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Martitz A, Schulz EG. Spatial orchestration of the genome: topological reorganisation during X-chromosome inactivation. Curr Opin Genet Dev 2024; 86:102198. [PMID: 38663040 DOI: 10.1016/j.gde.2024.102198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/21/2024] [Accepted: 04/05/2024] [Indexed: 06/11/2024]
Abstract
Genomes are organised through hierarchical structures, ranging from local kilobase-scale cis-regulatory contacts to large chromosome territories. Most notably, (sub)-compartments partition chromosomes according to transcriptional activity, while topologically associating domains (TADs) define cis-regulatory landscapes. The inactive X chromosome in mammals has provided unique insights into the regulation and function of the three-dimensional (3D) genome. Concurrent with silencing of the majority of genes and major alterations of its chromatin state, the X chromosome undergoes profound spatial rearrangements at multiple scales. These include the emergence of megadomains, alterations of the compartment structure and loss of the majority of TADs. Moreover, the Xist locus, which orchestrates X-chromosome inactivation, has provided key insights into regulation and function of regulatory domains. This review provides an overview of recent insights into the control of these structural rearrangements and contextualises them within a broader understanding of 3D genome organisation.
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Affiliation(s)
- Alexandra Martitz
- Systems Epigenetics, Otto Warburg Laboratories, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany; Department of Biology, Chemistry, Pharmacy, Freie Universität Berlin, 14195 Berlin, Germany
| | - Edda G Schulz
- Systems Epigenetics, Otto Warburg Laboratories, Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany.
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2
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Zhou T, Zhang R, Jia D, Doty RT, Munday AD, Gao D, Xin L, Abkowitz JL, Duan Z, Ma J. GAGE-seq concurrently profiles multiscale 3D genome organization and gene expression in single cells. Nat Genet 2024:10.1038/s41588-024-01745-3. [PMID: 38744973 DOI: 10.1038/s41588-024-01745-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 04/05/2024] [Indexed: 05/16/2024]
Abstract
The organization of mammalian genomes features a complex, multiscale three-dimensional (3D) architecture, whose functional significance remains elusive because of limited single-cell technologies that can concurrently profile genome organization and transcriptional activities. Here, we introduce genome architecture and gene expression by sequencing (GAGE-seq), a scalable, robust single-cell co-assay measuring 3D genome structure and transcriptome simultaneously within the same cell. Applied to mouse brain cortex and human bone marrow CD34+ cells, GAGE-seq characterized the intricate relationships between 3D genome and gene expression, showing that multiscale 3D genome features inform cell-type-specific gene expression and link regulatory elements to target genes. Integration with spatial transcriptomic data revealed in situ 3D genome variations in mouse cortex. Observations in human hematopoiesis unveiled discordant changes between 3D genome organization and gene expression, underscoring a complex, temporal interplay at the single-cell level. GAGE-seq provides a powerful, cost-effective approach for exploring genome structure and gene expression relationships at the single-cell level across diverse biological contexts.
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Affiliation(s)
- Tianming Zhou
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Ruochi Zhang
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
- Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Deyong Jia
- Department of Urology, University of Washington, Seattle, WA, USA
| | - Raymond T Doty
- Division of Hematology and Oncology, Department of Medicine/Fred Hutch Cancer Center, University of Washington, Seattle, WA, USA
| | - Adam D Munday
- Division of Hematology and Oncology, Department of Medicine/Fred Hutch Cancer Center, University of Washington, Seattle, WA, USA
| | - Daniel Gao
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
- Department of Chemistry, Pomona College, Claremont, CA, USA
| | - Li Xin
- Department of Urology, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
| | - Janis L Abkowitz
- Division of Hematology and Oncology, Department of Medicine/Fred Hutch Cancer Center, University of Washington, Seattle, WA, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA
| | - Zhijun Duan
- Division of Hematology and Oncology, Department of Medicine/Fred Hutch Cancer Center, University of Washington, Seattle, WA, USA.
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA.
| | - Jian Ma
- Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
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Irastorza-Azcarate I, Kukalev A, Kempfer R, Thieme CJ, Mastrobuoni G, Markowski J, Loof G, Sparks TM, Brookes E, Natarajan KN, Sauer S, Fisher AG, Nicodemi M, Ren B, Schwarz RF, Kempa S, Pombo A. Extensive folding variability between homologous chromosomes in mammalian cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.08.591087. [PMID: 38766012 PMCID: PMC11100664 DOI: 10.1101/2024.05.08.591087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Genetic variation and 3D chromatin structure have major roles in gene regulation. Due to challenges in mapping chromatin conformation with haplotype-specific resolution, the effects of genetic sequence variation on 3D genome structure and gene expression imbalance remain understudied. Here, we applied Genome Architecture Mapping (GAM) to a hybrid mouse embryonic stem cell (mESC) line with high density of single nucleotide polymorphisms (SNPs). GAM resolved haplotype-specific 3D genome structures with high sensitivity, revealing extensive allelic differences in chromatin compartments, topologically associating domains (TADs), long-range enhancer-promoter contacts, and CTCF loops. Architectural differences often coincide with allele-specific differences in gene expression, mediated by Polycomb repression. We show that histone genes are expressed with allelic imbalance in mESCs, are involved in haplotype-specific chromatin contact marked by H3K27me3, and are targets of Polycomb repression through conditional knockouts of Ezh2 or Ring1b. Our work reveals highly distinct 3D folding structures between homologous chromosomes, and highlights their intricate connections with allelic gene expression.
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Li Z, Schlick T. Hi-BDiSCO: folding 3D mesoscale genome structures from Hi-C data using brownian dynamics. Nucleic Acids Res 2024; 52:583-599. [PMID: 38015443 PMCID: PMC10810283 DOI: 10.1093/nar/gkad1121] [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: 07/06/2023] [Revised: 10/12/2023] [Accepted: 11/22/2023] [Indexed: 11/29/2023] Open
Abstract
The structure and dynamics of the eukaryotic genome are intimately linked to gene regulation and transcriptional activity. Many chromosome conformation capture experiments like Hi-C have been developed to detect genome-wide contact frequencies and quantify loop/compartment structures for different cellular contexts and time-dependent processes. However, a full understanding of these events requires explicit descriptions of representative chromatin and chromosome configurations. With the exponentially growing amount of data from Hi-C experiments, many methods for deriving 3D structures from contact frequency data have been developed. Yet, most reconstruction methods use polymer models with low resolution to predict overall genome structure. Here we present a Brownian Dynamics (BD) approach termed Hi-BDiSCO for producing 3D genome structures from Hi-C and Micro-C data using our mesoscale-resolution chromatin model based on the Discrete Surface Charge Optimization (DiSCO) model. Our approach integrates reconstruction with chromatin simulations at nucleosome resolution with appropriate biophysical parameters. Following a description of our protocol, we present applications to the NXN, HOXC, HOXA and Fbn2 mouse genes ranging in size from 50 to 100 kb. Such nucleosome-resolution genome structures pave the way for pursuing many biomedical applications related to the epigenomic regulation of chromatin and control of human disease.
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Affiliation(s)
- Zilong Li
- Department of Chemistry, 100 Washington Square East, Silver Building, New York University, New York, NY 10003, USA
- Simons Center for Computational Physical Chemistry, 24 Waverly Place, Silver Building, New York University, New York, NY 10003, USA
| | - Tamar Schlick
- Department of Chemistry, 100 Washington Square East, Silver Building, New York University, New York, NY 10003, USA
- Courant Institute of Mathematical Sciences, New York University, 251 Mercer St., New York, NY 10012, USA
- New York University-East China Normal University Center for Computational Chemistry, New York University Shanghai, Shanghai 200122, China
- Simons Center for Computational Physical Chemistry, 24 Waverly Place, Silver Building, New York University, New York, NY 10003, USA
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Moyano Rodriguez Y, Borensztein M. X-chromosome inactivation: a historic topic that's still hot. Development 2023; 150:dev202072. [PMID: 37997921 DOI: 10.1242/dev.202072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2023]
Abstract
The last edition of the X-chromosome inactivation (XCI) meeting was held as an EMBO workshop in Berlin on 19-22 June 2023. The conference took place at the Harnack-haus in the Dahlem district, birthplace of the first modern research campus, where notable scientists such as Lise Meitner, Hans Krebs and, briefly, Albert Einstein conducted their research. This special edition, also accessible online, was organized by Rafael Galupa (Centre for Integrative Biology of Toulouse, France), Joost Gribnau (Erasmus MC Rotterdam, The Netherlands), Claire Rougeulle (Université Paris Cité/CNRS, Epigenetics and Cell Fate Center, Paris, France), Edda Schulz (Max Planck Institute for Molecular Genetics, Berlin, Germany) and James Turner (The Francis Crick Institute, London, UK). Originally scheduled for 2021, to commemorate the 60th anniversary of Mary Lyon's hypothesis on X-chromosome inactivation in mammals and the 30th anniversary of XIST/Xist discovery, the meeting had to be postponed because of the COVID-19 pandemic. Seven years after the latest XCI meeting in London, the enthusiasm and expectations of the community were at their highest, bringing together over 160 scientists from around the world to share and discuss their research. Eighty posters and more than 40 talks were presented at this event, in a collegial and collaborative atmosphere. A historical session and several breakout discussions were also organized, as well as the now traditional boat trip, all thanks to great organization. Here, we debrief readers on this fantastic conference.
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Affiliation(s)
| | - Maud Borensztein
- IGMM, University of Montpellier, CNRS, 34090 Montpellier, France
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Wang F, Alinejad‐Rokny H, Lin J, Gao T, Chen X, Zheng Z, Meng L, Li X, Wong K. A Lightweight Framework For Chromatin Loop Detection at the Single-Cell Level. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2303502. [PMID: 37816141 PMCID: PMC10667817 DOI: 10.1002/advs.202303502] [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: 05/30/2023] [Revised: 08/10/2023] [Indexed: 10/12/2023]
Abstract
Single-cell Hi-C (scHi-C) has made it possible to analyze chromatin organization at the single-cell level. However, scHi-C experiments generate inherently sparse data, which poses a challenge for loop calling methods. The existing approach performs significance tests across the imputed dense contact maps, leading to substantial computational overhead and loss of information at the single-cell level. To overcome this limitation, a lightweight framework called scGSLoop is proposed, which sets a new paradigm for scHi-C loop calling by adapting the training and inferencing strategies of graph-based deep learning to leverage the sequence features and 1D positional information of genomic loci. With this framework, sparsity is no longer a challenge, but rather an advantage that the model leverages to achieve unprecedented computational efficiency. Compared to existing methods, scGSLoop makes more accurate predictions and is able to identify more loops that have the potential to play regulatory roles in genome functioning. Moreover, scGSLoop preserves single-cell information by identifying a distinct group of loops for each individual cell, which not only enables an understanding of the variability of chromatin looping states between cells, but also allows scGSLoop to be extended for the investigation of multi-connected hubs and their underlying mechanisms.
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Affiliation(s)
- Fuzhou Wang
- Department of Computer ScienceCity University of Hong KongKowloon TongHong Kong SAR
| | - Hamid Alinejad‐Rokny
- BioMedical Machine Learning Lab, Graduate School of Biomedical EngineeringUniversity of New South WalesSydney2052Australia
| | - Jiecong Lin
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General HospitalDepartment of PathologyHarvard Medical SchoolBostonMA02129USA
- Department of Computer ScienceThe University of Hong KongPok Fu LamHong Kong SAR
| | - Tingxiao Gao
- Department of Medical Biophysics, Faculty of MedicineUniversity of TorontoTorontoOntarioM5G1L7Canada
| | - Xingjian Chen
- Department of Computer ScienceCity University of Hong KongKowloon TongHong Kong SAR
| | - Zetian Zheng
- Department of Computer ScienceCity University of Hong KongKowloon TongHong Kong SAR
| | - Lingkuan Meng
- Department of Computer ScienceCity University of Hong KongKowloon TongHong Kong SAR
| | - Xiangtao Li
- School of Artificial IntelligenceJilin UniversityChangchun130012China
| | - Ka‐Chun Wong
- Department of Computer ScienceCity University of Hong KongKowloon TongHong Kong SAR
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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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Zhou Y, Li T, Choppavarapu L, Jin VX. Integration of scHi-C and scRNA-seq data defines distinct 3D-regulated and biological-context dependent cell subpopulations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.29.560193. [PMID: 37873257 PMCID: PMC10592853 DOI: 10.1101/2023.09.29.560193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
An integration of 3D chromatin structure and gene expression at single-cell resolution has yet been demonstrated. Here, we develop a computational method, a multiomic data integration (MUDI) algorithm, which integrates scHi-C and scRNA-seq data to precisely define the 3D-regulated and biological-context dependent cell subpopulations or topologically integrated subpopulations (TISPs). We demonstrate its algorithmic utility on the publicly available and newly generated scHi-C and scRNA-seq data. We then test and apply MUDI in a breast cancer cell model system to demonstrate its biological-context dependent utility. We found the newly defined topologically conserved associating domain (CAD) is the characteristic single-cell 3D chromatin structure and better characterizes chromatin domains in single-cell resolution. We further identify 20 TISPs uniquely characterizing 3D-regulated breast cancer cellular states. We reveal two of TISPs are remarkably resemble to high cycling breast cancer persister cells and chromatin modifying enzymes might be functional regulators to drive the alteration of the 3D chromatin structures. Our comprehensive integration of scHi-C and scRNA-seq data in cancer cells at single-cell resolution provides mechanistic insights into 3D-regulated heterogeneity of developing drug-tolerant cancer cells.
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Zhou T, Zhang R, Jia D, Doty RT, Munday AD, Gao D, Xin L, Abkowitz JL, Duan Z, Ma J. Concurrent profiling of multiscale 3D genome organization and gene expression in single mammalian cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.20.549578. [PMID: 37546900 PMCID: PMC10401946 DOI: 10.1101/2023.07.20.549578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
The organization of mammalian genomes within the nucleus features a complex, multiscale three-dimensional (3D) architecture. The functional significance of these 3D genome features, however, remains largely elusive due to limited single-cell technologies that can concurrently profile genome organization and transcriptional activities. Here, we report GAGE-seq, a highly scalable, robust single-cell co-assay that simultaneously measures 3D genome structure and transcriptome within the same cell. Employing GAGE-seq on mouse brain cortex and human bone marrow CD34+ cells, we comprehensively characterized the intricate relationships between 3D genome and gene expression. We found that these multiscale 3D genome features collectively inform cell type-specific gene expressions, hence contributing to defining cell identity at the single-cell level. Integration of GAGE-seq data with spatial transcriptomic data revealed in situ variations of the 3D genome in mouse cortex. Moreover, our observations of lineage commitment in normal human hematopoiesis unveiled notable discordant changes between 3D genome organization and gene expression, underscoring a complex, temporal interplay at the single-cell level that is more nuanced than previously appreciated. Together, GAGE-seq provides a powerful, cost-effective approach for interrogating genome structure and gene expression relationships at the single-cell level across diverse biological contexts.
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Affiliation(s)
- Tianming Zhou
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Ruochi Zhang
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
- Present address: Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Deyong Jia
- Department of Urology, University of Washington, Seattle, WA 98195, USA
| | - Raymond T. Doty
- Division of Hematology, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Adam D. Munday
- Division of Hematology, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Daniel Gao
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA 98109, USA
- Present address: Department of Chemistry, Pomona College, Claremont, CA 91711, USA
| | - Li Xin
- Department of Urology, University of Washington, Seattle, WA 98195, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA 98109, USA
| | - Janis L. Abkowitz
- Division of Hematology, Department of Medicine, University of Washington, Seattle, WA 98195, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA 98109, USA
| | - Zhijun Duan
- Division of Hematology, Department of Medicine, University of Washington, Seattle, WA 98195, USA
- Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA 98109, USA
| | - Jian Ma
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
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Demetci P, Santorella R, Chakravarthy M, Sandstede B, Singh R. SCOTv2: Single-Cell Multiomic Alignment with Disproportionate Cell-Type Representation. J Comput Biol 2022; 29:1213-1228. [PMID: 36251763 PMCID: PMC9805876 DOI: 10.1089/cmb.2022.0270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Multiomic single-cell data allow us to perform integrated analysis to understand genomic regulation of biological processes. However, most single-cell sequencing assays are performed on separately sampled cell populations, as applying them to the same single-cell is challenging. Existing unsupervised single-cell alignment algorithms have been primarily benchmarked on coassay experiments. Our investigation revealed that these methods do not perform well for noncoassay single-cell experiments when there is disproportionate cell-type representation across measurement domains. Therefore, we extend our previous work-Single Cell alignment using Optimal Transport (SCOT)-by using unbalanced Gromov-Wasserstein optimal transport to handle disproportionate cell-type representation and differing sample sizes across single-cell measurements. Our method, SCOTv2, gives state-of-the-art alignment performance across five non-coassay data sets (simulated and real world). It can also integrate multiple (M ≥ 2 ) single-cell measurements while preserving the self-tuning capabilities and computational tractability of its original version.
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Affiliation(s)
- Pinar Demetci
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA
- Department of Computer Science, Brown University, Providence, Rhode Island, USA
| | - Rebecca Santorella
- Division of Applied Mathematics, Brown University, Providence, Rhode Island, USA
| | - Manav Chakravarthy
- Department of Computer Science, Brown University, Providence, Rhode Island, USA
| | - Bjorn Sandstede
- Division of Applied Mathematics, Brown University, Providence, Rhode Island, USA
| | - Ritambhara Singh
- Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA
- Department of Computer Science, Brown University, Providence, Rhode Island, USA
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Chi Y, Shi J, Xing D, Tan L. Every gene everywhere all at once: High-precision measurement of 3D chromosome architecture with single-cell Hi-C. Front Mol Biosci 2022; 9:959688. [PMID: 36275628 PMCID: PMC9583135 DOI: 10.3389/fmolb.2022.959688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 09/06/2022] [Indexed: 11/13/2022] Open
Abstract
The three-dimensional (3D) structure of chromosomes influences essential biological processes such as gene expression, genome replication, and DNA damage repair and has been implicated in many developmental and degenerative diseases. In the past two centuries, two complementary genres of technology-microscopy, such as fluorescence in situ hybridization (FISH), and biochemistry, such as chromosome conformation capture (3C or Hi-C)-have revealed general principles of chromosome folding in the cell nucleus. However, the extraordinary complexity and cell-to-cell variability of the chromosome structure necessitate new tools with genome-wide coverage and single-cell precision. In the past decade, single-cell Hi-C emerges as a new approach that builds upon yet conceptually differs from bulk Hi-C assays. Instead of measuring population-averaged statistical properties of chromosome folding, single-cell Hi-C works as a proximity-based "biochemical microscope" that measures actual 3D structures of individual genomes, revealing features hidden in bulk Hi-C such as radial organization, multi-way interactions, and chromosome intermingling. Single-cell Hi-C has been used to study highly dynamic processes such as the cell cycle, cell-type-specific chromosome architecture ("structure types"), and structure-expression interplay, deepening our understanding of DNA organization and function.
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Affiliation(s)
- Yi Chi
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China,Innovation Center for Genomics, Peking University, Beijing, China
| | - Jenny Shi
- Department of Neurobiology, Stanford University, Stanford, CA, United States,Department of Chemistry, Stanford University, Stanford, CA, United States,Department of Bioengineering, Stanford University, Stanford, CA, United States
| | - Dong Xing
- Biomedical Pioneering Innovation Center, Peking University, Beijing, China,Innovation Center for Genomics, Peking University, Beijing, China,*Correspondence: Longzhi Tan, ; Dong Xing,
| | - Longzhi Tan
- Department of Neurobiology, Stanford University, Stanford, CA, United States,Department of Bioengineering, Stanford University, Stanford, CA, United States,*Correspondence: Longzhi Tan, ; Dong Xing,
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12
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Mapping nucleosome and chromatin architectures: A survey of computational methods. Comput Struct Biotechnol J 2022; 20:3955-3962. [PMID: 35950186 PMCID: PMC9340519 DOI: 10.1016/j.csbj.2022.07.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 07/22/2022] [Accepted: 07/22/2022] [Indexed: 11/21/2022] Open
Abstract
With ever-growing genomic sequencing data, the data variabilities and the underlying biases of the sequencing technologies pose significant computational challenges ranging from the need for accurately detecting the nucleosome positioning or chromatin interaction to the need for developing normalization methods to eliminate systematic biases. This review mainly surveys the computational methods for mapping the higher-resolution nucleosome and higher-order chromatin architectures. While a detailed discussion of the underlying algorithms is beyond the scope of our survey, we have discussed the methods and tools that can detect the nucleosomes in the genome, then demonstrated the computational methods for identifying 3D chromatin domains and interactions. We further illustrated computational approaches for integrating multi-omics data with Hi-C data and the advance of single-cell (sc)Hi-C data analysis. Our survey provides a comprehensive and valuable resource for biomedical scientists interested in studying nucleosome organization and chromatin structures as well as for computational scientists who are interested in improving upon them.
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