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. 2018 Dec;9(6):590-599.
doi: 10.1007/s12975-018-0609-z. Epub 2018 Jan 24.

Differential Proteomics for Distinguishing Ischemic Stroke from Controls: a Pilot Study of the SpecTRA Project

A M Penn  1 , V Saly  1 , A Trivedi  1 , M L Lesperance  2 , K Votova  3   4 , A M Jackson  5 , N S Croteau  2   6 , R F Balshaw  7   8 , M B Bibok  6 , D S Smith  5 , K K Lam  7 , J Morrison  6 , L Lu  2   6 , S B Coutts  9 , C H Borchers  5   10
Affiliations

Differential Proteomics for Distinguishing Ischemic Stroke from Controls: a Pilot Study of the SpecTRA Project

A M Penn et al. Transl Stroke Res. 2018 Dec.

Abstract

A diagnostic blood test for stroke is desirable but will likely require multiple proteins rather than a single "troponin." Validating large protein panels requires large patient numbers. Mass spectrometry (MS) is a cost-effective tool for this task. We compared differences in the abundance of 147 protein markers to distinguish 20 acute cerebrovascular syndrome (ACVS) patients who presented to the Emergency Department of one urban hospital within < 24 h from onset) and from 20 control patients who were enrolled via an outpatient neurology clinic. We targeted proteins from the stroke literature plus cardiovascular markers previously studied in our lab. One hundred forty-one proteins were quantified using MS, 8 were quantified using antibody protein enrichment with MS, and 32 were measured using ELISA, with some proteins measured by multiple techniques. Thirty proteins (4 by ELISA and 26 by the MS techniques) were differentially abundant between mimic and stroke after adjusting for age in robust regression analyses (FDR < 0.20). A logistic regression model using the first two principal components of the proteins significantly improved discrimination between strokes and controls compared to a model based on age alone (p < 0.001, cross-validated AUC 0.93 vs. 0.78). Significant proteins included markers of inflammation (47%), coagulation (40%), atrial fibrillation (7%), neurovascular unit injury (3%), and other (3%). These results suggest the potential value of plasma proteins as biomarkers for ACVS diagnosis and the role of plasma-based MS in this area.

Keywords: Hematologic tests; Infarction; Mass spectrometry; Plasma proteins; Proteomics; Stroke.

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Conflict of interest statement

Conflict of Interest

The authors declare they have no conflicts of interest.

Research Involving Human Participants

ᅟ

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institution and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study. Legally authorized representatives for consent were allowed.

Figures

Fig. 1
Fig. 1
Scatterplot of enriched MRM-MS quantitation of S100A12 (log2 relative area) versus corresponding ELISA-based measurements (log2 abundance) for 20 strokes (triangle) and 20 controls (circle). The Pearson sample correlation is r = 0.82
Fig. 2
Fig. 2
The first two principal components, PC1, PC2, of the 30 differentially abundant proteins clearly separate the strokes (triangle) and controls (circle)
Fig. 3
Fig. 3
Receiver operating characteristic (ROC) plot adjusted by leave-one-out cross-validation comparing logistic classifiers based on age alone (blue) and age plus the first two principal components of the differentially expressed proteins (red). The 95% confidence interval (CI) for AUC is shown
Fig. 4
Fig. 4
Functional interaction network of differentially abundant proteins visualized using STRING; interactions are coded by color and effects. See Table 3 for protein symbol reference
See this image and copyright information in PMC

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