Pedersen Ane Laura, Ernest Marion, Affolter Michael, Dayon Loïc
Proteomics, Bioanalytics Department, Nestlé Institute of Food Safety & Analytical Sciences, Nestlé Research, Lausanne, Switzerland.
Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Methods Mol Biol. 2025;2884:157-178. doi: 10.1007/978-1-0716-4298-6_11.
Protein biomarker discovery in human biological fluids has greatly developed over the past two decades thanks to technological advances allowing deeper proteome coverage and higher sample throughput, among others. While blood samples are most commonly investigated due to their moderate ease of collection and high information content, other biological fluids such as cerebrospinal fluid (CSF) and urine are highly relevant for specific pathologies, such as brain and urologic diseases, respectively. Independently of the biofluid of interest, platforms that can robustly handle a large number of samples are essential in the discovery phase of a clinical study.We have previously described a scalable automated proteomic pipeline (ASAP) for the sample preparation of hundreds to thousands of blood plasma, serum, and CSF samples before liquid chromatography (LC)-mass spectrometry (MS) analysis. Here, we describe how the workflow was further adapted to milk and urine samples, with small modifications at the beginning of the workflow. For blood and CSF samples, an optional immuno-affinity depletion step for abundant-protein removal constitutes the first step of the workflow. In the analysis of milk, a defatting step is incorporated before the samples are further processed with ASAP, while acetone precipitation is used for the analysis of urine samples. The main sample preparation steps then remain identical for all sample types, are automated on a liquid handling workstation, and include reduction of disulfide bridges, alkylation of free thiols, protein digestion, isobaric labeling of peptides, sample pooling, and purifications. The workflow is completed by LC-MS analysis of the samples and subsequent data processing.
在过去二十年中，得益于技术进步，人类生物体液中的蛋白质生物标志物发现取得了巨大进展，这些技术进步使得蛋白质组覆盖范围更广、样品通量更高等。由于血液样本采集相对容易且信息含量高，因此是最常被研究的样本类型。然而，其他生物体液，如脑脊液（CSF）和尿液，分别对于特定病理状况，如脑部疾病和泌尿系统疾病，具有高度相关性。无论所关注的生物体液是什么，能够稳健处理大量样本的平台在临床研究的发现阶段至关重要。我们之前描述了一种可扩展的自动化蛋白质组学流程（ASAP），用于在液相色谱（LC）-质谱（MS）分析之前对数百至数千份血浆、血清和脑脊液样本进行样品制备。在此，我们描述了如何对该工作流程进行进一步调整以适用于牛奶和尿液样本，只需在工作流程开始时进行小的修改。对于血液和脑脊液样本，用于去除丰度蛋白的可选免疫亲和去除步骤是工作流程的第一步。在牛奶分析中，在使用ASAP对样本进行进一步处理之前加入脱脂步骤，而尿液样本分析则采用丙酮沉淀法。然后，所有样本类型的主要样品制备步骤保持相同，在液体处理工作站上实现自动化，包括二硫键还原、游离巯基烷基化、蛋白质消化、肽段等压标记、样本合并和纯化。通过对样本进行LC-MS分析及后续数据处理完成整个工作流程。
