Combining a Suite of Rapid Profiling LC-MS/MS Methods and Ion Mobility Workflows to Investigate the Metabolome of Prostate Cancer Patients

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Adam King, Jade Talbot, Ian Wilson, Robert Plumb, Garth Maker, Lee Gethings, Tony Whetton, Paul Townsend
Murdoch University, Waters, The University of Manchester, Imperial College, University of Surrey
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Prostate cancer is ranked as the 6th leading cause of cancer related deaths globally and the second most common cancer in men. Currently, early detection of prostate cancer is determined using a PSA (prostate specific antigen) assay where increased concentration can be indicative of prostate cancer. Although, elevated PSA levels can also present in benign cases and inflammation. Therefore, further investigation is required to determine metabolic markers to reduce false positives. Identification of small molecule markers of disease statistically require analysis of large cohorts of samples from populations. Analysis of these can be time consuming and can impact on resource availability. This can be compounded when trying to obtain as much information as possible using complimentary separation techniques.        

Serum samples (n=350) from prostate cancer patients (disease and benign) and control sample donors were aliquoted (20 µL) into two 96 deep well plates for lipid and small molecule analysis. A pooled QC and phenotypic group pools were created from each sample. The lipid samples were extracted with isopropanol and small molecules with Water:MeCN. LC-MS data were collected by HDMSE data independent acquisition (DIA) mode in both positive and negative ESI with ion mobility separation with chromatographic separation comprised of rapid reversed-phase, HILIC and reversed-phase lipid chromatography using columns with 1 mm i.d.

Each batch consisted of 1157 injections for a single method and polarity. Using conventional methods (~10-minute gradient), the batch took ~8 days to complete. Using the rapid profiling methods, the batch was analysed within 3 days showing >60% reduction in acquisition time. With ion mobility, the number of detected peaks when compared to non-mobility data doubled. Furthermore, ion mobility generated collisional cross section (CCS) values for each compound, thereby improving the confidence of identifications when queried against in-house CCS libraries, covering both polar molecules and lipids.

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