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Qian J, Bao H, Shao X, Fang Y, Liao J, Chen Z, Li C, Guo W, Hu Y, Li A, Yao Y, Fan X, Cheng Y. Simulating multiple variability in spatially resolved transcriptomics with scCube. Nat Commun 2024; 15:5021. [PMID: 38866768 PMCID: PMC11169532 DOI: 10.1038/s41467-024-49445-0] [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: 09/28/2023] [Accepted: 06/03/2024] [Indexed: 06/14/2024] Open
Abstract
A pressing challenge in spatially resolved transcriptomics (SRT) is to benchmark the computational methods. A widely-used approach involves utilizing simulated data. However, biases exist in terms of the currently available simulated SRT data, which seriously affects the accuracy of method evaluation and validation. Herein, we present scCube ( https://github.com/ZJUFanLab/scCube ), a Python package for independent, reproducible, and technology-diverse simulation of SRT data. scCube not only enables the preservation of spatial expression patterns of genes in reference-based simulations, but also generates simulated data with different spatial variability (covering the spatial pattern type, the resolution, the spot arrangement, the targeted gene type, and the tissue slice dimension, etc.) in reference-free simulations. We comprehensively benchmark scCube with existing single-cell or SRT simulators, and demonstrate the utility of scCube in benchmarking spot deconvolution, gene imputation, and resolution enhancement methods in detail through three applications.
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Affiliation(s)
- Jingyang Qian
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Hudong Bao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Xin Shao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Yin Fang
- College of Computer Science and Technology, Zhejiang University, Hangzhou, 310013, China
| | - Jie Liao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Zhuo Chen
- College of Computer Science and Technology, Zhejiang University, Hangzhou, 310013, China
| | - Chengyu Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Wenbo Guo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Yining Hu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Anyao Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Yue Yao
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China
| | - Xiaohui Fan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Yiyu Cheng
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, 314100, Jiaxing, China.
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2
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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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3
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Liu Z, Xie H, Li L, Jiang D, Qian Y, Zhu X, Dai M, Li Y, Wei R, Luo Z, Xu W, Zheng Q, Shen J, Zhou M, Zeng W, Chen W. Single-cell landscape reveals the epithelial cell-centric pro-inflammatory immune microenvironment in dry eye development. Mucosal Immunol 2024; 17:491-507. [PMID: 38007004 DOI: 10.1016/j.mucimm.2023.11.008] [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: 07/07/2023] [Revised: 11/13/2023] [Accepted: 11/20/2023] [Indexed: 11/27/2023]
Abstract
Dry eye disease (DED) is a prevalent chronic eye disease characterized by an aberrant inflammatory response in ocular surface mucosa. The immunological alterations underlying DED remain largely unknown. In this study, we employed single-cell transcriptome sequencing of conjunctival tissue from environment-induced DED mice to investigate multicellular ecosystem and functional changes at different DED stages. Our results revealed an epithelial subtype with fibroblastic characteristics and pro-inflammatory effects emerging in the acute phase of DED. We also found that T helper (Th)1, Th17, and regulatory T cells (Treg) were the dominant clusters of differentiation (CD)4+ T-cell types involved in regulating immune responses and identified three distinct macrophage subtypes, with the CD72+CD11c+ subtype enhancing chronic inflammation. Furthermore, bulk transcriptome analysis of video display terminal-induced DED consistently suggested the presence of the pro-inflammatory epithelial subtype in human conjunctiva. Our findings have uncovered a DED-associated pro-inflammatory microenvironment in the conjunctiva, centered around epithelial cells, involving interactions with macrophages and CD4+ T cells, which deepens our understanding of ocular surface mucosal immune responses during DED progression.
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Affiliation(s)
- Zihao Liu
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China; State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - He Xie
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China; State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Ling Li
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China; State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou, China; The Affiliated Ningbo Eye Hospital of Wenzhou Medical University, Ningbo, Zhejiang, China
| | - Dan Jiang
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China; State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Yuna Qian
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China
| | - Xinhao Zhu
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China; State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Mali Dai
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China; State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Yanxiao Li
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China; State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Ruifen Wei
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China; State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Zan Luo
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China; State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Weihao Xu
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China; State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Qinxiang Zheng
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China; State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Jianliang Shen
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China; Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang, China; National Engineering Research Center of Ophthalmology and Optometry, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Meng Zhou
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China; State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Wenwen Zeng
- Institute for Immunology, School of Medicine, and Tsinghua-Peking Center for Life Sciences, Tsinghua University, Beijing, China.
| | - Wei Chen
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China; State Key Laboratory of Ophthalmology, Optometry and Visual Science, Eye Hospital, Wenzhou Medical University, Wenzhou, China.
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4
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Guo N, Vargas J, Reynoso S, Fritz D, Krishna R, Wang C, Zhang F. Uncover spatially informed variations for single-cell spatial transcriptomics with STew. BIOINFORMATICS ADVANCES 2024; 4:vbae064. [PMID: 38827413 PMCID: PMC11142628 DOI: 10.1093/bioadv/vbae064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 03/06/2024] [Accepted: 05/01/2024] [Indexed: 06/04/2024]
Abstract
Motivation The recent spatial transcriptomics (ST) technologies have enabled characterization of gene expression patterns and spatial information, advancing our understanding of cell lineages within diseased tissues. Several analytical approaches have been proposed for ST data, but effectively utilizing spatial information to unveil the shared variation with gene expression remains a challenge. Results We introduce STew, a Spatial Transcriptomic multi-viEW representation learning method, to jointly analyze spatial information and gene expression in a scalable manner, followed by a data-driven statistical framework to measure the goodness of model fit. Through benchmarking using human dorsolateral prefrontal cortex and mouse main olfactory bulb data with true manual annotations, STew achieved superior performance in both clustering accuracy and continuity of identified spatial domains compared with other methods. STew is also robust to generate consistent results insensitive to model parameters, including sparsity constraints. We next applied STew to various ST data acquired from 10× Visium, Slide-seqV2, and 10× Xenium, encompassing single-cell and multi-cellular resolution ST technologies, which revealed spatially informed cell type clusters and biologically meaningful axes. In particular, we identified a proinflammatory fibroblast spatial niche using ST data from psoriatic skins. Moreover, STew scales almost linearly with the number of spatial locations, guaranteeing its applicability to datasets with thousands of spatial locations to capture disease-relevant niches in complex tissues. Availability and implementation Source code and the R software tool STew are available from github.com/fanzhanglab/STew.
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Affiliation(s)
- Nanxi Guo
- Biostatistics and Informatics PhD Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
- Department of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
| | - Juan Vargas
- Department of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
- MPH Biostatistics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
| | - Samantha Reynoso
- Department of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
- Computational Bioscience PhD Program, University of Colorado School of Medicine, Aurora, CO 80045, United States
| | - Douglas Fritz
- Department of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
- Medical Scientist Training Program, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
| | - Revanth Krishna
- Department of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
- Division of Rheumatology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
| | - Chuangqi Wang
- Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
| | - Fan Zhang
- Department of Biomedical Informatics, Center for Health Artificial Intelligence, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
- Division of Rheumatology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, United States
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5
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Qin C, Zhang M, Mou DP, Zhou LQ, Dong MH, Huang L, Wang W, Cai SB, You YF, Shang K, Xiao J, Wang D, Li CR, Hao Y, Heming M, Wu LJ, Meyer Zu Hörste G, Dong C, Bu BT, Tian DS, Wang W. Single-cell analysis of anti-BCMA CAR T cell therapy in patients with central nervous system autoimmunity. Sci Immunol 2024; 9:eadj9730. [PMID: 38728414 DOI: 10.1126/sciimmunol.adj9730] [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: 07/26/2023] [Accepted: 04/12/2024] [Indexed: 05/12/2024]
Abstract
Chimeric antigen receptor (CAR) T cell immunotherapy for the treatment of neurological autoimmune diseases is promising, but CAR T cell kinetics and immune alterations after treatment are poorly understood. Here, we performed single-cell multi-omics sequencing of paired cerebrospinal fluid (CSF) and blood samples from patients with neuromyelitis optica spectrum disorder (NMOSD) treated with anti-B cell maturation antigen (BCMA) CAR T cells. Proliferating cytotoxic-like CD8+ CAR T cell clones were identified as the main effectors in autoimmunity. Anti-BCMA CAR T cells with enhanced features of chemotaxis efficiently crossed the blood-CSF barrier, eliminated plasmablasts and plasma cells in the CSF, and suppressed neuroinflammation. The CD44-expressing early memory phenotype in infusion products was potentially associated with CAR T cell persistence in autoimmunity. Moreover, CAR T cells from patients with NMOSD displayed distinctive features of suppressed cytotoxicity compared with those from hematological malignancies. Thus, we provide mechanistic insights into CAR T cell function in patients with neurological autoimmune disease.
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Affiliation(s)
- Chuan Qin
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- Hubei Key Laboratory of Neural Injury and Functional Reconstruction, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Min Zhang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- Hubei Key Laboratory of Neural Injury and Functional Reconstruction, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Da-Peng Mou
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology & Visual Science Key Lab, Beijing, China
| | - Luo-Qi Zhou
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- Hubei Key Laboratory of Neural Injury and Functional Reconstruction, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Ming-Hao Dong
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- Hubei Key Laboratory of Neural Injury and Functional Reconstruction, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Liang Huang
- Department of Hematology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wen Wang
- Nanjing IASO Biotechnology Co. Ltd., Nanjing, China
| | - Song-Bai Cai
- Nanjing IASO Biotechnology Co. Ltd., Nanjing, China
| | - Yun-Fan You
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- Hubei Key Laboratory of Neural Injury and Functional Reconstruction, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Ke Shang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- Hubei Key Laboratory of Neural Injury and Functional Reconstruction, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Jun Xiao
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- Hubei Key Laboratory of Neural Injury and Functional Reconstruction, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Di Wang
- Department of Hematology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Chun-Rui Li
- Department of Hematology, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Hao
- Department of Pathogen Biology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Michael Heming
- Department of Neurology with Institute of Translational Neurology, University Hospital Münster, Münster, Germany
| | - Long-Jun Wu
- Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Gerd Meyer Zu Hörste
- Department of Neurology with Institute of Translational Neurology, University Hospital Münster, Münster, Germany
| | - Chen Dong
- Shanghai Immune Therapy Institute, Shanghai Jiaotong University School of Medicine-affiliated Renji Hospital, Shanghai, China
| | - Bi-Tao Bu
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- Hubei Key Laboratory of Neural Injury and Functional Reconstruction, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Dai-Shi Tian
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- Hubei Key Laboratory of Neural Injury and Functional Reconstruction, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Wei Wang
- Department of Neurology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- Hubei Key Laboratory of Neural Injury and Functional Reconstruction, Huazhong University of Science and Technology, Wuhan 430030, China
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6
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Tao W, Yu Z, Han JDJ. Single-cell senescence identification reveals senescence heterogeneity, trajectory, and modulators. Cell Metab 2024; 36:1126-1143.e5. [PMID: 38604170 DOI: 10.1016/j.cmet.2024.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 12/15/2023] [Accepted: 03/13/2024] [Indexed: 04/13/2024]
Abstract
Cellular senescence underlies many aging-related pathologies, but its heterogeneity poses challenges for studying and targeting senescent cells. We present here a machine learning program senescent cell identification (SenCID), which accurately identifies senescent cells in both bulk and single-cell transcriptome. Trained on 602 samples from 52 senescence transcriptome datasets spanning 30 cell types, SenCID identifies six major senescence identities (SIDs). Different SIDs exhibit different senescence baselines, stemness, gene functions, and responses to senolytics. SenCID enables the reconstruction of senescent trajectories under normal aging, chronic diseases, and COVID-19. Additionally, when applied to single-cell Perturb-seq data, SenCID helps reveal a hierarchy of senescence modulators. Overall, SenCID is an essential tool for precise single-cell analysis of cellular senescence, enabling targeted interventions against senescent cells.
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Affiliation(s)
- Wanyu Tao
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, China
| | - Zhengqing Yu
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, China
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, China; Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies, Chengdu, China.
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7
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Duhan L, Kumari D, Naime M, Parmar VS, Chhillar AK, Dangi M, Pasrija R. Single-cell transcriptomics: background, technologies, applications, and challenges. Mol Biol Rep 2024; 51:600. [PMID: 38689046 DOI: 10.1007/s11033-024-09553-y] [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: 02/09/2024] [Accepted: 04/15/2024] [Indexed: 05/02/2024]
Abstract
Single-cell sequencing was developed as a high-throughput tool to elucidate unusual and transient cell states that are barely visible in the bulk. This technology reveals the evolutionary status of cells and differences between populations, helps to identify unique cell subtypes and states, reveals regulatory relationships between genes, targets and molecular mechanisms in disease processes, tumor heterogeneity, the state of the immune environment, etc. However, the high cost and technical limitations of single-cell sequencing initially prevented its widespread application, but with advances in research, numerous new single-cell sequencing techniques have been discovered, lowering the cost barrier. Many single-cell sequencing platforms and bioinformatics methods have recently become commercially available, allowing researchers to make fascinating observations. They are now increasingly being used in various industries. Several protocols have been discovered in this context and each technique has unique characteristics, capabilities and challenges. This review presents the latest advancements in single-cell transcriptomics technologies. This includes single-cell transcriptomics approaches, workflows and statistical approaches to data processing, as well as the potential advances, applications, opportunities and challenges of single-cell transcriptomics technology. You will also get an overview of the entry points for spatial transcriptomics and multi-omics.
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Affiliation(s)
- Lucky Duhan
- Department of Biochemistry, Maharshi Dayanand University, Rohtak, Haryana, 124001, India
| | - Deepika Kumari
- Department of Biochemistry, Maharshi Dayanand University, Rohtak, Haryana, 124001, India
| | - Mohammad Naime
- Central Research Institute of Unani Medicine (Under Central Council for Research in Unani Medicine, Ministry of Ayush, Govt of India), Uttar Pradesh, Lucknow, India
| | - Virinder S Parmar
- CUNY-Graduate Center and Departments of Chemistry, Nanoscience Program, City College & Medgar Evers College, The City University of New York, 1638 Bedford Avenue, Brooklyn, NY, 11225, USA
- Institute of Click Chemistry Research and Studies, Amity University, Noida, Uttar Pradesh, 201303, India
| | - Anil K Chhillar
- Centre for Biotechnology, Maharshi Dayanand University, Rohtak, Haryana, 124001, India
| | - Mehak Dangi
- Centre for Bioinformatics, Maharshi Dayanand University, Rohtak, Haryana, 124001, India
| | - Ritu Pasrija
- Department of Biochemistry, Maharshi Dayanand University, Rohtak, Haryana, 124001, India.
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8
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Ma Y, Guo W, Mou Q, Shao X, Lyu M, Garcia V, Kong L, Lewis W, Ward C, Yang Z, Pan X, Yi SS, Lu Y. Spatial imaging of glycoRNA in single cells with ARPLA. Nat Biotechnol 2024; 42:608-616. [PMID: 37217750 PMCID: PMC10663388 DOI: 10.1038/s41587-023-01801-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 04/24/2023] [Indexed: 05/24/2023]
Abstract
Little is known about the biological roles of glycosylated RNAs (glycoRNAs), a recently discovered class of glycosylated molecules, because of a lack of visualization methods. We report sialic acid aptamer and RNA in situ hybridization-mediated proximity ligation assay (ARPLA) to visualize glycoRNAs in single cells with high sensitivity and selectivity. The signal output of ARPLA occurs only when dual recognition of a glycan and an RNA triggers in situ ligation, followed by rolling circle amplification of a complementary DNA, which generates a fluorescent signal by binding fluorophore-labeled oligonucleotides. Using ARPLA, we detect spatial distributions of glycoRNAs on the cell surface and their colocalization with lipid rafts as well as the intracellular trafficking of glycoRNAs through SNARE protein-mediated secretory exocytosis. Studies in breast cell lines suggest that surface glycoRNA is inversely associated with tumor malignancy and metastasis. Investigation of the relationship between glycoRNAs and monocyte-endothelial cell interactions suggests that glycoRNAs may mediate cell-cell interactions during the immune response.
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Affiliation(s)
- Yuan Ma
- Department of Chemistry, The University of Texas at Austin, Austin, TX, USA
| | - Weijie Guo
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA
- Interdisciplinary Life Sciences Graduate Programs, The University of Texas at Austin, Austin, TX, USA
| | - Quanbing Mou
- Department of Chemistry, The University of Texas at Austin, Austin, TX, USA
| | - Xiangli Shao
- Department of Chemistry, The University of Texas at Austin, Austin, TX, USA
| | - Mingkuan Lyu
- Department of Chemistry, The University of Texas at Austin, Austin, TX, USA
| | - Valeria Garcia
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA
- Interdisciplinary Life Sciences Graduate Programs, The University of Texas at Austin, Austin, TX, USA
| | - Linggen Kong
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA
- Interdisciplinary Life Sciences Graduate Programs, The University of Texas at Austin, Austin, TX, USA
| | - Whitney Lewis
- Department of Chemistry, The University of Texas at Austin, Austin, TX, USA
- Interdisciplinary Life Sciences Graduate Programs, The University of Texas at Austin, Austin, TX, USA
| | - Carson Ward
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX, USA
| | - Zhenglin Yang
- Department of Chemistry, The University of Texas at Austin, Austin, TX, USA
| | - Xingxin Pan
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
| | - S Stephen Yi
- Interdisciplinary Life Sciences Graduate Programs, The University of Texas at Austin, Austin, TX, USA
- Department of Oncology, Livestrong Cancer Institutes, Dell Medical School, The University of Texas at Austin, Austin, TX, USA
- Oden Institute for Computational Engineering and Sciences (ICES), The University of Texas at Austin, Austin, TX, USA
| | - Yi Lu
- Department of Chemistry, The University of Texas at Austin, Austin, TX, USA.
- Interdisciplinary Life Sciences Graduate Programs, The University of Texas at Austin, Austin, TX, USA.
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9
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Wang X, Yang C, Ma X, Li X, Qi Y, Bai Z, Xu Y, Ma K, Luo Y, Song J, Jia W, He Z, Liu Z. A division-of-labor mode contributes to the cardioprotective potential of mesenchymal stem/stromal cells in heart failure post myocardial infarction. Front Immunol 2024; 15:1363517. [PMID: 38562923 PMCID: PMC10982400 DOI: 10.3389/fimmu.2024.1363517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 03/04/2024] [Indexed: 04/04/2024] Open
Abstract
Background Treatment of heart failure post myocardial infarction (post-MI HF) with mesenchymal stem/stromal cells (MSCs) holds great promise. Nevertheless, 2-dimensional (2D) GMP-grade MSCs from different labs and donor sources have different therapeutic efficacy and still in a low yield. Therefore, it is crucial to increase the production and find novel ways to assess the therapeutic efficacy of MSCs. Materials and methods hUC-MSCs were cultured in 3-dimensional (3D) expansion system for obtaining enough cells for clinical use, named as 3D MSCs. A post-MI HF mouse model was employed to conduct in vivo and in vitro experiments. Single-cell and bulk RNA-seq analyses were performed on 3D MSCs. A total of 125 combination algorithms were leveraged to screen for core ligand genes. Shinyapp and shinycell workflows were used for deploying web-server. Result 3D GMP-grade MSCs can significantly and stably reduce the extent of post-MI HF. To understand the stable potential cardioprotective mechanism, scRNA-seq revealed the heterogeneity and division-of-labor mode of 3D MSCs at the cellular level. Specifically, scissor phenotypic analysis identified a reported wound-healing CD142+ MSCs subpopulation that is also associated with cardiac protection ability and CD142- MSCs that is in proliferative state, contributing to the cardioprotective function and self-renewal, respectively. Differential expression analysis was conducted on CD142+ MSCs and CD142- MSCs and the differentially expressed ligand-related model was achieved by employing 125 combination algorithms. The present study developed a machine learning predictive model based on 13 ligands. Further analysis using CellChat demonstrated that CD142+ MSCs have a stronger secretion capacity compared to CD142- MSCs and Flow cytometry sorting of the CD142+ MSCs and qRT-PCR validation confirmed the significant upregulation of these 13 ligand factors in CD142+ MSCs. Conclusion Clinical GMP-grade 3D MSCs could serve as a stable cardioprotective cell product. Using scissor analysis on scRNA-seq data, we have clarified the potential functional and proliferative subpopulation, which cooperatively contributed to self-renewal and functional maintenance for 3D MSCs, named as "division of labor" mode of MSCs. Moreover, a ligand model was robustly developed for predicting the secretory efficacy of MSCs. A user-friendly web-server and a predictive model were constructed and available (https://wangxc.shinyapps.io/3D_MSCs/).
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Affiliation(s)
- Xicheng Wang
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Chao Yang
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Xiaoxue Ma
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Xiuhua Li
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Yiyao Qi
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Zhihui Bai
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Ying Xu
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Keming Ma
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Yi Luo
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Jiyang Song
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Wenwen Jia
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Zhiying He
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
| | - Zhongmin Liu
- Institute for Regenerative Medicine, Shanghai East Hospital, School of Life Sciences and Technology, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Research Center of Stem Cells Translational Medicine, Shanghai, China
- Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, China
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10
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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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11
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Baghdassarian HM, Lewis NE. Resource allocation in mammalian systems. Biotechnol Adv 2024; 71:108305. [PMID: 38215956 PMCID: PMC11182366 DOI: 10.1016/j.biotechadv.2023.108305] [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: 08/03/2023] [Revised: 12/17/2023] [Accepted: 12/18/2023] [Indexed: 01/14/2024]
Abstract
Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues.
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Affiliation(s)
- Hratch M Baghdassarian
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.
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12
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Peng L, Gao P, Xiong W, Li Z, Chen X. Identifying potential ligand-receptor interactions based on gradient boosted neural network and interpretable boosting machine for intercellular communication analysis. Comput Biol Med 2024; 171:108110. [PMID: 38367445 DOI: 10.1016/j.compbiomed.2024.108110] [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: 10/30/2023] [Revised: 01/24/2024] [Accepted: 02/04/2024] [Indexed: 02/19/2024]
Abstract
Cell-cell communication is essential to many key biological processes. Intercellular communication is generally mediated by ligand-receptor interactions (LRIs). Thus, building a comprehensive and high-quality LRI resource can significantly improve intercellular communication analysis. Meantime, due to lack of a "gold standard" dataset, it remains a challenge to evaluate LRI-mediated intercellular communication results. Here, we introduce CellGiQ, a high-confident LRI prediction framework for intercellular communication analysis. Highly confident LRIs are first inferred by LRI feature extraction with BioTriangle, LRI selection using LightGBM, and LRI classification based on ensemble of gradient boosted neural network and interpretable boosting machine. Subsequently, known and identified high-confident LRIs are filtered by combining single-cell RNA sequencing (scRNA-seq) data and further applied to intercellular communication inference through a quartile scoring strategy. To validation the predictions, CellGiQ exploited several evaluation strategies: using AUC and AUPR, it surpassed six competing LRI prediction models on four LRI datasets; through Venn diagrams and molecular docking, its predicted LRIs were validated by five other popular intercellular communication inference methods; based on the overlapping LRIs, it computed high Jaccard index with six other state-of-the-art intercellular communication prediction tools within human HNSCC tissues; by comparing with classical models and literature retrieve, its inferred HNSCC-related intercellular communication results was further validated. The novelty of this study is to identify high-confident LRIs based on machine learning as well as design several LRI validation ways, providing reference for computational LRI prediction. CellGiQ provides an open-source and useful tool to decompose LRI-mediated intercellular communication at single cell resolution. CellGiQ is freely available at https://github.com/plhhnu/CellGiQ.
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Affiliation(s)
- Lihong Peng
- College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Pengfei Gao
- College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Wei Xiong
- College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Zejun Li
- School of Computer Science and Engineering, Hunan Institute of Technology, Hengyang, 421002, Hunan, China.
| | - Xing Chen
- School of Science, Jiangnan University, Wuxi, 214122, Jiangsu, China.
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13
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Xianyu Z, Correia C, Ung CY, Zhu S, Billadeau DD, Li H. The Rise of Hypothesis-Driven Artificial Intelligence in Oncology. Cancers (Basel) 2024; 16:822. [PMID: 38398213 PMCID: PMC10886811 DOI: 10.3390/cancers16040822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 02/12/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
Cancer is a complex disease involving the deregulation of intricate cellular systems beyond genetic aberrations and, as such, requires sophisticated computational approaches and high-dimensional data for optimal interpretation. While conventional artificial intelligence (AI) models excel in many prediction tasks, they often lack interpretability and are blind to the scientific hypotheses generated by researchers to enable cancer discoveries. Here we propose that hypothesis-driven AI, a new emerging class of AI algorithm, is an innovative approach to uncovering the complex etiology of cancer from big omics data. This review exemplifies how hypothesis-driven AI is different from conventional AI by citing its application in various areas of oncology including tumor classification, patient stratification, cancer gene discovery, drug response prediction, and tumor spatial organization. Our aim is to stress the feasibility of incorporating domain knowledge and scientific hypotheses to craft the design of new AI algorithms. We showcase the power of hypothesis-driven AI in making novel cancer discoveries that can be overlooked by conventional AI methods. Since hypothesis-driven AI is still in its infancy, open questions such as how to better incorporate new knowledge and biological perspectives to ameliorate bias and improve interpretability in the design of AI algorithms still need to be addressed. In conclusion, hypothesis-driven AI holds great promise in the discovery of new mechanistic and functional insights that explain the complexity of cancer etiology and potentially chart a new roadmap to improve treatment regimens for individual patients.
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Affiliation(s)
- Zilin Xianyu
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (Z.X.); (C.C.); (C.Y.U.); (S.Z.); (D.D.B.)
| | - Cristina Correia
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (Z.X.); (C.C.); (C.Y.U.); (S.Z.); (D.D.B.)
| | - Choong Yong Ung
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (Z.X.); (C.C.); (C.Y.U.); (S.Z.); (D.D.B.)
| | - Shizhen Zhu
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (Z.X.); (C.C.); (C.Y.U.); (S.Z.); (D.D.B.)
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Daniel D. Billadeau
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (Z.X.); (C.C.); (C.Y.U.); (S.Z.); (D.D.B.)
- Department of Immunology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
| | - Hu Li
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (Z.X.); (C.C.); (C.Y.U.); (S.Z.); (D.D.B.)
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14
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Qi J, Zhu H, Li Y, Guan X, He Y, Ren G, Guo Q, Liu L, Gu Y, Dong X, Liu Y. Creation of a High-Throughput Microfluidic Platform for Single-Cell Transcriptome Sequencing of Cell-Cell Interactions. SMALL METHODS 2023; 7:e2300730. [PMID: 37712212 DOI: 10.1002/smtd.202300730] [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: 06/10/2023] [Revised: 08/21/2023] [Indexed: 09/16/2023]
Abstract
Cell-cell interaction is one of the major modalities for transmitting information between cells and activating the effects of functional cells. However, the construction of high-throughput analysis technologies from cell omics focusing on the impact of interactions of functional cells on targets has been relatively unexplored. Here, they propose a droplet-based microfluidic platform for cell-cell interaction sequencing (c-c-seq) and screening in vitro to address this challenge. A class of interacting cells is pre-labeled using cell molecular tags, and additional single-cell sequencing reagents are introduced to quickly form functional droplet mixes. Lastly, gene expression analysis is used to deduce the impact of the interaction, while molecular sequence tracing identifies the type of interaction. Research into the active effect between antigen-presenting cells and T cells, one of the most common cell-to-cell interactions, is crucial for the advancement of cancer therapy, particularly T cell receptor-engineered T cell therapy. As it allows for high throughput, this platform is superior to well plates as a research platform for cell-to-cell interactions. When combined with the next generation of sequencing, the platform may be able to more accurately evaluate interactions between epitopes and receptors and verify their functional relevance.
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Affiliation(s)
- Jingyu Qi
- BGI Research, Shenzhen, 518083, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | | | - Yijian Li
- BGI Research, Shenzhen, 518083, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Xiangyu Guan
- BGI Research, Shenzhen, 518083, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ying He
- Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - Guanhua Ren
- China National Institute of Standardization, Beijing, 100191, China
| | - Qiang Guo
- BGI Research, Shenzhen, 518083, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | | | - Ying Gu
- BGI Research, Shenzhen, 518083, China
| | - Xuan Dong
- BGI Research, Shenzhen, 518083, China
- Guangdong Provincial Key Laboratory of Human Disease Genomics, Shenzhen Key Laboratory of Genomics, Shenzhen, 518083, China
| | - Ya Liu
- BGI Research, Shenzhen, 518083, China
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15
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Fan Y, Jin L, He Z, Wei T, Luo T, Zhang J, Liu C, Dai C, A C, Liang Y, Tao X, Lv X, Gu Y, Li M. A cell transcriptomic profile provides insights into adipocytes of porcine mammary gland across development. J Anim Sci Biotechnol 2023; 14:126. [PMID: 37805503 PMCID: PMC10560433 DOI: 10.1186/s40104-023-00926-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 08/03/2023] [Indexed: 10/09/2023] Open
Abstract
BACKGROUND Studying the composition and developmental mechanisms in mammary gland is crucial for healthy growth of newborns. The mammary gland is inherently heterogeneous, and its physiological function dependents on the gene expression of multiple cell types. Most studies focused on epithelial cells, disregarding the role of neighboring adipocytes. RESULTS Here, we constructed the largest transcriptomic dataset of porcine mammary gland cells thus far. The dataset captured 126,829 high-quality nuclei from physiological mammary glands across five developmental stages (d 90 of gestation, G90; d 0 after lactation, L0; d 20 after lactation, L20; 2 d post natural involution, PI2; 7 d post natural involution, PI7). Seven cell types were identified, including epithelial cells, adipocytes, endothelial cells, fibroblasts cells, immune cells, myoepithelial cells and precursor cells. Our data indicate that mammary glands at different developmental stages have distinct phenotypic and transcriptional signatures. During late gestation (G90), the differentiation and proliferation of adipocytes were inhibited. Meanwhile, partly epithelial cells were completely differentiated. Pseudo-time analysis showed that epithelial cells undergo three stages to achieve lactation, including cellular differentiation, hormone sensing, and metabolic activation. During lactation (L0 and L20), adipocytes area accounts for less than 0.5% of mammary glands. To maintain their own survival, the adipocyte exhibited a poorly differentiated state and a proliferative capacity. Epithelial cells initiate lactation upon hormonal stimulation. After fulfilling lactation mission, their undergo physiological death under high intensity lactation. Interestingly, the physiological dead cells seem to be actively cleared by immune cells via CCL21-ACKR4 pathway. This biological process may be an important mechanism for maintaining homeostasis of the mammary gland. During natural involution (PI2 and PI7), epithelial cell populations dedifferentiate into mesenchymal stem cells to maintain the lactation potential of mammary glands for the next lactation cycle. CONCLUSION The molecular mechanisms of dedifferentiation, proliferation and redifferentiation of adipocytes and epithelial cells were revealed from late pregnancy to natural involution. This cell transcriptomic profile constitutes an essential reference for future studies in the development and remodeling of the mammary gland at different stages.
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Affiliation(s)
- Yongliang Fan
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130 China
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Southwest Minzu University, Chengdu, 610041 China
| | - Long Jin
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130 China
| | - Zhiping He
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu, 610000 China
| | - Tiantian Wei
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130 China
| | - Tingting Luo
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130 China
| | - Jiaman Zhang
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130 China
| | - Can Liu
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130 China
| | - Changjiu Dai
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130 China
| | - Chao A
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130 China
| | - Yan Liang
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu, 610000 China
| | - Xuan Tao
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu, 610000 China
| | - Xuebin Lv
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu, 610000 China
| | - Yiren Gu
- Key Laboratory of Qinghai-Tibetan Plateau Animal Genetic Resource Reservation and Utilization, Southwest Minzu University, Chengdu, 610041 China
- Animal Breeding and Genetics Key Laboratory of Sichuan Province, Sichuan Animal Science Academy, Chengdu, 610000 China
| | - Mingzhou Li
- State Key Laboratory of Swine and Poultry Breeding Industry, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu, 611130 China
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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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Shi Q, Chen X, Zhang Z. Decoding Human Biology and Disease Using Single-cell Omics Technologies. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:926-949. [PMID: 37739168 PMCID: PMC10928380 DOI: 10.1016/j.gpb.2023.06.003] [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: 01/19/2023] [Revised: 05/22/2023] [Accepted: 06/08/2023] [Indexed: 09/24/2023]
Abstract
Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.
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Affiliation(s)
- Qiang Shi
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Xueyan Chen
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Zemin Zhang
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Changping Laboratory, Beijing 102206, China.
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18
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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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19
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Luo X, Liu Z, Xu R. Adult tissue-specific stem cell interaction: novel technologies and research advances. Front Cell Dev Biol 2023; 11:1220694. [PMID: 37808078 PMCID: PMC10551553 DOI: 10.3389/fcell.2023.1220694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 09/11/2023] [Indexed: 10/10/2023] Open
Abstract
Adult tissue-specific stem cells play a dominant role in tissue homeostasis and regeneration. Various in vivo markers of adult tissue-specific stem cells have been increasingly reported by lineage tracing in genetic mouse models, indicating that marked cells differentiation is crucial during homeostasis and regeneration. How adult tissue-specific stem cells with indicated markers contact the adjacent lineage with indicated markers is of significance to be studied. Novel methods bring future findings. Recent advances in lineage tracing, synthetic receptor systems, proximity labeling, and transcriptomics have enabled easier and more accurate cell behavior visualization and qualitative and quantitative analysis of cell-cell interactions than ever before. These technological innovations have prompted researchers to re-evaluate previous experimental results, providing increasingly compelling experimental results for understanding the mechanisms of cell-cell interactions. This review aimed to describe the recent methodological advances of dual enzyme lineage tracing system, the synthetic receptor system, proximity labeling, single-cell RNA sequencing and spatial transcriptomics in the study of adult tissue-specific stem cells interactions. An enhanced understanding of the mechanisms of adult tissue-specific stem cells interaction is important for tissue regeneration and maintenance of homeostasis in organisms.
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Affiliation(s)
| | | | - Ruoshi Xu
- State Key Laboratory of Oral Diseases, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Department of Cariology and Endodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China
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20
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Peng L, Tan J, Xiong W, Zhang L, Wang Z, Yuan R, Li Z, Chen X. Deciphering ligand-receptor-mediated intercellular communication based on ensemble deep learning and the joint scoring strategy from single-cell transcriptomic data. Comput Biol Med 2023; 163:107137. [PMID: 37364528 DOI: 10.1016/j.compbiomed.2023.107137] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 05/18/2023] [Accepted: 06/04/2023] [Indexed: 06/28/2023]
Abstract
BACKGROUND Cell-cell communication in a tumor microenvironment is vital to tumorigenesis, tumor progression and therapy. Intercellular communication inference helps understand molecular mechanisms of tumor growth, progression and metastasis. METHODS Focusing on ligand-receptor co-expressions, in this study, we developed an ensemble deep learning framework, CellComNet, to decipher ligand-receptor-mediated cell-cell communication from single-cell transcriptomic data. First, credible LRIs are captured by integrating data arrangement, feature extraction, dimension reduction, and LRI classification based on an ensemble of heterogeneous Newton boosting machine and deep neural network. Next, known and identified LRIs are screened based on single-cell RNA sequencing (scRNA-seq) data in certain tissues. Finally, cell-cell communication is inferred by incorporating scRNA-seq data, the screened LRIs, a joint scoring strategy that combines expression thresholding and expression product of ligands and receptors. RESULTS The proposed CellComNet framework was compared with four competing protein-protein interaction prediction models (PIPR, XGBoost, DNNXGB, and OR-RCNN) and obtained the best AUCs and AUPRs on four LRI datasets, elucidating the optimal LRI classification ability. CellComNet was further applied to analyze intercellular communication in human melanoma and head and neck squamous cell carcinoma (HNSCC) tissues. The results demonstrate that cancer-associated fibroblasts highly communicate with melanoma cells and endothelial cells strong communicate with HNSCC cells. CONCLUSIONS The proposed CellComNet framework efficiently identified credible LRIs and significantly improved cell-cell communication inference performance. We anticipate that CellComNet can contribute to anticancer drug design and tumor-targeted therapy.
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Affiliation(s)
- Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China; College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Jingwei Tan
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Wei Xiong
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Li Zhang
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, Jiangsu, China
| | - Zhao Wang
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Ruya Yuan
- School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China
| | - Zejun Li
- School of Computer Science, Hunan Institute of Technology, Hengyang, 421002, Hunan, China.
| | - Xing Chen
- School of Science, Jiangnan University, Wuxi, 214122, Jiangsu, China.
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21
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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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22
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Mori T, Takase T, Lan KC, Yamane J, Alev C, Kimura A, Osafune K, Yamashita JK, Akutsu T, Kitano H, Fujibuchi W. eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio-temporal architectures of cells. BMC Bioinformatics 2023; 24:252. [PMID: 37322439 DOI: 10.1186/s12859-023-05355-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 05/25/2023] [Indexed: 06/17/2023] Open
Abstract
BACKGROUND Bioinformatics capability to analyze spatio-temporal dynamics of gene expression is essential in understanding animal development. Animal cells are spatially organized as functional tissues where cellular gene expression data contain information that governs morphogenesis during the developmental process. Although several computational tissue reconstruction methods using transcriptomics data have been proposed, those methods have been ineffective in arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided. RESULTS This study demonstrates stochastic self-organizing map clustering with Markov chain Monte Carlo calculations for optimizing informative genes effectively reconstruct any spatio-temporal topology of cells from their transcriptome profiles with only a coarse topological guideline. The method, eSPRESSO (enhanced SPatial REconstruction by Stochastic Self-Organizing Map), provides a powerful in silico spatio-temporal tissue reconstruction capability, as confirmed by using human embryonic heart and mouse embryo, brain, embryonic heart, and liver lobule with generally high reproducibility (average max. accuracy = 92.0%), while revealing topologically informative genes, or spatial discriminator genes. Furthermore, eSPRESSO was used for temporal analysis of human pancreatic organoids to infer rational developmental trajectories with several candidate 'temporal' discriminator genes responsible for various cell type differentiations. CONCLUSIONS eSPRESSO provides a novel strategy for analyzing mechanisms underlying the spatio-temporal formation of cellular organizations.
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Affiliation(s)
- Tomoya Mori
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan
| | - Toshiro Takase
- Life Sciences, IBM Consulting, IBM Japan Ltd., 19-21 Nihonbashi Hakozaki-cho , Chuo-ku, Tokyo, 103-8510, Japan
| | - Kuan-Chun Lan
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Sho-goin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Junko Yamane
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Sho-goin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Cantas Alev
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Yoshida-Konoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan
| | - Azuma Kimura
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Sho-goin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Kenji Osafune
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Sho-goin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Jun K Yamashita
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Sho-goin, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan
| | - Hiroaki Kitano
- The Systems Biology Institute, Tokyo, Japan
- Okinawa Institute of Science and Technology Graduate School, Okinawa, Japan
- Sony Computer Science Laboratories, Inc., Tokyo, Japan
- Sony AI, Inc., Tokyo, Japan
- The Alan Turing Institute, London, UK
| | - Wataru Fujibuchi
- Center for iPS Cell Research and Application (CiRA), Kyoto University, 53 Kawahara-cho, Sho-goin, Sakyo-ku, Kyoto, 606-8507, Japan.
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23
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He M, Jiang X, Miao J, Feng W, Xie T, Liao S, Qin Z, Tang H, Lin C, Li B, Xu J, Liu Y, Mo Z, Wei Q. A new insight of immunosuppressive microenvironment in osteosarcoma lung metastasis. Exp Biol Med (Maywood) 2023; 248:1056-1073. [PMID: 37439349 PMCID: PMC10581164 DOI: 10.1177/15353702231171900] [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: 10/19/2022] [Accepted: 03/08/2023] [Indexed: 07/14/2023] Open
Abstract
The lung is the primary organ for the metastasis of osteosarcoma. Although the application of neoadjuvant chemotherapy and surgery has remarkably improved the survival rate of patients with osteosarcoma, prognosis is still poor for those patients with metastasis. In this study, we performed further bioinformatics analysis on single-cell RNA sequencing (scRNA-seq) data published before, containing 75,317 cells from two osteosarcoma lung metastasis and five normal lung tissues. First, we classified 17 clusters, including macrophages, T cells, endothelial cells, and so on, indicating highly intratumoral heterogeneity in osteosarcoma lung metastasis. Next, we found macrophages in osteosarcoma lung metastasis did not have significant M1 or M2 polarizations. Then, we identified that T cells occupied the most abundant among all cell clusters, and found CD8+ T cells exhibited a low expression level of immune checkpoints in osteosarcoma lung metastasis. What is more, we identified C2_Malignant cells, and found CD63 might play vital roles in determining the infiltration of T cells and malignant cells in conventional-type osteosarcoma lung metastasis. Finally, we unveiled C1_Therapeutic cluster, a subcluster of malignant cells, was sensitive to oxfendazole and mevastatin, and the potential hydrogen-bond position and binding energy of oxfendazole-KIAA0907 and mevastatin-KIAA0907 were unveiled, respectively. Our results highlighted the power of scRNA-seq technique in identifying the complex tumor microenvironment of osteosarcoma lung metastasis, making it possible to devise precision therapeutic approaches.
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Affiliation(s)
- Mingwei He
- Department of Trauma Orthopedic and Hand Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
- Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-Constructed by the Province and Ministry, Guangxi Medical University, Nanning 530021, China
| | - Xiaohong Jiang
- Department of Trauma Orthopedic and Hand Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
| | - Jifeng Miao
- Orthopedics Department, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530005, China
| | - Wenyu Feng
- Orthopedics Department, The Second Affiliated Hospital of Guangxi Medical University, Nanning 530005, China
| | - Tianyu Xie
- Department of Trauma Orthopedic and Hand Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
| | - Shijie Liao
- Department of Trauma Orthopedic and Hand Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
| | - Zhaojie Qin
- Department of Orthopedic, The People’s Hospital of Hechi, Hechi 547600, China
| | - Haijun Tang
- Department of Spinal Bone Disease, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
| | - Chengsen Lin
- Department of Trauma Orthopedic and Hand Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
| | - Boxiang Li
- Department of Trauma Orthopedic and Hand Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
| | - Jiake Xu
- School of Biomedical Sciences, The University of Western Australia, Perth, WA 6009, Australia
| | - Yun Liu
- Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-Constructed by the Province and Ministry, Guangxi Medical University, Nanning 530021, China
- Department of Spinal Bone Disease, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
| | - Zengnan Mo
- Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning 530021, China
| | - Qingjun Wei
- Department of Trauma Orthopedic and Hand Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
- Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-Constructed by the Province and Ministry, Guangxi Medical University, Nanning 530021, China
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24
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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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25
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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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26
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Zhu M, Li C, Lv K, Guo H, Hou R, Tian G, Yang J. MLSpatial: A machine-learning method to reconstruct the spatial distribution of cells from scRNA-seq by extracting spatial features. Comput Biol Med 2023; 159:106873. [PMID: 37105115 DOI: 10.1016/j.compbiomed.2023.106873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 03/03/2023] [Accepted: 03/30/2023] [Indexed: 04/29/2023]
Abstract
MOTIVATION Single-cell RNA sequencing (scRNA-seq) technologies allow us to interrogate the state of an individual cell within its microenvironment. However, prior to sequencing, cells should be dissociated first, making it difficult to obtain their spatial information. Since the spatial distribution of cells is critical in a few circumstances such as cancer immunotherapy, we present MLSpatial, a novel computational method to learn the relationship between gene expression patterns and spatial locations of cells, and then predict cell-to-cell distance distribution based on scRNA-seq data alone. RESULTS We collected the drosophila embryo dataset, which contains both the fluorescence in situ hybridization (FISH) data and single cell RNA-seq (scRNA-seq) data of drosophila embryo. The FISH data provided the spatial position of 3039 cells and the expression of 84 genes for each cell. The scRNA-seq data contains the expressions of 8924 genes in 1297 high-quality cells with cell location unknown. For a comparison, we also collected the MERFISH data of 645 osteosarcoma cells with cell location and the expression status of 10,050 genes known. For each data, the cells were randomly divided into a training set and a test set, in the ratio of 7:3. The cell-to-cell distances our model extracted had a higher correspondence (i.e., correlation coefficient 0.99) with those of the real situation than those of existing methods in the FISH data of drosophila embryo. However, in the osteosarcoma data, our model captured the spatial relationship between cells, with a correlation of 0.514 to that of the real situation. We also applied the model trained using the FISH data of drosophila embryo into the single cell data of drosophila embryo, for which the real location of cells are unknown. The reconstructed pseudo drosophila embryo and the real embryo (as shown by the FISH data) had a high similarity in the spatial distribution of gene expression. CONCLUSION MLSpatial can accurately restore the relative position of cells from scRNA-seq data; however, the performance depends on the type of cells. The trained model might be useful in reconstructing the spatial distributions of single cells with only scRNA-seq data, provided that the scRNA-seq data and the FISH data are under similar background (i.e., the same tissue with similar disease background).
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Affiliation(s)
- Mengbo Zhu
- Department of Mathematics, Ocean University of China, Qingdao, 266100, China; Geneis Beijing Co., Ltd., Beijing, 100102, China
| | - Changjun Li
- Department of Mathematics, Ocean University of China, Qingdao, 266100, China.
| | - Kebo Lv
- Department of Mathematics, Ocean University of China, Qingdao, 266100, China
| | - Hongzhe Guo
- Geneis Beijing Co., Ltd., Beijing, 100102, China.
| | - Rui Hou
- Geneis Beijing Co., Ltd., Beijing, 100102, China; Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, 266000, China
| | - Geng Tian
- Geneis Beijing Co., Ltd., Beijing, 100102, China; Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, 266000, China
| | - Jialiang Yang
- Geneis Beijing Co., Ltd., Beijing, 100102, China; Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, 266000, China; Chifeng Municipal Hospital, Chifeng, Inner Mongolia, 024000, China; Academician Workstation, Changsha Medical University, Changsha, 410219, China.
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27
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Becker LM, Chen SH, Rodor J, de Rooij LPMH, Baker AH, Carmeliet P. Deciphering endothelial heterogeneity in health and disease at single-cell resolution: progress and perspectives. Cardiovasc Res 2023; 119:6-27. [PMID: 35179567 PMCID: PMC10022871 DOI: 10.1093/cvr/cvac018] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/16/2021] [Accepted: 02/16/2022] [Indexed: 11/14/2022] Open
Abstract
Endothelial cells (ECs) constitute the inner lining of vascular beds in mammals and are crucial for homeostatic regulation of blood vessel physiology, but also play a key role in pathogenesis of many diseases, thereby representing realistic therapeutic targets. However, it has become evident that ECs are heterogeneous, encompassing several subtypes with distinct functions, which makes EC targeting and modulation in diseases challenging. The rise of the new single-cell era has led to an emergence of studies aimed at interrogating transcriptome diversity along the vascular tree, and has revolutionized our understanding of EC heterogeneity from both a physiological and pathophysiological context. Here, we discuss recent landmark studies aimed at teasing apart the heterogeneous nature of ECs. We cover driving (epi)genetic, transcriptomic, and metabolic forces underlying EC heterogeneity in health and disease, as well as current strategies used to combat disease-enriched EC phenotypes, and propose strategies to transcend largely descriptive heterogeneity towards prioritization and functional validation of therapeutically targetable drivers of EC diversity. Lastly, we provide an overview of the most recent advances and hurdles in single EC OMICs.
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Affiliation(s)
| | | | | | | | - Andrew H Baker
- Corresponding authors. Tel: +32 16 32 62 47, E-mail: (P.C.); Tel: +44 (0)131 242 6774, E-mail: (A.H.B.)
| | - Peter Carmeliet
- Corresponding authors. Tel: +32 16 32 62 47, E-mail: (P.C.); Tel: +44 (0)131 242 6774, E-mail: (A.H.B.)
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Heidari A, Sentürk OI, Yang S, Joesaar A, Gobbo P, Mann S, de Greef TFA, Wegner SV. Orthogonal Light-Dependent Membrane Adhesion Induces Social Self-Sorting and Member-Specific DNA Communication in Synthetic Cell Communities. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2023; 19:e2206474. [PMID: 36599623 DOI: 10.1002/smll.202206474] [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: 10/20/2022] [Revised: 12/03/2022] [Indexed: 06/17/2023]
Abstract
Developing orthogonal chemical communication pathways in diverse synthetic cell communities is a considerable challenge due to the increased crosstalk and interference associated with large numbers of different types of sender-receiver pairs. Herein, the authors control which sender-receiver pairs communicate in a three-membered community of synthetic cells through red and blue light illumination. Semipermeable protein-polymer-based synthetic cells (proteinosomes) with complementary membrane-attached protein adhesion communicate through single-stranded DNA oligomers and synergistically process biochemical information within a community consisting of one sender and two different receiver populations. Different pairs of red and blue light-responsive protein-protein interactions act as membrane adhesion mediators between the sender and receivers such that they self-assemble and socially self-sort into different multicellular structures under red and blue light. Consequently, distinct sender-receiver pairs come into the signaling range depending on the light illumination and are able to communicate specifically without activation of the other receiver population. Overall, this work shows how photoswitchable membrane adhesion gives rise to different self-sorting protocell patterns that mediate member-specific DNA-based communication in ternary populations of synthetic cells and provides a step towards the design of orthogonal chemical communication networks in diverse communities of synthetic cells.
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Affiliation(s)
- Ali Heidari
- Institute of Physiological Chemistry and Pathobiochemistry University of Münster, Waldeyerstr. 15, 48149, Münster, Germany
| | - Oya I Sentürk
- Department of Physical Chemistry of Polymers, Max Planck Institute for Polymer Research, Ackermannweg 10, 55128, Mainz, Germany
| | - Shuo Yang
- Laboratory of Chemical Biology and Institute for Complex Molecular Systems, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, 5612 AZ, The Netherlands
| | - Alex Joesaar
- Laboratory of Chemical Biology and Institute for Complex Molecular Systems, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, 5612 AZ, The Netherlands
| | - Pierangelo Gobbo
- Department of Chemical and Pharmaceutical Sciences, University of Trieste, Trieste, 34127, Italy
| | - Stephen Mann
- Centre for Protolife Research and Centre for Organized Matter Chemistry, Max Planck Bristol Centre for Minimal Biology, School of Chemistry, University of Bristol, Bristol, BS8 1TS, UK
| | - Tom F A de Greef
- Laboratory of Chemical Biology and Institute for Complex Molecular Systems, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, 5612 AZ, The Netherlands
| | - Seraphine V Wegner
- Institute of Physiological Chemistry and Pathobiochemistry University of Münster, Waldeyerstr. 15, 48149, Münster, Germany
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Khozyainova AA, Valyaeva AA, Arbatsky MS, Isaev SV, Iamshchikov PS, Volchkov EV, Sabirov MS, Zainullina VR, Chechekhin VI, Vorobev RS, Menyailo ME, Tyurin-Kuzmin PA, Denisov EV. Complex Analysis of Single-Cell RNA Sequencing Data. BIOCHEMISTRY (MOSCOW) 2023; 88:231-252. [PMID: 37072324 PMCID: PMC10000364 DOI: 10.1134/s0006297923020074] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) is a revolutionary tool for studying the physiology of normal and pathologically altered tissues. This approach provides information about molecular features (gene expression, mutations, chromatin accessibility, etc.) of cells, opens up the possibility to analyze the trajectories/phylogeny of cell differentiation and cell-cell interactions, and helps in discovery of new cell types and previously unexplored processes. From a clinical point of view, scRNA-seq facilitates deeper and more detailed analysis of molecular mechanisms of diseases and serves as a basis for the development of new preventive, diagnostic, and therapeutic strategies. The review describes different approaches to the analysis of scRNA-seq data, discusses the advantages and disadvantages of bioinformatics tools, provides recommendations and examples of their successful use, and suggests potential directions for improvement. We also emphasize the need for creating new protocols, including multiomics ones, for the preparation of DNA/RNA libraries of single cells with the purpose of more complete understanding of individual cells.
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Affiliation(s)
- Anna A Khozyainova
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia.
| | - Anna A Valyaeva
- Faculty of Bioengineering and Bioinformatics, Lomonosov Moscow State University, Moscow, 119991, Russia
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Mikhail S Arbatsky
- Laboratory of Artificial Intelligence and Bioinformatics, The Russian Clinical Research Center for Gerontology, Pirogov Russian National Medical University, Moscow, 129226, Russia
- School of Public Administration, Lomonosov Moscow State University, Moscow, 119991, Russia
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Sergey V Isaev
- Research Institute of Personalized Medicine, National Center for Personalized Medicine of Endocrine Diseases, National Medical Research Center for Endocrinology, Moscow, 117036, Russia
| | - Pavel S Iamshchikov
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
- Laboratory of Complex Analysis of Big Bioimage Data, National Research Tomsk State University, Tomsk, 634050, Russia
| | - Egor V Volchkov
- Department of Oncohematology, Dmitry Rogachev National Research Center of Pediatric Hematology, Oncology and Immunology, Moscow, 117198, Russia
| | - Marat S Sabirov
- Laboratory of Bioinformatics and Molecular Genetics, Koltzov Institute of Developmental Biology of the Russian Academy of Sciences, Moscow, 119334, Russia
| | - Viktoria R Zainullina
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
| | - Vadim I Chechekhin
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Rostislav S Vorobev
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
| | - Maxim E Menyailo
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
| | - Pyotr A Tyurin-Kuzmin
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, 119991, Russia
| | - Evgeny V Denisov
- Laboratory of Cancer Progression Biology, Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, 634050, Russia
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Zhang C, Li J, Cheng Y, Meng F, Song JW, Fan X, Fan H, Li J, Fu YL, Zhou MJ, Hu W, Wang SY, Fu YJ, Zhang JY, Xu RN, Shi M, Hu X, Zhang Z, Ren X, Wang FS. Single-cell RNA sequencing reveals intrahepatic and peripheral immune characteristics related to disease phases in HBV-infected patients. Gut 2023; 72:153-167. [PMID: 35361683 PMCID: PMC9763233 DOI: 10.1136/gutjnl-2021-325915] [Citation(s) in RCA: 44] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 03/16/2022] [Indexed: 02/04/2023]
Abstract
OBJECTIVE A comprehensive immune landscape for HBV infection is pivotal to achieve HBV cure. DESIGN We performed single-cell RNA sequencing of 2 43 000 cells from 46 paired liver and blood samples of 23 individuals, including six immune tolerant, 5 immune active (IA), 3 acute recovery (AR), 3 chronic resolved and 6 HBV-free healthy controls (HCs). Flow cytometry and histological assays were applied in a second HBV cohort for validation. RESULTS Both IA and AR were characterised by high levels of intrahepatic exhausted CD8+ T (Tex) cells. In IA, Tex cells were mainly derived from liver-resident GZMK+ effector memory T cells and self-expansion. By contrast, peripheral CX3CR1+ effector T cells and GZMK+ effector memory T cells were the main source of Tex cells in AR. In IA but not AR, significant cell-cell interactions were observed between Tex cells and regulatory CD4+ T cells, as well as between Tex and FCGR3A+ macrophages. Such interactions were potentially mediated through human leukocyte antigen class I molecules together with their receptors CANX and LILRBs, respectively, contributing to the dysfunction of antiviral immune responses. By contrast, CX3CR1+GNLY+ central memory CD8+ T cells were concurrently expanded in both liver and blood of AR, providing a potential surrogate marker for viral resolution. In clinic, intrahepatic Tex cells were positively correlated with serum alanine aminotransferase levels and histological grading scores. CONCLUSION Our study dissects the coordinated immune responses for different HBV infection phases and provides a rich resource for fully understanding immunopathogenesis and developing effective therapeutic strategies.
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Affiliation(s)
- Chao Zhang
- Senior Department of Infectious Diseases, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jiesheng Li
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China,Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen, China
| | - Yongqian Cheng
- Senior Department of Infectious Diseases, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Fanping Meng
- Senior Department of Infectious Diseases, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Jin-Wen Song
- Senior Department of Infectious Diseases, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xing Fan
- Senior Department of Infectious Diseases, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Hongtao Fan
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China
| | - Jing Li
- Senior Department of Infectious Diseases, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yu-Long Fu
- Senior Department of Infectious Diseases, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Ming-Ju Zhou
- Senior Department of Infectious Diseases, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Wei Hu
- Senior Department of Infectious Diseases, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Si-Yu Wang
- Senior Department of Infectious Diseases, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Yuan-Jie Fu
- Senior Department of Infectious Diseases, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Ji-Yuan Zhang
- Senior Department of Infectious Diseases, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Ruo-Nan Xu
- Senior Department of Infectious Diseases, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Ming Shi
- Senior Department of Infectious Diseases, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xueda Hu
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China
| | - Zemin Zhang
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China .,Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen, China
| | - Xianwen Ren
- Biomedical Pioneering Innovation Center (BIOPIC), School of Life Sciences, Peking University, Beijing, China .,Changping Laboratory, Beijing, China
| | - Fu-Sheng Wang
- Senior Department of Infectious Diseases, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, China
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31
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Duan H, Cheng T, Cheng H. Spatially resolved transcriptomics: advances and applications. BLOOD SCIENCE 2023; 5:1-14. [PMID: 36742187 PMCID: PMC9891446 DOI: 10.1097/bs9.0000000000000141] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 10/19/2022] [Indexed: 11/06/2022] Open
Abstract
Spatial transcriptomics, which is capable of both measuring all gene activity in a tissue sample and mapping where this activity occurs, is vastly improving our understanding of biological processes and disease. The field has expanded rapidly in recent years, and the development of several new technologies has resulted in spatially resolved transcriptomics (SRT) becoming highly multiplexed, high-resolution, and high-throughput. Here, we summarize and compare the major methods of SRT, including imaging-based methods, sequencing-based methods, and in situ sequencing methods. We also highlight some typical applications of SRT in neuroscience, cancer biology, developmental biology, and hematology. Finally, we discuss future possibilities for improving spatially resolved transcriptomic methods and the expected applications of such methods, especially in the adult bone marrow, anticipating that new developments will unlock the full potential of spatially resolved multi-omics in both biological research and the clinic.
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Affiliation(s)
- Honglin Duan
- State Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, National Clinical Research Center for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Tao Cheng
- State Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, National Clinical Research Center for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Center for Stem Cell Medicine, Chinese Academy of Medical Sciences, Tianjin, China
- Department of Stem Cell & Regenerative Medicine, Peking Union Medical College, Tianjin, China
| | - Hui Cheng
- State Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, National Clinical Research Center for Blood Diseases, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
- Center for Stem Cell Medicine, Chinese Academy of Medical Sciences, Tianjin, China
- Department of Stem Cell & Regenerative Medicine, Peking Union Medical College, Tianjin, China
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32
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Xue Q, Peng W, Zhang S, Wei X, Ye L, Wang Z, Xiang X, Zhang P, Zhou Q. Promising immunotherapeutic targets in lung cancer based on single-cell RNA sequencing. Front Immunol 2023; 14:1148061. [PMID: 37187731 PMCID: PMC10175686 DOI: 10.3389/fimmu.2023.1148061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 04/17/2023] [Indexed: 05/17/2023] Open
Abstract
Immunotherapy has made great strides in the treatment of lung cancer, but a significant proportion of patients still do not respond to treatment. Therefore, the identification of novel targets is crucial to improving the response to immunotherapy. The tumor microenvironment (TME) is a complex niche composed of diverse pro-tumor molecules and cell populations, making the function and mechanism of a unique cell subset difficult to understand. However, the advent of single-cell RNA sequencing (scRNA-seq) technology has made it possible to identify cellular markers and understand their potential functions and mechanisms in the TME. In this review, we highlight recent advances emerging from scRNA-seq studies in lung cancer, with a particular focus on stromal cells. We elucidate the cellular developmental trajectory, phenotypic remodeling, and cell interactions during tumor progression. Our review proposes predictive biomarkers and novel targets for lung cancer immunotherapy based on cellular markers identified through scRNA-seq. The identification of novel targets could help improve the response to immunotherapy. The use of scRNA-seq technology could provide new strategies to understand the TME and develop personalized immunotherapy for lung cancer patients.
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33
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Kang B, Camps J, Fan B, Jiang H, Ibrahim MM, Hu X, Qin S, Kirchhoff D, Chiang DY, Wang S, Ye Y, Shen Z, Bu Z, Zhang Z, Roider HG. Parallel single-cell and bulk transcriptome analyses reveal key features of the gastric tumor microenvironment. Genome Biol 2022; 23:265. [PMID: 36550535 PMCID: PMC9773611 DOI: 10.1186/s13059-022-02828-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 12/01/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND The tumor microenvironment (TME) has been shown to strongly influence treatment outcome for cancer patients in various indications and to influence the overall survival. However, the cells forming the TME in gastric cancer have not been extensively characterized. RESULTS We combine bulk and single-cell RNA sequencing from tumors and matched normal tissue of 24 treatment-naïve GC patients to better understand which cell types and transcriptional programs are associated with malignant transformation of the stomach. Clustering 96,623 cells of non-epithelial origin reveals 81 well-defined TME cell types. We find that activated fibroblasts and endothelial cells are most prominently overrepresented in tumors. Intercellular network reconstruction and survival analysis of an independent cohort imply the importance of these cell types together with immunosuppressive myeloid cell subsets and regulatory T cells in establishing an immunosuppressive microenvironment that correlates with worsened prognosis and lack of response in anti-PD1-treated patients. In contrast, we find a subset of IFNγ activated T cells and HLA-II expressing macrophages that are linked to treatment response and increased overall survival. CONCLUSIONS Our gastric cancer single-cell TME compendium together with the matched bulk transcriptome data provides a unique resource for the identification of new potential biomarkers for patient stratification. This study helps further to elucidate the mechanism of gastric cancer and provides insights for therapy.
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Affiliation(s)
- Boxi Kang
- grid.11135.370000 0001 2256 9319BIOPIC, Beijing Advanced Innovation Centre for Genomics, and School of Life Sciences, Peking University, Beijing, China
| | - Jordi Camps
- grid.420044.60000 0004 0374 4101Biomedical Data Science, Research & Early Development Oncology, Bayer AG, Berlin, Germany
| | - Biao Fan
- grid.412474.00000 0001 0027 0586Department of Gastrointestinal Surgery, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Hongpeng Jiang
- grid.411610.30000 0004 1764 2878Department of General Surgery, Beijing Friendship Hospital, Capital Medical University, Beijing, China ,grid.411634.50000 0004 0632 4559Department of Gastroenterological Surgery, Peking University People’s Hospital, Beijing, China
| | - Mahmoud M. Ibrahim
- grid.420044.60000 0004 0374 4101Biomedical Data Science, Research & Early Development preMed, Bayer AG, Wuppertal, Germany
| | - Xueda Hu
- grid.11135.370000 0001 2256 9319BIOPIC, Beijing Advanced Innovation Centre for Genomics, and School of Life Sciences, Peking University, Beijing, China
| | - Shishang Qin
- grid.11135.370000 0001 2256 9319BIOPIC, Beijing Advanced Innovation Centre for Genomics, and School of Life Sciences, Peking University, Beijing, China
| | - Dennis Kirchhoff
- grid.420044.60000 0004 0374 4101Immuno Oncology, Research & Early Development Oncology, Bayer AG, Berlin, Germany
| | - Derek Y. Chiang
- Biomedical Data Science, Research & Early Development Oncology, Bayer US, Cambridge, MA USA
| | - Shan Wang
- grid.411634.50000 0004 0632 4559Department of Gastroenterological Surgery, Peking University People’s Hospital, Beijing, China ,grid.411634.50000 0004 0632 4559Laboratory of Surgical Oncology, Peking University People’s Hospital, Beijing, China ,Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Beijing, China
| | - Yingjiang Ye
- grid.411634.50000 0004 0632 4559Department of Gastroenterological Surgery, Peking University People’s Hospital, Beijing, China ,grid.411634.50000 0004 0632 4559Laboratory of Surgical Oncology, Peking University People’s Hospital, Beijing, China ,Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Beijing, China
| | - Zhanlong Shen
- grid.411634.50000 0004 0632 4559Department of Gastroenterological Surgery, Peking University People’s Hospital, Beijing, China ,grid.411634.50000 0004 0632 4559Laboratory of Surgical Oncology, Peking University People’s Hospital, Beijing, China ,Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Beijing, China
| | - Zhaode Bu
- grid.412474.00000 0001 0027 0586Department of Gastrointestinal Surgery, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Zemin Zhang
- grid.11135.370000 0001 2256 9319BIOPIC, Beijing Advanced Innovation Centre for Genomics, and School of Life Sciences, Peking University, Beijing, China ,grid.11135.370000 0001 2256 9319Peking-Tsinghua Centre for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Helge G. Roider
- grid.420044.60000 0004 0374 4101Oncology Precision Medicine, Research & Early Development Oncology, Bayer AG, Berlin, Germany
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Yuan Z, Li Y, Shi M, Yang F, Gao J, Yao J, Zhang MQ. SOTIP is a versatile method for microenvironment modeling with spatial omics data. Nat Commun 2022; 13:7330. [PMID: 36443314 PMCID: PMC9705407 DOI: 10.1038/s41467-022-34867-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 11/10/2022] [Indexed: 11/29/2022] Open
Abstract
The rapidly developing spatial omics generated datasets with diverse scales and modalities. However, most existing methods focus on modeling dynamics of single cells while ignore microenvironments (MEs). Here we present SOTIP (Spatial Omics mulTIPle-task analysis), a versatile method incorporating MEs and their interrelationships into a unified graph. Based on this graph, spatial heterogeneity quantification, spatial domain identification, differential microenvironment analysis, and other downstream tasks can be performed. We validate each module's accuracy, robustness, scalability and interpretability on various spatial omics datasets. In two independent mouse cerebral cortex spatial transcriptomics datasets, we reveal a gradient spatial heterogeneity pattern strongly correlated with the cortical depth. In human triple-negative breast cancer spatial proteomics datasets, we identify molecular polarizations and MEs associated with different patient survivals. Overall, by modeling biologically explainable MEs, SOTIP outperforms state-of-art methods and provides some perspectives for spatial omics data exploration and interpretation.
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Affiliation(s)
- Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.
- Tencent AI Lab, Shenzhen, China.
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Yisi Li
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China
| | - Minglei Shi
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, School of Medicine, Tsinghua University, Beijing, 100084, China
| | | | - Juntao Gao
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China
| | | | - Michael Q Zhang
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China.
- MOE Key Laboratory of Bioinformatics; Bioinformatics Division and Center for Synthetic & Systems Biology, School of Medicine, Tsinghua University, Beijing, 100084, China.
- Department of Biological Sciences, Center for Systems Biology, The University of Texas, Richardson, TX, 75080-3021, USA.
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35
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Sun H, Li L, Lao I, Li X, Xu B, Cao Y, Jin W. Single-cell RNA sequencing reveals cellular and molecular reprograming landscape of gliomas and lung cancer brain metastases. Clin Transl Med 2022; 12:e1101. [PMID: 36336787 PMCID: PMC9637666 DOI: 10.1002/ctm2.1101] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 10/13/2022] [Accepted: 10/14/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND Brain malignancies encompass gliomas and brain metastases originating from extracranial tumours including lung cancer. Approximately 50% of patients with lung adenocarcinoma (LUAD) will eventually develop brain metastases. However, the specific characteristics of gliomas and lung-to-brain metastases (LC) are largely unknown. METHODS We applied single-cell RNA sequencing to profile immune and nonimmune cells in 4 glioma and 10 LC samples. RESULTS Our analysis revealed that tumour microenvironment (TME) cells are present in heterogeneous subpopulations. LC reprogramed cells into immune suppressed state, including microglia, macrophages, endothelial cells, and CD8+ T cells, with unique cell proportions and gene signatures. Particularly, we identified that a subset of macrophages was associated with poor prognosis. ROS (reactive oxygen species)-producing neutrophils was found to participant in angiogenesis. Furthermore, endothelial cells participated in active communication with fibroblasts. Metastatic epithelial cells exhibited high heterogeneity in chromosomal instability (CIN) and cell population. CONCLUSIONS Our findings provide a comprehensive understanding of the heterogenicity of the tumor microenvironment and tumour cells and it will be crucial for successful immunotherapy development for brain metastasis of lung cancer.
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Affiliation(s)
- He‐Fen Sun
- Department of Breast SurgeryKey Laboratory of Breast Cancer in ShanghaiFudan University Shanghai Cancer CenterShanghaiChina,Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Liang‐Dong Li
- Department of Breast SurgeryKey Laboratory of Breast Cancer in ShanghaiFudan University Shanghai Cancer CenterShanghaiChina,Department of NeurosurgeryFudan University Shanghai Cancer CenterShanghaiChina,Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - I‐Weng Lao
- Department of PathologyFudan University Shanghai Cancer CenterShanghaiChina,Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Xuan Li
- Department of Breast SurgeryKey Laboratory of Breast Cancer in ShanghaiFudan University Shanghai Cancer CenterShanghaiChina,Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Bao‐Jin Xu
- Department of Breast SurgeryKey Laboratory of Breast Cancer in ShanghaiFudan University Shanghai Cancer CenterShanghaiChina,Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Yi‐Qun Cao
- Department of NeurosurgeryFudan University Shanghai Cancer CenterShanghaiChina,Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
| | - Wei Jin
- Department of Breast SurgeryKey Laboratory of Breast Cancer in ShanghaiFudan University Shanghai Cancer CenterShanghaiChina,Department of OncologyShanghai Medical CollegeFudan UniversityShanghaiChina
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Wen L, Li G, Huang T, Geng W, Pei H, Yang J, Zhu M, Zhang P, Hou R, Tian G, Su W, Chen J, Zhang D, Zhu P, Zhang W, Zhang X, Zhang N, Zhao Y, Cao X, Peng G, Ren X, Jiang N, Tian C, Chen ZJ. Single-cell technologies: From research to application. Innovation (N Y) 2022; 3:100342. [PMCID: PMC9637996 DOI: 10.1016/j.xinn.2022.100342] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 10/13/2022] [Indexed: 11/09/2022] Open
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37
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Xu C, Ma D, Ding Q, Zhou Y, Zheng H. PlantPhoneDB: A manually curated pan-plant database of ligand-receptor pairs infers cell-cell communication. PLANT BIOTECHNOLOGY JOURNAL 2022; 20:2123-2134. [PMID: 35842742 PMCID: PMC9616517 DOI: 10.1111/pbi.13893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 07/10/2022] [Accepted: 07/13/2022] [Indexed: 06/15/2023]
Abstract
Ligand-receptor pairs play important roles in cell-cell communication for multicellular organisms in response to environmental cues. Recently, the emergence of single-cell RNA-sequencing (scRNA-seq) provides unprecedented opportunities to investigate cellular communication based on ligand-receptor expression. However, so far, no reliable ligand-receptor interaction database is available for plant species. In this study, we developed PlantPhoneDB (https://jasonxu.shinyapps.io/PlantPhoneDB/), a pan-plant database comprising a large number of high-confidence ligand-receptor pairs manually curated from seven resources. Also, we developed a PlantPhoneDB R package, which not only provided optional four scoring approaches that calculate interaction scores of ligand-receptor pairs between cell types but also provided visualization functions to present analysis results. At the PlantPhoneDB web interface, the processed datasets and results can be searched, browsed, and downloaded. To uncover novel cell-cell communication events in plants, we applied the PlantPhoneDB R package on GSE121619 dataset to infer significant cell-cell interactions of heat-shocked root cells in Arabidopsis thaliana. As a result, the PlantPhoneDB predicted the actively communicating AT1G28290-AT2G14890 ligand-receptor pair in atrichoblast-cortex cell pair in Arabidopsis thaliana. Importantly, the downstream target genes of this ligand-receptor pair were significantly enriched in the ribosome pathway, which facilitated plants adapting to environmental changes. In conclusion, PlantPhoneDB provided researchers with integrated resources to infer cell-cell communication from scRNA-seq datasets.
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Affiliation(s)
- Chaoqun Xu
- Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and EcologyXiamen UniversityXiamenChina
| | - Dongna Ma
- Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and EcologyXiamen UniversityXiamenChina
| | - Qiansu Ding
- Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and EcologyXiamen UniversityXiamenChina
| | - Ying Zhou
- National Institute for Data Science in Health and Medicine, School of MedicineXiamen UniversityXiamenChina
| | - Hai‐Lei Zheng
- Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and EcologyXiamen UniversityXiamenChina
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Spatial transcriptome profiling of normal human liver. Sci Data 2022; 9:633. [PMID: 36261431 PMCID: PMC9581974 DOI: 10.1038/s41597-022-01676-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 09/05/2022] [Indexed: 11/21/2022] Open
Abstract
The comprehensive study of the spatial-cellular anatomy of the human liver is critical to addressing the cellular origins of liver disease. Here we conducted spatial transcriptomics on normal human liver tissue sections, providing detailed information of liver zonation at the transcriptional level. We present 6581 high-quality spots from normal livers of two human donors. In this dataset, cells were mainly hepatocytes, and we classified them into four sub-groups. Collectively, these data provide a reliable reference for studies on spatial heterogeneity of liver lobules. Measurement(s) | mRNA Expression | Technology Type(s) | spatial transcriptomics | Sample Characteristic - Organism | Homo sapiens |
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Li PH, Kong XY, He YZ, Liu Y, Peng X, Li ZH, Xu H, Luo H, Park J. Recent developments in application of single-cell RNA sequencing in the tumour immune microenvironment and cancer therapy. Mil Med Res 2022; 9:52. [PMID: 36154923 PMCID: PMC9511789 DOI: 10.1186/s40779-022-00414-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 08/20/2022] [Indexed: 11/10/2022] Open
Abstract
The advent of single-cell RNA sequencing (scRNA-seq) has provided insight into the tumour immune microenvironment (TIME). This review focuses on the application of scRNA-seq in investigation of the TIME. Over time, scRNA-seq methods have evolved, and components of the TIME have been deciphered with high resolution. In this review, we first introduced the principle of scRNA-seq and compared different sequencing approaches. Novel cell types in the TIME, a continuous transitional state, and mutual intercommunication among TIME components present potential targets for prognosis prediction and treatment in cancer. Thus, we concluded novel cell clusters of cancer-associated fibroblasts (CAFs), T cells, tumour-associated macrophages (TAMs) and dendritic cells (DCs) discovered after the application of scRNA-seq in TIME. We also proposed the development of TAMs and exhausted T cells, as well as the possible targets to interrupt the process. In addition, the therapeutic interventions based on cellular interactions in TIME were also summarized. For decades, quantification of the TIME components has been adopted in clinical practice to predict patient survival and response to therapy and is expected to play an important role in the precise treatment of cancer. Summarizing the current findings, we believe that advances in technology and wide application of single-cell analysis can lead to the discovery of novel perspectives on cancer therapy, which can subsequently be implemented in the clinic. Finally, we propose some future directions in the field of TIME studies that can be aided by scRNA-seq technology.
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Affiliation(s)
- Pei-Heng Li
- Department of Thyroid and Parathyroid Surgery, Laboratory of Thyroid and Parathyroid Disease, Frontiers Science Centre for Disease-Related Molecular Network, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, 610044, China
| | - Xiang-Yu Kong
- Department of Thyroid and Parathyroid Surgery, Laboratory of Thyroid and Parathyroid Disease, Frontiers Science Centre for Disease-Related Molecular Network, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, 610044, China
| | - Ya-Zhou He
- Department of Oncology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, 610044, China
| | - Yi Liu
- Department of Rheumatology and Immunology, Rare Diseases Centre, West China Hospital, Sichuan University, Chengdu, 610044, China
| | - Xi Peng
- College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Zhi-Hui Li
- Department of Thyroid and Parathyroid Surgery, Laboratory of Thyroid and Parathyroid Disease, Frontiers Science Centre for Disease-Related Molecular Network, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, 610044, China
| | - Heng Xu
- State Key Laboratory of Biotherapy and Cancer Centre, West China Hospital, Sichuan University and Collaborative Innovation Centre, Chengdu, 610044, China
| | - Han Luo
- Department of Thyroid and Parathyroid Surgery, Laboratory of Thyroid and Parathyroid Disease, Frontiers Science Centre for Disease-Related Molecular Network, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, 610044, China.
| | - Jihwan Park
- School of Life Sciences, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, Republic of Korea.
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Liu W, Jia J, Dai Y, Chen W, Pei G, Yan Q, Zhao Z. Delineating COVID-19 immunological features using single-cell RNA sequencing. Innovation (N Y) 2022; 3:100289. [PMID: 35879967 PMCID: PMC9299978 DOI: 10.1016/j.xinn.2022.100289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 07/16/2022] [Indexed: 11/24/2022] Open
Abstract
Understanding the molecular mechanisms of coronavirus disease 2019 (COVID-19) pathogenesis and immune response is vital for developing therapies. Single-cell RNA sequencing has been applied to delineate the cellular heterogeneity of the host response toward COVID-19 in multiple tissues and organs. Here, we review the applications and findings from over 80 original COVID-19 single-cell RNA sequencing studies as well as many secondary analysis studies. We describe that single-cell RNA sequencing reveals multiple features of COVID-19 patients with different severity, including cell populations with proportional alteration, COVID-19-induced genes and pathways, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection in single cells, and adaptation of immune repertoire. We also collect published single-cell RNA sequencing datasets from original studies. Finally, we discuss the limitations in current studies and perspectives for future advance.
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Affiliation(s)
- Wendao Liu
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Johnathan Jia
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Yulin Dai
- Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Wenhao Chen
- Immunobiology and Transplant Science Center, Department of Surgery, Houston Methodist Research Institute and Institute for Academic Medicine, Houston Methodist Hospital, Houston, TX 77030, USA
- Department of Surgery, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA
| | - Guangsheng Pei
- Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Qiheng Yan
- Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Zhongming Zhao
- The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
- Center for Precision Health, School of Biomedical Informatics, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Human Genetics Center, School of Public Health, the University of Texas Health Science Center at Houston, Houston, TX 77030, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
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Liu Q, Hsu CY, Shyr Y. Scalable and model-free detection of spatial patterns and colocalization. Genome Res 2022; 32:1736-1745. [PMID: 36223499 PMCID: PMC9528978 DOI: 10.1101/gr.276851.122] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 08/16/2022] [Indexed: 11/24/2022]
Abstract
The expeditious growth in spatial omics technologies enables the profiling of genome-wide molecular events at molecular and single-cell resolution, highlighting a need for fast and reliable methods to characterize spatial patterns. We developed SpaGene, a model-free method to discover spatial patterns rapidly in large-scale spatial omics studies. Analyzing simulation and a variety of spatially resolved transcriptomics data showed that SpaGene is more powerful and scalable than existing methods. Spatial expression patterns identified by SpaGene reconstruct unobserved tissue structures. SpaGene also successfully discovers ligand-receptor interactions through their colocalization.
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Affiliation(s)
- Qi Liu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee 37232, USA
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, Tennessee 37232, USA
| | - Chih-Yuan Hsu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee 37232, USA
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, Tennessee 37232, USA
| | - Yu Shyr
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee 37232, USA
- Center for Quantitative Sciences, Vanderbilt University Medical Center, Nashville, Tennessee 37232, USA
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Cuypers A, Truong ACK, Becker LM, Saavedra-García P, Carmeliet P. Tumor vessel co-option: The past & the future. Front Oncol 2022; 12:965277. [PMID: 36119528 PMCID: PMC9472251 DOI: 10.3389/fonc.2022.965277] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 08/04/2022] [Indexed: 12/11/2022] Open
Abstract
Tumor vessel co-option (VCO) is a non-angiogenic vascularization mechanism that is a possible cause of resistance to anti-angiogenic therapy (AAT). Multiple tumors are hypothesized to primarily rely on growth factor signaling-induced sprouting angiogenesis, which is often inhibited during AAT. During VCO however, tumors invade healthy tissues by hijacking pre-existing blood vessels of the host organ to secure their blood and nutrient supply. Although VCO has been described in the context of AAT resistance, the molecular mechanisms underlying this process and the profile and characteristics of co-opted vascular cell types (endothelial cells (ECs) and pericytes) remain poorly understood, resulting in the lack of therapeutic strategies to inhibit VCO (and to overcome AAT resistance). In the past few years, novel next-generation technologies (such as single-cell RNA sequencing) have emerged and revolutionized the way of analyzing and understanding cancer biology. While most studies utilizing single-cell RNA sequencing with focus on cancer vascularization have centered around ECs during sprouting angiogenesis, we propose that this and other novel technologies can be used in future investigations to shed light on tumor EC biology during VCO. In this review, we summarize the molecular mechanisms driving VCO known to date and introduce the models used to study this phenomenon to date. We highlight VCO studies that recently emerged using sequencing approaches and propose how these and other novel state-of-the-art methods can be used in the future to further explore ECs and other cell types in the VCO process and to identify potential vulnerabilities in tumors relying on VCO. A better understanding of VCO by using novel approaches could provide new answers to the many open questions, and thus pave the way to develop new strategies to control and target tumor vascularization.
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Affiliation(s)
- Anne Cuypers
- Laboratory of Angiogenesis and Vascular Metabolism, Center for Cancer Biology (CCB), Vlaams Instituut voor Biotechnologie (VIB) and Department of Oncology, Leuven Cancer Institute (LKI), KU Leuven, Leuven, Belgium
| | - Anh-Co Khanh Truong
- Laboratory of Angiogenesis and Vascular Metabolism, Center for Cancer Biology (CCB), Vlaams Instituut voor Biotechnologie (VIB) and Department of Oncology, Leuven Cancer Institute (LKI), KU Leuven, Leuven, Belgium
| | - Lisa M. Becker
- Laboratory of Angiogenesis and Vascular Metabolism, Center for Cancer Biology (CCB), Vlaams Instituut voor Biotechnologie (VIB) and Department of Oncology, Leuven Cancer Institute (LKI), KU Leuven, Leuven, Belgium
| | - Paula Saavedra-García
- Laboratory of Angiogenesis and Vascular Heterogeneity, Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Peter Carmeliet
- Laboratory of Angiogenesis and Vascular Metabolism, Center for Cancer Biology (CCB), Vlaams Instituut voor Biotechnologie (VIB) and Department of Oncology, Leuven Cancer Institute (LKI), KU Leuven, Leuven, Belgium
- Laboratory of Angiogenesis and Vascular Heterogeneity, Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Center for Biotechnology, Khalifa University of Science and Technology, Abu Dhabi, United Arab Emirates
- *Correspondence: Peter Carmeliet,
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Zhu J, Yan M, Yan H, Xu L, Jiang Z, Liao G, Zhou Y, Liu W, Liang X, Li X, Xiao Y, Zhang Y. Single-Cell Transcriptomic Analysis Reveals the Crosstalk Propensity Between the Tumor Intermediate State and the CD8+ T Exhausted State to be Associated with Clinical Benefits in Melanoma. Front Immunol 2022; 13:766852. [PMID: 35903095 PMCID: PMC9314667 DOI: 10.3389/fimmu.2022.766852] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Accepted: 06/20/2022] [Indexed: 11/14/2022] Open
Abstract
Heterogeneous crosstalk between tumor cells and CD8+ T cells leads to substantial variation in clinical benefits from immunotherapy in melanoma. Due to spatial distribution and functional state heterogeneity, it is still unknown whether there is a crosstalk propensity between tumor cells and CD8+ T cells in melanoma, and how this crosstalk propensity affects the clinical outcome of patients. Using public single-cell transcriptome data, extensive heterogeneous functional states and ligand–receptor interactions of tumor cells and CD8+ T cells were revealed in melanoma. Furthermore, based on the association between cell–cell communication intensity and cell state activity in a single cell, we identified a crosstalk propensity between the tumor intermediate state and the CD8+ T exhausted state. This crosstalk propensity was further verified by pseudo-spatial proximity, spatial co-location, and the intra/intercellular signal transduction network. At the sample level, the tumor intermediate state and the CD8+ T exhausted state synergistically indicated better prognosis and both reduced in immunotherapy-resistant samples. The risk groups defined based on these two cell states could comprehensively reflect tumor genomic mutations and anti-tumor immunity information. The low-risk group had a higher BRAF mutation fraction as well as stronger antitumor immune response. Our findings highlighted the crosstalk propensity between the tumor intermediate state and the CD8+ T exhausted state, which may serve as a reference to guide the development of diagnostic biomarkers for risk stratification and therapeutic targets for new therapeutic strategies.
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Affiliation(s)
- Jiali Zhu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Min Yan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Haoteng Yan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Liwen Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zedong Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Gaoming Liao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yao Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wei Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xin Liang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- Key Laboratory of High Throughput Omics Big Data for Cold Region’s Major Diseases in Heilongjiang Province, Harbin Medical University, Harbin, China
- *Correspondence: Yunpeng Zhang, ; Yun Xiao, ; Xia Li,
| | - Yun Xiao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- Key Laboratory of High Throughput Omics Big Data for Cold Region’s Major Diseases in Heilongjiang Province, Harbin Medical University, Harbin, China
- *Correspondence: Yunpeng Zhang, ; Yun Xiao, ; Xia Li,
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- Key Laboratory of High Throughput Omics Big Data for Cold Region’s Major Diseases in Heilongjiang Province, Harbin Medical University, Harbin, China
- *Correspondence: Yunpeng Zhang, ; Yun Xiao, ; Xia Li,
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Wang J, Li S, Chen L, Li SC. SPROUT: spectral sparsification helps restore the spatial structure at single-cell resolution. NAR Genom Bioinform 2022; 4:lqac069. [PMID: 36128423 PMCID: PMC9477078 DOI: 10.1093/nargab/lqac069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 07/11/2022] [Accepted: 08/26/2022] [Indexed: 11/23/2022] Open
Abstract
Single-cell RNA sequencing thoroughly quantifies the individual cell transcriptomes but renounces the spatial structure. Conversely, recently emerged spatial transcriptomics technologies capture the cellular spatial structure but skimp cell or gene resolutions. Ligand-receptor interactions reveal the potential of cell proximity since they are spatially constrained. Cell–cell affinity values estimated by ligand–receptor interaction can partially represent the structure of cells but falsely include the pseudo affinities between distant or indirectly interacting cells. Here, we develop a software package, SPROUT, to reconstruct the single-cell resolution spatial structure from the transcriptomics data through diminished pseudo ligand–receptor affinities. For spatial data, SPROUT first curates the representative single-cell profiles for each spatial spot from a candidate library, then reduces the pseudo affinities in the intercellular affinity matrix by partial correlation, spectral graph sparsification, and spatial coordinates refinement. SPROUT embeds the estimated interactions into a low-dimensional space with the cross-entropy objective to restore the intercellular structures, which facilitates the discovery of dominant ligand–receptor pairs between neighboring cells at single-cell resolution. SPROUT reconstructed structures achieved shape Pearson correlations ranging from 0.91 to 0.97 on the mouse hippocampus and human organ tumor microenvironment datasets. Furthermore, SPROUT can solely de novo reconstruct the structures at single-cell resolution, i.e., reaching the cell-type proximity correlations of 0.68 and 0.89 between reconstructed and immunohistochemistry-informed spatial structures on a human developing heart dataset and a tumor microenvironment dataset, respectively.
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Affiliation(s)
- Jingwan Wang
- Department of Computer Science, City University of Hong Kong , 83 Tat Chee Ave, Kowloon Tong, Hong Kong, China
- City University of Hong Kong Shenzhen Research Institute , Shenzhen, 518057 Guangdong, China
| | - Shiying Li
- Department of Computer Science, City University of Hong Kong , 83 Tat Chee Ave, Kowloon Tong, Hong Kong, China
- City University of Hong Kong Shenzhen Research Institute , Shenzhen, 518057 Guangdong, China
| | - Lingxi Chen
- Department of Computer Science, City University of Hong Kong , 83 Tat Chee Ave, Kowloon Tong, Hong Kong, China
- City University of Hong Kong Shenzhen Research Institute , Shenzhen, 518057 Guangdong, China
| | - Shuai Cheng Li
- Department of Computer Science, City University of Hong Kong , 83 Tat Chee Ave, Kowloon Tong, Hong Kong, China
- City University of Hong Kong Shenzhen Research Institute , Shenzhen, 518057 Guangdong, China
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Xin Y, Lyu P, Jiang J, Zhou F, Wang J, Blackshaw S, Qian J. LRLoop: a method to predict feedback loops in cell-cell communication. Bioinformatics 2022; 38:4117-4126. [PMID: 35788263 PMCID: PMC9438954 DOI: 10.1093/bioinformatics/btac447] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 06/24/2022] [Accepted: 07/03/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Intercellular communication (i.e. cell-cell communication) plays an essential role in multicellular organisms coordinating various biological processes. Previous studies discovered that feedback loops between two cell types are a widespread and vital signaling motif regulating development, regeneration and cancer progression. While many computational methods have been developed to predict cell-cell communication based on gene expression datasets, these methods often predict one-directional ligand-receptor interactions from sender to receiver cells and are not suitable to identify feedback loops. RESULTS Here, we describe ligand-receptor loop (LRLoop), a new method for analyzing cell-cell communication based on bi-directional ligand-receptor interactions, where two pairs of ligand-receptor interactions are identified that are responsive to each other and thereby form a closed feedback loop. We first assessed LRLoop using bulk datasets and found our method significantly reduces the false positive rate seen with existing methods. Furthermore, we developed a new strategy to assess the performance of these methods in single-cell datasets. We used the between-tissue interactions as an indicator of potential false-positive prediction and found that LRLoop produced a lower fraction of between-tissue interactions than traditional methods. Finally, we applied LRLoop to the single-cell datasets obtained from retinal development. We discovered many new bi-directional ligand-receptor interactions among individual cell types that potentially control proliferation, neurogenesis and/or cell fate specification. AVAILABILITY AND IMPLEMENTATION An R package is available at https://github.com/Pinlyu3/LRLoop. The source code can be found at figshare (https://doi.org/10.6084/m9.figshare.20126138.v1). The datasets can be found at figshare (https://doi.org/10.6084/m9.figshare.20126021.v1). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | - Junyao Jiang
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Fengquan Zhou
- Department of Orthopedic Surgery, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA,Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Jie Wang
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China
| | - Seth Blackshaw
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA,Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Jiang Qian
- To whom correspondence should be addressed.
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Wang R, Peng G, Tam PPL, Jing N. Integration of computational analysis and spatial transcriptomics in single-cell study. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022:S1672-0229(22)00084-5. [PMID: 35901961 PMCID: PMC10372908 DOI: 10.1016/j.gpb.2022.06.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 06/08/2022] [Accepted: 06/19/2022] [Indexed: 04/08/2023]
Abstract
Recent advances of single-cell transcriptomics technologies and allied computational methodologies have revolutionized molecular cell biology. Meanwhile, pioneering explorations in spatial transcriptomics have opened avenues to address fundamental biological questions in health and diseases. Here, we review the technical attributes of single-cell RNA sequencing and spatial transcriptomics, and the core concepts of computational data analysis. We further highlight the challenges in the application of data integration methodologies and the interpretation of the biological context of the findings.
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Affiliation(s)
- Ran Wang
- State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Guangdun Peng
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China
| | - Patrick P L Tam
- Embryology Research Unit, Children's Medical Research Institute, University of Sydney, Sydney, NSW 2145, Australia; School of Medical Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2145, Australia
| | - Naihe Jing
- State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China; Guangzhou Laboratory, Guangzhou 510005, China; CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou 510530, China; Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing 100101, China.
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Wu H, Fu R, Zhang YH, Liu Z, Chen ZH, Xu J, Tian Y, Jin W, Wong SZH, Wu QF. Single-Cell RNA Sequencing Unravels Upregulation of Immune Cell Crosstalk in Relapsed Pediatric Ependymoma. Front Immunol 2022; 13:903246. [PMID: 35844565 PMCID: PMC9281506 DOI: 10.3389/fimmu.2022.903246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 06/01/2022] [Indexed: 11/13/2022] Open
Abstract
Ependymoma (EPN) is a malignant glial tumor occurring throughout central nervous system, which commonly presents in children. Although recent studies have characterized EPN samples at both the bulk and single-cell level, intratumoral heterogeneity across subclones remains a confounding factor that impedes understanding of EPN biology. In this study, we generated a high-resolution single-cell dataset of pediatric ependymoma with a particular focus on the comparison of subclone differences within tumors and showed upregulation of cilium-associated genes in more highly differentiated subclone populations. As a proxy to traditional pseudotime analysis, we applied a novel trajectory scoring method to reveal cellular compositions associated with poor survival outcomes across primary and relapsed patients. Furthermore, we identified putative cell–cell communication features between relapsed and primary samples and showed upregulation of pathways associated with immune cell crosstalk. Our results revealed both inter- and intratumoral heterogeneity in EPN and provided a framework for studying transcriptomic signatures of individual subclones at single-cell resolution.
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Affiliation(s)
- Haoda Wu
- State Key Laboratory of Molecular Development Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- *Correspondence: Haoda Wu, ; Samuel Zheng Hao Wong, ; Wenfei Jin, ; Yongji Tian,
| | - Ruiqing Fu
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Yu-Hong Zhang
- State Key Laboratory of Molecular Development Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Zhiming Liu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhen-Hua Chen
- State Key Laboratory of Molecular Development Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jingkai Xu
- Department of Dermatology, China–Japan Friendship Hospital, Beijing, China
| | - Yongji Tian
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
- *Correspondence: Haoda Wu, ; Samuel Zheng Hao Wong, ; Wenfei Jin, ; Yongji Tian,
| | - Wenfei Jin
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
- *Correspondence: Haoda Wu, ; Samuel Zheng Hao Wong, ; Wenfei Jin, ; Yongji Tian,
| | - Samuel Zheng Hao Wong
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, United States
- *Correspondence: Haoda Wu, ; Samuel Zheng Hao Wong, ; Wenfei Jin, ; Yongji Tian,
| | - Qing-Feng Wu
- State Key Laboratory of Molecular Development Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, China
- Chinese Institute for Brain Research, Beijing, China
- Beijing Children Hospital, Capital Medical University, Beijing, China
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48
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Hoft SG, Pherson MD, DiPaolo RJ. Discovering Immune-Mediated Mechanisms of Gastric Carcinogenesis Through Single-Cell RNA Sequencing. Front Immunol 2022; 13:902017. [PMID: 35757757 PMCID: PMC9231461 DOI: 10.3389/fimmu.2022.902017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 04/27/2022] [Indexed: 12/17/2022] Open
Abstract
Single-cell RNA sequencing (scRNAseq) technology is still relatively new in the field of gastric cancer immunology but gaining significant traction. This technology now provides unprecedented insights into the intratumoral and intertumoral heterogeneities at the immunological, cellular, and molecular levels. Within the last few years, a volume of publications reported the usefulness of scRNAseq technology in identifying thus far elusive immunological mechanisms that may promote and impede gastric cancer development. These studies analyzed datasets generated from primary human gastric cancer tissues, metastatic ascites fluid from gastric cancer patients, and laboratory-generated data from in vitro and in vivo models of gastric diseases. In this review, we overview the exciting findings from scRNAseq datasets that uncovered the role of critical immune cells, including T cells, B cells, myeloid cells, mast cells, ILC2s, and other inflammatory stromal cells, like fibroblasts and endothelial cells. In addition, we also provide a synopsis of the initial scRNAseq findings on the interesting epithelial cell responses to inflammation. In summary, these new studies have implicated roles for T and B cells and subsets like NKT cells in tumor development and progression. The current studies identified diverse subsets of macrophages and mast cells in the tumor microenvironment, however, additional studies to determine their roles in promoting cancer growth are needed. Some groups specifically focus on the less prevalent ILC2 cell type that may contribute to early cancer development. ScRNAseq analysis also reveals that stromal cells, e.g., fibroblasts and endothelial cells, regulate inflammation and promote metastasis, making them key targets for future investigations. While evaluating the outcomes, we also highlight the gaps in the current findings and provide an assessment of what this technology holds for gastric cancer research in the coming years. With scRNAseq technology expanding rapidly, we stress the need for periodic review of the findings and assess the available scRNAseq analytical tools to guide future work on immunological mechanisms of gastric carcinogenesis.
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Affiliation(s)
- Stella G Hoft
- Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, St. Louis, MO, United States
| | - Michelle D Pherson
- Department of Biochemistry and Molecular Biology, Saint Louis University School of Medicine, St. Louis, MO, United States.,Genomics Core Facility, Saint Louis University School of Medicine, St. Louis, MO, United States
| | - Richard J DiPaolo
- Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, St. Louis, MO, United States
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49
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Williams CG, Lee HJ, Asatsuma T, Vento-Tormo R, Haque A. An introduction to spatial transcriptomics for biomedical research. Genome Med 2022; 14:68. [PMID: 35761361 PMCID: PMC9238181 DOI: 10.1186/s13073-022-01075-1] [Citation(s) in RCA: 223] [Impact Index Per Article: 111.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 06/19/2022] [Indexed: 01/04/2023] Open
Abstract
Single-cell transcriptomics (scRNA-seq) has become essential for biomedical research over the past decade, particularly in developmental biology, cancer, immunology, and neuroscience. Most commercially available scRNA-seq protocols require cells to be recovered intact and viable from tissue. This has precluded many cell types from study and largely destroys the spatial context that could otherwise inform analyses of cell identity and function. An increasing number of commercially available platforms now facilitate spatially resolved, high-dimensional assessment of gene transcription, known as 'spatial transcriptomics'. Here, we introduce different classes of method, which either record the locations of hybridized mRNA molecules in tissue, image the positions of cells themselves prior to assessment, or employ spatial arrays of mRNA probes of pre-determined location. We review sizes of tissue area that can be assessed, their spatial resolution, and the number and types of genes that can be profiled. We discuss if tissue preservation influences choice of platform, and provide guidance on whether specific platforms may be better suited to discovery screens or hypothesis testing. Finally, we introduce bioinformatic methods for analysing spatial transcriptomic data, including pre-processing, integration with existing scRNA-seq data, and inference of cell-cell interactions. Spatial -omics methods are already improving our understanding of human tissues in research, diagnostic, and therapeutic settings. To build upon these recent advancements, we provide entry-level guidance for those seeking to employ spatial transcriptomics in their own biomedical research.
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Affiliation(s)
- Cameron G Williams
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, 3000, Australia
| | - Hyun Jae Lee
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, 3000, Australia
| | - Takahiro Asatsuma
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, 3000, Australia
| | - Roser Vento-Tormo
- Cellular Genetics Group, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Ashraful Haque
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, 3000, Australia.
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50
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Pan Y, Cao W, Mu Y, Zhu Q. Microfluidics Facilitates the Development of Single-Cell RNA Sequencing. BIOSENSORS 2022; 12:bios12070450. [PMID: 35884253 PMCID: PMC9312765 DOI: 10.3390/bios12070450] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 06/20/2022] [Accepted: 06/21/2022] [Indexed: 12/12/2022]
Abstract
Single-cell RNA sequencing (scRNA-seq) technology provides a powerful tool for understanding complex biosystems at the single-cell and single-molecule level. The past decade has been a golden period for the development of single-cell sequencing, with scRNA-seq undergoing a tremendous leap in sensitivity and throughput. The application of droplet- and microwell-based microfluidics in scRNA-seq has contributed greatly to improving sequencing throughput. This review introduces the history of development and important technical factors of scRNA-seq. We mainly focus on the role of microfluidics in facilitating the development of scRNA-seq technology. To end, we discuss the future directions for scRNA-seq.
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Affiliation(s)
- Yating Pan
- Research Center for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.P.); (W.C.)
- College of Life Sciences, Zhejiang University, Hangzhou 310058, China
| | - Wenjian Cao
- Research Center for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.P.); (W.C.)
| | - Ying Mu
- Research Center for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.P.); (W.C.)
- Correspondence: (Y.M.); (Q.Z.); Tel.: +86-88208383 (Y.M.); +86-88208383 (Q.Z.)
| | - Qiangyuan Zhu
- Research Center for Analytical Instrumentation, State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China; (Y.P.); (W.C.)
- Huzhou Institute of Zhejiang University, Huzhou 313002, China
- Correspondence: (Y.M.); (Q.Z.); Tel.: +86-88208383 (Y.M.); +86-88208383 (Q.Z.)
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