1
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Subedi S, Sumida TS, Park YP. A scalable approach to topic modelling in single-cell data by approximate pseudobulk projection. Life Sci Alliance 2024; 7:e202402713. [PMID: 39107066 PMCID: PMC11303850 DOI: 10.26508/lsa.202402713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 07/29/2024] [Accepted: 07/30/2024] [Indexed: 08/09/2024] Open
Abstract
Probabilistic topic modelling has become essential in many types of single-cell data analysis. Based on probabilistic topic assignments in each cell, we identify the latent representation of cellular states. A dictionary matrix, consisting of topic-specific gene frequency vectors, provides interpretable bases to be compared with known cell type-specific marker genes and other pathway annotations. However, fitting a topic model on a large number of cells would require heavy computational resources-specialized computing units, computing time and memory. Here, we present a scalable approximation method customized for single-cell RNA-seq data analysis, termed ASAP, short for Annotating a Single-cell data matrix by Approximate Pseudobulk estimation. Our approach is more accurate than existing methods but requires orders of magnitude less computing time, leaving much lower memory consumption. We also show that our approach is widely applicable for atlas-scale data analysis; our method seamlessly integrates single-cell and bulk data in joint analysis, not requiring additional preprocessing or feature selection steps.
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Affiliation(s)
- Sishir Subedi
- https://ror.org/03rmrcq20Bioinformatics Graduate Program, University of British Columbia, Vancouver, Canada
- BC Cancer Research, Vancouver, Canada
| | - Tomokazu S Sumida
- Neurology, Program for Neuroinflammation, Yale School of Medicine, New Haven, CT, USA
| | - Yongjin P Park
- BC Cancer Research, Vancouver, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
- Department of Statistics, University of British Columbia, Vancouver, Canada
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2
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LeRoy N, Smith J, Zheng G, Rymuza J, Gharavi E, Brown D, Zhang A, Sheffield N. Fast clustering and cell-type annotation of scATAC data using pre-trained embeddings. NAR Genom Bioinform 2024; 6:lqae073. [PMID: 38974799 PMCID: PMC11224678 DOI: 10.1093/nargab/lqae073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 04/29/2024] [Accepted: 06/20/2024] [Indexed: 07/09/2024] Open
Abstract
Data from the single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) are now widely available. One major computational challenge is dealing with high dimensionality and inherent sparsity, which is typically addressed by producing lower dimensional representations of single cells for downstream clustering tasks. Current approaches produce such individual cell embeddings directly through a one-step learning process. Here, we propose an alternative approach by building embedding models pre-trained on reference data. We argue that this provides a more flexible analysis workflow that also has computational performance advantages through transfer learning. We implemented our approach in scEmbed, an unsupervised machine-learning framework that learns low-dimensional embeddings of genomic regulatory regions to represent and analyze scATAC-seq data. scEmbed performs well in terms of clustering ability and has the key advantage of learning patterns of region co-occurrence that can be transferred to other, unseen datasets. Moreover, models pre-trained on reference data can be exploited to build fast and accurate cell-type annotation systems without the need for other data modalities. scEmbed is implemented in Python and it is available to download from GitHub. We also make our pre-trained models available on huggingface for public use. scEmbed is open source and available at https://github.com/databio/geniml. Pre-trained models from this work can be obtained on huggingface: https://huggingface.co/databio.
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Affiliation(s)
- Nathan J LeRoy
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biomedical Engineering, School of Medicine, University of Virginia, Charlottesville, VA 22904, USA
| | - Jason P Smith
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Child Health Research Center, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Guangtao Zheng
- Department of Computer Science, School of Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Julia Rymuza
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
| | - Erfaneh Gharavi
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
| | - Donald E Brown
- School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
- Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Aidong Zhang
- Department of Biomedical Engineering, School of Medicine, University of Virginia, Charlottesville, VA 22904, USA
- Department of Computer Science, School of Engineering, University of Virginia, Charlottesville, VA 22908, USA
- School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
| | - Nathan C Sheffield
- Center for Public Health Genomics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Biomedical Engineering, School of Medicine, University of Virginia, Charlottesville, VA 22904, USA
- Department of Biochemistry and Molecular Genetics, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Child Health Research Center, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
- Department of Computer Science, School of Engineering, University of Virginia, Charlottesville, VA 22908, USA
- School of Data Science, University of Virginia, Charlottesville, VA 22904, USA
- Department of Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA 22908, USA
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3
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Rachid Zaim S, Pebworth MP, McGrath I, Okada L, Weiss M, Reading J, Czartoski JL, Torgerson TR, McElrath MJ, Bumol TF, Skene PJ, Li XJ. MOCHA's advanced statistical modeling of scATAC-seq data enables functional genomic inference in large human cohorts. Nat Commun 2024; 15:6828. [PMID: 39122670 PMCID: PMC11316085 DOI: 10.1038/s41467-024-50612-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 07/13/2024] [Indexed: 08/12/2024] Open
Abstract
Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) is being increasingly used to study gene regulation. However, major analytical gaps limit its utility in studying gene regulatory programs in complex diseases. In response, MOCHA (Model-based single cell Open CHromatin Analysis) presents major advances over existing analysis tools, including: 1) improving identification of sample-specific open chromatin, 2) statistical modeling of technical drop-out with zero-inflated methods, 3) mitigation of false positives in single cell analysis, 4) identification of alternative transcription-starting-site regulation, and 5) modules for inferring temporal gene regulatory networks from longitudinal data. These advances, in addition to open chromatin analyses, provide a robust framework after quality control and cell labeling to study gene regulatory programs in human disease. We benchmark MOCHA with four state-of-the-art tools to demonstrate its advances. We also construct cross-sectional and longitudinal gene regulatory networks, identifying potential mechanisms of COVID-19 response. MOCHA provides researchers with a robust analytical tool for functional genomic inference from scATAC-seq data.
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Affiliation(s)
| | | | | | - Lauren Okada
- Allen Institute for Immunology, Seattle, WA, USA
| | - Morgan Weiss
- Allen Institute for Immunology, Seattle, WA, USA
| | | | - Julie L Czartoski
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | - M Juliana McElrath
- Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | | | | | - Xiao-Jun Li
- Allen Institute for Immunology, Seattle, WA, USA.
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4
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Wang L, Wang C, Moriano JA, Chen S, Zuo G, Cebrián-Silla A, Zhang S, Mukhtar T, Wang S, Song M, de Oliveira LG, Bi Q, Augustin JJ, Ge X, Paredes MF, Huang EJ, Alvarez-Buylla A, Duan X, Li J, Kriegstein AR. Molecular and cellular dynamics of the developing human neocortex at single-cell resolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.16.575956. [PMID: 39131371 PMCID: PMC11312442 DOI: 10.1101/2024.01.16.575956] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
The development of the human neocortex is a highly dynamic process and involves complex cellular trajectories controlled by cell-type-specific gene regulation 1 . Here, we collected paired single-nucleus chromatin accessibility and transcriptome data from 38 human neocortical samples encompassing both the prefrontal cortex and primary visual cortex. These samples span five main developmental stages, ranging from the first trimester to adolescence. In parallel, we performed spatial transcriptomic analysis on a subset of the samples to illustrate spatial organization and intercellular communication. This atlas enables us to catalog cell type-, age-, and area-specific gene regulatory networks underlying neural differentiation. Moreover, combining single-cell profiling, progenitor purification, and lineage-tracing experiments, we have untangled the complex lineage relationships among progenitor subtypes during the transition from neurogenesis to gliogenesis in the human neocortex. We identified a tripotential intermediate progenitor subtype, termed Tri-IPC, responsible for the local production of GABAergic neurons, oligodendrocyte precursor cells, and astrocytes. Remarkably, most glioblastoma cells resemble Tri-IPCs at the transcriptomic level, suggesting that cancer cells hijack developmental processes to enhance growth and heterogeneity. Furthermore, by integrating our atlas data with large-scale GWAS data, we created a disease-risk map highlighting enriched ASD risk in second-trimester intratelencephalic projection neurons. Our study sheds light on the gene regulatory landscape and cellular dynamics of the developing human neocortex.
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5
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Tian T, Zhang J, Lin X, Wei Z, Hakonarson H. Dependency-aware deep generative models for multitasking analysis of spatial omics data. Nat Methods 2024; 21:1501-1513. [PMID: 38783067 DOI: 10.1038/s41592-024-02257-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 03/25/2024] [Indexed: 05/25/2024]
Abstract
Spatially resolved transcriptomics (SRT) technologies have significantly advanced biomedical research, but their data analysis remains challenging due to the discrete nature of the data and the high levels of noise, compounded by complex spatial dependencies. Here, we propose spaVAE, a dependency-aware, deep generative spatial variational autoencoder model that probabilistically characterizes count data while capturing spatial correlations. spaVAE introduces a hybrid embedding combining a Gaussian process prior with a Gaussian prior to explicitly capture spatial correlations among spots. It then optimizes the parameters of deep neural networks to approximate the distributions underlying the SRT data. With the approximated distributions, spaVAE can contribute to several analytical tasks that are essential for SRT data analysis, including dimensionality reduction, visualization, clustering, batch integration, denoising, differential expression, spatial interpolation, resolution enhancement and identification of spatially variable genes. Moreover, we have extended spaVAE to spaPeakVAE and spaMultiVAE to characterize spatial ATAC-seq (assay for transposase-accessible chromatin using sequencing) data and spatial multi-omics data, respectively.
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Affiliation(s)
- Tian Tian
- School of Computer Science, National Engineering Research Center for Multimedia Software, Institute of Artificial Intelligence, and Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University, Wuhan, Hubei, China
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jie Zhang
- National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, China
| | - Xiang Lin
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA
| | - Zhi Wei
- Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA.
| | - Hakon Hakonarson
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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6
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Thakore PI, Schnell A, Huang L, Zhao M, Hou Y, Christian E, Zaghouani S, Wang C, Singh V, Singaraju A, Krishnan RK, Kozoriz D, Ma S, Sankar V, Notarbartolo S, Buenrostro JD, Sallusto F, Patsopoulos NA, Rozenblatt-Rosen O, Kuchroo VK, Regev A. BACH2 regulates diversification of regulatory and proinflammatory chromatin states in T H17 cells. Nat Immunol 2024; 25:1395-1410. [PMID: 39009838 DOI: 10.1038/s41590-024-01901-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 06/18/2024] [Indexed: 07/17/2024]
Abstract
Interleukin-17 (IL-17)-producing helper T (TH17) cells are heterogenous and consist of nonpathogenic TH17 (npTH17) cells that contribute to tissue homeostasis and pathogenic TH17 (pTH17) cells that mediate tissue inflammation. Here, we characterize regulatory pathways underlying TH17 heterogeneity and discover substantial differences in the chromatin landscape of npTH17 and pTH17 cells both in vitro and in vivo. Compared to other CD4+ T cell subsets, npTH17 cells share accessible chromatin configurations with regulatory T cells, whereas pTH17 cells exhibit features of both npTH17 cells and type 1 helper T (TH1) cells. Integrating single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) and single-cell RNA sequencing (scRNA-seq), we infer self-reinforcing and mutually exclusive regulatory networks controlling different cell states and predicted transcription factors regulating TH17 cell pathogenicity. We validate that BACH2 promotes immunomodulatory npTH17 programs and restrains proinflammatory TH1-like programs in TH17 cells in vitro and in vivo. Furthermore, human genetics implicate BACH2 in multiple sclerosis. Overall, our work identifies regulators of TH17 heterogeneity as potential targets to mitigate autoimmunity.
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Affiliation(s)
- Pratiksha I Thakore
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Alexandra Schnell
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
| | - Linglin Huang
- The Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Maryann Zhao
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yu Hou
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Elena Christian
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sarah Zaghouani
- The Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Chao Wang
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Immunology, University of Toronto and Biological Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Vasundhara Singh
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Anvita Singaraju
- The Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Rajesh Kumar Krishnan
- The Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Deneen Kozoriz
- The Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Sai Ma
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Venkat Sankar
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Samuele Notarbartolo
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Bellinzona, Switzerland
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Infectious Diseases Unit, Milan, Italy
| | - Jason D Buenrostro
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Federica Sallusto
- Institute for Research in Biomedicine, Faculty of Biomedical Sciences, Università della Svizzera Italiana, Bellinzona, Switzerland
- Institute of Microbiology, ETH Zurich, Zurich, Switzerland
| | - Nikolaos A Patsopoulos
- Systems Biology and Computer Science Program, Ann Romney Center for Neurological Diseases, Department of Neurology, Brigham & Women's Hospital, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham & Women's Hospital, Harvard Medical School, Boston, MA, USA
- Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Vijay K Kuchroo
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- The Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Genentech, South San Francisco, CA, USA.
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7
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Yang X, Mann KK, Wu H, Ding J. scCross: a deep generative model for unifying single-cell multi-omics with seamless integration, cross-modal generation, and in silico exploration. Genome Biol 2024; 25:198. [PMID: 39075536 PMCID: PMC11285326 DOI: 10.1186/s13059-024-03338-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 07/16/2024] [Indexed: 07/31/2024] Open
Abstract
Single-cell multi-omics data reveal complex cellular states, providing significant insights into cellular dynamics and disease. Yet, integration of multi-omics data presents challenges. Some modalities have not reached the robustness or clarity of established transcriptomics. Coupled with data scarcity for less established modalities and integration intricacies, these challenges limit our ability to maximize single-cell omics benefits. We introduce scCross, a tool leveraging variational autoencoders, generative adversarial networks, and the mutual nearest neighbors (MNN) technique for modality alignment. By enabling single-cell cross-modal data generation, multi-omics data simulation, and in silico cellular perturbations, scCross enhances the utility of single-cell multi-omics studies.
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Affiliation(s)
- Xiuhui Yang
- School of Software, Shandong University, 1500 Shunhua, Jinan, 250101, Shandong, China
- Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, H4A 3J1, QC, Canada
- Quantitative Life Sciences, Faculty of Medicine & Health Sciences, McGill University, Montreal, QC, H3G 1Y6, Canada
| | - Koren K Mann
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, H3G 1Y6, Canada
| | - Hao Wu
- School of Software, Shandong University, 1500 Shunhua, Jinan, 250101, Shandong, China.
| | - Jun Ding
- Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, H4A 3J1, QC, Canada.
- Quantitative Life Sciences, Faculty of Medicine & Health Sciences, McGill University, Montreal, QC, H3G 1Y6, Canada.
- Mila-Quebec AI Institute, Montreal, QC, H2S 3H1, Canada.
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8
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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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9
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Dong C, Meng X, Zhang T, Guo Z, Liu Y, Wu P, Chen S, Zhou F, Ma Y, Xiong H, Shu S, He A. Single-cell EpiChem jointly measures drug-chromatin binding and multimodal epigenome. Nat Methods 2024:10.1038/s41592-024-02360-0. [PMID: 39025969 DOI: 10.1038/s41592-024-02360-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 06/25/2024] [Indexed: 07/20/2024]
Abstract
Studies of molecular and cellular functions of small-molecule inhibitors in cancer treatment, eliciting effects by targeting genome and epigenome associated proteins, requires measurement of drug-target engagement in single-cell resolution. Here we present EpiChem for in situ single-cell joint mapping of small molecules and multimodal epigenomic landscape. We demonstrate single-cell co-assays of three small molecules together with histone modifications, chromatin accessibility or target proteins in human colorectal cancer (CRC) organoids. Integrated multimodal analysis reveals diverse drug interactions in the context of chromatin states within heterogeneous CRC organoids. We further reveal drug genomic binding dynamics and adaptive epigenome across cell types after small-molecule drug treatment in CRC organoids. This method provides a unique tool to exploit the mechanisms of cell type-specific drug actions.
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Affiliation(s)
- Chao Dong
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Xiaoxuan Meng
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Tong Zhang
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Zhifang Guo
- State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Lymphoma, Peking University Cancer Hospital & Institute, Beijing, China
- Peking University International Cancer Institute, Beijing, China
- Peking University-Yunnan Baiyao International Medical Research Center, Beijing, China
| | - Yaxi Liu
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Peihuang Wu
- State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Lymphoma, Peking University Cancer Hospital & Institute, Beijing, China
| | - Shiwei Chen
- Peking University International Cancer Institute, Beijing, China
- Peking University-Yunnan Baiyao International Medical Research Center, Beijing, China
| | - Fanqi Zhou
- State Key Laboratory of Medical Molecular Biology, Haihe laboratory of Cell Ecosystem, Key Laboratory of RNA and Hematopoietic Regulation, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Yanni Ma
- State Key Laboratory of Medical Molecular Biology, Haihe laboratory of Cell Ecosystem, Key Laboratory of RNA and Hematopoietic Regulation, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Haiqing Xiong
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Shaokun Shu
- State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Lymphoma, Peking University Cancer Hospital & Institute, Beijing, China.
- Peking University International Cancer Institute, Beijing, China.
- Peking University-Yunnan Baiyao International Medical Research Center, Beijing, China.
| | - Aibin He
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.
- Key laboratory of Carcinogenesis and Translational Research of Ministry of Education of China, Peking University Cancer Hospital & Institute, Beijing, China.
- Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies, Chengdu, China.
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10
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Lyu Z, Dahal S, Zeng S, Wang J, Xu D, Joshi T. CrossMP: Enabling Cross-Modality Translation between Single-Cell RNA-Seq and Single-Cell ATAC-Seq through Web-Based Portal. Genes (Basel) 2024; 15:882. [PMID: 39062661 PMCID: PMC11276538 DOI: 10.3390/genes15070882] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2024] [Revised: 06/22/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024] Open
Abstract
In recent years, there has been a growing interest in profiling multiomic modalities within individual cells simultaneously. One such example is integrating combined single-cell RNA sequencing (scRNA-seq) data and single-cell transposase-accessible chromatin sequencing (scATAC-seq) data. Integrated analysis of diverse modalities has helped researchers make more accurate predictions and gain a more comprehensive understanding than with single-modality analysis. However, generating such multimodal data is technically challenging and expensive, leading to limited availability of single-cell co-assay data. Here, we propose a model for cross-modal prediction between the transcriptome and chromatin profiles in single cells. Our model is based on a deep neural network architecture that learns the latent representations from the source modality and then predicts the target modality. It demonstrates reliable performance in accurately translating between these modalities across multiple paired human scATAC-seq and scRNA-seq datasets. Additionally, we developed CrossMP, a web-based portal allowing researchers to upload their single-cell modality data through an interactive web interface and predict the other type of modality data, using high-performance computing resources plugged at the backend.
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Affiliation(s)
- Zhen Lyu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA; (Z.L.); (S.D.); (S.Z.); (D.X.)
| | - Sabin Dahal
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA; (Z.L.); (S.D.); (S.Z.); (D.X.)
| | - Shuai Zeng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA; (Z.L.); (S.D.); (S.Z.); (D.X.)
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Juexin Wang
- Department of BioHealth Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis, Indianapolis, IN 46202, USA;
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA; (Z.L.); (S.D.); (S.Z.); (D.X.)
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
| | - Trupti Joshi
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA; (Z.L.); (S.D.); (S.Z.); (D.X.)
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- Department of Biomedical Informatics, Biostatistics and Medical Epidemiology, University of Missouri, Columbia, MO 65211, USA
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11
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Bilous M, Hérault L, Gabriel AA, Teleman M, Gfeller D. Building and analyzing metacells in single-cell genomics data. Mol Syst Biol 2024; 20:744-766. [PMID: 38811801 PMCID: PMC11220014 DOI: 10.1038/s44320-024-00045-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/03/2024] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).
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Affiliation(s)
- Mariia Bilous
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Léonard Hérault
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Aurélie Ag Gabriel
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Matei Teleman
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland.
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland.
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland.
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12
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Chen H, Ryu J, Vinyard ME, Lerer A, Pinello L. SIMBA: single-cell embedding along with features. Nat Methods 2024; 21:1003-1013. [PMID: 37248389 PMCID: PMC11166568 DOI: 10.1038/s41592-023-01899-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 04/26/2023] [Indexed: 05/31/2023]
Abstract
Most current single-cell analysis pipelines are limited to cell embeddings and rely heavily on clustering, while lacking the ability to explicitly model interactions between different feature types. Furthermore, these methods are tailored to specific tasks, as distinct single-cell problems are formulated differently. To address these shortcomings, here we present SIMBA, a graph embedding method that jointly embeds single cells and their defining features, such as genes, chromatin-accessible regions and DNA sequences, into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for the study of cellular heterogeneity, clustering-free marker discovery, gene regulation inference, batch effect removal and omics data integration. We show that SIMBA provides a single framework that allows diverse single-cell problems to be formulated in a unified way and thus simplifies the development of new analyses and extension to new single-cell modalities. SIMBA is implemented as a comprehensive Python library ( https://simba-bio.readthedocs.io ).
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Affiliation(s)
- Huidong Chen
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jayoung Ryu
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Michael E Vinyard
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Adam Lerer
- Facebook AI Research, New York, NY, USA.
| | - Luca Pinello
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA.
- Department of Pathology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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13
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De Rop FV, Hulselmans G, Flerin C, Soler-Vila P, Rafels A, Christiaens V, González-Blas CB, Marchese D, Caratù G, Poovathingal S, Rozenblatt-Rosen O, Slyper M, Luo W, Muus C, Duarte F, Shrestha R, Bagdatli ST, Corces MR, Mamanova L, Knights A, Meyer KB, Mulqueen R, Taherinasab A, Maschmeyer P, Pezoldt J, Lambert CLG, Iglesias M, Najle SR, Dossani ZY, Martelotto LG, Burkett Z, Lebofsky R, Martin-Subero JI, Pillai S, Sebé-Pedrós A, Deplancke B, Teichmann SA, Ludwig LS, Braun TP, Adey AC, Greenleaf WJ, Buenrostro JD, Regev A, Aerts S, Heyn H. Systematic benchmarking of single-cell ATAC-sequencing protocols. Nat Biotechnol 2024; 42:916-926. [PMID: 37537502 PMCID: PMC11180611 DOI: 10.1038/s41587-023-01881-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 06/22/2023] [Indexed: 08/05/2023]
Abstract
Single-cell assay for transposase-accessible chromatin by sequencing (scATAC-seq) has emerged as a powerful tool for dissecting regulatory landscapes and cellular heterogeneity. However, an exploration of systemic biases among scATAC-seq technologies has remained absent. In this study, we benchmark the performance of eight scATAC-seq methods across 47 experiments using human peripheral blood mononuclear cells (PBMCs) as a reference sample and develop PUMATAC, a universal preprocessing pipeline, to handle the various sequencing data formats. Our analyses reveal significant differences in sequencing library complexity and tagmentation specificity, which impact cell-type annotation, genotype demultiplexing, peak calling, differential region accessibility and transcription factor motif enrichment. Our findings underscore the importance of sample extraction, method selection, data processing and total cost of experiments, offering valuable guidance for future research. Finally, our data and analysis pipeline encompasses 169,000 PBMC scATAC-seq profiles and a best practices code repository for scATAC-seq data analysis, which are freely available to extend this benchmarking effort to future protocols.
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Grants
- R01 DA047237 NIDA NIH HHS
- R00 AG059918 NIA NIH HHS
- U19 AI057266 NIAID NIH HHS
- G0B5619N Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)
- RF1 MH128842 NIMH NIH HHS
- UM1 HG009436 NHGRI NIH HHS
- 1S80920N Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)
- UM1 HG012076 NHGRI NIH HHS
- RM1 HG007735 NHGRI NIH HHS
- G094121N Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)
- R35 GM124704 NIGMS NIH HHS
- UM1 HG009442 NHGRI NIH HHS
- Wellcome Trust
- H.H. received support for the project PID2020-115439GB-I00- funded by MCIN/AEI/ 10.13039/501100011033. This publication is also supported as part of a project (BCLLATLAS and ESPACE) that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement No 810287 and 874710).
- M.R.C. is supported by the National Institutes on Aging K99/R00AG059918.
- K.B.M. is supported by Wellcome (WT211276/Z/18/Z and Sanger core grant WT206194).
- S.A.T. is supported by Wellcome (WT211276/Z/18/Z and Sanger core grant WT206194).
- This work was supported by funding from the Rita Allen Foundation (W.J.G.), the Human Frontiers Science (RGY006S) (W.J.G.). W.J.G. is a Chan Zuckerberg Biohub investigator and acknowledges grants 2017-174468 and 2018-182817 from the Chan Zuckerberg Initiative, and the National Institutes of Health grants RM1-HG007735, UM1-HG009442, UM1-HG009436, R01- HG00990901, and U19- AI057266 (to W.J.G.). W.J.G. acknowledges funding from Emerson Collective.
- This work was supported by an ERC Consolidator Grant to S.A. (no. 724226_cis- CONTROL), KU Leuven (grant no. C14/22/125 to S.A.), Foundation Against Cancer (grant no, F/2020/1396 to S.A.), F.W.O. (grants G0I2722N, G0B5619N and G094121N to S.A.), Aligning Science Across Parkinson’s (ASAP, grant no. ASAP-000430 to S.A.)
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Affiliation(s)
- Florian V De Rop
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Gert Hulselmans
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Chris Flerin
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Paula Soler-Vila
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Albert Rafels
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Valerie Christiaens
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Carmen Bravo González-Blas
- VIB Center for Brain and Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Domenica Marchese
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Ginevra Caratù
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | | | | | | | - Wendy Luo
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Fabiana Duarte
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Rojesh Shrestha
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - M Ryan Corces
- Gladstone Institute of Neurological Disease, San Francisco, CA, USA
| | | | | | | | - Ryan Mulqueen
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Akram Taherinasab
- Division of Hematology & Medical Oncology, Knight Cancer Institute, Oregon Health & Sciences University, Portland, OR, USA
- Division of Oncologic Sciences, Knight Cancer Institute, Oregon Health & Sciences University, Portland, OR, USA
| | - Patrick Maschmeyer
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
| | - Jörn Pezoldt
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Camille Lucie Germaine Lambert
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Marta Iglesias
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Sebastián R Najle
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Zain Y Dossani
- Vitalant Research Institute, San Francisco, CA, USA
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
| | - Luciano G Martelotto
- Adelaide Centre for Epigenetics and the South Australian Immunogenomics Cancer Institute, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- University of Melbourne Centre for Cancer Research, Victoria Comprehensive Cancer Centre, Melbourne, Victoria, Australia
| | - Zach Burkett
- Digital Biology Group, Bio-Rad, Pleasanton, CA, USA
| | | | - José Ignacio Martin-Subero
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Departament de Fonaments Clínics, Facultat de Medicina, Universitat de Barcelona, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
| | - Satish Pillai
- Vitalant Research Institute, San Francisco, CA, USA
- Department of Laboratory Medicine, University of California, San Francisco, CA, USA
| | - Arnau Sebé-Pedrós
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- ICREA, Barcelona, Spain
| | - Bart Deplancke
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Sarah A Teichmann
- Wellcome Sanger Institute, Cambridge, UK
- Department of Physics/Cavendish Laboratory, University of Cambridge, Cambridge, UK
| | - Leif S Ludwig
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
- Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin Institute for Medical Systems Biology (BIMSB), Berlin, Germany
| | - Theodore P Braun
- Division of Hematology & Medical Oncology, Knight Cancer Institute, Oregon Health & Sciences University, Portland, OR, USA
- Division of Oncologic Sciences, Knight Cancer Institute, Oregon Health & Sciences University, Portland, OR, USA
| | - Andrew C Adey
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - William J Greenleaf
- Department of Genetics, Stanford University, Stanford, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Jason D Buenrostro
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Koch Institute of Integrative Cancer Research, Cambridge, MA, USA
- Howard Hughes Medical Institute, Department of Biology, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Stein Aerts
- VIB Center for Brain and Disease Research, Leuven, Belgium.
- Department of Human Genetics, KU Leuven, Leuven, Belgium.
| | - Holger Heyn
- CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, Spain.
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14
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Schiroli G, Kartha V, Duarte FM, Kristiansen TA, Mayerhofer C, Shrestha R, Earl A, Hu Y, Tay T, Rhee C, Buenrostro JD, Scadden DT. Cell of origin epigenetic priming determines susceptibility to Tet2 mutation. Nat Commun 2024; 15:4325. [PMID: 38773071 PMCID: PMC11109152 DOI: 10.1038/s41467-024-48508-6] [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: 06/17/2023] [Accepted: 04/30/2024] [Indexed: 05/23/2024] Open
Abstract
Hematopoietic stem cell (HSC) mutations can result in clonal hematopoiesis (CH) with heterogeneous clinical outcomes. Here, we investigate how the cell state preceding Tet2 mutation impacts the pre-malignant phenotype. Using an inducible system for clonal analysis of myeloid progenitors, we find that the epigenetic features of clones at similar differentiation status are highly heterogeneous and functionally respond differently to Tet2 mutation. Cell differentiation stage also influences Tet2 mutation response indicating that the cell of origin's epigenome modulates clone-specific behaviors in CH. Molecular features associated with higher risk outcomes include Sox4 that sensitizes cells to Tet2 inactivation, inducing dedifferentiation, altered metabolism and increasing the in vivo clonal output of mutant cells, as confirmed in primary GMP and HSC models. Our findings validate the hypothesis that epigenetic features can predispose specific clones for dominance, explaining why identical genetic mutations can result in different phenotypes.
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Affiliation(s)
- Giulia Schiroli
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
| | - Vinay Kartha
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Fabiana M Duarte
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Trine A Kristiansen
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
| | - Christina Mayerhofer
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
| | - Rojesh Shrestha
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Andrew Earl
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Yan Hu
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Tristan Tay
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Catherine Rhee
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA
| | - Jason D Buenrostro
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA.
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA.
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
| | - David T Scadden
- Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, 02138, USA.
- Harvard Stem Cell Institute, Cambridge, MA, 02138, USA.
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15
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Hu Z, Przytycki PF, Pollard KS. CellWalker2: multi-omic discovery of hierarchical cell type relationships and their associations with genomic annotations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.17.594770. [PMID: 38798605 PMCID: PMC11118555 DOI: 10.1101/2024.05.17.594770] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
CellWalker2 is a graph diffusion-based method for single-cell genomics data integration. It extends the CellWalker model by incorporating hierarchical relationships between cell types, providing estimates of statistical significance, and adding data structures for analyzing multi-omics data so that gene expression and open chromatin can be jointly modeled. Our open-source software enables users to annotate cells using existing ontologies and to probabilistically match cell types between two or more contexts, including across species. CellWalker2 can also map genomic regions to cell ontologies, enabling precise annotation of elements derived from bulk data, such as enhancers, genetic variants, and sequence motifs. Through simulation studies, we show that CellWalker2 performs better than existing methods in cell type annotation and mapping. We then use data from the brain and immune system to demonstrate CellWalker2's ability to discover cell type-specific regulatory programs and both conserved and divergent cell type relationships in complex tissues.
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Affiliation(s)
- Zhirui Hu
- Gladstone Institute of Data Science & Biotechnology, 1650 Owens Street, San Francisco, 94158, CA, USA
| | - Pawel F Przytycki
- Gladstone Institute of Data Science & Biotechnology, 1650 Owens Street, San Francisco, 94158, CA, USA
- Faculty of Computing & Data Sciences, Boston University, 665 Commonwealth Avenue, Boston, 02215, MA, USA
| | - Katherine S Pollard
- Gladstone Institute of Data Science & Biotechnology, 1650 Owens Street, San Francisco, 94158, CA, USA
- Department of Epidemiology & Biostatistics, University of California, 1650 Owens Street, San Francisco, 94158, CA, USA
- Chan Zuckerberg Biohub SF, 499 Illinois Street, San Francisco, 94158, CA, USA
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16
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Gong X, He W, Jin W, Ma H, Wang G, Li J, Xiao Y, Zhao Y, Chen Q, Guo H, Yang J, Qi Y, Dong W, Fu M, Li X, Liu J, Liu X, Yin A, Zhang Y, Wei Y. Disruption of maternal vascular remodeling by a fetal endoretrovirus-derived gene in preeclampsia. Genome Biol 2024; 25:117. [PMID: 38715110 PMCID: PMC11075363 DOI: 10.1186/s13059-024-03265-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 04/30/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND Preeclampsia, one of the most lethal pregnancy-related diseases, is associated with the disruption of uterine spiral artery remodeling during placentation. However, the early molecular events leading to preeclampsia remain unknown. RESULTS By analyzing placentas from preeclampsia, non-preeclampsia, and twin pregnancies with selective intrauterine growth restriction, we show that the pathogenesis of preeclampsia is attributed to immature trophoblast and maldeveloped endothelial cells. Delayed epigenetic reprogramming during early extraembryonic tissue development leads to generation of excessive immature trophoblast cells. We find reduction of de novo DNA methylation in these trophoblast cells results in selective overexpression of maternally imprinted genes, including the endoretrovirus-derived gene PEG10 (paternally expressed gene 10). PEG10 forms virus-like particles, which are transferred from the trophoblast to the closely proximate endothelial cells. In normal pregnancy, only a low amount of PEG10 is transferred to maternal cells; however, in preeclampsia, excessive PEG10 disrupts maternal vascular development by inhibiting TGF-beta signaling. CONCLUSIONS Our study reveals the intricate epigenetic mechanisms that regulate trans-generational genetic conflict and ultimately ensure proper maternal-fetal interface formation.
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Affiliation(s)
- Xiaoli Gong
- Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China
| | - Wei He
- Medical Genetic Center, Guangdong Women and Children Hospital, Guangzhou, China
| | - Wan Jin
- Euler Technology, Beijing, China
- Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Hongwei Ma
- Department of Obstetrics and Gynecology, West China Second University Hospital of Sichuan University, Chengdu, China
- Department Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China
| | - Gang Wang
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
- Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China
- Human Genetic Resources Preservation Center of Hubei Province, Wuhan, China
- Laboratory of Precision Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Jiaxin Li
- Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China
| | - Yu Xiao
- Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China
- Department of Biological Repositories, Zhongnan Hospital of Wuhan University, Wuhan, China
- Human Genetic Resources Preservation Center of Hubei Province, Wuhan, China
- Laboratory of Precision Medicine, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yangyu Zhao
- Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China
| | | | | | - Jiexia Yang
- Medical Genetic Center, Guangdong Women and Children Hospital, Guangzhou, China
| | - Yiming Qi
- Medical Genetic Center, Guangdong Women and Children Hospital, Guangzhou, China
| | - Wei Dong
- Maternity Ward, Haidian Maternal and Child Health Hospital, Beijing, China
| | - Meng Fu
- Department of Obstetrics and Gynecology, Haidian Maternal and Child Health Hospital, Beijing, China
| | - Xiaojuan Li
- Euler Technology, Beijing, China
- Present Address: International Max Planck Research School for Genome Science, and University of Göttingen, Göttingen Center for Molecular Biosciences, Göttingen, Germany
| | | | - Xinghui Liu
- Department of Obstetrics and Gynecology, West China Second University Hospital of Sichuan University, Chengdu, China.
- Department Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China.
| | - Aihua Yin
- Medical Genetic Center, Guangdong Women and Children Hospital, Guangzhou, China.
| | - Yi Zhang
- Euler Technology, Beijing, China.
| | - Yuan Wei
- Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China.
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17
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Mannens CCA, Hu L, Lönnerberg P, Schipper M, Reagor CC, Li X, He X, Barker RA, Sundström E, Posthuma D, Linnarsson S. Chromatin accessibility during human first-trimester neurodevelopment. Nature 2024:10.1038/s41586-024-07234-1. [PMID: 38693260 DOI: 10.1038/s41586-024-07234-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 02/02/2024] [Indexed: 05/03/2024]
Abstract
The human brain develops through a tightly organized cascade of patterning events, induced by transcription factor expression and changes in chromatin accessibility. Although gene expression across the developing brain has been described at single-cell resolution1, similar atlases of chromatin accessibility have been primarily focused on the forebrain2-4. Here we describe chromatin accessibility and paired gene expression across the entire developing human brain during the first trimester (6-13 weeks after conception). We defined 135 clusters and used multiomic measurements to link candidate cis-regulatory elements to gene expression. The number of accessible regions increased both with age and along neuronal differentiation. Using a convolutional neural network, we identified putative functional transcription factor-binding sites in enhancers characterizing neuronal subtypes. We applied this model to cis-regulatory elements linked to ESRRB to elucidate its activation mechanism in the Purkinje cell lineage. Finally, by linking disease-associated single nucleotide polymorphisms to cis-regulatory elements, we validated putative pathogenic mechanisms in several diseases and identified midbrain-derived GABAergic neurons as being the most vulnerable to major depressive disorder-related mutations. Our findings provide a more detailed view of key gene regulatory mechanisms underlying the emergence of brain cell types during the first trimester and a comprehensive reference for future studies related to human neurodevelopment.
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Affiliation(s)
- Camiel C A Mannens
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden
| | - Lijuan Hu
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden
| | - Peter Lönnerberg
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden
| | - Marijn Schipper
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Caleb C Reagor
- Howard Hughes Medical Institute and Laboratory of Sensory Neuroscience, The Rockefeller University, New York, NY, USA
| | - Xiaofei Li
- Division of Neurodegeneration, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden
| | - Xiaoling He
- John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Roger A Barker
- John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Erik Sundström
- Division of Neurodegeneration, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sten Linnarsson
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden.
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18
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Wei Z. Discrete latent embeddings illuminate cellular diversity in single-cell epigenomics. NATURE COMPUTATIONAL SCIENCE 2024; 4:316-317. [PMID: 38811822 DOI: 10.1038/s43588-024-00634-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2024]
Affiliation(s)
- Zhi Wei
- Department of Computer Science, Ying Wu College of Computing, New Jersey Institute of Technology, Newark, NJ, USA.
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19
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Cui X, Chen X, Li Z, Gao Z, Chen S, Jiang R. Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity. NATURE COMPUTATIONAL SCIENCE 2024; 4:346-359. [PMID: 38730185 DOI: 10.1038/s43588-024-00625-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 04/05/2024] [Indexed: 05/12/2024]
Abstract
Single-cell epigenomic data has been growing continuously at an unprecedented pace, but their characteristics such as high dimensionality and sparsity pose substantial challenges to downstream analysis. Although deep learning models-especially variational autoencoders-have been widely used to capture low-dimensional feature embeddings, the prevalent Gaussian assumption somewhat disagrees with real data, and these models tend to struggle to incorporate reference information from abundant cell atlases. Here we propose CASTLE, a deep generative model based on the vector-quantized variational autoencoder framework to extract discrete latent embeddings that interpretably characterize single-cell chromatin accessibility sequencing data. We validate the performance and robustness of CASTLE for accurate cell-type identification and reasonable visualization compared with state-of-the-art methods. We demonstrate the advantages of CASTLE for effective incorporation of existing massive reference datasets in a weakly supervised or supervised manner. We further demonstrate CASTLE's capacity for intuitively distilling cell-type-specific feature spectra that unveil cell heterogeneity and biological implications quantitatively.
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Affiliation(s)
- Xuejian Cui
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, China
| | - Xiaoyang Chen
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, China
| | - Zhen Li
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, China
| | - Zijing Gao
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, China
| | - Shengquan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China.
| | - Rui Jiang
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, China.
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20
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Zhang H, Mulqueen RM, Iannuzo N, Farrera DO, Polverino F, Galligan JJ, Ledford JG, Adey AC, Cusanovich DA. txci-ATAC-seq: a massive-scale single-cell technique to profile chromatin accessibility. Genome Biol 2024; 25:78. [PMID: 38519979 PMCID: PMC10958877 DOI: 10.1186/s13059-023-03150-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 12/20/2023] [Indexed: 03/25/2024] Open
Abstract
We develop a large-scale single-cell ATAC-seq method by combining Tn5-based pre-indexing with 10× Genomics barcoding, enabling the indexing of up to 200,000 nuclei across multiple samples in a single reaction. We profile 449,953 nuclei across diverse tissues, including the human cortex, mouse brain, human lung, mouse lung, mouse liver, and lung tissue from a club cell secretory protein knockout (CC16-/-) model. Our study of CC16-/- nuclei uncovers previously underappreciated technical artifacts derived from remnant 129 mouse strain genetic material, which cause profound cell-type-specific changes in regulatory elements near many genes, thereby confounding the interpretation of this commonly referenced mouse model.
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Affiliation(s)
- Hao Zhang
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA
| | - Ryan M Mulqueen
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Natalie Iannuzo
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA
| | - Dominique O Farrera
- Department of Pharmacology and Toxicology, University of Arizona, Tucson, AZ, USA
| | - Francesca Polverino
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Arizona, Tucson, AZ, USA
- Banner - University Medicine North, Pulmonary - Clinic F, Tucson, AZ, USA
| | - James J Galligan
- Department of Pharmacology and Toxicology, University of Arizona, Tucson, AZ, USA
| | - Julie G Ledford
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA
| | - Andrew C Adey
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA.
- Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR, USA.
- Oregon Health & Science University, Knight Cancer Institute, Portland, OR, USA.
- Oregon Health & Science University, Knight Cardiovascular Institute, Portland, OR, USA.
| | - Darren A Cusanovich
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA.
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA.
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21
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Gong M, Yu Y, Wang Z, Zhang J, Wang X, Fu C, Zhang Y, Wang X. scAuto as a comprehensive framework for single-cell chromatin accessibility data analysis. Comput Biol Med 2024; 171:108230. [PMID: 38442554 DOI: 10.1016/j.compbiomed.2024.108230] [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: 10/11/2023] [Revised: 02/06/2024] [Accepted: 02/25/2024] [Indexed: 03/07/2024]
Abstract
Interpreting single-cell chromatin accessibility data is crucial for understanding intercellular heterogeneity regulation. Despite the progress in computational methods for analyzing this data, there is still a lack of a comprehensive analytical framework and a user-friendly online analysis tool. To fill this gap, we developed a pre-trained deep learning-based framework, single-cell auto-correlation transformers (scAuto), to overcome the challenge. Following DNABERT's methodology of pre-training and fine-tuning, scAuto learns a general understanding of DNA sequence's grammar by being pre-trained on unlabeled human genome via self-supervision; it is then transferred to the single-cell chromatin accessibility analysis task of scATAC-seq data for supervised fine-tuning. We extensively validated scAuto on the Buenrostro2018 dataset, demonstrating its superior performance on chromatin accessibility prediction, single-cell clustering, and data denoising. Based on scAuto, we further developed an interactive web server for single-cell chromatin accessibility data analysis. It integrates tutorial-style interfaces for those with limited programming skills. The platform is accessible at http://zhanglab.icaup.cn. To our knowledge, this work is expected to help analyze single-cell chromatin accessibility data and facilitate the development of precision medicine.
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Affiliation(s)
- Meiqin Gong
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Yun Yu
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Zixuan Wang
- College of Electronics and information Engineering, SiChuan University, Chengdu, 610065, China
| | - Junming Zhang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Xiongyi Wang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Cheng Fu
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Yongqing Zhang
- School of Computer Science, Chengdu University of Information Technology, Chengdu, 610225, China
| | - Xiaodong Wang
- Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
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22
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Chen JP, Diekmann C, Wu H, Chen C, Della Chiara G, Berrino E, Georgiadis KL, Bouwman BAM, Virdi M, Harbers L, Bellomo SE, Marchiò C, Bienko M, Crosetto N. scCircle-seq unveils the diversity and complexity of extrachromosomal circular DNAs in single cells. Nat Commun 2024; 15:1768. [PMID: 38409079 PMCID: PMC10897160 DOI: 10.1038/s41467-024-45972-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 02/08/2024] [Indexed: 02/28/2024] Open
Abstract
Extrachromosomal circular DNAs (eccDNAs) have emerged as important intra-cellular mobile genetic elements that affect gene copy number and exert in trans regulatory roles within the cell nucleus. Here, we describe scCircle-seq, a method for profiling eccDNAs and unraveling their diversity and complexity in single cells. We implement and validate scCircle-seq in normal and cancer cell lines, demonstrating that most eccDNAs vary largely between cells and are stochastically inherited during cell division, although their genomic landscape is cell type-specific and can be used to accurately cluster cells of the same origin. eccDNAs are preferentially produced from chromatin regions enriched in H3K9me3 and H3K27me3 histone marks and are induced during replication stress conditions. Concomitant sequencing of eccDNAs and RNA from the same cell uncovers the absence of correlation between eccDNA copy number and gene expression levels, except for a few oncogenes, including MYC, contained within a large eccDNA in colorectal cancer cells. Lastly, we apply scCircle-seq to one prostate cancer and two breast cancer specimens, revealing cancer-specific eccDNA landscapes and a higher propensity of eccDNAs to form in amplified genomic regions. scCircle-seq is a scalable tool that can be used to dissect the complexity of eccDNAs across different cell and tissue types, and further expands the potential of eccDNAs for cancer diagnostics.
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Affiliation(s)
- Jinxin Phaedo Chen
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 17177, Sweden.
- Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden.
| | - Constantin Diekmann
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 17177, Sweden
- Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden
| | - Honggui Wu
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, PR China
- School of Life Sciences, Peking University, Beijing, PR China
| | - Chong Chen
- State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, PR China
| | | | - Enrico Berrino
- Candiolo Cancer Institute, FPO - IRCCS, Candiolo, SP142, km 3,95, 10060, Turin, Italy
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Konstantinos L Georgiadis
- Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden
- Department of Oncology and Pathology, Karolinska Institutet, Stockholm, 17177, Sweden
| | - Britta A M Bouwman
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 17177, Sweden
- Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden
| | - Mohit Virdi
- Human Technopole, Viale Rita Levi-Montalcini 1, 22157, Milan, Italy
| | - Luuk Harbers
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 17177, Sweden
- Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden
| | - Sara Erika Bellomo
- Candiolo Cancer Institute, FPO - IRCCS, Candiolo, SP142, km 3,95, 10060, Turin, Italy
| | - Caterina Marchiò
- Candiolo Cancer Institute, FPO - IRCCS, Candiolo, SP142, km 3,95, 10060, Turin, Italy
- Department of Medical Sciences, University of Turin, Turin, Italy
| | - Magda Bienko
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 17177, Sweden.
- Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden.
- Human Technopole, Viale Rita Levi-Montalcini 1, 22157, Milan, Italy.
| | - Nicola Crosetto
- Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, 17177, Sweden.
- Science for Life Laboratory, Tomtebodavägen 23A, Solna, 17165, Sweden.
- Human Technopole, Viale Rita Levi-Montalcini 1, 22157, Milan, Italy.
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23
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Gómez-Márquez C, Morales JA, Romero-Gutiérrez T, Paredes O, Borrayo E. Decoding semiotic minimal genome: a non-genocentric approach. Front Microbiol 2024; 15:1356050. [PMID: 38476952 PMCID: PMC10929006 DOI: 10.3389/fmicb.2024.1356050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 02/02/2024] [Indexed: 03/14/2024] Open
Abstract
The search for the minimum information required for an organism to sustain a cellular system network has rendered both the identification of a fixed number of known genes and those genes whose function remains to be identified. The approaches used in such search generally focus their analysis on coding genomic regions, based on the genome to proteic-product perspective. Such approaches leave other fundamental processes aside, mainly those that include higher-level information management. To cope with this limitation, a non-genocentric approach based on genomic sequence analysis using language processing tools and gene ontology may prove an effective strategy for the identification of those fundamental genomic elements for life autonomy. Additionally, this approach will provide us with an integrative analysis of the information value present in all genomic elements, regardless of their coding status.
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Affiliation(s)
- Carolina Gómez-Márquez
- Biodigital Innovation Lab, Translational Bioengineering Department, Exact Sciences and Engineering University Center, Universidad de Guadalajara, Guadalajara, Mexico
| | - J. Alejandro Morales
- Biodigital Innovation Lab, Translational Bioengineering Department, Exact Sciences and Engineering University Center, Universidad de Guadalajara, Guadalajara, Mexico
| | - Teresa Romero-Gutiérrez
- Biodigital Innovation Lab, Translational Bioengineering Department, Exact Sciences and Engineering University Center, Universidad de Guadalajara, Guadalajara, Mexico
- Technological Innovation Department, Tlajomulco University Center, Universidad de Guadalajara, Guadalajara, Mexico
| | - Omar Paredes
- Biodigital Innovation Lab, Translational Bioengineering Department, Exact Sciences and Engineering University Center, Universidad de Guadalajara, Guadalajara, Mexico
| | - Ernesto Borrayo
- Biodigital Innovation Lab, Translational Bioengineering Department, Exact Sciences and Engineering University Center, Universidad de Guadalajara, Guadalajara, Mexico
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24
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Kazwini NE, Sanguinetti G. SHARE-Topic: Bayesian interpretable modeling of single-cell multi-omic data. Genome Biol 2024; 25:55. [PMID: 38395871 PMCID: PMC10885556 DOI: 10.1186/s13059-024-03180-3] [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: 02/03/2023] [Accepted: 01/31/2024] [Indexed: 02/25/2024] Open
Abstract
Multi-omic single-cell technologies, which simultaneously measure the transcriptional and epigenomic state of the same cell, enable understanding epigenetic mechanisms of gene regulation. However, noisy and sparse data pose fundamental statistical challenges to extract biological knowledge from complex datasets. SHARE-Topic, a Bayesian generative model of multi-omic single cell data using topic models, aims to address these challenges. SHARE-Topic identifies common patterns of co-variation between different omic layers, providing interpretable explanations for the data complexity. Tested on data from different technological platforms, SHARE-Topic provides low dimensional representations recapitulating known biology and defines associations between genes and distal regulators in individual cells.
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Affiliation(s)
- Nour El Kazwini
- Theoretical and Scientific Data Science, Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy
| | - Guido Sanguinetti
- Theoretical and Scientific Data Science, Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy.
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25
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Zhang K, Zemke NR, Armand EJ, Ren B. A fast, scalable and versatile tool for analysis of single-cell omics data. Nat Methods 2024; 21:217-227. [PMID: 38191932 PMCID: PMC10864184 DOI: 10.1038/s41592-023-02139-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 11/23/2023] [Indexed: 01/10/2024]
Abstract
Single-cell omics technologies have revolutionized the study of gene regulation in complex tissues. A major computational challenge in analyzing these datasets is to project the large-scale and high-dimensional data into low-dimensional space while retaining the relative relationships between cells. This low dimension embedding is necessary to decompose cellular heterogeneity and reconstruct cell-type-specific gene regulatory programs. Traditional dimensionality reduction techniques, however, face challenges in computational efficiency and in comprehensively addressing cellular diversity across varied molecular modalities. Here we introduce a nonlinear dimensionality reduction algorithm, embodied in the Python package SnapATAC2, which not only achieves a more precise capture of single-cell omics data heterogeneities but also ensures efficient runtime and memory usage, scaling linearly with the number of cells. Our algorithm demonstrates exceptional performance, scalability and versatility across diverse single-cell omics datasets, including single-cell assay for transposase-accessible chromatin using sequencing, single-cell RNA sequencing, single-cell Hi-C and single-cell multi-omics datasets, underscoring its utility in advancing single-cell analysis.
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Affiliation(s)
- Kai Zhang
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Westlake Laboratory of Life Sciences and Biomedicine, School of Life Sciences, Westlake University, Hangzhou, China
| | - Nathan R Zemke
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Ethan J Armand
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Bing Ren
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA.
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, USA.
- Ludwig Institute for Cancer Research, La Jolla, CA, USA.
- Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA.
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26
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Mihai IS, Chafle S, Henriksson J. Representing and extracting knowledge from single-cell data. Biophys Rev 2024; 16:29-56. [PMID: 38495441 PMCID: PMC10937862 DOI: 10.1007/s12551-023-01091-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 06/28/2023] [Indexed: 03/19/2024] Open
Abstract
Single-cell analysis is currently one of the most high-resolution techniques to study biology. The large complex datasets that have been generated have spurred numerous developments in computational biology, in particular the use of advanced statistics and machine learning. This review attempts to explain the deeper theoretical concepts that underpin current state-of-the-art analysis methods. Single-cell analysis is covered from cell, through instruments, to current and upcoming models. The aim of this review is to spread concepts which are not yet in common use, especially from topology and generative processes, and how new statistical models can be developed to capture more of biology. This opens epistemological questions regarding our ontology and models, and some pointers will be given to how natural language processing (NLP) may help overcome our cognitive limitations for understanding single-cell data.
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Affiliation(s)
- Ionut Sebastian Mihai
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå, Sweden
- Umeå Centre for Microbial Research (UCMR), Department of Molecular Biology, Umeå University, Umeå, Sweden
- Industrial Doctoral School, Umeå University, Umeå, Sweden
| | - Sarang Chafle
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå, Sweden
- Umeå Centre for Microbial Research (UCMR), Department of Molecular Biology, Umeå University, Umeå, Sweden
| | - Johan Henriksson
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå, Sweden
- Umeå Centre for Microbial Research (UCMR), Department of Molecular Biology, Umeå University, Umeå, Sweden
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27
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Zhang Z, Ruf-Zamojski F, Zamojski M, Bernard D, Chen X, Troyanskaya O, Sealfon S. Peak-agnostic high-resolution cis-regulatory circuitry mapping using single cell multiome data. Nucleic Acids Res 2024; 52:572-582. [PMID: 38084892 PMCID: PMC10810203 DOI: 10.1093/nar/gkad1166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 11/15/2023] [Accepted: 11/27/2023] [Indexed: 01/26/2024] Open
Abstract
Single same cell RNAseq/ATACseq multiome data provide unparalleled potential to develop high resolution maps of the cell-type specific transcriptional regulatory circuitry underlying gene expression. We present CREMA, a framework that recovers the full cis-regulatory circuitry by modeling gene expression and chromatin activity in individual cells without peak-calling or cell type labeling constraints. We demonstrate that CREMA overcomes the limitations of existing methods that fail to identify about half of functional regulatory elements which are outside the called chromatin 'peaks'. These circuit sites outside called peaks are shown to be important cell type specific functional regulatory loci, sufficient to distinguish individual cell types. Analysis of mouse pituitary data identifies a Gata2-circuit for the gonadotrope-enriched disease-associated Pcsk1 gene, which is experimentally validated by reduced gonadotrope expression in a gonadotrope conditional Gata2-knockout model. We present a web accessible human immune cell regulatory circuit resource, and provide CREMA as an R package.
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Affiliation(s)
- Zidong Zhang
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Frederique Ruf-Zamojski
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Michel Zamojski
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
| | - Daniel J Bernard
- Department of Pharmacology and Therapeutics, McGill University, Montreal, QC H3G 1Y6, Canada
| | - Xi Chen
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
| | - Olga G Troyanskaya
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Center for Computational Biology, Flatiron Institute, Simons Foundation, New York, NY, USA
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Stuart C Sealfon
- Department of Neurology, Center for Advanced Research on Diagnostic Assays, Icahn School of Medicine at Mount Sinai (ISMMS), New York, NY, USA
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28
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Lu C, Wei Y, Abbas M, Agula H, Wang E, Meng Z, Zhang R. Application of Single-Cell Assay for Transposase-Accessible Chromatin with High Throughput Sequencing in Plant Science: Advances, Technical Challenges, and Prospects. Int J Mol Sci 2024; 25:1479. [PMID: 38338756 PMCID: PMC10855595 DOI: 10.3390/ijms25031479] [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: 11/28/2023] [Revised: 01/16/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024] Open
Abstract
The Single-cell Assay for Transposase-Accessible Chromatin with high throughput sequencing (scATAC-seq) has gained increasing popularity in recent years, allowing for chromatin accessibility to be deciphered and gene regulatory networks (GRNs) to be inferred at single-cell resolution. This cutting-edge technology now enables the genome-wide profiling of chromatin accessibility at the cellular level and the capturing of cell-type-specific cis-regulatory elements (CREs) that are masked by cellular heterogeneity in bulk assays. Additionally, it can also facilitate the identification of rare and new cell types based on differences in chromatin accessibility and the charting of cellular developmental trajectories within lineage-related cell clusters. Due to technical challenges and limitations, the data generated from scATAC-seq exhibit unique features, often characterized by high sparsity and noise, even within the same cell type. To address these challenges, various bioinformatic tools have been developed. Furthermore, the application of scATAC-seq in plant science is still in its infancy, with most research focusing on root tissues and model plant species. In this review, we provide an overview of recent progress in scATAC-seq and its application across various fields. We first conduct scATAC-seq in plant science. Next, we highlight the current challenges of scATAC-seq in plant science and major strategies for cell type annotation. Finally, we outline several future directions to exploit scATAC-seq technologies to address critical challenges in plant science, ranging from plant ENCODE(The Encyclopedia of DNA Elements) project construction to GRN inference, to deepen our understanding of the roles of CREs in plant biology.
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Affiliation(s)
- Chao Lu
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (C.L.); (Y.W.)
- Key Laboratory of Herbage & Endemic Crop Biology, Ministry of Education, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Yunxiao Wei
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (C.L.); (Y.W.)
| | - Mubashir Abbas
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (C.L.); (Y.W.)
| | - Hasi Agula
- Key Laboratory of Herbage & Endemic Crop Biology, Ministry of Education, School of Life Sciences, Inner Mongolia University, Hohhot 010070, China
| | - Edwin Wang
- Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Zhigang Meng
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (C.L.); (Y.W.)
| | - Rui Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (C.L.); (Y.W.)
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29
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Yu J, Leng J, Hou Z, Sun D, Wu LY. Incorporating network diffusion and peak location information for better single-cell ATAC-seq data analysis. Brief Bioinform 2024; 25:bbae093. [PMID: 38493346 PMCID: PMC10944575 DOI: 10.1093/bib/bbae093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 12/22/2023] [Accepted: 02/20/2024] [Indexed: 03/18/2024] Open
Abstract
Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data provided new insights into the understanding of epigenetic heterogeneity and transcriptional regulation. With the increasing abundance of dataset resources, there is an urgent need to extract more useful information through high-quality data analysis methods specifically designed for scATAC-seq. However, analyzing scATAC-seq data poses challenges due to its near binarization, high sparsity and ultra-high dimensionality properties. Here, we proposed a novel network diffusion-based computational method to comprehensively analyze scATAC-seq data, named Single-Cell ATAC-seq Analysis via Network Refinement with Peaks Location Information (SCARP). SCARP formulates the Network Refinement diffusion method under the graph theory framework to aggregate information from different network orders, effectively compensating for missing signals in the scATAC-seq data. By incorporating distance information between adjacent peaks on the genome, SCARP also contributes to depicting the co-accessibility of peaks. These two innovations empower SCARP to obtain lower-dimensional representations for both cells and peaks more effectively. We have demonstrated through sufficient experiments that SCARP facilitated superior analyses of scATAC-seq data. Specifically, SCARP exhibited outstanding cell clustering performance, enabling better elucidation of cell heterogeneity and the discovery of new biologically significant cell subpopulations. Additionally, SCARP was also instrumental in portraying co-accessibility relationships of accessible regions and providing new insight into transcriptional regulation. Consequently, SCARP identified genes that were involved in key Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diseases and predicted reliable cis-regulatory interactions. To sum up, our studies suggested that SCARP is a promising tool to comprehensively analyze the scATAC-seq data.
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Affiliation(s)
- Jiating Yu
- School of Mathematics and Statistics, Nanjing University of Information Science & Technology, Nanjing 210044, China
- IAM, MADIS, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiacheng Leng
- IAM, MADIS, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- Zhejiang Lab, Hangzhou 311121, China
| | - Zhichao Hou
- IAM, MADIS, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Duanchen Sun
- School of Mathematics, Shandong University, Jinan 250100, China
| | - Ling-Yun Wu
- IAM, MADIS, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
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30
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Chen Y, Zheng R, Liu J, Li M. scMLC: an accurate and robust multiplex community detection method for single-cell multi-omics data. Brief Bioinform 2024; 25:bbae101. [PMID: 38493339 PMCID: PMC10944569 DOI: 10.1093/bib/bbae101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 01/03/2024] [Accepted: 02/15/2024] [Indexed: 03/18/2024] Open
Abstract
Clustering cells based on single-cell multi-modal sequencing technologies provides an unprecedented opportunity to create high-resolution cell atlas, reveal cellular critical states and study health and diseases. However, effectively integrating different sequencing data for cell clustering remains a challenging task. Motivated by the successful application of Louvain in scRNA-seq data, we propose a single-cell multi-modal Louvain clustering framework, called scMLC, to tackle this problem. scMLC builds multiplex single- and cross-modal cell-to-cell networks to capture modal-specific and consistent information between modalities and then adopts a robust multiplex community detection method to obtain the reliable cell clusters. In comparison with 15 state-of-the-art clustering methods on seven real datasets simultaneously measuring gene expression and chromatin accessibility, scMLC achieves better accuracy and stability in most datasets. Synthetic results also indicate that the cell-network-based integration strategy of multi-omics data is superior to other strategies in terms of generalization. Moreover, scMLC is flexible and can be extended to single-cell sequencing data with more than two modalities.
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Affiliation(s)
- Yuxuan Chen
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Ruiqing Zheng
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Jin Liu
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha 410083, China
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31
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Xiong H, Wang Q, Li CC, He A. Single-cell joint profiling of multiple epigenetic proteins and gene transcription. SCIENCE ADVANCES 2024; 10:eadi3664. [PMID: 38170774 PMCID: PMC10796078 DOI: 10.1126/sciadv.adi3664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024]
Abstract
Sculpting the epigenome with a combination of histone modifications and transcription factor occupancy determines gene transcription and cell fate specification. Here, we first develop uCoTarget, utilizing a split-pool barcoding strategy for realizing ultrahigh-throughput single-cell joint profiling of multiple epigenetic proteins. Through extensive optimization for sensitivity and multimodality resolution, we demonstrate that uCoTarget enables simultaneous detection of five histone modifications (H3K27ac, H3K4me3, H3K4me1, H3K36me3, and H3K27me3) in 19,860 single cells. We applied uCoTarget to the in vitro generation of hematopoietic stem/progenitor cells (HSPCs) from human embryonic stem cells, presenting multimodal epigenomic profiles in 26,418 single cells. uCoTarget reveals establishment of pairing of HSPC enhancers (H3K27ac) and promoters (H3K4me3) and RUNX1 engagement priming for H3K27ac activation along the HSPC path. We then develop uCoTargetX, an expansion of uCoTarget to simultaneously measure transcriptome and multiple epigenome targets. Together, our methods enable generalizable, versatile multimodal profiles for reconstructing comprehensive epigenome and transcriptome landscapes and analyzing the regulatory interplay at single-cell level.
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Affiliation(s)
- Haiqing Xiong
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - Qianhao Wang
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Chen C. Li
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
| | - Aibin He
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China
- Key laboratory of Carcinogenesis and Translational Research of Ministry of Education of China, Peking University Cancer Hospital & Institute, Peking University, Beijing 100142, China
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32
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Qian FC, Zhou LW, Zhu YB, Li YY, Yu ZM, Feng CC, Fang QL, Zhao Y, Cai FH, Wang QY, Tang HF, Li CQ. scATAC-Ref: a reference of scATAC-seq with known cell labels in multiple species. Nucleic Acids Res 2024; 52:D285-D292. [PMID: 37897340 PMCID: PMC10767920 DOI: 10.1093/nar/gkad924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/14/2023] [Accepted: 10/16/2023] [Indexed: 10/30/2023] Open
Abstract
Chromatin accessibility profiles at single cell resolution can reveal cell type-specific regulatory programs, help dissect highly specialized cell functions and trace cell origin and evolution. Accurate cell type assignment is critical for effectively gaining biological and pathological insights, but is difficult in scATAC-seq. Hence, by extensively reviewing the literature, we designed scATAC-Ref (https://bio.liclab.net/scATAC-Ref/), a manually curated scATAC-seq database aimed at providing a comprehensive, high-quality source of chromatin accessibility profiles with known cell labels across broad cell types. Currently, scATAC-Ref comprises 1 694 372 cells with known cell labels, across various biological conditions, >400 cell/tissue types and five species. We used uniform system environment and software parameters to perform comprehensive downstream analysis on these chromatin accessibility profiles with known labels, including gene activity score, TF enrichment score, differential chromatin accessibility regions, pathway/GO term enrichment analysis and co-accessibility interactions. The scATAC-Ref also provided a user-friendly interface to query, browse and visualize cell types of interest, thereby providing a valuable resource for exploring epigenetic regulation in different tissues and cell types.
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Affiliation(s)
- Feng-Cui Qian
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Li-Wei Zhou
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, China
| | - Yan-Bing Zhu
- Beijing Clinical Research Institute, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Yan-Yu Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Zheng-Min Yu
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
| | - Chen-Chen Feng
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China
| | - Qiao-Li Fang
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
| | - Yu Zhao
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
| | - Fu-Hong Cai
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
| | - Qiu-Yu Wang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
| | - Hui-Fang Tang
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Department of Cardiology, Hengyang Medical School, University of South China, Hengyang, China
| | - Chun-Quan Li
- The First Affiliated Hospital & Hunan Provincial Key Laboratory of Multi-omics And Artificial Intelligence of Cardiovascular Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Hunan Provincial Maternal and Child Health Care Hospital, National Health Commission Key Laboratory of Birth Defect Research and Prevention, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences & MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
- School of Computer, University of South China, Hengyang, Hunan, 421001, China
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China
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33
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Miao Z, Kim J. Uniform quantification of single-nucleus ATAC-seq data with Paired-Insertion Counting (PIC) and a model-based insertion rate estimator. Nat Methods 2024; 21:32-36. [PMID: 38049698 PMCID: PMC10776405 DOI: 10.1038/s41592-023-02103-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 10/25/2023] [Indexed: 12/06/2023]
Abstract
Existing approaches to scoring single-nucleus assay for transposase-accessible chromatin with sequencing (snATAC-seq) feature matrices from sequencing reads are inconsistent, affecting downstream analyses and displaying artifacts. We show that, even with sparse single-cell data, quantitative counts are informative for estimating the regulatory state of a cell, which calls for a consistent treatment. We propose Paired-Insertion Counting as a uniform method for snATAC-seq feature characterization and provide a probability model for inferring latent insertion dynamics from snATAC-seq count matrices.
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Affiliation(s)
- Zhen Miao
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Junhyong Kim
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Biology, University of Pennsylvania, Philadelphia, PA, USA.
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34
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Bravo González-Blas C, Matetovici I, Hillen H, Taskiran II, Vandepoel R, Christiaens V, Sansores-García L, Verboven E, Hulselmans G, Poovathingal S, Demeulemeester J, Psatha N, Mauduit D, Halder G, Aerts S. Single-cell spatial multi-omics and deep learning dissect enhancer-driven gene regulatory networks in liver zonation. Nat Cell Biol 2024; 26:153-167. [PMID: 38182825 PMCID: PMC10791584 DOI: 10.1038/s41556-023-01316-4] [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: 11/22/2022] [Accepted: 11/15/2023] [Indexed: 01/07/2024]
Abstract
In the mammalian liver, hepatocytes exhibit diverse metabolic and functional profiles based on their location within the liver lobule. However, it is unclear whether this spatial variation, called zonation, is governed by a well-defined gene regulatory code. Here, using a combination of single-cell multiomics, spatial omics, massively parallel reporter assays and deep learning, we mapped enhancer-gene regulatory networks across mouse liver cell types. We found that zonation affects gene expression and chromatin accessibility in hepatocytes, among other cell types. These states are driven by the repressors TCF7L1 and TBX3, alongside other core hepatocyte transcription factors, such as HNF4A, CEBPA, FOXA1 and ONECUT1. To examine the architecture of the enhancers driving these cell states, we trained a hierarchical deep learning model called DeepLiver. Our study provides a multimodal understanding of the regulatory code underlying hepatocyte identity and their zonation state that can be used to engineer enhancers with specific activity levels and zonation patterns.
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Affiliation(s)
- Carmen Bravo González-Blas
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Irina Matetovici
- VIB Center for Brain & Disease Research, Leuven, Belgium
- VIB Center for AI and Computational Biology (VIB.AI), Leuven, Belgium
- VIB Tech Watch, VIB Headquarters, Ghent, Belgium
| | - Hanne Hillen
- VIB Center for Cancer Biology, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - Ibrahim Ihsan Taskiran
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Center for AI and Computational Biology (VIB.AI), Leuven, Belgium
| | - Roel Vandepoel
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Center for AI and Computational Biology (VIB.AI), Leuven, Belgium
| | - Valerie Christiaens
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Center for AI and Computational Biology (VIB.AI), Leuven, Belgium
| | - Leticia Sansores-García
- VIB Center for Cancer Biology, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - Elisabeth Verboven
- VIB Center for Cancer Biology, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - Gert Hulselmans
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Center for AI and Computational Biology (VIB.AI), Leuven, Belgium
| | | | - Jonas Demeulemeester
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Nikoleta Psatha
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - David Mauduit
- VIB Center for Brain & Disease Research, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Center for AI and Computational Biology (VIB.AI), Leuven, Belgium
| | - Georg Halder
- VIB Center for Cancer Biology, Leuven, Belgium
- Department of Oncology, KU Leuven, Leuven, Belgium
| | - Stein Aerts
- VIB Center for Brain & Disease Research, Leuven, Belgium.
- Department of Human Genetics, KU Leuven, Leuven, Belgium.
- VIB Center for AI and Computational Biology (VIB.AI), Leuven, Belgium.
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35
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Martens LD, Fischer DS, Yépez VA, Theis FJ, Gagneur J. Modeling fragment counts improves single-cell ATAC-seq analysis. Nat Methods 2024; 21:28-31. [PMID: 38049697 PMCID: PMC10776385 DOI: 10.1038/s41592-023-02112-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 10/25/2023] [Indexed: 12/06/2023]
Abstract
Single-cell ATAC sequencing coverage in regulatory regions is typically binarized as an indicator of open chromatin. Here we show that binarization is an unnecessary step that neither improves goodness of fit, clustering, cell type identification nor batch integration. Fragment counts, but not read counts, should instead be modeled, which preserves quantitative regulatory information. These results have immediate implications for single-cell ATAC sequencing analysis.
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Affiliation(s)
- Laura D Martens
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany
- Helmholtz Association, Munich School for Data Science (MUDS), Munich, Germany
| | - David S Fischer
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Vicente A Yépez
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Fabian J Theis
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany.
- Helmholtz Association, Munich School for Data Science (MUDS), Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
| | - Julien Gagneur
- School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Computational Health Center, Helmholtz Center Munich, Neuherberg, Germany.
- Helmholtz Association, Munich School for Data Science (MUDS), Munich, Germany.
- Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany.
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36
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Neijzen D, Lunter G. Unsupervised learning for medical data: A review of probabilistic factorization methods. Stat Med 2023; 42:5541-5554. [PMID: 37850249 DOI: 10.1002/sim.9924] [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: 06/19/2023] [Accepted: 09/13/2023] [Indexed: 10/19/2023]
Abstract
We review popular unsupervised learning methods for the analysis of high-dimensional data encountered in, for example, genomics, medical imaging, cohort studies, and biobanks. We show that four commonly used methods, principal component analysis, K-means clustering, nonnegative matrix factorization, and latent Dirichlet allocation, can be written as probabilistic models underpinned by a low-rank matrix factorization. In addition to highlighting their similarities, this formulation clarifies the various assumptions and restrictions of each approach, which eases identifying the appropriate method for specific applications for applied medical researchers. We also touch upon the most important aspects of inference and model selection for the application of these methods to health data.
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Affiliation(s)
- Dorien Neijzen
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Gerton Lunter
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Weatherall Institute of Molecular Medicine, Oxford University, Oxford, UK
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37
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Zhu Q, Zhao X, Zhang Y, Li Y, Liu S, Han J, Sun Z, Wang C, Deng D, Wang S, Tang Y, Huang Y, Jiang S, Tian C, Chen X, Yuan Y, Li Z, Yang T, Lai T, Liu Y, Yang W, Zou X, Zhang M, Cui H, Liu C, Jin X, Hu Y, Chen A, Xu X, Li G, Hou Y, Liu L, Liu S, Fang L, Chen W, Wu L. Single cell multi-omics reveal intra-cell-line heterogeneity across human cancer cell lines. Nat Commun 2023; 14:8170. [PMID: 38071219 PMCID: PMC10710513 DOI: 10.1038/s41467-023-43991-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 11/27/2023] [Indexed: 12/18/2023] Open
Abstract
Human cancer cell lines have long served as tools for cancer research and drug discovery, but the presence and the source of intra-cell-line heterogeneity remain elusive. Here, we perform single-cell RNA-sequencing and ATAC-sequencing on 42 and 39 human cell lines, respectively, to illustrate both transcriptomic and epigenetic heterogeneity within individual cell lines. Our data reveal that transcriptomic heterogeneity is frequently observed in cancer cell lines of different tissue origins, often driven by multiple common transcriptional programs. Copy number variation, as well as epigenetic variation and extrachromosomal DNA distribution all contribute to the detected intra-cell-line heterogeneity. Using hypoxia treatment as an example, we demonstrate that transcriptomic heterogeneity could be reshaped by environmental stress. Overall, our study performs single-cell multi-omics of commonly used human cancer cell lines and offers mechanistic insights into the intra-cell-line heterogeneity and its dynamics, which would serve as an important resource for future cancer cell line-based studies.
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Affiliation(s)
- Qionghua Zhu
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, 518055, Shenzhen, China.
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, 518055, Shenzhen, China.
| | - Xin Zhao
- BGI Research, 518083, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Yuanhang Zhang
- BGI Research, 518083, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Yanping Li
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, 518055, Shenzhen, China
| | - Shang Liu
- BGI Research, 518083, Shenzhen, China
| | - Jingxuan Han
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, 518055, Shenzhen, China
| | - Zhiyuan Sun
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, 518055, Shenzhen, China
| | - Chunqing Wang
- BGI Research, 518083, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Daqi Deng
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, 518055, Shenzhen, China
| | | | - Yisen Tang
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, 518055, Shenzhen, China
| | | | - Siyuan Jiang
- BGI Research, 518083, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Chi Tian
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, 518055, Shenzhen, China
| | - Xi Chen
- BGI Research, 518083, Shenzhen, China
| | - Yue Yuan
- BGI Research, 518083, Shenzhen, China
| | - Zeyu Li
- BGI Research, 518083, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China
| | - Tao Yang
- China National GeneBank, 518120, Shenzhen, China
| | - Tingting Lai
- China National GeneBank, 518120, Shenzhen, China
| | - Yiqun Liu
- China National GeneBank, 518120, Shenzhen, China
| | - Wenzhen Yang
- China National GeneBank, 518120, Shenzhen, China
| | - Xuanxuan Zou
- BGI Research, 518083, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, 100049, Beijing, China
| | | | - Huanhuan Cui
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, 518055, Shenzhen, China
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, 518055, Shenzhen, China
- Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, 518055, Shenzhen, China
| | | | - Xin Jin
- BGI Research, 518083, Shenzhen, China
| | - Yuhui Hu
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, 518055, Shenzhen, China
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, 518055, Shenzhen, China
- Department of Pharmacology, School of Medicine, Southern University of Science and Technology, 518055, Shenzhen, China
| | - Ao Chen
- BGI Research, 518083, Shenzhen, China
- JFL-BGI STOmics Center, Jinfeng Laboratory, 401329, Chongqing, China
- The Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong, China
| | - Xun Xu
- BGI Research, 518083, Shenzhen, China
| | - Guipeng Li
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, 518055, Shenzhen, China
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, 518055, Shenzhen, China
- Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, 518055, Shenzhen, China
| | - Yong Hou
- BGI Research, 518083, Shenzhen, China
- Shenzhen Key Laboratory of Single-Cell Omics, BGI-Shenzhen, 518100, Shenzhen, China
| | - Longqi Liu
- BGI Research, 518083, Shenzhen, China.
- BGI Research, 310012, Hangzhou, China.
- Shenzhen Bay Laboratory, 518000, Shenzhen, China.
| | - Shiping Liu
- BGI Research, 518083, Shenzhen, China.
- The Guangdong-Hong Kong Joint Laboratory on Immunological and Genetic Kidney Diseases, Guangdong, China.
- Shenzhen Key Laboratory of Single-Cell Omics, BGI-Shenzhen, 518100, Shenzhen, China.
- BGI Research, 310012, Hangzhou, China.
- Shenzhen Bay Laboratory, 518000, Shenzhen, China.
| | - Liang Fang
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, 518055, Shenzhen, China.
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, 518055, Shenzhen, China.
- Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, 518055, Shenzhen, China.
| | - Wei Chen
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, 518055, Shenzhen, China.
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, 518055, Shenzhen, China.
| | - Liang Wu
- BGI Research, 518083, Shenzhen, China.
- JFL-BGI STOmics Center, Jinfeng Laboratory, 401329, Chongqing, China.
- BGI Research, 401329, Chongqing, China.
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38
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Godoy PM, Oyedeji A, Mudd JL, Morikis VA, Zarov AP, Longmore GD, Fields RC, Kaufman CK. Functional analysis of recurrent CDC20 promoter variants in human melanoma. Commun Biol 2023; 6:1216. [PMID: 38030698 PMCID: PMC10686982 DOI: 10.1038/s42003-023-05526-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 10/30/2023] [Indexed: 12/01/2023] Open
Abstract
Small nucleotide variants in non-coding regions of the genome can alter transcriptional regulation, leading to changes in gene expression which can activate oncogenic gene regulatory networks. Melanoma is heavily burdened by non-coding variants, representing over 99% of total genetic variation, including the well-characterized TERT promoter mutation. However, the compendium of regulatory non-coding variants is likely still functionally under-characterized. We developed a pipeline to identify hotspots, i.e. recurrently mutated regions, in melanoma containing putatively functional non-coding somatic variants that are located within predicted melanoma-specific regulatory regions. We identified hundreds of statistically significant hotspots, including the hotspot containing the TERT promoter variants, and focused on a hotspot in the promoter of CDC20. We found that variants in the promoter of CDC20, which putatively disrupt an ETS motif, lead to lower transcriptional activity in reporter assays. Using CRISPR/Cas9, we generated an indel in the CDC20 promoter in human A375 melanoma cell lines and observed decreased expression of CDC20, changes in migration capabilities, increased growth of xenografts, and an altered transcriptional state previously associated with a more proliferative and less migratory state. Overall, our analysis prioritized several recurrent functional non-coding variants that, through downregulation of CDC20, led to perturbation of key melanoma phenotypes.
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Affiliation(s)
- Paula M Godoy
- Division of Medical Oncology, Department of Medicine and Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
| | - Abimbola Oyedeji
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University in Saint Louis, St. Louis, MO, USA
| | - Jacqueline L Mudd
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University in Saint Louis, St. Louis, MO, USA
| | - Vasilios A Morikis
- Departments of Medicine (Oncology) and Cell Biology and Physiology and the ICCE Institute, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Anna P Zarov
- Division of Medical Oncology, Department of Medicine and Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA
| | - Gregory D Longmore
- Siteman Cancer Center, Washington University in Saint Louis, St. Louis, MO, USA
- Departments of Medicine (Oncology) and Cell Biology and Physiology and the ICCE Institute, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Ryan C Fields
- Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Center, Washington University in Saint Louis, St. Louis, MO, USA
| | - Charles K Kaufman
- Division of Medical Oncology, Department of Medicine and Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA.
- Siteman Cancer Center, Washington University in Saint Louis, St. Louis, MO, USA.
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39
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Akhtyamov P, Shaheen L, Raevskiy M, Stupnikov A, Medvedeva YA. scATAC-seq preprocessing and imputation evaluation system for visualization, clustering and digital footprinting. Brief Bioinform 2023; 25:bbad447. [PMID: 38084919 PMCID: PMC10714317 DOI: 10.1093/bib/bbad447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/29/2023] [Accepted: 11/14/2023] [Indexed: 12/18/2023] Open
Abstract
Single-cell ATAC-seq (scATAC-seq) is a recently developed approach that provides means to investigate open chromatin at single cell level, to assess epigenetic regulation and transcription factors binding landscapes. The sparsity of the scATAC-seq data calls for imputation. Similarly, preprocessing (filtering) may be required to reduce computational load due to the large number of open regions. However, optimal strategies for both imputation and preprocessing have not been yet evaluated together. We present SAPIEnS (scATAC-seq Preprocessing and Imputation Evaluation System), a benchmark for scATAC-seq imputation frameworks, a combination of state-of-the-art imputation methods with commonly used preprocessing techniques. We assess different types of scATAC-seq analysis, i.e. clustering, visualization and digital genomic footprinting, and attain optimal preprocessing-imputation strategies. We discuss the benefits of the imputation framework depending on the task and the number of the dataset features (peaks). We conclude that the preprocessing with the Boruta method is beneficial for the majority of tasks, while imputation is helpful mostly for small datasets. We also implement a SAPIEnS database with pre-computed transcription factor footprints based on imputed data with their activity scores in a specific cell type. SAPIEnS is published at: https://github.com/lab-medvedeva/SAPIEnS. SAPIEnS database is available at: https://sapiensdb.com.
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Affiliation(s)
- Pavel Akhtyamov
- Department of Biomedical Physics, Moscow Institute of Physics and Technology (National Research University), 9 Institutskiy per., 141701, Moscow Region, Russian Federation
- The National Medical Research Center for Endocrinology, Dm. Ulyanova, 11, 117036, Moscow, Russian Federation
| | - Layal Shaheen
- Department of Biomedical Physics, Moscow Institute of Physics and Technology (National Research University), 9 Institutskiy per., 141701, Moscow Region, Russian Federation
- The National Medical Research Center for Endocrinology, Dm. Ulyanova, 11, 117036, Moscow, Russian Federation
| | - Mikhail Raevskiy
- Department, École Polytechnique Fédérale de Lausanne, Rte Cantonale, 1015, Lausanne, Vaud, Switzerland
| | - Alexey Stupnikov
- Department of Biomedical Physics, Moscow Institute of Physics and Technology (National Research University), 9 Institutskiy per., 141701, Moscow Region, Russian Federation
- The National Medical Research Center for Endocrinology, Dm. Ulyanova, 11, 117036, Moscow, Russian Federation
- Institute of Bioengineering, Research Center of Biotechnology, Russian Academy of Science, Leninsky prospect, 33, build. 2, 119071, Moscow, Russian Federation
| | - Yulia A Medvedeva
- Department of Biomedical Physics, Moscow Institute of Physics and Technology (National Research University), 9 Institutskiy per., 141701, Moscow Region, Russian Federation
- The National Medical Research Center for Endocrinology, Dm. Ulyanova, 11, 117036, Moscow, Russian Federation
- Institute of Bioengineering, Research Center of Biotechnology, Russian Academy of Science, Leninsky prospect, 33, build. 2, 119071, Moscow, Russian Federation
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40
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Nikopoulou C, Kleinenkuhnen N, Parekh S, Sandoval T, Ziegenhain C, Schneider F, Giavalisco P, Donahue KF, Vesting AJ, Kirchner M, Bozukova M, Vossen C, Altmüller J, Wunderlich T, Sandberg R, Kondylis V, Tresch A, Tessarz P. Spatial and single-cell profiling of the metabolome, transcriptome and epigenome of the aging mouse liver. NATURE AGING 2023; 3:1430-1445. [PMID: 37946043 PMCID: PMC10645594 DOI: 10.1038/s43587-023-00513-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 09/27/2023] [Indexed: 11/12/2023]
Abstract
Tissues within an organism and even cell types within a tissue can age with different velocities. However, it is unclear whether cells of one type experience different aging trajectories within a tissue depending on their spatial location. Here, we used spatial transcriptomics in combination with single-cell ATAC-seq and RNA-seq, lipidomics and functional assays to address how cells in the male murine liver are affected by age-related changes in the microenvironment. Integration of the datasets revealed zonation-specific and age-related changes in metabolic states, the epigenome and transcriptome. The epigenome changed in a zonation-dependent manner and functionally, periportal hepatocytes were characterized by decreased mitochondrial fitness, whereas pericentral hepatocytes accumulated large lipid droplets. Together, we provide evidence that changing microenvironments within a tissue exert strong influences on their resident cells that can shape epigenetic, metabolic and phenotypic outputs.
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Affiliation(s)
- Chrysa Nikopoulou
- Max Planck Research Group 'Chromatin and Ageing', Max Planck Institute for Biology of Ageing, Cologne, Germany
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-associated Diseases (CECAD), Cologne, Germany
| | - Niklas Kleinenkuhnen
- Max Planck Research Group 'Chromatin and Ageing', Max Planck Institute for Biology of Ageing, Cologne, Germany
- Institute of Medical Statistics and Computational Biology, Faculty of Medicine, University of Cologne, Cologne, Germany
| | - Swati Parekh
- Max Planck Research Group 'Chromatin and Ageing', Max Planck Institute for Biology of Ageing, Cologne, Germany
- Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharma, Biberach, Germany
| | - Tonantzi Sandoval
- Max Planck Research Group 'Chromatin and Ageing', Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Christoph Ziegenhain
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Farina Schneider
- Institute for Pathology, University Hospital Cologne, Cologne, Germany
| | - Patrick Giavalisco
- Metabolic Core Facility, Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Kat-Folz Donahue
- FACS and Imaging Core Facility, Max Planck Institute for Biology of Ageing, Cologne, Germany
| | | | - Marcel Kirchner
- FACS and Imaging Core Facility, Max Planck Institute for Biology of Ageing, Cologne, Germany
| | - Mihaela Bozukova
- Max Planck Research Group 'Chromatin and Ageing', Max Planck Institute for Biology of Ageing, Cologne, Germany
| | | | - Janine Altmüller
- Cologne Center for Genomics, University of Cologne, Cologne, Germany; Berlin Institute of Health at Charité, Core Facility Genomics, Berlin, Germany; Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
| | - Thomas Wunderlich
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-associated Diseases (CECAD), Cologne, Germany
- Max Planck Institute for Metabolism Research, Cologne, Germany
| | - Rickard Sandberg
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Vangelis Kondylis
- Institute for Pathology, University Hospital Cologne, Cologne, Germany
- Department of Gastroenterology, Hepatology and Infectious Diseases, University Hospital Düsseldorf, Medical Faculty at Heinrich-Heine-University, Duesseldorf, Germany
| | - Achim Tresch
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-associated Diseases (CECAD), Cologne, Germany.
- Institute of Medical Statistics and Computational Biology, Faculty of Medicine, University of Cologne, Cologne, Germany.
| | - Peter Tessarz
- Max Planck Research Group 'Chromatin and Ageing', Max Planck Institute for Biology of Ageing, Cologne, Germany.
- Cologne Excellence Cluster on Cellular Stress Responses in Aging-associated Diseases (CECAD), Cologne, Germany.
- Department of Human Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, The Netherlands.
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41
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Klie A, Laub D, Talwar JV, Stites H, Jores T, Solvason JJ, Farley EK, Carter H. Predictive analyses of regulatory sequences with EUGENe. NATURE COMPUTATIONAL SCIENCE 2023; 3:946-956. [PMID: 38177592 PMCID: PMC10768637 DOI: 10.1038/s43588-023-00544-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 09/27/2023] [Indexed: 01/06/2024]
Abstract
Deep learning has become a popular tool to study cis-regulatory function. Yet efforts to design software for deep-learning analyses in regulatory genomics that are findable, accessible, interoperable and reusable (FAIR) have fallen short of fully meeting these criteria. Here we present elucidating the utility of genomic elements with neural nets (EUGENe), a FAIR toolkit for the analysis of genomic sequences with deep learning. EUGENe consists of a set of modules and subpackages for executing the key functionality of a genomics deep learning workflow: (1) extracting, transforming and loading sequence data from many common file formats; (2) instantiating, initializing and training diverse model architectures; and (3) evaluating and interpreting model behavior. We designed EUGENe as a simple, flexible and extensible interface for streamlining and customizing end-to-end deep-learning sequence analyses, and illustrate these principles through application of the toolkit to three predictive modeling tasks. We hope that EUGENe represents a springboard towards a collaborative ecosystem for deep-learning applications in genomics research.
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Affiliation(s)
- Adam Klie
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | - David Laub
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | - James V Talwar
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | | | - Tobias Jores
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Joe J Solvason
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- Department of Molecular Biology, University of California San Diego, La Jolla, CA, USA
| | - Emma K Farley
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- Department of Molecular Biology, University of California San Diego, La Jolla, CA, USA
| | - Hannah Carter
- Department of Medicine, University of California San Diego, La Jolla, CA, USA.
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA.
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42
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Badia-I-Mompel P, Wessels L, Müller-Dott S, Trimbour R, Ramirez Flores RO, Argelaguet R, Saez-Rodriguez J. Gene regulatory network inference in the era of single-cell multi-omics. Nat Rev Genet 2023; 24:739-754. [PMID: 37365273 DOI: 10.1038/s41576-023-00618-5] [Citation(s) in RCA: 48] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/12/2023] [Indexed: 06/28/2023]
Abstract
The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory networks (GRNs). The study of GRNs is useful to understand how cellular identity is established, maintained and disrupted in disease. GRNs can be inferred from experimental data - historically, bulk omics data - and/or from the literature. The advent of single-cell multi-omics technologies has led to the development of novel computational methods that leverage genomic, transcriptomic and chromatin accessibility information to infer GRNs at an unprecedented resolution. Here, we review the key principles of inferring GRNs that encompass transcription factor-gene interactions from transcriptomics and chromatin accessibility data. We focus on the comparison and classification of methods that use single-cell multimodal data. We highlight challenges in GRN inference, in particular with respect to benchmarking, and potential further developments using additional data modalities.
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Affiliation(s)
- Pau Badia-I-Mompel
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Lorna Wessels
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
- Department of Vascular Biology and Tumor Angiogenesis, European Center for Angioscience, Medical Faculty, MannHeim Heidelberg University, Mannheim, Germany
| | - Sophia Müller-Dott
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | - Rémi Trimbour
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
- Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Machine Learning for Integrative Genomics Group, Paris, France
| | - Ricardo O Ramirez Flores
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany
| | | | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg, Germany.
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43
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Tzaferis C, Karatzas E, Baltoumas FA, Pavlopoulos GA, Kollias G, Konstantopoulos D. SCALA: A complete solution for multimodal analysis of single-cell Next Generation Sequencing data. Comput Struct Biotechnol J 2023; 21:5382-5393. [PMID: 38022693 PMCID: PMC10651449 DOI: 10.1016/j.csbj.2023.10.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 10/16/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Analysis and interpretation of high-throughput transcriptional and chromatin accessibility data at single-cell (sc) resolution are still open challenges in the biomedical field. The existence of countless bioinformatics tools, for the different analytical steps, increases the complexity of data interpretation and the difficulty to derive biological insights. In this article, we present SCALA, a bioinformatics tool for analysis and visualization of single-cell RNA sequencing (scRNA-seq) and Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) datasets, enabling either independent or integrative analysis of the two modalities. SCALA combines standard types of analysis by integrating multiple software packages varying from quality control to the identification of distinct cell populations and cell states. Additional analysis options enable functional enrichment, cellular trajectory inference, ligand-receptor analysis, and regulatory network reconstruction. SCALA is fully parameterizable, presenting data in tabular format and producing publication-ready visualizations. The different available analysis modules can aid biomedical researchers in exploring, analyzing, and visualizing their data without any prior experience in coding. We demonstrate the functionality of SCALA through two use-cases related to TNF-driven arthritic mice, handling both scRNA-seq and scATAC-seq datasets. SCALA is developed in R, Shiny and JavaScript and is mainly available as a standalone version, while an online service of more limited capacity can be found at http://scala.pavlopouloslab.info or https://scala.fleming.gr.
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Affiliation(s)
- Christos Tzaferis
- Institute for Bioinnovation, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece
| | - Fotis A. Baltoumas
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece
- Research Institute of New Biotechnologies and Precision Medicine, National and Kapodistrian University of Athens, Greece
| | - George Kollias
- Institute for Bioinnovation, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece
- Research Institute of New Biotechnologies and Precision Medicine, National and Kapodistrian University of Athens, Greece
- Department of Physiology, Medical School, National and Kapodistrian University of Athens, Greece
| | - Dimitris Konstantopoulos
- Institute for Bioinnovation, Biomedical Sciences Research Center "Alexander Fleming", Vari, Greece
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44
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Carbonetto P, Luo K, Sarkar A, Hung A, Tayeb K, Pott S, Stephens M. GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership. Genome Biol 2023; 24:236. [PMID: 37858253 PMCID: PMC10588049 DOI: 10.1186/s13059-023-03067-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 09/20/2023] [Indexed: 10/21/2023] Open
Abstract
Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets.
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Affiliation(s)
- Peter Carbonetto
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Research Computing Center, University of Chicago, Chicago, IL, USA
| | - Kaixuan Luo
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Abhishek Sarkar
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Vesalius Therapeutics, Cambridge, MA, USA
| | - Anthony Hung
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
| | - Karl Tayeb
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
| | - Sebastian Pott
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
| | - Matthew Stephens
- Department of Human Genetics, University of Chicago, Chicago, IL, USA.
- Department of Statistics, University of Chicago, Chicago, IL, USA.
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45
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Fukuyama J, Sankaran K, Symul L. Multiscale analysis of count data through topic alignment. Biostatistics 2023; 24:1045-1065. [PMID: 35657012 DOI: 10.1093/biostatistics/kxac018] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 03/10/2022] [Accepted: 03/21/2022] [Indexed: 10/19/2023] Open
Abstract
Topic modeling is a popular method used to describe biological count data. With topic models, the user must specify the number of topics $K$. Since there is no definitive way to choose $K$ and since a true value might not exist, we develop a method, which we call topic alignment, to study the relationships across models with different $K$. In addition, we present three diagnostics based on the alignment. These techniques can show how many topics are consistently present across different models, if a topic is only transiently present, or if a topic splits into more topics when $K$ increases. This strategy gives more insight into the process of generating the data than choosing a single value of $K$ would. We design a visual representation of these cross-model relationships, show the effectiveness of these tools for interpreting the topics on simulated and real data, and release an accompanying R package, alto.
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Affiliation(s)
- Julia Fukuyama
- Department of Statistics, Indiana University Bloomington, 919 E 10th Street, Bloomington, IN 47408, USA
| | - Kris Sankaran
- Department of Statistics, University of Wisconsin - Madison, 1300 University Ave, Madison, WI 53706, USA
| | - Laura Symul
- Department of Statistics, Stanford University, 390 Jane Stanford Way, Stanford, CA 94305, USA
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46
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Velmeshev D, Perez Y, Yan Z, Valencia JE, Castaneda-Castellanos DR, Wang L, Schirmer L, Mayer S, Wick B, Wang S, Nowakowski TJ, Paredes M, Huang EJ, Kriegstein AR. Single-cell analysis of prenatal and postnatal human cortical development. Science 2023; 382:eadf0834. [PMID: 37824647 PMCID: PMC11005279 DOI: 10.1126/science.adf0834] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 08/31/2023] [Indexed: 10/14/2023]
Abstract
We analyzed >700,000 single-nucleus RNA sequencing profiles from 106 donors during prenatal and postnatal developmental stages and identified lineage-specific programs that underlie the development of specific subtypes of excitatory cortical neurons, interneurons, glial cell types, and brain vasculature. By leveraging single-nucleus chromatin accessibility data, we delineated enhancer gene regulatory networks and transcription factors that control commitment of specific cortical lineages. By intersecting our results with genetic risk factors for human brain diseases, we identified the cortical cell types and lineages most vulnerable to genetic insults of different brain disorders, especially autism. We find that lineage-specific gene expression programs up-regulated in female cells are especially enriched for the genetic risk factors of autism. Our study captures the molecular progression of cortical lineages across human development.
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Affiliation(s)
- Dmitry Velmeshev
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA 94143
- Department of Neurology, University of California, San Francisco, CA 94143
- Department of Neurobiology, Duke University School of Medicine, Durham, NC 27710
| | - Yonatan Perez
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA 94143
- Department of Neurology, University of California, San Francisco, CA 94143
| | - Zihan Yan
- Department of Neurobiology, Duke University School of Medicine, Durham, NC 27710
| | | | | | - Li Wang
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA 94143
- Department of Neurology, University of California, San Francisco, CA 94143
| | - Lucas Schirmer
- Division of Neuroimmunology, Department of Neurology, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, 68167
- Mannheim Center for Translational Neuroscience and Institute for Innate Immunoscience, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany, 68167
- Interdisciplinary Center for Neurosciences, Heidelberg University, Heidelberg, Germany, 68167
| | - Simone Mayer
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA 94143
- Department of Neurology, University of California, San Francisco, CA 94143
- Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany 72076
| | - Brittney Wick
- UC Santa Cruz Genomics Institute, Santa Cruz, CA 95060
| | - Shaohui Wang
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA 94143
- Department of Neurology, University of California, San Francisco, CA 94143
| | - Tomasz Jan Nowakowski
- Department of Neurological Surgery, University of California, San Francisco, CA 94143
| | - Mercedes Paredes
- Department of Neurology, University of California, San Francisco, CA 94143
| | - Eric J Huang
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA 94143
- Department of Pathology, University of California, San Francisco, CA 94115
| | - Arnold R Kriegstein
- Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California, San Francisco, CA 94143
- Department of Neurology, University of California, San Francisco, CA 94143
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47
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Purwin TJ, Caksa S, Sacan A, Capparelli C, Aplin AE. Gene signature reveals decreased SOX10-dependent transcripts in malignant cells from immune checkpoint inhibitor-resistant cutaneous melanomas. iScience 2023; 26:107472. [PMID: 37636077 PMCID: PMC10450419 DOI: 10.1016/j.isci.2023.107472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 06/18/2023] [Accepted: 07/21/2023] [Indexed: 08/29/2023] Open
Abstract
Evidence is mounting for cross-resistance between immune checkpoint and targeted kinase inhibitor therapies in cutaneous melanoma patients. Since the loss of the transcription factor, SOX10, causes tolerance to MAPK pathway inhibitors, we used bioinformatic techniques to determine if reduced SOX10 expression/activity is associated with immune checkpoint inhibitor resistance. We integrated SOX10 ChIP-seq, knockout RNA-seq, and knockdown ATAC-seq data from melanoma cell models to develop a robust SOX10 gene signature. We used computational methods to validate this signature as a measure of SOX10-dependent activity in independent single-cell and bulk RNA-seq SOX10 knockdown, cell line panel, and MAPK inhibitor drug-resistant datasets. Evaluation of patient single-cell RNA-seq data revealed lower levels of SOX10-dependent transcripts in immune checkpoint inhibitor-resistant tumors. Our results suggest that SOX10-deficient melanoma cells are associated with cross-resistance between targeted and immune checkpoint inhibitors and highlight the need to identify therapeutic strategies that target this subpopulation.
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Affiliation(s)
- Timothy J. Purwin
- Department of Pharmacology, Physiology, and Cancer Biology, Thomas Jefferson University, Philadelphia, PA 19107, USA
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA 19104, USA
| | - Signe Caksa
- Department of Pharmacology, Physiology, and Cancer Biology, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Ahmet Sacan
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA 19104, USA
| | - Claudia Capparelli
- Medical Oncology, Thomas Jefferson University, Philadelphia, PA 19107, USA
- Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Andrew E. Aplin
- Department of Pharmacology, Physiology, and Cancer Biology, Thomas Jefferson University, Philadelphia, PA 19107, USA
- Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA 19107, USA
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48
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Zhang K, Zemke NR, Armand EJ, Ren B. SnapATAC2: a fast, scalable and versatile tool for analysis of single-cell omics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.11.557221. [PMID: 37745443 PMCID: PMC10515871 DOI: 10.1101/2023.09.11.557221] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Single-cell omics technologies have ushered in a new era for the study of dynamic gene regulation in complex tissues during development and disease pathogenesis. A major computational challenge in analyzing these datasets is to project the large-scale and high dimensional data into low-dimensional space while retaining the relative relationships between cells in order to decompose the cellular heterogeneity and reconstruct cell-type-specific gene regulatory programs. Conventional dimensionality reduction methods suffer from computational inefficiency, difficulty to capture the full spectrum of cellular heterogeneity, or inability to apply across diverse molecular modalities. Here, we report a fast and nonlinear dimensionality reduction algorithm that not only more accurately captures the heterogeneities of single-cell omics data, but also features runtime and memory usage that is computational efficient and linearly proportional to cell numbers. We implement this algorithm in a Python package named SnapATAC2, and demonstrate its superior performance, remarkable scalability and general adaptability using an array of single-cell omics data types, including single-cell ATAC-seq, single-cell RNA-seq, single-cell Hi-C, and single-cell multiomics datasets.
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49
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Carbonetto P, Luo K, Sarkar A, Hung A, Tayeb K, Pott S, Stephens M. GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.03.531029. [PMID: 36945441 PMCID: PMC10028846 DOI: 10.1101/2023.03.03.531029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets.
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Affiliation(s)
- Peter Carbonetto
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Research Computing Center, University of Chicago, Chicago, IL, USA
| | - Kaixuan Luo
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Abhishek Sarkar
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Vesalius Therapeutics, Cambridge, MA, USA
| | - Anthony Hung
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
| | - Karl Tayeb
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
| | - Sebastian Pott
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Section of Genetic Medicine, University of Chicago, Chicago, IL, USA
| | - Matthew Stephens
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Department of Statistics, University of Chicago, Chicago, IL, USA
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50
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Bhatia P, Yang L, Luo JXJ, Xu M, Renthal W. Epigenomic profiling of mouse nucleus accumbens at single-cell resolution. Mol Cell Neurosci 2023; 126:103857. [PMID: 37137383 PMCID: PMC10525004 DOI: 10.1016/j.mcn.2023.103857] [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: 01/15/2023] [Revised: 04/21/2023] [Accepted: 04/26/2023] [Indexed: 05/05/2023] Open
Abstract
The nucleus accumbens (NAc) is a key brain region involved in reward processing and is linked to multiple neuropsychiatric conditions such as substance use disorder, depression, and chronic pain. Recent studies have begun to investigate NAc gene expression at a single-cell resolution, however, our understanding of the cellular heterogeneity of the NAc epigenomic landscape remains limited. In this study, we utilize single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq) to map cell-type-specific differences in chromatin accessibility in the NAc. Our findings not only reveal the transcription factors and putative gene regulatory elements that may contribute to these cell-type-specific epigenomic differences but also provide a valuable resource for future studies investigating epigenomic changes that occur in neuropsychiatric disorders.
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Affiliation(s)
- Parth Bhatia
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, USA
| | - Lite Yang
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, USA
| | - Jay X J Luo
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, USA
| | - Mengyi Xu
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, USA
| | - William Renthal
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, USA.
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