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Liu X, Ren X. Computational Strategies and Algorithms for Inferring Cellular Composition of Spatial Transcriptomics Data. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae057. [PMID: 39110523 PMCID: PMC11398939 DOI: 10.1093/gpbjnl/qzae057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 07/26/2024] [Accepted: 08/02/2024] [Indexed: 09/15/2024]
Abstract
Spatial transcriptomics technology has been an essential and powerful method for delineating tissue architecture at the molecular level. However, due to the limitations of the current spatial techniques, the cellular information cannot be directly measured but instead spatial spots typically varying from a diameter of 0.2 to 100 µm are characterized. Therefore, it is vital to apply computational strategies for inferring the cellular composition within each spatial spot. The main objective of this review is to summarize the most recent progresses in estimating the exact cellular proportions for each spatial spot, and to prospect the future directions of this field.
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Jena SG, Verma A, Engelhardt BE. Answering open questions in biology using spatial genomics and structured methods. BMC Bioinformatics 2024; 25:291. [PMID: 39232666 PMCID: PMC11375982 DOI: 10.1186/s12859-024-05912-5] [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: 10/10/2023] [Accepted: 08/22/2024] [Indexed: 09/06/2024] Open
Abstract
Genomics methods have uncovered patterns in a range of biological systems, but obscure important aspects of cell behavior: the shapes, relative locations, movement, and interactions of cells in space. Spatial technologies that collect genomic or epigenomic data while preserving spatial information have begun to overcome these limitations. These new data promise a deeper understanding of the factors that affect cellular behavior, and in particular the ability to directly test existing theories about cell state and variation in the context of morphology, location, motility, and signaling that could not be tested before. Rapid advancements in resolution, ease-of-use, and scale of spatial genomics technologies to address these questions also require an updated toolkit of statistical methods with which to interrogate these data. We present a framework to respond to this new avenue of research: four open biological questions that can now be answered using spatial genomics data paired with methods for analysis. We outline spatial data modalities for each open question that may yield specific insights, discuss how conflicting theories may be tested by comparing the data to conceptual models of biological behavior, and highlight statistical and machine learning-based tools that may prove particularly helpful to recover biological understanding.
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Affiliation(s)
- Siddhartha G Jena
- Department of Stem Cell and Regenerative Biology, Harvard, 7 Divinity Ave, Cambridge, MA, USA
| | - Archit Verma
- Gladstone Institutes, 1650 Owens Street, San Francisco, CA, 94158, USA
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3
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Chen J, Larsson L, Swarbrick A, Lundeberg J. Spatial landscapes of cancers: insights and opportunities. Nat Rev Clin Oncol 2024; 21:660-674. [PMID: 39043872 DOI: 10.1038/s41571-024-00926-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/28/2024] [Indexed: 07/25/2024]
Abstract
Solid tumours comprise many different cell types organized in spatially structured arrangements, with substantial intratumour and intertumour heterogeneity. Advances in spatial profiling technologies over the past decade hold promise to capture the complexity of these cellular architectures to build a holistic view of the intricate molecular mechanisms that shape the tumour ecosystem. Some of these mechanisms act at the cellular scale and are controlled by cell-autonomous programmes or communication between nearby cells, whereas other mechanisms result from coordinated efforts between large networks of cells and extracellular molecules organized into tissues and organs. In this Review we provide insights into the application of single-cell and spatial profiling tools, with a focus on spatially resolved transcriptomic tools developed to understand the cellular architecture of the tumour microenvironment and identify opportunities to use them to improve clinical management of cancers.
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Affiliation(s)
- Julia Chen
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
- Department of Medical Oncology, St George Hospital, Sydney, New South Wales, Australia
| | - Ludvig Larsson
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden
| | - Alexander Swarbrick
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.
- School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia.
| | - Joakim Lundeberg
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Stockholm, Sweden.
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4
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Almet AA, Tsai YC, Watanabe M, Nie Q. Inferring pattern-driving intercellular flows from single-cell and spatial transcriptomics. Nat Methods 2024:10.1038/s41592-024-02380-w. [PMID: 39187683 DOI: 10.1038/s41592-024-02380-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 07/23/2024] [Indexed: 08/28/2024]
Abstract
From single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST), one can extract high-dimensional gene expression patterns that can be described by intercellular communication networks or decoupled gene modules. These two descriptions of information flow are often assumed to occur independently. However, intercellular communication drives directed flows of information that are mediated by intracellular gene modules, in turn triggering outflows of other signals. Methodologies to describe such intercellular flows are lacking. We present FlowSig, a method that infers communication-driven intercellular flows from scRNA-seq or ST data using graphical causal modeling and conditional independence. We benchmark FlowSig using newly generated experimental cortical organoid data and synthetic data generated from mathematical modeling. We demonstrate FlowSig's utility by applying it to various studies, showing that FlowSig can capture stimulation-induced changes to paracrine signaling in pancreatic islets, demonstrate shifts in intercellular flows due to increasing COVID-19 severity and reconstruct morphogen-driven activator-inhibitor patterns in mouse embryogenesis.
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Affiliation(s)
- Axel A Almet
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA
- NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA
| | - Yuan-Chen Tsai
- Department of Anatomy & Neurobiology, University of California, Irvine, Irvine, CA, USA
- Sue & Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, USA
- School of Medicine, University of California, Irvine, Irvine, CA, USA
| | - Momoko Watanabe
- Department of Anatomy & Neurobiology, University of California, Irvine, Irvine, CA, USA
- Sue & Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, USA
- School of Medicine, University of California, Irvine, Irvine, CA, USA
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA.
- NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA.
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA.
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5
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Yang W, Wang P, Xu S, Wang T, Luo M, Cai Y, Xu C, Xue G, Que J, Ding Q, Jin X, Yang Y, Pang F, Pang B, Lin Y, Nie H, Xu Z, Ji Y, Jiang Q. Deciphering cell-cell communication at single-cell resolution for spatial transcriptomics with subgraph-based graph attention network. Nat Commun 2024; 15:7101. [PMID: 39155292 PMCID: PMC11330978 DOI: 10.1038/s41467-024-51329-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 08/01/2024] [Indexed: 08/20/2024] Open
Abstract
The inference of cell-cell communication (CCC) is crucial for a better understanding of complex cellular dynamics and regulatory mechanisms in biological systems. However, accurately inferring spatial CCCs at single-cell resolution remains a significant challenge. To address this issue, we present a versatile method, called DeepTalk, to infer spatial CCC at single-cell resolution by integrating single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data. DeepTalk utilizes graph attention network (GAT) to integrate scRNA-seq and ST data, which enables accurate cell-type identification for single-cell ST data and deconvolution for spot-based ST data. Then, DeepTalk can capture the connections among cells at multiple levels using subgraph-based GAT, and further achieve spatially resolved CCC inference at single-cell resolution. DeepTalk achieves excellent performance in discovering meaningful spatial CCCs on multiple cross-platform datasets, which demonstrates its superior ability to dissect cellular behavior within intricate biological processes.
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Affiliation(s)
- Wenyi Yang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Pingping Wang
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, China
| | - Shouping Xu
- Department of Breast Cancer, Harbin Medical University Cancer Hospital, Harbin, China
| | - Tao Wang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Meng Luo
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yideng Cai
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Chang Xu
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Guangfu Xue
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Jinhao Que
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Qian Ding
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xiyun Jin
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, China
| | - Yuexin Yang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Fenglan Pang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Boran Pang
- Center for Difficult and Complicated Abdominal Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yi Lin
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, China
| | - Huan Nie
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Zhaochun Xu
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, China.
| | - Yong Ji
- State Key Laboratory of Frigid Zone Cardiovascular Diseases (SKLFZCD), Harbin Medical University, Harbin, China.
| | - Qinghua Jiang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China.
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, China.
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Su J, Song Y, Zhu Z, Huang X, Fan J, Qiao J, Mao F. Cell-cell communication: new insights and clinical implications. Signal Transduct Target Ther 2024; 9:196. [PMID: 39107318 PMCID: PMC11382761 DOI: 10.1038/s41392-024-01888-z] [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: 12/29/2023] [Revised: 05/09/2024] [Accepted: 06/02/2024] [Indexed: 09/11/2024] Open
Abstract
Multicellular organisms are composed of diverse cell types that must coordinate their behaviors through communication. Cell-cell communication (CCC) is essential for growth, development, differentiation, tissue and organ formation, maintenance, and physiological regulation. Cells communicate through direct contact or at a distance using ligand-receptor interactions. So cellular communication encompasses two essential processes: cell signal conduction for generation and intercellular transmission of signals, and cell signal transduction for reception and procession of signals. Deciphering intercellular communication networks is critical for understanding cell differentiation, development, and metabolism. First, we comprehensively review the historical milestones in CCC studies, followed by a detailed description of the mechanisms of signal molecule transmission and the importance of the main signaling pathways they mediate in maintaining biological functions. Then we systematically introduce a series of human diseases caused by abnormalities in cell communication and their progress in clinical applications. Finally, we summarize various methods for monitoring cell interactions, including cell imaging, proximity-based chemical labeling, mechanical force analysis, downstream analysis strategies, and single-cell technologies. These methods aim to illustrate how biological functions depend on these interactions and the complexity of their regulatory signaling pathways to regulate crucial physiological processes, including tissue homeostasis, cell development, and immune responses in diseases. In addition, this review enhances our understanding of the biological processes that occur after cell-cell binding, highlighting its application in discovering new therapeutic targets and biomarkers related to precision medicine. This collective understanding provides a foundation for developing new targeted drugs and personalized treatments.
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Affiliation(s)
- Jimeng Su
- Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, China
- Cancer Center, Peking University Third Hospital, Beijing, China
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China
| | - Ying Song
- Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, China
- Cancer Center, Peking University Third Hospital, Beijing, China
| | - Zhipeng Zhu
- Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, China
- Cancer Center, Peking University Third Hospital, Beijing, China
| | - Xinyue Huang
- Biomedical Research Institute, Shenzhen Peking University-the Hong Kong University of Science and Technology Medical Center, Shenzhen, China
| | - Jibiao Fan
- College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, China
| | - Jie Qiao
- State Key Laboratory of Female Fertility Promotion, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China.
- National Clinical Research Center for Obstetrics and Gynecology (Peking University Third Hospital), Beijing, China.
- Key Laboratory of Assisted Reproduction (Peking University), Ministry of Education, Beijing, China.
- Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Beijing, China.
| | - Fengbiao Mao
- Institute of Medical Innovation and Research, Peking University Third Hospital, Beijing, China.
- Cancer Center, Peking University Third Hospital, Beijing, China.
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7
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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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Sudhakar M, Vignesh H, Natarajan KN. Crosstalk between tumor and microenvironment: Insights from spatial transcriptomics. Adv Cancer Res 2024; 163:187-222. [PMID: 39271263 DOI: 10.1016/bs.acr.2024.06.009] [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: 09/15/2024]
Abstract
Cancer is a dynamic disease, and clonal heterogeneity plays a fundamental role in tumor development, progression, and resistance to therapies. Single-cell and spatial multimodal technologies can provide a high-resolution molecular map of underlying genomic, epigenomic, and transcriptomic alterations involved in inter- and intra-tumor heterogeneity and interactions with the microenvironment. In this review, we provide a perspective on factors driving cancer heterogeneity, tumor evolution, and clonal states. We briefly describe spatial transcriptomic technologies and summarize recent literature that sheds light on the dynamical interactions between tumor states, cell-to-cell communication, and remodeling local microenvironment.
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Affiliation(s)
- Malvika Sudhakar
- DTU Bioengineering, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Harie Vignesh
- DTU Bioengineering, Technical University of Denmark, Kongens Lyngby, Denmark
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9
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Yan G, Hua SH, Li JJ. Categorization of 31 computational methods to detect spatially variable genes from spatially resolved transcriptomics data. ARXIV 2024:arXiv:2405.18779v2. [PMID: 38855546 PMCID: PMC11160866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review 31 state-of-the-art methods, categorizing SVGs into three types: overall, cell-type-specific, and spatial-domain-marker SVGs. Our review explains the intuitions underlying these methods, summarizes their applications, and categorizes the hypothesis tests they use in the trade-off between generality and specificity for SVG detection. We discuss challenges in SVG detection and propose future directions for improvement. Our review offers insights for method developers and users, advocating for category-specific benchmarking.
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Affiliation(s)
- Guanao Yan
- Department of Statistics, University of California, Los Angeles, CA 90095-1554
| | - Shuo Harper Hua
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305
| | - Jingyi Jessica Li
- Department of Statistics, University of California, Los Angeles, CA 90095-1554
- Department of Human Genetics, University of California, Los Angeles, CA 90095-7088
- Department of Computational Medicine, University of California, Los Angeles, CA 90095-1766
- Department of Biostatistics, University of California, Los Angeles, CA 90095-1772
- Radcliffe Institute for Advanced Study, Harvard University, Cambridge, MA 02138
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10
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Zhang Y, Yang Y, Ren L, Zhan M, Sun T, Zou Q, Zhang Y. Predicting intercellular communication based on metabolite-related ligand-receptor interactions with MRCLinkdb. BMC Biol 2024; 22:152. [PMID: 38978014 PMCID: PMC11232326 DOI: 10.1186/s12915-024-01950-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Accepted: 07/03/2024] [Indexed: 07/10/2024] Open
Abstract
BACKGROUND Metabolite-associated cell communications play critical roles in maintaining human biological function. However, most existing tools and resources focus only on ligand-receptor interaction pairs where both partners are proteinaceous, neglecting other non-protein molecules. To address this gap, we introduce the MRCLinkdb database and algorithm, which aggregates and organizes data related to non-protein L-R interactions in cell-cell communication, providing a valuable resource for predicting intercellular communication based on metabolite-related ligand-receptor interactions. RESULTS Here, we manually curated the metabolite-ligand-receptor (ML-R) interactions from the literature and known databases, ultimately collecting over 790 human and 670 mouse ML-R interactions. Additionally, we compiled information on over 1900 enzymes and 260 transporter entries associated with these metabolites. We developed Metabolite-Receptor based Cell Link Database (MRCLinkdb) to store these ML-R interactions data. Meanwhile, the platform also offers extensive information for presenting ML-R interactions, including fundamental metabolite information and the overall expression landscape of metabolite-associated gene sets (such as receptor, enzymes, and transporter proteins) based on single-cell transcriptomics sequencing (covering 35 human and 26 mouse tissues, 52 human and 44 mouse cell types) and bulk RNA-seq/microarray data (encompassing 62 human and 39 mouse tissues). Furthermore, MRCLinkdb introduces a web server dedicated to the analysis of intercellular communication based on ML-R interactions. MRCLinkdb is freely available at https://www.cellknowledge.com.cn/mrclinkdb/ . CONCLUSIONS In addition to supplementing ligand-receptor databases, MRCLinkdb may provide new perspectives for decoding the intercellular communication and advancing related prediction tools based on ML-R interactions.
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Affiliation(s)
- Yuncong Zhang
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Jinan University, Zhuhai, Guangdong, China
| | - Yu Yang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Liping Ren
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Meixiao Zhan
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Jinan University, Zhuhai, Guangdong, China
| | - Taoping Sun
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Jinan University, Zhuhai, Guangdong, China.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
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11
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Sarkar H, Chitra U, Gold J, Raphael BJ. A count-based model for delineating cell-cell interactions in spatial transcriptomics data. Bioinformatics 2024; 40:i481-i489. [PMID: 38940134 PMCID: PMC11211854 DOI: 10.1093/bioinformatics/btae219] [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] [Indexed: 06/29/2024] Open
Abstract
MOTIVATION Cell-cell interactions (CCIs) consist of cells exchanging signals with themselves and neighboring cells by expressing ligand and receptor molecules and play a key role in cellular development, tissue homeostasis, and other critical biological functions. Since direct measurement of CCIs is challenging, multiple methods have been developed to infer CCIs by quantifying correlations between the gene expression of the ligands and receptors that mediate CCIs, originally from bulk RNA-sequencing data and more recently from single-cell or spatially resolved transcriptomics (SRT) data. SRT has a particular advantage over single-cell approaches, since ligand-receptor correlations can be computed between cells or spots that are physically close in the tissue. However, the transcript counts of individual ligands and receptors in SRT data are generally low, complicating the inference of CCIs from expression correlations. RESULTS We introduce Copulacci, a count-based model for inferring CCIs from SRT data. Copulacci uses a Gaussian copula to model dependencies between the expression of ligands and receptors from nearby spatial locations even when the transcript counts are low. On simulated data, Copulacci outperforms existing CCI inference methods based on the standard Spearman and Pearson correlation coefficients. Using several real SRT datasets, we show that Copulacci discovers biologically meaningful ligand-receptor interactions that are lowly expressed and undiscoverable by existing CCI inference methods. AVAILABILITY AND IMPLEMENTATION Copulacci is implemented in Python and available at https://github.com/raphael-group/copulacci.
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Affiliation(s)
- Hirak Sarkar
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, United States
- Ludwig Cancer Institute, Princeton Branch, Princeton University, Princeton, NJ, 08540, United States
| | - Uthsav Chitra
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, United States
| | - Julian Gold
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, United States
- Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, 08540, United States
| | - Benjamin J Raphael
- Department of Computer Science, Princeton University, Princeton, NJ, 08540, United States
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12
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McGuire D, Markus H, Yang L, Xu J, Montgomery A, Berg A, Li Q, Carrel L, Liu DJ, Jiang B. Dissecting heritability, environmental risk, and air pollution causal effects using > 50 million individuals in MarketScan. Nat Commun 2024; 15:5357. [PMID: 38918381 PMCID: PMC11199552 DOI: 10.1038/s41467-024-49566-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 06/10/2024] [Indexed: 06/27/2024] Open
Abstract
Large national-level electronic health record (EHR) datasets offer new opportunities for disentangling the role of genes and environment through deep phenotype information and approximate pedigree structures. Here we use the approximate geographical locations of patients as a proxy for spatially correlated community-level environmental risk factors. We develop a spatial mixed linear effect (SMILE) model that incorporates both genetics and environmental contribution. We extract EHR and geographical locations from 257,620 nuclear families and compile 1083 disease outcome measurements from the MarketScan dataset. We augment the EHR with publicly available environmental data, including levels of particulate matter 2.5 (PM2.5), nitrogen dioxide (NO2), climate, and sociodemographic data. We refine the estimates of genetic heritability and quantify community-level environmental contributions. We also use wind speed and direction as instrumental variables to assess the causal effects of air pollution. In total, we find PM2.5 or NO2 have statistically significant causal effects on 135 diseases, including respiratory, musculoskeletal, digestive, metabolic, and sleep disorders, where PM2.5 and NO2 tend to affect biologically distinct disease categories. These analyses showcase several robust strategies for jointly modeling genetic and environmental effects on disease risk using large EHR datasets and will benefit upcoming biobank studies in the era of precision medicine.
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Affiliation(s)
- Daniel McGuire
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, 17033, USA
| | - Havell Markus
- MD/PhD Program, Penn State College of Medicine of Medicine, Hershey, PA, 17033, USA
- Bioinformatics and Genomics PhD Program, Penn State College of Medicine, Hershey, PA, 17033, USA
- Institute for Personalized Medicine, Penn State College of Medicine, Hershey, PA, 17033, USA
| | - Lina Yang
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, 17033, USA
| | - Jingyu Xu
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, 17033, USA
| | - Austin Montgomery
- MD/PhD Program, Penn State College of Medicine of Medicine, Hershey, PA, 17033, USA
| | - Arthur Berg
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, 17033, USA
| | - Qunhua Li
- Department of Statistics, Penn State University, University Park, PA, USA
| | - Laura Carrel
- Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, PA, 17033, USA
| | - Dajiang J Liu
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, 17033, USA.
| | - Bibo Jiang
- Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA, 17033, USA.
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13
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Kim H, Kim KE, Madan E, Martin P, Gogna R, Rhee HW, Won KJ. Unveiling contact-mediated cellular crosstalk. Trends Genet 2024:S0168-9525(24)00132-X. [PMID: 38906738 DOI: 10.1016/j.tig.2024.05.010] [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: 04/12/2024] [Revised: 05/28/2024] [Accepted: 05/29/2024] [Indexed: 06/23/2024]
Abstract
Cell-cell interactions orchestrate complex functions in multicellular organisms, forming a regulatory network for diverse biological processes. Their disruption leads to disease states. Recent advancements - including single-cell sequencing and spatial transcriptomics, coupled with powerful bioengineering and molecular tools - have revolutionized our understanding of how cells respond to each other. Notably, spatial transcriptomics allows us to analyze gene expression changes based on cell proximity, offering a unique window into the impact of cell-cell contact. Additionally, computational approaches are being developed to decipher how cell contact governs the symphony of cellular responses. This review explores these cutting-edge approaches, providing valuable insights into deciphering the intricate cellular changes influenced by cell-cell communication.
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Affiliation(s)
- Hyobin Kim
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West, Hollywood, CA, USA
| | - Kwang-Eun Kim
- Department of Convergence Medicine, Yonsei University Wonju College of Medicine, Wonju, South Korea; Department of Chemistry, Seoul National University, Seoul, South Korea
| | - Esha Madan
- Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, USA; School of Medicine, Institute of Molecular Medicine, Virginia Commonwealth University, Richmond, VA, USA; Department of Human and Molecular Genetics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Patrick Martin
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West, Hollywood, CA, USA
| | - Rajan Gogna
- Massey Cancer Center, Virginia Commonwealth University, Richmond, VA, USA; School of Medicine, Institute of Molecular Medicine, Virginia Commonwealth University, Richmond, VA, USA; Department of Human and Molecular Genetics, School of Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Hyun-Woo Rhee
- Department of Chemistry, Seoul National University, Seoul, South Korea.
| | - Kyoung-Jae Won
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West, Hollywood, CA, USA.
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14
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Li Y, Zhang J, Gao X, Zhang QC. Tissue module discovery in single-cell-resolution spatial transcriptomics data via cell-cell interaction-aware cell embedding. Cell Syst 2024; 15:578-592.e7. [PMID: 38823396 DOI: 10.1016/j.cels.2024.05.001] [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: 05/25/2023] [Revised: 01/08/2024] [Accepted: 05/07/2024] [Indexed: 06/03/2024]
Abstract
Computational methods are desired for single-cell-resolution spatial transcriptomics (ST) data analysis to uncover spatial organization principles for how individual cells exert tissue-specific functions. Here, we present ST data analysis via interaction-aware cell embedding (SPACE), a deep-learning method for cell-type identification and tissue module discovery from single-cell-resolution ST data by learning a cell representation that captures its gene expression profile and interactions with its spatial neighbors. SPACE identified spatially informed cell subtypes defined by their special spatial distribution patterns and distinct proximal-interacting cell types. SPACE also automatically discovered "cell communities"-tissue modules with discernible boundaries and a uniform spatial distribution of constituent cell types. For each cell community, SPACE outputs a characteristic proximal cell-cell interaction network associated with physiological processes, which can be used to refine ligand-receptor-based intercellular signaling analyses. We envision that SPACE can be used in large-scale ST projects to understand how proximal cell-cell interactions contribute to emergent biological functions within cell communities. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Yuzhe Li
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Jinsong Zhang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China; Shanghai Qi Zhi Institute, Shanghai 200030, China
| | - Xin Gao
- Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia; KAUST Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia; BioMap, Beijing 100086, China.
| | - Qiangfeng Cliff Zhang
- MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology & Frontier Research Center for Biological Structure, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China.
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15
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Zuo C, Xia J, Chen L. Dissecting tumor microenvironment from spatially resolved transcriptomics data by heterogeneous graph learning. Nat Commun 2024; 15:5057. [PMID: 38871687 DOI: 10.1038/s41467-024-49171-7] [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: 06/10/2023] [Accepted: 05/22/2024] [Indexed: 06/15/2024] Open
Abstract
Spatially resolved transcriptomics (SRT) has enabled precise dissection of tumor-microenvironment (TME) by analyzing its intracellular molecular networks and intercellular cell-cell communication (CCC). However, lacking computational exploration of complicated relations between cells, genes, and histological regions, severely limits the ability to interpret the complex structure of TME. Here, we introduce stKeep, a heterogeneous graph (HG) learning method that integrates multimodality and gene-gene interactions, in unraveling TME from SRT data. stKeep leverages HG to learn both cell-modules and gene-modules by incorporating features of diverse nodes including genes, cells, and histological regions, allows for identifying finer cell-states within TME and cell-state-specific gene-gene relations, respectively. Furthermore, stKeep employs HG to infer CCC for each cell, while ensuring that learned CCC patterns are comparable across different cell-states through contrastive learning. In various cancer samples, stKeep outperforms other tools in dissecting TME such as detecting bi-potent basal populations, neoplastic myoepithelial cells, and metastatic cells distributed within the tumor or leading-edge regions. Notably, stKeep identifies key transcription factors, ligands, and receptors relevant to disease progression, which are further validated by the functional and survival analysis of independent clinical data, thereby highlighting its clinical prognostic and immunotherapy applications.
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Affiliation(s)
- Chunman Zuo
- Institute of Artificial Intelligence, Shanghai Engineering Research Center of Industrial Big Data and Intelligent System, Donghua University, Shanghai, 201620, China.
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130022, China.
| | - Junjie Xia
- Institute of Artificial Intelligence, Shanghai Engineering Research Center of Industrial Big Data and Intelligent System, Donghua University, Shanghai, 201620, China
- Department of Applied Mathematics, Donghua University, Shanghai, 201620, China
| | - Luonan Chen
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, 200031, China.
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, 310024, China.
- West China Biomedical Big Data Center, Med-X center for informatics, West China Hospital, Sichuan University, Chengdu, 610041, China.
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16
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Armingol E, Baghdassarian HM, Lewis NE. The diversification of methods for studying cell-cell interactions and communication. Nat Rev Genet 2024; 25:381-400. [PMID: 38238518 PMCID: PMC11139546 DOI: 10.1038/s41576-023-00685-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/01/2023] [Indexed: 05/20/2024]
Abstract
No cell lives in a vacuum, and the molecular interactions between cells define most phenotypes. Transcriptomics provides rich information to infer cell-cell interactions and communication, thus accelerating the discovery of the roles of cells within their communities. Such research relies heavily on algorithms that infer which cells are interacting and the ligands and receptors involved. Specific pressures on different research niches are driving the evolution of next-generation computational tools, enabling new conceptual opportunities and technological advances. More sophisticated algorithms now account for the heterogeneity and spatial organization of cells, multiple ligand types and intracellular signalling events, and enable the use of larger and more complex datasets, including single-cell and spatial transcriptomics. Similarly, new high-throughput experimental methods are increasing the number and resolution of interactions that can be analysed simultaneously. Here, we explore recent progress in cell-cell interaction research and highlight the diversification of the next generation of tools, which have yielded a rich ecosystem of tools for different applications and are enabling invaluable discoveries.
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Affiliation(s)
- Erick Armingol
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA.
- Department of Paediatrics, University of California, San Diego, La Jolla, CA, USA.
| | - Hratch M Baghdassarian
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA, USA
- Department of Paediatrics, University of California, San Diego, La Jolla, CA, USA
| | - Nathan E Lewis
- Department of Paediatrics, University of California, San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
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17
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Tian J, Bai X, Quek C. Single-Cell Informatics for Tumor Microenvironment and Immunotherapy. Int J Mol Sci 2024; 25:4485. [PMID: 38674070 PMCID: PMC11050520 DOI: 10.3390/ijms25084485] [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: 03/08/2024] [Revised: 04/12/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
Cancer comprises malignant cells surrounded by the tumor microenvironment (TME), a dynamic ecosystem composed of heterogeneous cell populations that exert unique influences on tumor development. The immune community within the TME plays a substantial role in tumorigenesis and tumor evolution. The innate and adaptive immune cells "talk" to the tumor through ligand-receptor interactions and signaling molecules, forming a complex communication network to influence the cellular and molecular basis of cancer. Such intricate intratumoral immune composition and interactions foster the application of immunotherapies, which empower the immune system against cancer to elicit durable long-term responses in cancer patients. Single-cell technologies have allowed for the dissection and characterization of the TME to an unprecedented level, while recent advancements in bioinformatics tools have expanded the horizon and depth of high-dimensional single-cell data analysis. This review will unravel the intertwined networks between malignancy and immunity, explore the utilization of computational tools for a deeper understanding of tumor-immune communications, and discuss the application of these approaches to aid in diagnosis or treatment decision making in the clinical setting, as well as the current challenges faced by the researchers with their potential future improvements.
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Affiliation(s)
| | | | - Camelia Quek
- Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; (J.T.); (X.B.)
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18
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Yu S, Li WV. spVC for the detection and interpretation of spatial gene expression variation. Genome Biol 2024; 25:103. [PMID: 38641849 PMCID: PMC11027374 DOI: 10.1186/s13059-024-03245-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 04/10/2024] [Indexed: 04/21/2024] Open
Abstract
Spatially resolved transcriptomics technologies have opened new avenues for understanding gene expression heterogeneity in spatial contexts. However, existing methods for identifying spatially variable genes often focus solely on statistical significance, limiting their ability to capture continuous expression patterns and integrate spot-level covariates. To address these challenges, we introduce spVC, a statistical method based on a generalized Poisson model. spVC seamlessly integrates constant and spatially varying effects of covariates, facilitating comprehensive exploration of gene expression variability and enhancing interpretability. Simulation and real data applications confirm spVC's accuracy in these tasks, highlighting its versatility in spatial transcriptomics analysis.
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Affiliation(s)
- Shan Yu
- Department of Statistics, Unversity of Virginia, Charlottesville, 22903, VA, USA.
| | - Wei Vivian Li
- Department of Statistics, University of California, Riverside, 92521, CA, USA.
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19
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Samadi Z, Askary A. Spatial motifs reveal patterns in cellular architecture of complex tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.08.588586. [PMID: 38645046 PMCID: PMC11030378 DOI: 10.1101/2024.04.08.588586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Spatial organization of cells is crucial to both proper physiological function of tissues and pathological conditions like cancer. Recent advances in spatial transcriptomics have enabled joint profiling of gene expression and spatial context of the cells. The outcome is an information rich map of the tissue where individual cells, or small regions, can be labeled based on their gene expression state. While spatial transcriptomics excels in its capacity to profile numerous genes within the same sample, most existing methods for analysis of spatial data only examine distribution of one or two labels at a time. These approaches overlook the potential for identifying higher-order associations between cell types - associations that can play a pivotal role in understanding development and function of complex tissues. In this context, we introduce a novel method for detecting motifs in spatial neighborhood graphs. Each motif represents a spatial arrangement of cell types that occurs in the tissue more frequently than expected by chance. To identify spatial motifs, we developed an algorithm for uniform sampling of paths from neighborhood graphs and combined it with a motif finding algorithm on graphs inspired by previous methods for finding motifs in DNA sequences. Using synthetic data with known ground truth, we show that our method can identify spatial motifs with high accuracy and sensitivity. Applied to spatial maps of mouse retinal bipolar cells and hypothalamic preoptic region, our method reveals previously unrecognized patterns in cell type arrangements. In some cases, cells within these spatial patterns differ in their gene expression from other cells of the same type, providing insights into the functional significance of the spatial motifs. These results suggest that our method can illuminate the substantial complexity of neural tissues, provide novel insight even in well studied models, and generate experimentally testable hypotheses.
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Affiliation(s)
- Zainalabedin Samadi
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, 90095, CA, USA
| | - Amjad Askary
- Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, 90095, CA, USA
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20
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Mulholland EJ, Leedham SJ. Redefining clinical practice through spatial profiling: a revolution in tissue analysis. Ann R Coll Surg Engl 2024; 106:305-312. [PMID: 38555868 PMCID: PMC10981989 DOI: 10.1308/rcsann.2023.0091] [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] [Accepted: 10/25/2023] [Indexed: 04/02/2024] Open
Abstract
Spatial biology, which combines molecular biology and advanced imaging, enhances our understanding of tissue cellular organisation. Despite its potential, spatial omics encounters challenges related to data complexity, computational requirements and standardisation of analysis. In clinical applications, spatial omics has the potential to revolutionise biomarker discovery, disease stratification and personalised treatments. It can identify disease-specific cell patterns, and could help risk stratify patients for clinical trials and disease-appropriate therapies. Although there are challenges in adopting it in clinical practice, spatial omics has the potential to significantly enhance patient outcomes. In this paper, we discuss the recent evolution of spatial biology, and its potential for improving our tissue level understanding and treatment of disease, to help advance precision and effectiveness in healthcare interventions.
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21
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Li R, Chen X, Yang X. Navigating the landscapes of spatial transcriptomics: How computational methods guide the way. WILEY INTERDISCIPLINARY REVIEWS. RNA 2024; 15:e1839. [PMID: 38527900 DOI: 10.1002/wrna.1839] [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: 11/23/2023] [Revised: 02/24/2024] [Accepted: 03/04/2024] [Indexed: 03/27/2024]
Abstract
Spatially resolved transcriptomics has been dramatically transforming biological and medical research in various fields. It enables transcriptome profiling at single-cell, multi-cellular, or sub-cellular resolution, while retaining the information of geometric localizations of cells in complex tissues. The coupling of cell spatial information and its molecular characteristics generates a novel multi-modal high-throughput data source, which poses new challenges for the development of analytical methods for data-mining. Spatial transcriptomic data are often highly complex, noisy, and biased, presenting a series of difficulties, many unresolved, for data analysis and generation of biological insights. In addition, to keep pace with the ever-evolving spatial transcriptomic experimental technologies, the existing analytical theories and tools need to be updated and reformed accordingly. In this review, we provide an overview and discussion of the current computational approaches for mining of spatial transcriptomics data. Future directions and perspectives of methodology design are proposed to stimulate further discussions and advances in new analytical models and algorithms. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA Evolution and Genomics > Computational Analyses of RNA RNA Export and Localization > RNA Localization.
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Affiliation(s)
- Runze Li
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Xu Chen
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Xuerui Yang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
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22
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Caetano A, Sharpe P. Redefining Mucosal Inflammation with Spatial Genomics. J Dent Res 2024; 103:129-137. [PMID: 38166489 PMCID: PMC10845836 DOI: 10.1177/00220345231216114] [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: 01/04/2024] Open
Abstract
The human oral mucosa contains one of the most complex cellular systems that are essential for normal physiology and defense against a wide variety of local pathogens. Evolving techniques and experimental systems have helped refine our understanding of this complex cellular network. Current single-cell RNA sequencing methods can resolve subtle differences between cell types and states, thus providing a great tool for studying the molecular and cellular repertoire of the oral mucosa in health and disease. However, it requires the dissociation of tissue samples, which means that the interrelationships between cells are lost. Spatial transcriptomic methods bypass tissue dissociation and retain this spatial information, thereby allowing gene expression to be assessed across thousands of cells within the context of tissue structural organization. Here, we discuss the contribution of spatial technologies in shaping our understanding of this complex system. We consider the impact on identifying disease cellular neighborhoods and how space defines cell state. We also discuss the limitations and future directions of spatial sequencing technologies with recent advances in machine learning. Finally, we offer a perspective on open questions about mucosal homeostasis that these technologies are well placed to address.
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Affiliation(s)
- A.J. Caetano
- Centre for Oral Immunobiology and Regenerative Medicine, Barts Centre for Squamous Cancer, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, UK
| | - P.T. Sharpe
- Centre for Craniofacial and Regenerative Biology, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London, UK
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23
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Zheng H, Harcum SW, Pei J, Xie W. Stochastic biological system-of-systems modelling for iPSC culture. Commun Biol 2024; 7:39. [PMID: 38191636 PMCID: PMC10774284 DOI: 10.1038/s42003-023-05653-w] [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: 07/29/2023] [Accepted: 11/30/2023] [Indexed: 01/10/2024] Open
Abstract
Large-scale manufacturing of induced pluripotent stem cells (iPSCs) is essential for cell therapies and regenerative medicines. Yet, iPSCs form large cell aggregates in suspension bioreactors, resulting in insufficient nutrient supply and extra metabolic waste build-up for the cells located at the core. Since subtle changes in micro-environment can lead to a heterogeneous cell population, a novel Biological System-of-Systems (Bio-SoS) framework is proposed to model cell-to-cell interactions, spatial and metabolic heterogeneity, and cell response to micro-environmental variation. Building on stochastic metabolic reaction network, aggregation kinetics, and reaction-diffusion mechanisms, the Bio-SoS model characterizes causal interdependencies at individual cell, aggregate, and cell population levels. It has a modular design that enables data integration and improves predictions for different monolayer and aggregate culture processes. In addition, a variance decomposition analysis is derived to quantify the impact of factors (i.e., aggregate size) on cell product health and quality heterogeneity.
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Affiliation(s)
- Hua Zheng
- Mechanical and Industrial Engineering, Northeastern University, Boston, MA, 02115, USA
| | | | - Jinxiang Pei
- Mechanical and Industrial Engineering, Northeastern University, Boston, MA, 02115, USA
| | - Wei Xie
- Mechanical and Industrial Engineering, Northeastern University, Boston, MA, 02115, USA.
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24
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Yang X, Huang K, Yang D, Zhao W, Zhou X. Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review. GLOBAL CHALLENGES (HOBOKEN, NJ) 2024; 8:2300163. [PMID: 38223896 PMCID: PMC10784210 DOI: 10.1002/gch2.202300163] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Revised: 09/20/2023] [Indexed: 01/16/2024]
Abstract
The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.
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Affiliation(s)
- Xue Yang
- Department of Pancreatic Surgery and West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu610041China
| | - Kexin Huang
- Department of Pancreatic Surgery and West China Biomedical Big Data CenterWest China HospitalSichuan UniversityChengdu610041China
| | - Dewei Yang
- College of Advanced Manufacturing EngineeringChongqing University of Posts and TelecommunicationsChongqingChongqing400000China
| | - Weiling Zhao
- Center for Systems MedicineSchool of Biomedical InformaticsUTHealth at HoustonHoustonTX77030USA
| | - Xiaobo Zhou
- Center for Systems MedicineSchool of Biomedical InformaticsUTHealth at HoustonHoustonTX77030USA
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25
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Harrer DC, Lüke F, Pukrop T, Ghibelli L, Reichle A, Heudobler D. Addressing Genetic Tumor Heterogeneity, Post-Therapy Metastatic Spread, Cancer Repopulation, and Development of Acquired Tumor Cell Resistance. Cancers (Basel) 2023; 16:180. [PMID: 38201607 PMCID: PMC10778239 DOI: 10.3390/cancers16010180] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/08/2023] [Accepted: 12/12/2023] [Indexed: 01/12/2024] Open
Abstract
The concept of post-therapy metastatic spread, cancer repopulation and acquired tumor cell resistance (M-CRAC) rationalizes tumor progression because of tumor cell heterogeneity arising from post-therapy genetic damage and subsequent tissue repair mechanisms. Therapeutic strategies designed to specifically address M-CRAC involve tissue editing approaches, such as low-dose metronomic chemotherapy and the use of transcriptional modulators with or without targeted therapies. Notably, tumor tissue editing holds the potential to treat patients, who are refractory to or relapsing (r/r) after conventional chemotherapy, which is usually based on administering a maximum tolerable dose of a cytostatic drugs. Clinical trials enrolling patients with r/r malignancies, e.g., non-small cell lung cancer, Hodgkin's lymphoma, Langerhans cell histiocytosis and acute myelocytic leukemia, indicate that tissue editing approaches could yield tangible clinical benefit. In contrast to conventional chemotherapy or state-of-the-art precision medicine, tissue editing employs a multi-pronged approach targeting important drivers of M-CRAC across various tumor entities, thereby, simultaneously engaging tumor cell differentiation, immunomodulation, and inflammation control. In this review, we highlight the M-CRAC concept as a major factor in resistance to conventional cancer therapies and discusses tissue editing as a potential treatment.
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Affiliation(s)
- Dennis Christoph Harrer
- Department of Internal Medicine III, Hematology and Oncology, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053 Regensburg, Germany; (D.C.H.); (F.L.); (T.P.); (D.H.)
| | - Florian Lüke
- Department of Internal Medicine III, Hematology and Oncology, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053 Regensburg, Germany; (D.C.H.); (F.L.); (T.P.); (D.H.)
- Division of Personalized Tumor Therapy, Fraunhofer Institute for Toxicology and Experimental Medicine, 30625 Regensburg, Germany
| | - Tobias Pukrop
- Department of Internal Medicine III, Hematology and Oncology, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053 Regensburg, Germany; (D.C.H.); (F.L.); (T.P.); (D.H.)
- Bavarian Cancer Research Center (BZKF), University Hospital Regensburg, 93053 Regensburg, Germany
| | - Lina Ghibelli
- Department of Biology, University of Rome “Tor Vergata”, 00133 Rome, Italy;
| | - Albrecht Reichle
- Department of Internal Medicine III, Hematology and Oncology, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053 Regensburg, Germany; (D.C.H.); (F.L.); (T.P.); (D.H.)
| | - Daniel Heudobler
- Department of Internal Medicine III, Hematology and Oncology, University Hospital Regensburg, Franz-Josef-Strauß-Allee 11, 93053 Regensburg, Germany; (D.C.H.); (F.L.); (T.P.); (D.H.)
- Bavarian Cancer Research Center (BZKF), University Hospital Regensburg, 93053 Regensburg, Germany
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26
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Wang S, Rong R, Zhou Q, Yang DM, Zhang X, Zhan X, Bishop J, Chi Z, Wilhelm CJ, Zhang S, Pickering CR, Kris MG, Minna J, Xie Y, Xiao G. Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images. Nat Commun 2023; 14:7872. [PMID: 38081823 PMCID: PMC10713592 DOI: 10.1038/s41467-023-43172-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Accepted: 11/02/2023] [Indexed: 12/18/2023] Open
Abstract
Recent advancements in tissue imaging techniques have facilitated the visualization and identification of various cell types within physiological and pathological contexts. Despite the emergence of cell-cell interaction studies, there is a lack of methods for evaluating individual spatial interactions. In this study, we introduce Ceograph, a cell spatial organization-based graph convolutional network designed to analyze cell spatial organization (for example,. the cell spatial distribution, morphology, proximity, and interactions) derived from pathology images. Ceograph identifies key cell spatial organization features by accurately predicting their influence on patient clinical outcomes. In patients with oral potentially malignant disorders, our model highlights reduced structural concordance and increased closeness in epithelial substrata as driving features for an elevated risk of malignant transformation. In lung cancer patients, Ceograph detects elongated tumor nuclei and diminished stroma-stroma closeness as biomarkers for insensitivity to EGFR tyrosine kinase inhibitors. With its potential to predict various clinical outcomes, Ceograph offers a deeper understanding of biological processes and supports the development of personalized therapeutic strategies.
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Affiliation(s)
- Shidan Wang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA.
| | - Ruichen Rong
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Qin Zhou
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Donghan M Yang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Xinyi Zhang
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Xiaowei Zhan
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Justin Bishop
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Zhikai Chi
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Clare J Wilhelm
- Department of Thoracic Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Siyuan Zhang
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Mark G Kris
- Department of Thoracic Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - John Minna
- Hamon Center for Therapeutic Oncology Research, UT Southwestern Medical Center, Dallas, TX, USA
- Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Yang Xie
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA
- Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, TX, USA.
- Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX, USA.
- Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, USA.
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27
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Choi J, Ehrlich ME, Roussos P, Wang P, Yuan GC, Song X. QuadST: A Powerful and Robust Approach for Identifying Cell-Cell Interaction-Changed Genes on Spatially Resolved Transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.04.570019. [PMID: 38106025 PMCID: PMC10723309 DOI: 10.1101/2023.12.04.570019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Spatially resolved transcriptomics (SRT) have enabled profiling spatial organization of cells and their transcriptome in situ. Various analytical methods have been developed to uncover cell-cell interaction processes using SRT data. To improve upon existing efforts, we developed a novel statistical framework called QuadST for the robust and powerful identification of interaction-changed genes (ICGs) for cell-type-pair specific interactions on a single-cell SRT dataset. QuadST is motivated by the idea that in the presence of cell-cell interaction, gene expression level can vary with cell-cell distance between cell type pairs, which can be particularly pronounced within and in the vicinity of cell-cell interaction distance. Specifically, QuadST infers ICGs in a specific cell type pair's interaction based on a quantile regression model, which allows us to assess the strength of distance-expression association across entire distance quantiles conditioned on gene expression level. To identify ICGs, QuadST performs a hypothesis testing with an empirically estimated FDR, whose upper bound is determined by the ratio of cumulative associations at symmetrically smaller and larger distance quantiles simultaneously across all genes. Simulation studies illustrate that QuadST provides consistent FDR control and better power performance than other compared methods. Its application on SRT datasets profiled from mouse brains demonstrates that QuadST can identify ICGs presumed to play a role in specific cell type pair interactions (e.g., synaptic pathway genes among excitatory neuron cell interactions). These results suggest that QuadST can be a useful tool to discover genes and regulatory processes involved in specific cell type pair interactions.
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Affiliation(s)
- Jinmyung Choi
- Institute for Health Care Delivery Science, Department of Population Health Science and Policy, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michelle E. Ehrlich
- Departments of Neurology, Pediatrics, and Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Department of Psychiatry, Department of Genetics and Genomics Sciences, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, NY, USA; Mental Illness Research Education, and Clinical Center (VISN 2 South), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Pei Wang
- Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Xiaoyu Song
- Institute for Health Care Delivery Science, Department of Population Health Science and Policy, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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28
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Toninelli M, Rossetti G, Pagani M. Charting the tumor microenvironment with spatial profiling technologies. Trends Cancer 2023; 9:1085-1096. [PMID: 37673713 DOI: 10.1016/j.trecan.2023.08.004] [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: 05/25/2023] [Revised: 08/04/2023] [Accepted: 08/10/2023] [Indexed: 09/08/2023]
Abstract
In recent years technologies that can achieve readouts at cellular resolution such as single-cell RNA sequencing (scRNA-seq) have provided a comprehensive characterization of the cellular proportions and phenotypes that populate the tumor microenvironment (TME). However, because of the sample dissociation steps required by these protocols, they fail to capture information related to the intricate spatial context in which cells operate as well as their dense networks of interactions. Spatial profiling technologies have recently emerged as a valuable way to investigate the physical organization of cells crowding the TME in intact tissues. In this review we first discuss how spatial profiling technologies have propelled TME characterization, and then explore their potential to improve both diagnosis and prognosis for cancer patients in the clinic.
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Affiliation(s)
- Mattia Toninelli
- IFOM ETS, The AIRC Institute of Molecular Oncology, Milan, Italy
| | - Grazisa Rossetti
- IFOM ETS, The AIRC Institute of Molecular Oncology, Milan, Italy
| | - Massimiliano Pagani
- IFOM ETS, The AIRC Institute of Molecular Oncology, Milan, Italy; Department of Medical Biotechnology and Translational Medicine (BIOMETRA), Università degli Studi, Milan, Italy.
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29
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Zhang C, Dong K, Aihara K, Chen L, Zhang S. STAMarker: determining spatial domain-specific variable genes with saliency maps in deep learning. Nucleic Acids Res 2023; 51:e103. [PMID: 37811885 PMCID: PMC10639070 DOI: 10.1093/nar/gkad801] [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: 12/08/2022] [Revised: 08/26/2023] [Accepted: 09/19/2023] [Indexed: 10/10/2023] Open
Abstract
Spatial transcriptomics characterizes gene expression profiles while retaining the information of the spatial context, providing an unprecedented opportunity to understand cellular systems. One of the essential tasks in such data analysis is to determine spatially variable genes (SVGs), which demonstrate spatial expression patterns. Existing methods only consider genes individually and fail to model the inter-dependence of genes. To this end, we present an analytic tool STAMarker for robustly determining spatial domain-specific SVGs with saliency maps in deep learning. STAMarker is a three-stage ensemble framework consisting of graph-attention autoencoders, multilayer perceptron (MLP) classifiers, and saliency map computation by the backpropagated gradient. We illustrate the effectiveness of STAMarker and compare it with serveral commonly used competing methods on various spatial transcriptomic data generated by different platforms. STAMarker considers all genes at once and is more robust when the dataset is very sparse. STAMarker could identify spatial domain-specific SVGs for characterizing spatial domains and enable in-depth analysis of the region of interest in the tissue section.
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Affiliation(s)
- Chihao Zhang
- NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kangning Dong
- NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kazuyuki Aihara
- International Research Center for Neurointelligence, The University of Tokyo Institutes for Advanced Study, The University of Tokyo, Tokyo 113-0033, Japan
| | - Luonan Chen
- Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
- School of Life Science and Technology, Shanghai Tech University, Shanghai 201210, China
- Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai, Guangdong 519031, China
| | - Shihua Zhang
- NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China
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30
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Kim H, Kumar A, Lövkvist C, Palma AM, Martin P, Kim J, Bhoopathi P, Trevino J, Fisher P, Madan E, Gogna R, Won KJ. CellNeighborEX: deciphering neighbor-dependent gene expression from spatial transcriptomics data. Mol Syst Biol 2023; 19:e11670. [PMID: 37815040 PMCID: PMC10632736 DOI: 10.15252/msb.202311670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 09/14/2023] [Accepted: 09/18/2023] [Indexed: 10/11/2023] Open
Abstract
Cells have evolved their communication methods to sense their microenvironments and send biological signals. In addition to communication using ligands and receptors, cells use diverse channels including gap junctions to communicate with their immediate neighbors. Current approaches, however, cannot effectively capture the influence of various microenvironments. Here, we propose a novel approach to investigate cell neighbor-dependent gene expression (CellNeighborEX) in spatial transcriptomics (ST) data. To categorize cells based on their microenvironment, CellNeighborEX uses direct cell location or the mixture of transcriptome from multiple cells depending on ST technologies. For each cell type, CellNeighborEX identifies diverse gene sets associated with partnering cell types, providing further insight. We found that cells express different genes depending on their neighboring cell types in various tissues including mouse embryos, brain, and liver cancer. Those genes are associated with critical biological processes such as development or metastases. We further validated that gene expression is induced by neighboring partners via spatial visualization. The neighbor-dependent gene expression suggests new potential genes involved in cell-cell interactions beyond what ligand-receptor co-expression can discover.
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Affiliation(s)
- Hyobin Kim
- Department of Computational BiomedicineCedars‐Sinai Medical CenterHollywoodCAUSA
- Biotech Research and Innovation Centre (BRIC)University of CopenhagenCopenhagenDenmark
| | - Amit Kumar
- Massey Cancer CenterVirginia Commonwealth UniversityRichmondVAUSA
- School of Medicine, Institute of Molecular MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Department of Human and Molecular Genetics, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
| | - Cecilia Lövkvist
- Novo Nordisk Foundation Center for Stem Cell Medicine, reNEWUniversity of CopenhagenCopenhagenDenmark
| | - António M Palma
- Massey Cancer CenterVirginia Commonwealth UniversityRichmondVAUSA
- School of Medicine, Institute of Molecular MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Instituto Superior TecnicoUniversidade de LisboaLisboaPortugal
| | - Patrick Martin
- Department of Computational BiomedicineCedars‐Sinai Medical CenterHollywoodCAUSA
- Biotech Research and Innovation Centre (BRIC)University of CopenhagenCopenhagenDenmark
| | - Junil Kim
- School of Systems Biomedical ScienceSoongsil UniversitySeoulKorea
| | - Praveen Bhoopathi
- Massey Cancer CenterVirginia Commonwealth UniversityRichmondVAUSA
- School of Medicine, Institute of Molecular MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Department of Human and Molecular Genetics, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
| | - Jose Trevino
- Massey Cancer CenterVirginia Commonwealth UniversityRichmondVAUSA
- Department of Surgery, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
| | - Paul Fisher
- Massey Cancer CenterVirginia Commonwealth UniversityRichmondVAUSA
- School of Medicine, Institute of Molecular MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Department of Human and Molecular Genetics, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
| | - Esha Madan
- Massey Cancer CenterVirginia Commonwealth UniversityRichmondVAUSA
- School of Medicine, Institute of Molecular MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Department of Human and Molecular Genetics, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Department of Surgery, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
| | - Rajan Gogna
- Massey Cancer CenterVirginia Commonwealth UniversityRichmondVAUSA
- School of Medicine, Institute of Molecular MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Department of Human and Molecular Genetics, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
- Department of Surgery, School of MedicineVirginia Commonwealth UniversityRichmondVAUSA
| | - Kyoung Jae Won
- Department of Computational BiomedicineCedars‐Sinai Medical CenterHollywoodCAUSA
- Biotech Research and Innovation Centre (BRIC)University of CopenhagenCopenhagenDenmark
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31
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Alexandrov T, Saez‐Rodriguez J, Saka SK. Enablers and challenges of spatial omics, a melting pot of technologies. Mol Syst Biol 2023; 19:e10571. [PMID: 37842805 PMCID: PMC10632737 DOI: 10.15252/msb.202110571] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 10/17/2023] Open
Abstract
Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.
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Affiliation(s)
- Theodore Alexandrov
- Structural and Computational Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- BioInnovation InstituteCopenhagenDenmark
| | - Julio Saez‐Rodriguez
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Faculty of Medicine and Heidelberg University Hospital, Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Sinem K Saka
- Genome Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
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32
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Wang X, Almet AA, Nie Q. The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics. Semin Cancer Biol 2023; 95:42-51. [PMID: 37454878 PMCID: PMC10627116 DOI: 10.1016/j.semcancer.2023.07.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 03/02/2023] [Accepted: 07/13/2023] [Indexed: 07/18/2023]
Abstract
Cell-cell interactions instruct cell fate and function. These interactions are hijacked to promote cancer development. Single-cell transcriptomics and spatial transcriptomics have become powerful new tools for researchers to profile the transcriptional landscape of cancer at unparalleled genetic depth. In this review, we discuss the rapidly growing array of computational tools to infer cell-cell interactions from non-spatial single-cell RNA-sequencing and the limited but growing number of methods for spatial transcriptomics data. Downstream analyses of these computational tools and applications to cancer studies are highlighted. We finish by suggesting several directions for further extensions that anticipate the increasing availability of multi-omics cancer data.
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Affiliation(s)
- Xinyi Wang
- Department of Mathematics, University of California, Irvine, Irvine, CA, United States
| | - Axel A Almet
- Department of Mathematics, University of California, Irvine, Irvine, CA, United States; The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, United States.
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, Irvine, CA, United States; The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, United States; Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, United States.
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33
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Velten B, Stegle O. Principles and challenges of modeling temporal and spatial omics data. Nat Methods 2023; 20:1462-1474. [PMID: 37710019 DOI: 10.1038/s41592-023-01992-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/31/2023] [Indexed: 09/16/2023]
Abstract
Studies with temporal or spatial resolution are crucial to understand the molecular dynamics and spatial dependencies underlying a biological process or system. With advances in high-throughput omic technologies, time- and space-resolved molecular measurements at scale are increasingly accessible, providing new opportunities to study the role of timing or structure in a wide range of biological questions. At the same time, analyses of the data being generated in the context of spatiotemporal studies entail new challenges that need to be considered, including the need to account for temporal and spatial dependencies and compare them across different scales, biological samples or conditions. In this Review, we provide an overview of common principles and challenges in the analysis of temporal and spatial omics data. We discuss statistical concepts to model temporal and spatial dependencies and highlight opportunities for adapting existing analysis methods to data with temporal and spatial dimensions.
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Affiliation(s)
- Britta Velten
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Cellular Genetics Programme, Wellcome Sanger Institute, Hinxton, Cambridge, UK.
- Centre for Organismal Studies (COS) and Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany.
| | - Oliver Stegle
- Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Cellular Genetics Programme, Wellcome Sanger Institute, Hinxton, Cambridge, UK.
- Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
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34
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Li H, Ma T, Hao M, Guo W, Gu J, Zhang X, Wei L. Decoding functional cell-cell communication events by multi-view graph learning on spatial transcriptomics. Brief Bioinform 2023; 24:bbad359. [PMID: 37824741 DOI: 10.1093/bib/bbad359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/25/2023] [Accepted: 09/18/2023] [Indexed: 10/14/2023] Open
Abstract
Cell-cell communication events (CEs) are mediated by multiple ligand-receptor (LR) pairs. Usually only a particular subset of CEs directly works for a specific downstream response in a particular microenvironment. We name them as functional communication events (FCEs) of the target responses. Decoding FCE-target gene relations is: important for understanding the mechanisms of many biological processes, but has been intractable due to the mixing of multiple factors and the lack of direct observations. We developed a method HoloNet for decoding FCEs using spatial transcriptomic data by integrating LR pairs, cell-type spatial distribution and downstream gene expression into a deep learning model. We modeled CEs as a multi-view network, developed an attention-based graph learning method to train the model for generating target gene expression with the CE networks, and decoded the FCEs for specific downstream genes by interpreting trained models. We applied HoloNet on three Visium datasets of breast cancer and liver cancer. The results detangled the multiple factors of FCEs by revealing how LR signals and cell types affect specific biological processes, and specified FCE-induced effects in each single cell. We conducted simulation experiments and showed that HoloNet is more reliable on LR prioritization in comparison with existing methods. HoloNet is a powerful tool to illustrate cell-cell communication landscapes and reveal vital FCEs that shape cellular phenotypes. HoloNet is available as a Python package at https://github.com/lhc17/HoloNet.
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Affiliation(s)
- Haochen Li
- School of Medicine, Tsinghua University, Beijing 100084, China
| | - Tianxing Ma
- MOE Key Lab of Bioinformatics, Bioinformatics Division of BNRIST and Department of Automation, Tsinghua University, Beijing 100084, China
| | - Minsheng Hao
- MOE Key Lab of Bioinformatics, Bioinformatics Division of BNRIST and Department of Automation, Tsinghua University, Beijing 100084, China
| | - Wenbo Guo
- MOE Key Lab of Bioinformatics, Bioinformatics Division of BNRIST and Department of Automation, Tsinghua University, Beijing 100084, China
| | - Jin Gu
- MOE Key Lab of Bioinformatics, Bioinformatics Division of BNRIST and Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xuegong Zhang
- School of Medicine, Tsinghua University, Beijing 100084, China
- MOE Key Lab of Bioinformatics, Bioinformatics Division of BNRIST and Department of Automation, Tsinghua University, Beijing 100084, China
| | - Lei Wei
- MOE Key Lab of Bioinformatics, Bioinformatics Division of BNRIST and Department of Automation, Tsinghua University, Beijing 100084, China
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35
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Cheng M, Jiang Y, Xu J, Mentis AFA, Wang S, Zheng H, Sahu SK, Liu L, Xu X. Spatially resolved transcriptomics: a comprehensive review of their technological advances, applications, and challenges. J Genet Genomics 2023; 50:625-640. [PMID: 36990426 DOI: 10.1016/j.jgg.2023.03.011] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 03/11/2023] [Accepted: 03/16/2023] [Indexed: 03/29/2023]
Abstract
The ability to explore life kingdoms is largely driven by innovations and breakthroughs in technology, from the invention of the microscope 350 years ago to the recent emergence of single-cell sequencing, by which the scientific community has been able to visualize life at an unprecedented resolution. Most recently, the Spatially Resolved Transcriptomics (SRT) technologies have filled the gap in probing the spatial or even three-dimensional organization of the molecular foundation behind the molecular mysteries of life, including the origin of different cellular populations developed from totipotent cells and human diseases. In this review, we introduce recent progresses and challenges on SRT from the perspectives of technologies and bioinformatic tools, as well as the representative SRT applications. With the currently fast-moving progress of the SRT technologies and promising results from early adopted research projects, we can foresee the bright future of such new tools in understanding life at the most profound analytical level.
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Affiliation(s)
| | - Yujia Jiang
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China
| | | | | | - Shuai Wang
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | | | - Sunil Kumar Sahu
- BGI-Shenzhen, Shenzhen, Guangdong 518103, China; State Key Laboratory of Agricultural Genomics, BGI-Shenzhen, Shenzhen, Guangdong 518083, China
| | - Longqi Liu
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Xun Xu
- BGI-Hangzhou, Hangzhou, Zhejiang 310012, China; BGI-Shenzhen, Shenzhen, Guangdong 518103, China; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Guangdong Provincial Key Laboratory of Genome Read and Write, Shenzhen, Guangdong 518120, China.
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36
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Maffuid K, Cao Y. Decoding the Complexity of Immune-Cancer Cell Interactions: Empowering the Future of Cancer Immunotherapy. Cancers (Basel) 2023; 15:4188. [PMID: 37627216 PMCID: PMC10453128 DOI: 10.3390/cancers15164188] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/16/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023] Open
Abstract
The tumor and tumor microenvironment (TME) consist of a complex network of cells, including malignant, immune, fibroblast, and vascular cells, which communicate with each other. Disruptions in cell-cell communication within the TME, caused by a multitude of extrinsic and intrinsic factors, can contribute to tumorigenesis, hinder the host immune system, and enable tumor evasion. Understanding and addressing intercellular miscommunications in the TME are vital for combating these processes. The effectiveness of immunotherapy and the heterogeneous response observed among patients can be attributed to the intricate cellular communication between immune cells and cancer cells. To unravel these interactions, various experimental, statistical, and computational techniques have been developed. These include ligand-receptor analysis, intercellular proximity labeling approaches, and imaging-based methods, which provide insights into the distorted cell-cell interactions within the TME. By characterizing these interactions, we can enhance the design of cancer immunotherapy strategies. In this review, we present recent advancements in the field of mapping intercellular communication, with a particular focus on immune-tumor cellular interactions. By modeling these interactions, we can identify critical factors and develop strategies to improve immunotherapy response and overcome treatment resistance.
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Affiliation(s)
- Kaitlyn Maffuid
- Division of Pharmacotherapy and Experimental Therapeutics, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;
| | - Yanguang Cao
- Division of Pharmacotherapy and Experimental Therapeutics, School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;
- Lineberger Comprehensive Cancer Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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Wang WJ, Chu LX, He LY, Zhang MJ, Dang KT, Gao C, Ge QY, Wang ZG, Zhao XW. Spatial transcriptomics: recent developments and insights in respiratory research. Mil Med Res 2023; 10:38. [PMID: 37592342 PMCID: PMC10433685 DOI: 10.1186/s40779-023-00471-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/24/2023] [Indexed: 08/19/2023] Open
Abstract
The respiratory system's complex cellular heterogeneity presents unique challenges to researchers in this field. Although bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) have provided insights into cell types and heterogeneity in the respiratory system, the relevant specific spatial localization and cellular interactions have not been clearly elucidated. Spatial transcriptomics (ST) has filled this gap and has been widely used in respiratory studies. This review focuses on the latest iterative technology of ST in recent years, summarizing how ST can be applied to the physiological and pathological processes of the respiratory system, with emphasis on the lungs. Finally, the current challenges and potential development directions are proposed, including high-throughput full-length transcriptome, integration of multi-omics, temporal and spatial omics, bioinformatics analysis, etc. These viewpoints are expected to advance the study of systematic mechanisms, including respiratory studies.
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Affiliation(s)
- Wen-Jia Wang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Liu-Xi Chu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Li-Yong He
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Ming-Jing Zhang
- Orthopaedic Bioengineering Research Group, Division of Surgery and Interventional Science, University College London, London, HA7 4LP, UK
| | - Kai-Tong Dang
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Chen Gao
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Qin-Yu Ge
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Zhou-Guang Wang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
| | - Xiang-Wei Zhao
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China.
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38
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Souto-Maior C, Serrano Negron YL, Harbison ST. Nonlinear expression patterns and multiple shifts in gene network interactions underlie robust phenotypic change in Drosophila melanogaster selected for night sleep duration. PLoS Comput Biol 2023; 19:e1011389. [PMID: 37561813 PMCID: PMC10443883 DOI: 10.1371/journal.pcbi.1011389] [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: 02/15/2023] [Revised: 08/22/2023] [Accepted: 07/25/2023] [Indexed: 08/12/2023] Open
Abstract
All but the simplest phenotypes are believed to result from interactions between two or more genes forming complex networks of gene regulation. Sleep is a complex trait known to depend on the system of feedback loops of the circadian clock, and on many other genes; however, the main components regulating the phenotype and how they interact remain an unsolved puzzle. Genomic and transcriptomic data may well provide part of the answer, but a full account requires a suitable quantitative framework. Here we conducted an artificial selection experiment for sleep duration with RNA-seq data acquired each generation. The phenotypic results are robust across replicates and previous experiments, and the transcription data provides a high-resolution, time-course data set for the evolution of sleep-related gene expression. In addition to a Hierarchical Generalized Linear Model analysis of differential expression that accounts for experimental replicates we develop a flexible Gaussian Process model that estimates interactions between genes. 145 gene pairs are found to have interactions that are different from controls. Our method appears to be not only more specific than standard correlation metrics but also more sensitive, finding correlations not significant by other methods. Statistical predictions were compared to experimental data from public databases on gene interactions. Mutations of candidate genes implicated by our results affected night sleep, and gene expression profiles largely met predicted gene-gene interactions.
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Affiliation(s)
- Caetano Souto-Maior
- Laboratory of Systems Genetics, Systems Biology Center, National Heart Lung and Blood Institute, Bethesda, Maryland, United States of America
| | - Yazmin L. Serrano Negron
- Laboratory of Systems Genetics, Systems Biology Center, National Heart Lung and Blood Institute, Bethesda, Maryland, United States of America
| | - Susan T. Harbison
- Laboratory of Systems Genetics, Systems Biology Center, National Heart Lung and Blood Institute, Bethesda, Maryland, United States of America
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39
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Heumos L, Schaar AC, Lance C, Litinetskaya A, Drost F, Zappia L, Lücken MD, Strobl DC, Henao J, Curion F, Schiller HB, Theis FJ. Best practices for single-cell analysis across modalities. Nat Rev Genet 2023; 24:550-572. [PMID: 37002403 PMCID: PMC10066026 DOI: 10.1038/s41576-023-00586-w] [Citation(s) in RCA: 168] [Impact Index Per Article: 168.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2023] [Indexed: 04/03/2023]
Abstract
Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.
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Affiliation(s)
- Lukas Heumos
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Anna C Schaar
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany
| | - Christopher Lance
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Paediatrics, Dr von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Anastasia Litinetskaya
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Felix Drost
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Luke Zappia
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Malte D Lücken
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity, Helmholtz Munich, Munich, Germany
| | - Daniel C Strobl
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Juan Henao
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
| | - Fabiola Curion
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Herbert B Schiller
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany.
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40
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Cheng C, Chen W, Jin H, Chen X. A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell-Cell Communication. Cells 2023; 12:1970. [PMID: 37566049 PMCID: PMC10417635 DOI: 10.3390/cells12151970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/10/2023] [Accepted: 07/21/2023] [Indexed: 08/12/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of cellular heterogeneity, identification of rare but significant cell types, and exploration of cell-cell communications and interactions. Its broad applications span both basic and clinical research domains. In this comprehensive review, we survey the current landscape of scRNA-seq analysis methods and tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, and the inference of cell-cell communication. We review the challenges encountered in scRNA-seq analysis, including issues of sparsity or low expression, reliability of cell annotation, and assumptions in data integration, and discuss the potential impact of suboptimal clustering and differential expression analysis tools on downstream analyses, particularly in identifying cell subpopulations. Finally, we discuss recent advancements and future directions for enhancing scRNA-seq analysis. Specifically, we highlight the development of novel tools for annotating single-cell data, integrating and interpreting multimodal datasets covering transcriptomics, epigenomics, and proteomics, and inferring cellular communication networks. By elucidating the latest progress and innovation, we provide a comprehensive overview of the rapidly advancing field of scRNA-seq analysis.
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Affiliation(s)
- Changde Cheng
- Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA;
| | - Wenan Chen
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; (W.C.); (H.J.)
| | - Hongjian Jin
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA; (W.C.); (H.J.)
| | - Xiang Chen
- Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA;
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41
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Li Z, Wang T, Liu P, Huang Y. SpatialDM for rapid identification of spatially co-expressed ligand-receptor and revealing cell-cell communication patterns. Nat Commun 2023; 14:3995. [PMID: 37414760 PMCID: PMC10325966 DOI: 10.1038/s41467-023-39608-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 06/21/2023] [Indexed: 07/08/2023] Open
Abstract
Cell-cell communication is a key aspect of dissecting the complex cellular microenvironment. Existing single-cell and spatial transcriptomics-based methods primarily focus on identifying cell-type pairs for a specific interaction, while less attention has been paid to the prioritisation of interaction features or the identification of interaction spots in the spatial context. Here, we introduce SpatialDM, a statistical model and toolbox leveraging a bivariant Moran's statistic to detect spatially co-expressed ligand and receptor pairs, their local interacting spots (single-spot resolution), and communication patterns. By deriving an analytical null distribution, this method is scalable to millions of spots and shows accurate and robust performance in various simulations. On multiple datasets including melanoma, Ventricular-Subventricular Zone, and intestine, SpatialDM reveals promising communication patterns and identifies differential interactions between conditions, hence enabling the discovery of context-specific cell cooperation and signalling.
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Affiliation(s)
- Zhuoxuan Li
- School of Biomedical Sciences, University of Hong Kong, Hong Kong SAR, China
| | - Tianjie Wang
- Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong SAR, China
| | - Pentao Liu
- School of Biomedical Sciences, University of Hong Kong, Hong Kong SAR, China.
- Center for Translational Stem Cell Biology, Hong Kong Science and Technology Park, Hong Kong SAR, China.
| | - Yuanhua Huang
- School of Biomedical Sciences, University of Hong Kong, Hong Kong SAR, China.
- Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong SAR, China.
- Center for Translational Stem Cell Biology, Hong Kong Science and Technology Park, Hong Kong SAR, China.
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42
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Wang S, Rong R, Yang DM, Zhang X, Zhan X, Bishop J, Wilhelm CJ, Zhang S, Pickering CR, Kris MG, Minna J, Xie Y, Xiao G. Deep Learning of Cell Spatial Organizations Identifies Clinically Relevant Insights in Tissue Images. RESEARCH SQUARE 2023:rs.3.rs-2928838. [PMID: 37461694 PMCID: PMC10350240 DOI: 10.21203/rs.3.rs-2928838/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Abstract
Recent advancements in tissue imaging techniques have facilitated the visualization and identification of various cell types within physiological and pathological contexts. Despite the emergence of cell-cell interaction studies, there is a lack of methods for evaluating individual spatial interactions. In this study, we introduce Ceograph, a novel cell spatial organization-based graph convolutional network designed to analyze cell spatial organization (i.e. the cell spatial distribution, morphology, proximity, and interactions) derived from pathology images. Ceograph identifies key cell spatial organization features by accurately predicting their influence on patient clinical outcomes. In patients with oral potentially malignant disorders, our model highlights reduced structural concordance and increased closeness in epithelial substrata as driving features for an elevated risk of malignant transformation. In lung cancer patients, Ceograph detects elongated tumor nuclei and diminished stroma-stroma closeness as biomarkers for insensitivity to EGFR tyrosine kinase inhibitors. With its potential to predict various clinical outcomes, Ceograph offers a deeper understanding of biological processes and supports the development of personalized therapeutic strategies.
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Affiliation(s)
- Shidan Wang
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Ruichen Rong
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Donghan M. Yang
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Xinyi Zhang
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Xiaowei Zhan
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Justin Bishop
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Clare J. Wilhelm
- Department of Thoracic Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Siyuan Zhang
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | | | - Mark G. Kris
- Department of Thoracic Oncology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - John Minna
- Hamon Center for Therapeutic Oncology Research, UT Southwestern Medical Center, Texas, USA
- Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Yang Xie
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Department of Pharmacology and Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas, USA
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Department of Pharmacology and Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA
- Department of Bioinformatics, UT Southwestern Medical Center, Dallas, Texas, USA
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43
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Jin J, Yu S, Lu P, Cao P. Deciphering plant cell-cell communications using single-cell omics data. Comput Struct Biotechnol J 2023; 21:3690-3695. [PMID: 37576747 PMCID: PMC10412842 DOI: 10.1016/j.csbj.2023.06.016] [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: 10/27/2022] [Revised: 06/16/2023] [Accepted: 06/16/2023] [Indexed: 08/15/2023] Open
Abstract
Plants have various cell types that respond to different environmental factors, and cell-cell communication is the fundamental process that controls these plant responses. The emergence of single-cell techniques provides opportunities to explore features unique to each cell type and construct a comprehensive cell-cell communication (CCC) network. Although the most current successes of CCC inference were achieved in animal research, computational methods can also be directly applied to plants. This review describes the current major models for cell-cell communication inference and summarizes the computational tools based on single-cell omics datasets. In addition, we discuss the limitations of plant cell-cell communication research and propose new directions to expand the field in meaningful ways.
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Affiliation(s)
- Jingjing Jin
- China Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China
| | - Shizhou Yu
- Molecular Genetics Key Laboratory of China Tobacco, Guizhou Academy of Tobacco Science, Guiyang 550081, China
| | - Peng Lu
- China Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China
| | - Peijian Cao
- China Tobacco Gene Research Center, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450001, China
- School of Agricultural Sciences, Zhengzhou University, Zhengzhou 450001, China
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44
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Ritter U. In situ veritas: combining omics and multiplex imaging can facilitate the detection and characterization of cell-cell interactions in tissues. Front Med (Lausanne) 2023; 10:1155057. [PMID: 37332762 PMCID: PMC10270289 DOI: 10.3389/fmed.2023.1155057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 04/25/2023] [Indexed: 06/20/2023] Open
Affiliation(s)
- Uwe Ritter
- Chair for Immunology, University of Regensburg, Regensburg, Germany
- Department for Immunology, Leibniz Institute for Immunotherapy (LIT), Regensburg, Germany
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45
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Meng-Lin K, Ung CY, Zhang C, Weiskittel TM, Wisniewski P, Zhang Z, Tan SH, Yeo KS, Zhu S, Correia C, Li H. SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes. Biomolecules 2023; 13:895. [PMID: 37371475 PMCID: PMC10296445 DOI: 10.3390/biom13060895] [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: 04/27/2023] [Revised: 05/23/2023] [Accepted: 05/23/2023] [Indexed: 06/29/2023] Open
Abstract
Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of differentially expressed genes and permits us to understand how genes and associated molecular processes are spatially distributed within cellular niches. However, the expression activities of SVGs fail to encode all information inherent in the spatial distribution of cells. Here, we devised a deep learning model, Spatially Informed Artificial Intelligence (SPIN-AI), to identify spatially predictive genes (SPGs), whose expression can predict how cells are organized in space. We used SPIN-AI on spatial transcriptomic data from squamous cell carcinoma (SCC) as a proof of concept. Our results demonstrate that SPGs not only recapitulate the biology of SCC but also identify genes distinct from SVGs. Moreover, we found a substantial number of ribosomal genes that were SPGs but not SVGs. Since SPGs possess the capability to predict spatial cellular organization, we reason that SPGs capture more biologically relevant information for a given cellular niche than SVGs. Thus, SPIN-AI has broad applications for detecting SPGs and uncovering which biological processes play important roles in governing cellular organization.
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Affiliation(s)
- Kevin Meng-Lin
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA; (K.M.-L.); (C.-Y.U.); (C.Z.); (T.M.W.); (P.W.); (Z.Z.); (S.-H.T.)
| | - Choong-Yong Ung
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA; (K.M.-L.); (C.-Y.U.); (C.Z.); (T.M.W.); (P.W.); (Z.Z.); (S.-H.T.)
| | - Cheng Zhang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA; (K.M.-L.); (C.-Y.U.); (C.Z.); (T.M.W.); (P.W.); (Z.Z.); (S.-H.T.)
| | - Taylor M. Weiskittel
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA; (K.M.-L.); (C.-Y.U.); (C.Z.); (T.M.W.); (P.W.); (Z.Z.); (S.-H.T.)
| | - Philip Wisniewski
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA; (K.M.-L.); (C.-Y.U.); (C.Z.); (T.M.W.); (P.W.); (Z.Z.); (S.-H.T.)
| | - Zhuofei Zhang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA; (K.M.-L.); (C.-Y.U.); (C.Z.); (T.M.W.); (P.W.); (Z.Z.); (S.-H.T.)
| | - Shyang-Hong Tan
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA; (K.M.-L.); (C.-Y.U.); (C.Z.); (T.M.W.); (P.W.); (Z.Z.); (S.-H.T.)
| | - Kok-Siong Yeo
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (K.-S.Y.); (S.Z.)
| | - Shizhen Zhu
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (K.-S.Y.); (S.Z.)
| | - Cristina Correia
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA; (K.M.-L.); (C.-Y.U.); (C.Z.); (T.M.W.); (P.W.); (Z.Z.); (S.-H.T.)
| | - Hu Li
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA; (K.M.-L.); (C.-Y.U.); (C.Z.); (T.M.W.); (P.W.); (Z.Z.); (S.-H.T.)
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46
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Li X, Xiao C, Qi J, Xue W, Xu X, Mu Z, Zhang J, Li CY, Ding W. STellaris: a web server for accurate spatial mapping of single cells based on spatial transcriptomics data. Nucleic Acids Res 2023:7177883. [PMID: 37224539 DOI: 10.1093/nar/gkad419] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 04/23/2023] [Accepted: 05/08/2023] [Indexed: 05/26/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) provides insights into gene expression heterogeneities in diverse cell types underlying homeostasis, development and pathological states. However, the loss of spatial information hinders its applications in deciphering spatially related features, such as cell-cell interactions in a spatial context. Here, we present STellaris (https://spatial.rhesusbase.com), a web server aimed to rapidly assign spatial information to scRNA-seq data based on their transcriptomic similarity with public spatial transcriptomics (ST) data. STellaris is founded on 101 manually curated ST datasets comprising 823 sections across different organs, developmental stages and pathological states from humans and mice. STellaris accepts raw count matrix and cell type annotation of scRNA-seq data as the input, and maps single cells to spatial locations in the tissue architecture of properly matched ST section. Spatially resolved information for intercellular communications, such as spatial distance and ligand-receptor interactions (LRIs), are further characterized between annotated cell types. Moreover, we also expanded the application of STellaris in spatial annotation of multiple regulatory levels with single-cell multiomics data, using the transcriptome as a bridge. STellaris was applied to several case studies to showcase its utility of adding value to the ever-growing scRNA-seq data from a spatial perspective.
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Affiliation(s)
- Xiangshang Li
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
| | - Chunfu Xiao
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
| | - Juntian Qi
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
| | | | - Xinwei Xu
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
- College of Life Science, Peking University, Beijing 100871, China
| | - Zelin Mu
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
- School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, China
| | - Jie Zhang
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
| | - Chuan-Yun Li
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
- Chinese Institute for Brain Research, Beijing 102206, China
| | - Wanqiu Ding
- Laboratory of Bioinformatics and Genomic Medicine, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
- Bioinformatics Core, Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing 100871, China
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47
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Aoki T, Steidl C. Novel insights into Hodgkin lymphoma biology by single-cell analysis. Blood 2023; 141:1791-1801. [PMID: 36548960 PMCID: PMC10646771 DOI: 10.1182/blood.2022017147] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 12/15/2022] [Accepted: 12/21/2022] [Indexed: 12/24/2022] Open
Abstract
The emergence and rapid development of single-cell technologies mark a paradigm shift in cancer research. Various technology implementations represent powerful tools to understand cellular heterogeneity, identify minor cell populations that were previously hard to detect and define, and make inferences about cell-to-cell interactions at single-cell resolution. Applied to lymphoma, recent advances in single-cell RNA sequencing have broadened opportunities to delineate previously underappreciated heterogeneity of malignant cell differentiation states and presumed cell of origin, and to describe the composition and cellular subsets in the ecosystem of the tumor microenvironment (TME). Clinical deployment of an expanding armamentarium of immunotherapy options that rely on targets and immune cell interactions in the TME emphasizes the requirement for a deeper understanding of immune biology in lymphoma. In particular, classic Hodgkin lymphoma (CHL) can serve as a study paradigm because of its unique TME, featuring infrequent tumor cells among numerous nonmalignant immune cells with significant interpatient and intrapatient variability. Synergistic to advances in single-cell sequencing, multiplexed imaging techniques have added a new dimension to describing cellular cross talk in various lymphoma entities. Here, we comprehensively review recent progress using novel single-cell technologies with an emphasis on the TME biology of CHL as an application field. The described technologies, which are applicable to peripheral blood, fresh tissues, and formalin-fixed samples, hold the promise to accelerate biomarker discovery for novel immunotherapeutic approaches and to serve as future assay platforms for biomarker-informed treatment selection, including immunotherapies.
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Affiliation(s)
- Tomohiro Aoki
- Centre for Lymphoid Cancer, British Columbia Cancer, Vancouver, BC, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Christian Steidl
- Centre for Lymphoid Cancer, British Columbia Cancer, Vancouver, BC, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
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48
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Lee AJ, Cahill R, Abbasi-Asl R. Machine Learning for Uncovering Biological Insights in Spatial Transcriptomics Data. ARXIV 2023:arXiv:2303.16725v1. [PMID: 37033464 PMCID: PMC10081350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 04/11/2023]
Abstract
Development and homeostasis in multicellular systems both require exquisite control over spatial molecular pattern formation and maintenance. Advances in spatially-resolved and high-throughput molecular imaging methods such as multiplexed immunofluorescence and spatial transcriptomics (ST) provide exciting new opportunities to augment our fundamental understanding of these processes in health and disease. The large and complex datasets resulting from these techniques, particularly ST, have led to rapid development of innovative machine learning (ML) tools primarily based on deep learning techniques. These ML tools are now increasingly featured in integrated experimental and computational workflows to disentangle signals from noise in complex biological systems. However, it can be difficult to understand and balance the different implicit assumptions and methodologies of a rapidly expanding toolbox of analytical tools in ST. To address this, we summarize major ST analysis goals that ML can help address and current analysis trends. We also describe four major data science concepts and related heuristics that can help guide practitioners in their choices of the right tools for the right biological questions.
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49
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de Visser KE, Joyce JA. The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth. Cancer Cell 2023; 41:374-403. [PMID: 36917948 DOI: 10.1016/j.ccell.2023.02.016] [Citation(s) in RCA: 582] [Impact Index Per Article: 582.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/28/2023] [Accepted: 02/14/2023] [Indexed: 03/14/2023]
Abstract
Cancers represent complex ecosystems comprising tumor cells and a multitude of non-cancerous cells, embedded in an altered extracellular matrix. The tumor microenvironment (TME) includes diverse immune cell types, cancer-associated fibroblasts, endothelial cells, pericytes, and various additional tissue-resident cell types. These host cells were once considered bystanders of tumorigenesis but are now known to play critical roles in the pathogenesis of cancer. The cellular composition and functional state of the TME can differ extensively depending on the organ in which the tumor arises, the intrinsic features of cancer cells, the tumor stage, and patient characteristics. Here, we review the importance of the TME in each stage of cancer progression, from tumor initiation, progression, invasion, and intravasation to metastatic dissemination and outgrowth. Understanding the complex interplay between tumor cell-intrinsic, cell-extrinsic, and systemic mediators of disease progression is critical for the rational development of effective anti-cancer treatments.
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Affiliation(s)
- Karin E de Visser
- Division of Tumor Biology and Immunology, Oncode Institute, The Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands; Department of Immunology, Leiden University Medical Center, 2333 ZA Leiden, the Netherlands.
| | - Johanna A Joyce
- Department of Oncology, University of Lausanne, 1011 Lausanne, Switzerland; Ludwig Institute for Cancer Research, 1011 Lausanne, Switzerland; Agora Cancer Center Lausanne, and Swiss Cancer Center Léman, 1011 Lausanne, Switzerland.
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50
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Zhu J, Shang L, Zhou X. SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics. Genome Biol 2023; 24:39. [PMID: 36869394 PMCID: PMC9983268 DOI: 10.1186/s13059-023-02879-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 02/16/2023] [Indexed: 03/05/2023] Open
Abstract
Spatially resolved transcriptomics (SRT)-specific computational methods are often developed, tested, validated, and evaluated in silico using simulated data. Unfortunately, existing simulated SRT data are often poorly documented, hard to reproduce, or unrealistic. Single-cell simulators are not directly applicable for SRT simulation as they cannot incorporate spatial information. We present SRTsim, an SRT-specific simulator for scalable, reproducible, and realistic SRT simulations. SRTsim not only maintains various expression characteristics of SRT data but also preserves spatial patterns. We illustrate the benefits of SRTsim in benchmarking methods for spatial clustering, spatial expression pattern detection, and cell-cell communication identification.
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Affiliation(s)
- Jiaqiang Zhu
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Lulu Shang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA.
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA.
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