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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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Zhang T, Wu Z, Li L, Ren J, Zhang Z, Wang G. CPPLS-MLP: a method for constructing cell-cell communication networks and identifying related highly variable genes based on single-cell sequencing and spatial transcriptomics data. Brief Bioinform 2024; 25:bbae198. [PMID: 38678387 PMCID: PMC11056015 DOI: 10.1093/bib/bbae198] [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: 01/31/2024] [Revised: 03/08/2024] [Accepted: 04/10/2024] [Indexed: 04/30/2024] Open
Abstract
In the growth and development of multicellular organisms, the immune processes of the immune system and the maintenance of the organism's internal environment, cell communication plays a crucial role. It exerts a significant influence on regulating internal cellular states such as gene expression and cell functionality. Currently, the mainstream methods for studying intercellular communication are focused on exploring the ligand-receptor-transcription factor and ligand-receptor-subunit scales. However, there is relatively limited research on the association between intercellular communication and highly variable genes (HVGs). As some HVGs are closely related to cell communication, accurately identifying these HVGs can enhance the accuracy of constructing cell communication networks. The rapid development of single-cell sequencing (scRNA-seq) and spatial transcriptomics technologies provides a data foundation for exploring the relationship between intercellular communication and HVGs. Therefore, we propose CPPLS-MLP, which can identify HVGs closely related to intercellular communication and further analyze the impact of Multiple Input Multiple Output cellular communication on the differential expression of these HVGs. By comparing with the commonly used method CCPLS for constructing intercellular communication networks, we validated the superior performance of our method in identifying cell-type-specific HVGs and effectively analyzing the influence of neighboring cell types on HVG expression regulation. Source codes for the CPPLS_MLP R, python packages and the related scripts are available at 'CPPLS_MLP Github [https://github.com/wuzhenao/CPPLS-MLP]'.
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Affiliation(s)
- Tianjiao Zhang
- College of Computer and Control Engineering, Northeast Forestry University Harbin, 150040, China
| | - Zhenao Wu
- College of Computer and Control Engineering, Northeast Forestry University Harbin, 150040, China
| | - Liangyu Li
- College of Computer and Control Engineering, Northeast Forestry University Harbin, 150040, China
| | - Jixiang Ren
- College of Computer and Control Engineering, Northeast Forestry University Harbin, 150040, China
| | - Ziheng Zhang
- College of Computer and Control Engineering, Northeast Forestry University Harbin, 150040, China
| | - Guohua Wang
- College of Computer and Control Engineering, Northeast Forestry University Harbin, 150040, China
- Faculty of Computing, Harbin Institute of Technology Harbin, 150001, China
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Tsuyuzaki K, Ishii M, Nikaido I. Sctensor detects many-to-many cell-cell interactions from single cell RNA-sequencing data. BMC Bioinformatics 2023; 24:420. [PMID: 37936079 PMCID: PMC10631077 DOI: 10.1186/s12859-023-05490-y] [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: 07/05/2023] [Accepted: 09/21/2023] [Indexed: 11/09/2023] Open
Abstract
BACKGROUND Complex biological systems are described as a multitude of cell-cell interactions (CCIs). Recent single-cell RNA-sequencing studies focus on CCIs based on ligand-receptor (L-R) gene co-expression but the analytical methods are not appropriate to detect many-to-many CCIs. RESULTS In this work, we propose scTensor, a novel method for extracting representative triadic relationships (or hypergraphs), which include ligand-expression, receptor-expression, and related L-R pairs. CONCLUSIONS Through extensive studies with simulated and empirical datasets, we have shown that scTensor can detect some hypergraphs that cannot be detected using conventional CCI detection methods, especially when they include many-to-many relationships. scTensor is implemented as a freely available R/Bioconductor package.
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Affiliation(s)
- Koki Tsuyuzaki
- Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan.
- Japan Science and Technology Agency, PRESTO, 7 Gobancho, Chiyoda-ku, Tokyo, 102-0076, Japan.
| | - Manabu Ishii
- Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Itoshi Nikaido
- Laboratory for Bioinformatics Research RIKEN Center for Biosystems Dynamics Research, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan.
- Department of Functional Genome Informatics, Division of Biological Data Science, Medical Research Institute, Tokyo Medical and Dental University (TMDU), 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan.
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Sun YH, Wu YL, Liao BY. Phenotypic heterogeneity in human genetic diseases: ultrasensitivity-mediated threshold effects as a unifying molecular mechanism. J Biomed Sci 2023; 30:58. [PMID: 37525275 PMCID: PMC10388531 DOI: 10.1186/s12929-023-00959-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 07/26/2023] [Indexed: 08/02/2023] Open
Abstract
Phenotypic heterogeneity is very common in genetic systems and in human diseases and has important consequences for disease diagnosis and treatment. In addition to the many genetic and non-genetic (e.g., epigenetic, environmental) factors reported to account for part of the heterogeneity, we stress the importance of stochastic fluctuation and regulatory network topology in contributing to phenotypic heterogeneity. We argue that a threshold effect is a unifying principle to explain the phenomenon; that ultrasensitivity is the molecular mechanism for this threshold effect; and discuss the three conditions for phenotypic heterogeneity to occur. We suggest that threshold effects occur not only at the cellular level, but also at the organ level. We stress the importance of context-dependence and its relationship to pleiotropy and edgetic mutations. Based on this model, we provide practical strategies to study human genetic diseases. By understanding the network mechanism for ultrasensitivity and identifying the critical factor, we may manipulate the weak spot to gently nudge the system from an ultrasensitive state to a stable non-disease state. Our analysis provides a new insight into the prevention and treatment of genetic diseases.
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Affiliation(s)
- Y Henry Sun
- Institute of Molecular and Genomic Medicine, National Health Research Institute, Zhunan, Miaoli, Taiwan.
- Institute of Molecular Biology, Academia Sinica, Taipei, Taiwan.
| | - Yueh-Lin Wu
- Institute of Molecular and Genomic Medicine, National Health Research Institute, Zhunan, Miaoli, Taiwan
- Division of Nephrology, Department of Internal Medicine, Wei-Gong Memorial Hospital, Miaoli, Taiwan
- Division of Nephrology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan
- TMU Research Center of Urology and Kidney, Taipei Medical University, Taipei, Taiwan
- Division of Nephrology, Department of Internal Medicine, Wan Fang Hospital, Taipei Medical University, Taipei City, Taiwan
| | - Ben-Yang Liao
- Institute of Population Health Sciences, National Health Research Institute, Zhunan, Miaoli, Taiwan
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Moiseeva V, Cisneros A, Sica V, Deryagin O, Lai Y, Jung S, Andrés E, An J, Segalés J, Ortet L, Lukesova V, Volpe G, Benguria A, Dopazo A, Benitah SA, Urano Y, Del Sol A, Esteban MA, Ohkawa Y, Serrano AL, Perdiguero E, Muñoz-Cánoves P. Senescence atlas reveals an aged-like inflamed niche that blunts muscle regeneration. Nature 2023; 613:169-178. [PMID: 36544018 DOI: 10.1038/s41586-022-05535-x] [Citation(s) in RCA: 84] [Impact Index Per Article: 84.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 11/07/2022] [Indexed: 12/24/2022]
Abstract
Tissue regeneration requires coordination between resident stem cells and local niche cells1,2. Here we identify that senescent cells are integral components of the skeletal muscle regenerative niche that repress regeneration at all stages of life. The technical limitation of senescent-cell scarcity3 was overcome by combining single-cell transcriptomics and a senescent-cell enrichment sorting protocol. We identified and isolated different senescent cell types from damaged muscles of young and old mice. Deeper transcriptome, chromatin and pathway analyses revealed conservation of cell identity traits as well as two universal senescence hallmarks (inflammation and fibrosis) across cell type, regeneration time and ageing. Senescent cells create an aged-like inflamed niche that mirrors inflammation associated with ageing (inflammageing4) and arrests stem cell proliferation and regeneration. Reducing the burden of senescent cells, or reducing their inflammatory secretome through CD36 neutralization, accelerates regeneration in young and old mice. By contrast, transplantation of senescent cells delays regeneration. Our results provide a technique for isolating in vivo senescent cells, define a senescence blueprint for muscle, and uncover unproductive functional interactions between senescent cells and stem cells in regenerative niches that can be overcome. As senescent cells also accumulate in human muscles, our findings open potential paths for improving muscle repair throughout life.
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Affiliation(s)
- Victoria Moiseeva
- Department of Medicine and Life Sciences, Pompeu Fabra University, Barcelona, Spain.,CIBERNED, Barcelona, Spain
| | - Andrés Cisneros
- Department of Medicine and Life Sciences, Pompeu Fabra University, Barcelona, Spain.,CIBERNED, Barcelona, Spain
| | - Valentina Sica
- Department of Medicine and Life Sciences, Pompeu Fabra University, Barcelona, Spain.,CIBERNED, Barcelona, Spain
| | - Oleg Deryagin
- Department of Medicine and Life Sciences, Pompeu Fabra University, Barcelona, Spain.,CIBERNED, Barcelona, Spain
| | - Yiwei Lai
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Key Laboratory of Regenerative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Sascha Jung
- CIC bioGUNE-BRTA (Basque Research and Technology Alliance), Bizkaia Technology Park, Derio, Spain
| | - Eva Andrés
- Department of Medicine and Life Sciences, Pompeu Fabra University, Barcelona, Spain.,CIBERNED, Barcelona, Spain
| | - Juan An
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Key Laboratory of Regenerative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,University of Science and Technology of China, Hefei, China
| | - Jessica Segalés
- Department of Medicine and Life Sciences, Pompeu Fabra University, Barcelona, Spain.,CIBERNED, Barcelona, Spain
| | - Laura Ortet
- Department of Medicine and Life Sciences, Pompeu Fabra University, Barcelona, Spain.,CIBERNED, Barcelona, Spain
| | - Vera Lukesova
- Department of Medicine and Life Sciences, Pompeu Fabra University, Barcelona, Spain.,CIBERNED, Barcelona, Spain
| | - Giacomo Volpe
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Key Laboratory of Regenerative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
| | - Alberto Benguria
- Genomic Unit, Centro Nacional de Investigaciones Cardiovasculares and CIBERCV, Madrid, Spain
| | - Ana Dopazo
- Genomic Unit, Centro Nacional de Investigaciones Cardiovasculares and CIBERCV, Madrid, Spain
| | - Salvador Aznar Benitah
- ICREA, Barcelona, Spain.,Institute for Research in Biomedicine and BIST, Barcelona, Spain
| | - Yasuteru Urano
- Laboratory of Chemistry & Biology, Graduate School of Pharmaceutical Sciences and School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Antonio Del Sol
- CIC bioGUNE-BRTA (Basque Research and Technology Alliance), Bizkaia Technology Park, Derio, Spain.,Computational Biology Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.,IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
| | - Miguel A Esteban
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Key Laboratory of Regenerative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Guangdong Provincial Key Laboratory of Stem Cells and Regenerative Medicine, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.,Bioland Laboratory, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, China
| | - Yasuyuki Ohkawa
- Division of Transcriptomics. Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - Antonio L Serrano
- Department of Medicine and Life Sciences, Pompeu Fabra University, Barcelona, Spain.,CIBERNED, Barcelona, Spain.,Altos labs Inc, San Diego, CA, USA
| | - Eusebio Perdiguero
- Department of Medicine and Life Sciences, Pompeu Fabra University, Barcelona, Spain. .,CIBERNED, Barcelona, Spain. .,Altos labs Inc, San Diego, CA, USA.
| | - Pura Muñoz-Cánoves
- Department of Medicine and Life Sciences, Pompeu Fabra University, Barcelona, Spain. .,CIBERNED, Barcelona, Spain. .,ICREA, Barcelona, Spain. .,Altos labs Inc, San Diego, CA, USA. .,Cardiovascular Regeneration Program, CNIC Centro Nacional de Investigaciones Cardiovasculares, Madrid, Spain.
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