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Cell composition inference and identification of layer-specific spatial transcriptional profiles with POLARIS
Spatial transcriptomics (ST) technology, providing spatially resolved transcriptional profiles, facilitates advanced understanding of key biological processes related to health and disease. Sequencing-based ST technologies provide whole-transcriptome profiles but are limited by the non–single cell–l...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9977174/ https://www.ncbi.nlm.nih.gov/pubmed/36857450 http://dx.doi.org/10.1126/sciadv.add9818 |
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author | Chen, Jiawen Luo, Tianyou Jiang, Minzhi Liu, Jiandong Gupta, Gaorav P. Li, Yun |
author_facet | Chen, Jiawen Luo, Tianyou Jiang, Minzhi Liu, Jiandong Gupta, Gaorav P. Li, Yun |
author_sort | Chen, Jiawen |
collection | PubMed |
description | Spatial transcriptomics (ST) technology, providing spatially resolved transcriptional profiles, facilitates advanced understanding of key biological processes related to health and disease. Sequencing-based ST technologies provide whole-transcriptome profiles but are limited by the non–single cell–level resolution. Lack of knowledge in the number of cells or cell type composition at each spot can lead to invalid downstream analysis, which is a critical issue recognized in ST data analysis. Methods developed, however, tend to underuse histological images, which conceptually provide important and complementary information including anatomical structure and distribution of cells. To fill in the gaps, we present POLARIS, a versatile ST analysis method that can perform cell type deconvolution, identify anatomical or functional layer-wise differentially expressed (LDE) genes, and enable cell composition inference from histology images. Applied to four tissues, POLARIS demonstrates high deconvolution accuracy, accurately predicts cell composition solely from images, and identifies LDE genes that are biologically relevant and meaningful. |
format | Online Article Text |
id | pubmed-9977174 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-99771742023-03-02 Cell composition inference and identification of layer-specific spatial transcriptional profiles with POLARIS Chen, Jiawen Luo, Tianyou Jiang, Minzhi Liu, Jiandong Gupta, Gaorav P. Li, Yun Sci Adv Biomedicine and Life Sciences Spatial transcriptomics (ST) technology, providing spatially resolved transcriptional profiles, facilitates advanced understanding of key biological processes related to health and disease. Sequencing-based ST technologies provide whole-transcriptome profiles but are limited by the non–single cell–level resolution. Lack of knowledge in the number of cells or cell type composition at each spot can lead to invalid downstream analysis, which is a critical issue recognized in ST data analysis. Methods developed, however, tend to underuse histological images, which conceptually provide important and complementary information including anatomical structure and distribution of cells. To fill in the gaps, we present POLARIS, a versatile ST analysis method that can perform cell type deconvolution, identify anatomical or functional layer-wise differentially expressed (LDE) genes, and enable cell composition inference from histology images. Applied to four tissues, POLARIS demonstrates high deconvolution accuracy, accurately predicts cell composition solely from images, and identifies LDE genes that are biologically relevant and meaningful. American Association for the Advancement of Science 2023-03-01 /pmc/articles/PMC9977174/ /pubmed/36857450 http://dx.doi.org/10.1126/sciadv.add9818 Text en Copyright © 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY). https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution license (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Biomedicine and Life Sciences Chen, Jiawen Luo, Tianyou Jiang, Minzhi Liu, Jiandong Gupta, Gaorav P. Li, Yun Cell composition inference and identification of layer-specific spatial transcriptional profiles with POLARIS |
title | Cell composition inference and identification of layer-specific spatial transcriptional profiles with POLARIS |
title_full | Cell composition inference and identification of layer-specific spatial transcriptional profiles with POLARIS |
title_fullStr | Cell composition inference and identification of layer-specific spatial transcriptional profiles with POLARIS |
title_full_unstemmed | Cell composition inference and identification of layer-specific spatial transcriptional profiles with POLARIS |
title_short | Cell composition inference and identification of layer-specific spatial transcriptional profiles with POLARIS |
title_sort | cell composition inference and identification of layer-specific spatial transcriptional profiles with polaris |
topic | Biomedicine and Life Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9977174/ https://www.ncbi.nlm.nih.gov/pubmed/36857450 http://dx.doi.org/10.1126/sciadv.add9818 |
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