Visualizing somatic alterations in spatial transcriptomics data of skin cancer

Abstract

Tools to visualize genetic alterations within tissues remain underdeveloped despite the growth of spatial transcriptomic technologies, which measure gene expression in different regions of tissues. Since genetic alterations can be detected in RNA-sequencing data, we explored the feasibility of observing somatic alterations in spatial transcriptomics data. Extracting genetic information from spatial transcriptomic data would illuminate the spatial distribution of clones and allow for correlations with regional changes in gene expression to support genotype-phenotype studies. Recent work demonstrates that copy number alterations can be inferred from spatial transcriptomics data1. Here, we describe new software to further enhance the inference of copy number from spatial transcriptomics data. Moreover, we demonstrate that single nucleotide variants are also detectable in spatial transcriptomic data. We applied these approaches to map the location of point mutations, copy number alterations, and allelic imbalances in spatial transcriptomic data of two cutaneous squamous cell carcinomas. We show that both tumors are dominated by a single clone of cells, suggesting that their regional variations in gene expression2 are likely driven by non-genetic factors. Furthermore, we observe mutant cells in histologically normal tissue surrounding one tumor, which were not discernible upon histopathologic evaluation. Finally, we detected mono-allelic expression of immunoglobulin heavy chains in B-cells, revealing clonal populations of plasma cells surrounding one tumor. In summary, we put forward solutions to add the genetic dimension to spatial transcriptomic datasets, augmenting the potential of this new technology.

Exploring the Landscape of Spatial Transcriptome Analysis: Introducing STASH, a Database of Spatial Transcriptome Tools

“…Another exciting area is the spatial reconstruction of scRNA-seq data, where ST data can be used as a reference to assess the spatial location of cells, as demonstrated by Tangram [4]. Also, several tools are being developed to detect biological signals other than expression in ST data, such as fusion transcripts [29], alternative polyadenylation [30], point mutation detection [31], CNV detection, and clone inference [32]. These tools expand the scope of what can be observed in ST data and offer new avenues for biological discovery.…”