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NIPMAP: niche-phenotype mapping of multiplex histology data by community ecology

Advances in multiplex histology allow surveying millions of cells, dozens of cell types, and up to thousands of phenotypes within the spatial context of tissue sections. This leads to a combinatorial challenge in (a) summarizing the cellular and phenotypic architecture of tissues and (b) identifying...

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Autores principales: El Marrahi, Anissa, Lipreri, Fabio, Kang, Ziqi, Gsell, Louise, Eroglu, Alper, Alber, David, Hausser, Jean
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10630431/
https://www.ncbi.nlm.nih.gov/pubmed/37935691
http://dx.doi.org/10.1038/s41467-023-42878-z
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author El Marrahi, Anissa
Lipreri, Fabio
Kang, Ziqi
Gsell, Louise
Eroglu, Alper
Alber, David
Hausser, Jean
author_facet El Marrahi, Anissa
Lipreri, Fabio
Kang, Ziqi
Gsell, Louise
Eroglu, Alper
Alber, David
Hausser, Jean
author_sort El Marrahi, Anissa
collection PubMed
description Advances in multiplex histology allow surveying millions of cells, dozens of cell types, and up to thousands of phenotypes within the spatial context of tissue sections. This leads to a combinatorial challenge in (a) summarizing the cellular and phenotypic architecture of tissues and (b) identifying phenotypes with interesting spatial architecture. To address this, we combine ideas from community ecology and machine learning into niche-phenotype mapping (NIPMAP). NIPMAP takes advantage of geometric constraints on local cellular composition imposed by the niche structure of tissues in order to automatically segment tissue sections into niches and their interfaces. Projecting phenotypes on niches and their interfaces identifies previously-reported and previously-unreported spatially-driven phenotypes, concisely summarizes the phenotypic architecture of tissues, and reveals fundamental properties of tissue architecture. NIPMAP is applicable to both protein and RNA multiplex histology of healthy and diseased tissue. An open-source R/Python package implements NIPMAP.
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spelling pubmed-106304312023-11-07 NIPMAP: niche-phenotype mapping of multiplex histology data by community ecology El Marrahi, Anissa Lipreri, Fabio Kang, Ziqi Gsell, Louise Eroglu, Alper Alber, David Hausser, Jean Nat Commun Article Advances in multiplex histology allow surveying millions of cells, dozens of cell types, and up to thousands of phenotypes within the spatial context of tissue sections. This leads to a combinatorial challenge in (a) summarizing the cellular and phenotypic architecture of tissues and (b) identifying phenotypes with interesting spatial architecture. To address this, we combine ideas from community ecology and machine learning into niche-phenotype mapping (NIPMAP). NIPMAP takes advantage of geometric constraints on local cellular composition imposed by the niche structure of tissues in order to automatically segment tissue sections into niches and their interfaces. Projecting phenotypes on niches and their interfaces identifies previously-reported and previously-unreported spatially-driven phenotypes, concisely summarizes the phenotypic architecture of tissues, and reveals fundamental properties of tissue architecture. NIPMAP is applicable to both protein and RNA multiplex histology of healthy and diseased tissue. An open-source R/Python package implements NIPMAP. Nature Publishing Group UK 2023-11-07 /pmc/articles/PMC10630431/ /pubmed/37935691 http://dx.doi.org/10.1038/s41467-023-42878-z Text en © The Author(s) 2023 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
El Marrahi, Anissa
Lipreri, Fabio
Kang, Ziqi
Gsell, Louise
Eroglu, Alper
Alber, David
Hausser, Jean
NIPMAP: niche-phenotype mapping of multiplex histology data by community ecology
title NIPMAP: niche-phenotype mapping of multiplex histology data by community ecology
title_full NIPMAP: niche-phenotype mapping of multiplex histology data by community ecology
title_fullStr NIPMAP: niche-phenotype mapping of multiplex histology data by community ecology
title_full_unstemmed NIPMAP: niche-phenotype mapping of multiplex histology data by community ecology
title_short NIPMAP: niche-phenotype mapping of multiplex histology data by community ecology
title_sort nipmap: niche-phenotype mapping of multiplex histology data by community ecology
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10630431/
https://www.ncbi.nlm.nih.gov/pubmed/37935691
http://dx.doi.org/10.1038/s41467-023-42878-z
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