Single-cell gene expression prediction using H and E images based on spatial transcriptomics

In situ RNA capturing represents an excellent opportunity for bridging transcriptomic data to a spatial domain, making it possible to map the gene expression to the corresponding anatomical structure. As a result, scientists can better understand the transcriptional heterogeneity with spatially resolved, anatomical, and pathophysiological contexts. However, high throughput sequencing technologies paired with histological images suffer from lower resolution mapping between transcriptome and imaging data. Here, we present Spatial Transcriptome Auto-encoder and Deconvolution (ST-AnD), a scalable deep generative model for predicting gene expression at cellular or nuclei level based on H&E imaging and in situ RNA capturing, thus allowing a better understanding of the tissue microenvironment.