Analysis of RNA processing directly from spatial transcriptomics data reveals previously unknown regulation

Abstract

Technical advances have led to an explosion in the amount of biological data available in recent years, especially in the field of RNA sequencing. Specifically, spatial transcriptomics (ST) datasets, which allow each RNA molecule to be mapped to the 2D location it originated from within a tissue, have become readily available. Due to computational challenges, ST data has rarely been used to study RNA processing such as splicing or differential UTR usage. We apply the ReadZS and the SpliZ, methods developed to analyze RNA process in scRNA-seq data, to analyze spatial localization of RNA processing directly from ST data for the first time. Using Moran's I metric for spatial autocorrelation, we identify genes with spatially regulated RNA processing in the mouse brain and kidney, re-discovering known spatial regulation in Myl6 and identifying previously-unknown spatial regulation in genes such as Rps24, Gng13, Slc8a1, Gpm6a, Gpx3, ActB, Rps8, and S100A9. The rich set of discoveries made here from commonly used reference datasets provides a small taste of what can be learned by applying this technique more broadly to the large quantity of Visium data currently being created.

RPS24 alternative splicing is a marker of cancer progression and epithelial-mesenchymal transition

“…A recent study suggested that proteins derived from the ex4:22 bp isoform exhibit increased stability, potentially promoting hypoxic cell survival and growth 33 . Another study conducted a spatial transcriptomic analysis of mouse organs and revealed the topological regulation of the Rps24 AS isoforms in brain and kidney tissues, suggesting a crucial role for this isoform in cell fate determination 38 . This evidence suggested that RPS24 AS isoforms may contribute to the creation of heterogeneity in ribosomes, which could in turn alter the ribosome’s translational properties and consequently affect tumorigenesis.…”