Unveiling hidden depths: A high-throughput plasma proteomics workflow on Orbitrap astral mass spectrometer for enhanced biomarker discovery

Introduction: Cancer is the second leading cause of death, accounting for millions of deaths annually. Late diagnosis hinders prognoses and highlights the urgency for improved early detection tools. Existing diagnostic tests lack sensitivity or require invasive biopsies. While analyzing blood plasma for cancer biomarkers holds promise, complex workflows involved limit progress. We address these challenges by developing a high-throughput plasma proteomics workflow on the cutting-edge Thermo Scientific™ Orbitrap™ Astral™ mass spectrometer, which yields the deepest plasma proteome coverage yet. We have evaluated the performance of this workflow in a mini cohort of diverse cancer and non-cancer samples. This paves the way for rapid cancer cohort and translational research studies, offering the depth needed for early disease detection through comprehensive plasma analysis.

Methods: Plasma samples were processed with a standard plasma proteomics workflow using the Proteograph XT Assay from Seer or top 14 High Select™ Depletion Spin Columns from Thermo Fisher Scientific. Resulting peptides were then analyzed using a robust liquid chromatography (LC) method with EASY-Spray™ PepMap™ Neo columns on a Vanquish™ Neo UHPLC system coupled to the new Orbitrap Astral mass spectrometer in Data Independent Acquisition (DIA) mode with few variations of LC-MS methods which provided a variety of high- throughput options to enable translational researchers to choose a workflow optimized for their cohort size and preferred turnaround time. The LC-MS data analysis was done using Thermo Scientific™ Proteome Discoverer software 3.1 and Seer"s Proteograph Analysis Suite.

Preliminary Data: Our preliminary results show over 700 protein groups identified in neat plasma with the standard high-throughput proteomics workflows through LFQ-DIA analysis on an Orbitrap Astral mass spectrometer. The top 14 depletion column enables identification of 1500 protein groups, which doubled the plasma proteome coverage without sacrificing the throughput, showing an economical method to capture subtle disease biomarkers. When coupled with the Proteograph XT Assay sample processing, we were able to identify 7,000 protein groups, which represents a 10-fold increase in proteome coverage. We also evaluated the quantitation accuracy by mixing different quantities of human and chicken plasma. The results showed that we were able to obtain a ratio that is close to the expected ratio, highlighting the quantitation accuracy from our plasma proteomics workflow.

Together, the novel DIA LC-MS workflow represents a powerful combination of scale and depth of coverage, empowering the development of improved classification models and enhancing biomarker discovery through robust and larger statistically powered early-stage biomarker discovery studies.