Discovery and generalization of tissue structures from spatial omics data

Wu, Zhenqin; Kondo, Ayano; McGrady, Monee; Baker, Ethan A.G.; Chidester, Benjamin; Wu, Eric; Rahim, Maha K.; Bracey, Nathan A.; Charu, Vivek; Cho, Raymond J.; Cheng, Jeffrey B.; Afkarian, Maryam; Zou, James; Mayer, Aaron T.; Trevino, Alexandro E.

doi:10.1016/j.crmeth.2024.100838

Cell Reports Methods

2024

DOI: 10.1016/j.crmeth.2024.100838

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Discovery and generalization of tissue structures from spatial omics data

Zhenqin Wu,

Ayano Kondo,

Monee McGrady

et al.

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2024

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ROSIE: AI generation of multiplex immunofluorescence staining from histopathology images

Wu,

Bieniosek,

et al. 2024

Preprint

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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 1000 paired and aligned H&E and multiplex immunofluorescence (mIF) samples from 20 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.

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ROSIE: AI generation of multiplex immunofluorescence staining from histopathology images

Wu,

Bieniosek,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

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Discovery and generalization of tissue structures from spatial omics data

Cited by 1 publication

References 55 publications

ROSIE: AI generation of multiplex immunofluorescence staining from histopathology images

ROSIE: AI generation of multiplex immunofluorescence staining from histopathology images

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