Problem
Research questions and friction points this paper is trying to address.
Spatial Transcriptomics
Multimodal Pretraining
Histology-Gene Alignment
Spatial Dependencies
Gene Selection
PaSTel: Anchoring Histology in Spatial Transcriptomics via Multi-Scale Hierarchical Bio-Prior Contrastive Pretraining
🤖 AI Summary
This study addresses the limitations of weak gene discriminability and neglected spatial dependencies in spatial transcriptomics pre-training by proposing PaSTel, a hierarchical multimodal framework. This method introduces a novel three-scale biologically informed contrastive learning mechanism spanning point, functional, and regional levels. By integrating TF-IDF reweighting, KEGG pathway anchoring, and spatial clustering, PaSTel achieves deep alignment between histology and gene expression. Experimental results demonstrate that PaSTel consistently outperforms existing vision and omics encoders across multiple downstream tasks. Crucially, this approach effectively bridges the gap between global semantics and spatial structure, significantly enhancing both representation informativeness and transferability for spatial transcriptomic analysis.
📝 Abstract
Spatial transcriptomics (ST) links tissue morphology with molecular programs, motivating multimodal pretraining methods that align histology images with gene expression. However, existing approaches suffer from two key limitations: spatially informative gene selection is often dominated by ubiquitous housekeeping genes, leading to weakly discriminative representations, and independent spot-patch alignment fails to capture spatial dependencies that are critical for tissue organization. To address these challenges, we introduce PaSTel, a hierarchical multimodal pretraining framework that integrates biological priors at three levels. At the spot level, TF-IDF reweighting is used to identify spatially informative genes; at the functional level, curated KEGG pathways serve as anchors for encoding global biological semantics; and at the regional level, spatial clustering aggregates neighboring spots to model meso-scale tissue structure. Across multiple downstream tasks, PaSTel consistently outperforms existing vision and vision-omics encoders, demonstrating that incorporating multiscale biological priors yields more informative and transferable representations for spatial transcriptomics.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Hierarchical Bio-Prior
Contrastive Pretraining
Spatial Transcriptomics
Multi-Scale Representation
TF-IDF Reweighting
🔎 Similar Papers
No similar papers found.
