PRSs). RESULTS Single-cell RNA sequencing analyses of VFOs revealed differences in developmental trajectory between schizophrenia and control individuals in which inhibitory neuronal cells from the patients exhibited accelerated maturation. Furthermore, upregulated genes in inhibitory neurons in schizophrenia VFOs showed a significant overlap with upregulated genes in postmortem caudate tissue of individuals with schizophrenia compared with control individuals, including the donors of the iPSC cohort. CONCLUSIONS The findings suggest that striatal neurons derived from high-PRS individuals with schizophrenia carry abnormalities that originated during early brain development and that the VFO model can recapitulate disease-relevant cell type-specific neurodevelopmental phenotypes in a dish.
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Affiliation(s)
- Tomoyo Sawada
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | | | - Bruno Araujo
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | | | - Laura D’Ignazio
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Kynon J. M. Benjamin
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Bonna Sheehan
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Michael Zabolocki
- South Australian Health and Medical Research Institute (SAHMRI), Laboratory for Human Neurophysiology and Genetics, Adelaide, SA, Australia
- Flinders University, Flinders Health and Medical Research Institute (FHMRI), College of Medicine and Public Health, Adelaide, SA, Australia
| | - Arthur Feltrin
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Ria Arora
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | | | - Joel E. Kleinman
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Cedric Bardy
- South Australian Health and Medical Research Institute (SAHMRI), Laboratory for Human Neurophysiology and Genetics, Adelaide, SA, Australia
- Flinders University, Flinders Health and Medical Research Institute (FHMRI), College of Medicine and Public Health, Adelaide, SA, Australia
| | - Daniel R. Weinberger
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Apuā C. M. Paquola
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Jennifer A. Erwin
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
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12
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Wang L, Khunsriraksakul C, Markus H, Chen D, Zhang F, Chen F, Zhan X, Carrel L, Liu DJ, Jiang B. Integrating single cell expression quantitative trait loci summary statistics to understand complex trait risk genes. Nat Commun 2024; 15:4260. [PMID: 38769300 DOI: 10.1038/s41467-024-48143-1] [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: 02/09/2023] [Accepted: 04/22/2024] [Indexed: 05/22/2024] Open
Abstract
Transcriptome-wide association study (TWAS) is a popular approach to dissect the functional consequence of disease associated non-coding variants. Most existing TWAS use bulk tissues and may not have the resolution to reveal cell-type specific target genes. Single-cell expression quantitative trait loci (sc-eQTL) datasets are emerging. The largest bulk- and sc-eQTL datasets are most conveniently available as summary statistics, but have not been broadly utilized in TWAS. Here, we present a new method EXPRESSO (EXpression PREdiction with Summary Statistics Only), to analyze sc-eQTL summary statistics, which also integrates 3D genomic data and epigenomic annotation to prioritize causal variants. EXPRESSO substantially improves existing methods. We apply EXPRESSO to analyze multi-ancestry GWAS datasets for 14 autoimmune diseases. EXPRESSO uniquely identifies 958 novel gene x trait associations, which is 26% more than the second-best method. Among them, 492 are unique to cell type level analysis and missed by TWAS using whole blood. We also develop a cell type aware drug repurposing pipeline, which leverages EXPRESSO results to identify drug compounds that can reverse disease gene expressions in relevant cell types. Our results point to multiple drugs with therapeutic potentials, including metformin for type 1 diabetes, and vitamin K for ulcerative colitis.
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Affiliation(s)
- Lida Wang
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Chachrit Khunsriraksakul
- Bioinformatics and Genomics PhD Program; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Institute for Personalized Medicine; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Havell Markus
- Bioinformatics and Genomics PhD Program; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Institute for Personalized Medicine; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Dieyi Chen
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Fan Zhang
- Bioinformatics and Genomics PhD Program; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Fang Chen
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Xiaowei Zhan
- Department of Statistical Science, Southern Methodist University, Dallas, TX, US
- Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX, US
- Center for Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX, US
| | - Laura Carrel
- Department of Biochemistry and Molecular Biology; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.
| | - Dajiang J Liu
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.
- Bioinformatics and Genomics PhD Program; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.
- Department of Statistical Science, Southern Methodist University, Dallas, TX, US.
| | - Bibo Jiang
- Department of Public Health Sciences; Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA.
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13
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Zhou W, Cuomo ASE, Xue A, Kanai M, Chau G, Krishna C, Xavier RJ, MacArthur DG, Powell JE, Daly MJ, Neale BM. Efficient and accurate mixed model association tool for single-cell eQTL analysis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.15.24307317. [PMID: 38798318 PMCID: PMC11118640 DOI: 10.1101/2024.05.15.24307317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Understanding the genetic basis of gene expression can help us understand the molecular underpinnings of human traits and disease. Expression quantitative trait locus (eQTL) mapping can help in studying this relationship but have been shown to be very cell-type specific, motivating the use of single-cell RNA sequencing and single-cell eQTLs to obtain a more granular view of genetic regulation. Current methods for single-cell eQTL mapping either rely on the "pseudobulk" approach and traditional pipelines for bulk transcriptomics or do not scale well to large datasets. Here, we propose SAIGE-QTL, a robust and scalable tool that can directly map eQTLs using single-cell profiles without needing aggregation at the pseudobulk level. Additionally, SAIGE-QTL allows for testing the effects of less frequent/rare genetic variation through set-based tests, which is traditionally excluded from eQTL mapping studies. We evaluate the performance of SAIGE-QTL on both real and simulated data and demonstrate the improved power for eQTL mapping over existing pipelines.
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14
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Popp JM, Rhodes K, Jangi R, Li M, Barr K, Tayeb K, Battle A, Gilad Y. Cell-type and dynamic state govern genetic regulation of gene expression in heterogeneous differentiating cultures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.02.592174. [PMID: 38746382 PMCID: PMC11092595 DOI: 10.1101/2024.05.02.592174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Identifying the molecular effects of human genetic variation across cellular contexts is crucial for understanding the mechanisms underlying disease-associated loci, yet many cell-types and developmental stages remain underexplored. Here we harnessed the potential of heterogeneous differentiating cultures ( HDCs ), an in vitro system in which pluripotent cells asynchronously differentiate into a broad spectrum of cell-types. We generated HDCs for 53 human donors and collected single-cell RNA-sequencing data from over 900,000 cells. We identified expression quantitative trait loci in 29 cell-types and characterized regulatory dynamics across diverse differentiation trajectories. This revealed novel regulatory variants for genes involved in key developmental and disease-related processes while replicating known effects from primary tissues, and dynamic regulatory effects associated with a range of complex traits.
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15
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Dou J, Tan Y, Kock KH, Wang J, Cheng X, Tan LM, Han KY, Hon CC, Park WY, Shin JW, Jin H, Wang Y, Chen H, Ding L, Prabhakar S, Navin N, Chen R, Chen K. Single-nucleotide variant calling in single-cell sequencing data with Monopogen. Nat Biotechnol 2024; 42:803-812. [PMID: 37592035 PMCID: PMC11098741 DOI: 10.1038/s41587-023-01873-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: 12/05/2022] [Accepted: 06/21/2023] [Indexed: 08/19/2023]
Abstract
Single-cell omics technologies enable molecular characterization of diverse cell types and states, but how the resulting transcriptional and epigenetic profiles depend on the cell's genetic background remains understudied. We describe Monopogen, a computational tool to detect single-nucleotide variants (SNVs) from single-cell sequencing data. Monopogen leverages linkage disequilibrium from external reference panels to identify germline SNVs and detects putative somatic SNVs using allele cosegregating patterns at the cell population level. It can identify 100 K to 3 M germline SNVs achieving a genotyping accuracy of 95%, together with hundreds of putative somatic SNVs. Monopogen-derived genotypes enable global and local ancestry inference and identification of admixed samples. It identifies variants associated with cardiomyocyte metabolic levels and epigenomic programs. It also improves putative somatic SNV detection that enables clonal lineage tracing in primary human clonal hematopoiesis. Monopogen brings together population genetics, cell lineage tracing and single-cell omics to uncover genetic determinants of cellular processes.
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Affiliation(s)
- Jinzhuang Dou
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yukun Tan
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kian Hong Kock
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Jun Wang
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Xuesen Cheng
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Le Min Tan
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Kyung Yeon Han
- Samsung Genome Institute, Samsung Medical Center, Seoul, South Korea
| | - Chung-Chau Hon
- Laboratory for Genome Information Analysis, RIKEN center for Integrative Medical Sciences, Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Japan
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Seoul, South Korea
| | - Jay W Shin
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
- Laboratory for Advanced Genomics Circuit, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Haijing Jin
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yujia Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center, Houston, TX, USA
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, USA
| | - Li Ding
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Shyam Prabhakar
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Nicholas Navin
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rui Chen
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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16
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Farbehi N, Neavin DR, Cuomo ASE, Studer L, MacArthur DG, Powell JE. Integrating population genetics, stem cell biology and cellular genomics to study complex human diseases. Nat Genet 2024; 56:758-766. [PMID: 38741017 DOI: 10.1038/s41588-024-01731-9] [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: 01/24/2023] [Accepted: 03/20/2024] [Indexed: 05/16/2024]
Abstract
Human pluripotent stem (hPS) cells can, in theory, be differentiated into any cell type, making them a powerful in vitro model for human biology. Recent technological advances have facilitated large-scale hPS cell studies that allow investigation of the genetic regulation of molecular phenotypes and their contribution to high-order phenotypes such as human disease. Integrating hPS cells with single-cell sequencing makes identifying context-dependent genetic effects during cell development or upon experimental manipulation possible. Here we discuss how the intersection of stem cell biology, population genetics and cellular genomics can help resolve the functional consequences of human genetic variation. We examine the critical challenges of integrating these fields and approaches to scaling them cost-effectively and practically. We highlight two areas of human biology that can particularly benefit from population-scale hPS cell studies, elucidating mechanisms underlying complex disease risk loci and evaluating relationships between common genetic variation and pharmacotherapeutic phenotypes.
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Affiliation(s)
- Nona Farbehi
- Garvan Weizmann Center for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Graduate School of Biomedical Engineering, University of New South Wales, Sydney, New South Wales, Australia
- Aligning Science Across Parkinson's Collaborative Research Network, Chevy Chase, MD, USA
| | - Drew R Neavin
- Garvan Weizmann Center for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
| | - Anna S E Cuomo
- Garvan Weizmann Center for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Centre for Population Genomics, Garvan Institute of Medical Research, University of New South Wales, Sydney, New South Wales, Australia
| | - Lorenz Studer
- Aligning Science Across Parkinson's Collaborative Research Network, Chevy Chase, MD, USA
- The Center for Stem Cell Biology and Developmental Biology Program, Sloan-Kettering Institute for Cancer Research, New York, NY, USA
| | - Daniel G MacArthur
- Centre for Population Genomics, Garvan Institute of Medical Research, University of New South Wales, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Joseph E Powell
- Garvan Weizmann Center for Cellular Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
- Aligning Science Across Parkinson's Collaborative Research Network, Chevy Chase, MD, USA.
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia.
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17
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Wigdor EM, Samocha KE, Eberhardt RY, Chundru VK, Firth HV, Wright CF, Hurles ME, Martin HC. Investigating the role of common cis-regulatory variants in modifying penetrance of putatively damaging, inherited variants in severe neurodevelopmental disorders. Sci Rep 2024; 14:8708. [PMID: 38622173 PMCID: PMC11018828 DOI: 10.1038/s41598-024-58894-y] [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: 04/19/2023] [Accepted: 04/04/2024] [Indexed: 04/17/2024] Open
Abstract
Recent work has revealed an important role for rare, incompletely penetrant inherited coding variants in neurodevelopmental disorders (NDDs). Additionally, we have previously shown that common variants contribute to risk for rare NDDs. Here, we investigate whether common variants exert their effects by modifying gene expression, using multi-cis-expression quantitative trait loci (cis-eQTL) prediction models. We first performed a transcriptome-wide association study for NDDs using 6987 probands from the Deciphering Developmental Disorders (DDD) study and 9720 controls, and found one gene, RAB2A, that passed multiple testing correction (p = 6.7 × 10-7). We then investigated whether cis-eQTLs modify the penetrance of putatively damaging, rare coding variants inherited by NDD probands from their unaffected parents in a set of 1700 trios. We found no evidence that unaffected parents transmitting putatively damaging coding variants had higher genetically-predicted expression of the variant-harboring gene than their child. In probands carrying putatively damaging variants in constrained genes, the genetically-predicted expression of these genes in blood was lower than in controls (p = 2.7 × 10-3). However, results for proband-control comparisons were inconsistent across different sets of genes, variant filters and tissues. We find limited evidence that common cis-eQTLs modify penetrance of rare coding variants in a large cohort of NDD probands.
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Affiliation(s)
- Emilie M Wigdor
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
| | - Kaitlin E Samocha
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, USA
| | - Ruth Y Eberhardt
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - V Kartik Chundru
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, UK
| | - Helen V Firth
- Department of Medical Genetics, Addenbrooke's Hospital, Cambridge University Hospitals, Cambridge, UK
| | - Caroline F Wright
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, UK
| | - Matthew E Hurles
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Hilary C Martin
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
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18
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Huckins LM, Brennand K, Bulik CM. Dissecting the biology of feeding and eating disorders. Trends Mol Med 2024; 30:380-391. [PMID: 38431502 DOI: 10.1016/j.molmed.2024.01.009] [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: 11/15/2023] [Revised: 01/28/2024] [Accepted: 01/31/2024] [Indexed: 03/05/2024]
Abstract
Feeding and eating disorders (FEDs) are heterogenous and characterized by varying patterns of dysregulated eating and weight. Genome-wide association studies (GWASs) are clarifying their underlying biology and their genetic relationship to other psychiatric and metabolic/anthropometric traits. Genetic research on anorexia nervosa (AN) has identified eight significant loci and uncovered genetic correlations implicating both psychiatric and metabolic/anthropometric risk factors. Careful explication of these metabolic contributors may be key to developing effective and enduring treatments for devastating, life-altering, and frequently lethal illnesses. We discuss clinical phenomenology, genomics, phenomics, intestinal microbiota, and functional genomics and propose a path that translates variants to genes, genes to pathways, and pathways to metabolic outcomes to advance the science and eventually treatment of FEDs.
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Affiliation(s)
- Laura M Huckins
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Kristen Brennand
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511, USA; Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511, USA
| | - Cynthia M Bulik
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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19
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Sakaue S, Weinand K, Isaac S, Dey KK, Jagadeesh K, Kanai M, Watts GFM, Zhu Z, Brenner MB, McDavid A, Donlin LT, Wei K, Price AL, Raychaudhuri S. Tissue-specific enhancer-gene maps from multimodal single-cell data identify causal disease alleles. Nat Genet 2024; 56:615-626. [PMID: 38594305 DOI: 10.1038/s41588-024-01682-1] [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: 03/07/2023] [Accepted: 02/07/2024] [Indexed: 04/11/2024]
Abstract
Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer-gene maps from disease-relevant tissues. Building enhancer-gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer-gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer-gene maps, essential for defining noncoding variant function.
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Affiliation(s)
- Saori Sakaue
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kathryn Weinand
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Shakson Isaac
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Kushal K Dey
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Karthik Jagadeesh
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Masahiro Kanai
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Gerald F M Watts
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Zhu Zhu
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Michael B Brenner
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrew McDavid
- Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA
| | - Laura T Donlin
- Hospital for Special Surgery, New York, NY, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Kevin Wei
- Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Alkes L Price
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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20
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Li Z, Brittan M, Mills NL. A Multimodal Omics Framework to Empower Target Discovery for Cardiovascular Regeneration. Cardiovasc Drugs Ther 2024; 38:223-236. [PMID: 37421484 PMCID: PMC10959818 DOI: 10.1007/s10557-023-07484-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/19/2023] [Indexed: 07/10/2023]
Abstract
Ischaemic heart disease is a global healthcare challenge with high morbidity and mortality. Early revascularisation in acute myocardial infarction has improved survival; however, limited regenerative capacity and microvascular dysfunction often lead to impaired function and the development of heart failure. New mechanistic insights are required to identify robust targets for the development of novel strategies to promote regeneration. Single-cell RNA sequencing (scRNA-seq) has enabled profiling and analysis of the transcriptomes of individual cells at high resolution. Applications of scRNA-seq have generated single-cell atlases for multiple species, revealed distinct cellular compositions for different regions of the heart, and defined multiple mechanisms involved in myocardial injury-induced regeneration. In this review, we summarise findings from studies of healthy and injured hearts in multiple species and spanning different developmental stages. Based on this transformative technology, we propose a multi-species, multi-omics, meta-analysis framework to drive the discovery of new targets to promote cardiovascular regeneration.
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Affiliation(s)
- Ziwen Li
- BHF Centre for Cardiovascular Science, The Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK.
| | - Mairi Brittan
- BHF Centre for Cardiovascular Science, The Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Nicholas L Mills
- BHF Centre for Cardiovascular Science, The Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
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21
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Yu E, Larivière R, Thomas RA, Liu L, Senkevich K, Rahayel S, Trempe JF, Fon EA, Gan-Or Z. Machine learning nominates the inositol pathway and novel genes in Parkinson's disease. Brain 2024; 147:887-899. [PMID: 37804111 PMCID: PMC10907089 DOI: 10.1093/brain/awad345] [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: 07/04/2023] [Revised: 09/01/2023] [Accepted: 09/24/2023] [Indexed: 10/08/2023] Open
Abstract
There are 78 loci associated with Parkinson's disease in the most recent genome-wide association study (GWAS), yet the specific genes driving these associations are mostly unknown. Herein, we aimed to nominate the top candidate gene from each Parkinson's disease locus and identify variants and pathways potentially involved in Parkinson's disease. We trained a machine learning model to predict Parkinson's disease-associated genes from GWAS loci using genomic, transcriptomic and epigenomic data from brain tissues and dopaminergic neurons. We nominated candidate genes in each locus and identified novel pathways potentially involved in Parkinson's disease, such as the inositol phosphate biosynthetic pathway (INPP5F, IP6K2, ITPKB and PPIP5K2). Specific common coding variants in SPNS1 and MLX may be involved in Parkinson's disease, and burden tests of rare variants further support that CNIP3, LSM7, NUCKS1 and the polyol/inositol phosphate biosynthetic pathway are associated with the disease. Functional studies are needed to further analyse the involvements of these genes and pathways in Parkinson's disease.
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Affiliation(s)
- Eric Yu
- Department of Human Genetics, McGill University, Montreal, Quebec H3A 0G4, Canada
- The Neuro (Montreal Neurological Institute-Hospital), Montreal, Quebec H3A 2B4, Canada
| | - Roxanne Larivière
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada
| | - Rhalena A Thomas
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada
- Early Drug Discovery Unit (EDDU), Montreal Neurological Institute-Hospital (The Neuro), Montreal, Quebec H3A 2B4, Canada
| | - Lang Liu
- Department of Human Genetics, McGill University, Montreal, Quebec H3A 0G4, Canada
- The Neuro (Montreal Neurological Institute-Hospital), Montreal, Quebec H3A 2B4, Canada
| | - Konstantin Senkevich
- The Neuro (Montreal Neurological Institute-Hospital), Montreal, Quebec H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada
| | - Shady Rahayel
- Centre for Advanced Research in Sleep Medicine, Hôpital du Sacré-Cœur de Montréal, Montreal, Quebec H4J 1C5, Canada
- Department of Medicine, University of Montreal, Montreal, Quebec H3C 3J7, Canada
| | - Jean-François Trempe
- Department of Pharmacology and Therapeutics and Centre de Recherche en Biologie Structurale, McGill University, Montreal, Quebec H3A 0G4, Canada
| | - Edward A Fon
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada
- Early Drug Discovery Unit (EDDU), Montreal Neurological Institute-Hospital (The Neuro), Montreal, Quebec H3A 2B4, Canada
| | - Ziv Gan-Or
- Department of Human Genetics, McGill University, Montreal, Quebec H3A 0G4, Canada
- The Neuro (Montreal Neurological Institute-Hospital), Montreal, Quebec H3A 2B4, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec H3A 0G4, Canada
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22
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Panten J, Heinen T, Ernst C, Eling N, Wagner RE, Satorius M, Marioni JC, Stegle O, Odom DT. The dynamic genetic determinants of increased transcriptional divergence in spermatids. Nat Commun 2024; 15:1272. [PMID: 38341412 PMCID: PMC10858866 DOI: 10.1038/s41467-024-45133-1] [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: 02/07/2023] [Accepted: 01/16/2024] [Indexed: 02/12/2024] Open
Abstract
Cis-genetic effects are key determinants of transcriptional divergence in discrete tissues and cell types. However, how cis- and trans-effects act across continuous trajectories of cellular differentiation in vivo is poorly understood. Here, we quantify allele-specific expression during spermatogenic differentiation at single-cell resolution in an F1 hybrid mouse system, allowing for the comprehensive characterisation of cis- and trans-genetic effects, including their dynamics across cellular differentiation. Collectively, almost half of the genes subject to genetic regulation show evidence for dynamic cis-effects that vary during differentiation. Our system also allows us to robustly identify dynamic trans-effects, which are less pervasive than cis-effects. In aggregate, genetic effects were strongest in round spermatids, which parallels their increased transcriptional divergence we identified between species. Our approach provides a comprehensive quantification of the variability of genetic effects in vivo, and demonstrates a widely applicable strategy to dissect the impact of regulatory variants on gene regulation in dynamic systems.
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Affiliation(s)
- Jasper Panten
- Division of Regulatory Genomics and Cancer Evolution, German Cancer Research Centre (DKFZ), 69120, Heidelberg, Germany
- Division of Computational Genomics and Systems Genetics, German Cancer Research Centre (DKFZ), 69120, Heidelberg, Germany
- Faculty of Biosciences, Heidelberg University, 69117, Heidelberg, Germany
| | - Tobias Heinen
- Division of Computational Genomics and Systems Genetics, German Cancer Research Centre (DKFZ), 69120, Heidelberg, Germany
- Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany
- European Molecular Biology Laboratory, Genome Biology Unit, 69117, Heidelberg, Germany
| | - Christina Ernst
- School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland
| | - Nils Eling
- University of Zurich, Department of Quantitative Biomedicine, Zurich, 8057, Switzerland
- ETH Zurich, Institute for Molecular Health Sciences, Zurich, 8093, Switzerland
| | - Rebecca E Wagner
- Faculty of Biosciences, Heidelberg University, 69117, Heidelberg, Germany
- Division of Mechanisms Regulating Gene Expression, German Cancer Research Centre (DKFZ), 69120, Heidelberg, Germany
| | - Maja Satorius
- Division of Regulatory Genomics and Cancer Evolution, German Cancer Research Centre (DKFZ), 69120, Heidelberg, Germany
| | - John C Marioni
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Oliver Stegle
- Division of Computational Genomics and Systems Genetics, German Cancer Research Centre (DKFZ), 69120, Heidelberg, Germany.
- European Molecular Biology Laboratory, Genome Biology Unit, 69117, Heidelberg, Germany.
| | - Duncan T Odom
- Division of Regulatory Genomics and Cancer Evolution, German Cancer Research Centre (DKFZ), 69120, Heidelberg, Germany.
- Faculty of Biosciences, Heidelberg University, 69117, Heidelberg, Germany.
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
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23
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Boye C, Kalita CA, Findley AS, Alazizi A, Wei J, Wen X, Pique-Regi R, Luca F. Characterization of caffeine response regulatory variants in vascular endothelial cells. eLife 2024; 13:e85235. [PMID: 38334359 PMCID: PMC10901511 DOI: 10.7554/elife.85235] [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: 11/30/2022] [Accepted: 02/08/2024] [Indexed: 02/10/2024] Open
Abstract
Genetic variants in gene regulatory sequences can modify gene expression and mediate the molecular response to environmental stimuli. In addition, genotype-environment interactions (GxE) contribute to complex traits such as cardiovascular disease. Caffeine is the most widely consumed stimulant and is known to produce a vascular response. To investigate GxE for caffeine, we treated vascular endothelial cells with caffeine and used a massively parallel reporter assay to measure allelic effects on gene regulation for over 43,000 genetic variants. We identified 665 variants with allelic effects on gene regulation and 6 variants that regulate the gene expression response to caffeine (GxE, false discovery rate [FDR] < 5%). When overlapping our GxE results with expression quantitative trait loci colocalized with coronary artery disease and hypertension, we dissected their regulatory mechanisms and showed a modulatory role for caffeine. Our results demonstrate that massively parallel reporter assay is a powerful approach to identify and molecularly characterize GxE in the specific context of caffeine consumption.
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Affiliation(s)
- Carly Boye
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Cynthia A Kalita
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Anthony S Findley
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Adnan Alazizi
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Julong Wei
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
| | - Xiaoquan Wen
- Department of Biostatistics, University of MichiganAnn ArborUnited States
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
- Department of Obstetrics and Gynecology, Wayne State UniversityDetroitUnited States
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State UniversityDetroitUnited States
- Department of Obstetrics and Gynecology, Wayne State UniversityDetroitUnited States
- Department of Biology, University of Rome Tor VergataRomeItaly
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24
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Strober BJ, Tayeb K, Popp J, Qi G, Gordon MG, Perez R, Ye CJ, Battle A. SURGE: uncovering context-specific genetic-regulation of gene expression from single-cell RNA sequencing using latent-factor models. Genome Biol 2024; 25:28. [PMID: 38254214 PMCID: PMC10801966 DOI: 10.1186/s13059-023-03152-z] [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: 12/22/2022] [Accepted: 12/20/2023] [Indexed: 01/24/2024] Open
Abstract
Genetic regulation of gene expression is a complex process, with genetic effects known to vary across cellular contexts such as cell types and environmental conditions. We developed SURGE, a method for unsupervised discovery of context-specific expression quantitative trait loci (eQTLs) from single-cell transcriptomic data. This allows discovery of the contexts or cell types modulating genetic regulation without prior knowledge. Applied to peripheral blood single-cell eQTL data, SURGE contexts capture continuous representations of distinct cell types and groupings of biologically related cell types. We demonstrate the disease-relevance of SURGE context-specific eQTLs using colocalization analysis and stratified LD-score regression.
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Affiliation(s)
- Benjamin J Strober
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Karl Tayeb
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Joshua Popp
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Guanghao Qi
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - M Grace Gordon
- Biological and Medical Informatics Graduate Program, University of California, San Francisco, CA, USA
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
| | - Richard Perez
- Institute for Human Genetics, University of California, San Francisco, CA, USA
| | - Chun Jimmie Ye
- Division of Rheumatology, Department of Medicine, University of California, San Francisco, CA, USA
- Institute for Human Genetics, University of California, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA
- Institute for Computational Health Sciences, University of California, San Francisco, San Francisco, CA, USA
- Chan-Zuckerberg Biohub, San Francisco, CA, USA
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
- Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA.
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25
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Ding R, Wang Q, Gong L, Zhang T, Zou X, Xiong K, Liao Q, Plass M, Li L. scQTLbase: an integrated human single-cell eQTL database. Nucleic Acids Res 2024; 52:D1010-D1017. [PMID: 37791879 PMCID: PMC10767909 DOI: 10.1093/nar/gkad781] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 08/24/2023] [Accepted: 09/15/2023] [Indexed: 10/05/2023] Open
Abstract
Genome-wide association studies (GWAS) have identified numerous genetic variants associated with diseases and traits. However, the functional interpretation of these variants remains challenging. Expression quantitative trait loci (eQTLs) have been widely used to identify mutations linked to disease, yet they explain only 20-50% of disease-related variants. Single-cell eQTLs (sc-eQTLs) studies provide an immense opportunity to identify new disease risk genes with expanded eQTL scales and transcriptional regulation at a much finer resolution. However, there is no comprehensive database dedicated to single-cell eQTLs that users can use to search, analyse and visualize them. Therefore, we developed the scQTLbase (http://bioinfo.szbl.ac.cn/scQTLbase), the first integrated human sc-eQTLs portal, featuring 304 datasets spanning 57 cell types and 95 cell states. It contains ∼16 million SNPs significantly associated with cell-type/state gene expression and ∼0.69 million disease-associated sc-eQTLs from 3 333 traits/diseases. In addition, scQTLbase offers sc-eQTL search, gene expression visualization in UMAP plots, a genome browser, and colocalization visualization based on the GWAS dataset of interest. scQTLbase provides a one-stop portal for sc-eQTLs that will significantly advance the discovery of disease susceptibility genes.
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Affiliation(s)
- Ruofan Ding
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Qixuan Wang
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Lihai Gong
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Ting Zhang
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Xudong Zou
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Kewei Xiong
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
| | - Qi Liao
- School of Public Health, Health Science Center, Ningbo University, Ningbo 315211, China
| | - Mireya Plass
- Gene Regulation of Cell Identity Group, Regenerative Medicine Program, Bellvitge Institute for Biomedical Research (IDIBELL), and Program for Advancing Clinical Translation of Regenerative Medicine of Catalonia, P-CMR[C], L′Hospitalet de Llobregat, Barcelona, Spain
- Center for Networked Biomedical Research on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Lei Li
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518055, China
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26
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Conte MI, Fuentes-Trillo A, Domínguez Conde C. Opportunities and tradeoffs in single-cell transcriptomic technologies. Trends Genet 2024; 40:83-93. [PMID: 37953195 DOI: 10.1016/j.tig.2023.10.003] [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: 07/11/2023] [Revised: 09/26/2023] [Accepted: 10/03/2023] [Indexed: 11/14/2023]
Abstract
Recent technological and algorithmic advances enable single-cell transcriptomic analysis with remarkable depth and breadth. Nonetheless, a persistent challenge is the compromise between the ability to profile high numbers of cells and the achievement of full-length transcript coverage. Currently, the field is progressing and developing new and creative solutions that improve cellular throughput, gene detection sensitivity and full-length transcript capture. Furthermore, long-read sequencing approaches for single-cell transcripts are breaking frontiers that have previously blocked full transcriptome characterization. We here present a comprehensive overview of available options for single-cell transcriptome profiling, highlighting the key advantages and disadvantages of each approach.
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Affiliation(s)
- Matilde I Conte
- Human Technopole, Viale Rita Levi-Montalcini 1, 20157 Milan, Italy
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27
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Seah C, Signer R, Deans M, Bader H, Rusielewicz T, Hicks EM, Young H, Cote A, Townsley K, Xu C, Hunter CJ, McCarthy B, Goldberg J, Dobariya S, Holtzherimer PE, Young KA, Noggle SA, Krystal JH, Paull D, Girgenti MJ, Yehuda R, Brennand KJ, Huckins LM. Common genetic variation impacts stress response in the brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.27.573459. [PMID: 38234801 PMCID: PMC10793429 DOI: 10.1101/2023.12.27.573459] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
To explain why individuals exposed to identical stressors experience divergent clinical outcomes, we determine how molecular encoding of stress modifies genetic risk for brain disorders. Analysis of post-mortem brain (n=304) revealed 8557 stress-interactive expression quantitative trait loci (eQTLs) that dysregulate expression of 915 eGenes in response to stress, and lie in stress-related transcription factor binding sites. Response to stress is robust across experimental paradigms: up to 50% of stress-interactive eGenes validate in glucocorticoid treated hiPSC-derived neurons (n=39 donors). Stress-interactive eGenes show brain region- and cell type-specificity, and, in post-mortem brain, implicate glial and endothelial mechanisms. Stress dysregulates long-term expression of disorder risk genes in a genotype-dependent manner; stress-interactive transcriptomic imputation uncovered 139 novel genes conferring brain disorder risk only in the context of traumatic stress. Molecular stress-encoding explains individualized responses to traumatic stress; incorporating trauma into genomic studies of brain disorders is likely to improve diagnosis, prognosis, and drug discovery.
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28
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Abe H, Lin P, Zhou D, Ruderfer DM, Gamazon ER. Mapping the landscape of lineage-specific dynamic regulation of gene expression using single-cell transcriptomics and application to genetics of complex disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.10.24.23297476. [PMID: 37961453 PMCID: PMC10635195 DOI: 10.1101/2023.10.24.23297476] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Single-cell transcriptome data can provide insights into how genetic variation influences biological processes involved in human biology and disease. However, the identification of gene-level associations in distinct cell types faces several challenges, including the limited reference resource from population scale studies, data sparsity in single-cell RNA sequencing, and the complex cell-state pattern of expression within individual cell types. Here we develop genetic models of cell type specific and cell state adjusted gene expression in mid-brain neurons in the process of specializing from induced pluripotent stem cells. The resulting framework quantifies the dynamics of the genetic regulation of gene expression and estimates its cell type specificity. As an application, we show that the approach detects known and new genes associated with schizophrenia and enables insights into context-dependent disease mechanisms. We provide a genomic resource from a phenome-wide application of our models to more than 1500 phenotypes from the UK Biobank. Using longitudinal genetically determined expression, we implement a predictive causality framework, evaluating the prediction of future values of a target gene expression using prior values of a putative regulatory gene. Collectively, this work demonstrates the insights that can be gained into the molecular underpinnings of diseases by quantifying the genetic control of gene expression at single-cell resolution.
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Affiliation(s)
- Hanna Abe
- Vanderbilt University, Nashville, TN
| | - Phillip Lin
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Dan Zhou
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Douglas M Ruderfer
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
- Department of Biomedical Informatics and Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Eric R Gamazon
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
- Clare Hall, University of Cambridge, Cambridge, England
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29
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Farjood F, Manos JD, Wang Y, Williams AL, Zhao C, Borden S, Alam N, Prusky G, Temple S, Stern JH, Boles NC. Identifying biomarkers of heterogeneity and transplantation efficacy in retinal pigment epithelial cells. J Exp Med 2023; 220:e20230913. [PMID: 37728563 PMCID: PMC10510736 DOI: 10.1084/jem.20230913] [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: 05/26/2023] [Revised: 08/15/2023] [Accepted: 08/18/2023] [Indexed: 09/21/2023] Open
Abstract
Transplantation of retinal pigment epithelial (RPE) cells holds great promise for patients with retinal degenerative diseases, such as age-related macular degeneration. In-depth characterization of RPE cell product identity and critical quality attributes are needed to enhance efficacy and safety of replacement therapy strategies. Here, we characterized an adult RPE stem cell-derived (RPESC-RPE) cell product using bulk and single-cell RNA sequencing (scRNA-seq), assessing functional cell integration in vitro into a mature RPE monolayer and in vivo efficacy by vision rescue in the Royal College of Surgeons rats. scRNA-seq revealed several distinct subpopulations in the RPESC-RPE product, some with progenitor markers. We identified RPE clusters expressing genes associated with in vivo efficacy and increased cell integration capability. Gene expression analysis revealed lncRNA (TREX) as a predictive marker of in vivo efficacy. TREX knockdown decreased cell integration while overexpression increased integration in vitro and improved vision rescue in the RCS rats.
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Affiliation(s)
| | | | - Yue Wang
- Neural Stem Cell Institute, Rensselaer, NY, USA
| | | | | | | | - Nazia Alam
- Burke Neurological Institute at Weill Cornell Medicine, White Plains, NY, USA
| | - Glen Prusky
- Burke Neurological Institute at Weill Cornell Medicine, White Plains, NY, USA
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30
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Adegunsoye A, Gonzales NM, Gilad Y. Induced Pluripotent Stem Cells in Disease Biology and the Evidence for Their In Vitro Utility. Annu Rev Genet 2023; 57:341-360. [PMID: 37708421 DOI: 10.1146/annurev-genet-022123-090319] [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] [Indexed: 09/16/2023]
Abstract
Many human phenotypes are impossible to recapitulate in model organisms or immortalized human cell lines. Induced pluripotent stem cells (iPSCs) offer a way to study disease mechanisms in a variety of differentiated cell types while circumventing ethical and practical issues associated with finite tissue sources and postmortem states. Here, we discuss the broad utility of iPSCs in genetic medicine and describe how they are being used to study musculoskeletal, pulmonary, neurologic, and cardiac phenotypes. We summarize the particular challenges presented by each organ system and describe how iPSC models are being used to address them. Finally, we discuss emerging iPSC-derived organoid models and the potential value that they can bring to studies of human disease.
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Affiliation(s)
- Ayodeji Adegunsoye
- Genetics, Genomics, and Systems Biology, Section of Pulmonary and Critical Care, and the Department of Medicine, University of Chicago, Chicago, Illinois, USA;
| | - Natalia M Gonzales
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, USA; ,
| | - Yoav Gilad
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, Illinois, USA; ,
- Department of Human Genetics, University of Chicago, Chicago, Illinois, USA
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31
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Minow MAA, Marand AP, Schmitz RJ. Leveraging Single-Cell Populations to Uncover the Genetic Basis of Complex Traits. Annu Rev Genet 2023; 57:297-319. [PMID: 37562412 PMCID: PMC10775913 DOI: 10.1146/annurev-genet-022123-110824] [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] [Indexed: 08/12/2023]
Abstract
The ease and throughput of single-cell genomics have steadily improved, and its current trajectory suggests that surveying single-cell populations will become routine. We discuss the merger of quantitative genetics with single-cell genomics and emphasize how this synergizes with advantages intrinsic to plants. Single-cell population genomics provides increased detection resolution when mapping variants that control molecular traits, including gene expression or chromatin accessibility. Additionally, single-cell population genomics reveals the cell types in which variants act and, when combined with organism-level phenotype measurements, unveils which cellular contexts impact higher-order traits. Emerging technologies, notably multiomics, can facilitate the measurement of both genetic changes and genomic traits in single cells, enabling single-cell genetic experiments. The implementation of single-cell genetics will advance the investigation of the genetic architecture of complex molecular traits and provide new experimental paradigms to study eukaryotic genetics.
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Affiliation(s)
- Mark A A Minow
- Department of Genetics, University of Georgia, Athens, Georgia, USA;
| | | | - Robert J Schmitz
- Department of Genetics, University of Georgia, Athens, Georgia, USA;
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32
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Qi G, Strober BJ, Popp JM, Keener R, Ji H, Battle A. Single-cell allele-specific expression analysis reveals dynamic and cell-type-specific regulatory effects. Nat Commun 2023; 14:6317. [PMID: 37813843 PMCID: PMC10562474 DOI: 10.1038/s41467-023-42016-9] [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: 01/06/2023] [Accepted: 09/27/2023] [Indexed: 10/11/2023] Open
Abstract
Differential allele-specific expression (ASE) is a powerful tool to study context-specific cis-regulation of gene expression. Such effects can reflect the interaction between genetic or epigenetic factors and a measured context or condition. Single-cell RNA sequencing (scRNA-seq) allows the measurement of ASE at individual-cell resolution, but there is a lack of statistical methods to analyze such data. We present Differential Allelic Expression using Single-Cell data (DAESC), a powerful method for differential ASE analysis using scRNA-seq from multiple individuals, with statistical behavior confirmed through simulation. DAESC accounts for non-independence between cells from the same individual and incorporates implicit haplotype phasing. Application to data from 105 induced pluripotent stem cell (iPSC) lines identifies 657 genes dynamically regulated during endoderm differentiation, with enrichment for changes in chromatin state. Application to a type-2 diabetes dataset identifies several differentially regulated genes between patients and controls in pancreatic endocrine cells. DAESC is a powerful method for single-cell ASE analysis and can uncover novel insights on gene regulation.
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Affiliation(s)
- Guanghao Qi
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA
| | - Benjamin J Strober
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Joshua M Popp
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Rebecca Keener
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Hongkai Ji
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA.
- Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, 21205, USA.
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Şapcı AOB, Lu S, Yan S, Ay F, Tastan O, Keleş S. MuDCoD: multi-subject community detection in personalized dynamic gene networks from single-cell RNA sequencing. Bioinformatics 2023; 39:btad592. [PMID: 37740957 PMCID: PMC10564618 DOI: 10.1093/bioinformatics/btad592] [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: 03/17/2023] [Revised: 08/24/2023] [Accepted: 09/21/2023] [Indexed: 09/25/2023] Open
Abstract
MOTIVATION With the wide availability of single-cell RNA-seq (scRNA-seq) technology, population-scale scRNA-seq datasets across multiple individuals and time points are emerging. While the initial investigations of these datasets tend to focus on standard analysis of clustering and differential expression, leveraging the power of scRNA-seq data at the personalized dynamic gene co-expression network level has the potential to unlock subject and/or time-specific network-level variation, which is critical for understanding phenotypic differences. Community detection from co-expression networks of multiple time points or conditions has been well-studied; however, none of the existing settings included networks from multiple subjects and multiple time points simultaneously. To address this, we develop Multi-subject Dynamic Community Detection (MuDCoD) for multi-subject community detection in personalized dynamic gene networks from scRNA-seq. MuDCoD builds on the spectral clustering framework and promotes information sharing among the networks of the subjects as well as networks at different time points. It clusters genes in the personalized dynamic gene networks and reveals gene communities that are variable or shared not only across time but also among subjects. RESULTS Evaluation and benchmarking of MuDCoD against existing approaches reveal that MuDCoD effectively leverages apparent shared signals among networks of the subjects at individual time points, and performs robustly when there is no or little information sharing among the networks. Applications to population-scale scRNA-seq datasets of human-induced pluripotent stem cells during dopaminergic neuron differentiation and CD4+ T cell activation indicate that MuDCoD enables robust inference for identifying time-varying personalized gene modules. Our results illustrate how personalized dynamic community detection can aid in the exploration of subject-specific biological processes that vary across time. AVAILABILITY AND IMPLEMENTATION MuDCoD is publicly available at https://github.com/bo1929/MuDCoD as a Python package. Implementation includes simulation and real-data experiments together with extensive documentation.
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Affiliation(s)
- Ali Osman Berk Şapcı
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA 92093, United States
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul 34956, Turkey
| | - Shan Lu
- Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, United States
| | - Shuchen Yan
- Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, United States
| | - Ferhat Ay
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92093, United States
- Centers for Autoimmunity, Inflammation and Cancer Immunotherapy, La Jolla Institute for Immunology, La Jolla, CA 92037, United States
| | - Oznur Tastan
- Faculty of Engineering and Natural Sciences, Sabancı University, Istanbul 34956, Turkey
| | - Sündüz Keleş
- Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, United States
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, United States
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34
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Velten B, Stegle O. Principles and challenges of modeling temporal and spatial omics data. Nat Methods 2023; 20:1462-1474. [PMID: 37710019 DOI: 10.1038/s41592-023-01992-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/31/2023] [Indexed: 09/16/2023]
Abstract
Studies with temporal or spatial resolution are crucial to understand the molecular dynamics and spatial dependencies underlying a biological process or system. With advances in high-throughput omic technologies, time- and space-resolved molecular measurements at scale are increasingly accessible, providing new opportunities to study the role of timing or structure in a wide range of biological questions. At the same time, analyses of the data being generated in the context of spatiotemporal studies entail new challenges that need to be considered, including the need to account for temporal and spatial dependencies and compare them across different scales, biological samples or conditions. In this Review, we provide an overview of common principles and challenges in the analysis of temporal and spatial omics data. We discuss statistical concepts to model temporal and spatial dependencies and highlight opportunities for adapting existing analysis methods to data with temporal and spatial dimensions.
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Affiliation(s)
- Britta Velten
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Cellular Genetics Programme, Wellcome Sanger Institute, Hinxton, Cambridge, UK.
- Centre for Organismal Studies (COS) and Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany.
| | - Oliver Stegle
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Cellular Genetics Programme, Wellcome Sanger Institute, Hinxton, Cambridge, UK.
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
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35
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Smullen M, Olson MN, Reichert JM, Dawes P, Murray LF, Baer CE, Wang Q, Readhead B, Church GM, Lim ET, Chan Y. Reliable multiplex generation of pooled induced pluripotent stem cells. CELL REPORTS METHODS 2023; 3:100570. [PMID: 37751688 PMCID: PMC10545906 DOI: 10.1016/j.crmeth.2023.100570] [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: 06/28/2022] [Revised: 06/23/2023] [Accepted: 08/04/2023] [Indexed: 09/28/2023]
Abstract
Reprogramming somatic cells into pluripotent stem cells (iPSCs) enables the study of systems in vitro. To increase the throughput of reprogramming, we present induction of pluripotency from pooled cells (iPPC)-an efficient, scalable, and reliable reprogramming procedure. Using our deconvolution algorithm that employs pooled sequencing of single-nucleotide polymorphisms (SNPs), we accurately estimated individual donor proportions of the pooled iPSCs. With iPPC, we concurrently reprogrammed over one hundred donor lymphoblastoid cell lines (LCLs) into iPSCs and found strong correlations of individual donors' reprogramming ability across multiple experiments. Individual donors' reprogramming ability remains consistent across both same-day replicates and multiple experimental runs, and the expression of certain immunoglobulin precursor genes may impact reprogramming ability. The pooled iPSCs were also able to differentiate into cerebral organoids. Our procedure enables a multiplex framework of using pooled libraries of donor iPSCs for downstream research and investigation of in vitro phenotypes.
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Affiliation(s)
- Molly Smullen
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; NeuroNexus Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Meagan N Olson
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; NeuroNexus Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Julia M Reichert
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; NeuroNexus Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Pepper Dawes
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; NeuroNexus Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Liam F Murray
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; NeuroNexus Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Christina E Baer
- Department of Microbiology and Physiological Systems, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Qi Wang
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ 85281, USA
| | - Benjamin Readhead
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ 85281, USA
| | - George M Church
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Elaine T Lim
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; NeuroNexus Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA
| | - Yingleong Chan
- Department of Neurology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA; NeuroNexus Institute, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA.
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36
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Wang M, Li Y, Wang H, Li M, Wang X, Liu R, Zhang D, Xu W. Corneal regeneration strategies: From stem cell therapy to tissue engineered stem cell scaffolds. Biomed Pharmacother 2023; 165:115206. [PMID: 37494785 DOI: 10.1016/j.biopha.2023.115206] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 07/12/2023] [Accepted: 07/18/2023] [Indexed: 07/28/2023] Open
Abstract
Corneal epithelial defects and excessive wound healing might lead to severe complications. As stem cells can self-renew infinitely, they are a promising solution for regenerating the corneal epithelium and treating severe corneal epithelial injury. The chemical and biophysical properties of biological scaffolds, such as the amniotic membrane, fibrin, and hydrogels, can provide the necessary signals for stem cell proliferation and differentiation. Multiple researchers have conducted investigations on these scaffolds and evaluated them as potential therapeutic interventions for corneal disorders. These studies have identified various inherent benefits and drawbacks associated with these scaffolds. In this study, we provided a comprehensive overview of the history and use of various stem cells in corneal repair. We mainly discussed biological scaffolds that are used in stem cell transplantation and innovative materials that are under investigation.
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Affiliation(s)
- Mengyuan Wang
- Institute of Regenerative Medicine and Laboratory Technology Innovation, Qingdao University, Qingdao, Shandong 266071, PR China
| | - Ying Li
- Institute of Regenerative Medicine and Laboratory Technology Innovation, Qingdao University, Qingdao, Shandong 266071, PR China
| | - Hongqiao Wang
- Blood Purification Department, Qingdao Hospital of Traditional Chinese Medicine, Qingdao Hiser Hospital, Qingdao, Shandong 266071, PR China
| | - Meng Li
- Institute of Regenerative Medicine and Laboratory Technology Innovation, Qingdao University, Qingdao, Shandong 266071, PR China
| | - Xiaomin Wang
- Institute of Regenerative Medicine and Laboratory Technology Innovation, Qingdao University, Qingdao, Shandong 266071, PR China
| | - Rongzhen Liu
- Institute of Regenerative Medicine and Laboratory Technology Innovation, Qingdao University, Qingdao, Shandong 266071, PR China
| | - Daijun Zhang
- Medical College of Qingdao University, Qingdao, Shandong 266071, PR China.
| | - Wenhua Xu
- Institute of Regenerative Medicine and Laboratory Technology Innovation, Qingdao University, Qingdao, Shandong 266071, PR China.
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37
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Kang JB, Raveane A, Nathan A, Soranzo N, Raychaudhuri S. Methods and Insights from Single-Cell Expression Quantitative Trait Loci. Annu Rev Genomics Hum Genet 2023; 24:277-303. [PMID: 37196361 PMCID: PMC10784788 DOI: 10.1146/annurev-genom-101422-100437] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.
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Affiliation(s)
- Joyce B Kang
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
| | | | - Aparna Nathan
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
| | - Nicole Soranzo
- Human Technopole, Milan, Italy; ,
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, United Kingdom
- British Heart Foundation Centre of Research Excellence and Department of Haematology, University of Cambridge, Cambridge, United Kingdom
| | - Soumya Raychaudhuri
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
- Centre for Genetics and Genomics Versus Arthritis, University of Manchester, Manchester, United Kingdom
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38
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Fouché A, Zinovyev A. Omics data integration in computational biology viewed through the prism of machine learning paradigms. FRONTIERS IN BIOINFORMATICS 2023; 3:1191961. [PMID: 37600970 PMCID: PMC10436311 DOI: 10.3389/fbinf.2023.1191961] [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: 03/22/2023] [Accepted: 07/26/2023] [Indexed: 08/22/2023] Open
Abstract
Important quantities of biological data can today be acquired to characterize cell types and states, from various sources and using a wide diversity of methods, providing scientists with more and more information to answer challenging biological questions. Unfortunately, working with this amount of data comes at the price of ever-increasing data complexity. This is caused by the multiplication of data types and batch effects, which hinders the joint usage of all available data within common analyses. Data integration describes a set of tasks geared towards embedding several datasets of different origins or modalities into a joint representation that can then be used to carry out downstream analyses. In the last decade, dozens of methods have been proposed to tackle the different facets of the data integration problem, relying on various paradigms. This review introduces the most common data types encountered in computational biology and provides systematic definitions of the data integration problems. We then present how machine learning innovations were leveraged to build effective data integration algorithms, that are widely used today by computational biologists. We discuss the current state of data integration and important pitfalls to consider when working with data integration tools. We eventually detail a set of challenges the field will have to overcome in the coming years.
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Affiliation(s)
- Aziz Fouché
- Institut Curie, PSL Research University, Paris, France
- Institut National de la Santé et de la Recherche Médicale, Paris, France
- CBIO-Centre for Computational Biology, ParisTech, PSL Research University, Paris, France
- Ecole Normale Supérieure Paris-Saclay, Cachan, France
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39
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Vandereyken K, Sifrim A, Thienpont B, Voet T. Methods and applications for single-cell and spatial multi-omics. Nat Rev Genet 2023; 24:494-515. [PMID: 36864178 PMCID: PMC9979144 DOI: 10.1038/s41576-023-00580-2] [Citation(s) in RCA: 263] [Impact Index Per Article: 263.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/20/2023] [Indexed: 03/04/2023]
Abstract
The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome from single cells is transforming our understanding of cell biology in health and disease. In less than a decade, the field has seen tremendous technological revolutions that enable crucial new insights into the interplay between intracellular and intercellular molecular mechanisms that govern development, physiology and pathogenesis. In this Review, we highlight advances in the fast-developing field of single-cell and spatial multi-omics technologies (also known as multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers. We demonstrate their impact on fundamental cell biology and translational research, discuss current challenges and provide an outlook to the future.
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Affiliation(s)
- Katy Vandereyken
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Alejandro Sifrim
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Bernard Thienpont
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Thierry Voet
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium.
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium.
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA.
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40
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Cuomo ASE, Nathan A, Raychaudhuri S, MacArthur DG, Powell JE. Single-cell genomics meets human genetics. Nat Rev Genet 2023; 24:535-549. [PMID: 37085594 PMCID: PMC10784789 DOI: 10.1038/s41576-023-00599-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/29/2023] [Indexed: 04/23/2023]
Abstract
Single-cell genomic technologies are revealing the cellular composition, identities and states in tissues at unprecedented resolution. They have now scaled to the point that it is possible to query samples at the population level, across thousands of individuals. Combining single-cell information with genotype data at this scale provides opportunities to link genetic variation to the cellular processes underpinning key aspects of human biology and disease. This strategy has potential implications for disease diagnosis, risk prediction and development of therapeutic solutions. But, effectively integrating large-scale single-cell genomic data, genetic variation and additional phenotypic data will require advances in data generation and analysis methods. As single-cell genetics begins to emerge as a field in its own right, we review its current state and the challenges and opportunities ahead.
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Affiliation(s)
- Anna S E Cuomo
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
| | - Aparna Nathan
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Soumya Raychaudhuri
- Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Divisions of Rheumatology and Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel G MacArthur
- Centre for Population Genomics, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- Centre for Population Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Joseph E Powell
- Garvan Institute of Medical Research, Darlinghurst, Sydney, New South Wales, Australia.
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, New South Wales, Australia.
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41
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Shevade K, Peddada S, Mader K, Przybyla L. Functional genomics in stem cell models: considerations and applications. Front Cell Dev Biol 2023; 11:1236553. [PMID: 37554308 PMCID: PMC10404852 DOI: 10.3389/fcell.2023.1236553] [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: 06/07/2023] [Accepted: 07/13/2023] [Indexed: 08/10/2023] Open
Abstract
Protocols to differentiate human pluripotent stem cells have advanced in terms of cell type specificity and tissue-level complexity over the past 2 decades, which has facilitated human disease modeling in the most relevant cell types. The ability to generate induced PSCs (iPSCs) from patients further enables the study of disease mutations in an appropriate cellular context to reveal the mechanisms that underlie disease etiology and progression. As iPSC-derived disease models have improved in robustness and scale, they have also been adopted more widely for use in drug screens to discover new therapies and therapeutic targets. Advancement in genome editing technologies, in particular the discovery of CRISPR-Cas9, has further allowed for rapid development of iPSCs containing disease-causing mutations. CRISPR-Cas9 technologies have now evolved beyond creating single gene edits, aided by the fusion of inhibitory (CRISPRi) or activation (CRISPRa) domains to a catalytically dead Cas9 protein, enabling inhibition or activation of endogenous gene loci. These tools have been used in CRISPR knockout, CRISPRi, or CRISPRa screens to identify genetic modifiers that synergize or antagonize with disease mutations in a systematic and unbiased manner, resulting in identification of disease mechanisms and discovery of new therapeutic targets to accelerate drug discovery research. However, many technical challenges remain when applying large-scale functional genomics approaches to differentiated PSC populations. Here we review current technologies in the field of iPSC disease modeling and CRISPR-based functional genomics screens and practical considerations for implementation across a range of modalities, applications, and disease areas, as well as explore CRISPR screens that have been performed in iPSC models to-date and the insights and therapies these screens have produced.
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Affiliation(s)
- Kaivalya Shevade
- Laboratory for Genomics Research, San Francisco, CA, United States
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, United States
| | - Sailaja Peddada
- Laboratory for Genomics Research, San Francisco, CA, United States
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, United States
| | - Karl Mader
- Laboratory for Genomics Research, San Francisco, CA, United States
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, United States
| | - Laralynne Przybyla
- Laboratory for Genomics Research, San Francisco, CA, United States
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, United States
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42
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Atwell S, Waibel DJE, Boushehri SS, Wiedenmann S, Marr C, Meier M. Label-free imaging of 3D pluripotent stem cell differentiation dynamics on chip. CELL REPORTS METHODS 2023; 3:100523. [PMID: 37533640 PMCID: PMC10391578 DOI: 10.1016/j.crmeth.2023.100523] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 05/09/2023] [Accepted: 06/15/2023] [Indexed: 08/04/2023]
Abstract
Massive, parallelized 3D stem cell cultures for engineering in vitro human cell types require imaging methods with high time and spatial resolution to fully exploit technological advances in cell culture technologies. Here, we introduce a large-scale integrated microfluidic chip platform for automated 3D stem cell differentiation. To fully enable dynamic high-content imaging on the chip platform, we developed a label-free deep learning method called Bright2Nuc to predict in silico nuclear staining in 3D from confocal microscopy bright-field images. Bright2Nuc was trained and applied to hundreds of 3D human induced pluripotent stem cell cultures differentiating toward definitive endoderm on a microfluidic platform. Combined with existing image analysis tools, Bright2Nuc segmented individual nuclei from bright-field images, quantified their morphological properties, predicted stem cell differentiation state, and tracked the cells over time. Our methods are available in an open-source pipeline, enabling researchers to upscale image acquisition and phenotyping of 3D cell culture.
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Affiliation(s)
- Scott Atwell
- Helmholtz Pioneer Campus, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Dominik Jens Elias Waibel
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- TUM School of Life Sciences, Technical University of Munich, Weihenstephan, Germany
| | - Sayedali Shetab Boushehri
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Department of Mathematics, Technical University of Munich, Munich, Germany
- Data & Analytics, Pharmaceutical Research and Early Development, Roche Innovation Center Munich (RICM), Penzberg, Germany
| | - Sandra Wiedenmann
- Helmholtz Pioneer Campus, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Carsten Marr
- Institute of AI for Health, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Matthias Meier
- Helmholtz Pioneer Campus, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
- Center for Biotechnology and Biomedicine, University of Leipzig, Leipzig, Germany
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43
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Liu W, Deng W, Chen M, Dong Z, Zhu B, Yu Z, Tang D, Sauler M, Lin C, Wain LV, Cho MH, Kaminski N, Zhao H. A statistical framework to identify cell types whose genetically regulated proportions are associated with complex diseases. PLoS Genet 2023; 19:e1010825. [PMID: 37523391 PMCID: PMC10414598 DOI: 10.1371/journal.pgen.1010825] [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: 12/20/2022] [Revised: 08/10/2023] [Accepted: 06/12/2023] [Indexed: 08/02/2023] Open
Abstract
Finding disease-relevant tissues and cell types can facilitate the identification and investigation of functional genes and variants. In particular, cell type proportions can serve as potential disease predictive biomarkers. In this manuscript, we introduce a novel statistical framework, cell-type Wide Association Study (cWAS), that integrates genetic data with transcriptomics data to identify cell types whose genetically regulated proportions (GRPs) are disease/trait-associated. On simulated and real GWAS data, cWAS showed good statistical power with newly identified significant GRP associations in disease-associated tissues. More specifically, GRPs of endothelial and myofibroblasts in lung tissue were associated with Idiopathic Pulmonary Fibrosis and Chronic Obstructive Pulmonary Disease, respectively. For breast cancer, the GRP of blood CD8+ T cells was negatively associated with breast cancer (BC) risk as well as survival. Overall, cWAS is a powerful tool to reveal cell types associated with complex diseases mediated by GRPs.
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Affiliation(s)
- Wei Liu
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
| | - Wenxuan Deng
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
| | - Ming Chen
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
| | - Zihan Dong
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
| | - Biqing Zhu
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
| | - Zhaolong Yu
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
| | - Daiwei Tang
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
| | - Maor Sauler
- Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America
| | - Chen Lin
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
| | - Louise V. Wain
- Department of Health Sciences, University of Leicester, Leicester, United Kingdom
- National Institute for Health Research, Leicester Respiratory Biomedical Research Centre, Glenfield Hospital, Leicester, United Kingdom
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Naftali Kaminski
- Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, Yale University, New Haven, Connecticut, United States of America
| | - Hongyu Zhao
- Program of Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, United States of America
- Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, Connecticut, United States of America
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44
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Qiu Y, Wang H, Fan M, Pan H, Guan J, Jiang Y, Jia Z, Wu K, Zhou H, Zhuang Q, Lei Z, Ding X, Cai H, Dong Y, Yan L, Lin A, Fu Y, Zhang D, Yan Q, Wang Q. Impaired AIF-CHCHD4 interaction and mitochondrial calcium overload contribute to auditory neuropathy spectrum disorder in patient-iPSC-derived neurons with AIFM1 variant. Cell Death Dis 2023; 14:375. [PMID: 37365177 PMCID: PMC10293272 DOI: 10.1038/s41419-023-05899-6] [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: 02/01/2023] [Revised: 06/08/2023] [Accepted: 06/16/2023] [Indexed: 06/28/2023]
Abstract
Auditory neuropathy spectrum disorder (ANSD) is a hearing impairment caused by dysfunction of inner hair cells, ribbon synapses, spiral ganglion neurons and/or the auditory nerve itself. Approximately 1/7000 newborns have abnormal auditory nerve function, accounting for 10%-14% of cases of permanent hearing loss in children. Although we previously identified the AIFM1 c.1265 G > A variant to be associated with ANSD, the mechanism by which ANSD is associated with AIFM1 is poorly understood. We generated induced pluripotent stem cells (iPSCs) from peripheral blood mononuclear cells (PBMCs) via nucleofection with episomal plasmids. The patient-specific iPSCs were edited via CRISPR/Cas9 technology to generate gene-corrected isogenic iPSCs. These iPSCs were further differentiated into neurons via neural stem cells (NSCs). The pathogenic mechanism was explored in these neurons. In patient cells (PBMCs, iPSCs, and neurons), the AIFM1 c.1265 G > A variant caused a novel splicing variant (c.1267-1305del), resulting in AIF p.R422Q and p.423-435del proteins, which impaired AIF dimerization. Such impaired AIF dimerization then weakened the interaction between AIF and coiled-coil-helix-coiled-coil-helix domain-containing protein 4 (CHCHD4). On the one hand, the mitochondrial import of ETC complex subunits was inhibited, subsequently leading to an increased ADP/ATP ratio and elevated ROS levels. On the other hand, MICU1-MICU2 heterodimerization was impaired, leading to mCa2+ overload. Calpain was activated by mCa2+ and subsequently cleaved AIF for its translocation into the nucleus, ultimately resulting in caspase-independent apoptosis. Interestingly, correction of the AIFM1 variant significantly restored the structure and function of AIF, further improving the physiological state of patient-specific iPSC-derived neurons. This study demonstrates that the AIFM1 variant is one of the molecular bases of ANSD. Mitochondrial dysfunction, especially mCa2+ overload, plays a prominent role in ANSD associated with AIFM1. Our findings help elucidate the mechanism of ANSD and may lead to the provision of novel therapies.
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Affiliation(s)
- Yue Qiu
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Hongyang Wang
- Senior Department of Otolaryngology, Head and Neck Surgery, Chinese PLA Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, 100853, China
| | - Mingjie Fan
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
- Department of Pediatrics, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, China
| | - Huaye Pan
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Jing Guan
- Senior Department of Otolaryngology, Head and Neck Surgery, Chinese PLA Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, 100853, China
| | - Yangwei Jiang
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Zexiao Jia
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Kaiwen Wu
- Senior Department of Otolaryngology, Head and Neck Surgery, Chinese PLA Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, 100853, China
| | - Hui Zhou
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Qianqian Zhuang
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Zhaoying Lei
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Xue Ding
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Huajian Cai
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Yufei Dong
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Lei Yan
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Aifu Lin
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China
| | - Yong Fu
- The Children's Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310052, China
| | - Dong Zhang
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
| | - Qingfeng Yan
- College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang, 310058, China.
- Department of Pediatrics, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310003, China.
- Key Laboratory for Cell and Gene Engineering of Zhejiang Province, Hangzhou, Zhejiang, 310058, China.
| | - Qiuju Wang
- Senior Department of Otolaryngology, Head and Neck Surgery, Chinese PLA Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, 100853, China.
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45
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Sun Y, Shim WJ, Shen S, Sinniah E, Pham D, Su Z, Mizikovsky D, White MD, Ho JK, Nguyen Q, Bodén M, Palpant N. Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity. Nucleic Acids Res 2023; 51:e62. [PMID: 37125641 PMCID: PMC10287941 DOI: 10.1093/nar/gkad307] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 04/28/2023] [Indexed: 05/02/2023] Open
Abstract
Methods for cell clustering and gene expression from single-cell RNA sequencing (scRNA-seq) data are essential for biological interpretation of cell processes. Here, we present TRIAGE-Cluster which uses genome-wide epigenetic data from diverse bio-samples to identify genes demarcating cell diversity in scRNA-seq data. By integrating patterns of repressive chromatin deposited across diverse cell types with weighted density estimation, TRIAGE-Cluster determines cell type clusters in a 2D UMAP space. We then present TRIAGE-ParseR, a machine learning method which evaluates gene expression rank lists to define gene groups governing the identity and function of cell types. We demonstrate the utility of this two-step approach using atlases of in vivo and in vitro cell diversification and organogenesis. We also provide a web accessible dashboard for analysis and download of data and software. Collectively, genome-wide epigenetic repression provides a versatile strategy to define cell diversity and study gene regulation of scRNA-seq data.
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Affiliation(s)
- Yuliangzi Sun
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Woo Jun Shim
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Sophie Shen
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Enakshi Sinniah
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Duy Pham
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Zezhuo Su
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China
| | - Dalia Mizikovsky
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Melanie D White
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Joshua W K Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
- Laboratory of Data Discovery for Health Limited (D24H), Hong Kong Science Park, Hong Kong SAR, China
| | - Quan Nguyen
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Mikael Bodén
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
| | - Nathan J Palpant
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
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46
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Neavin DR, Steinmann AM, Farbehi N, Chiu HS, Daniszewski MS, Arora H, Bermudez Y, Moutinho C, Chan CL, Bax M, Tyebally M, Gnanasambandapillai V, Lam CE, Nguyen U, Hernández D, Lidgerwood GE, Graham RM, Hewitt AW, Pébay A, Palpant NJ, Powell JE. A village in a dish model system for population-scale hiPSC studies. Nat Commun 2023; 14:3240. [PMID: 37296104 PMCID: PMC10256711 DOI: 10.1038/s41467-023-38704-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 04/26/2023] [Indexed: 06/12/2023] Open
Abstract
The mechanisms by which DNA alleles contribute to disease risk, drug response, and other human phenotypes are highly context-specific, varying across cell types and different conditions. Human induced pluripotent stem cells are uniquely suited to study these context-dependent effects but cell lines from hundreds or thousands of individuals are required. Village cultures, where multiple induced pluripotent stem lines are cultured and differentiated in a single dish, provide an elegant solution for scaling induced pluripotent stem experiments to the necessary sample sizes required for population-scale studies. Here, we show the utility of village models, demonstrating how cells can be assigned to an induced pluripotent stem line using single-cell sequencing and illustrating that the genetic, epigenetic or induced pluripotent stem line-specific effects explain a large percentage of gene expression variation for many genes. We demonstrate that village methods can effectively detect induced pluripotent stem line-specific effects, including sensitive dynamics of cell states.
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Affiliation(s)
- Drew R Neavin
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia
| | - Angela M Steinmann
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia
| | - Nona Farbehi
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia
- Graduate School of Biomedical Engineering, University of New South Wales, Kensington, 2033, Sydney, Australia
| | - Han Sheng Chiu
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Maciej S Daniszewski
- Department of Anatomy and Physiology, the University of Melbourne, Melbourne, Australia
| | - Himanshi Arora
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia
| | - Yasmin Bermudez
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia
| | - Cátia Moutinho
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia
| | - Chia-Ling Chan
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia
| | - Monique Bax
- Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia
- UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
| | - Mubarika Tyebally
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia
| | | | - Chuan E Lam
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia
| | - Uyen Nguyen
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia
| | - Damián Hernández
- Department of Anatomy and Physiology, the University of Melbourne, Melbourne, Australia
| | - Grace E Lidgerwood
- Department of Anatomy and Physiology, the University of Melbourne, Melbourne, Australia
| | - Robert M Graham
- Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia
- UNSW Medicine & Health, UNSW Sydney, Kensington, NSW, Australia
- St Vincent's Hospital, Darlinghurst, 2010, NSW, Australia
| | - Alex W Hewitt
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Australia
- School of Medicine, Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
| | - Alice Pébay
- Department of Anatomy and Physiology, the University of Melbourne, Melbourne, Australia
- Department of Surgery, Royal Melbourne Hospital, Anatomy and Neuroscience, the University of Melbourne, Melbourne, Australia
| | - Nathan J Palpant
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Joseph E Powell
- Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Darlinghurst, 2010, Sydney, Australia.
- UNSW Cellular Genomics Futures Institute, School of Medical Sciences, University of New South Wales, 2052, Sydney, Australia.
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47
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Benaglio P, Newsome J, Han JY, Chiou J, Aylward A, Corban S, Miller M, Okino ML, Kaur J, Preissl S, Gorkin DU, Gaulton KJ. Mapping genetic effects on cell type-specific chromatin accessibility and annotating complex immune trait variants using single nucleus ATAC-seq in peripheral blood. PLoS Genet 2023; 19:e1010759. [PMID: 37289818 DOI: 10.1371/journal.pgen.1010759] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 04/25/2023] [Indexed: 06/10/2023] Open
Abstract
Gene regulation is highly cell type-specific and understanding the function of non-coding genetic variants associated with complex traits requires molecular phenotyping at cell type resolution. In this study we performed single nucleus ATAC-seq (snATAC-seq) and genotyping in peripheral blood mononuclear cells from 13 individuals. Clustering chromatin accessibility profiles of 96,002 total nuclei identified 17 immune cell types and sub-types. We mapped chromatin accessibility QTLs (caQTLs) in each immune cell type and sub-type using individuals of European ancestry which identified 6,901 caQTLs at FDR < .10 and 4,220 caQTLs at FDR < .05, including those obscured from assays of bulk tissue such as with divergent effects on different cell types. For 3,941 caQTLs we further annotated putative target genes of variant activity using single cell co-accessibility, and caQTL variants were significantly correlated with the accessibility level of linked gene promoters. We fine-mapped loci associated with 16 complex immune traits and identified immune cell caQTLs at 622 candidate causal variants, including those with cell type-specific effects. At the 6q15 locus associated with type 1 diabetes, in line with previous reports, variant rs72928038 was a naïve CD4+ T cell caQTL linked to BACH2 and we validated the allelic effects of this variant on regulatory activity in Jurkat T cells. These results highlight the utility of snATAC-seq for mapping genetic effects on accessible chromatin in specific cell types.
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Affiliation(s)
- Paola Benaglio
- Department of Pediatrics, University of California San Diego, San Diego, California, United States of America
| | - Jacklyn Newsome
- Bioinformatics and Systems Biology Program, University of California San Diego, San Diego, California, United States of America
| | - Jee Yun Han
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, San Diego, California, United States of America
| | - Joshua Chiou
- Biomedical Sciences Graduate Program. University of California San Diego, San Diego, California, United States of America
| | - Anthony Aylward
- Bioinformatics and Systems Biology Program, University of California San Diego, San Diego, California, United States of America
| | - Sierra Corban
- Department of Pediatrics, University of California San Diego, San Diego, California, United States of America
| | - Michael Miller
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, San Diego, California, United States of America
| | - Mei-Lin Okino
- Department of Pediatrics, University of California San Diego, San Diego, California, United States of America
| | - Jaspreet Kaur
- Department of Pediatrics, University of California San Diego, San Diego, California, United States of America
| | - Sebastian Preissl
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, San Diego, California, United States of America
| | - David U Gorkin
- Center for Epigenomics, Department of Cellular and Molecular Medicine, University of California San Diego, San Diego, California, United States of America
| | - Kyle J Gaulton
- Department of Pediatrics, University of California San Diego, San Diego, California, United States of America
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48
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Isoda M, Sanosaka T, Tomooka R, Mabuchi Y, Shinozaki M, Andoh-Noda T, Banno S, Mizota N, Yamaguchi R, Okano H, Kohyama J. Mesenchymal properties of iPSC-derived neural progenitors that generate undesired grafts after transplantation. Commun Biol 2023; 6:611. [PMID: 37286713 DOI: 10.1038/s42003-023-04995-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 05/30/2023] [Indexed: 06/09/2023] Open
Abstract
Although neural stem/progenitor cells derived from human induced pluripotent stem cells (hiPSC-NS/PCs) are expected to be a cell source for cell-based therapy, tumorigenesis of hiPSC-NS/PCs is a potential problem for clinical applications. Therefore, to understand the mechanisms of tumorigenicity in NS/PCs, we clarified the cell populations of NS/PCs. We established single cell-derived NS/PC clones (scNS/PCs) from hiPSC-NS/PCs that generated undesired grafts. Additionally, we performed bioassays on scNS/PCs, which classified cell types within parental hiPSC-NS/PCs. Interestingly, we found unique subsets of scNS/PCs, which exhibited the transcriptome signature of mesenchymal lineages. Furthermore, these scNS/PCs expressed both neural (PSA-NCAM) and mesenchymal (CD73 and CD105) markers, and had an osteogenic differentiation capacity. Notably, eliminating CD73+ CD105+ cells from among parental hiPSC-NS/PCs ensured the quality of hiPSC-NS/PCs. Taken together, the existence of unexpected cell populations among NS/PCs may explain their tumorigenicity leading to potential safety issues of hiPSC-NS/PCs for future regenerative medicine.
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Affiliation(s)
- Miho Isoda
- Department of Physiology, Keio University School of Medicine, Shinjuku-ku, Tokyo, 160-8582, Japan
- Regenerative & Cellular Medicine Kobe Center, Sumitomo Pharma Co., Ltd., Kobe, Hyogo, 650-0047, Japan
| | - Tsukasa Sanosaka
- Department of Physiology, Keio University School of Medicine, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Ryo Tomooka
- Department of Physiology, Keio University School of Medicine, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Yo Mabuchi
- Department of Physiology, Keio University School of Medicine, Shinjuku-ku, Tokyo, 160-8582, Japan
- Intractable Disease Research Centre, Juntendo University School of Medicine, Bunkyo-ku, Tokyo, 113-8421, Japan
- Department of Clinical Regenerative Medicine, Fujita Health University, Toyoake, Aichi, 470-1192, Japan
| | - Munehisa Shinozaki
- Department of Physiology, Keio University School of Medicine, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Tomoko Andoh-Noda
- Department of Physiology, Keio University School of Medicine, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Satoe Banno
- Department of Physiology, Keio University School of Medicine, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Noriko Mizota
- Department of Physiology, Keio University School of Medicine, Shinjuku-ku, Tokyo, 160-8582, Japan
| | - Ryo Yamaguchi
- Department of Physiology, Keio University School of Medicine, Shinjuku-ku, Tokyo, 160-8582, Japan
- Regenerative & Cellular Medicine Kobe Center, Sumitomo Pharma Co., Ltd., Kobe, Hyogo, 650-0047, Japan
| | - Hideyuki Okano
- Department of Physiology, Keio University School of Medicine, Shinjuku-ku, Tokyo, 160-8582, Japan.
| | - Jun Kohyama
- Department of Physiology, Keio University School of Medicine, Shinjuku-ku, Tokyo, 160-8582, Japan.
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49
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Resztak JA, Wei J, Zilioli S, Sendler E, Alazizi A, Mair-Meijers HE, Wu P, Wen X, Slatcher RB, Zhou X, Luca F, Pique-Regi R. Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single-cell resolution. Genome Res 2023; 33:839-856. [PMID: 37442575 PMCID: PMC10519413 DOI: 10.1101/gr.276765.122] [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: 03/17/2022] [Accepted: 06/08/2023] [Indexed: 07/15/2023]
Abstract
Synthetic glucocorticoids, such as dexamethasone, have been used as a treatment for many immune conditions, such as asthma and, more recently, severe COVID-19. Single-cell data can capture more fine-grained details on transcriptional variability and dynamics to gain a better understanding of the molecular underpinnings of inter-individual variation in drug response. Here, we used single-cell RNA-seq to study the dynamics of the transcriptional response to glucocorticoids in activated peripheral blood mononuclear cells from 96 African American children. We used novel statistical approaches to calculate a mean-independent measure of gene expression variability and a measure of transcriptional response pseudotime. Using these approaches, we showed that glucocorticoids reverse the effects of immune stimulation on both gene expression mean and variability. Our novel measure of gene expression response dynamics, based on the diagonal linear discriminant analysis, separated individual cells by response status on the basis of their transcriptional profiles and allowed us to identify different dynamic patterns of gene expression along the response pseudotime. We identified genetic variants regulating gene expression mean and variability, including treatment-specific effects, and showed widespread genetic regulation of the transcriptional dynamics of the gene expression response.
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Affiliation(s)
- Justyna A Resztak
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA
| | - Julong Wei
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA
| | - Samuele Zilioli
- Department of Psychology, Wayne State University, Detroit, Michigan 48201, USA
- Department of Family Medicine and Public Health Sciences, Wayne State University, Detroit, Michigan 48201, USA
| | - Edward Sendler
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA
| | - Adnan Alazizi
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA
| | - Henriette E Mair-Meijers
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA
| | - Peijun Wu
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Xiaoquan Wen
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Richard B Slatcher
- Department of Psychology, University of Georgia, Athens, Georgia 30602, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Francesca Luca
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA;
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan 48201, USA
- Department of Biology, University of Rome "Tor Vergata," 00133 Rome, Italy
| | - Roger Pique-Regi
- Center for Molecular Medicine and Genetics, Wayne State University, Detroit, Michigan 48201, USA;
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, Michigan 48201, USA
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50
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Lu S, Keleş S. Debiased personalized gene coexpression networks for population-scale scRNA-seq data. Genome Res 2023; 33:932-947. [PMID: 37295843 PMCID: PMC10519377 DOI: 10.1101/gr.277363.122] [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: 09/28/2022] [Accepted: 06/07/2023] [Indexed: 06/12/2023]
Abstract
Population-scale single-cell RNA-seq (scRNA-seq) data sets create unique opportunities for quantifying expression variation across individuals at the gene coexpression network level. Estimation of coexpression networks is well established for bulk RNA-seq; however, single-cell measurements pose novel challenges owing to technical limitations and noise levels of this technology. Gene-gene correlation estimates from scRNA-seq tend to be severely biased toward zero for genes with low and sparse expression. Here, we present Dozer to debias gene-gene correlation estimates from scRNA-seq data sets and accurately quantify network-level variation across individuals. Dozer corrects correlation estimates in the general Poisson measurement model and provides a metric to quantify genes measured with high noise. Computational experiments establish that Dozer estimates are robust to mean expression levels of the genes and the sequencing depths of the data sets. Compared with alternatives, Dozer results in fewer false-positive edges in the coexpression networks, yields more accurate estimates of network centrality measures and modules, and improves the faithfulness of networks estimated from separate batches of the data sets. We showcase unique analyses enabled by Dozer in two population-scale scRNA-seq applications. Coexpression network-based centrality analysis of multiple differentiating human induced pluripotent stem cell (iPSC) lines yields biologically coherent gene groups that are associated with iPSC differentiation efficiency. Application with population-scale scRNA-seq of oligodendrocytes from postmortem human tissues of Alzheimer's disease and controls uniquely reveals coexpression modules of innate immune response with distinct coexpression levels between the diagnoses. Dozer represents an important advance in estimating personalized coexpression networks from scRNA-seq data.
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Affiliation(s)
- Shan Lu
- Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706, USA
| | - Sündüz Keleş
- Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706, USA;
- Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin 53706, USA
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