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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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Yu Q, Liu X, Fang J, Wu H, Guo C, Zhang W, Liu N, Jiang C, Sha Q, Yuan X, Wang Z, Qu K. Dynamics and regulation of mitotic chromatin accessibility bookmarking at single-cell resolution. SCIENCE ADVANCES 2023; 9:eadd2175. [PMID: 36696508 PMCID: PMC9876548 DOI: 10.1126/sciadv.add2175] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 12/29/2022] [Indexed: 06/17/2023]
Abstract
Although mitotic chromosomes are highly compacted and transcriptionally inert, some active chromatin features are retained during mitosis to ensure the proper postmitotic reestablishment of maternal transcriptional programs, a phenomenon termed "mitotic bookmarking." However, the dynamics and regulation of mitotic bookmarking have not been systemically surveyed. Using single-cell transposase-accessible chromatin sequencing (scATAC-seq), we examined 6538 mitotic L02 human liver cells of variable stages and found that chromatin accessibility remained changing throughout cell division, with a constant decrease until metaphase and a gradual increase as chromosomes segregated. In particular, a subset of chromatin regions were identified to remain open throughout mitosis, and genes associated with these bookmarked regions are primarily linked to rapid reactivation upon mitotic exit. We also demonstrated that nuclear transcription factor Y subunit α (NF-YA) preferentially occupied bookmarked regions and contributed to transcriptional reactivation after mitosis. Our study uncovers the dynamic and regulatory blueprint of mitotic bookmarking.
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Affiliation(s)
- Qiaoni Yu
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230021, China
| | - Xu Liu
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
- Keck Center for Organoids Plasticity, Morehouse School of Medicine, Atlanta, GA, USA
| | - Jingwen Fang
- Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
- HanGene Biotech, Xiaoshan Innovation Polis, Hangzhou, Zhejiang 311200, China
| | - Huihui Wu
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
- Keck Center for Organoids Plasticity, Morehouse School of Medicine, Atlanta, GA, USA
| | - Chuang Guo
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Wen Zhang
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Nianping Liu
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Chen Jiang
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Qing Sha
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
| | - Xiao Yuan
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
- Keck Center for Organoids Plasticity, Morehouse School of Medicine, Atlanta, GA, USA
| | - Zhikai Wang
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
- Keck Center for Organoids Plasticity, Morehouse School of Medicine, Atlanta, GA, USA
- Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
| | - Kun Qu
- MOE Key Laboratory for Cellular Dynamics, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
- Department of Oncology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230021, China
- Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
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Intrinsic bias estimation for improved analysis of bulk and single-cell chromatin accessibility profiles using SELMA. Nat Commun 2022; 13:5533. [PMID: 36130957 PMCID: PMC9492688 DOI: 10.1038/s41467-022-33194-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 09/08/2022] [Indexed: 11/25/2022] Open
Abstract
Genome-wide profiling of chromatin accessibility by DNase-seq or ATAC-seq has been widely used to identify regulatory DNA elements and transcription factor binding sites. However, enzymatic DNA cleavage exhibits intrinsic sequence biases that confound chromatin accessibility profiling data analysis. Existing computational tools are limited in their ability to account for such intrinsic biases and not designed for analyzing single-cell data. Here, we present Simplex Encoded Linear Model for Accessible Chromatin (SELMA), a computational method for systematic estimation of intrinsic cleavage biases from genomic chromatin accessibility profiling data. We demonstrate that SELMA yields accurate and robust bias estimation from both bulk and single-cell DNase-seq and ATAC-seq data. SELMA can utilize internal mitochondrial DNA data to improve bias estimation. We show that transcription factor binding inference from DNase footprints can be improved by incorporating estimated biases using SELMA. Furthermore, we show strong effects of intrinsic biases in single-cell ATAC-seq data, and develop the first single-cell ATAC-seq intrinsic bias correction model to improve cell clustering. SELMA can enhance the performance of existing bioinformatics tools and improve the analysis of both bulk and single-cell chromatin accessibility sequencing data. Genome-wide profiling of chromatin accessibility by DNase-seq or ATAC-seq has been widely used to identify regulatory DNA elements and transcription factor binding sites. Here the authors develop a computational model, SELMA, to estimate and correct enzymatic cleavage biases in chromatin accessibility profiling data.
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Shi P, Nie Y, Yang J, Zhang W, Tang Z, Xu J. Fundamental and practical approaches for single-cell ATAC-seq analysis. ABIOTECH 2022; 3:212-223. [PMID: 36313930 PMCID: PMC9590475 DOI: 10.1007/s42994-022-00082-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 09/07/2022] [Indexed: 11/28/2022]
Abstract
Assays for transposase-accessible chromatin through high-throughput sequencing (ATAC-seq) are effective tools in the study of genome-wide chromatin accessibility landscapes. With the rapid development of single-cell technology, open chromatin regions that play essential roles in epigenetic regulation have been measured at the single-cell level using single-cell ATAC-seq approaches. The application of scATAC-seq has become as popular as that of scRNA-seq. However, owing to the nature of scATAC-seq data, which are sparse and noisy, processing the data requires different methodologies and empirical experience. This review presents a practical guide for processing scATAC-seq data, from quality evaluation to downstream analysis, for various applications. In addition to the epigenomic profiling from scATAC-seq, we also discuss recent studies in which the function of non-coding variants has been investigated based on cell type-specific cis-regulatory elements and how to use the by-product genetic information obtained from scATAC-seq to infer single-cell copy number variants and trace cell lineage. We anticipate that this review will assist researchers in designing and implementing scATAC-seq assays to facilitate research in diverse fields.
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Affiliation(s)
- Peiyu Shi
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275 China
| | - Yage Nie
- Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou, 510275 China
| | - Jiawen Yang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275 China
| | - Weixing Zhang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275 China
| | - Zhongjie Tang
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275 China
| | - Jin Xu
- State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-Sen University, Guangzhou, 510275 China
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5
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Dong K, Zhang S. Network diffusion for scalable embedding of massive single-cell ATAC-seq data. Sci Bull (Beijing) 2021; 66:2271-2276. [PMID: 36654454 DOI: 10.1016/j.scib.2021.05.014] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 04/08/2021] [Accepted: 05/12/2021] [Indexed: 02/03/2023]
Affiliation(s)
- Kangning Dong
- NCMIS, CEMS, RCSDS, 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
| | - Shihua Zhang
- NCMIS, CEMS, RCSDS, 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; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China; Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.
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6
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Liu Y, Zhang J, Wang S, Zeng X, Zhang W. Are dropout imputation methods for scRNA-seq effective for scATAC-seq data? Brief Bioinform 2021; 23:6412397. [PMID: 34718405 DOI: 10.1093/bib/bbab442] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2021] [Revised: 09/08/2021] [Accepted: 09/27/2021] [Indexed: 11/12/2022] Open
Abstract
The tremendous progress of single-cell sequencing technology has given researchers the opportunity to study cell development and differentiation processes at single-cell resolution. Assay of Transposase-Accessible Chromatin by deep sequencing (ATAC-seq) was proposed for genome-wide analysis of chromatin accessibility. Due to technical limitations or other reasons, dropout events are almost a common occurrence for extremely sparse single-cell ATAC-seq data, leading to confusion in downstream analysis (such as clustering). Although considerable progress has been made in the estimation of scRNA-seq data, there is currently no specific method for the inference of dropout events in single-cell ATAC-seq data. In this paper, we select several state-of-the-art scRNA-seq imputation methods (including MAGIC, SAVER, scImpute, deepImpute, PRIME, bayNorm and knn-smoothing) in recent years to infer dropout peaks in scATAC-seq data, and perform a systematic evaluation of these methods through several downstream analyses. Specifically, we benchmarked these methods in terms of correlation with meta-cell, clustering, subpopulations distance analysis, imputation performance for corruption datasets, identification of TF motifs and computation time. The experimental results indicated that most of the imputed peaks increased the correlation with the reference meta-cell, while the performance of different methods on different datasets varied greatly in different downstream analyses, thus should be used with caution. In general, MAGIC performed better than the other methods most consistently across all assessments. Our source code is freely available at https://github.com/yueyueliu/scATAC-master.
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Affiliation(s)
- Yue Liu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Junfeng Zhang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Shulin Wang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Xiangxiang Zeng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China
| | - Wei Zhang
- College of Computer Engineering & Applied Mathematics, Changsha University, Changsha, Hunan 410003, China
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Schwartz GW, Zhou Y, Petrovic J, Pear WS, Faryabi RB. TooManyPeaks identifies drug-resistant-specific regulatory elements from single-cell leukemic epigenomes. Cell Rep 2021; 36:109575. [PMID: 34433064 PMCID: PMC8409102 DOI: 10.1016/j.celrep.2021.109575] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 03/30/2021] [Accepted: 07/29/2021] [Indexed: 12/13/2022] Open
Abstract
Emerging single-cell epigenomic assays are used to investigate the heterogeneity of chromatin activity and its function. However, identifying cells with distinct regulatory elements and clearly visualizing their relationships remains challenging. To this end, we introduce TooManyPeaks to address the need for the simultaneous study of chromatin state heterogeneity in both rare and abundant subpopulations. Our analyses of existing data from three widely used single-cell assays for transposase-accessible chromatin using sequencing (scATAC-seq) show the superior performance of TooManyPeaks in delineating and visualizing pure clusters of rare and abundant subpopulations. Furthermore, the application of TooManyPeaks to new scATAC-seq data from drug-naive and drug-resistant leukemic T cells clearly visualizes relationships among these cells and stratifies a rare "resistant-like" drug-naive sub-clone with distinct cis-regulatory elements.
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Affiliation(s)
- Gregory W Schwartz
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn Epigenetics Institute, University of Pennsylvania, Philadelphia, PA, USA; Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yeqiao Zhou
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn Epigenetics Institute, University of Pennsylvania, Philadelphia, PA, USA; Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jelena Petrovic
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn Epigenetics Institute, University of Pennsylvania, Philadelphia, PA, USA; Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Warren S Pear
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA; Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Robert B Faryabi
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA; Penn Epigenetics Institute, University of Pennsylvania, Philadelphia, PA, USA; Abramson Family Cancer Research Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Li Y, Li K, Zhu L, Li B, Zong D, Cai P, Jiang C, Du P, Lin J, Qu K. Development of double-positive thymocytes at single-cell resolution. Genome Med 2021; 13:49. [PMID: 33771202 PMCID: PMC8004397 DOI: 10.1186/s13073-021-00861-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 02/25/2021] [Indexed: 12/03/2022] Open
Abstract
BACKGROUND T cells generated from thymopoiesis are essential for the immune system, and recent single-cell studies have contributed to our understanding of the development of thymocytes at the genetic and epigenetic levels. However, the development of double-positive (DP) T cells, which comprise the majority of thymocytes, has not been well investigated. METHODS We applied single-cell sequencing to mouse thymocytes and analyzed the transcriptome data using Seurat. By applying unsupervised clustering, we defined thymocyte subtypes and validated DP cell subtypes by flow cytometry. We classified the cell cycle phases of each cell according to expression of cell cycle phase-specific genes. For immune synapse detection, we used immunofluorescent staining and ImageStream-based flow cytometry. We studied and integrated human thymocyte data to verify the conservation of our findings and also performed cross-species comparisons to examine species-specific gene regulation. RESULTS We classified blast, rearrangement, and selection subtypes of DP thymocytes and used the surface markers CD2 and Ly6d to identify these subtypes by flow cytometry. Based on this new classification, we found that the proliferation of blast DP cells is quite different from that of double-positive cells and other cell types, which tend to exit the cell cycle after a single round. At the DP cell selection stage, we observed that CD8-associated immune synapses formed between thymocytes, indicating that CD8sp selection occurred among thymocytes themselves. Moreover, cross-species comparison revealed species-specific transcription factors (TFs) that contribute to the transcriptional differences of thymocytes from humans and mice. CONCLUSIONS Our study classified DP thymocyte subtypes of different developmental stages and provided new insight into the development of DP thymocytes at single-cell resolution, furthering our knowledge of the fundamental immunological process of thymopoiesis.
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Affiliation(s)
- Young Li
- Department of oncology, The First Affiliated Hospital of USTC, Division of Molecular Medicine, Hefei National Laboratory for Physical Sciences at Microscale, School of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230021, Anhui, China
| | - Kun Li
- Department of oncology, The First Affiliated Hospital of USTC, Division of Molecular Medicine, Hefei National Laboratory for Physical Sciences at Microscale, School of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230021, Anhui, China
| | - Lianbang Zhu
- Department of oncology, The First Affiliated Hospital of USTC, Division of Molecular Medicine, Hefei National Laboratory for Physical Sciences at Microscale, School of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230021, Anhui, China
| | - Bin Li
- Department of oncology, The First Affiliated Hospital of USTC, Division of Molecular Medicine, Hefei National Laboratory for Physical Sciences at Microscale, School of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230021, Anhui, China
| | - Dandan Zong
- Department of oncology, The First Affiliated Hospital of USTC, Division of Molecular Medicine, Hefei National Laboratory for Physical Sciences at Microscale, School of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230021, Anhui, China
| | - Pengfei Cai
- Department of oncology, The First Affiliated Hospital of USTC, Division of Molecular Medicine, Hefei National Laboratory for Physical Sciences at Microscale, School of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230021, Anhui, China
| | - Chen Jiang
- Department of oncology, The First Affiliated Hospital of USTC, Division of Molecular Medicine, Hefei National Laboratory for Physical Sciences at Microscale, School of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230021, Anhui, China
| | - Pengcheng Du
- Department of oncology, The First Affiliated Hospital of USTC, Division of Molecular Medicine, Hefei National Laboratory for Physical Sciences at Microscale, School of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230021, Anhui, China
| | - Jun Lin
- Department of oncology, The First Affiliated Hospital of USTC, Division of Molecular Medicine, Hefei National Laboratory for Physical Sciences at Microscale, School of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230021, Anhui, China
| | - Kun Qu
- Department of oncology, The First Affiliated Hospital of USTC, Division of Molecular Medicine, Hefei National Laboratory for Physical Sciences at Microscale, School of Basic Medicine, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230021, Anhui, China.
- The CAS Key Laboratory of Innate Immunity and Chronic Disease, CAS Center for Excellence in Molecular Cell Sciences, University of Science and Technology of China, Hefei, 230021, Anhui, China.
- School of Data Science, University of Science and Technology of China, Hefei, 230027, Anhui, China.
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9
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Navidi Z, Zhang L, Wang B. simATAC: a single-cell ATAC-seq simulation framework. Genome Biol 2021; 22:74. [PMID: 33663563 PMCID: PMC7934446 DOI: 10.1186/s13059-021-02270-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 01/13/2021] [Indexed: 12/21/2022] Open
Abstract
Single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) identifies regulated chromatin accessibility modules at the single-cell resolution. Robust evaluation is critical to the development of scATAC-seq pipelines, which calls for reproducible datasets for benchmarking. We hereby present the simATAC framework, an R package that generates scATAC-seq count matrices that highly resemble real scATAC-seq datasets in library size, sparsity, and chromatin accessibility signals. simATAC deploys statistical models derived from analyzing 90 real scATAC-seq cell groups. simATAC provides a robust and systematic approach to generate in silico scATAC-seq samples with known cell labels for assessing analytical pipelines.
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Affiliation(s)
- Zeinab Navidi
- Peter Munk Cardiac Centre, University Health Network, Toronto, Canada
| | - Lin Zhang
- Department of Statistical Sciences, University of Toronto, Toronto, Canada
| | - Bo Wang
- Peter Munk Cardiac Centre, University Health Network, Toronto, Canada. .,Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada. .,Department of Computer Science, University of Toronto, Toronto, Canada. .,Vector Institute, Toronto, Canada.
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Ma S, Zhang Y. Profiling chromatin regulatory landscape: insights into the development of ChIP-seq and ATAC-seq. MOLECULAR BIOMEDICINE 2020; 1:9. [PMID: 34765994 PMCID: PMC7546943 DOI: 10.1186/s43556-020-00009-w] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 09/04/2020] [Indexed: 12/16/2022] Open
Abstract
Chromatin regulatory landscape plays a critical role in many disease processes and embryo development. Epigenome sequencing technologies such as chromatin immunoprecipitation sequencing (ChIP-seq) and assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) have enabled us to dissect the pan-genomic regulatory landscape of cells and tissues in both time and space dimensions by detecting specific chromatin state and its corresponding transcription factors. Pioneered by the advancement of chromatin immunoprecipitation-chip (ChIP-chip) technology, abundant epigenome profiling technologies have become available such as ChIP-seq, DNase I hypersensitive site sequencing (DNase-seq), ATAC-seq and so on. The advent of single-cell sequencing has revolutionized the next-generation sequencing, applications in single-cell epigenetics are enriched rapidly. Epigenome sequencing technologies have evolved from low-throughput to high-throughput and from bulk sample to the single-cell scope, which unprecedentedly benefits scientists to interpret life from different angles. In this review, after briefly introducing the background knowledge of epigenome biology, we discuss the development of epigenome sequencing technologies, especially ChIP-seq & ATAC-seq and their current applications in scientific research. Finally, we provide insights into future applications and challenges.
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Affiliation(s)
- Shaoqian Ma
- School of Life Sciences, Xiamen University, Xiamen, 361102 Fujian China
| | - Yongyou Zhang
- School of Life Sciences, Xiamen University, Xiamen, 361102 Fujian China
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