1
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Sun Y, Kong L, Huang J, Deng H, Bian X, Li X, Cui F, Dou L, Cao C, Zou Q, Zhang Z. A comprehensive survey of dimensionality reduction and clustering methods for single-cell and spatial transcriptomics data. Brief Funct Genomics 2024:elae023. [PMID: 38860675 DOI: 10.1093/bfgp/elae023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 02/29/2024] [Accepted: 05/27/2024] [Indexed: 06/12/2024] Open
Abstract
In recent years, the application of single-cell transcriptomics and spatial transcriptomics analysis techniques has become increasingly widespread. Whether dealing with single-cell transcriptomic or spatial transcriptomic data, dimensionality reduction and clustering are indispensable. Both single-cell and spatial transcriptomic data are often high-dimensional, making the analysis and visualization of such data challenging. Through dimensionality reduction, it becomes possible to visualize the data in a lower-dimensional space, allowing for the observation of relationships and differences between cell subpopulations. Clustering enables the grouping of similar cells into the same cluster, aiding in the identification of distinct cell subpopulations and revealing cellular diversity, providing guidance for downstream analyses. In this review, we systematically summarized the most widely recognized algorithms employed for the dimensionality reduction and clustering analysis of single-cell transcriptomic and spatial transcriptomic data. This endeavor provides valuable insights and ideas that can contribute to the development of novel tools in this rapidly evolving field.
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Affiliation(s)
- Yidi Sun
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Lingling Kong
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Jiayi Huang
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Hongyan Deng
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Xinling Bian
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Xingfeng Li
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Feifei Cui
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Lijun Dou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland, OH 44106, United States
| | - Chen Cao
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 210029, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
| | - Zilong Zhang
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
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2
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Bilous M, Hérault L, Gabriel AA, Teleman M, Gfeller D. Building and analyzing metacells in single-cell genomics data. Mol Syst Biol 2024:10.1038/s44320-024-00045-6. [PMID: 38811801 DOI: 10.1038/s44320-024-00045-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/03/2024] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).
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Affiliation(s)
- Mariia Bilous
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Léonard Hérault
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Aurélie Ag Gabriel
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Matei Teleman
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland.
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland.
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland.
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3
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Emani PS, Liu JJ, Clarke D, Jensen M, Warrell J, Gupta C, Meng R, Lee CY, Xu S, Dursun C, Lou S, Chen Y, Chu Z, Galeev T, Hwang A, Li Y, Ni P, Zhou X, Bakken TE, Bendl J, Bicks L, Chatterjee T, Cheng L, Cheng Y, Dai Y, Duan Z, Flaherty M, Fullard JF, Gancz M, Garrido-Martín D, Gaynor-Gillett S, Grundman J, Hawken N, Henry E, Hoffman GE, Huang A, Jiang Y, Jin T, Jorstad NL, Kawaguchi R, Khullar S, Liu J, Liu J, Liu S, Ma S, Margolis M, Mazariegos S, Moore J, Moran JR, Nguyen E, Phalke N, Pjanic M, Pratt H, Quintero D, Rajagopalan AS, Riesenmy TR, Shedd N, Shi M, Spector M, Terwilliger R, Travaglini KJ, Wamsley B, Wang G, Xia Y, Xiao S, Yang AC, Zheng S, Gandal MJ, Lee D, Lein ES, Roussos P, Sestan N, Weng Z, White KP, Won H, Girgenti MJ, Zhang J, Wang D, Geschwind D, Gerstein M. Single-cell genomics and regulatory networks for 388 human brains. Science 2024; 384:eadi5199. [PMID: 38781369 DOI: 10.1126/science.adi5199] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 04/05/2024] [Indexed: 05/25/2024]
Abstract
Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type-specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.
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Affiliation(s)
- Prashant S Emani
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Jason J Liu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Declan Clarke
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Matthew Jensen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Chirag Gupta
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Ran Meng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Che Yu Lee
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Siwei Xu
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Cagatay Dursun
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Shaoke Lou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Yuhang Chen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Zhiyuan Chu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
| | - Timur Galeev
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Ahyeon Hwang
- Department of Computer Science, University of California, Irvine, CA 92697, USA
- Mathematical, Computational and Systems Biology, University of California, Irvine, CA 92697, USA
| | - Yunyang Li
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, New Haven, CT 06520, USA
| | - Pengyu Ni
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Xiao Zhou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | | | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Lucy Bicks
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Tanima Chatterjee
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | | | - Yuyan Cheng
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yi Dai
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Ziheng Duan
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | | | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Michael Gancz
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Diego Garrido-Martín
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona, Barcelona 08028, Spain
| | - Sophia Gaynor-Gillett
- Tempus Labs, Chicago, IL 60654, USA
- Department of Biology, Cornell College, Mount Vernon, IA 52314, USA
| | - Jennifer Grundman
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Natalie Hawken
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Ella Henry
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Ao Huang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
| | - Yunzhe Jiang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Ting Jin
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | | | - Riki Kawaguchi
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA 90095, USA
| | - Saniya Khullar
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Jianyin Liu
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Junhao Liu
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Shuang Liu
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Shaojie Ma
- Department of Neuroscience, Yale University, New Haven, CT 06510, USA
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Samantha Mazariegos
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Jill Moore
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | | | - Eric Nguyen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Nishigandha Phalke
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Milos Pjanic
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Henry Pratt
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Diana Quintero
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | | | - Tiernon R Riesenmy
- Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA
| | - Nicole Shedd
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | | | | | - Rosemarie Terwilliger
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
| | | | - Brie Wamsley
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Gaoyuan Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Yan Xia
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Shaohua Xiao
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Andrew C Yang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Suchen Zheng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Michael J Gandal
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles CA, 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Nenad Sestan
- Department of Neuroscience, Yale University, New Haven, CT 06510, USA
| | - Zhiping Weng
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Kevin P White
- Yong Loo Lin School of Medicine, National University of Singapore, 117597 Singapore
| | - Hyejung Won
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT 06520, USA
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT 06516, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Daifeng Wang
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Daniel Geschwind
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, New Haven, CT 06520, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA
- Department of Biomedical Informatics & Data Science, Yale University, New Haven, CT 06520, USA
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4
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Gan D, Zhu Y, Lu X, Li J. SCIPAC: quantitative estimation of cell-phenotype associations. Genome Biol 2024; 25:119. [PMID: 38741183 DOI: 10.1186/s13059-024-03263-1] [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: 01/30/2023] [Accepted: 04/30/2024] [Indexed: 05/16/2024] Open
Abstract
Numerous algorithms have been proposed to identify cell types in single-cell RNA sequencing data, yet a fundamental problem remains: determining associations between cells and phenotypes such as cancer. We develop SCIPAC, the first algorithm that quantitatively estimates the association between each cell in single-cell data and a phenotype. SCIPAC also provides a p-value for each association and applies to data with virtually any type of phenotype. We demonstrate SCIPAC's accuracy in simulated data. On four real cancerous or noncancerous datasets, insights from SCIPAC help interpret the data and generate new hypotheses. SCIPAC requires minimum tuning and is computationally very fast.
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Affiliation(s)
- Dailin Gan
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, 46556, IN, USA
| | - Yini Zhu
- Department of Biological Sciences, Boler-Parseghian Center for Rare and Neglected Diseases, Harper Cancer Research Institute, Integrated Biomedical Sciences Graduate Program, University of Notre Dame, Notre Dame, 46556, IN, USA
| | - Xin Lu
- Department of Biological Sciences, Boler-Parseghian Center for Rare and Neglected Diseases, Harper Cancer Research Institute, Integrated Biomedical Sciences Graduate Program, University of Notre Dame, Notre Dame, 46556, IN, USA
- Tumor Microenvironment and Metastasis Program, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, 46202, IN, USA
| | - Jun Li
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, 46556, IN, USA.
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5
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Barboy O, Bercovich A, Li H, Eyal-Lubling Y, Yalin A, Shapir Itai Y, Abadie K, Zada M, David E, Shlomi-Loubaton S, Katzenelenbogen Y, Jaitin DA, Gur C, Yofe I, Feferman T, Cohen M, Dahan R, Newell EW, Lifshitz A, Tanay A, Amit I. Modeling T cell temporal response to cancer immunotherapy rationalizes development of combinatorial treatment protocols. NATURE CANCER 2024; 5:742-759. [PMID: 38429414 DOI: 10.1038/s43018-024-00734-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 01/19/2024] [Indexed: 03/03/2024]
Abstract
Successful immunotherapy relies on triggering complex responses involving T cell dynamics in tumors and the periphery. Characterizing these responses remains challenging using static human single-cell atlases or mouse models. To address this, we developed a framework for in vivo tracking of tumor-specific CD8+ T cells over time and at single-cell resolution. Our tools facilitate the modeling of gene program dynamics in the tumor microenvironment (TME) and the tumor-draining lymph node (tdLN). Using this approach, we characterize two modes of anti-programmed cell death protein 1 (PD-1) activity, decoupling induced differentiation of tumor-specific activated precursor cells from conventional type 1 dendritic cell (cDC1)-dependent proliferation and recruitment to the TME. We demonstrate that combining anti-PD-1 therapy with anti-4-1BB agonist enhances the recruitment and proliferation of activated precursors, resulting in tumor control. These data suggest that effective response to anti-PD-1 therapy is dependent on sufficient influx of activated precursor CD8+ cells to the TME and highlight the importance of understanding system-level dynamics in optimizing immunotherapies.
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Affiliation(s)
- Oren Barboy
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Akhiad Bercovich
- Department of Computer Science and Applied Mathematics and Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Hanjie Li
- Department of Synthetic Immunology, Shenzhen Institutes of Advanced Technology, Shenzhen, China
| | - Yaniv Eyal-Lubling
- Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge, UK
| | - Adam Yalin
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Yuval Shapir Itai
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Kathleen Abadie
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Mor Zada
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Eyal David
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Shir Shlomi-Loubaton
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | | | - Diego Adhemar Jaitin
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Chamutal Gur
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
- The Hebrew University, Jerusalem, Israel
| | - Ido Yofe
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Tali Feferman
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Merav Cohen
- Department of Clinical Microbiology and Immunology, Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Rony Dahan
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Evan W Newell
- Fred Hutchinson Cancer Research Center, University of Washington, Seattle, WA, USA
| | - Aviezer Lifshitz
- Department of Computer Science and Applied Mathematics and Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Amos Tanay
- Department of Computer Science and Applied Mathematics and Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
| | - Ido Amit
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel.
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6
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Putri GH, Howitt G, Marsh-Wakefield F, Ashhurst TM, Phipson B. SuperCellCyto: enabling efficient analysis of large scale cytometry datasets. Genome Biol 2024; 25:89. [PMID: 38589921 PMCID: PMC11003185 DOI: 10.1186/s13059-024-03229-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 03/27/2024] [Indexed: 04/10/2024] Open
Abstract
Advancements in cytometry technologies have enabled quantification of up to 50 proteins across millions of cells at single cell resolution. Analysis of cytometry data routinely involves tasks such as data integration, clustering, and dimensionality reduction. While numerous tools exist, many require extensive run times when processing large cytometry data containing millions of cells. Existing solutions, such as random subsampling, are inadequate as they risk excluding rare cell subsets. To address this, we propose SuperCellCyto, an R package that builds on the SuperCell tool which groups highly similar cells into supercells. SuperCellCyto is available on GitHub ( https://github.com/phipsonlab/SuperCellCyto ) and Zenodo ( https://doi.org/10.5281/zenodo.10521294 ).
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Affiliation(s)
- Givanna H Putri
- The Walter and Eliza Hall Institute of Medical Research and The Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia.
| | - George Howitt
- Peter MacCallum Cancer Centre and The Sir Peter MacCallum, Department of Oncology, The University of Melbourne, Parkville, VIC, Australia
| | - Felix Marsh-Wakefield
- Centenary Institute of Cancer Medicine and Cell Biology, The University of Sydney, Sydney, NSW, Australia
| | - Thomas M Ashhurst
- Sydney Cytometry Core Research Facility and School of Medical Sciences, The University of Sydney, Sydney, NSW, Australia
| | - Belinda Phipson
- The Walter and Eliza Hall Institute of Medical Research and The Department of Medical Biology, The University of Melbourne, Parkville, VIC, Australia.
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7
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Emani PS, Liu JJ, Clarke D, Jensen M, Warrell J, Gupta C, Meng R, Lee CY, Xu S, Dursun C, Lou S, Chen Y, Chu Z, Galeev T, Hwang A, Li Y, Ni P, Zhou X, Bakken TE, Bendl J, Bicks L, Chatterjee T, Cheng L, Cheng Y, Dai Y, Duan Z, Flaherty M, Fullard JF, Gancz M, Garrido-Martín D, Gaynor-Gillett S, Grundman J, Hawken N, Henry E, Hoffman GE, Huang A, Jiang Y, Jin T, Jorstad NL, Kawaguchi R, Khullar S, Liu J, Liu J, Liu S, Ma S, Margolis M, Mazariegos S, Moore J, Moran JR, Nguyen E, Phalke N, Pjanic M, Pratt H, Quintero D, Rajagopalan AS, Riesenmy TR, Shedd N, Shi M, Spector M, Terwilliger R, Travaglini KJ, Wamsley B, Wang G, Xia Y, Xiao S, Yang AC, Zheng S, Gandal MJ, Lee D, Lein ES, Roussos P, Sestan N, Weng Z, White KP, Won H, Girgenti MJ, Zhang J, Wang D, Geschwind D, Gerstein M. Single-cell genomics and regulatory networks for 388 human brains. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.18.585576. [PMID: 38562822 PMCID: PMC10983939 DOI: 10.1101/2024.03.18.585576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.
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Affiliation(s)
- Prashant S Emani
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Jason J Liu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Declan Clarke
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Matthew Jensen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Chirag Gupta
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Ran Meng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Che Yu Lee
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Siwei Xu
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Cagatay Dursun
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Shaoke Lou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Yuhang Chen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Zhiyuan Chu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Timur Galeev
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Ahyeon Hwang
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
- Mathematical, Computational and Systems Biology, University of California, Irvine, CA, 92697, USA
| | - Yunyang Li
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA
| | - Pengyu Ni
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Xiao Zhou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | | | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Lucy Bicks
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Tanima Chatterjee
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | | | - Yuyan Cheng
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Department of Opthalmology, Perlman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Yi Dai
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Ziheng Duan
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | | | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Michael Gancz
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Diego Garrido-Martín
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona, Barcelona, 08028, Spain
| | - Sophia Gaynor-Gillett
- Tempus Labs, Inc., Chicago, IL, 60654, USA
- Department of Biology, Cornell College, Mount Vernon, IA, 52314, USA
| | - Jennifer Grundman
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Natalie Hawken
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Ella Henry
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
| | - Ao Huang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Yunzhe Jiang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Ting Jin
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | | | - Riki Kawaguchi
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA, 90095, USA
| | - Saniya Khullar
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Jianyin Liu
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Junhao Liu
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Shuang Liu
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Shaojie Ma
- Department of Neuroscience, Yale University, New Haven, CT, 06510, USA
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Michael Margolis
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Samantha Mazariegos
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Jill Moore
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | | | - Eric Nguyen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Nishigandha Phalke
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Milos Pjanic
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Henry Pratt
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Diana Quintero
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | | | - Tiernon R Riesenmy
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
| | - Nicole Shedd
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Manman Shi
- Tempus Labs, Inc., Chicago, IL, 60654, USA
| | | | - Rosemarie Terwilliger
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06520, USA
| | | | - Brie Wamsley
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Gaoyuan Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Yan Xia
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Shaohua Xiao
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Andrew C Yang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Suchen Zheng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Michael J Gandal
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, 98109, USA
- Department of Neurological Surgery, University of Washington, Seattle, WA, 98195, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, 98195, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
| | - Nenad Sestan
- Department of Neuroscience, Yale University, New Haven, CT, 06510, USA
| | - Zhiping Weng
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Kevin P White
- Yong Loo Lin School of Medicine, National University of Singapore, 117597, Singapore
| | - Hyejung Won
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT, 06520, USA
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT, 06516, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Daifeng Wang
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Daniel Geschwind
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA, 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
- Department of Biomedical Informatics & Data Science, Yale University, New Haven, CT, 06520, USA
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8
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Li S, Li Y, Sun Y, Li Y, Chen X, Tang S, Chen S. EpiCarousel: memory- and time-efficient identification of metacells for atlas-level single-cell chromatin accessibility data. Bioinformatics 2024; 40:btae191. [PMID: 38588573 PMCID: PMC11037479 DOI: 10.1093/bioinformatics/btae191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 03/02/2024] [Accepted: 04/05/2024] [Indexed: 04/10/2024] Open
Abstract
SUMMARY Recent technical advancements in single-cell chromatin accessibility sequencing (scCAS) have brought new insights to the characterization of epigenetic heterogeneity. As single-cell genomics experiments scale up to hundreds of thousands of cells, the demand for computational resources for downstream analysis grows intractably large and exceeds the capabilities of most researchers. Here, we propose EpiCarousel, a tailored Python package based on lazy loading, parallel processing, and community detection for memory- and time-efficient identification of metacells, i.e. the emergence of homogenous cells, in large-scale scCAS data. Through comprehensive experiments on five datasets of various protocols, sample sizes, dimensions, number of cell types, and degrees of cell-type imbalance, EpiCarousel outperformed baseline methods in systematic evaluation of memory usage, computational time, and multiple downstream analyses including cell type identification. Moreover, EpiCarousel executes preprocessing and downstream cell clustering on the atlas-level dataset with 707 043 cells and 1 154 611 peaks within 2 h consuming <75 GB of RAM and provides superior performance for characterizing cell heterogeneity than state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION The EpiCarousel software is well-documented and freely available at https://github.com/biox-nku/epicarousel. It can be seamlessly interoperated with extensive scCAS analysis toolkits.
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Affiliation(s)
- Sijie Li
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China
| | - Yuxi Li
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China
| | - Yu Sun
- Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Yaru Li
- Institute of Health Service and Transfusion Medicine, Beijing 100850, China
| | - Xiaoyang Chen
- MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Songming Tang
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China
| | - Shengquan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China
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9
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Vasighizaker A, Hora S, Zeng R, Rueda L. SEGCECO: Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication. Brief Bioinform 2024; 25:bbae160. [PMID: 38605638 PMCID: PMC11009470 DOI: 10.1093/bib/bbae160] [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: 11/07/2023] [Revised: 01/30/2024] [Accepted: 03/13/2024] [Indexed: 04/13/2024] Open
Abstract
Recent advances in single-cell RNA sequencing technology have eased analyses of signaling networks of cells. Recently, cell-cell interaction has been studied based on various link prediction approaches on graph-structured data. These approaches have assumptions about the likelihood of node interaction, thus showing high performance for only some specific networks. Subgraph-based methods have solved this problem and outperformed other approaches by extracting local subgraphs from a given network. In this work, we present a novel method, called Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication (SEGCECO), which uses an attributed graph convolutional neural network to predict cell-cell communication from single-cell RNA-seq data. SEGCECO captures the latent and explicit attributes of undirected, attributed graphs constructed from the gene expression profile of individual cells. High-dimensional and sparse single-cell RNA-seq data make converting the data into a graphical format a daunting task. We successfully overcome this limitation by applying SoptSC, a similarity-based optimization method in which the cell-cell communication network is built using a cell-cell similarity matrix which is learned from gene expression data. We performed experiments on six datasets extracted from the human and mouse pancreas tissue. Our comparative analysis shows that SEGCECO outperforms latent feature-based approaches, and the state-of-the-art method for link prediction, WLNM, with 0.99 ROC and 99% prediction accuracy. The datasets can be found at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84133 and the code is publicly available at Github https://github.com/sheenahora/SEGCECO and Code Ocean https://codeocean.com/capsule/8244724/tree.
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Affiliation(s)
| | - Sheena Hora
- Software Development Department, Amazon, USA
| | - Raymond Zeng
- School of Computer Science, University of Windsor, Windsor, ON, Canada
| | - Luis Rueda
- School of Computer Science, University of Windsor, Windsor, ON, Canada
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10
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Vlasschaert C, Robinson-Cohen C, Chen J, Akwo E, Parker AC, Silver SA, Bhatraju PK, Poisner H, Cao S, Jiang M, Wang Y, Niu A, Siew E, Van Amburg JC, Kramer HJ, Kottgen A, Franceschini N, Psaty BM, Tracy RP, Alonso A, Arking DE, Coresh J, Ballantyne CM, Boerwinkle E, Grams M, Zhang MZ, Kestenbaum B, Lanktree MB, Rauh MJ, Harris RC, Bick AG. Clonal hematopoiesis of indeterminate potential is associated with acute kidney injury. Nat Med 2024; 30:810-817. [PMID: 38454125 PMCID: PMC10957477 DOI: 10.1038/s41591-024-02854-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 02/01/2024] [Indexed: 03/09/2024]
Abstract
Age is a predominant risk factor for acute kidney injury (AKI), yet the biological mechanisms underlying this risk are largely unknown. Clonal hematopoiesis of indeterminate potential (CHIP) confers increased risk for several chronic diseases associated with aging. Here we sought to test whether CHIP increases the risk of AKI. In three population-based epidemiology cohorts, we found that CHIP was associated with a greater risk of incident AKI, which was more pronounced in patients with AKI requiring dialysis and in individuals with somatic mutations in genes other than DNMT3A, including mutations in TET2 and JAK2. Mendelian randomization analyses supported a causal role for CHIP in promoting AKI. Non-DNMT3A-CHIP was also associated with a nonresolving pattern of injury in patients with AKI. To gain mechanistic insight, we evaluated the role of Tet2-CHIP and Jak2V617F-CHIP in two mouse models of AKI. In both models, CHIP was associated with more severe AKI, greater renal proinflammatory macrophage infiltration and greater post-AKI kidney fibrosis. In summary, this work establishes CHIP as a genetic mechanism conferring impaired kidney function recovery after AKI via an aberrant inflammatory response mediated by renal macrophages.
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Affiliation(s)
| | - Cassianne Robinson-Cohen
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt O'Brien Center for Kidney Disease, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jianchun Chen
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt O'Brien Center for Kidney Disease, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Elvis Akwo
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt O'Brien Center for Kidney Disease, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Alyssa C Parker
- Division of Genetic Medicine, Department of Medicine, School of Medicine, Vanderbilt University, Nashville, TN, USA
| | - Samuel A Silver
- Department of Medicine, Queen's University, Kingston, Ontario, Canada
| | - Pavan K Bhatraju
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Hannah Poisner
- Division of Genetic Medicine, Department of Medicine, School of Medicine, Vanderbilt University, Nashville, TN, USA
| | - Shirong Cao
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt O'Brien Center for Kidney Disease, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Ming Jiang
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt O'Brien Center for Kidney Disease, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yinqiu Wang
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt O'Brien Center for Kidney Disease, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Aolei Niu
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt O'Brien Center for Kidney Disease, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Edward Siew
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt O'Brien Center for Kidney Disease, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Joseph C Van Amburg
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt O'Brien Center for Kidney Disease, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Holly J Kramer
- Departments of Public Health Sciences and Medicine, Loyola University Chicago, Maywood IL, USA
| | - Anna Kottgen
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Nora Franceschini
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology and Health Systems and Population Health, University of Washington, Seattle, WA, USA
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Russell P Tracy
- Pathology and Biochemistry, University of Vermont, Burlington, VT, USA
| | - Alvaro Alonso
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Dan E Arking
- McKusick-Nathans Institute, Department of Genetic Medicine, John Hopkins University School of Medicine, Baltimore, MD, USA
| | - Josef Coresh
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
| | | | - Eric Boerwinkle
- Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Morgan Grams
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University, Baltimore, MD, USA
- Division of Nephrology, Department of Internal Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Ming-Zhi Zhang
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt O'Brien Center for Kidney Disease, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bryan Kestenbaum
- Kidney Research Institute, Division of Nephrology, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Matthew B Lanktree
- Department of Medicine and Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada
- St. Joseph's Healthcare Hamilton, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton, Ontario, Canada
| | - Michael J Rauh
- Department of Pathology and Molecular Medicine, Queen's University, Kingston, Ontario, Canada
| | - Raymond C Harris
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt O'Brien Center for Kidney Disease, Vanderbilt University Medical Center, Nashville, TN, USA.
- U.S Department of Veterans Affairs, Nashville, TN, USA.
| | - Alexander G Bick
- Division of Genetic Medicine, Department of Medicine, School of Medicine, Vanderbilt University, Nashville, TN, USA.
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11
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Shapir Itai Y, Barboy O, Salomon R, Bercovich A, Xie K, Winter E, Shami T, Porat Z, Erez N, Tanay A, Amit I, Dahan R. Bispecific dendritic-T cell engager potentiates anti-tumor immunity. Cell 2024; 187:375-389.e18. [PMID: 38242085 DOI: 10.1016/j.cell.2023.12.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 10/24/2023] [Accepted: 12/05/2023] [Indexed: 01/21/2024]
Abstract
Immune checkpoint inhibition treatment using aPD-1 monoclonal antibodies is a promising cancer immunotherapy approach. However, its effect on tumor immunity is narrow, as most patients do not respond to the treatment or suffer from recurrence. We show that the crosstalk between conventional type I dendritic cells (cDC1) and T cells is essential for an effective aPD-1-mediated anti-tumor response. Accordingly, we developed a bispecific DC-T cell engager (BiCE), a reagent that facilitates physical interactions between PD-1+ T cells and cDC1. BiCE treatment promotes the formation of active dendritic/T cell crosstalk in the tumor and tumor-draining lymph nodes. In vivo, single-cell and physical interacting cell analysis demonstrates the distinct and superior immune reprogramming of the tumors and tumor-draining lymph nodes treated with BiCE as compared to conventional aPD-1 treatment. By bridging immune cells, BiCE potentiates cell circuits and communication pathways needed for effective anti-tumor immunity.
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Affiliation(s)
- Yuval Shapir Itai
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Oren Barboy
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Ran Salomon
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Akhiad Bercovich
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Ken Xie
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Eitan Winter
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Tamar Shami
- Department of Pathology, Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Ziv Porat
- Flow Cytometry Unit, Life Science Core Facility, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Neta Erez
- Department of Pathology, Faculty of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Amos Tanay
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Ido Amit
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel.
| | - Rony Dahan
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot 7610001, Israel.
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12
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Persad S, Choo ZN, Dien C, Sohail N, Masilionis I, Chaligné R, Nawy T, Brown CC, Sharma R, Pe'er I, Setty M, Pe'er D. SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data. Nat Biotechnol 2023; 41:1746-1757. [PMID: 36973557 PMCID: PMC10713451 DOI: 10.1038/s41587-023-01716-9] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 02/20/2023] [Indexed: 03/29/2023]
Abstract
Metacells are cell groupings derived from single-cell sequencing data that represent highly granular, distinct cell states. Here we present single-cell aggregation of cell states (SEACells), an algorithm for identifying metacells that overcome the sparsity of single-cell data while retaining heterogeneity obscured by traditional cell clustering. SEACells outperforms existing algorithms in identifying comprehensive, compact and well-separated metacells in both RNA and assay for transposase-accessible chromatin (ATAC) modalities across datasets with discrete cell types and continuous trajectories. We demonstrate the use of SEACells to improve gene-peak associations, compute ATAC gene scores and infer the activities of critical regulators during differentiation. Metacell-level analysis scales to large datasets and is particularly well suited for patient cohorts, where per-patient aggregation provides more robust units for data integration. We use our metacells to reveal expression dynamics and gradual reconfiguration of the chromatin landscape during hematopoietic differentiation and to uniquely identify CD4 T cell differentiation and activation states associated with disease onset and severity in a Coronavirus Disease 2019 (COVID-19) patient cohort.
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Affiliation(s)
- Sitara Persad
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Computer Science, Fu Foundation School of Engineering & Applied Science, Columbia University, New York, NY, USA
| | - Zi-Ning Choo
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Christine Dien
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA
- Computational Biology Program, Public Health Sciences Division and Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Noor Sohail
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ignas Masilionis
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ronan Chaligné
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Tal Nawy
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Chrysothemis C Brown
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Pediatrics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Roshan Sharma
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Itsik Pe'er
- Department of Computer Science, Fu Foundation School of Engineering & Applied Science, Columbia University, New York, NY, USA
| | - Manu Setty
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
- Computational Biology Program, Public Health Sciences Division and Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle, WA, USA.
| | - Dana Pe'er
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Howard Hughes Medical Institute, New York, NY, USA.
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13
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Lareau C. Subtle cell states resolved in single-cell data. Nat Biotechnol 2023; 41:1690-1691. [PMID: 37198441 PMCID: PMC10654257 DOI: 10.1038/s41587-023-01797-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Affiliation(s)
- Caleb Lareau
- Department of Pathology, Stanford University, Palo Alto, CA, USA.
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14
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Ben-Kiki O, Bercovich A, Lifshitz A, Raz O, Brook D, Tanay A. MCProj: metacell projection for interpretable and quantitative use of transcriptional atlases. Genome Biol 2023; 24:220. [PMID: 37798781 PMCID: PMC10552220 DOI: 10.1186/s13059-023-03069-7] [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: 12/04/2022] [Accepted: 09/20/2023] [Indexed: 10/07/2023] Open
Abstract
We describe MCProj-an algorithm for analyzing query scRNA-seq data by projections over reference single-cell atlases. We represent the reference as a manifold of annotated metacell gene expression distributions. We then interpret query metacells as mixtures of atlas distributions while correcting for technology-specific gene biases. This approach distinguishes and tags query cells that are consistent with atlas states from unobserved (novel or artifactual) behaviors. It also identifies expression differences observed in successfully mapped query states. We showcase MCProj functionality by projecting scRNA-seq data on a blood cell atlas, deriving precise, quantitative, and interpretable results across technologies and datasets.
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Affiliation(s)
- Oren Ben-Kiki
- Department of Applied Math and Computer Science, Weizmann Institute of Science, Rehovot, Israel
| | - Akhiad Bercovich
- Department of Applied Math and Computer Science, Weizmann Institute of Science, Rehovot, Israel
| | - Aviezer Lifshitz
- Department of Applied Math and Computer Science, Weizmann Institute of Science, Rehovot, Israel
| | - Ofir Raz
- Department of Applied Math and Computer Science, Weizmann Institute of Science, Rehovot, Israel
| | - Dror Brook
- Department of Applied Math and Computer Science, Weizmann Institute of Science, Rehovot, Israel
| | - Amos Tanay
- Department of Applied Math and Computer Science, Weizmann Institute of Science, Rehovot, Israel.
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15
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Wang Z, Wu Z, Wang H, Feng R, Wang G, Li M, Wang SY, Chen X, Su Y, Wang J, Zhang W, Bao Y, Lan Z, Song Z, Wang Y, Luo X, Zhao L, Hou A, Tian S, Gao H, Miao W, Liu Y, Wang H, Yin C, Ji ZL, Feng M, Liu H, Diao L, Amit I, Chen Y, Zeng Y, Ginhoux F, Wu X, Zhu Y, Li H. An immune cell atlas reveals the dynamics of human macrophage specification during prenatal development. Cell 2023; 186:4454-4471.e19. [PMID: 37703875 DOI: 10.1016/j.cell.2023.08.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 05/26/2023] [Accepted: 08/17/2023] [Indexed: 09/15/2023]
Abstract
Macrophages are heterogeneous and play critical roles in development and disease, but their diversity, function, and specification remain inadequately understood during human development. We generated a single-cell RNA sequencing map of the dynamics of human macrophage specification from PCW 4-26 across 19 tissues. We identified a microglia-like population and a proangiogenic population in 15 macrophage subtypes. Microglia-like cells, molecularly and morphologically similar to microglia in the CNS, are present in the fetal epidermis, testicle, and heart. They are the major immune population in the early epidermis, exhibit a polarized distribution along the dorsal-lateral-ventral axis, and interact with neural crest cells, modulating their differentiation along the melanocyte lineage. Through spatial and differentiation trajectory analysis, we also showed that proangiogenic macrophages are perivascular across fetal organs and likely yolk-sac-derived as microglia. Our study provides a comprehensive map of the heterogeneity and developmental dynamics of human macrophages and unravels their diverse functions during development.
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Affiliation(s)
- Zeshuai Wang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; College of Life Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Zhisheng Wu
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; School of Chemistry and Chemical Engineering, Southeast University, Nanjing, China
| | - Hao Wang
- Maternal Fetal Medicine Institute, Department of Obstetrics and Gynaecology, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China
| | - Ruoqing Feng
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Guanlin Wang
- Shanghai Key Laboratory of Metabolic Remodeling and Health, Institute of Metabolism and Integrative Biology, Centre for Evolutionary Biology, Fudan University, Shanghai, China; Shanghai Qi Zhi Institute, Shanghai, China.
| | - Muxi Li
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, China
| | - Shuang-Yin Wang
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Xiaoyan Chen
- Maternal Fetal Medicine Institute, Department of Obstetrics and Gynaecology, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China
| | - Yiyi Su
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; College of Life Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Jun Wang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Weiwen Zhang
- Department of Gynaecology & Obstetrics, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China
| | - Yuzhou Bao
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, China
| | - Zhenwei Lan
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, China
| | - Zhuo Song
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Maternal Fetal Medicine Institute, Department of Obstetrics and Gynaecology, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China
| | - Yiheng Wang
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; College of Biological Sciences, China Agricultural University, Beijing, China
| | - Xianyang Luo
- The Brain Cognition and Brain Disease Institute, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Lingyu Zhao
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Graduate School of Peking Union Medical College, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Anli Hou
- University of Chinese Academy of Sciences Shenzhen Hospital, Shenzhen, China
| | - Shuye Tian
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Hongliang Gao
- University of Chinese Academy of Sciences Shenzhen Hospital, Shenzhen, China
| | - Wenbin Miao
- University of Chinese Academy of Sciences Shenzhen Hospital, Shenzhen, China
| | - Yingyu Liu
- Maternal Fetal Medicine Institute, Department of Obstetrics and Gynaecology, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China
| | - Huilin Wang
- Maternal Fetal Medicine Institute, Department of Obstetrics and Gynaecology, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China
| | - Cui Yin
- Department of Gynaecology & Obstetrics, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China
| | - Zhi-Liang Ji
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, China
| | - Mingqian Feng
- College of Life Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Hongkun Liu
- Jinxin Fertility Group Limited, Chengdu, China
| | - Lianghui Diao
- Shenzhen Key Laboratory for Reproductive Immunology of Peri-Implantation, Shenzhen Zhongshan Institute for Reproduction and Genetics, Shenzhen Zhongshan Urology Hospital, Shenzhen, China
| | - Ido Amit
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Yun Chen
- School of Chemistry and Chemical Engineering, Southeast University, Nanjing, China; Department of Immunology, Nanjing Medical University, Nanjing, China
| | - Yong Zeng
- Shenzhen Key Laboratory for Reproductive Immunology of Peri-Implantation, Shenzhen Zhongshan Institute for Reproduction and Genetics, Shenzhen Zhongshan Urology Hospital, Shenzhen, China
| | - Florent Ginhoux
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; INSERM U1015, Gustave Roussy Cancer Campus, Villejuif 94800, France; Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A(∗)STAR), 8A Biomedical Grove, Immunos, Singapore 138648, Singapore; Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore 169856, Singapore.
| | - Xueqing Wu
- Department of Gynaecology & Obstetrics, Shenzhen University General Hospital, Shenzhen University, Shenzhen, China.
| | - Yuanfang Zhu
- Maternal Fetal Medicine Institute, Department of Obstetrics and Gynaecology, Shenzhen Baoan Women's and Children's Hospital, Jinan University, Shenzhen, China.
| | - Hanjie Li
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
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16
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Rappoport N, Chomsky E, Nagano T, Seibert C, Lubling Y, Baran Y, Lifshitz A, Leung W, Mukamel Z, Shamir R, Fraser P, Tanay A. Single cell Hi-C identifies plastic chromosome conformations underlying the gastrulation enhancer landscape. Nat Commun 2023; 14:3844. [PMID: 37386027 PMCID: PMC10310791 DOI: 10.1038/s41467-023-39549-4] [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: 11/27/2022] [Accepted: 06/19/2023] [Indexed: 07/01/2023] Open
Abstract
Embryonic development involves massive proliferation and differentiation of cell lineages. This must be supported by chromosome replication and epigenetic reprogramming, but how proliferation and cell fate acquisition are balanced in this process is not well understood. Here we use single cell Hi-C to map chromosomal conformations in post-gastrulation mouse embryo cells and study their distributions and correlations with matching embryonic transcriptional atlases. We find that embryonic chromosomes show a remarkably strong cell cycle signature. Despite that, replication timing, chromosome compartment structure, topological associated domains (TADs) and promoter-enhancer contacts are shown to be variable between distinct epigenetic states. About 10% of the nuclei are identified as primitive erythrocytes, showing exceptionally compact and organized compartment structure. The remaining cells are broadly associated with ectoderm and mesoderm identities, showing only mild differentiation of TADs and compartment structures, but more specific localized contacts in hundreds of ectoderm and mesoderm promoter-enhancer pairs. The data suggest that while fully committed embryonic lineages can rapidly acquire specific chromosomal conformations, most embryonic cells are showing plastic signatures driven by complex and intermixed enhancer landscapes.
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Affiliation(s)
- Nimrod Rappoport
- Department of Computer Science and Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Elad Chomsky
- Department of Computer Science and Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel
| | - Takashi Nagano
- Laboratory for Nuclear Dynamics, Institute for Protein Research, Osaka University, Osaka, Japan
- Nuclear Dynamics Programme, The Babraham Institute, Cambridge, UK
| | - Charlie Seibert
- Department of Biological Science, Florida State University, Tallahassee, FL, USA
| | - Yaniv Lubling
- Department of Computer Science and Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel
| | - Yael Baran
- Department of Computer Science and Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel
| | - Aviezer Lifshitz
- Department of Computer Science and Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel
| | - Wing Leung
- Laboratory for Nuclear Dynamics, Institute for Protein Research, Osaka University, Osaka, Japan
- Nuclear Dynamics Programme, The Babraham Institute, Cambridge, UK
| | - Zohar Mukamel
- Department of Computer Science and Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel
| | - Ron Shamir
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - Peter Fraser
- Nuclear Dynamics Programme, The Babraham Institute, Cambridge, UK.
- Department of Biological Science, Florida State University, Tallahassee, FL, USA.
| | - Amos Tanay
- Department of Computer Science and Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel.
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17
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Morabito S, Reese F, Rahimzadeh N, Miyoshi E, Swarup V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. CELL REPORTS METHODS 2023; 3:100498. [PMID: 37426759 PMCID: PMC10326379 DOI: 10.1016/j.crmeth.2023.100498] [Citation(s) in RCA: 51] [Impact Index Per Article: 51.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 02/13/2023] [Accepted: 05/16/2023] [Indexed: 07/11/2023]
Abstract
Biological systems are immensely complex, organized into a multi-scale hierarchy of functional units based on tightly regulated interactions between distinct molecules, cells, organs, and organisms. While experimental methods enable transcriptome-wide measurements across millions of cells, popular bioinformatic tools do not support systems-level analysis. Here we present hdWGCNA, a comprehensive framework for analyzing co-expression networks in high-dimensional transcriptomics data such as single-cell and spatial RNA sequencing (RNA-seq). hdWGCNA provides functions for network inference, gene module identification, gene enrichment analysis, statistical tests, and data visualization. Beyond conventional single-cell RNA-seq, hdWGCNA is capable of performing isoform-level network analysis using long-read single-cell data. We showcase hdWGCNA using data from autism spectrum disorder and Alzheimer's disease brain samples, identifying disease-relevant co-expression network modules. hdWGCNA is directly compatible with Seurat, a widely used R package for single-cell and spatial transcriptomics analysis, and we demonstrate the scalability of hdWGCNA by analyzing a dataset containing nearly 1 million cells.
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Affiliation(s)
- Samuel Morabito
- Mathematical, Computational, and Systems Biology (MCSB) Program, University of California, Irvine, Irvine, CA, USA
- Center for Complex Biological Systems (CCBS), University of California, Irvine, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, Irvine, CA, USA
| | - Fairlie Reese
- Center for Complex Biological Systems (CCBS), University of California, Irvine, Irvine, CA, USA
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA
| | - Negin Rahimzadeh
- Mathematical, Computational, and Systems Biology (MCSB) Program, University of California, Irvine, Irvine, CA, USA
- Center for Complex Biological Systems (CCBS), University of California, Irvine, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, Irvine, CA, USA
| | - Emily Miyoshi
- Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, Irvine, CA, USA
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Vivek Swarup
- Center for Complex Biological Systems (CCBS), University of California, Irvine, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders (MIND), University of California, Irvine, Irvine, CA, USA
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
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18
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Mayshar Y, Raz O, Cheng S, Ben-Yair R, Hadas R, Reines N, Mittnenzweig M, Ben-Kiki O, Lifshitz A, Tanay A, Stelzer Y. Time-aligned hourglass gastrulation models in rabbit and mouse. Cell 2023; 186:2610-2627.e18. [PMID: 37209682 DOI: 10.1016/j.cell.2023.04.037] [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: 11/17/2022] [Revised: 03/07/2023] [Accepted: 04/26/2023] [Indexed: 05/22/2023]
Abstract
The hourglass model describes the convergence of species within the same phylum to a similar body plan during development; however, the molecular mechanisms underlying this phenomenon in mammals remain poorly described. Here, we compare rabbit and mouse time-resolved differentiation trajectories to revisit this model at single-cell resolution. We modeled gastrulation dynamics using hundreds of embryos sampled between gestation days 6.0 and 8.5 and compared the species using a framework for time-resolved single-cell differentiation-flows analysis. We find convergence toward similar cell-state compositions at E7.5, supported by the quantitatively conserved expression of 76 transcription factors, despite divergence in surrounding trophoblast and hypoblast signaling. However, we observed noticeable changes in specification timing of some lineages and divergence of primordial germ cell programs, which in the rabbit do not activate mesoderm genes. Comparative analysis of temporal differentiation models provides a basis for studying the evolution of gastrulation dynamics across mammals.
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Affiliation(s)
- Yoav Mayshar
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Ofir Raz
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel; Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
| | - Saifeng Cheng
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Raz Ben-Yair
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Ron Hadas
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Netta Reines
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Markus Mittnenzweig
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel; Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
| | - Oren Ben-Kiki
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel; Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
| | - Aviezer Lifshitz
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel; Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
| | - Amos Tanay
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel; Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
| | - Yonatan Stelzer
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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19
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Zioni N, Bercovich AA, Chapal-Ilani N, Bacharach T, Rappoport N, Solomon A, Avraham R, Kopitman E, Porat Z, Sacma M, Hartmut G, Scheller M, Muller-Tidow C, Lipka D, Shlush E, Minden M, Kaushansky N, Shlush LI. Inflammatory signals from fatty bone marrow support DNMT3A driven clonal hematopoiesis. Nat Commun 2023; 14:2070. [PMID: 37045808 PMCID: PMC10097668 DOI: 10.1038/s41467-023-36906-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 02/20/2023] [Indexed: 04/14/2023] Open
Abstract
Both fatty bone marrow (FBM) and somatic mutations in hematopoietic stem cells (HSCs), also termed clonal hematopoiesis (CH) accumulate with human aging. However it remains unclear whether FBM can modify the evolution of CH. To address this question, we herein present the interaction between CH and FBM in two preclinical male mouse models: after sub-lethal irradiation or after castration. An adipogenesis inhibitor (PPARγ inhibitor) is used in both models as a control. A significant increase in self-renewal can be detected in both human and rodent DNMT3AMut-HSCs when exposed to FBM. DNMT3AMut-HSCs derived from older mice interacting with FBM have even higher self-renewal in comparison to DNMT3AMut-HSCs derived from younger mice. Single cell RNA-sequencing on rodent HSCs after exposing them to FBM reveal a 6-10 fold increase in DNMT3AMut-HSCs and an activated inflammatory signaling. Cytokine analysis of BM fluid and BM derived adipocytes grown in vitro demonstrates an increased IL-6 levels under FBM conditions. Anti-IL-6 neutralizing antibodies significantly reduce the selective advantage of DNMT3AMut-HSCs exposed to FBM. Overall, paracrine FBM inflammatory signals promote DNMT3A-driven clonal hematopoiesis, which can be inhibited by blocking the IL-6 pathway.
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Affiliation(s)
- N Zioni
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - A Akhiad Bercovich
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
| | - N Chapal-Ilani
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Tal Bacharach
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - N Rappoport
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | - A Solomon
- Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel
| | - R Avraham
- Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel
| | - E Kopitman
- Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Z Porat
- Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - M Sacma
- Institute of Molecular Medicine Ulm University, Ulm, Germany
| | - G Hartmut
- Institute of Molecular Medicine Ulm University, Ulm, Germany
| | - M Scheller
- Department of Medicine, Hematology, Oncology and Rheumatology, University Hospital Heidelberg, Heidelberg, Germany
| | - C Muller-Tidow
- Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany
- European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Partner Site Heidelberg, Heidelberg, Germany
| | - D Lipka
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Partner Site Heidelberg, Heidelberg, Germany
| | - E Shlush
- IVF Unit, Galilee Medical Center, Nahariya, Israel
| | - M Minden
- Princess Margaret Cancer Centre, University Health Network (UHN), Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
- Division of Medical Oncology and Hematology, University Health Network, Toronto, ON, Canada
- Division of Hematology, University Health Network, Toronto, ON, Canada
| | - N Kaushansky
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Liran I Shlush
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
- Hematology and Bone Marrow Transplantation Institute Rambam Healthcare campus Haifa, Haifa, Israel.
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20
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Lau KYC, Rubinstein H, Gantner CW, Hadas R, Amadei G, Stelzer Y, Zernicka-Goetz M. Mouse embryo model derived exclusively from embryonic stem cells undergoes neurulation and heart development. Cell Stem Cell 2022; 29:1445-1458.e8. [PMID: 36084657 PMCID: PMC9648694 DOI: 10.1016/j.stem.2022.08.013] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/15/2022] [Accepted: 08/26/2022] [Indexed: 11/03/2022]
Abstract
Several in vitro models have been developed to recapitulate mouse embryogenesis solely from embryonic stem cells (ESCs). Despite mimicking many aspects of early development, they fail to capture the interactions between embryonic and extraembryonic tissues. To overcome this difficulty, we have developed a mouse ESC-based in vitro model that reconstitutes the pluripotent ESC lineage and the two extraembryonic lineages of the post-implantation embryo by transcription-factor-mediated induction. This unified model recapitulates developmental events from embryonic day 5.5 to 8.5, including gastrulation; formation of the anterior-posterior axis, brain, and a beating heart structure; and the development of extraembryonic tissues, including yolk sac and chorion. Comparing single-cell RNA sequencing from individual structures with time-matched natural embryos identified remarkably similar transcriptional programs across lineages but also showed when and where the model diverges from the natural program. Our findings demonstrate an extraordinary plasticity of ESCs to self-organize and generate a whole-embryo-like structure.
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Affiliation(s)
- Kasey Y C Lau
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge CB2 3EG, UK
| | - Hernan Rubinstein
- Department of Molecular Cell Biology, Weizmann Institute of Science, 7610001 Rehovot, Israel
| | - Carlos W Gantner
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge CB2 3EG, UK
| | - Ron Hadas
- Department of Molecular Cell Biology, Weizmann Institute of Science, 7610001 Rehovot, Israel; California Institute of Technology, Division of Biology and Biological Engineering, 1200 E. California Boulevard, Pasadena, CA 91125, USA
| | - Gianluca Amadei
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge CB2 3EG, UK
| | - Yonatan Stelzer
- Department of Molecular Cell Biology, Weizmann Institute of Science, 7610001 Rehovot, Israel.
| | - Magdalena Zernicka-Goetz
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge CB2 3EG, UK; California Institute of Technology, Division of Biology and Biological Engineering, 1200 E. California Boulevard, Pasadena, CA 91125, USA.
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