bioRxivBiochemistry
Development and Validation of an LC-MS/MS Based Quantitative Assay for Marmoset Insulin
R. W. Goy, H. K. Shrestha, T. E. Ziegler, N. J. Dukes, R. J. Colman, A. Kapoor
Abstract
IntroductionInsulin is a peptide hormone that is secreted by the {beta} cells of the pancreas and is essential to the metabolism of carbohydrates, fats, and proteins in the body. The marmoset insulin peptide is not homologous with human insulin and therefore commonly available assays do not work for this species. Due to the increasing popularity of marmoset research, a simple, specific assay for the quantitation of marmoset insulin is needed. This study aimed to develop and validate a bottom-up proteomic workflow with trypsin digestion and analysis using LC coupled with triple quadrupole mass spectrometry (LC-MS/MS). MethodsMarmoset serum proteins were subjected to denaturation, reduction, and enzymatic cleavage to extract a unique, 7 amino acid peptide for quantitation of marmoset insulin.Resolution of the peptide was achieved by LC-MS/MS using electrospray ionization operating in positive mode. Calibration was achieved by aliquot dilution of fully synthetic marmoset insulin tryptic peptide into macaque serum. A stable-isotope labeled (13C, 15N) synthetic marmoset insulin tryptic peptide was used as internal standard. ResultsThe assay was fully validated according to bioanalytical method validation guidelines for linearity, precision, and dilution linearity using purified marmoset insulin. The limit of detection was 4.28 pmol/L and the limit of quantitation was 8.62 pmol/L. Biological validation was achieved by comparison of samples previously run by radioimmunoassay and measurement of the marmoset insulin response to glucose via an oral glucose tolerance test (OGTT). The physiological range of marmoset insulin was shown to be 84.5 to 1222 pmol/L. ConclusionsThis paper presents a simple, reproducible method to measure marmoset insulin using LC-MS/MS.
- organism: mouse
- assay: proteomics
- research focus: protein structure prediction
- approach: experimental assay
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