miniMTI: minimal multiplex tissue imaging enhances biomarker expression prediction from histology

TL;DR

miniMTI combines H&E histology with a few measured markers to accurately predict large multiplex tissue imaging data, improving biomarker prediction and preserving biological and clinical information.

Contribution

miniMTI introduces a minimal set of molecular markers combined with H&E to reconstruct full multiplex imaging data, enabling scalable and interpretable virtual staining.

Findings

miniMTI reduces a 40-marker MTI assay to H&E plus three measured markers while preserving cellular phenotypes and spatial architecture.

The method accurately recovers withheld markers and disease-associated molecular programs like Gleason grade-linked signatures.

miniMTI outperforms H&E-only virtual staining by integrating sparse molecular data with histology context.

Abstract

Virtual multiplexing from routine histology has advanced rapidly, yet morphology alone provides limited access to molecular state, imposing an intrinsic ceiling on H&E-only inference. Here, we introduce miniMTI, a molecularly anchored virtual staining framework that determines the minimal set of experimentally measured markers required, alongside H&E, to accurately reconstruct large multiplex tissue imaging (MTI) panels while preserving biologically and clinically relevant information. miniMTI learns from paired same-section H&E–MTI data using a unified multimodal generative model that can condition on arbitrary combinations of measured marker channels, coupled with an iterative panel selection strategy to rank informative molecular anchors. Across colorectal and prostate cancer cohorts spanning two MTI platforms and over 40 million cells, miniMTI reduces a 40-marker MTI assay to H&E plus as few as three measured molecular markers, while accurately recovering withheld markers, preserving cellular phenotypes and spatial tissue architecture, and disease-associated molecular programs, including Gleason grade-linked signatures. By integrating histology context with sparse molecular grounding, miniMTI overcomes the limitations of morphology-only virtual staining and provides a scalable, cost-effective approach for expanding MTI-level biomarker coverage with retained biological interpretability and clinical relevance.

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