Analysis of cell-specific transcriptional responses in human colon tissue using CIBERSORTx

Diet is an important determinant of overall health, and has been linked to the risk of various cancers. To understand the mechanisms involved, transcriptomic responses from human intervention studies are very informative. However, gene expression analysis of human biopsy material only represents the average profile of a mixture of cell types that can mask more subtle, but relevant cell-specific changes. Here, we use the CIBERSORTx algorithm to generate single-cell gene expression from human multicellular colon tissue. We applied the CIBERSORTx to microarray data from the PHYTOME study, which investigated the effects of different types of meat on transcriptional and biomarker changes relevant to colorectal cancer (CRC) risk. First, we used single-cell mRNA sequencing data from healthy colon tissue to generate a novel signature matrix in CIBERSORTx, then we determined the proportions and gene expression of each separate cell type. After comparison, cell proportion analysis showed a continuous upward trend in the abundance of goblet cells and stem cells, and a continuous downward trend in transit amplifying cells after the addition of phytochemicals in red meat products. The dietary intervention influenced the expression of genes involved in the growth and division of stem cells, the metabolism and detoxification of enterocytes, the translation and glycosylation of goblet cells, and the inflammatory response of innate lymphoid cells. These results show that our approach offers novel insights into the heterogeneous gene expression responses of different cell types in colon tissue during a dietary intervention.