Abstract
We have developed a novel plasma protein analysis platform with optimized sample preparation, chromatography, and MS analysis protocols. The workflow, which utilizes chemical isobaric mass tag labeling for relative quantification of plasma proteins, achieves far greater depth of proteome detection and quantification while simultaneously having increased sample throughput than prior methods. We applied the new workflow to a time series of plasma samples from patients undergoing a therapeutic, "planned" myocardial infarction for hypertrophic cardiomyopathy, a unique human model in which each person serves as their own biologic control. Over 5300 proteins were confidently identified in our experiments with an average of 4600 proteins identified per sample (with two or more distinct peptides identified per protein) using iTRAQ four-plex labeling. Nearly 3400 proteins were quantified in common across all 16 patient samples. Compared with a previously published label-free approach, the new method quantified almost fivefold more proteins/sample and provided a six- to nine-fold increase in sample analysis throughput. Moreover, this study provides the largest high-confidence plasma proteome dataset available to date. The reliability of relative quantification was also greatly improved relative to the label-free approach, with measured iTRAQ ratios and temporal trends correlating well with results from a 23-plex immunoMRM (iMRM) assay containing a subset of the candidate proteins applied to the same patient samples. The functional importance of improved detection and quantification was reflected in a markedly expanded list of significantly regulated proteins that provided many new candidate biomarker proteins. Preliminary evaluation of plasma sample labeling with TMT six-plex and ten-plex reagents suggests that even further increases in multiplexing of plasma analysis are practically achievable without significant losses in depth of detection relative to iTRAQ four-plex. These results obtained with our novel platform provide clear demonstration of the value of using isobaric mass tag reagents in plasma-based biomarker discovery experiments.
Highlights
From the ‡Broad Institute of MIT and Harvard, 415 Main St., Cambridge, Massachusetts 02142; §Massachusetts General Hospital, 55 Fruit St., Boston, Massachusetts 02114
To reconcile the vast dynamic range and complexity of plasma with the hypothesis that disease-specific markers are likely to be of relatively low abundance, plasma samples for biomarker discovery are often extensively processed before liquid chromatography tandem MS (LC-MS/MS)1 analysis
As an example of the merits and limitations of such a strategy, we have reported previously on plasma-based biomarker discovery for myocardial injury using a label-free workflow consisting of depletion of the most abundant plasma proteins followed by digestion and extensive fractionation of peptides by SCX chromatography prior to LC-MS/MS analysis [4]
Summary
Typical approaches to improve depth of detection in plasma include immunoaffinity-based depletion of abundant proteins and offline chromatography at the protein or peptide level using approaches based on separation techniques that differ from the final RP-HPLC separation into the mass spectrometer (e.g. strong cation exchange chromatography (SCX), size exclusion chromatography, or reversed phase chromatography at basic pH). Increasing the fraction number in plasma discovery experiments has been shown to increase both the total number of peptides/proteins confidently identified and the relative enrichment of low abundance proteins, [1] with accumulating data supporting reversed phase at basic pH as a effective fractionation strategy [1,2,3] Such intensive processing of individual samples improves depth of coverage at the dual cost of increased preanalytical variability and decreased throughput. Detection of up to 900 proteins per sample time point was achieved by fractionation into 80 sub-samples and analyzing each using a 90 min effective gradient (150 min inject-to-inject) Such extensive fractionation markedly constrained the number of samples that could be analyzed, and the attendant pre-analytical variability contributed to the determination that only relatively large fold-changes in abundance (Ͼ5ϫ) could be reliably distinguished by this label-free approach. We deployed and tested these process improvements in the context of ongoing discovery and verification of plasma biomarkers of acute myocardial injury, using the planned myocardial infarction model described previously [4]
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