Comprehensive and Scalable Highly Automated MS-Based Proteomic Workflow for Clinical Biomarker Discovery in Human Plasma

Abstract

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.

Analysis of the plasma proteome using iTRAQ and TMT‐based Isobaric labeling

“…are performed. Dayon et al demonstrated the scalability of depletion, TMT labeling and ESI analysis, with cited a throughput of 2 × 96 samples in 3 weeks and 1005 samples in 15 weeks . Such measurements would allow plasma proteomics to catch up to some degree with genomics measurements.…”

“…Using samples collected from a large study cohort, Cominetti et al analyzed in the order of 1000 plasma samples from 525 individuals enrolled in a study related to diet, obesity and genetics (DiOGenes project) . Using a semi‐automated platform and work flow (presented in an earlier publication by Dayon et al ( vide supra ), a total of 1005 samples were analyzed. They observed that CRP and S100A9 were correlated with BMI and that pregnancy zone protein (PZP), SHBG and APOC‐II provided potential gender discrimination.…”

Quantitative, multiplexed workflow for deep analysis of human blood plasma and biomarker discovery by mass spectrometry

“…This unfortunate tradeoff between depth and throughput has meant that studies in plasma proteomics that include relatively large numbers of samples (many tens to >100) typically have shallow depth of coverage (e.g., Cominetti et al 17 and Dayon et al 18 ), whereas studies emphasizing depth of coverage typically have small sample numbers (e.g., refs. 19–21 ).…”

“…After the depletion of six abundant proteins, Cole et al performed 8-plex iTRAQ labeling and SCX separation into 24 fractions, resulting in the quantification of ~900 proteins across 500 patient plasma samples spanning 72 iTRAQ experiments 34 . In the largest patient study yet reported, Cominetti et al applied an automated plasma processing workflow that included depletion of 14 abundant proteins, 6-plex TMT peptide labeling and online LC–MS/MS analysis without fractionation to >1,000 patient samples 17,18 . Although each sample required only 3 h of instrument time, this study identified only 365 proteins in total across all samples.…”

Large-Scale and Deep Quantitative Proteome Profiling Using Isobaric Labeling Coupled with Two-Dimensional LC–MS/MS

“… 1 Incorporating the automated solutions in proteomics high throughput sample preparation needs to be tailored to the specific workflow and has to be determined by the number of samples, the size and type of these samples, experimental protocols applied, and instruments available in the laboratory [15]. It is problematic to establish a fully automated protocol in extensive and elaborate proteomics workflows due to all the variables in the multiple often disjointed sample preparation steps.…”