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

Journal: Nature communications
Published Date: Jan 2, 2024

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.

Authors

  • Muhammad Shaban
    Tissue Image Analytics Lab, Department of Computer Science, University of Warwick, Coventry, United Kingdom.
  • Yunhao Bai
    Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
  • Huaying Qiu
    Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
  • Shulin Mao
    Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
  • Jason Yeung
    Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
  • Yao Yu Yeo
    Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
  • Vignesh Shanmugam
    Harvard Medical School, Brigham and Women's Hospital, Boston, Massachusetts.
  • Han Chen
    School of Statistics, University of Minnesota at Twin Cities.
  • Bokai Zhu
    Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
  • Jason L Weirather
    Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA.
  • Garry P Nolan
    Department of Microbiology & Immunology, Stanford University, CA, USA.
  • Margaret A Shipp
    Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
  • Scott J Rodig
    Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Sizun Jiang
    Broad Institute of Harvard and MIT, Cambridge, MA, USA. sjiang3@bidmc.harvard.edu.
  • Faisal Mahmood
    Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. faisalmahmood@bwh.harvard.edu.

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