Spatial transcriptomics at subspot resolution with BayesSpace

Recent spatial gene expression technologies enable comprehensive measurement of transcriptomic profiles while retaining spatial context. However, existing analysis methods do not address the limited resolution of the technology or use the spatial information efficiently. Researchers from the Fred Hutchinson Cancer Research Center and the University of Washington have developed BayesSpace, a fully Bayesian statistical method that uses the information from spatial neighborhoods for resolution enhancement of spatial transcriptomic data and for clustering analysis. The researchers benchmark BayesSpace against current methods for spatial and non-spatial clustering and show that it improves identification of distinct intra-tissue transcriptional profiles from samples of the brain, melanoma, invasive ductal carcinoma and ovarian adenocarcinoma. Using immunohistochemistry and an in silico dataset constructed from scRNA-seq data, they show that BayesSpace resolves tissue structure that is not detectable at the original resolution and identifies transcriptional heterogeneity inaccessible to histological analysis. These results illustrate BayesSpace’s utility in facilitating the discovery of biological insights from spatial transcriptomic datasets.

The BayesSpace workflow

a, The BayesSpace workflow begins with preprocessed ST or Visium data. Data are spatially clustered to infer regions with similar expression profiles. These clusters can be refined via enhanced clustering to provide a higher-resolution spatial map. Enhanced clustering also provides the basis for predicting gene expression at the higher resolution, which can be used in further differential expression analyses. b, From geometric representations of spatial distribution of spots in the ST and Visium technologies, neighbors can be identified for each spot based on shared edges (top). Each spot can be subdivided into subspots, which again have natural edge-based neighbors (bottom).