Integration of transcriptomic and proteomic data identifies biological functions in cell populations from human infant lung

摘要

Systems biology uses computational approaches to integrate diverse data types to understand cell and organ behavior. Data derived from complementary technologies, for example transcriptomic and proteomic analyses, are providing new insights into development and disease. We compared tnRNA and protein profiles from purified endothelial, epithelial, immune, and mesenchy-mal cells from normal human infant lung tissue. Signatures for each cell type were identified and compared at both mRNA and protein levels. Cell-specific biological processes and pathways were predicted by analysis of concordant and discordant RNA-protein pairs. Cell clustering and gene set enrichment comparisons identified shared versus unique processes associated with transcriptomic and/or proteomic data. Clear cell-cell correlations between mRNA and protein data were obtained from each cell type. Approximately 40 of RNA-protein pairs were coherently expressed.