Multiplexed, Quantitative Workflow for Sensitive Biomarker Discovery in Plasma Yields Novel Candidates for Early Myocardial Injury

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.

Fig. 1.

Diagram of improved workflow for discovery proteomics in plasma. Samples from four different time points of planned MI (PMI) patients were depleted from abundant proteins, reduced, alkylated and digested by LysC/Trypsin. Following desalting, samples were labeled by four-plex iTRAQ reagent, and mixed after evaluating label incorporation. Sample was then fractionated using reversed phase chromatography at high pH into 30 pooled, concatenated fractions. Fractions were analyzed by data dependent analysis on a Q Exactive mass spectrometer using 75 μm picofrit columns packed in-house with 1.9 μm beads to 20 cm length. See Methods for details.

Fig. 2.

Identification summary statistics in the four PMI patient samples. Table enumerates the number of spectra collected, distinct peptides and proteins identified along with the FDR values achieved in four PMI patient samples as reported by Spectrum Mill (see “Methods” for details). Venn diagram shows the overlap of proteins quantified in four PMI patient samples. ^aProteins identified in at least two patients with two or more peptides. ^bSubset of identified proteins with two or more distinct peptides observed in at least one patient. ^cProtein subgroups (groups); that is, 5304 distinct protein subgroups were identified within 4555 protein groups. Proteins that share a detected distinct peptide (length >8) are combined into a group. A protein group is parsimoniously expanded to one or more subgroups to distinguish proteins that also have one or more distinct peptides that are not shared with the rest of the group, typically isoforms and family members.

Fig. 3.

Scatter plots of protein iTRAQ ratios. Protein iTRAQ ratios are plotted for a representative set of two patients for 10 min versus baseline (A), 1 h versus baseline (B), and 4 h versus baseline (C). Red dots indicate statistically significant proteins using moderated F-test with Benjamini-Hochberg corrected p value < 0.05 and these proteins are common across all scatter plots because they are determined using all patients and time points.

Fig. 4.

Cluster analysis of 333 regulated proteins. 323 out of the 333 regulated proteins (p-Value < 0.05) in iTRAQ discovery study are grouped in 5 distinct clusters using fuzzy C-means clustering (13). Each line represents temporal behavior of a protein over the time course. X-axis represents the time points (baseline, 10 min, 1 h, and 4 h), and Y-axis represents normalized protein abundance. Proteins were assigned to a cluster based on the membership value of > 0.7. Proteins with membership value in between 0.5 and 0.7 were not assigned to any cluster. Bar graph on the lower right corner shows the number of proteins in each cluster.

Fig. 5.

Correlation of iTRAQ discovery results with verification by iMRM. Line plots show change over the time course for ACLP1 (A), FHL1 (B) and FSTL1 (C) as observed in iTRAQ discovery (top panel) and in iMRM verification (bottom panel) experiments for 3 out of the 4 PMI patients shown in black, red, and blue colors. For iTRAQ plots used normalized median protein ratio at 10min/BL, 1 h/BL and 4 h/BL for ACLP1, FHL1 and FSTL1. For iMRM plots ratios were obtained using peptide concentrations calculated for baseline, 10min, 1 h, and 4 h based on light to heavy peptide ratios observed for DTPVLSELPEPVVAR (ACLP1), AIVAGDQNVEYK (FHL1), and IQVDYDGHCK (FSTL1) peptides.