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An Integrated Quantitative Proteomics Workflow for Biomarker Discovery and Validation in Plasma

ABSTRACT: Blood plasma is one of the most widely used samples for biomarker discovery research as well as clinical investigations for diagnostic and therapeutic purposes. However, the plasma proteome is extremely complex due to its wide dynamic range of protein concentrations and the presence of high-abundance proteins. Here we are describing an optimized integrated quantitative proteomics pipeline combining the label-free and multiplexed-labeling-based (Isobaric tags for relative and absolute quantitation) proteome profiling methods for biomarker discovery, followed by the targeted approaches for validation of the identified potential marker proteins. In this workflow, the targeted quantitation of proteins is carried out by multiple-reaction monitoring (MRM) and parallel-reaction monitoring (PRM) mass spectrometry. Thus, our approach enables both unbiased screenings of biomarkers and their subsequent selective validation in human plasma. The overall procedure takes only 1–2 days to complete including the time for data acquisition (excluding database searching). This protocol is quick, flexible and eliminates the need for a separate immunoassay-based validation workflow in blood biomarker investigations.

INSTRUMENT(S): Orbitrap Fusion

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Plasma

SUBMITTER: Vipin Kumar

LAB HEAD: Prof. Sanjeeva Srivastava

PROVIDER: PXD017834 | Pride | 2020-09-07

REPOSITORIES: Pride

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			Action	DRS
	03112019_Fusion_1050_VK_Plasma_LFQ_120min_AR1.msf	Msf
	03112019_Fusion_1050_VK_Plasma_LFQ_120min_AR1.pdResult	Other
	03112019_Fusion_1050_VK_Plasma_LFQ_120min_AR1.raw	Raw
	03112019_Fusion_1050_VK_Plasma_LFQ_120min_AR2.raw	Raw
	03112019_Fusion_1050_VK_Plasma_LFQ_120min_AR3.raw	Raw

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Project description:Background Hypertrophic cardiomyopathy (HCM) is defined clinically by pathological left ventricular hypertrophy (LVH). We have previously developed a plasma proteomics biomarker panel that correlates with clinical markers of disease severity and sudden cardiac death (SCD) risk in adult patients with HCM. The aim of this study was to validate the adult biomarkers and perform new discovery proteomics in childhood-onset HCM. Methods Fifty-nine protein biomarkers were identified from an exploratory plasma proteomics screen in children with HCM and augmented into our existing multiplexed targeted liquid chromatography-tandem/mass spectrometry-based assay. The association of these biomarkers with clinical phenotypes and outcomes was prospectively tested in plasma collected from 148 children with HCM and 50 healthy controls. Machine learning techniques were used to develop novel paediatric plasma proteomic biomarker panels.Results Four previously validated adult HCM markers (Aldolase Fructose-Bisphosphate A, Complement C3a, Talin-1 and Thrombospondin 1) and three new markers (Glycogen Phosphorylase B, Lipoprotein A and Profilin 1) were elevated in paediatric HCM. Using supervised machine learning applied to training (n=137) and validation cohorts (n=61), this 7-biomarker panel differentiated HCM from healthy controls with an area under the curve of 1.0 in the training dataset (sensitivity 100% [95% confidence interval: 95–100]; specificity 100% [96–100]) and 0.82 in the validation dataset (sensitivity 75% [59–86]; specificity 88% [75–94]). Reduced circulating levels of 4 other peptides (Apolipoprotein L1-, Complement 5b-, Immunoglobulin Heavy Constant Epsilon- and Serum Amyloid A4-peptide) found in children with high SCD risk provided complete separation from the low and intermediate risk groups, and predicted mortality and adverse cardiovascular outcomes (hazard ratio 1.53 [1.2 –2.0], p = 0.001). ConclusionIn children, a 7-biomarker proteomics panel can distinguish HCM from controls with high sensitivity and specificity, and a second 4-biomarker panel identifies those at high risk of adverse outcomes, including sudden cardiac death.

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An Integrated Quantitative Proteomics Workflow for Biomarker Discovery and Validation in Plasma

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