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双 UHPLC-HRMS 代谢组学和脂质组学及自动化数据处理工作流程用于全面高通量肠道表型分析。

Dual UHPLC-HRMS Metabolomics and Lipidomics and Automated Data Processing Workflow for Comprehensive High-Throughput Gut Phenotyping.

机构信息

Laboratory of Integrative Metabolomics (LIMET), Department of Translational Physiology, Infectiology and Public Health, Faculty of Veterinary Medicine, Ghent University, Salisburylaan 133, 9820 Merelbeke, Belgium.

Department of Microbiology and Immunology, Rega Institute, KU Leuven, Herestraat 49, 3000 Leuven, Belgium.

出版信息

Anal Chem. 2023 Jun 6;95(22):8461-8468. doi: 10.1021/acs.analchem.2c05371. Epub 2023 May 23.

Abstract

In recent years, feces has surfaced as the matrix of choice for investigating the gut microbiome-health axis because of its non-invasive sampling and the unique reflection it offers of an individual's lifestyle. In cohort studies where the number of samples required is large, but availability is scarce, a clear need exists for high-throughput analyses. Such analyses should combine a wide physicochemical range of molecules with a minimal amount of sample and resources and downstream data processing workflows that are as automated and time efficient as possible. We present a dual fecal extraction and ultra high performance liquid chromatography-high resolution-quadrupole-orbitrap-mass spectrometry (UHPLC-HR-Q-Orbitrap-MS)-based workflow that enables widely targeted and untargeted metabolome and lipidome analysis. A total of 836 in-house standards were analyzed, of which 360 metabolites and 132 lipids were consequently detected in feces. Their targeted profiling was validated successfully with respect to repeatability (78% CV < 20%), reproducibility (82% CV < 20%), and linearity (81% > 0.9), while also enabling holistic untargeted fingerprinting (15,319 features, CV < 30%). To automate targeted processing, we optimized an R-based targeted peak extraction (TaPEx) algorithm relying on a database comprising retention time and mass-to-charge ratio (360 metabolites and 132 lipids), with batch-specific quality control curation. The latter was benchmarked toward vendor-specific targeted and untargeted software and our isotopologue parameter optimization/XCMS-based untargeted pipeline in LifeLines Deep cohort samples ( = 97). TaPEx clearly outperformed the untargeted approaches (81.3 vs 56.7-66.0% compounds detected). Finally, our novel dual fecal metabolomics-lipidomics-TaPEx method was successfully applied to Flemish Gut Flora Project cohort ( = 292) samples, leading to a sample-to-result time reduction of 60%.

摘要

近年来,粪便因其非侵入性采样以及对个体生活方式的独特反映,已成为研究肠道微生物组-健康轴的首选基质。在需要大量样本但可用性有限的队列研究中,对高通量分析的需求非常迫切。此类分析应将广泛的物理化学分子范围与尽可能少的样本和资源相结合,并尽可能实现自动化和高效的下游数据处理工作流程。我们提出了一种基于双重粪便提取和超高效液相色谱-高分辨率-四极杆-轨道阱质谱(UHPLC-HR-Q-Orbitrap-MS)的工作流程,可实现广泛靶向和非靶向代谢组学和脂质组学分析。总共分析了 836 个内部标准,其中 360 种代谢物和 132 种脂质随后在粪便中被检测到。它们的靶向分析在重复性(78%CV<20%)、再现性(82%CV<20%)和线性度(81%>0.9)方面得到了成功验证,同时还实现了全面的非靶向指纹分析(15319 个特征,CV<30%)。为了实现靶向处理的自动化,我们优化了基于数据库的基于 R 的靶向峰提取(TaPEx)算法,该数据库包含保留时间和质荷比(360 种代谢物和 132 种脂质),以及批特异性质量控制策管。后者被基准测试针对供应商特定的靶向和非靶向软件以及我们基于同位素优化/XCMS 的非靶向管道在 LifeLines Deep 队列样本中的性能(n=97)。TaPEx 明显优于非靶向方法(81.3%对 56.7-66.0%检测到的化合物)。最后,我们的新型双重粪便代谢组学-脂质组学-TaPEx 方法成功应用于佛兰德肠道菌群项目队列(n=292)样本,将样本到结果的时间缩短了 60%。

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