The spatial landscape of gene expression isoforms in tissue sections

capturing technologies add tissue context to gene expression data, with the potential of providing a greater understanding of complex biological systems. However, splicing variants and full-length sequence heterogeneity cannot be characterized with current methods. Here, we introduce Spatial Isoform Transcriptomics (SiT), an explorative method for characterizing spatial isoform and sequence heterogeneity in tissue sections, and show how it can be used to profile isoform expression and sequence heterogeneity in a tissue context