Human pluripotent stem cells (hPSCs) are invaluable tools in regenerative medicine, drug development, and disease modelling due to their ability to differentiate into any cell type in the human body and self-renew indefinitely. Although the primary factors that control pluripotency have been identified, there are still significant knowledge gaps regarding the regulation of pluripotency, particularly through proteomic signaling and interactions within the microenvironment. To bridge this gap, this study leverages computational proteomics and proteogenomics analysis of hPSCs, using both the total proteome and the secretome.

We have established a computational proteogenomics workflow that integrates DNA- and RNA-seq data with mass spectrometry proteomics data from the same samples, allowing construction of a tailored database to enhance the interpretation of the mass spectra. The workflow enables revealing pluripotency-associated protein variants that may be missed using conventional methods. In particular, our analysis revealed several differentially expressed proteins in the total proteome and in the secretome of hPSCs, indicating multiple uncharacterised pluripotency-associated factors. These findings emphasize the value of computational techniques in proteogenomics to provide deeper insights into the regulation of hPSCs.