Fully Automated Peptide Mapping Protocol for Multi-Attribute Method by Liquid Chromatography–Tandem Mass Spectroscopy with a High-Throughput Robotic Liquid Handling System

Author:

Qian Chen,Niu BenORCID,Jimenez Rod Brian,Wang Jihong,Albarghouthi Methal,Chen Xiaoyu

Abstract

ABSTRACTThe multi-attribute method (MAM) based on liquid chromatography–tandem mass spectroscopy is emerging as a powerful tool to directly monitor multiple product quality attributes simultaneously. Preparation of samples for MAM, however, is labor intensive, involving protein denaturation, disulfide bond reduction, free cysteine alkylation, and enzymatic digestion steps, which require significant analyst hands-on time while limiting result turnaround. Such complexity can also render nontrivial variations across analysts and laboratories. We describe the development of a fully automated peptide mapping procedure with a high-throughput robotic liquid handling system to improve sample handling capability and outcome reproducibility while saving analyst hands-on time. The automated procedure is completely hands-free, and setup requires the analyst only to prenormalize the sample concentrations and load buffers and reagents at their designated positions on the robotic deck. The robotic liquid handler performs all the subsequent preparation steps and stores the digested samples on a chiller unit to await retrieval. The convenience and flexibility provided by this automated peptide mapping method provides substantial benefits over manual sample preparation protocols. The optimized, automated procedure showed good reproducibility and results that were comparable to those of the manual procedure with respect to sequence coverage, digestion completeness, and quantification of posttranslational modifications. With this increased throughput, coupled with fast MAM analysis, more comprehensive characterization can be achieved.

Publisher

Cold Spring Harbor Laboratory

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Combating COVID-19: Study of robotic solutions for COVID-19;MATERIALS, MECHANICS & MODELING (NCMMM-2020);2021

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