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Nakandakari-Higa S. Universal LIPSTIC: a new tool for decoding cellular interactions. Nat Rev Immunol 2024; 24:458. [PMID: 38783094 DOI: 10.1038/s41577-024-01047-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
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Liao X, Scheidereit E, Kuppe C. New tools to study renal fibrogenesis. Curr Opin Nephrol Hypertens 2024; 33:420-426. [PMID: 38587103 PMCID: PMC11139246 DOI: 10.1097/mnh.0000000000000988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
PURPOSE OF REVIEW Kidney fibrosis is a key pathological aspect and outcome of chronic kidney disease (CKD). The advent of multiomic analyses using human kidney tissue, enabled by technological advances, marks a new chapter of discovery in fibrosis research of the kidney. This review highlights the rapid advancements of single-cell and spatial multiomic techniques that offer new avenues for exploring research questions related to human kidney fibrosis development. RECENT FINDINGS We recently focused on understanding the origin and transition of myofibroblasts in kidney fibrosis using single-cell RNA sequencing (scRNA-seq) [1] . We analysed cells from healthy human kidneys and compared them to patient samples with CKD. We identified PDGFRα+/PDGFRβ+ mesenchymal cells as the primary cellular source of extracellular matrix (ECM) in human kidney fibrosis. We found several commonly shared cell states of fibroblasts and myofibroblasts and provided insights into molecular regulators. Novel single-cell and spatial multiomics tools are now available to shed light on cell lineages, the plasticity of kidney cells and cell-cell communication in fibrosis. SUMMARY As further single-cell and spatial multiomic approaches are being developed, opportunities to apply these methods to human kidney tissues expand similarly. Careful design and optimisation of the multiomic experiments are needed to answer questions related to cell lineages, plasticity and cell-cell communication in kidney fibrosis.
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Affiliation(s)
- Xian Liao
- Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany
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Chen SD, Chu CY, Wang CB, Yang Y, Xu ZY, Qu YL, Man Y. Integrated-omics profiling unveils the disparities of host defense to ECM scaffolds during wound healing in aged individuals. Biomaterials 2024; 311:122685. [PMID: 38944969 DOI: 10.1016/j.biomaterials.2024.122685] [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: 02/05/2024] [Revised: 06/11/2024] [Accepted: 06/23/2024] [Indexed: 07/02/2024]
Abstract
Extracellular matrix (ECM) scaffold membranes have exhibited promising potential to better the outcomes of wound healing by creating a regenerative microenvironment around. However, when compared to the application in younger individuals, the performance of the same scaffold membrane in promoting re-epithelialization and collagen deposition was observed dissatisfying in aged mice. To comprehensively explore the mechanisms underlying this age-related disparity, we conducted the integrated analysis, combing single-cell RNA sequencing (scRNA-Seq) with spatial transcriptomics, and elucidated six functionally and spatially distinctive macrophage groups and lymphocytes surrounding the ECM scaffolds. Through intergroup comparative analysis and cell-cell communication, we characterized the dysfunction of Spp1+ macrophages in aged mice impeded the activation of the type Ⅱ immune response, thus inhibiting the repair ability of epidermal cells and fibroblasts around the ECM scaffolds. These findings contribute to a deeper understanding of biomaterial applications in varied physiological contexts, thereby paving the way for the development of precision-based biomaterials tailored specifically for aged individuals in future therapeutic strategies.
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Affiliation(s)
- Shuai-Dong Chen
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Chen-Yu Chu
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Chen-Bing Wang
- College & Hospital of Stomatology, Anhui Medical University, Key Lab. of Oral Diseases Research of Anhui Province, Hefei, 230032, China
| | - Yang Yang
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Zhao-Yu Xu
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Yi-Li Qu
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Prosthodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Yi Man
- State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, 610041, Sichuan, China.
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Palmer JA, Rosenthal N, Teichmann SA, Litvinukova M. Revisiting Cardiac Biology in the Era of Single Cell and Spatial Omics. Circ Res 2024; 134:1681-1702. [PMID: 38843288 PMCID: PMC11149945 DOI: 10.1161/circresaha.124.323672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 04/16/2024] [Accepted: 04/24/2024] [Indexed: 06/09/2024]
Abstract
Throughout our lifetime, each beat of the heart requires the coordinated action of multiple cardiac cell types. Understanding cardiac cell biology, its intricate microenvironments, and the mechanisms that govern their function in health and disease are crucial to designing novel therapeutical and behavioral interventions. Recent advances in single-cell and spatial omics technologies have significantly propelled this understanding, offering novel insights into the cellular diversity and function and the complex interactions of cardiac tissue. This review provides a comprehensive overview of the cellular landscape of the heart, bridging the gap between suspension-based and emerging in situ approaches, focusing on the experimental and computational challenges, comparative analyses of mouse and human cardiac systems, and the rising contextualization of cardiac cells within their niches. As we explore the heart at this unprecedented resolution, integrating insights from both mouse and human studies will pave the way for novel diagnostic tools and therapeutic interventions, ultimately improving outcomes for patients with cardiovascular diseases.
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Affiliation(s)
- Jack A. Palmer
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom (J.A.P., S.A.T.)
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus (J.A.P., S.A.T.), University of Cambridge, United Kingdom
| | - Nadia Rosenthal
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME (N.R.)
- National Heart and Lung Institute, Imperial College London, United Kingdom (N.R.)
| | - Sarah A. Teichmann
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom (J.A.P., S.A.T.)
- Wellcome-MRC Cambridge Stem Cell Institute, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus (J.A.P., S.A.T.), University of Cambridge, United Kingdom
- Theory of Condensed Matter Group, Department of Physics, Cavendish Laboratory (S.A.T.), University of Cambridge, United Kingdom
| | - Monika Litvinukova
- University Hospital Würzburg, Germany (M.L.)
- Würzburg Institute of Systems Immunology, Max Planck Research Group at the Julius-Maximilians-Universität Würzburg, Germany (M.L.)
- Helmholtz Pioneer Campus, Helmholtz Munich, Germany (M.L.)
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Farr E, Dimitrov D, Schmidt C, Turei D, Lobentanzer S, Dugourd A, Saez-Rodriguez J. MetalinksDB: a flexible and contextualizable resource of metabolite-protein interactions. Brief Bioinform 2024; 25:bbae347. [PMID: 39038934 DOI: 10.1093/bib/bbae347] [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: 02/06/2024] [Revised: 05/29/2024] [Accepted: 07/08/2024] [Indexed: 07/24/2024] Open
Abstract
From the catalytic breakdown of nutrients to signaling, interactions between metabolites and proteins play an essential role in cellular function. An important case is cell-cell communication, where metabolites, secreted into the microenvironment, initiate signaling cascades by binding to intra- or extracellular receptors of neighboring cells. Protein-protein cell-cell communication interactions are routinely predicted from transcriptomic data. However, inferring metabolite-mediated intercellular signaling remains challenging, partially due to the limited size of intercellular prior knowledge resources focused on metabolites. Here, we leverage knowledge-graph infrastructure to integrate generalistic metabolite-protein with curated metabolite-receptor resources to create MetalinksDB. MetalinksDB is an order of magnitude larger than existing metabolite-receptor resources and can be tailored to specific biological contexts, such as diseases, pathways, or tissue/cellular locations. We demonstrate MetalinksDB's utility in identifying deregulated processes in renal cancer using multi-omics bulk data. Furthermore, we infer metabolite-driven intercellular signaling in acute kidney injury using spatial transcriptomics data. MetalinksDB is a comprehensive and customizable database of intercellular metabolite-protein interactions, accessible via a web interface (https://metalinks.omnipathdb.org/) and programmatically as a knowledge graph (https://github.com/biocypher/metalinks). We anticipate that by enabling diverse analyses tailored to specific biological contexts, MetalinksDB will facilitate the discovery of disease-relevant metabolite-mediated intercellular signaling processes.
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Affiliation(s)
- Elias Farr
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, United Kingdom
| | - Daniel Dimitrov
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany
| | - Christina Schmidt
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany
| | - Denes Turei
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany
| | - Sebastian Lobentanzer
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany
| | - Aurelien Dugourd
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany
- EMBL European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SA, United Kingdom
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany
- EMBL European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SA, United Kingdom
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Baghdassarian HM, Dimitrov D, Armingol E, Saez-Rodriguez J, Lewis NE. Combining LIANA and Tensor-cell2cell to decipher cell-cell communication across multiple samples. CELL REPORTS METHODS 2024; 4:100758. [PMID: 38631346 PMCID: PMC11046036 DOI: 10.1016/j.crmeth.2024.100758] [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: 08/10/2023] [Revised: 12/22/2023] [Accepted: 03/22/2024] [Indexed: 04/19/2024]
Abstract
In recent years, data-driven inference of cell-cell communication has helped reveal coordinated biological processes across cell types. Here, we integrate two tools, LIANA and Tensor-cell2cell, which, when combined, can deploy multiple existing methods and resources to enable the robust and flexible identification of cell-cell communication programs across multiple samples. In this work, we show how the integration of our tools facilitates the choice of method to infer cell-cell communication and subsequently perform an unsupervised deconvolution to obtain and summarize biological insights. We explain how to perform the analysis step by step in both Python and R and provide online tutorials with detailed instructions available at https://ccc-protocols.readthedocs.io/. This workflow typically takes ∼1.5 h to complete from installation to downstream visualizations on a graphics processing unit-enabled computer for a dataset of ∼63,000 cells, 10 cell types, and 12 samples.
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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
| | - Daniel Dimitrov
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, 69120 Heidelberg, Germany
| | - 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
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, 69120 Heidelberg, Germany.
| | - 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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Wheeler MA. Interactions between immune cells recorded. Nature 2024; 627:277-279. [PMID: 38448528 PMCID: PMC10998074 DOI: 10.1038/d41586-024-00426-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
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