SIRV: Spatial inference of RNA velocity at the single-cell resolution

Abstract Studying cellular differentiation using single-cell RNA sequencing (scRNA-seq) rapidly expands our understanding of cellular development processes. Recently, RNA velocity has created new possibilities in studying these cellular differentiation processes, as differentiation dynamics can be obtained from measured spliced and unspliced mRNA expression. However, to map these differentiation processes to developments within a tissue, the spatial context of the tissue should be taken into account, which is not possible with current approaches as they start from dissociated cells. We present SIRV (Spatially Inferred RNA Velocity), a method to infer spatial differentiation trajectories within the spatial context of a tissue at the single-cell resolution. SIRV integrates spatial transcriptomics data with reference scRNA-seq data, to enrich the spatially measured genes with spliced and unspliced expressions from the scRNA-seq data. Next, SIRV calculates RNA velocity vectors for every spatially measured cell and maps these vectors to the spatial coordinates within the tissue. We tested SIRV on the Developing Mouse Brain Atlas data and obtained biologically relevant spatial differentiation trajectories. Additionally, SIRV annotates spatial cells with cellular identities and the region of origin which are transferred from the annotated reference scRNA-seq data. Altogether, with SIRV, we introduce a new tool to enrich spatial transcriptomics data that can assist in understanding how tissues develop.