Identification of treatment-responsive genes in spatial transcriptomics data by leveraging injection site information

Abstract

Spatial Transcriptomics assays allow to study gene expression as a function of the spatial position of cells across a tissue sample. Although several methods have been proposed to identify spatially variable genes, they do not take into account the position of the injection site in the case of treated samples. In this study, we developed a method to identify treatment-responsive genes based on the assumption that the distance of the cells from the injection site across the tissue would affect the corresponding transcriptional response. In particular, we tested our approach using a publicly available ST dataset obtained after injection of heme into the striatum nucleus of a murine brain. We observed that several biologically relevant genes were detected by our method as showing a distance-dependent expression trend. We finally compared the results against a ground-truth gene set and a state-of art pattern-based method.