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spaCI: deciphering spatial cellular communications through adaptive graph model
Cell–cell communications are vital for biological signalling and play important roles in complex diseases. Recent advances in single-cell spatial transcriptomics (SCST) technologies allow examining the spatial cell communication landscapes and hold the promise for disentangling the complex ligand–re...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9851335/ https://www.ncbi.nlm.nih.gov/pubmed/36545790 http://dx.doi.org/10.1093/bib/bbac563 |
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author | Tang, Ziyang Zhang, Tonglin Yang, Baijian Su, Jing Song, Qianqian |
author_facet | Tang, Ziyang Zhang, Tonglin Yang, Baijian Su, Jing Song, Qianqian |
author_sort | Tang, Ziyang |
collection | PubMed |
description | Cell–cell communications are vital for biological signalling and play important roles in complex diseases. Recent advances in single-cell spatial transcriptomics (SCST) technologies allow examining the spatial cell communication landscapes and hold the promise for disentangling the complex ligand–receptor (L–R) interactions across cells. However, due to frequent dropout events and noisy signals in SCST data, it is challenging and lack of effective and tailored methods to accurately infer cellular communications. Herein, to decipher the cell-to-cell communications from SCST profiles, we propose a novel adaptive graph model with attention mechanisms named spaCI. spaCI incorporates both spatial locations and gene expression profiles of cells to identify the active L–R signalling axis across neighbouring cells. Through benchmarking with currently available methods, spaCI shows superior performance on both simulation data and real SCST datasets. Furthermore, spaCI is able to identify the upstream transcriptional factors mediating the active L–R interactions. For biological insights, we have applied spaCI to the seqFISH+ data of mouse cortex and the NanoString CosMx Spatial Molecular Imager (SMI) data of non-small cell lung cancer samples. spaCI reveals the hidden L–R interactions from the sparse seqFISH+ data, meanwhile identifies the inconspicuous L–R interactions including THBS1−ITGB1 between fibroblast and tumours in NanoString CosMx SMI data. spaCI further reveals that SMAD3 plays an important role in regulating the crosstalk between fibroblasts and tumours, which contributes to the prognosis of lung cancer patients. Collectively, spaCI addresses the challenges in interrogating SCST data for gaining insights into the underlying cellular communications, thus facilitates the discoveries of disease mechanisms, effective biomarkers and therapeutic targets. |
format | Online Article Text |
id | pubmed-9851335 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-98513352023-01-20 spaCI: deciphering spatial cellular communications through adaptive graph model Tang, Ziyang Zhang, Tonglin Yang, Baijian Su, Jing Song, Qianqian Brief Bioinform Problem Solving Protocol Cell–cell communications are vital for biological signalling and play important roles in complex diseases. Recent advances in single-cell spatial transcriptomics (SCST) technologies allow examining the spatial cell communication landscapes and hold the promise for disentangling the complex ligand–receptor (L–R) interactions across cells. However, due to frequent dropout events and noisy signals in SCST data, it is challenging and lack of effective and tailored methods to accurately infer cellular communications. Herein, to decipher the cell-to-cell communications from SCST profiles, we propose a novel adaptive graph model with attention mechanisms named spaCI. spaCI incorporates both spatial locations and gene expression profiles of cells to identify the active L–R signalling axis across neighbouring cells. Through benchmarking with currently available methods, spaCI shows superior performance on both simulation data and real SCST datasets. Furthermore, spaCI is able to identify the upstream transcriptional factors mediating the active L–R interactions. For biological insights, we have applied spaCI to the seqFISH+ data of mouse cortex and the NanoString CosMx Spatial Molecular Imager (SMI) data of non-small cell lung cancer samples. spaCI reveals the hidden L–R interactions from the sparse seqFISH+ data, meanwhile identifies the inconspicuous L–R interactions including THBS1−ITGB1 between fibroblast and tumours in NanoString CosMx SMI data. spaCI further reveals that SMAD3 plays an important role in regulating the crosstalk between fibroblasts and tumours, which contributes to the prognosis of lung cancer patients. Collectively, spaCI addresses the challenges in interrogating SCST data for gaining insights into the underlying cellular communications, thus facilitates the discoveries of disease mechanisms, effective biomarkers and therapeutic targets. Oxford University Press 2022-12-21 /pmc/articles/PMC9851335/ /pubmed/36545790 http://dx.doi.org/10.1093/bib/bbac563 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Problem Solving Protocol Tang, Ziyang Zhang, Tonglin Yang, Baijian Su, Jing Song, Qianqian spaCI: deciphering spatial cellular communications through adaptive graph model |
title | spaCI: deciphering spatial cellular communications through adaptive graph model |
title_full | spaCI: deciphering spatial cellular communications through adaptive graph model |
title_fullStr | spaCI: deciphering spatial cellular communications through adaptive graph model |
title_full_unstemmed | spaCI: deciphering spatial cellular communications through adaptive graph model |
title_short | spaCI: deciphering spatial cellular communications through adaptive graph model |
title_sort | spaci: deciphering spatial cellular communications through adaptive graph model |
topic | Problem Solving Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9851335/ https://www.ncbi.nlm.nih.gov/pubmed/36545790 http://dx.doi.org/10.1093/bib/bbac563 |
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