Despite the growing popularity of liquid chromatography-mass spectrometry (LC-MS)-based metabolomics, no study has yet to systematically compare the performance of different data acquisition modes in the discovery of significantly altered metabolic features, which is an important task of untargeted metabolomics for identifying clinical biomarkers and elucidating disease mechanism in comparative samples. In this work, we performed a comprehensive comparison of three most commonly used data acquisition modes, including full-scan, data-dependent acquisition (DDA), and data-independent acquisition (DIA), using a metabolomics study of human plasma samples from leukemia patients before and after one-month chemotherapy. After optimization of data processing parameters, we extracted and compared statistically significant metabolic features from the results of each data acquisition mode. We found that most significant features can be consistently found in all three data acquisition modes with similar statistical performance as evaluated by Pearson correlation and receiver operating characteristic (ROC) analysis. Upon comparison, DDA mode consistently generated fewer uniquely found significant features than full-scan and DIA modes. We then manually inspected over 2000 uniquely discovered significant features in each data acquisition mode and showed that these features can be generally categorized into four major types. Many significant features were missed in DDA mode, primarily due to its low capability of detecting or extracting these features from raw LC-MS data. We thus proposed a bioinformatic solution to rescue these missing significant features from the raw DDA data with good reproducibility and accuracy. Overall, our work asserts that data acquisition modes can influence metabolomics results, suggesting room for improvement of data acquisition modes for untargeted metabolomics.

中文翻译：

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在基于质谱的非靶向代谢组学中评估从不同数据采集模式中发现的重要特征

尽管基于液相色谱-质谱 (LC-MS) 的代谢组学越来越受欢迎，但还没有研究系统地比较不同数据采集模式在发现显着改变的代谢特征方面的性能，这是非靶向代谢组学的一项重要任务用于鉴定临床生物标志物并阐明比较样本中的疾病机制。在这项工作中，我们使用对白血病人血浆样本的代谢组学研究，对三种最常用的数据采集模式进行了全面比较，包括全扫描、数据依赖采集 (DDA) 和数据独立采集 (DIA)患者化疗前后一个月。数据处理参数优化后，我们从每种数据采集模式的结果中提取并比较了具有统计意义的代谢特征。我们发现最重要的特征可以在所有三种数据采集模式中一致地找到，其统计性能与 Pearson 相关和接收器操作特征 (ROC) 分析评估的相似。相比之下，DDA 模式始终比全扫描和 DIA 模式生成更少的独特发现的重要特征。然后，我们在每种数据采集模式中手动检查了 2000 多个独特发现的重要特征，并表明这些特征通常可以分为四大类。DDA 模式遗漏了许多重要特征，主要是因为它从原始 LC-MS 数据中检测或提取这些特征的能力较低。因此，我们提出了一种生物信息学解决方案，以良好的重现性和准确性从原始 DDA 数据中拯救这些缺失的重要特征。总的来说，我们的工作断言数据采集模式会影响代谢组学结果，这表明非靶向代谢组学的数据采集模式有改进的空间。