2023-11-13 カロリンスカ研究所(KI)
◆新しい手法は臨床試験での適用が次のステップであり、がん免疫療法への応答差異や乳がん患者の化学療法必要性などの課題に取り組む予定です。
<関連情報>
- https://news.ki.se/researchers-take-new-ai-approach-to-analyse-tumours
- https://www.nature.com/articles/s41467-023-42878-z
NIPMAP: コミュニティ生態学によるマルチプレックス組織学データのニッチ表現型マッピング NIPMAP: niche-phenotype mapping of multiplex histology data by community ecology
Anissa El Marrahi,Fabio Lipreri,Ziqi Kang,Louise Gsell,Alper Eroglu,David Alber & Jean Hausser
Nature Communications Published:07 November 2023
DOI:https://doi.org/10.1038/s41467-023-42878-z
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.
