Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.
Barbara Davis Center for Diabetes, School of Medicine, University of Colorado, Aurora, CO, USA.
Nat Protoc. 2021 Aug;16(8):3737-3760. doi: 10.1038/s41596-021-00566-6. Epub 2021 Jul 9.
Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.
基于质谱的蛋白质组学分析是发现新疾病生物标志物的有力方法。然而，在实验设计和执行过程中，通常会忽略或低估研究设计中某些关键步骤，如队列选择、统计功效评估、样本盲法和随机化以及样本/数据质量控制。本教程讨论了设计和实施基于液相色谱-质谱的生物标志物发现研究的重要步骤。我们描述了这些研究中每个步骤的原理、考虑因素和可能的失败，包括实验设计、样本采集和处理以及数据采集。我们还为有意义的生物学解释的主要数据处理和最终统计分析步骤提供了指导，以及几个成功的生物标志物研究的亮点。从研究设计到实施再到数据解释的提供的指南可作为提高生物标志物开发研究严谨性和可重复性的参考。
