前胶原肽酶
色谱法
化学
蛋白质前体
串联质谱法
免疫分析
质谱法
肽
分子生物学
生物化学
生物
抗体
基因
免疫学
作者
Huu‐Hien Huynh,Lili Barahona‐Carrillo,Danielle A. Moncrieffe,David Cowan,Katrina Forrest,Jessica O. Becker,Michelle A. Emrick,Andreas Thomas,Mario Thevis,Daniel Eichner,Peter H. Byers,Geoffrey D. Miller,Andrew N. Hoofnagle
摘要
The amino-terminal propeptide of type III procollagen (P-III-NP) is used with IGF-I to detect the illicit use of growth hormone and to monitor growth hormone therapy. However, the only currently available assays for P-III-NP are immunoassays, which are not well harmonized. In addition, other fragments of type III procollagen may better evaluate collagen turnover. We aimed to develop a high-throughput assay using immunoaffinity enrichment coupled to ultra-high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) to quantify peptides belonging to three different regions of type III procollagen in human serum simultaneously. To facilitate higher throughput, we transferred the assay from microcentrifuge tubes to a 96-well plate format with partially automated pipetting. The method was linear (Pearson's R ≥ 0.994) over an estimated concentration range of 1.35-13.3 nM, 0.04-2.28 nM, and 0.26-5.1 nM for each surrogate peptide of P-III-NP, collagen degradation products, and the carboxyl-terminal propeptide, respectively. Intra-day and inter-day imprecision were both < 13.6%, and the results of robustness testing were also encouraging. The method was successfully applied to capillary blood samples obtained using Tasso+ microsampling devices. Modest correlation of P-III-NP concentration was observed between our new method and a WADA-approved immunoassay (N = 40, Pearson's R = 0.789) with a significant bias of -87.8%. Our method simultaneously quantifies four peptides belonging to three regions of type III procollagen in human serum. High bias between assays highlights the need for common higher-order calibrators or reference materials to help improve the comparability of results across laboratories.
科研通智能强力驱动
Strongly Powered by AbleSci AI