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<p>Hematoxylin and eosin (H&amp;E) is a common and inexpensive histopathology assay. Though widely used and information-rich, it cannot directly inform about specific molecular markers, which require additional experiments to assess. To address this gap, we present ROSIE, a deep-learning framework that computationally imputes the expression and localization of dozens of proteins from H&amp;E images. Our model is trained on a dataset of over 1300 paired and aligned H&amp;E and multiplex immunofluorescence (mIF) samples from over a dozen tissues and disease conditions, spanning over 16 million cells. Validation of our in silico <i>mIF</i> staining method on held-out H&amp;E samples demonstrates that the predicted biomarkers are effective in identifying cell phenotypes, particularly distinguishing lymphocytes such as B cells and T cells, which are not readily discernible with H&amp;E staining alone. Additionally, ROSIE facilitates the robust identification of stromal and epithelial microenvironments and immune cell subtypes like tumor-infiltrating lymphocytes (TILs), which are important for understanding tumor-immune interactions and can help inform treatment strategies in cancer research.</p>

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ROSIE: AI generation of multiplex immunofluorescence staining from histopathology images

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月16日

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  • Eric Wu,
  • Matthew Bieniosek,
  • Zhenqin Wu,
  • Nitya Thakkar,
  • Gregory W. Charville,
  • Ahmad Makky,
  • Christian M. Schürch,
  • Jeroen R. Huyghe,
  • Ulrike Peters,
  • Christopher I. Li,
  • Li Li,
  • Hannah Giba,
  • Vivek Behera,
  • Arjun Raman,
  • Alexandro E. Trevino,
  • Aaron T. Mayer,
  • James Zou

摘要

Hematoxylin and eosin (H&E) is a common and inexpensive histopathology assay. Though widely used and information-rich, it cannot directly inform about specific molecular markers, which require additional experiments to assess. To address this gap, we present ROSIE, a deep-learning framework that computationally imputes the expression and localization of dozens of proteins from H&E images. Our model is trained on a dataset of over 1300 paired and aligned H&E and multiplex immunofluorescence (mIF) samples from over a dozen tissues and disease conditions, spanning over 16 million cells. Validation of our in silico mIF staining method on held-out H&E samples demonstrates that the predicted biomarkers are effective in identifying cell phenotypes, particularly distinguishing lymphocytes such as B cells and T cells, which are not readily discernible with H&E staining alone. Additionally, ROSIE facilitates the robust identification of stromal and epithelial microenvironments and immune cell subtypes like tumor-infiltrating lymphocytes (TILs), which are important for understanding tumor-immune interactions and can help inform treatment strategies in cancer research.