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

Muhammad Shaban, Yunhao Bai, Huaying Qiu, Shulin Mao, Jason Yeung, Yao Yu Yeo, Vignesh Shanmugam, Han Chen, Bokai Zhu, Jason L. Weirather, Garry P. Nolan, Margaret A. Shipp, Scott J. Rodig, Sizun Jiang () and Faisal Mahmood ()
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Muhammad Shaban: Harvard Medical School
Yunhao Bai: Stanford University School of Medicine
Huaying Qiu: Harvard Medical School
Shulin Mao: Harvard Medical School
Jason Yeung: Harvard Medical School
Yao Yu Yeo: Harvard Medical School
Vignesh Shanmugam: Harvard Medical School
Han Chen: Stanford University School of Medicine
Bokai Zhu: Stanford University School of Medicine
Jason L. Weirather: Dana-Farber Cancer Institute
Garry P. Nolan: Stanford University School of Medicine
Margaret A. Shipp: Harvard Medical School
Scott J. Rodig: Harvard Medical School
Sizun Jiang: Broad Institute of Harvard and MIT
Faisal Mahmood: Harvard Medical School

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract 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 typically 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.

Date: 2024
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DOI: 10.1038/s41467-023-44188-w

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