代谢组学
肌萎缩侧索硬化
代谢组
OPL公司
脑脊液
色谱法
质谱法
化学
生物标志物发现
高分辨率
蛋白质组学
医学
病理
疾病
生物化学
氢键
地质学
基因
有机化学
遥感
分子
作者
Hélène Blasco,Philippe Corcia,Pierre‐François Pradat,Cinzia Bocca,Gordon Gilbert,Charlotte Veyrat-Durebex,Sylvie Mavel,Lydie Nadal‐Desbarats,Caroline Moreau,David Devos,Christian Andrés,Patrick Emond
摘要
Amyotrophic lateral sclerosis (ALS) is characterized by the absence of reliable diagnostic biomarkers. The aim of the study was to (i) devise an untargeted metabolomics methodology that reliably compares cerebrospinal fluid (CSF) from ALS patients and controls by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS); (ii) ascertain a metabolic signature of ALS by use of the LC-HRMS platform; (iii) identify metabolites for use as diagnostic or pathophysiologic markers. We developed a method to analyze CSF components by UPLC coupled with a Q-Exactive mass spectrometer that uses electrospray ionization. Metabolomic profiles were created from the CSF obtained at diagnosis from ALS patients and patients with other neurological conditions. We performed multivariate analyses (OPLS-DA) and univariate analyses to assess the contribution of individual metabolites as well as compounds identified in other studies. Sixty-six CSF samples from ALS patients and 128 from controls were analyzed. Metabolome analysis correctly predicted the diagnosis of ALS in more than 80% of cases. OPLS-DA identified four features that discriminated diagnostic group (p < 0.004). Our data demonstrate that untargeted metabolomics with LC-HRMS is a robust procedure to generate a specific metabolic profile for ALS from CSF and could be an important aid to the development of biomarkers for the disease.
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