Inferring multi-slice spatially resolved gene expression from H&E-stained histology images with STMCL.

Spatial transcriptomics has significantly advanced the measurement of spatial gene expression in the field of biology. However, the high cost of ST limits its application in large-scale studies. Using deep learning to predict spatial gene expression from H&E-stained histology images offers a more cost-effective alternative, but existing methods fail to fully leverage the multimodal information provided by Spatial transcriptomics and pathology images.

Multimodal contrastive learning for spatial gene expression prediction using histology images.

In recent years, the advent of spatial transcriptomics (ST) technology has unlocked unprecedented opportunities for delving into the complexities of gene expression patterns within intricate biological systems. Despite its transformative potential, the prohibitive cost of ST technology remains a significant barrier to its widespread adoption in large-scale studies. An alternative, more cost-effective strategy involves employing artificial intelligence to predict gene expression levels using readily accessible whole-slide images stained with Hematoxylin and Eosin (H&E).