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Spatially visualized single-cell pathology of highly multiplexed protein profiles in health and disease
Deep molecular profiling of biological tissues is an indicator of health and disease. We used imaging mass cytometry (IMC) to acquire spatially resolved 20-plex protein data in tissue sections from normal and chronic tonsillitis cases. We present SpatialViz, a suite of algorithms to explore spatial...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8160218/ https://www.ncbi.nlm.nih.gov/pubmed/34045665 http://dx.doi.org/10.1038/s42003-021-02166-2 |
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author | Allam, Mayar Hu, Thomas Cai, Shuangyi Laxminarayanan, Krishnan Hughley, Robert B. Coskun, Ahmet F. |
author_facet | Allam, Mayar Hu, Thomas Cai, Shuangyi Laxminarayanan, Krishnan Hughley, Robert B. Coskun, Ahmet F. |
author_sort | Allam, Mayar |
collection | PubMed |
description | Deep molecular profiling of biological tissues is an indicator of health and disease. We used imaging mass cytometry (IMC) to acquire spatially resolved 20-plex protein data in tissue sections from normal and chronic tonsillitis cases. We present SpatialViz, a suite of algorithms to explore spatial relationships in multiplexed tissue images by visualizing and quantifying single-cell granularity and anatomical complexity in diverse multiplexed tissue imaging data. Single-cell and spatial maps confirmed that CD68+ cells were correlated with the enhanced Granzyme B expression and CD3+ cells exhibited enrichment of CD4+ phenotype in chronic tonsillitis. SpatialViz revealed morphological distributions of cellular organizations in distinct anatomical areas, spatially resolved single-cell associations across anatomical categories, and distance maps between the markers. Spatial topographic maps showed the unique organization of different tissue layers. The spatial reference framework generated network-based comparisons of multiplex data from healthy and diseased tonsils. SpatialViz is broadly applicable to multiplexed tissue biology. |
format | Online Article Text |
id | pubmed-8160218 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81602182021-06-10 Spatially visualized single-cell pathology of highly multiplexed protein profiles in health and disease Allam, Mayar Hu, Thomas Cai, Shuangyi Laxminarayanan, Krishnan Hughley, Robert B. Coskun, Ahmet F. Commun Biol Article Deep molecular profiling of biological tissues is an indicator of health and disease. We used imaging mass cytometry (IMC) to acquire spatially resolved 20-plex protein data in tissue sections from normal and chronic tonsillitis cases. We present SpatialViz, a suite of algorithms to explore spatial relationships in multiplexed tissue images by visualizing and quantifying single-cell granularity and anatomical complexity in diverse multiplexed tissue imaging data. Single-cell and spatial maps confirmed that CD68+ cells were correlated with the enhanced Granzyme B expression and CD3+ cells exhibited enrichment of CD4+ phenotype in chronic tonsillitis. SpatialViz revealed morphological distributions of cellular organizations in distinct anatomical areas, spatially resolved single-cell associations across anatomical categories, and distance maps between the markers. Spatial topographic maps showed the unique organization of different tissue layers. The spatial reference framework generated network-based comparisons of multiplex data from healthy and diseased tonsils. SpatialViz is broadly applicable to multiplexed tissue biology. Nature Publishing Group UK 2021-05-27 /pmc/articles/PMC8160218/ /pubmed/34045665 http://dx.doi.org/10.1038/s42003-021-02166-2 Text en © The Author(s) 2021 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 Allam, Mayar Hu, Thomas Cai, Shuangyi Laxminarayanan, Krishnan Hughley, Robert B. Coskun, Ahmet F. Spatially visualized single-cell pathology of highly multiplexed protein profiles in health and disease |
title | Spatially visualized single-cell pathology of highly multiplexed protein profiles in health and disease |
title_full | Spatially visualized single-cell pathology of highly multiplexed protein profiles in health and disease |
title_fullStr | Spatially visualized single-cell pathology of highly multiplexed protein profiles in health and disease |
title_full_unstemmed | Spatially visualized single-cell pathology of highly multiplexed protein profiles in health and disease |
title_short | Spatially visualized single-cell pathology of highly multiplexed protein profiles in health and disease |
title_sort | spatially visualized single-cell pathology of highly multiplexed protein profiles in health and disease |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8160218/ https://www.ncbi.nlm.nih.gov/pubmed/34045665 http://dx.doi.org/10.1038/s42003-021-02166-2 |
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