Investigating Differences Between scRNA-seq and Spatial Transcriptomics

Abstract

Single-cell RNA sequencing (scRNA-seq), a technology used to study the transcriptome at single-cell resolution, has allowed for many advances in biomedicine. However, the dissociation of cells in the scRNA-seq protocol takes away spatial information. Spatial transcriptomics technologies emerged as a way to fill this gap. Because spatial transcriptomics technologies do not capture the transcriptome at a single-cell resolution, integration with scRNA-seq data is needed for a deeper understanding of the transcriptome of a region of interest with a spatial context. In order to explore the spatial dynamics that contribute to cell differentiation during branching morphogenesis of the mouse lung, we applied a spatial transcriptomics technology, deterministic barcoding in tissue for spatial omics sequencing (DBiT-seq), to sections of the mouse lung at timepoints E11.5-E.13.5. The DBiT-seq replicates were integrated with scRNA-seq data from published papers to form an object for transcriptomic analysis. The presence of DBiT-seq data and scRNA-seq data from the same region and timepoints allowed for comparison of the two technologies. DBiT-seq data has a higher and more variable proportion of unspliced mRNA. There is also a set of genes that is expressed only in the DBiT-seq data, which does not align with the fact that DBiT-seq has less sampling-depth. Investigation of this gene set revealed that these genes were smaller and enriched for septins. Spatial mapping of the gene set to the replicates reveals no clear localization pattern. An optimization step added to our DBiT-seq protocol was hypothesized to be the reason why these genes were captured. However, comparison of this gene set to that of an older DBiT-seq paper which lacked the optimization step ruled this theory out.