分析脑脊液蛋白质组以表征中枢神经系统疾病:一种基于高自动化质谱的生物标志物发现流程。

Analyzing Cerebrospinal Fluid Proteomes to Characterize Central Nervous System Disorders: A Highly Automated Mass Spectrometry-Based Pipeline for Biomarker Discovery.

作者信息

Núñez Galindo Antonio, Macron Charlotte, Cominetti Ornella, Dayon Loïc

机构信息

Proteomics, Nestlé Institute of Health Sciences, Nestlé Research, Lausanne, Switzerland.

出版信息

Methods Mol Biol. 2019;1959:89-112. doi: 10.1007/978-1-4939-9164-8_6.

Abstract

Over the past decade, liquid chromatography tandem mass spectrometry (LC MS/MS)-based workflows become standard for biomarker discovery in proteomics. These medium- to high-throughput (in terms of protein content) profiling approaches have been applied to clinical research. As a result, human proteomes have been characterized to a greater extent than ever before. However, proteomics in clinical research and biomarker discovery studies has generally been performed with small cohorts of subjects (or pooled samples from larger cohorts). This is problematic, as when aiming to identify novel biomarkers, small studies suffer from inherent and important limitations, as a result of the reduced biological diversity and representativity of human populations. Consequently, larger-scale proteomics will be key to delivering robust biomarker candidates and enabling translation to clinical practice.Cerebrospinal fluid (CSF) is a highly clinically relevant body fluid, and an important source of potential biomarkers for brain-associated damage, such as that induced by traumatic brain injury and stroke, and brain diseases, such as Alzheimer's disease and Parkinson's disease. We have developed a scalable automated proteomic pipeline (ASAP) for biomarker discovery. This workflow is compatible with larger clinical research studies in terms of sample size, while still allowing several hundred proteins to be measured in CSF by MS. In this chapter, we describe the whole proteomic workflow to analyze human CSF. We further illustrate our protocol with some examples from an analysis of hundreds of human CSF samples, in the specific context of biomarker discovery to characterize central nervous system disorders.

摘要

在过去十年中,基于液相色谱串联质谱(LC MS/MS)的工作流程已成为蛋白质组学中生物标志物发现的标准方法。这些中高通量(就蛋白质含量而言)的分析方法已应用于临床研究。因此,人类蛋白质组得到了前所未有的深入表征。然而,临床研究和生物标志物发现研究中的蛋白质组学通常是在小样本队列(或来自大样本队列的混合样本)中进行的。这是个问题,因为在旨在识别新型生物标志物时,小型研究存在固有的重要局限性,这是由于人类群体的生物多样性和代表性降低所致。因此,大规模蛋白质组学对于提供可靠的生物标志物候选物并实现向临床实践的转化至关重要。脑脊液(CSF)是一种与临床高度相关的体液,是与脑相关损伤(如创伤性脑损伤和中风所致)以及脑部疾病(如阿尔茨海默病和帕金森病)相关的潜在生物标志物的重要来源。我们已经开发了一种用于生物标志物发现的可扩展自动化蛋白质组学流程(ASAP)。该工作流程在样本量方面与更大规模的临床研究兼容,同时仍允许通过质谱对脑脊液中的数百种蛋白质进行检测。在本章中,我们描述了分析人类脑脊液的完整蛋白质组学工作流程。我们还通过对数百份人类脑脊液样本进行分析的一些示例,在生物标志物发现以表征中枢神经系统疾病的特定背景下,进一步阐述我们的方案。

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