Development of an Automated, High‐throughput Sample Preparation Protocol for Proteomics Analysis

色谱法 样品制备 蛋白质组学 洗脱 协议(科学) 化学 鸟枪蛋白质组学 样品(材料) 质谱法 生物化学 医学 基因 病理 替代医学
作者
Albert B. Arul,Munkhtsolmon Byambadorj,Nayoung Han,Jong Moon Park,Hookeun Lee
出处
期刊:Bulletin of The Korean Chemical Society [Wiley]
卷期号:36 (7): 1791-1798 被引量:13
标识
DOI:10.1002/bkcs.10338
摘要

Sample preparation for proteomics analysis is one of the most crucial processes that is often time consuming and laborious, which makes parallel handling of high‐throughput samples difficult. In proteomics studies, availability of reproducible and sensitive methods is a prerequisite for qualitative and quantitative analyses of high‐throughput data to identify potential biomarker candidates from biological samples. The greatest strength of any sample preparation protocol is the reproducibility of extraction, solubilization, and digestion of proteins. Therefore, the present study was planned to automate filter‐aided sample preparation ( FASP ) protocol that is widely used at present in proteomics analysis. We tested the efficiency of an automated sample preparation platform for handling 96 samples in parallel, which reduced the time and cost of analysis. Samples were added to a 96‐well filter plate, and proteins present in the samples were trapped, denatured, reduced, alkylated, and digested with trypsin. The eluted peptides were analyzed using mass spectrometry MS / MS . Buffer exchanges, which were previously performed using centrifugation, were performed by controlling the vacuum pressure. Different concentrations of bovine serum albumin ( BSA ) were used for optimizing the pressure and time required for the efficient elution and identification of peptides. The optimized conditions were evaluated using tissue samples with manual and automated FASP protocols and the eluted peptides were analyzed using liquid chromatography LC ‐ MS / MS . Results showed that the automated FASP ( a‐FASP ) protocol could efficiently handle large sample sets in a reproducible and quantifiable manner. Thus, inclusion of this automated protocol in proteomics analysis could help in handling high‐throughput data in less time and with minimal error.
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