High-Resolution Spatial Transcriptomics from Histology Images using HisToSGE
This work addresses the cost and resolution limitations in spatial transcriptomics for researchers in genomics and pathology, representing a novel method for a known bottleneck.
The paper tackled the problem of high costs and sparse resolution in spatial transcriptomics by developing HisToSGE, a deep learning method that predicts high-resolution gene expression profiles from histological images, and it outperformed five state-of-the-art baseline methods on four datasets.
Spatial transcriptomics (ST) is a groundbreaking genomic technology that enables spatial localization analysis of gene expression within tissue sections. However, it is significantly limited by high costs and sparse spatial resolution. An alternative, more cost-effective strategy is to use deep learning methods to predict high-density gene expression profiles from histological images. However, existing methods struggle to capture rich image features effectively or rely on low-dimensional positional coordinates, making it difficult to accurately predict high-resolution gene expression profiles. To address these limitations, we developed HisToSGE, a method that employs a Pathology Image Large Model (PILM) to extract rich image features from histological images and utilizes a feature learning module to robustly generate high-resolution gene expression profiles. We evaluated HisToSGE on four ST datasets, comparing its performance with five state-of-the-art baseline methods. The results demonstrate that HisToSGE excels in generating high-resolution gene expression profiles and performing downstream tasks such as spatial domain identification. All code and public datasets used in this paper are available at https://github.com/wenwenmin/HisToSGE and https://zenodo.org/records/12792163.