An Open-Source Tool for Investigation of Differential RNA Expression Between Spinal Cord Cells of Male and Female Mice

Abstract

Chronic pain is a highly debilitating condition that differs by type, prevalence, and severity between men and women. To uncover the molecular underpinnings of these differences, it is critical to analyze the transcriptomes of spinal cord pain-processing networks for both sexes. Despite several recently published single-nucleus RNA-sequencing (snRNA-seq) studies on the function and composition of the mouse spinal cord, a gene expression analysis investigating the differences between males and females has yet to be performed. Here, we combined data from three different large-scale snRNA-seq studies, which used sex-identified adult mice. Using SeqSeek, we classified more than 37,000 unique viable cells within predicted cell types with the use of machine learning. We then utilized DESeq2 to identify significant differentially expressed genes (DEGs) between males and females in a variety of cell populations, including superficial dorsal horn (SDH) neurons. We found a large number of DEGs between males and females in all cells, in neurons, and in SDH neurons of the mouse spinal cord, with a greater level of differential expression in inhibitory SDH neurons compared to excitatory SDH neurons. The results of these analyses are available on an open-source web-app: https://justinbellavance.shinyapps.io/snRNA_Visualization/. Lastly, we used gene set enrichment analysis to identify sex-enriched pathways from our previously identified DEGs. Through this, we have identified specific genetic players within the rodent spinal cord that diverge between males and females, which may underlie reported sex differences in spinal nociceptive mechanisms and pain processing.