Introduction

Data analysis during UPLC-MS non-targeted metabolomics introduces variation as different manufacturers use specific algorithms for data treatment and this makes untargeted metabolomics an application for the discovery of new biomarkers with low confidence in the reproducibility of the results under the use of different metabolomics platforms.

Objectives

This study compared the ability of two platforms (Agilent UPLC-ESI-QTOF-MS and Waters UPLC-IMS-QTOF-MS) to identify biomarkers in butterhead and romaine lettuce cultivars.

Methods

Two case studies by different metabolomics platforms: (1) Waters and Agilent datasets processed by the same data pre-processing software (Progenesis QI), and (2) Datasets processed by different data pre-processing software.

Results

A higher number of candidate biomarkers shared between sample groups in case 2 (101) than in case 1 (26) was found. Thirteen metabolites were common to both cases. Romaine lettuce was characterised by phenolic compounds including flavonoids, hydroxycinnamate derivatives, and 9-undecenal, while Butterhead showed sesquiterpene lactones and xanthosine. This study demonstrates that high percentages of the most discriminatory entities can be obtained by using the manufacturers’ embedded pre-processing software and following the recommended processing data guidelines using commercial software to normalise the data matrix.

中文翻译：

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我们可以信任使用不同的非靶向代谢组学平台识别的生物标志物吗？通过 UPLC-QTOF-MS 对生菜品种进行多平台、实验室间比较代谢组学分析。

 介绍

UPLC-MS 非靶向代谢组学期间的数据分析引入了差异，因为不同的制造商使用特定的算法进行数据处理，这使得非靶向代谢组学成为发现新生物标志物的应用，但在使用不同代谢组学平台时结果的重现性信心不足。

 目标

本研究比较了两个平台（安捷伦 UPLC-ESI-QTOF-MS 和沃特世 UPLC-IMS-QTOF-MS）识别黄油头和长叶生菜品种中生物标志物的能力。

 方法

不同代谢组学平台的两个案例研究：（1）由同一数据预处理软件（Progenesis QI）处理的 Waters 和 Agilent 数据集，以及（2）由不同数据预处理软件处理的数据集。

 结果

案例 2 (101) 中样本组之间共有的候选生物标志物数量高于案例 1 (26)。两种情况共有 13 种代谢物。长叶生菜的特征在于酚类化合物，包括类黄酮、羟基肉桂酸酯衍生物和 9-十一烯醛，而黄油头则含有倍半萜内酯和黄苷。这项研究表明，通过使用制造商的嵌入式预处理软件并遵循建议的处理数据指南，使用商业软件对数据矩阵进行标准化，可以获得高比例的最具歧视性的实体。