Evaluation of plasma sample preparation methods for high-throughput mass spectrometry based proteomics and application to colorectal cancer

Introduction and Objectives The significance of plasma as a source of potential biomarkers is immense. In recent years, methods for high-throughput plasma proteomics based on mass spectrometry have emerged that enable profiling of large clinical cohorts. The aim of our project is to compare and implement a high-throughput plasma proteomics workflow that enables profiling at a depth of >500 proteins per sample in single shot.

Methods Plasma processing methods of interest enable sample processing at a throughput of 30 samples per day and compatible with low sample input (ideally a few ml). Sample preparation methods that are being explored: 1. In-solution digestion; 2. Suspension Traping filter (Strap)-based sample processing (Li et al., 2023); and 3. a precipitation method using perchloric acid (Viode et al., 2023). To this end, aliquots of one donor sample will be used for analysis of workflow triplicates. Moreover, we explored timsTOF high-throughput quantitative performance using different plasma protein input (400ng, 500ng, 600ng).

Results To determine the most optimal workflow, the data are being compared in terms of total number of protein identifications, unique identifications, reproducibility of protein identification and quantification. We attempt several alterations and their combinations to ensure the Viode et al method can be tailored for our requirements, the precipitation was carried out as described in the article, with volume adjustments when using HLB cartridges. So far our results show that the Perchloric acid (perCA) depletion method yields the highest number of proteins (330 proteins) per sample at good reproducibility (CV=13%). Since the aim amount of proteins is over 500, we will try to use DIA instead of DDA to analyze plasma proteins.

Conclusion The best performing method will be applied to a clinical cohort of plasma samples of patients with colorectal cancer and control subjects to explore the detection of previously defined colon cancer secretome biomarker candidates.