Estimating cell-type specific gene expression in mouse spinal cord injury through deconvolution of bulk RNA-seq data

Advancements in single-cell RNA-Sequencing (scRNA-Seq) have allowed for the characterization of individual cell-type gene expression profiles. However, adult nerve cells exposed to a traumatic injury, such as cells in the spinal cord, are difficult to keep alive and viable for scRNA-Seq after isolation, making it difficult to study individual cell-type response to injury. Here, we use computational methods to deconvolve bulk RNA-Seq data obtained from mixtures of cells in the injured mouse spinal cord into individual cell types using healthy mouse scRNA-Seq Data. Through this deconvolution, we deduce that the mixtures mainly consist of neurons, oligodendrocytes, and astrocytes which make up approximately 54%, 24%, and 16% of the total cell population, respectively. These cell proportions and the differential gene expression between mixtures are then used to estimate the changes in cell-type specific gene expression between experimental conditions. The resulting gene expression profiles are then compared in a differential gene expression analysis (DGE) to provide evidence of the biological effects of gene therapies on neuron, oligodendrocyte, and microglia cell populations. Through the DGE analysis, we identified an average of 650 differentially expressed genes in neurons, 147 in oligodendrocytes, and 40 in microglia across experimental conditions. This approach provides an accessible and useful method for identifying the gene expression profiles of various cell-types after injury.M.S.Includes bibliographical reference