No abstract

We established a robust capillary-flow data-independent acquisition MS platform capable of measuring 31 plasma proteomes per day without the need of repeated acquisition of the same sample. We acquired 1508 samples of the DiOGenes study (multicentered, Europa-wide caloric restriction weight loss and maintenance study of overweight and obese, non-diabetic participants). This was achieved using a single analytical column. Comprehensive biological reactions to weight loss and maintenance were observed.

Over the past decade, mass spectrometric performance has greatly improved in terms of sensitivity, dynamic range, and speed. By contrast, only limited progress has been accomplished with regard to automation, throughput, and robustness of the proteomic sample preparation process upstream of mass spectrometry. The present work delivers an optimized analysis of human plasma samples in both small preclinical and large clinical studies, enabled by the development of a highly automated quantitative proteomic workflow. Several iterative evaluation and validation steps were performed before process "design freeze" and development completion. A robotic liquid handling workflow and platform (including reduction, alkylation, digestion, TMT labeling, pooling, and purification) were shown to provide better quantitative trueness and precision than manual operation at the bench. Depletion of the most abundant human plasma proteins and subsequent buffer exchange were also developed and integrated. Finally, 96 identical pooled human plasma samples were prepared in a 96-well plate format, and each sample was individually subjected to our developed workflow. This test revealed increased throughput and robustness compared with to-date published manual or less automated workflows. Our workflow is ready-to-use for future (pre-) clinical studies. We expect our work to facilitate, accelerate, and improve clinical proteomic discovery in human blood plasma.

The overall impact of proteomics on clinical research and its translation has lagged behind expectations. One recognized caveat is the limited size (subject numbers) of (pre)clinical studies performed at the discovery stage, the findings of which fail to be replicated in larger verification/validation trials. Compromised study designs and insufficient statistical power are consequences of the to-date still limited capacity of mass spectrometry (MS)-based workflows to handle large numbers of samples in a realistic time frame, while delivering comprehensive proteome coverages. We developed a highly automated proteomic biomarker discovery workflow. Herein, we have applied this approach to analyze 1000 plasma samples from the multicentered human dietary intervention study "DiOGenes". Study design, sample randomization, tracking, and logistics were the foundations of our large-scale study. We checked the quality of the MS data and provided descriptive statistics. The data set was interrogated for proteins with most stable expression levels in that set of plasma samples. We evaluated standard clinical variables that typically impact forthcoming results and assessed body mass index-associated and gender-specific proteins at two time points. We demonstrate that analyzing a large number of human plasma samples for biomarker discovery with MS using isobaric tagging is feasible, providing robust and consistent biological results.