Complementary proteome and glycoproteome access revealed through comparative analysis of reversed phase and porous...

Continual developments in instrumental and analytical techniques have aided in establishing rigorous connections between protein glycosylation and human illness. These illnesses, such as various forms of cancer, are often associated with poor prognoses, prompting the need for more comprehensive characterization of the glycoproteome. While innovative instrumental and computational strategies have largely benefited glycoproteomic analyses, less attention is given to benefits gained through alternative, optimized chromatographic techniques. Porous graphitic carbon (PGC) chromatography has gained considerable interest in glycomics research due to its mobile phase flexibility, increased retention of polar analytes, and improved structural elucidation at higher temperatures. PGC has yet to be systematically compared against or in tandem with standard reversed phase liquid chromatography (RPLC) in high-throughput bottom-up glycoproteomic experiments, leaving the potential benefits unexplored. Performing comparative analysis of single and biphasic separation regimes at a range of column temperatures illustrates complementary advantages for each method. PGC separation is shown to selectively retain shorter, more hydrophilic glycopeptide species, imparting higher average charge, and exhibiting greater microheterogeneity coverage for identified glycosites. Additionally, we demonstrate that liquid-phase separation of glycopeptide isomers may be achieved through both single and biphasic PGC separations, providing a means towards facile, multidimensional glycopeptide characterization. Beyond this, we demonstrate how utilization of multiple separation regimes and column temperatures can aid in profiling the glycoproteome in tumorigenic and aggressive prostate cancer cells. RAW MS proteomic and glycoproteomic datasets have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD024196 (10.6019/PXD024196) and PXD024195, respectively.