Roux-Dalvai Florence, Leclercq Mickaël, Gotti Clarisse, Droit Arnaud
Proteomics Platform, CHU de Québec - Université Laval Research Center, Québec City, QC, Canada.
Computational Biology Laboratory, CHU de Québec - Université Laval Research Center, Québec City, QC, Canada.
Methods Mol Biol. 2022;2456:299-317. doi: 10.1007/978-1-0716-2124-0_21.
Identification of bacterial species in biological samples is essential in many applications. However, the standard methods usually use a time-consuming bacterial culture (24-48 h) and sometimes lack in specificity. To overcome these limitations, we developed a new protocol, combining LC-MS/MS analysis in Data Independent Acquisition mode and machine learning algorithms, enabling the accurate identification of the bacterial species contaminating a sample in a few hours without bacterial culture. In this chapter, we describe the three steps of the protocol (spectral libraries generation, training step, identification step) to generate customized peptide signatures and use them for bacterial identification in biological samples through targeted proteomics analyses and prediction models.
在许多应用中，鉴定生物样品中的细菌种类至关重要。然而，标准方法通常采用耗时的细菌培养（24 - 48小时），且有时缺乏特异性。为克服这些局限性，我们开发了一种新方案，将数据非依赖采集模式下的液相色谱 - 串联质谱（LC-MS/MS）分析与机器学习算法相结合，能够在无需细菌培养的情况下，几小时内准确鉴定污染样品的细菌种类。在本章中，我们描述了该方案的三个步骤（谱库生成、训练步骤、鉴定步骤），以生成定制化的肽段特征，并通过靶向蛋白质组学分析和预测模型将其用于生物样品中的细菌鉴定。
