Instrumental Drift in Untargeted Metabolomics: Optimizing Data Quality with Intrastudy QC Samples

Abstract

Untargeted metabolomics is an important tool in studying health and disease and is employed in fields such as biomarker discovery and drug development, as well as precision medicine. Although significant technical advances were made in the field of mass-spectrometry driven metabolomics, instrumental drifts, such as fluctuations in retention time and signal intensity, remain a challenge, particularly in large untargeted metabolomics studies. Therefore, it is crucial to consider these variations during data processing to ensure high-quality data. Here, we will provide recommendations for an optimal data processing workflow using intrastudy quality control (QC) samples that identifies errors resulting from instrumental drifts, such as shifts in retention time and metabolite intensities. Furthermore, we provide an in-depth comparison of the performance of three popular batch-effect correction methods of different complexity. By using different evaluation metrics based on QC samples and a machine learning approach based on biological samples, the performance of the batch-effect correction methods were evaluated. Here, the method TIGER demonstrated the overall best performance by reducing the relative standard deviation of the QCs and dispersion-ratio the most, as well as demonstrating the highest area under the receiver operating characteristic with three different probabilistic classifiers (Logistic regression, Random Forest, and Support Vector Machine). In summary, our recommendations will help to generate high-quality data that are suitable for further downstream processing, leading to more accurate and meaningful insights into the underlying biological processes.

Quality Control in Targeted GC-MS for Amino Acid-OMICS

“…In GC-MS- and LC-MS-based untargeted metabolomics, analytical errors may result, among others, from instrumental drifts, such as shifts in retention time (GC-related factors) and metabolite intensities (MS-related factors). These kinds of errors and the utility of so-called intra-study QC samples were recently reviewed and discussed in detail, and helpful recommendations were made [ 29 ].…”

Stable Isotope Tracing Analysis in Cancer Research: Advancements and Challenges in Identifying Dysregulated Cancer Metabolism and Treatment Strategies

“…Additionally, MS itself is not inherently quantitative. The intensity of MS signals can be influenced by various experimental factors, such as ionization efficiency, matrix effects, and instrument settings, and hence, directly comparing datasets from different batches or across laboratories can be challenging [68][69][70]. However, it is worth noting that Clark et al report qualitatively similar PCA plots in their interlaboratory study, indicating that while direct data comparison is often difficult, the overall trends observed in data across laboratories largely align [69].…”