TissueMosaic: Self-supervised learning of tissue representations enables differential spatial transcriptomics across samples.

Abstract

Spatial transcriptomics allows for the measurement of gene expression within the native tissue context. However, despite technological advancements, computational methods to link cell states with their microenvironment and compare these relationships across samples and conditions remain limited. To address this, we introduce Tissue Motif-Based Spatial Inference across Conditions (TissueMosaic), a self-supervised convolutional neural network designed to discover and represent tissue architectural motifs from multi-sample spatial transcriptomic datasets. TissueMosaic further links these motifs to gene expression, enabling the study of how changes in tissue structure impact cell-intrinsic function. TissueMosaic increases the signal-to-noise ratio of spatial differential expression analysis through a motif enrichment strategy, resulting in more reliable detection of genes that covary with tissue structure changes. Here, we demonstrate that TissueMosaic learns representations that outperform neighborhood cell-type composition baselines and existing methods on downstream tasks. These findings underscore the potential of self-supervised learning to advance spatial transcriptomics discovery.