Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, USA.
Center for Biotechnology, University of Nebraska-Lincoln, Lincoln, NE, USA.
Methods Mol Biol. 2024;2758:125-150. doi: 10.1007/978-1-0716-3646-6_7.
Liquid chromatography-mass spectrometry (LC-MS)-based peptidomics methods allow for the detection and identification of many peptides in a complex biological mixture in an untargeted manner. Quantitative peptidomics approaches allow for comparisons of peptide abundance between different samples, allowing one to draw conclusions about peptide differences as a function of experimental treatment or physiology. While stable isotope labeling is a powerful approach for quantitative proteomics and peptidomics, advances in mass spectrometry instrumentation and analysis tools have allowed label-free methods to gain popularity in recent years. In a general label-free quantitative peptidomics experiment, peak intensity information for each peptide is compared across multiple LC-MS runs. Here, we outline a general approach for label-free quantitative peptidomics experiments, including steps for sample preparation, LC-MS data acquisition, data processing, and statistical analysis. Special attention is paid to address run-to-run variability, which can lead to several major problems in label-free experiments. Overall, our method provides researchers with a framework for the development of their own quantitative peptidomics workflows applicable to quantitation of peptides from a wide variety of different biological sources.
基于液相色谱-质谱(LC-MS)的肽组学方法能够以非靶向的方式检测和鉴定复杂生物混合物中的许多肽。定量肽组学方法允许比较不同样品中肽的丰度,从而可以根据实验处理或生理学得出关于肽差异的结论。虽然稳定同位素标记是定量蛋白质组学和肽组学的强大方法,但质谱仪器和分析工具的进步使得无标记方法在近年来变得流行。在一般的无标记定量肽组学实验中,比较多个 LC-MS 运行中每个肽的峰强度信息。在这里,我们概述了一种用于无标记定量肽组学实验的一般方法,包括样品制备、LC-MS 数据采集、数据处理和统计分析步骤。特别关注解决运行间变异性问题,这可能会导致无标记实验中的几个主要问题。总的来说,我们的方法为研究人员提供了一个框架,用于开发适用于从各种不同生物来源定量肽的定量肽组学工作流程。