Histopathology-based Protein Multiplex Generation using Deep Learning.

ABSTRACT: Multiplexed protein imaging offers valuable insights into interactions between tumors and their surrounding tumor microenvironment (TME), but its widespread use is limited by cost, time, and tissue availability. We present HistoPlexer, a deep learning framework that generates spatially resolved protein multiplexes directly from standard hematoxylin and eosin (H&E) histopathology images. HistoPlexer jointly predicts multiple tumor and immune markers using a conditional generative adversarial architecture with custom loss functions designed to ensure pixel- and embedding-level similarity while mitigating slice-to-slice variations. A comprehensive evaluation on metastatic melanoma samples demonstrates that HistoPlexer-generated protein maps closely resemble real maps, as validated by expert a

SUBMITTER: Andani S 

PROVIDER: S-EPMC11643202 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature