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

Anissa El Marrahi, Fabio Lipreri, Ziqi Kang, Louise Gsell, Alper Eroglu, David Alber and Jean Hausser ()
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Anissa El Marrahi: Karolinska Institutet
Fabio Lipreri: Karolinska Institutet
Ziqi Kang: Karolinska Institutet
Louise Gsell: Karolinska Institutet
Alper Eroglu: Karolinska Institutet
David Alber: Karolinska Institutet
Jean Hausser: Karolinska Institutet

Nature Communications, 2023, vol. 14, issue 1, 1-17

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41467-023-42878-z

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