Biology guided method improves gene expression prediction from tissue images
Biology-Guided Context-Aware Residual Propagation for Spatial Gene Expression Prediction from Histology
Machine Learning
Summary
Spatial transcriptomics shows where genes are active in a tissue but is expensive and complex to do on a large scale. The authors propose a method called BCARP that predicts these gene activity patterns using regular stained tissue images. Instead of guessing gene activity directly from images, BCARP improves its predictions step-by-step by considering cell types nearby and overall tissue structure. This approach worked better than others when tested on several datasets.
What this means in practice
- •For biomedical data engineers: Integrate BCARP to predict spatial gene activity from standard stained tissue slides, reducing reliance on costly spatial transcriptomics experiments.
- •For digital pathology teams: Use BCARP to enhance histology image analysis by linking tissue morphology with underlying gene expression patterns for improved diagnosis or research.
Authors
Vu Van An, Long D. Nguyen, Ho Viet Duc Luong, Binh P. Nguyen
Abstract
Spatial transcriptomics provides spatially resolved molecular profiling, but its cost, limited throughput and experimental complexity hinder routine large-scale use. Predicting spatial gene expression from hematoxylin-and-eosin-stained histology images offers a scalable alternative, yet remains challenging because histology provides only an indirect morphological view of molecular activity. Existing methods typically decode expression from visual features in a single stage, even when enhanced with spatial aggregation, retrieval or multi-resolution fusion. This formulation is restrictive because spot-level expression reflects not only local morphology but also cellular identity, neighborhood composition and tissue-level organization. We propose BCARP, a Biology-guided Context-Aware Residual Propagation framework for histology-guided spatial transcriptomics prediction. Instead of directly predicting gene expression from histology embeddings, BCARP treats them as morphology visual priors and progressively refines spot representations in a transcriptomics-oriented latent space. Each refinement step integrates nuclei-guided local cellular context with prototype-guided global context propagation, enabling information exchange across both neighboring and spatially distant spots. A pattern-scale decoder further separates spatial expression into pattern prediction from raw expression with scale calibration. Evaluated on three ST datasets and ten HEST-1k cohorts, BCARP achieves the highest mean correlation-based performance against other baselines. Numerical results support biology-guided local-global residual propagation as an effective alternative to visual decoding for histology-to-transcriptomics prediction.