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Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk
Spatially resolved transcriptomics provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9338929/ https://www.ncbi.nlm.nih.gov/pubmed/35908020 http://dx.doi.org/10.1038/s41467-022-32111-8 |
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author | Shao, Xin Li, Chengyu Yang, Haihong Lu, Xiaoyan Liao, Jie Qian, Jingyang Wang, Kai Cheng, Junyun Yang, Penghui Chen, Huajun Xu, Xiao Fan, Xiaohui |
author_facet | Shao, Xin Li, Chengyu Yang, Haihong Lu, Xiaoyan Liao, Jie Qian, Jingyang Wang, Kai Cheng, Junyun Yang, Penghui Chen, Huajun Xu, Xiao Fan, Xiaohui |
author_sort | Shao, Xin |
collection | PubMed |
description | Spatially resolved transcriptomics provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells by dissecting cell-type composition through a non-negative linear model and spatial mapping between single-cell transcriptomic and spatially resolved transcriptomic data. The benchmarked performance of SpaTalk on public single-cell spatial transcriptomic datasets is superior to that of existing inference methods. Then we apply SpaTalk to STARmap, Slide-seq, and 10X Visium data, revealing the in-depth communicative mechanisms underlying normal and disease tissues with spatial structure. SpaTalk can uncover spatially resolved cell-cell communications for single-cell and spot-based spatially resolved transcriptomic data universally, providing valuable insights into spatial inter-cellular tissue dynamics. |
format | Online Article Text |
id | pubmed-9338929 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-93389292022-08-01 Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk Shao, Xin Li, Chengyu Yang, Haihong Lu, Xiaoyan Liao, Jie Qian, Jingyang Wang, Kai Cheng, Junyun Yang, Penghui Chen, Huajun Xu, Xiao Fan, Xiaohui Nat Commun Article Spatially resolved transcriptomics provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells by dissecting cell-type composition through a non-negative linear model and spatial mapping between single-cell transcriptomic and spatially resolved transcriptomic data. The benchmarked performance of SpaTalk on public single-cell spatial transcriptomic datasets is superior to that of existing inference methods. Then we apply SpaTalk to STARmap, Slide-seq, and 10X Visium data, revealing the in-depth communicative mechanisms underlying normal and disease tissues with spatial structure. SpaTalk can uncover spatially resolved cell-cell communications for single-cell and spot-based spatially resolved transcriptomic data universally, providing valuable insights into spatial inter-cellular tissue dynamics. Nature Publishing Group UK 2022-07-30 /pmc/articles/PMC9338929/ /pubmed/35908020 http://dx.doi.org/10.1038/s41467-022-32111-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Shao, Xin Li, Chengyu Yang, Haihong Lu, Xiaoyan Liao, Jie Qian, Jingyang Wang, Kai Cheng, Junyun Yang, Penghui Chen, Huajun Xu, Xiao Fan, Xiaohui Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk |
title | Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk |
title_full | Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk |
title_fullStr | Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk |
title_full_unstemmed | Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk |
title_short | Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk |
title_sort | knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with spatalk |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9338929/ https://www.ncbi.nlm.nih.gov/pubmed/35908020 http://dx.doi.org/10.1038/s41467-022-32111-8 |
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