Cedars-Sinai Parker Lab - CAM_DIA_FPplex

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LAD_DIA_openSWATH_Result_2020-02-19_18-30-05.sky.zip2020-02-25 09:59:402,19213,44914,67288,03299
AA_DIA_openSWATH_Result_2020-02-19_10-27-32.sky.zip2020-02-25 08:16:011,83111,31212,32473,94499
DIA_FPplex_Run2_13Oct17_PoolQC_reinject_2019-11-13_15-22-45.sky.zip2019-12-12 14:08:104376100262109
Skyline_PureSample_VisualizationOpenSwath_2019-11-25_13-31-23.sky.zip2019-12-12 14:08:101,85611,37712,60275,61210
Identification of Putative Early Atherosclerosis Biomarkers by Unsupervised Deconvolution of Heterogeneous Vascular Proteomes

  • Organism: Homo sapiens
  • Instrument: TripleTOF 6600,QTRAP 6500+
  • SpikeIn: No
  • Keywords: Atherosclerosis, Tissue Fibrous Plaque proteome
  • Lab head: Sarah Parker
Abstract
Coronary artery disease remains a leading cause of death in industrialized nations, and early detection of disease is a critical intervention target in order to effectively treat patients and manage risk. Proteomic analysis of mixed tissue homogenates may obscure subtle protein changes that occur uniquely in underlying tissue subtypes. The unsupervised ‘convex analysis of mixtures’ (CAM) tool has previously been shown to effectively segregate cellular subtypes from mixed expression data. In this study, we hypothesized that CAM would identify proteomic information specifically informative to early atherosclerosis lesion involvement that could lead to potential markers of early disease detection. We quantified the proteome of 99 paired Abdominal Aorta (AA) and Left Anterior Descending Coronary Artery (LAD) specimens (N=198 specimens total) acquired during autopsy of young adults free of diagnosed cardiac disease. The CAM tool was then used to segregate protein subsets uniquely associated with different underlying tissue types, yielding markers of normal and fibrous plaque (FP) tissues in LAD and AA (N=62 lesions markers). CAM-derived FP marker expression was validated against pathologist estimated luminal surface involvement of FP, as well as in an orthogonal cohort of ‘pure’ fibrous plaque, fatty streak, and normal vascular specimens. A targeted mass spectrometry (MS) analysis quantified 39 of 62 CAM-FP markers in plasma from women with angiographically verified coronary artery disease (CAD, N=46) or free from apparent CAD (control, N=40). Elastic net variable selection with logistic regression reduced this list to 10 proteins capable of classifying CAD status in this cohort with <6% misclassification error, and a mean area under the receiver operating characteristic curve of 0.992 (Confidence Interval 0.968-0.998) after cross validation. The proteomics-CAM workflow identified lesion-specific molecular biomarker candidates by distilling the most representative molecules from heterogenous tissue types.
Experiment Description
DIA-MS data from human vascular tissues with variable lesion composition was analyzed by CAM to identify putative fibrous plaque marker proteins. These CAM-identified markers were validated in pathologist-identified 'pure' samples of normal, fatty streak, and fibrous plaque lesions by a second DIA-MS analysis, the raw data from which are shown here. Targeted MRM data were captured by first querying all peptides of CAM-FP proteins from the DIA-MS library for detectability in a generic human plasma DIA-MS file. Then, the strongest performing peptides were selected and built into a scheduled, Tier 3 level MRM experiment. This scheduled MRM experiment was run on N=86 human plasma samples from post-menopausal women with (N=46) or without (N=40) angiographically verified Coronary Artery Disease (CAD). NOTE: while select SIS heavy peptides were spiked into and monitored for a handful of proteins in this experiment, these data were not used for this body of work.
Sample Description
Human aortic specimens containing no lesion (N=3), or entire luminal surface area involvement of fatty streak (N=3) or fibrous plaque (N=4) Human Plasma from women with (N=46) and without (N=40) verified CAD.
Created on 12/12/19, 2:30 PM

This data is available under the CC BY 4.0 license.