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Johnston MJ, Lee JJY, Hu B, Nikolic A, Hasheminasabgorji E, Baguette A, Paik S, Chen H, Kumar S, Chen CCL, Jessa S, Balin P, Fong V, Zwaig M, Michealraj KA, Chen X, Zhang Y, Varadharajan S, Billon P, Juretic N, Daniels C, Rao AN, Giannini C, Thompson EM, Garami M, Hauser P, Pocza T, Ra YS, Cho BK, Kim SK, Wang KC, Lee JY, Grajkowska W, Perek-Polnik M, Agnihotri S, Mack S, Ellezam B, Weil A, Rich J, Bourque G, Chan JA, Yong VW, Lupien M, Ragoussis J, Kleinman C, Majewski J, Blanchette M, Jabado N, Taylor MD, Gallo M. TULIPs decorate the three-dimensional genome of PFA ependymoma. Cell 2024:S0092-8674(24)00697-4. [PMID: 38986619 DOI: 10.1016/j.cell.2024.06.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 05/26/2022] [Accepted: 06/18/2024] [Indexed: 07/12/2024]
Abstract
Posterior fossa group A (PFA) ependymoma is a lethal brain cancer diagnosed in infants and young children. The lack of driver events in the PFA linear genome led us to search its 3D genome for characteristic features. Here, we reconstructed 3D genomes from diverse childhood tumor types and uncovered a global topology in PFA that is highly reminiscent of stem and progenitor cells in a variety of human tissues. A remarkable feature exclusively present in PFA are type B ultra long-range interactions in PFAs (TULIPs), regions separated by great distances along the linear genome that interact with each other in the 3D nuclear space with surprising strength. TULIPs occur in all PFA samples and recur at predictable genomic coordinates, and their formation is induced by expression of EZHIP. The universality of TULIPs across PFA samples suggests a conservation of molecular principles that could be exploited therapeutically.
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Affiliation(s)
- Michael J Johnston
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - John J Y Lee
- The Arthur and Sonia Labatt Brain Tumor Research Center, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Bo Hu
- Department of Human Genetics, McGill University, Montreal, QC H2A 1B1, Canada; Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Ana Nikolic
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Elham Hasheminasabgorji
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Audrey Baguette
- Quantitative Life Sciences, McGill University, Montreal, QC H3A 1B9, Canada
| | - Seungil Paik
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Haifen Chen
- Department of Human Genetics, McGill University, Montreal, QC H2A 1B1, Canada
| | - Sachin Kumar
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Carol C L Chen
- Department of Human Genetics, McGill University, Montreal, QC H2A 1B1, Canada
| | - Selin Jessa
- Quantitative Life Sciences, McGill University, Montreal, QC H3A 1B9, Canada
| | - Polina Balin
- The Arthur and Sonia Labatt Brain Tumor Research Center, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Vernon Fong
- The Arthur and Sonia Labatt Brain Tumor Research Center, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Melissa Zwaig
- Department of Human Genetics, McGill University, Montreal, QC H2A 1B1, Canada
| | | | - Xun Chen
- Department of Anatomy and Cell Biology, Kyoto University, Kyoto 606-8501, Japan
| | - Yanlin Zhang
- School of Computer Science, McGill University, Montreal, QC H3A 2A7, Canada
| | - Srinidhi Varadharajan
- Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Pierre Billon
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Nikoleta Juretic
- Department of Pediatrics, McGill University and The Research Institute of the McGill University Health Centre, Montreal, QC H4A 3J1, Canada
| | - Craig Daniels
- The Arthur and Sonia Labatt Brain Tumor Research Center, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | | | - Caterina Giannini
- Pediatric Hematology-Oncology, Mayo Clinic, Rochester, MN 55905, USA
| | - Eric M Thompson
- Department of Neurosurgery, Duke University Medical Center, Durham, NC 27710, USA
| | - Miklos Garami
- Department of Pediatrics, Semmelweis University, H-1094 Budapest, Hungary
| | - Peter Hauser
- Department of Pediatrics, Semmelweis University, H-1094 Budapest, Hungary
| | - Timea Pocza
- Department of Pediatrics, Semmelweis University, H-1094 Budapest, Hungary
| | - Young Shin Ra
- Department of Neurosurgery, University of Ulsan, Asan Medical Center, Seoul 05505, South Korea
| | - Byung-Kyu Cho
- Department of Neurosurgery, Division of Pediatric Neurosurgery, Seoul National University Children's Hospital, Seoul 30322, South Korea
| | - Seung-Ki Kim
- Department of Neurosurgery, Division of Pediatric Neurosurgery, Seoul National University Children's Hospital, Seoul 30322, South Korea
| | - Kyu-Chang Wang
- Department of Neurosurgery, Division of Pediatric Neurosurgery, Seoul National University Children's Hospital, Seoul 30322, South Korea
| | - Ji Yeoun Lee
- Department of Neurosurgery, Division of Pediatric Neurosurgery, Seoul National University Children's Hospital, Seoul 30322, South Korea
| | - Wieslawa Grajkowska
- Department of Pathology, The Children's Memorial Health Institute, University of Warsaw, 04-730 Warsaw, Poland
| | - Marta Perek-Polnik
- Department of Oncology, The Children's Memorial Health Institute, University of Warsaw, 04-730 Warsaw, Poland
| | - Sameer Agnihotri
- Department of Neurosurgery, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, United States of America
| | - Stephen Mack
- Department of Developmental Neurobiology, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - Benjamin Ellezam
- Department of Pathology, Centre Hospitalier Universitaire Sainte-Justine, Université de Montréal, Montreal, QC H3T 1C5, Canada
| | - Alex Weil
- Department of Pediatric Neurosurgery, Centre Hospitalier Universitaire Sainte-Justine, Université de Montréal, Montreal, QC H3T 1C5, Canada
| | - Jeremy Rich
- University of Pittsburgh Medical Center, Hillman Cancer Center, Pittsburgh, PA 15213, USA; Department of Neurology, University of Pittsburgh Medical Center, Pittsburgh, PA 15213, USA
| | - Guillaume Bourque
- Department of Human Genetics, McGill University, Montreal, QC H2A 1B1, Canada; McGill Genome Centre, Montreal, QC H3A 0G1, Canada
| | - Jennifer A Chan
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - V Wee Yong
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; Department of Oncology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada
| | - Mathieu Lupien
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada; Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada
| | - Jiannis Ragoussis
- Department of Human Genetics, McGill University, Montreal, QC H2A 1B1, Canada; McGill Genome Centre, Montreal, QC H3A 0G1, Canada
| | - Claudia Kleinman
- Department of Human Genetics, McGill University, Montreal, QC H2A 1B1, Canada; Lady Davis Research Institute, Jewish General Hospital, Montreal, QC H3T 1E2, Canada
| | - Jacek Majewski
- Department of Human Genetics, McGill University, Montreal, QC H2A 1B1, Canada
| | - Mathieu Blanchette
- Quantitative Life Sciences, McGill University, Montreal, QC H3A 1B9, Canada; School of Computer Science, McGill University, Montreal, QC H3A 2A7, Canada
| | - Nada Jabado
- Department of Human Genetics, McGill University, Montreal, QC H2A 1B1, Canada; Department of Pediatrics, McGill University and The Research Institute of the McGill University Health Centre, Montreal, QC H4A 3J1, Canada; Division of Experimental Medicine, Department of Medicine, McGill University, Montreal, QC H3A 3J1, Canada.
| | - Michael D Taylor
- The Arthur and Sonia Labatt Brain Tumor Research Center, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Developmental & Stem Cell Biology Program, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5G 1L7, Canada; Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA; Cancer and Hematology Center, Texas Children's Hospital, Houston, TX 77030, USA; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Marco Gallo
- Arnie Charbonneau Cancer Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; Alberta Children's Hospital Research Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada; Department of Pediatrics, Baylor College of Medicine, Houston, TX 77030, USA; Cancer and Hematology Center, Texas Children's Hospital, Houston, TX 77030, USA; Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA.
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2
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Xu J, Xu X, Huang D, Luo Y, Lin L, Bai X, Zheng Y, Yang Q, Cheng Y, Huang A, Shi J, Bo X, Gu J, Chen H. A comprehensive benchmarking with interpretation and operational guidance for the hierarchy of topologically associating domains. Nat Commun 2024; 15:4376. [PMID: 38782890 PMCID: PMC11116433 DOI: 10.1038/s41467-024-48593-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 05/03/2024] [Indexed: 05/25/2024] Open
Abstract
Topologically associating domains (TADs), megabase-scale features of chromatin spatial architecture, are organized in a domain-within-domain TAD hierarchy. Within TADs, the inner and smaller subTADs not only manifest cell-to-cell variability, but also precisely regulate transcription and differentiation. Although over 20 TAD callers are able to detect TAD, their usability in biomedicine is confined by a disagreement of outputs and a limit in understanding TAD hierarchy. We compare 13 computational tools across various conditions and develop a metric to evaluate the similarity of TAD hierarchy. Although outputs of TAD hierarchy at each level vary among callers, data resolutions, sequencing depths, and matrices normalization, they are more consistent when they have a higher similarity of larger TADs. We present comprehensive benchmarking of TAD hierarchy callers and operational guidance to researchers of life science researchers. Moreover, by simulating the mixing of different types of cells, we confirm that TAD hierarchy is generated not simply from stacking Hi-C heatmaps of heterogeneous cells. Finally, we propose an air conditioner model to decipher the role of TAD hierarchy in transcription.
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Affiliation(s)
- Jingxuan Xu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Surgery, Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Xiang Xu
- Academy of Military Medical Science, Beijing, 100850, China
| | - Dandan Huang
- Department of Oncology, Peking University Shougang Hospital, Beijing, China
- Center for Precision Diagnosis and Treatment of Colorectal Cancer and Inflammatory Diseases, Peking University Health Science Center, Beijing, China
| | - Yawen Luo
- Academy of Military Medical Science, Beijing, 100850, China
| | - Lin Lin
- Academy of Military Medical Science, Beijing, 100850, China
- School of Computer Science and Information Technology& KLAS, Northeast Normal University, Changchun, China
| | - Xuemei Bai
- Academy of Military Medical Science, Beijing, 100850, China
| | - Yang Zheng
- Academy of Military Medical Science, Beijing, 100850, China
| | - Qian Yang
- Academy of Military Medical Science, Beijing, 100850, China
| | - Yu Cheng
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Surgery, Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - An Huang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Surgery, Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Jingyi Shi
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Surgery, Peking University Cancer Hospital & Institute, Beijing, 100142, China
| | - Xiaochen Bo
- Academy of Military Medical Science, Beijing, 100850, China.
| | - Jin Gu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Surgery, Peking University Cancer Hospital & Institute, Beijing, 100142, China.
- Department of Oncology, Peking University Shougang Hospital, Beijing, China.
- Center for Precision Diagnosis and Treatment of Colorectal Cancer and Inflammatory Diseases, Peking University Health Science Center, Beijing, China.
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.
- Peking University International Cancer Institute, Beijing, China.
| | - Hebing Chen
- Academy of Military Medical Science, Beijing, 100850, China.
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3
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Banerjee A, Zhang S, Bahar I. Genome structural dynamics: insights from Gaussian network analysis of Hi-C data. Brief Funct Genomics 2024:elae014. [PMID: 38654598 DOI: 10.1093/bfgp/elae014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 03/11/2024] [Accepted: 04/02/2024] [Indexed: 04/26/2024] Open
Abstract
Characterization of the spatiotemporal properties of the chromatin is essential to gaining insights into the physical bases of gene co-expression, transcriptional regulation and epigenetic modifications. The Gaussian network model (GNM) has proven in recent work to serve as a useful tool for modeling chromatin structural dynamics, using as input high-throughput chromosome conformation capture data. We focus here on the exploration of the collective dynamics of chromosomal structures at hierarchical levels of resolution, from single gene loci to topologically associating domains or entire chromosomes. The GNM permits us to identify long-range interactions between gene loci, shedding light on the role of cross-correlations between distal regions of the chromosomes in regulating gene expression. Notably, GNM analysis performed across diverse cell lines highlights the conservation of the global/cooperative movements of the chromatin across different types of cells. Variations driven by localized couplings between genomic loci, on the other hand, underlie cell differentiation, underscoring the significance of the four-dimensional properties of the genome in defining cellular identity. Finally, we demonstrate the close relation between the cell type-dependent mobility profiles of gene loci and their gene expression patterns, providing a clear demonstration of the role of chromosomal 4D features in defining cell-specific differential expression of genes.
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Affiliation(s)
- Anupam Banerjee
- Laufer Center for Physical & Quantitative Biology, Stony Brook University, NY 11794, USA
| | - She Zhang
- OpenEye, Cadence Molecular Sciences, Santa Fe, NM 87508, USA
| | - Ivet Bahar
- Laufer Center for Physical & Quantitative Biology, Stony Brook University, NY 11794, USA
- Department of Biochemistry and Cell Biology, Renaissance School of Medicine, Stony Brook University, NY 11794, USA
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4
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Liu R, Xu R, Yan S, Li P, Jia C, Sun H, Sheng K, Wang Y, Zhang Q, Guo J, Xin X, Li X, Guo D. Hi-C, a chromatin 3D structure technique advancing the functional genomics of immune cells. Front Genet 2024; 15:1377238. [PMID: 38586584 PMCID: PMC10995239 DOI: 10.3389/fgene.2024.1377238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 03/13/2024] [Indexed: 04/09/2024] Open
Abstract
The functional performance of immune cells relies on a complex transcriptional regulatory network. The three-dimensional structure of chromatin can affect chromatin status and gene expression patterns, and plays an important regulatory role in gene transcription. Currently available techniques for studying chromatin spatial structure include chromatin conformation capture techniques and their derivatives, chromatin accessibility sequencing techniques, and others. Additionally, the recently emerged deep learning technology can be utilized as a tool to enhance the analysis of data. In this review, we elucidate the definition and significance of the three-dimensional chromatin structure, summarize the technologies available for studying it, and describe the research progress on the chromatin spatial structure of dendritic cells, macrophages, T cells, B cells, and neutrophils.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | - Dianhao Guo
- School of Clinical and Basic Medical Sciences, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
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5
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Liu E, Lyu H, Liu Y, Fu L, Cheng X, Yin X. Identifying TAD-like domains on single-cell Hi-C data by graph embedding and changepoint detection. Bioinformatics 2024; 40:btae138. [PMID: 38449288 PMCID: PMC10960928 DOI: 10.1093/bioinformatics/btae138] [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: 07/03/2023] [Revised: 01/10/2024] [Accepted: 03/05/2024] [Indexed: 03/08/2024] Open
Abstract
MOTIVATION Topologically associating domains (TADs) are fundamental building blocks of 3D genome. TAD-like domains in single cells are regarded as the underlying genesis of TADs discovered in bulk cells. Understanding the organization of TAD-like domains helps to get deeper insights into their regulatory functions. Unfortunately, it remains a challenge to identify TAD-like domains on single-cell Hi-C data due to its ultra-sparsity. RESULTS We propose scKTLD, an in silico tool for the identification of TAD-like domains on single-cell Hi-C data. It takes Hi-C contact matrix as the adjacency matrix for a graph, embeds the graph structures into a low-dimensional space with the help of sparse matrix factorization followed by spectral propagation, and the TAD-like domains can be identified using a kernel-based changepoint detection in the embedding space. The results tell that our scKTLD is superior to the other methods on the sparse contact matrices, including downsampled bulk Hi-C data as well as simulated and experimental single-cell Hi-C data. Besides, we demonstrated the conservation of TAD-like domain boundaries at single-cell level apart from heterogeneity within and across cell types, and found that the boundaries with higher frequency across single cells are more enriched for architectural proteins and chromatin marks, and they preferentially occur at TAD boundaries in bulk cells, especially at those with higher hierarchical levels. AVAILABILITY AND IMPLEMENTATION scKTLD is freely available at https://github.com/lhqxinghun/scKTLD.
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Affiliation(s)
- Erhu Liu
- School of Information and Control Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China
| | - Hongqiang Lyu
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi'an 710049, China
| | - Yuan Liu
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi'an 710049, China
| | - Laiyi Fu
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi'an 710049, China
| | - Xiaoliang Cheng
- Department of Pharmacy, The First Affiliated Hospital of Xi’an Jiaotong University, Xi'an 710061, China
| | - Xiaoran Yin
- Department of Oncology, The Second Affiliated Hospital of Xi’an Jiaotong University, Xi'an 710004, China
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6
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Gong H, Zhang D, Zhang X. TOAST: A novel method for identifying topologically associated domains based on graph auto-encoders and clustering. Comput Struct Biotechnol J 2023; 21:4759-4768. [PMID: 37822562 PMCID: PMC10562672 DOI: 10.1016/j.csbj.2023.09.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/16/2023] [Accepted: 09/16/2023] [Indexed: 10/13/2023] Open
Abstract
Topologically associated domains (TADs) play a pivotal role in disease detection. This study introduces a novel TADs recognition approach named TOAST, leveraging graph auto-encoders and clustering techniques. TOAST conceptualizes each genomic bin as a node of a graph and employs the Hi-C contact matrix as the graph's adjacency matrix. By employing graph auto-encoders, TOAST generates informative embeddings as features. Subsequently, the unsupervised clustering algorithm HDBSCAN is utilized to assign labels to each genomic bin, facilitating the identification of contiguous regions with the same label as TADs. Our experimental analysis of several simulated Hi-C data sets shows that TOAST can quickly and accurately identify TADs from different types of simulated Hi-C contact matrices, outperforming existing algorithms. We also determined the anchoring ratio of TAD boundaries by analyzing different TAD recognition algorithms, and obtained an average ratio of anchoring CTCF, SMC3, RAD21, POLR2A, H3K36me3, H3K9me3, H3K4me3, H3K4me1, Enhancer, and Promoters of 0.66, 0.47, 0.54, 0.27, 0.24, 0.12, 0.32, 0.41, 0.26, and 0.13, respectively. In conclusion, TOAST is a method that can quickly identify TAD boundary parameters that are easy to understand and have important biological significance. The TOAST web server can be accessed via http://223.223.185.189:4005/. The code of TOAST is available online at https://github.com/ghaiyan/TOAST.
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Affiliation(s)
- Haiyan Gong
- Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, 100083, China
- School of Computer and Communication Engineering, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, China
- Shunde innovation School, University of Science and Technology Beijing, Foshan, 528399, Guangdong, China
| | - Dawei Zhang
- Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, 100083, China
- Shunde innovation School, University of Science and Technology Beijing, Foshan, 528399, Guangdong, China
| | - Xiaotong Zhang
- School of Computer and Communication Engineering, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing, 100083, China
- Shunde innovation School, University of Science and Technology Beijing, Foshan, 528399, Guangdong, China
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7
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Raffo A, Paulsen J. The shape of chromatin: insights from computational recognition of geometric patterns in Hi-C data. Brief Bioinform 2023; 24:bbad302. [PMID: 37646128 PMCID: PMC10516369 DOI: 10.1093/bib/bbad302] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/05/2023] [Accepted: 08/03/2023] [Indexed: 09/01/2023] Open
Abstract
The three-dimensional organization of chromatin plays a crucial role in gene regulation and cellular processes like deoxyribonucleic acid (DNA) transcription, replication and repair. Hi-C and related techniques provide detailed views of spatial proximities within the nucleus. However, data analysis is challenging partially due to a lack of well-defined, underpinning mathematical frameworks. Recently, recognizing and analyzing geometric patterns in Hi-C data has emerged as a powerful approach. This review provides a summary of algorithms for automatic recognition and analysis of geometric patterns in Hi-C data and their correspondence with chromatin structure. We classify existing algorithms on the basis of the data representation and pattern recognition paradigm they make use of. Finally, we outline some of the challenges ahead and promising future directions.
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Affiliation(s)
- Andrea Raffo
- Department of Biosciences, University of Oslo, 0316 Oslo, Norway
| | - Jonas Paulsen
- Department of Biosciences, University of Oslo, 0316 Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, 0316 Oslo, Norway
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8
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Li K, Zhang P, Wang Z, Shen W, Sun W, Xu J, Wen Z, Li L. iEnhance: a multi-scale spatial projection encoding network for enhancing chromatin interaction data resolution. Brief Bioinform 2023; 24:bbad245. [PMID: 37381618 DOI: 10.1093/bib/bbad245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/06/2023] [Accepted: 06/12/2023] [Indexed: 06/30/2023] Open
Abstract
Although sequencing-based high-throughput chromatin interaction data are widely used to uncover genome-wide three-dimensional chromatin architecture, their sparseness and high signal-noise-ratio greatly restrict the precision of the obtained structural elements. To improve data quality, we here present iEnhance (chromatin interaction data resolution enhancement), a multi-scale spatial projection and encoding network, to predict high-resolution chromatin interaction matrices from low-resolution and noisy input data. Specifically, iEnhance projects the input data into matrix spaces to extract multi-scale global and local feature sets, then hierarchically fused these features by attention mechanism. After that, dense channel encoding and residual channel decoding are used to effectively infer robust chromatin interaction maps. iEnhance outperforms state-of-the-art Hi-C resolution enhancement tools in both visual and quantitative evaluation. Comprehensive analysis shows that unlike other tools, iEnhance can recover both short-range structural elements and long-range interaction patterns precisely. More importantly, iEnhance can be transferred to data enhancement of other tissues or cell lines of unknown resolution. Furthermore, iEnhance performs robustly in enhancement of diverse chromatin interaction data including those from single-cell Hi-C and Micro-C experiments.
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Affiliation(s)
- Kai Li
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Ping Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Zilin Wang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Wei Shen
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Weicheng Sun
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Jinsheng Xu
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Zi Wen
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Li Li
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
- Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
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9
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Wang J, Xue Y, He Y, Quan H, Zhang J, Gao YQ. Characterization of network hierarchy reflects cell state specificity in genome organization. Genome Res 2023; 33:247-260. [PMID: 36828586 PMCID: PMC10069467 DOI: 10.1101/gr.277206.122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 01/31/2023] [Indexed: 02/26/2023]
Abstract
Dynamic chromatin structure acts as the regulator of transcription program in crucial processes including cancer and cell development, but a unified framework for characterizing chromatin structural evolution remains to be established. Here, we performed graph inferences on Hi-C data sets and derived the chromatin contact networks. We discovered significant decreases in information transmission efficiencies in chromatin of colorectal cancer (CRC) and T-cell acute lymphoblastic leukemia (T-ALL) compared to corresponding normal controls through graph statistics. Using network embedding in the Poincaré disk, the hierarchy depths of chromatin from CRC and T-ALL patients were found to be significantly shallower compared to their normal controls. A reverse trend of change in chromatin structure was observed during early embryo development. We found tissue-specific conservation of hierarchy order in chromatin contact networks. Our findings reveal the top-down hierarchy of chromatin organization, which is significantly attenuated in cancer.
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Affiliation(s)
- Jingyao Wang
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
| | - Yue Xue
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
| | - Yueying He
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
| | - Hui Quan
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
| | - Jun Zhang
- Changping Laboratory, Beijing, 102206, China
| | - Yi Qin Gao
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China; .,Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, 100871, China.,Changping Laboratory, Beijing, 102206, China
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10
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Rosen J, Lee L, Abnousi A, Chen J, Wen J, Hu M, Li Y. HPTAD: A computational method to identify topologically associating domains from HiChIP and PLAC-seq datasets. Comput Struct Biotechnol J 2023; 21:931-939. [PMID: 38213897 PMCID: PMC10782010 DOI: 10.1016/j.csbj.2023.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 01/06/2023] [Accepted: 01/07/2023] [Indexed: 01/11/2023] Open
Abstract
High-throughput chromatin conformation capture technologies, such as Hi-C and Micro-C, have enabled genome-wide view of chromatin spatial organization. Most recently, Hi-C-derived enrichment-based technologies, including HiChIP and PLAC-seq, offer attractive alternatives due to their high signal-to-noise ratio and low cost. While a series of computational tools have been developed for Hi-C data, methods tailored for HiChIP and PLAC-seq data are still under development. Here we present HPTAD, a computational method to identify topologically associating domains (TADs) from HiChIP and PLAC-seq data. We performed comprehensive benchmark analysis to demonstrate its superior performance over existing TAD callers designed for Hi-C data. HPTAD is freely available at https://github.com/yunliUNC/HPTAD.
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Affiliation(s)
- Jonathan Rosen
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Lindsay Lee
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Armen Abnousi
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Jiawen Chen
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Jia Wen
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Yun Li
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
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11
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Gong H, Yang Y, Zhang X, Li M, Zhang S, Chen Y. CASPIAN: A method to identify chromatin topological associated domains based on spatial density cluster. Comput Struct Biotechnol J 2022; 20:4816-4824. [PMID: 36147659 PMCID: PMC9464881 DOI: 10.1016/j.csbj.2022.08.059] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 08/10/2022] [Accepted: 08/27/2022] [Indexed: 11/25/2022] Open
Abstract
With the development of Hi-C technology, the detection of topologically associated domains (TADs) boundaries plays an important role in exploring the relationship between gene structure and expression. However, a method that can identify accurate TAD boundaries from the Hi-C contact matrix with different resolutions is currently lacking. We proposed a method named CASPIAN that can identify chromatin TAD boundaries based on the spatial density clustering algorithm. CASPIAN requires few parameters to call TADs. This method is realized using the hierarchical density-based clustering method HDBSCAN, where the distance of pairwise bins is calculated based on three distance metrics (Euclidean, Manhattan, and Chebyshev distance metric) to adapt to the characteristics of the Hi-C contact matrix generated from simulation experiments or normalized methods. Our results show that, same as standard methods (e.g., Insulation Score, TopDom), CASPIAN can enrich factors related to promoting the gene expression, such as CTCF, H3K4me1, H3K4me3, RAD21, POLR2A, and SMC3. We also calculated the approximate proportion of various factors anchored at the TAD boundaries to observe the distribution of these factors surrounding the TAD boundaries. In conclusion, CASPIAN is an easy method to explore the relationship between transcription factors and TAD boundaries. CASPIAN is available online (https://gitee.com/ghaiyan/caspian).
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Affiliation(s)
- Haiyan Gong
- School of Computer and Communication Engineering, Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Yi Yang
- School of Computer and Communication Engineering, Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Xiaotong Zhang
- School of Computer and Communication Engineering, Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China.,Shunde Graduate School, University of Science and Technology Beijing, Foshan 528399, Guangdong, China
| | - Minghong Li
- School of Computer and Communication Engineering, Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Sichen Zhang
- School of Computer and Communication Engineering, Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Yang Chen
- The State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China
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12
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Liu E, Lyu H, Peng Q, Liu Y, Wang T, Han J. TADfit is a multivariate linear regression model for profiling hierarchical chromatin domains on replicate Hi-C data. Commun Biol 2022; 5:608. [PMID: 35725901 PMCID: PMC9209495 DOI: 10.1038/s42003-022-03546-y] [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: 11/10/2021] [Accepted: 05/31/2022] [Indexed: 11/26/2022] Open
Abstract
Topologically associating domains (TADs) are fundamental building blocks of three dimensional genome, and organized into complex hierarchies. Identifying hierarchical TADs on Hi-C data helps to understand the relationship between genome architectures and gene regulation. Herein we propose TADfit, a multivariate linear regression model for profiling hierarchical chromatin domains, which tries to fit the interaction frequencies in Hi-C contact matrix with and without replicates using all-possible hierarchical TADs, and the significant ones can be determined by the regression coefficients obtained with the help of an online learning solver called Follow-The-Regularized-Leader (FTRL). Beyond the existing methods, TADfit has an ability to handle multiple contact matrix replicates and find partially overlapping TADs on them, which helps to find the comprehensive underlying TADs across replicates from different experiments. The comparative results tell that TADfit has better accuracy and reproducibility, and the hierarchical TADs called by it exhibit a reasonable biological relevance. TADfit is a computational method that can identify hierarchical or partially overlapping TADs from Hi-C data, in part by using information from multiple replicates to improve detection power.
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Affiliation(s)
- Erhu Liu
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Shaanxi, 710049, China
| | - Hongqiang Lyu
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Shaanxi, 710049, China. .,Guangdong Artificial Intelligence and Digital Economy Laboratory, Guangdong, 510335, China.
| | - Qinke Peng
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Shaanxi, 710049, China
| | - Yuan Liu
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Shaanxi, 710049, China
| | - Tian Wang
- Institute of Artificial Intelligence, Beihang University, Beijing, 100191, China
| | - Jiuqiang Han
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Shaanxi, 710049, China.,Guangdong Artificial Intelligence and Digital Economy Laboratory, Guangdong, 510335, China
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13
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Nagano M, Hu B, Yokobayashi S, Yamamura A, Umemura F, Coradin M, Ohta H, Yabuta Y, Ishikura Y, Okamoto I, Ikeda H, Kawahira N, Nosaka Y, Shimizu S, Kojima Y, Mizuta K, Kasahara T, Imoto Y, Meehan K, Stocsits R, Wutz G, Hiraoka Y, Murakawa Y, Yamamoto T, Tachibana K, Peters JM, Mirny LA, Garcia BA, Majewski J, Saitou M. Nucleome programming is required for the foundation of totipotency in mammalian germline development. EMBO J 2022; 41:e110600. [PMID: 35703121 DOI: 10.15252/embj.2022110600] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 04/27/2022] [Accepted: 04/29/2022] [Indexed: 11/09/2022] Open
Abstract
Germ cells are unique in engendering totipotency, yet the mechanisms underlying this capacity remain elusive. Here, we perform comprehensive and in-depth nucleome analysis of mouse germ-cell development in vitro, encompassing pluripotent precursors, primordial germ cells (PGCs) before and after epigenetic reprogramming, and spermatogonia/spermatogonial stem cells (SSCs). Although epigenetic reprogramming, including genome-wide DNA de-methylation, creates broadly open chromatin with abundant enhancer-like signatures, the augmented chromatin insulation safeguards transcriptional fidelity. These insulatory constraints are then erased en masse for spermatogonial development. Notably, despite distinguishing epigenetic programming, including global DNA re-methylation, the PGCs-to-spermatogonia/SSCs development entails further euchromatization. This accompanies substantial erasure of lamina-associated domains, generating spermatogonia/SSCs with a minimal peripheral attachment of chromatin except for pericentromeres-an architecture conserved in primates. Accordingly, faulty nucleome maturation, including persistent insulation and improper euchromatization, leads to impaired spermatogenic potential. Given that PGCs after epigenetic reprogramming serve as oogenic progenitors as well, our findings elucidate a principle for the nucleome programming that creates gametogenic progenitors in both sexes, defining a basis for nuclear totipotency.
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Affiliation(s)
- Masahiro Nagano
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan.,Department of Anatomy and Cell Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Bo Hu
- Department of Anatomy and Cell Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Shihori Yokobayashi
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan.,Department of Anatomy and Cell Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan
| | - Akitoshi Yamamura
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan.,Department of Anatomy and Cell Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Fumiya Umemura
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan.,Department of Anatomy and Cell Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Mariel Coradin
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA, USA.,Penn Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Molecular, Cellular, and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA
| | - Hiroshi Ohta
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan.,Department of Anatomy and Cell Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yukihiro Yabuta
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan.,Department of Anatomy and Cell Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yukiko Ishikura
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan.,Department of Anatomy and Cell Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Ikuhiro Okamoto
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan.,Department of Anatomy and Cell Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Hiroki Ikeda
- Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan.,Department of Embryology, Nara Medical University, Nara, Japan
| | - Naofumi Kawahira
- Department of Molecular Cell Developmental Biology, School of Life Science, University of California, Los Angeles, CA, USA.,Laboratory for Developmental Morphogeometry, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Yoshiaki Nosaka
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan.,Department of Anatomy and Cell Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Sakura Shimizu
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan.,Department of Anatomy and Cell Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yoji Kojima
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan.,Department of Anatomy and Cell Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan
| | - Ken Mizuta
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan.,Department of Anatomy and Cell Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tomoko Kasahara
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan.,RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yusuke Imoto
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
| | - Killian Meehan
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
| | - Roman Stocsits
- Research Institute of Molecular Pathology, Vienna BioCenter, Vienna, Austria
| | - Gordana Wutz
- Research Institute of Molecular Pathology, Vienna BioCenter, Vienna, Austria
| | - Yasuaki Hiraoka
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
| | - Yasuhiro Murakawa
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan.,RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Takuya Yamamoto
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan.,Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan.,Medical-risk Avoidance based on iPS Cells Team, RIKEN Center for Advanced Intelligence Project, Kyoto, Japan
| | - Kikue Tachibana
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna BioCenter, Vienna, Austria.,Department of Totipotency, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Jan-Michel Peters
- Research Institute of Molecular Pathology, Vienna BioCenter, Vienna, Austria
| | - Leonid A Mirny
- Institute for Medical Engineering and Science, and Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Benjamin A Garcia
- Department of Biochemistry and Biophysics, University of Pennsylvania, Philadelphia, PA, USA.,Penn Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, MO, USA
| | - Jacek Majewski
- Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Mitinori Saitou
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan.,Department of Anatomy and Cell Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan.,Center for iPS Cell Research and Application (CiRA), Kyoto University, Kyoto, Japan
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14
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Liu T, Wang Z. scHiCEmbed: Bin-Specific Embeddings of Single-Cell Hi-C Data Using Graph Auto-Encoders. Genes (Basel) 2022; 13:genes13061048. [PMID: 35741810 PMCID: PMC9222580 DOI: 10.3390/genes13061048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/08/2022] [Accepted: 06/09/2022] [Indexed: 02/05/2023] Open
Abstract
Most publicly accessible single-cell Hi-C data are sparse and cannot reach a higher resolution. Therefore, learning latent representations (bin-specific embeddings) of sparse single-cell Hi-C matrices would provide us with a novel way of mining valuable information hidden in the limited number of single-cell Hi-C contacts. We present scHiCEmbed, an unsupervised computational method for learning bin-specific embeddings of single-cell Hi-C data, and the computational system is applied to the tasks of 3D structure reconstruction of whole genomes and detection of topologically associating domains (TAD). The only input of scHiCEmbed is a raw or scHiCluster-imputed single-cell Hi-C matrix. The main process of scHiCEmbed is to embed each node/bin in a higher dimensional space using graph auto-encoders. The learned n-by-3 bin-specific embedding/latent matrix is considered the final reconstructed 3D genome structure. For TAD detection, we use constrained hierarchical clustering on the latent matrix to classify bins: S_Dbw is used to determine the optimal number of clusters, and each cluster is considered as one potential TAD. Our reconstructed 3D structures for individual chromatins at different cell stages reveal the expanding process of chromatins during the cell cycle. We observe that the TADs called from single-cell Hi-C data are not shared across individual cells and that the TAD boundaries called from raw or imputed single-cell Hi-C are significantly different from those called from bulk Hi-C, confirming the cell-to-cell variability in terms of TAD definitions. The source code for scHiCEmbed is publicly available, and the URL can be found in the conclusion section.
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15
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de Lima MF, Lisboa MDO, Terceiro LEL, Rangel-Pozzo A, Mai S. Chromosome Territories in Hematological Malignancies. Cells 2022; 11:cells11081368. [PMID: 35456046 PMCID: PMC9028803 DOI: 10.3390/cells11081368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 04/12/2022] [Accepted: 04/15/2022] [Indexed: 11/21/2022] Open
Abstract
Chromosomes are organized in distinct nuclear areas designated as chromosome territories (CT). The structural formation of CT is a consequence of chromatin packaging and organization that ultimately affects cell function. Chromosome positioning can identify structural signatures of genomic organization, especially for diseases where changes in gene expression contribute to a given phenotype. The study of CT in hematological diseases revealed chromosome position as an important factor for specific chromosome translocations. In this review, we highlight the history of CT theory, current knowledge on possible clinical applications of CT analysis, and the impact of CT in the development of hematological neoplasia such as multiple myeloma, leukemia, and lymphomas. Accumulating data on nuclear architecture in cancer allow one to propose the three-dimensional nuclear genomic landscape as a novel cancer biomarker for the future.
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Affiliation(s)
- Matheus Fabiao de Lima
- Department of Physiology and Pathophysiology, CancerCare Manitoba Research Institute, University of Manitoba, Winnipeg, MB R3E 0V9, Canada;
| | - Mateus de Oliveira Lisboa
- Core for Cell Technology, School of Medicine, Pontifícia Universidade Católica do Paraná—PUCPR, Curitiba 80215-901, Brazil;
| | - Lucas E. L. Terceiro
- Department of Pathology, Max Rady College of Medicine, University of Manitoba, Winnipeg, MB R3E 3P5, Canada;
| | - Aline Rangel-Pozzo
- Department of Physiology and Pathophysiology, CancerCare Manitoba Research Institute, University of Manitoba, Winnipeg, MB R3E 0V9, Canada;
- Correspondence: (A.R.-P.); (S.M.); Tel.: +1-204-787-2135 (S.M.)
| | - Sabine Mai
- Department of Physiology and Pathophysiology, CancerCare Manitoba Research Institute, University of Manitoba, Winnipeg, MB R3E 0V9, Canada;
- Correspondence: (A.R.-P.); (S.M.); Tel.: +1-204-787-2135 (S.M.)
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16
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Sefer E. A comparison of topologically associating domain callers over mammals at high resolution. BMC Bioinformatics 2022; 23:127. [PMID: 35413815 PMCID: PMC9006547 DOI: 10.1186/s12859-022-04674-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 04/07/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Topologically associating domains (TADs) are locally highly-interacting genome regions, which also play a critical role in regulating gene expression in the cell. TADs have been first identified while investigating the 3D genome structure over High-throughput Chromosome Conformation Capture (Hi-C) interaction dataset. Substantial degree of efforts have been devoted to develop techniques for inferring TADs from Hi-C interaction dataset. Many TAD-calling methods have been developed which differ in their criteria and assumptions in TAD inference. Correspondingly, TADs inferred via these callers vary in terms of both similarities and biological features they are enriched in. RESULT We have carried out a systematic comparison of 27 TAD-calling methods over mammals. We use Micro-C, a recent high-resolution variant of Hi-C, to compare TADs at a very high resolution, and classify the methods into 3 categories: feature-based methods, Clustering methods, Graph-partitioning methods. We have evaluated TAD boundaries, gaps between adjacent TADs, and quality of TADs across various criteria. We also found particularly CTCF and Cohesin proteins to be effective in formation of TADs with corner dots. We have also assessed the callers performance on simulated datasets since a gold standard for TADs is missing. TAD sizes and numbers change remarkably between TAD callers and dataset resolutions, indicating that TADs are hierarchically-organized domains, instead of disjoint regions. A core subset of feature-based TAD callers regularly perform the best while inferring reproducible domains, which are also enriched for TAD related biological properties. CONCLUSION We have analyzed the fundamental principles of TAD-calling methods, and identified the existing situation in TAD inference across high resolution Micro-C interaction datasets over mammals. We come up with a systematic, comprehensive, and concise framework to evaluate the TAD-calling methods performance across Micro-C datasets. Our research will be useful in selecting appropriate methods for TAD inference and evaluation based on available data, experimental design, and biological question of interest. We also introduce our analysis as a benchmarking tool with publicly available source code.
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Affiliation(s)
- Emre Sefer
- Department of Computer Science, Ozyegin University, Istanbul, Turkey.
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17
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Li CC, Zhang G, Du J, Liu D, Li Z, Ni Y, Zhou J, Li Y, Hou S, Zheng X, Lan Y, Liu B, He A. Pre-configuring chromatin architecture with histone modifications guides hematopoietic stem cell formation in mouse embryos. Nat Commun 2022; 13:346. [PMID: 35039499 PMCID: PMC8764075 DOI: 10.1038/s41467-022-28018-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 01/03/2022] [Indexed: 11/09/2022] Open
Abstract
The gene activity underlying cell differentiation is regulated by a diverse set of transcription factors (TFs), histone modifications, chromatin structures and more. Although definitive hematopoietic stem cells (HSCs) are known to emerge via endothelial-to-hematopoietic transition (EHT), how the multi-layered epigenome is sequentially unfolded in a small portion of endothelial cells (ECs) transitioning into the hematopoietic fate remains elusive. With optimized low-input itChIP-seq and Hi-C assays, we performed multi-omics dissection of the HSC ontogeny trajectory across early arterial ECs (eAECs), hemogenic endothelial cells (HECs), pre-HSCs and long-term HSCs (LT-HSCs) in mouse embryos. Interestingly, HSC regulatory regions are already pre-configurated with active histone modifications as early as eAECs, preceding chromatin looping dynamics within topologically associating domains. Chromatin looping structures between enhancers and promoters only become gradually strengthened over time. Notably, RUNX1, a master TF for hematopoiesis, enriched at half of these loops is observed early from eAECs through pre-HSCs but its enrichment further increases in HSCs. RUNX1 and co-TFs together constitute a central, progressively intensified enhancer-promoter interactions. Thus, our study provides a framework to decipher how temporal epigenomic configurations fulfill cell lineage specification during development.
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Affiliation(s)
- Chen C Li
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, Peking University, 100871, Beijing, China
| | - Guangyu Zhang
- State Key Laboratory of Proteomics, Academy of Military Medical Sciences, Academy of Military Sciences, 100850, Beijing, China
| | - Junjie Du
- State Key Laboratory of Proteomics, Academy of Military Medical Sciences, Academy of Military Sciences, 100850, Beijing, China
| | - Di Liu
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, Peking University, 100871, Beijing, China
| | - Zongcheng Li
- State Key Laboratory of Experimental Hematology, Institute of Hematology, Fifth Medical Center of Chinese PLA General Hospital, 100850, Beijing, China
| | - Yanli Ni
- State Key Laboratory of Experimental Hematology, Institute of Hematology, Fifth Medical Center of Chinese PLA General Hospital, 100850, Beijing, China
| | - Jie Zhou
- State Key Laboratory of Experimental Hematology, Institute of Hematology, Fifth Medical Center of Chinese PLA General Hospital, 100850, Beijing, China
| | - Yunqiao Li
- State Key Laboratory of Proteomics, Academy of Military Medical Sciences, Academy of Military Sciences, 100850, Beijing, China
| | - Siyuan Hou
- Key Laboratory for Regenerative Medicine of Ministry of Education, Institute of Hematology, School of Medicine, Jinan University, Guangzhou, China
| | - Xiaona Zheng
- State Key Laboratory of Proteomics, Academy of Military Medical Sciences, Academy of Military Sciences, 100850, Beijing, China
| | - Yu Lan
- Key Laboratory for Regenerative Medicine of Ministry of Education, Institute of Hematology, School of Medicine, Jinan University, Guangzhou, China.
| | - Bing Liu
- State Key Laboratory of Proteomics, Academy of Military Medical Sciences, Academy of Military Sciences, 100850, Beijing, China.
- State Key Laboratory of Experimental Hematology, Institute of Hematology, Fifth Medical Center of Chinese PLA General Hospital, 100850, Beijing, China.
- Key Laboratory for Regenerative Medicine of Ministry of Education, Institute of Hematology, School of Medicine, Jinan University, Guangzhou, China.
| | - Aibin He
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, Peking-Tsinghua Center for Life Sciences, Peking University, 100871, Beijing, China.
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18
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Jerkovic I, Cavalli G. Understanding 3D genome organization by multidisciplinary methods. Nat Rev Mol Cell Biol 2021; 22:511-528. [PMID: 33953379 DOI: 10.1038/s41580-021-00362-w] [Citation(s) in RCA: 146] [Impact Index Per Article: 48.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/16/2021] [Indexed: 02/03/2023]
Abstract
Understanding how chromatin is folded in the nucleus is fundamental to understanding its function. Although 3D genome organization has been historically difficult to study owing to a lack of relevant methodologies, major technological breakthroughs in genome-wide mapping of chromatin contacts and advances in imaging technologies in the twenty-first century considerably improved our understanding of chromosome conformation and nuclear architecture. In this Review, we discuss methods of 3D genome organization analysis, including sequencing-based techniques, such as Hi-C and its derivatives, Micro-C, DamID and others; microscopy-based techniques, such as super-resolution imaging coupled with fluorescence in situ hybridization (FISH), multiplex FISH, in situ genome sequencing and live microscopy methods; and computational and modelling approaches. We describe the most commonly used techniques and their contribution to our current knowledge of nuclear architecture and, finally, we provide a perspective on up-and-coming methods that open possibilities for future major discoveries.
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Affiliation(s)
- Ivana Jerkovic
- Institute of Human Genetics, CNRS, University of Montpellier, Montpellier, France
| | - Giacomo Cavalli
- Institute of Human Genetics, CNRS, University of Montpellier, Montpellier, France.
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19
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Liu Y, Nanni L, Sungalee S, Zufferey M, Tavernari D, Mina M, Ceri S, Oricchio E, Ciriello G. Systematic inference and comparison of multi-scale chromatin sub-compartments connects spatial organization to cell phenotypes. Nat Commun 2021; 12:2439. [PMID: 33972523 PMCID: PMC8110550 DOI: 10.1038/s41467-021-22666-3] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Accepted: 03/16/2021] [Indexed: 12/21/2022] Open
Abstract
Chromatin compartmentalization reflects biological activity. However, inference of chromatin sub-compartments and compartment domains from chromosome conformation capture (Hi-C) experiments is limited by data resolution. As a result, these have been characterized only in a few cell types and systematic comparisons across multiple tissues and conditions are missing. Here, we present Calder, an algorithmic approach that enables the identification of multi-scale sub-compartments at variable data resolution. Calder allows to infer and compare chromatin sub-compartments and compartment domains in >100 cell lines. Our results reveal sub-compartments enriched for poised chromatin states and undergoing spatial repositioning during lineage differentiation and oncogenic transformation.
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Affiliation(s)
- Yuanlong Liu
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Cancer Center Leman, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Luca Nanni
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Cancer Center Leman, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Stephanie Sungalee
- Swiss Cancer Center Leman, Lausanne, Switzerland
- Swiss Institute for Experimental Cancer Research (ISREC) School of Life Sciences, EPFL, Épalinges, Switzerland
| | - Marie Zufferey
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Cancer Center Leman, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Daniele Tavernari
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Cancer Center Leman, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Marco Mina
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Cancer Center Leman, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Stefano Ceri
- Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Elisa Oricchio
- Swiss Cancer Center Leman, Lausanne, Switzerland
- Swiss Institute for Experimental Cancer Research (ISREC) School of Life Sciences, EPFL, Épalinges, Switzerland
| | - Giovanni Ciriello
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- Swiss Cancer Center Leman, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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20
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Gong H, Yang Y, Zhang S, Li M, Zhang X. Application of Hi-C and other omics data analysis in human cancer and cell differentiation research. Comput Struct Biotechnol J 2021; 19:2070-2083. [PMID: 33995903 PMCID: PMC8086027 DOI: 10.1016/j.csbj.2021.04.016] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 04/04/2021] [Accepted: 04/04/2021] [Indexed: 02/07/2023] Open
Abstract
With the development of 3C (chromosome conformation capture) and its derivative technology Hi-C (High-throughput chromosome conformation capture) research, the study of the spatial structure of the genomic sequence in the nucleus helps researchers understand the functions of biological processes such as gene transcription, replication, repair, and regulation. In this paper, we first introduce the research background and purpose of Hi-C data visualization analysis. After that, we discuss the Hi-C data analysis methods from genome 3D structure, A/B compartment, TADs (topologically associated domain), and loop detection. We also discuss how to apply genome visualization technologies to the identification of chromosome feature structures. We continue with a review of correlation analysis differences among multi-omics data, and how to apply Hi-C and other omics data analysis into cancer and cell differentiation research. Finally, we summarize the various problems in joint analyses based on Hi-C and other multi-omics data. We believe this review can help researchers better understand the progress and applications of 3D genome technology.
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Affiliation(s)
- Haiyan Gong
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
- Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China
- Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China
- Shunde Graduate School of University of Science and Technology Beijing, Foshan 528000, China
| | - Yi Yang
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Sichen Zhang
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Minghong Li
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
| | - Xiaotong Zhang
- Department of Computer Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
- Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing, Beijing 100083, China
- Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China
- Shunde Graduate School of University of Science and Technology Beijing, Foshan 528000, China
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21
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Du G, Li H, Ding Y, Jiang S, Hong H, Gan J, Wang L, Yang Y, Li Y, Huang X, Sun Y, Tao H, Li Y, Xu X, Zheng Y, Wang J, Bai X, Xu K, Li Y, Jiang Q, Li C, Chen H, Bo X. The hierarchical folding dynamics of topologically associating domains are closely related to transcriptional abnormalities in cancers. Comput Struct Biotechnol J 2021; 19:1684-1693. [PMID: 33897976 PMCID: PMC8050718 DOI: 10.1016/j.csbj.2021.03.018] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 03/14/2021] [Accepted: 03/16/2021] [Indexed: 01/08/2023] Open
Abstract
The hierarchical levels of TAD boundaries were tissue- and cell type-specific. The TAD nesting level of genes in tumors is different from that in normal tissue. Hierarchical TAD level of genes is related to abnormal transcription and prognosis in cancers.
Recent studies have shown that the three-dimensional (3D) structure of chromatin is associated with cancer progression. However, the roles of the 3D genome structure and its dynamics in cancer remains largely unknown. In this study, we investigated hierarchical topologically associating domain (TAD) structures in cancers and defined a “TAD hierarchical score (TH score)” for genes, which allowed us to assess the TAD nesting level of all genes in a simplified way. We demonstrated that the TAD nesting levels of genes in a tumor differ from those in normal tissue. Furthermore, the hierarchical TAD level dynamics were related to transcriptional changes in cancer, and some of the genes in which the hierarchical level was altered were significantly related to the prognosis of cancer patients. Overall, the results of this study suggest that the folding dynamics of TADs are closely related to transcriptional abnormalities in cancers, emphasizing that the function of hierarchical chromatin organization goes beyond simple chromatin packaging efficiency.
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Affiliation(s)
- Guifang Du
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Hao Li
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Yang Ding
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Shuai Jiang
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Hao Hong
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Jingbo Gan
- Center for Statistical Science, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing 100871, China
| | - Longteng Wang
- Center for Statistical Science, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing 100871, China
| | - Yuanping Yang
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510320, China
| | - Yinyin Li
- Department of Liver Disease, Fifth Medical Center, Chinese People's Liberation Army General Hospital, Beijing 100069, China
| | - Xin Huang
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Yu Sun
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Huan Tao
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Yaru Li
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Xiang Xu
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Yang Zheng
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Junting Wang
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Xuemei Bai
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Kang Xu
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Yaoshen Li
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou 510320, China
| | - Qi Jiang
- Tongfang Cloud (Beijing) Technology Co., Ltd, Beijing 100083, China
| | - Cheng Li
- Center for Statistical Science, Center for Bioinformatics, School of Life Sciences, Peking University, Beijing 100871, China
| | - Hebing Chen
- Beijing Institute of Radiation Medicine, Beijing 100850, China
| | - Xiaochen Bo
- Beijing Institute of Radiation Medicine, Beijing 100850, China
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22
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Lee BH, Rhie SK. Molecular and computational approaches to map regulatory elements in 3D chromatin structure. Epigenetics Chromatin 2021; 14:14. [PMID: 33741028 PMCID: PMC7980343 DOI: 10.1186/s13072-021-00390-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 03/08/2021] [Indexed: 12/19/2022] Open
Abstract
Epigenetic marks do not change the sequence of DNA but affect gene expression in a cell-type specific manner by altering the activities of regulatory elements. Development of new molecular biology assays, sequencing technologies, and computational approaches enables us to profile the human epigenome in three-dimensional structure genome-wide. Here we describe various molecular biology techniques and bioinformatic tools that have been developed to measure the activities of regulatory elements and their chromatin interactions. Moreover, we list currently available three-dimensional epigenomic data sets that are generated in various human cell types and tissues to assist in the design and analysis of research projects.
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Affiliation(s)
- Beoung Hun Lee
- Department of Biochemistry and Molecular Medicine and the Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA
| | - Suhn K Rhie
- Department of Biochemistry and Molecular Medicine and the Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90089, USA.
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