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An MRM-Based Multiplexed Quantification Assay for Human Adipokines and Apolipoproteins
An MRM-Based Multiplexed Quantification Assay for Human Adipokines and Apolipoproteins
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An MRM-Based Multiplexed Quantification Assay for Human Adipokines and Apolipoproteins
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An MRM-Based Multiplexed Quantification Assay for Human Adipokines and Apolipoproteins
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An MRM-Based Multiplexed Quantification Assay for Human Adipokines and Apolipoproteins
An MRM-Based Multiplexed Quantification Assay for Human Adipokines and Apolipoproteins
Journal Article

An MRM-Based Multiplexed Quantification Assay for Human Adipokines and Apolipoproteins

2020
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Overview
Adipokines and apolipoproteins are key regulators and potential biomarkers in obesity and associated diseases and their quantitative assessment is crucial for functional analyses to understand disease mechanisms. Compared to routinely used ELISAs, multiple reaction monitoring (MRM)-based mass spectrometry allows multiplexing and detection of proteins for which antibodies are not available. Thus, we established an MRM method to quantify 9 adipokines and 10 apolipoproteins in human serum. We optimized sample preparation by depleting the two most abundant serum proteins for improved detectability of low abundant proteins. Intra-day and inter-day imprecision were below 16.5%, demonstrating a high accuracy. In 50 serum samples from participants with either normal weight or obesity, we quantified 8 adipokines and 10 apolipoproteins. Significantly different abundances were observed for five adipokines (adipsin, adiponectin, chemerin, leptin, vaspin) and four apolipoproteins (apo-B100/-C2/-C4/-D) between the body mass index (BMI) groups. Additionally, we applied our MRM assay to serum samples from normal weight children and human adipocyte cell culture supernatants to proof the feasibility for large cohort studies and distinct biological matrices. In summary, this multiplexed assay facilitated the investigation of relationships between adipokines or apolipoproteins and phenotypes or clinical parameters in large cohorts, which may contribute to disease prediction approaches in the future.

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