MAPS: Pathologist-level cell type annotation from tissue images through machine learning.

Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.
Competing Interests: CONFLICT OF INTERESTS S.J.R. has received research support from Affimed, Merck, and Bristol-Myers Squibb (BMS), he is on the Scientific Advisory Board for Immunitas Therapeutics and part of Bristol Myers Squibb International Immuno-Oncology Network (II-ON). M.A.S. has research funding from BMS, Bayer, Abbvie, and AstraZeneca, and is on advisory boards for AstraZeneca and BMS. G.P.N., is co-founder and stockholder of IonPath Inc, which manufactures the instrument used in this manuscript, is a co-founder and stockholder of Akoya Biosciences, Inc. and inventor on patent US9909167, and is a Scientific Advisory Board member for Akoya Biosciences, Inc. The other authors declare no competing interests.