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Jin Y, Wu Y, Reuben A, Zhu L, Gay CM, Wu Q, Zhou X, Mo H, Zheng Q, Ren J, Fang Z, Peng T, Wang N, Ma L, Fan Y, Song H, Zhang J, Chen M. Single-cell and spatial proteo-transcriptomic profiling reveals immune infiltration heterogeneity associated with neuroendocrine features in small cell lung cancer. Cell Discov 2024; 10:93. [PMID: 39231924 PMCID: PMC11375181 DOI: 10.1038/s41421-024-00703-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 06/23/2024] [Indexed: 09/06/2024] Open
Abstract
Small cell lung cancer (SCLC) is an aggressive pulmonary neuroendocrine malignancy featured by cold tumor immune microenvironment (TIME), limited benefit from immunotherapy, and poor survival. The spatial heterogeneity of TIME significantly associated with anti-tumor immunity has not been systemically studied in SCLC. We performed ultra-high-plex Digital Spatial Profiling on 132 tissue microarray cores from 44 treatment-naive limited-stage SCLC tumors. Incorporating single-cell RNA-sequencing data from a local cohort and published SCLC data, we established a spatial proteo-transcriptomic landscape covering over 18,000 genes and 60 key immuno-oncology proteins that participate in signaling pathways affecting tumorigenesis, immune regulation, and cancer metabolism across 3 pathologically defined spatial compartments (pan-CK-positive tumor nest; CD45/CD3-positive tumor stroma; para-tumor). Our study depicted the spatial transcriptomic and proteomic TIME architecture of SCLC, indicating clear intra-tumor heterogeneity dictated via canonical neuroendocrine subtyping markers; revealed the enrichment of innate immune cells and functionally impaired B cells in tumor nest and suggested potentially important immunoregulatory roles of monocytes/macrophages. We identified RE1 silencing factor (REST) as a potential biomarker for SCLC associated with low neuroendocrine features, more active anti-tumor immunity, and prolonged survival.
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Affiliation(s)
- Ying Jin
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
- Zhejiang Key Laboratory of Radiation Oncology, Hangzhou, Zhejiang, China
| | - Yuefeng Wu
- The MOE Key Laboratory of Biosystems Homeostasis and Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang, China
- School of Life Sciences, Westlake University, Hangzhou, Zhejiang, China
- Department of Cardiovascular Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
- Zhejiang University-University of Edinburgh Institute (ZJU-UoE), School of Medicine, Zhejiang University, Haining, Zhejiang, China
| | - Alexandre Reuben
- Department of Thoracic/Head and Neck Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Liang Zhu
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Carl M Gay
- Department of Thoracic/Head and Neck Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Qingzhe Wu
- The MOE Key Laboratory of Biosystems Homeostasis and Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xintong Zhou
- The MOE Key Laboratory of Biosystems Homeostasis and Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang, China
| | - Haomin Mo
- The MOE Key Laboratory of Biosystems Homeostasis and Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang, China
| | - Qi Zheng
- Department of Cardiovascular Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Junyu Ren
- College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Zhaoyuan Fang
- Zhejiang University-University of Edinburgh Institute (ZJU-UoE), School of Medicine, Zhejiang University, Haining, Zhejiang, China
| | - Teng Peng
- Department of Cardiovascular Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Nan Wang
- Cosmos Wisdom Biotech Co. Ltd., Hangzhou, Zhejiang, China
| | - Liang Ma
- Department of Cardiovascular Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yun Fan
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
| | - Hai Song
- The MOE Key Laboratory of Biosystems Homeostasis and Protection, Zhejiang Provincial Key Laboratory for Cancer Molecular Cell Biology and Innovation Center for Cell Signaling Network, Life Sciences Institute, Zhejiang University, Hangzhou, Zhejiang, China.
- Center for Oncology Medicine, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu, Zhejiang, China.
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- Department of Genomic Medicine, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Ming Chen
- Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
- State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China.
- United Laboratory of Frontier Radiotherapy Technology of Sun Yat-sen University & Chinese Academy of Sciences Ion Medical Technology Co., Ltd, Guangzhou, Guangdong, China.
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Yang W, Wang P, Xu S, Wang T, Luo M, Cai Y, Xu C, Xue G, Que J, Ding Q, Jin X, Yang Y, Pang F, Pang B, Lin Y, Nie H, Xu Z, Ji Y, Jiang Q. Deciphering cell-cell communication at single-cell resolution for spatial transcriptomics with subgraph-based graph attention network. Nat Commun 2024; 15:7101. [PMID: 39155292 PMCID: PMC11330978 DOI: 10.1038/s41467-024-51329-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 08/01/2024] [Indexed: 08/20/2024] Open
Abstract
The inference of cell-cell communication (CCC) is crucial for a better understanding of complex cellular dynamics and regulatory mechanisms in biological systems. However, accurately inferring spatial CCCs at single-cell resolution remains a significant challenge. To address this issue, we present a versatile method, called DeepTalk, to infer spatial CCC at single-cell resolution by integrating single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data. DeepTalk utilizes graph attention network (GAT) to integrate scRNA-seq and ST data, which enables accurate cell-type identification for single-cell ST data and deconvolution for spot-based ST data. Then, DeepTalk can capture the connections among cells at multiple levels using subgraph-based GAT, and further achieve spatially resolved CCC inference at single-cell resolution. DeepTalk achieves excellent performance in discovering meaningful spatial CCCs on multiple cross-platform datasets, which demonstrates its superior ability to dissect cellular behavior within intricate biological processes.
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Affiliation(s)
- Wenyi Yang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Pingping Wang
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, China
| | - Shouping Xu
- Department of Breast Cancer, Harbin Medical University Cancer Hospital, Harbin, China
| | - Tao Wang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Meng Luo
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yideng Cai
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Chang Xu
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Guangfu Xue
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Jinhao Que
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Qian Ding
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Xiyun Jin
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, China
| | - Yuexin Yang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Fenglan Pang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Boran Pang
- Center for Difficult and Complicated Abdominal Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yi Lin
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, China
| | - Huan Nie
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Zhaochun Xu
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, China.
| | - Yong Ji
- State Key Laboratory of Frigid Zone Cardiovascular Diseases (SKLFZCD), Harbin Medical University, Harbin, China.
| | - Qinghua Jiang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin, China.
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, China.
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3
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Zhang Y, Yang Y, Ren L, Zhan M, Sun T, Zou Q, Zhang Y. Predicting intercellular communication based on metabolite-related ligand-receptor interactions with MRCLinkdb. BMC Biol 2024; 22:152. [PMID: 38978014 PMCID: PMC11232326 DOI: 10.1186/s12915-024-01950-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Accepted: 07/03/2024] [Indexed: 07/10/2024] Open
Abstract
BACKGROUND Metabolite-associated cell communications play critical roles in maintaining human biological function. However, most existing tools and resources focus only on ligand-receptor interaction pairs where both partners are proteinaceous, neglecting other non-protein molecules. To address this gap, we introduce the MRCLinkdb database and algorithm, which aggregates and organizes data related to non-protein L-R interactions in cell-cell communication, providing a valuable resource for predicting intercellular communication based on metabolite-related ligand-receptor interactions. RESULTS Here, we manually curated the metabolite-ligand-receptor (ML-R) interactions from the literature and known databases, ultimately collecting over 790 human and 670 mouse ML-R interactions. Additionally, we compiled information on over 1900 enzymes and 260 transporter entries associated with these metabolites. We developed Metabolite-Receptor based Cell Link Database (MRCLinkdb) to store these ML-R interactions data. Meanwhile, the platform also offers extensive information for presenting ML-R interactions, including fundamental metabolite information and the overall expression landscape of metabolite-associated gene sets (such as receptor, enzymes, and transporter proteins) based on single-cell transcriptomics sequencing (covering 35 human and 26 mouse tissues, 52 human and 44 mouse cell types) and bulk RNA-seq/microarray data (encompassing 62 human and 39 mouse tissues). Furthermore, MRCLinkdb introduces a web server dedicated to the analysis of intercellular communication based on ML-R interactions. MRCLinkdb is freely available at https://www.cellknowledge.com.cn/mrclinkdb/ . CONCLUSIONS In addition to supplementing ligand-receptor databases, MRCLinkdb may provide new perspectives for decoding the intercellular communication and advancing related prediction tools based on ML-R interactions.
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Affiliation(s)
- Yuncong Zhang
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Jinan University, Zhuhai, Guangdong, China
| | - Yu Yang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Liping Ren
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China
| | - Meixiao Zhan
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Jinan University, Zhuhai, Guangdong, China
| | - Taoping Sun
- Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Jinan University, Zhuhai, Guangdong, China.
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
| | - Yang Zhang
- Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China.
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4
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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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5
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Ramirez Flores RO, Schäfer PSL, Küchenhoff L, Saez-Rodriguez J. Complementing Cell Taxonomies with a Multicellular Analysis of Tissues. Physiology (Bethesda) 2024; 39:0. [PMID: 38319138 DOI: 10.1152/physiol.00001.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 01/31/2024] [Indexed: 02/07/2024] Open
Abstract
The application of single-cell molecular profiling coupled with spatial technologies has enabled charting of cellular heterogeneity in reference tissues and in disease. This new wave of molecular data has highlighted the expected diversity of single-cell dynamics upon shared external queues and spatial organizations. However, little is known about the relationship between single-cell heterogeneity and the emergence and maintenance of robust multicellular processes in developed tissues and its role in (patho)physiology. Here, we present emerging computational modeling strategies that use increasingly available large-scale cross-condition single-cell and spatial datasets to study multicellular organization in tissues and complement cell taxonomies. This perspective should enable us to better understand how cells within tissues collectively process information and adapt synchronized responses in disease contexts and to bridge the gap between structural changes and functions in tissues.
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Affiliation(s)
- Ricardo Omar Ramirez Flores
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Philipp Sven Lars Schäfer
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Leonie Küchenhoff
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
| | - Julio Saez-Rodriguez
- Faculty of Medicine, Heidelberg University and Institute for Computational Biomedicine, Heidelberg University Hospital, Heidelberg, Germany
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6
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Jiang A, Han K, Wei J, Su X, Wang R, Zhang W, Liu X, Qiao J, Liu P, Liu Q, Zhang J, Zhang N, Ge Y, Zhuang Y, Yu H, Wang S, Chen K, Lu W, Xu X, Yang H, Fan G, Dong B. Spatially resolved single-cell atlas of ascidian endostyle provides insight into the origin of vertebrate pharyngeal organs. SCIENCE ADVANCES 2024; 10:eadi9035. [PMID: 38552007 PMCID: PMC10980280 DOI: 10.1126/sciadv.adi9035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 02/26/2024] [Indexed: 04/01/2024]
Abstract
The pharyngeal endoderm, an innovation of deuterostome ancestors, contributes to pharyngeal development by influencing the patterning and differentiation of pharyngeal structures in vertebrates; however, the evolutionary origin of the pharyngeal organs in vertebrates is largely unknown. The endostyle, a distinct pharyngeal organ exclusively present in basal chordates, represents a good model for understanding pharyngeal organ origins. Using Stereo-seq and single-cell RNA sequencing, we constructed aspatially resolved single-cell atlas for the endostyle of the ascidian Styela clava. We determined the cell composition of the hemolymphoid region, which illuminates a mixed ancestral structure for the blood and lymphoid system. In addition, we discovered a cluster of hair cell-like cells in zone 3, which has transcriptomic similarity with the hair cells of the vertebrate acoustico-lateralis system. These findings reshape our understanding of the pharynx of the basal chordate and provide insights into the evolutionary origin of multiplexed pharyngeal organs.
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Affiliation(s)
- An Jiang
- Fang Zongxi Center for Marine EvoDevo, MoE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
| | - Kai Han
- BGI Research, Qingdao 266555, China
| | - Jiankai Wei
- Fang Zongxi Center for Marine EvoDevo, MoE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
- Laboratory for Marine Biology and Biotechnology, Qingdao Marine Science and Technology Center, Qingdao 266237, China
| | | | - Rui Wang
- BGI Research, Qingdao 266555, China
| | - Wei Zhang
- Fang Zongxi Center for Marine EvoDevo, MoE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
| | | | - Jinghan Qiao
- Fang Zongxi Center for Marine EvoDevo, MoE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
| | - Penghui Liu
- Fang Zongxi Center for Marine EvoDevo, MoE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
| | - Qun Liu
- BGI Research, Qingdao 266555, China
| | - Jin Zhang
- Fang Zongxi Center for Marine EvoDevo, MoE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
| | | | - Yonghang Ge
- Fang Zongxi Center for Marine EvoDevo, MoE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
| | - Yuan Zhuang
- Fang Zongxi Center for Marine EvoDevo, MoE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
| | - Haiyan Yu
- Fang Zongxi Center for Marine EvoDevo, MoE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
| | - Shi Wang
- Fang Zongxi Center for Marine EvoDevo, MoE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
- Laboratory for Marine Biology and Biotechnology, Qingdao Marine Science and Technology Center, Qingdao 266237, China
| | - Kai Chen
- State Key Laboratory of Primate Biomedical Research and Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
| | - Wange Lu
- State Key Laboratory of Medicinal Chemical Biology and College of Life Sciences, Nankai University, 94 Weijin Road, Tianjin 300071, China
| | - Xun Xu
- BGI Research, Shenzhen 518083, China
| | | | - Guangyi Fan
- BGI Research, Qingdao 266555, China
- BGI Research, Shenzhen 518083, China
- Qingdao Key Laboratory of Marine Genomics BGI Research, Qingdao 266555, China
| | - Bo Dong
- Fang Zongxi Center for Marine EvoDevo, MoE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
- Laboratory for Marine Biology and Biotechnology, Qingdao Marine Science and Technology Center, Qingdao 266237, China
- MoE Key Laboratory of Evolution and Marine Biodiversity, Institute of Evolution and Marine Biodiversity, Ocean University of China, Qingdao 266003, China
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7
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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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8
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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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9
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Teng P, Wu Y, Chen R, Hong L, Wu B, Liu L, Ma L, Zhao H, Wu S. Pulsed field ablation as a precise approach for cardiac arrhythmia treatment via cardiac microenvironment remodeling. Bioelectrochemistry 2023; 154:108502. [PMID: 37453203 DOI: 10.1016/j.bioelechem.2023.108502] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 07/02/2023] [Accepted: 07/03/2023] [Indexed: 07/18/2023]
Abstract
PFA uses short-duration, high-voltage electrical pulses to induce transient or irreversible electroporation on cell membranes, causing cell death. Selective inhibition of chaotic electrical signals in morbid cardiomyocytes significantly aids the treatment of atrial fibrillation, ventricular tachycardia, and other heart arrhythmias. Recent preclinical and clinical studies have only investigated physical changes, such as lesion size and myocardial scar. Compared to radiofrequency ablation and cryoballoon ablation, PFA causes less postoperative myocardial cell fibrosis and inflammatory reaction and does not result in myocardial necrosis or tissue scar formation. However, the regulatory mechanism of cellular stress following PFA treatment remains unknown. This study aimed to analyze the transcriptome of the mouse ventricle after PFA treatment. The animals were subjected to a 225-V electric pulse with a 1.5-mm gap between the positive and negative electrodes. Hearts were harvested at 3, 6, 12, 24 h, and 2, 5 days for myocardial zymogram testing. PFA-treated ventricular regions were selected for single-nucleus sequencing. We discovered that PFA remodeled the cardiac microenvironment as a whole. Further, we discussed the possible stress response and wound-healing mechanism in non-targeted cells. In conclusion, PFA allowed effective and selective ventricular myocardium ablation with controllable inflammation.
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Affiliation(s)
- Peng Teng
- Department of Cardiovascular Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China; Key Laboratory of Pulsed Power Translational Medicine of Zhejiang Province, Hangzhou, 310003, China
| | - Yuefeng Wu
- Department of Cardiovascular Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China; The Lab of Biomed-X, Zhejiang University-University of Edinburgh Institute (ZJU-UoE), School of Medicine, Zhejiang University, Haining, 310000, China
| | - Ruoshi Chen
- Department of Cardiovascular Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China
| | - Liangjie Hong
- Key Laboratory of Pulsed Power Translational Medicine of Zhejiang Province, Hangzhou, 310003, China
| | - Bin Wu
- Key Laboratory of Pulsed Power Translational Medicine of Zhejiang Province, Hangzhou, 310003, China
| | - Lingshan Liu
- Department of Cardiovascular Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China
| | - Liang Ma
- Department of Cardiovascular Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China
| | - Haige Zhao
- Department of Cardiovascular Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China.
| | - Shengjun Wu
- Department of Cardiovascular Surgery, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310009, China; Key Laboratory of Pulsed Power Translational Medicine of Zhejiang Province, Hangzhou, 310003, China.
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10
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Luo J, Deng M, Zhang X, Sun X. ESICCC as a systematic computational framework for evaluation, selection, and integration of cell-cell communication inference methods. Genome Res 2023; 33:1788-1805. [PMID: 37827697 PMCID: PMC10691505 DOI: 10.1101/gr.278001.123] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 09/21/2023] [Indexed: 10/14/2023]
Abstract
Cell-cell communication (CCC) is critical for determining cell fates and functions in multicellular organisms. With the advent of single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics (ST), an increasing number of CCC inference methods have been developed. Nevertheless, a thorough comparison of their performances is yet to be conducted. To fill this gap, we developed a systematic benchmark framework called ESICCC to evaluate 18 ligand-receptor (LR) inference methods and five ligand/receptor-target inference methods using a total of 116 data sets, including 15 ST data sets, 15 sets of cell line perturbation data, two sets of cell type-specific expression/proteomics data, and 84 sets of sampled or unsampled scRNA-seq data. We evaluated and compared the agreement, accuracy, robustness, and usability of these methods. Regarding accuracy evaluation, RNAMagnet, CellChat, and scSeqComm emerge as the three best-performing methods for intercellular ligand-receptor inference based on scRNA-seq data, whereas stMLnet and HoloNet are the best methods for predicting ligand/receptor-target regulation using ST data. To facilitate the practical applications, we provide a decision-tree-style guideline for users to easily choose best tools for their specific research concerns in CCC inference, and develop an ensemble pipeline CCCbank that enables versatile combinations of methods and databases. Moreover, our comparative results also uncover several critical influential factors for CCC inference, such as prior interaction information, ligand-receptor scoring algorithm, intracellular signaling complexity, and spatial relationship, which may be considered in the future studies to advance the development of new methodologies.
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Affiliation(s)
- Jiaxin Luo
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China
| | - Minghua Deng
- School of Mathematical Sciences, Peking University, Beijing, 100871, China
| | - Xuegong Zhang
- Bioinformatics Division of BNRIST and Department of Automation, MOE Key Lab of Bioinformatics, Tsinghua University, Beijing, 100084, China
| | - Xiaoqiang Sun
- School of Mathematics, Sun Yat-sen University, Guangzhou 510275, China;
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11
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Zhang C, Hu Y, Gao L. Defining and identifying cell sub-crosstalk pairs for characterizing cell-cell communication patterns. Sci Rep 2023; 13:15746. [PMID: 37735248 PMCID: PMC10514069 DOI: 10.1038/s41598-023-42883-8] [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: 03/13/2023] [Accepted: 09/15/2023] [Indexed: 09/23/2023] Open
Abstract
Current cell-cell communication analysis focuses on quantifying intercellular interactions at cell type level. In the tissue microenvironment, one type of cells could be divided into multiple cell subgroups that function differently and communicate with other cell types or subgroups via different ligand-receptor-mediated signaling pathways. Given two cell types, we define a cell sub-crosstalk pair (CSCP) as a combination of two cell subgroups with strong and similar intercellular crosstalk signals and identify CSCPs based on coupled non-negative matrix factorization. Using single-cell spatial transcriptomics data of mouse olfactory bulb and visual cortex, we find that cells of different types within CSCPs are significantly spatially closer with each other than those in the whole single-cell spatial map. To demonstrate the utility of CSCPs, we apply 13 cell-cell communication analysis methods to sampled single-cell transcriptomics datasets at CSCP level and reveal ligand-receptor interactions masked at cell type level. Furthermore, by analyzing single-cell transcriptomics data from 29 breast cancer patients with different immunotherapy responses, we find that CSCPs are useful predictive features to discriminate patients responding to anti-PD-1 therapy from non-responders. Taken together, partitioning a cell type pair into CSCPs enables fine-grained characterization of cell-cell communication in tissue and tumor microenvironments.
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Affiliation(s)
- Chenxing Zhang
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, China
| | - Yuxuan Hu
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an, 710071, China.
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12
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Xie Z, Li X, Mora A. A Comparison of Cell-Cell Interaction Prediction Tools Based on scRNA-seq Data. Biomolecules 2023; 13:1211. [PMID: 37627276 PMCID: PMC10452151 DOI: 10.3390/biom13081211] [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: 06/18/2023] [Revised: 07/29/2023] [Accepted: 07/31/2023] [Indexed: 08/27/2023] Open
Abstract
Computational prediction of cell-cell interactions (CCIs) is becoming increasingly important for understanding disease development and progression. We present a benchmark study of available CCI prediction tools based on single-cell RNA sequencing (scRNA-seq) data. By comparing prediction outputs with a manually curated gold standard for idiopathic pulmonary fibrosis (IPF), we evaluated prediction performance and processing time of several CCI prediction tools, including CCInx, CellChat, CellPhoneDB, iTALK, NATMI, scMLnet, SingleCellSignalR, and an ensemble of tools. According to our results, CellPhoneDB and NATMI are the best performer CCI prediction tools, among the ones analyzed, when we define a CCI as a source-target-ligand-receptor tetrad. In addition, we recommend specific tools according to different types of research projects and discuss the possible future paths in the field.
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Affiliation(s)
- Zihong Xie
- Joint School of Life Sciences, Guangzhou Medical University and Guangzhou Institutes of Biomedicine and Health (Chinese Academy of Sciences), Guangzhou 511436, China;
| | - Xuri Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou 510060, China
| | - Antonio Mora
- Joint School of Life Sciences, Guangzhou Medical University and Guangzhou Institutes of Biomedicine and Health (Chinese Academy of Sciences), Guangzhou 511436, China;
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13
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Laub V, Devraj K, Elias L, Schulte D. Bioinformatics for wet-lab scientists: practical application in sequencing analysis. BMC Genomics 2023; 24:382. [PMID: 37420172 PMCID: PMC10326960 DOI: 10.1186/s12864-023-09454-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 06/15/2023] [Indexed: 07/09/2023] Open
Abstract
BACKGROUND Genomics data is available to the scientific community after publication of research projects and can be investigated for a multitude of research questions. However, in many cases deposited data is only assessed and used for the initial publication, resulting in valuable resources not being exploited to their full depth. MAIN: A likely reason for this is that many wetlab-based researchers are not formally trained to apply bioinformatic tools and may therefore assume that they lack the necessary experience to do so themselves. In this article, we present a series of freely available, predominantly web-based platforms and bioinformatic tools that can be combined in analysis pipelines to interrogate different types of next-generation sequencing data. Additionally to the presented exemplary route, we also list a number of alternative tools that can be combined in a mix-and-match fashion. We place special emphasis on tools that can be followed and used correctly without extensive prior knowledge in programming. Such analysis pipelines can be applied to existing data downloaded from the public domain or be compared to the results of own experiments. CONCLUSION Integrating transcription factor binding to chromatin (ChIP-seq) with transcriptional output (RNA-seq) and chromatin accessibility (ATAC-seq) can not only assist to form a deeper understanding of the molecular interactions underlying transcriptional regulation but will also help establishing new hypotheses and pre-testing them in silico.
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Affiliation(s)
- Vera Laub
- Neurological Institute (Edinger Institute), University Hospital Frankfurt, Goethe University, Frankfurt, Germany.
| | - Kavi Devraj
- Neurological Institute (Edinger Institute), University Hospital Frankfurt, Goethe University, Frankfurt, Germany
- Department of Biological Sciences, Birla Institute of Technology and Science Pilani, Hyderabad Campus, Hyderabad, Telangana, India
| | - Lena Elias
- Neurological Institute (Edinger Institute), University Hospital Frankfurt, Goethe University, Frankfurt, Germany
| | - Dorothea Schulte
- Neurological Institute (Edinger Institute), University Hospital Frankfurt, Goethe University, Frankfurt, Germany
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14
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Ghaddar A, Armingol E, Huynh C, Gevirtzman L, Lewis NE, Waterston R, O’Rourke EJ. Whole-body gene expression atlas of an adult metazoan. SCIENCE ADVANCES 2023; 9:eadg0506. [PMID: 37352352 PMCID: PMC10289653 DOI: 10.1126/sciadv.adg0506] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 05/17/2023] [Indexed: 06/25/2023]
Abstract
Gene activity defines cell identity, drives intercellular communication, and underlies the functioning of multicellular organisms. We present the single-cell resolution atlas of gene activity of a fertile adult metazoan: Caenorhabditis elegans. This compendium comprises 180 distinct cell types and 19,657 expressed genes. We predict 7541 transcription factor expression profile associations likely responsible for defining cellular identity. We predict thousands of intercellular interactions across the C. elegans body and the ligand-receptor pairs that mediate them, some of which we experimentally validate. We identify 172 genes that show consistent expression across cell types, are involved in basic and essential functions, and are conserved across phyla; therefore, we present them as experimentally validated housekeeping genes. We developed the WormSeq application to explore these data. In addition to the integrated gene-to-systems biology, we present genome-scale single-cell resolution testable hypotheses that we anticipate will advance our understanding of the molecular mechanisms, underlying the functioning of a multicellular organism and the perturbations that lead to its malfunction.
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Affiliation(s)
- Abbas Ghaddar
- Department of Biology, College of Arts and Sciences, University of Virginia, Charlottesville, VA 22903, USA
| | - Erick Armingol
- 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
| | - Chau Huynh
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Louis Gevirtzman
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, 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
| | - Robert Waterston
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Eyleen J. O’Rourke
- Department of Biology, College of Arts and Sciences, University of Virginia, Charlottesville, VA 22903, USA
- Department of Cell Biology, University of Virginia School of Medicine, Charlottesville, VA 22903, USA
- Robert M. Berne Cardiovascular Research Center, School of Medicine, University of Virginia, Charlottesville, VA 22903, USA
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15
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Polyakova N, Kalashnikova M, Belyavsky A. Non-Classical Intercellular Communications: Basic Mechanisms and Roles in Biology and Medicine. Int J Mol Sci 2023; 24:ijms24076455. [PMID: 37047428 PMCID: PMC10095225 DOI: 10.3390/ijms24076455] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/27/2023] [Accepted: 03/28/2023] [Indexed: 04/03/2023] Open
Abstract
In multicellular organisms, interactions between cells and intercellular communications form the very basis of the organism’s survival, the functioning of its systems, the maintenance of homeostasis and adequate response to the environment. The accumulated experimental data point to the particular importance of intercellular communications in determining the fate of cells, as well as their differentiation and plasticity. For a long time, it was believed that the properties and behavior of cells were primarily governed by the interactions of secreted or membrane-bound ligands with corresponding receptors, as well as direct intercellular adhesion contacts. In this review, we describe various types of other, non-classical intercellular interactions and communications that have recently come into the limelight—in particular, the broad repertoire of extracellular vesicles and membrane protrusions. These communications are mediated by large macromolecular structural and functional ensembles, and we explore here the mechanisms underlying their formation and present current data that reveal their roles in multiple biological processes. The effects mediated by these new types of intercellular communications in normal and pathological states, as well as therapeutic applications, are also discussed. The in-depth study of novel intercellular interaction mechanisms is required for the establishment of effective approaches for the control and modification of cell properties both for basic research and the development of radically new therapeutic strategies.
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Affiliation(s)
- Natalia Polyakova
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Vavilova 32, 119991 Moscow, Russia
| | - Maria Kalashnikova
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Vavilova 32, 119991 Moscow, Russia
- Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Butlerova 5A, 117485 Moscow, Russia
| | - Alexander Belyavsky
- Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Vavilova 32, 119991 Moscow, Russia
- Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Butlerova 5A, 117485 Moscow, Russia
- Correspondence:
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16
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Cang Z, Zhao Y, Almet AA, Stabell A, Ramos R, Plikus MV, Atwood SX, Nie Q. Screening cell-cell communication in spatial transcriptomics via collective optimal transport. Nat Methods 2023; 20:218-228. [PMID: 36690742 PMCID: PMC9911355 DOI: 10.1038/s41592-022-01728-4] [Citation(s) in RCA: 77] [Impact Index Per Article: 77.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 11/21/2022] [Indexed: 01/24/2023]
Abstract
Spatial transcriptomic technologies and spatially annotated single-cell RNA sequencing datasets provide unprecedented opportunities to dissect cell-cell communication (CCC). However, incorporation of the spatial information and complex biochemical processes required in the reconstruction of CCC remains a major challenge. Here, we present COMMOT (COMMunication analysis by Optimal Transport) to infer CCC in spatial transcriptomics, which accounts for the competition between different ligand and receptor species as well as spatial distances between cells. A collective optimal transport method is developed to handle complex molecular interactions and spatial constraints. Furthermore, we introduce downstream analysis tools to infer spatial signaling directionality and genes regulated by signaling using machine learning models. We apply COMMOT to simulation data and eight spatial datasets acquired with five different technologies to show its effectiveness and robustness in identifying spatial CCC in data with varying spatial resolutions and gene coverages. Finally, COMMOT identifies new CCCs during skin morphogenesis in a case study of human epidermal development.
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Affiliation(s)
- Zixuan Cang
- Department of Mathematics and Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC, USA
| | - Yanxiang Zhao
- Department of Mathematics, The George Washington University, Washington, DC, USA
| | - Axel A Almet
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA
| | - Adam Stabell
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA
| | - Raul Ramos
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA
| | - Maksim V Plikus
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA
| | - Scott X Atwood
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA
| | - Qing Nie
- Department of Mathematics, University of California, Irvine, Irvine, CA, USA.
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, Irvine, CA, USA.
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA.
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