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Liu W, Zhong W, Giusti-Rodríguez P, Jiang Z, Wang GW, Sun H, Hu M, Li Y. SnapHiC-G: identifying long-range enhancer-promoter interactions from single-cell Hi-C data via a global background model. Brief Bioinform 2024; 25:bbae426. [PMID: 39222061 PMCID: PMC11367764 DOI: 10.1093/bib/bbae426] [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: 01/20/2024] [Revised: 07/05/2024] [Accepted: 08/13/2024] [Indexed: 09/04/2024] Open
Abstract
Harnessing the power of single-cell genomics technologies, single-cell Hi-C (scHi-C) and its derived technologies provide powerful tools to measure spatial proximity between regulatory elements and their target genes in individual cells. Using a global background model, we propose SnapHiC-G, a computational method, to identify long-range enhancer-promoter interactions from scHi-C data. We applied SnapHiC-G to scHi-C datasets generated from mouse embryonic stem cells and human brain cortical cells. SnapHiC-G achieved high sensitivity in identifying long-range enhancer-promoter interactions. Moreover, SnapHiC-G can identify putative target genes for noncoding genome-wide association study (GWAS) variants, and the genetic heritability of neuropsychiatric diseases is enriched for single-nucleotide polymorphisms (SNPs) within SnapHiC-G-identified interactions in a cell-type-specific manner. In sum, SnapHiC-G is a powerful tool for characterizing cell-type-specific enhancer-promoter interactions from complex tissues and can facilitate the discovery of chromatin interactions important for gene regulation in biologically relevant cell types.
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Affiliation(s)
- Weifang Liu
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC 27599, United States
| | - Wujuan Zhong
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., 126 East Lincoln Ave, Rahway, New Jersey 07065, United States
| | - Paola Giusti-Rodríguez
- Department of Psychiatry, University of Florida, 1149 Newel Dr., Gainesville, FL 32611, United States
| | - Zhiyun Jiang
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, United States
| | - Geoffery W Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC 27599, United States
| | - Huaigu Sun
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, United States
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH 44196, United States
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC 27599, United States
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, United States
- Department of Computer Science, University of North Carolina at Chapel Hill, 201 S. Columbia St, Chapel Hill, NC 27599, United States
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2
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Park JC, Han JW, Lee W, Kim J, Lee SE, Lee D, Choi H, Han J, Kang YJ, Diep YN, Cho H, Kang R, Yu WJ, Lee J, Choi M, Im SW, Kim JI, Mook-Jung I. Microglia Gravitate toward Amyloid Plaques Surrounded by Externalized Phosphatidylserine via TREM2. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2400064. [PMID: 38981007 DOI: 10.1002/advs.202400064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 05/08/2024] [Indexed: 07/11/2024]
Abstract
Microglia play a crucial role in synaptic elimination by engulfing dystrophic neurons via triggering receptors expressed on myeloid cells 2 (TREM2). They are also involved in the clearance of beta-amyloid (Aβ) plaques in Alzheimer's disease (AD); nonetheless, the driving force behind TREM2-mediated phagocytosis of beta-amyloid (Aβ) plaques remains unknown. Here, using advanced 2D/3D/4D co-culture systems with loss-of-function mutations in TREM2 (a frameshift mutation engineered in exon 2) brain organoids/microglia/assembloids, it is identified that the clearance of Aβ via TREM2 is accelerated by externalized phosphatidylserine (ePtdSer) generated from dystrophic neurons surrounding the Aβ plaques. Moreover, it is investigated whether microglia from both sporadic (CRISPR-Cas9-based APOE4 lines) and familial (APPNL-G-F/MAPT double knock-in mice) AD models show reduced levels of TREM2 and lack of phagocytic activity toward ePtdSer-positive Aβ plaques. Herein new insight is provided into TREM2-dependent microglial phagocytosis of Aβ plaques in the context of the presence of ePtdSer during AD progression.
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Affiliation(s)
- Jong-Chan Park
- Department of Biophysics, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Department of Metabiohealth, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Jong Won Han
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
| | - Woochan Lee
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
- Genome Medicine Institute, Medical Research Center, Seoul National University, Seoul, 03080, Republic of Korea
| | - Jieun Kim
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
| | - Sang-Eun Lee
- Department of Physiology and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
- BK21 FOUR Biomedical Science Program, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
- UK Dementia Research Institute, Institute of Neurology, University College London, Gower Street, London, WC1E 6BT, UK
- Neuroscience Research Institute, Seoul National University Medical Research Center, Seoul, 03080, Republic of Korea
| | - Dongjoon Lee
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
| | - Hayoung Choi
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
| | - Jihui Han
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
| | - You Jung Kang
- Department of Biophysics, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Yen N Diep
- Department of Biophysics, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Hansang Cho
- Department of Biophysics, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Rian Kang
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Department of Metabiohealth, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Won Jong Yu
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Department of Metabiohealth, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Jean Lee
- Department of Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
| | - Murim Choi
- Department of Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
| | - Sun-Wha Im
- Department of Biochemistry and Molecular Biology, Kangwon National University School of Medicine, Gangwon, Seoul, 24341, Republic of Korea
| | - Jong-Il Kim
- Genome Medicine Institute, Medical Research Center, Seoul National University, Seoul, 03080, Republic of Korea
- Department of Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Inhee Mook-Jung
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
- Convergence Dementia Research Center, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
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3
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Zhang Y, Boninsegna L, Yang M, Misteli T, Alber F, Ma J. Computational methods for analysing multiscale 3D genome organization. Nat Rev Genet 2024; 25:123-141. [PMID: 37673975 PMCID: PMC11127719 DOI: 10.1038/s41576-023-00638-1] [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] [Accepted: 07/12/2023] [Indexed: 09/08/2023]
Abstract
Recent progress in whole-genome mapping and imaging technologies has enabled the characterization of the spatial organization and folding of the genome in the nucleus. In parallel, advanced computational methods have been developed to leverage these mapping data to reveal multiscale three-dimensional (3D) genome features and to provide a more complete view of genome structure and its connections to genome functions such as transcription. Here, we discuss how recently developed computational tools, including machine-learning-based methods and integrative structure-modelling frameworks, have led to a systematic, multiscale delineation of the connections among different scales of 3D genome organization, genomic and epigenomic features, functional nuclear components and genome function. However, approaches that more comprehensively integrate a wide variety of genomic and imaging datasets are still needed to uncover the functional role of 3D genome structure in defining cellular phenotypes in health and disease.
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Affiliation(s)
- Yang Zhang
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Lorenzo Boninsegna
- Department of Microbiology, Immunology and Molecular Genetics and Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Muyu Yang
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Tom Misteli
- Center for Cancer Research, National Cancer Institute, Bethesda, MD, USA.
| | - Frank Alber
- Department of Microbiology, Immunology and Molecular Genetics and Institute for Quantitative and Computational Biosciences, University of California Los Angeles, Los Angeles, CA, USA.
| | - Jian Ma
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
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4
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Hua D, Gu M, Zhang X, Du Y, Xie H, Qi L, Du X, Bai Z, Zhu X, Tian D. DiffDomain enables identification of structurally reorganized topologically associating domains. Nat Commun 2024; 15:502. [PMID: 38218905 PMCID: PMC10787792 DOI: 10.1038/s41467-024-44782-6] [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: 12/16/2022] [Accepted: 01/02/2024] [Indexed: 01/15/2024] Open
Abstract
Topologically associating domains (TADs) are critical structural units in three-dimensional genome organization of mammalian genome. Dynamic reorganizations of TADs between health and disease states are associated with essential genome functions. However, computational methods for identifying reorganized TADs are still in the early stages of development. Here, we present DiffDomain, an algorithm leveraging high-dimensional random matrix theory to identify structurally reorganized TADs using high-throughput chromosome conformation capture (Hi-C) contact maps. Method comparison using multiple real Hi-C datasets reveals that DiffDomain outperforms alternative methods for false positive rates, true positive rates, and identifying a new subtype of reorganized TADs. Applying DiffDomain to Hi-C data from different cell types and disease states demonstrates its biological relevance. Identified reorganized TADs are associated with structural variations and epigenomic changes such as changes in CTCF binding sites. By applying to a single-cell Hi-C data from mouse neuronal development, DiffDomain can identify reorganized TADs between cell types with reasonable reproducibility using pseudo-bulk Hi-C data from as few as 100 cells per condition. Moreover, DiffDomain reveals differential cell-to-population variability and heterogeneous cell-to-cell variability in TADs. Therefore, DiffDomain is a statistically sound method for better comparative analysis of TADs using both Hi-C and single-cell Hi-C data.
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Affiliation(s)
- Dunming Hua
- Department of Biostatistics and Systems Biology, School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, 510275, China
- Department of Biostatistics and Systems Biology, School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China
| | - Ming Gu
- Department of Biostatistics and Systems Biology, School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, 510275, China
- Department of Biostatistics and Systems Biology, School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China
| | - Xiao Zhang
- Department of Biostatistics and Systems Biology, School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, 510275, China
- Department of Biostatistics and Systems Biology, School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China
| | - Yanyi Du
- Department of Biostatistics and Systems Biology, School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, 510275, China
- Department of Biostatistics and Systems Biology, School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China
| | - Hangcheng Xie
- Department of Biostatistics and Systems Biology, School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, 510275, China
- Department of Biostatistics and Systems Biology, School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China
| | - Li Qi
- Chongqing Municipal Center for Disease Control and Prevention, Chongqing, 400042, China
| | - Xiangjun Du
- Department of Biostatistics and Systems Biology, School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, 510275, China
- Department of Biostatistics and Systems Biology, School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China
| | - Zhidong Bai
- KLASMOE & School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, 130024, China
| | - Xiaopeng Zhu
- MyCellome LLC., Allison Park, PA, 15101, USA
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
| | - Dechao Tian
- Department of Biostatistics and Systems Biology, School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, Guangdong, 510275, China.
- Department of Biostatistics and Systems Biology, School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China.
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5
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Gunsalus LM, Keiser MJ, Pollard KS. ChromaFactor: deconvolution of single-molecule chromatin organization with non-negative matrix factorization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.22.568268. [PMID: 38045231 PMCID: PMC10690235 DOI: 10.1101/2023.11.22.568268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
The investigation of chromatin organization in single cells holds great promise for identifying causal relationships between genome structure and function. However, analysis of single-molecule data is hampered by extreme yet inherent heterogeneity, making it challenging to determine the contributions of individual chromatin fibers to bulk trends. To address this challenge, we propose ChromaFactor, a novel computational approach based on non-negative matrix factorization that deconvolves single-molecule chromatin organization datasets into their most salient primary components. ChromaFactor provides the ability to identify trends accounting for the maximum variance in the dataset while simultaneously describing the contribution of individual molecules to each component. Applying our approach to two single-molecule imaging datasets across different genomic scales, we find that these primary components demonstrate significant correlation with key functional phenotypes, including active transcription, enhancer-promoter distance, and genomic compartment. ChromaFactor offers a robust tool for understanding the complex interplay between chromatin structure and function on individual DNA molecules, pinpointing which subpopulations drive functional changes and fostering new insights into cellular heterogeneity and its implications for bulk genomic phenomena.
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Affiliation(s)
- Laura M. Gunsalus
- Gladstone Institutes, San Francisco, CA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA
| | - Michael J. Keiser
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA
- Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA
- Department of Epidemiology & Biostatistics, University of California, San Francisco, San Francisco, CA
| | - Katherine S. Pollard
- Gladstone Institutes, San Francisco, CA
- Institute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA
- Chan Zuckerberg Biohub, San Francisco, CA
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6
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龚 海, 麻 付, 张 晓. [Advances in methods and applications of single-cell Hi-C data analysis]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2023; 40:1033-1039. [PMID: 37879935 PMCID: PMC10600426 DOI: 10.7507/1001-5515.202303046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 08/29/2023] [Indexed: 10/27/2023]
Abstract
Chromatin three-dimensional genome structure plays a key role in cell function and gene regulation. Single-cell Hi-C techniques can capture genomic structure information at the cellular level, which provides an opportunity to study changes in genomic structure between different cell types. Recently, some excellent computational methods have been developed for single-cell Hi-C data analysis. In this paper, the available methods for single-cell Hi-C data analysis were first reviewed, including preprocessing of single-cell Hi-C data, multi-scale structure recognition based on single-cell Hi-C data, bulk-like Hi-C contact matrix generation based on single-cell Hi-C data sets, pseudo-time series analysis, and cell classification. Then the application of single-cell Hi-C data in cell differentiation and structural variation was described. Finally, the future development direction of single-cell Hi-C data analysis was also prospected.
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Affiliation(s)
- 海燕 龚
- 北京科技大学 新材料技术研究院 (北京 100083)Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, P. R. China
- 北京科技大学 计算机与通信工程学院(北京 100083)School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, P. R. China
| | - 付强 麻
- 北京科技大学 新材料技术研究院 (北京 100083)Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, P. R. China
| | - 晓彤 张
- 北京科技大学 新材料技术研究院 (北京 100083)Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, P. R. China
- 北京科技大学 计算机与通信工程学院(北京 100083)School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, P. R. China
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7
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Liu H, Ma W. scHiCDiff: detecting differential chromatin interactions in single-cell Hi-C data. Bioinformatics 2023; 39:btad625. [PMID: 37847655 PMCID: PMC10598576 DOI: 10.1093/bioinformatics/btad625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 08/15/2023] [Accepted: 10/16/2023] [Indexed: 10/19/2023] Open
Abstract
SUMMARY Here, we presented the scHiCDiff software tool that provides both nonparametric tests and parametirc models to detect differential chromatin interactions (DCIs) from single-cell Hi-C data. We thoroughly evaluated the scHiCDiff methods on both simulated and real data. Our results demonstrated that scHiCDiff, especially the zero-inflated negative binomial model option, can effectively detect reliable and consistent single-cell DCIs between two conditions, thereby facilitating the study of cell type-specific variations of chromatin structures at the single-cell level. AVAILABILITY AND IMPLEMENTATION scHiCDiff is implemented in R and freely available at GitHub (https://github.com/wmalab/scHiCDiff).
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Affiliation(s)
- Huiling Liu
- Department of Statistics, University of California Riverside, Riverside, CA 92521, United States
| | - Wenxiu Ma
- Department of Statistics, University of California Riverside, Riverside, CA 92521, United States
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8
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Lee L, Yu M, Li X, Zhu C, Zhang Y, Yu H, Chen Z, Mishra S, Ren B, Li Y, Hu M. SnapHiC-D: a computational pipeline to identify differential chromatin contacts from single-cell Hi-C data. Brief Bioinform 2023; 24:bbad315. [PMID: 37649383 PMCID: PMC10516352 DOI: 10.1093/bib/bbad315] [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: 05/25/2023] [Revised: 08/04/2023] [Accepted: 08/07/2023] [Indexed: 09/01/2023] Open
Abstract
Single-cell high-throughput chromatin conformation capture technologies (scHi-C) has been used to map chromatin spatial organization in complex tissues. However, computational tools to detect differential chromatin contacts (DCCs) from scHi-C datasets in development and through disease pathogenesis are still lacking. Here, we present SnapHiC-D, a computational pipeline to identify DCCs between two scHi-C datasets. Compared to methods designed for bulk Hi-C data, SnapHiC-D detects DCCs with high sensitivity and accuracy. We used SnapHiC-D to identify cell-type-specific chromatin contacts at 10 Kb resolution in mouse hippocampal and human prefrontal cortical tissues, demonstrating that DCCs detected in the hippocampal and cortical cell types are generally associated with cell-type-specific gene expression patterns and epigenomic features. SnapHiC-D is freely available at https://github.com/HuMingLab/SnapHiC-D.
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Affiliation(s)
- Lindsay Lee
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Miao Yu
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
| | - Xiaoqi Li
- Carolina Health Informatics Program, University of North Carolina, Chapel Hill, NC, USA
| | - Chenxu Zhu
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
- New York Genome Center, New York, NY, USA
- Department of Physiology and Biophysics, Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, USA
| | - Yanxiao Zhang
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
- Westlake University, Hangzhou, Zhejiang, China
| | - Hongyu Yu
- Department of Statistics, University of Wisconsin Madison, Madison, WI, USA
- Department of Biochemistry, University of Wisconsin Madison, Madison, WI, USA
| | - Ziyin Chen
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
| | - Shreya Mishra
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Bing Ren
- Ludwig Institute for Cancer Research, La Jolla, CA, USA
- Center for Epigenomics & Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
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9
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Wang Y, Guo Z, Cheng J. Single-cell Hi-C data enhancement with deep residual and generative adversarial networks. Bioinformatics 2023; 39:btad458. [PMID: 37498561 PMCID: PMC10403428 DOI: 10.1093/bioinformatics/btad458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 07/19/2023] [Accepted: 07/25/2023] [Indexed: 07/28/2023] Open
Abstract
MOTIVATION The spatial genome organization of a eukaryotic cell is important for its function. The development of single-cell technologies for probing the 3D genome conformation, especially single-cell chromosome conformation capture techniques, has enabled us to understand genome function better than before. However, due to extreme sparsity and high noise associated with single-cell Hi-C data, it is still difficult to study genome structure and function using the HiC-data of one single cell. RESULTS In this work, we developed a deep learning method ScHiCEDRN based on deep residual networks and generative adversarial networks for the imputation and enhancement of Hi-C data of a single cell. In terms of both image evaluation and Hi-C reproducibility metrics, ScHiCEDRN outperforms the four deep learning methods (DeepHiC, HiCPlus, HiCSR, and Loopenhance) on enhancing the raw single-cell Hi-C data of human and Drosophila. The experiments also show that it can generate single-cell Hi-C data more suitable for identifying topologically associating domain boundaries and reconstructing 3D chromosome structures than the existing methods. Moreover, ScHiCEDRN's performance generalizes well across different single cells and cell types, and it can be applied to improving population Hi-C data. AVAILABILITY AND IMPLEMENTATION The source code of ScHiCEDRN is available at the GitHub repository: https://github.com/BioinfoMachineLearning/ScHiCEDRN.
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Affiliation(s)
- Yanli Wang
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, United States
- NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, United States
| | - Zhiye Guo
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, United States
- NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, United States
| | - Jianlin Cheng
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, United States
- NextGen Precision Health Institute, University of Missouri, Columbia, MO 65211, United States
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10
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Li Z, Portillo-Ledesma S, Schlick T. Techniques for and challenges in reconstructing 3D genome structures from 2D chromosome conformation capture data. Curr Opin Cell Biol 2023; 83:102209. [PMID: 37506571 PMCID: PMC10529954 DOI: 10.1016/j.ceb.2023.102209] [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: 05/15/2023] [Revised: 06/07/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023]
Abstract
Chromosome conformation capture technologies that provide frequency information for contacts between genomic regions have been crucial for increasing our understanding of genome folding and regulation. However, such data do not provide direct evidence of the spatial 3D organization of chromatin. In this opinion article, we discuss the development and application of computational methods to reconstruct chromatin 3D structures from experimental 2D contact data, highlighting how such modeling provides biological insights and can suggest mechanisms anchored to experimental data. By applying different reconstruction methods to the same contact data, we illustrate some state-of-the-art of these techniques and discuss our gene resolution approach based on Brownian dynamics and Monte Carlo sampling.
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Affiliation(s)
- Zilong Li
- Department of Chemistry, New York University, 100 Washington Square East, Silver Building, New York, 10003, NY, USA; Simons Center for Computational Physical Chemistry, New York University, 24 Waverly Place, Silver Building, New York, NY, 10003, USA
| | - Stephanie Portillo-Ledesma
- Department of Chemistry, New York University, 100 Washington Square East, Silver Building, New York, 10003, NY, USA; Simons Center for Computational Physical Chemistry, New York University, 24 Waverly Place, Silver Building, New York, NY, 10003, USA
| | - Tamar Schlick
- Department of Chemistry, New York University, 100 Washington Square East, Silver Building, New York, 10003, NY, USA; Courant Institute of Mathematical Sciences, New York University, 251 Mercer St., New York, 10012, NY, USA; New York University-East China Normal University Center for Computational Chemistry, New York University Shanghai, Room 340, Geography Building, 3663 North Zhongshan Road, Shanghai, 200122, China; Simons Center for Computational Physical Chemistry, New York University, 24 Waverly Place, Silver Building, New York, NY, 10003, USA.
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11
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Liu Z, Chen Y, Xia Q, Liu M, Xu H, Chi Y, Deng Y, Xing D. Linking genome structures to functions by simultaneous single-cell Hi-C and RNA-seq. Science 2023; 380:1070-1076. [PMID: 37289875 DOI: 10.1126/science.adg3797] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 05/07/2023] [Indexed: 06/10/2023]
Abstract
Much progress has been made recently in single-cell chromosome conformation capture technologies. However, a method that allows simultaneous profiling of chromatin architecture and gene expression has not been reported. Here, we developed an assay named "Hi-C and RNA-seq employed simultaneously" (HiRES) and performed it on thousands of single cells from developing mouse embryos. Single-cell three-dimensional genome structures, despite being heavily determined by the cell cycle and developmental stages, gradually diverged in a cell type-specific manner as development progressed. By comparing the pseudotemporal dynamics of chromatin interactions with gene expression, we found a widespread chromatin rewiring that occurred before transcription activation. Our results demonstrate that the establishment of specific chromatin interactions is tightly related to transcriptional control and cell functions during lineage specification.
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Affiliation(s)
- Zhiyuan Liu
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Peking University, Beijing, China
| | - Yujie Chen
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Peking University, Beijing, China
| | - Qimin Xia
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Peking University, Beijing, China
| | - Menghan Liu
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Peking University, Beijing, China
| | - Heming Xu
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Peking University, Beijing, China
| | - Yi Chi
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Peking University, Beijing, China
| | - Yujing Deng
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Peking University, Beijing, China
| | - Dong Xing
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China
- Beijing Advanced Innovation Center for Genomics (ICG), Peking University, Beijing, China
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