Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States.
Proteomics Core, Mayo Clinic, Rochester, Minnesota 55905, United States.
J Am Soc Mass Spectrom. 2023 Jul 5;34(7):1225-1229. doi: 10.1021/jasms.3c00099. Epub 2023 Jun 2.
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.
激光捕获显微切割(LCM)已成为临床和研究环境中从福尔马林固定石蜡包埋(FFPE)组织样本中特定区域获得的基于质谱的蛋白质组学分析不可或缺的工具。LCM 样本中蛋白质产量低,以及繁琐的样品处理步骤,对蛋白质组学分析提出了挑战,如果不牺牲蛋白质和肽的回收,就需要克服这些挑战。样品制备工作流程的自动化仍在开发中,特别是对于激光捕获微切割组织等样品。在这里,我们提出了一种简化和快速的工作流程,使用自适应聚焦声学(AFA)技术进行高通量基于 FFPE 的蛋白质组学的样品处理。我们评估了三种不同的工作流程:标准提取方法,然后进行过夜胰蛋白酶消化,AFA 辅助提取和过夜胰蛋白酶消化,以及 AFA 辅助提取同时与胰蛋白酶消化进行。AFA 基超声处理的使用能够实现 LCM-FFPE 组织的高通量自动样品处理,适用于 96 孔和 384 孔格式。此外,加速的胰蛋白酶消化与 AFA 结合使用可大大缩短总体处理时间。LC-MS/MS 分析显示,与标准和 AFA 过夜工作流程相比,AFA 加速工作流程中鉴定的蛋白质和肽的数量略多。此外,我们在三个不同的工作流程中都没有观察到肽鉴定的比例有任何差异,包括漏切肽和脱酰胺肽。总体而言,我们的结果表明,本研究中描述的工作流程能够实现快速、高通量的样品处理,大大减少了样品处理量,并且易于自动化。