蛋白质组学
工作流程
样品制备
激光捕获显微切割
胰蛋白酶
化学
质谱法
色谱法
自动化
吞吐量
样品(材料)
计算生物学
消化(炼金术)
蛋白质组
计算机科学
数据库
生物化学
生物
基因
电信
酶
基因表达
工程类
无线
机械工程
作者
Ganesh P. Pujari,Kiran K. Mangalaparthi,Benjamin J. Madden,Firdous Ahmad Bhat,M. Cristine Charlesworth,Amy J. French,Gunveen S. Sachdeva,Eugenio Daviso,U. Thomann,Patrick McCarthy,Sameer Vasantgadkar,Debadeep Bhattacharyya,Akhilesh Pandey
标识
DOI:10.1021/jasms.3c00099
摘要
Laser capture microdissection (LCM) has become an indispensable tool for mass spectrometry-based proteomic analysis of specific regions obtained from formalin-fixed paraffin-embedded (FFPE) tissue samples in both clinical and research settings. Low protein yields from LCM samples along with laborious sample processing steps present challenges for proteomic analysis without sacrificing protein and peptide recovery. Automation of sample preparation workflows is still under development, especially for samples such as laser-capture microdissected tissues. Here, we present a simplified and rapid workflow using adaptive focused acoustics (AFA) technology for sample processing for high-throughput FFPE-based proteomics. We evaluated three different workflows: standard extraction method followed by overnight trypsin digestion, AFA-assisted extraction and overnight trypsin digestion, and AFA-assisted extraction simultaneously performed with trypsin digestion. The use of AFA-based ultrasonication enables automated sample processing for high-throughput proteomic analysis of LCM-FFPE tissues in 96-well and 384-well formats. Further, accelerated trypsin digestion combined with AFA dramatically reduced the overall processing times. LC-MS/MS analysis revealed a slightly higher number of protein and peptide identifications in AFA accelerated workflows compared to standard and AFA overnight workflows. Further, we did not observe any difference in the proportion of peptides identified with missed cleavages or deamidated peptides across the three different workflows. Overall, our results demonstrate that the workflow described in this study enables rapid and high-throughput sample processing with greatly reduced sample handling, which is amenable to automation.
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