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MAPS: pathologist-level cell type annotation from tissue images through machine learning.
Shaban, Muhammad; Bai, Yunhao; Qiu, Huaying; Mao, Shulin; Yeung, Jason; Yeo, Yao Yu; Shanmugam, Vignesh; Chen, Han; Zhu, Bokai; Weirather, Jason L; Nolan, Garry P; Shipp, Margaret A; Rodig, Scott J; Jiang, Sizun; Mahmood, Faisal.
Afiliação
  • Shaban M; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Bai Y; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Qiu H; Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA.
  • Mao S; Broad Institute of Harvard and MIT, Cambridge, MA, USA.
  • Yeung J; Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
  • Yeo YY; Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
  • Shanmugam V; Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
  • Chen H; Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
  • Zhu B; Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
  • Weirather JL; Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
  • Nolan GP; Broad Institute of Harvard and MIT, Cambridge, MA, USA.
  • Shipp MA; Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
  • Rodig SJ; Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
  • Jiang S; Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA.
  • Mahmood F; Center for Immuno-oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
Nat Commun ; 15(1): 28, 2024 01 02.
Article em En | MEDLINE | ID: mdl-38167832
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado de Máquina / Patologistas Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Revista: Nat Commun Assunto da revista: BIOLOGIA / CIENCIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Aprendizado de Máquina / Patologistas Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Revista: Nat Commun Assunto da revista: BIOLOGIA / CIENCIA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos