HECLIP: Histology-Enhanced Contrastive Learning for Imputation of Transcriptomics Profiles

TL;DR

HECLIP is a deep learning framework that predicts gene expression profiles directly from histology images, reducing the need for costly spatial transcriptomics and enhancing molecular insights from tissue morphology.

Contribution

HECLIP introduces a novel contrastive learning approach that effectively links histology images to gene expression profiles, enabling accurate imputation without expensive assays.

Findings

Outperforms existing methods in gene expression prediction accuracy.

Demonstrates robustness across multiple datasets.

Provides biologically meaningful molecular insights from histology images.

Abstract

Histopathology, particularly hematoxylin and eosin (H\&E) staining, plays a critical role in diagnosing and characterizing pathological conditions by highlighting tissue morphology. However, H\&E-stained images inherently lack molecular information, requiring costly and resource-intensive methods like spatial transcriptomics to map gene expression with spatial resolution. To address these challenges, we introduce HECLIP (Histology-Enhanced Contrastive Learning for Imputation of Profiles), an innovative deep learning framework that bridges the gap between histological imaging and molecular profiling. HECLIP is specifically designed to infer gene expression profiles directly from H\&E-stained images, eliminating the need for expensive spatial transcriptomics assays. HECLIP leverages an advanced image-centric contrastive loss function to optimize image representation learning, ensuring that critical morphological patterns in histology images are effectively captured and translated into accurate gene expression profiles. This design enhances the predictive power of the image modality while minimizing reliance on gene expression data. Through extensive benchmarking on publicly available datasets, HECLIP demonstrates superior performance compared to existing approaches, delivering robust and biologically meaningful predictions. Detailed ablation studies further underscore its effectiveness in extracting molecular insights from histology images. Additionally, HECLIP's scalable and cost-efficient approach positions it as a transformative tool for both research and clinical applications, driving advancements in precision medicine. The source code for HECLIP is openly available at https://github.com/QSong-github/HECLIP.