Spatial domain detection using contrastive self-supervised learning for spatial multi-omics technologies

Recent advances in spatially-resolved single-omics and multi-omics technologies have led to the emergence of computational tools to detect or predict spatial domains. Additionally, histological images and immunofluorescence (IF) staining of proteins and cell types provide multiple perspectives and a more complete understanding of tissue architecture. Here, we introduce Proust, a scalable tool to predict discrete domains using spatial multi-omics data by combining the low-dimensional representation of biological profiles based on graph-based contrastive self-supervised learning. Our scalable method integrates multiple data modalities, such as RNA, protein, and H&E images, and predicts spatial domains within tissue samples. Through the integration of multiple modalities, Proust consistently demonstrates enhanced accuracy in detecting spatial domains, as evidenced across various benchmark datasets and technological platforms. ### Competing Interest Statement The authors have declared no competing interest. * Abeta : amyloid- beta AD : Alzheimer’s disease ARI : adjusted Rand index DLPFC : dorsolateral prefrontal cortex H&E : hematoxylin and eosin IF : immunofluorescence PCA : Principal component analysis pTau : hyperphosphorylated tau SRT : spatially-resolved transcriptomics Visium SPG : 10x Genomics Visium Spatial Proteogenomics platform UMAP : uniform manifold approximation and projection WM : white matter