Prostate cancer is a leading cause of cancer deaths for men in the U.S., with around 1 in 9 men being diagnosed with the disease each year. Numerous OMIC-based studies into the disease have been conducted, proposing potential markers. However, in order to provide a comprehensive and statistically valid data set, samples from a large cohort of individuals are required. This ultimately provides an analytical challenge, particularly for proteomics research, where nanoscale chromatography is routinely adopted. Here, we initially evaluate Cyclic IMS for the analysis of proteomic standards and then demonstrate high throughput strategies for proteomic profiling of plasma from prostate cancer individuals.
Samples of E.Coli and Human K562 tryptic digests were analysed using nanoscale LC at a flow rate of 300nL/min. For the plasma proteomics, eight pooled samples were created from 520 prostate cancer patients, with the pools corresponding to different disease states or treatments. These plasma samples were subjected to reduction, alkylation and trypsin digestion. Plasma digest samples were separated using 2.1mm scale chromatography at a flow rate of 150uL/min with a turnaround time of 25 minutes. Each liquid chromatography system was coupled to an cIMS oa-QTof mass spectrometer and data were obtained using an ion mobility enabled DIA method, HDMSE.
Optimum loading amounts were found to be 50 to 75ng for nanoscale LC coupled to Cyclic IMS, identifying approximately 4500 proteins in each injection and resulting in >4300 identifications for two out of three injections for the Human K562 sample, searched against a reviewed Uniprot Homo sapien database at 1% FDR. Investigations were also carried out to determine the benefits of multiple passes through the Cyclic IMS for discovery-based experiments.
The acquired dataset was imported and processed using both ProteinLynx Global Server and Progenesis QI for Proteomics (Waters Corporation) and searched against a Uniprot Homo sapien database. The samples were then assigned to their pooled groups, revealing a significant number of proteins with differential regulation between the sample groups. Proteins occurring in a minimum of two biological replicates with ANOVA p <0.05 were considered as significant and peptides associated with these were further analysed through Skyline (Macoss Lab) and EZInfo (Umetrics) where multivariate analysis showed clear separation between the different groups. Adoption of the high throughput schema demonstrated highly reproducible chromatography, with retention times typically within 1% coefficient of variation (CV). Curated data was then subjected to pathway analysis in order to provide their biological significance.