Normalization Approach by a Reference Material to Improve LC–MS-Based Metabolomic Data Comparability of Multibatch Samples

Abstract

Large cohorts of samples from multiple batches are usually required for global metabolomic studies to characterize the metabolic state of human disease. As such, it is critical to eliminate systematic variation and truly reveal the biologically associated alterations. In this study, we proposed a reference material-based approach (Ref-M) for data correction by liquid chromatography− mass spectrometry and represented by an analysis of multibatch human serum samples. The reference material was generated by mixing serum from healthy donors and distributed to each extraction batch of subject samples. Pooled quality control samples and isotopic internal standards were then applied in each acquisition batch for data quality control. Finally, each metabolite in subject samples was normalized by its counterpart in the reference serum. We demonstrated that Ref-M significantly enhanced the numbers of efficient features and effectively eliminated the batch variation of 522 serum samples of healthy individuals, benign pulmonary nodules, and lung cancer patients. Twenty differential metabolites were identified to distinguish lung cancer from healthy controls in the training set. The discriminant model was validated in an independent data set with an area under the receiver operating characteristics (ROC) curve (AUC) of 0.853. Another 40 serum samples further tested with Ref-M were achieved an AUC of 0.843 by the established model. Our results showed that the reference material-based approach presents the potential to improve the data comparability and precision for biomarker discovery in large-scale metabolomic studies.

Improving the Data Quality of Untargeted Metabolomics through a Targeted Data-Dependent Acquisition Based on an Inclusion List of Differential and Preidentified Ions

“…Therefore, to evaluate whether dpDDA can address this problem to some extent, we compared the RSD values of QC samples obtained by dpDDA, DDA, and DIA for data normalization. In this study, peaks with RSD ≤ 30% in QC samples were considered efficient features for potential biomarker discovery Figure A indicates that dpDDA had a significantly higher proportion of effective features (63.81%) than DDA (15.31%) and DIA (39.20%) before normalization.…”

“…In this study, peaks with RSD ≤ 30% in QC samples were considered efficient features for potential biomarker discovery. 22 Figure 4A indicates that dpDDA had a significantly higher proportion of effective features (63.81%) than DDA (15.31%) and DIA (39.20%) before normalization. As shown in Figure 4B, after data normalization, the proportion of efficient features detected by dpDDA reached 92.22%, significantly higher than those obtained by DDA (58.91%) and DIA (80.30%).…”

“…Large-scale, diverse, and longitudinal cohort studies are essential to accelerate the development of precision medicine . Thus, clinical metabolomics with large-scale samples is expected to inform the practice of precision medicine and is highly complementary to other technologies, such as genomics . Owing to the limitations of liquid chromatography, mass spectrometry, and data processing, the standard workflow of conventional untargeted metabolomics still has room for improvement in dealing with metabolomics of large-scale samples .…”

“…Therefore, the dpDDA workflow has the advantage of more stable MS 1 data compared with conventional DDA and DIA and is very promising for clinical metabolomics with large-scale samples. In clinical metabolomics with large-scale samples, signal drift caused by decreased sensitivity of instrumental detectors is one of the most common issues . Therefore, to evaluate whether dpDDA can address this problem to some extent, we compared the RSD values of QC samples obtained by dpDDA, DDA, and DIA for data normalization.…”

“…21 Thus, clinical metabolomics with large-scale samples is expected to inform the practice of precision medicine and is highly complementary to other technologies, such as genomics. 22 Owing to the limitations of liquid chromatography, mass spectrometry, and data processing, the standard workflow of conventional untargeted metabolomics still has room for improvement in dealing with metabolomics of large-scale samples. 23 Our previous study found that the dpDDA workflow can acquire more stable MS 1 information of characteristic ions than the conventional DDA and DIA (Figure 2B).…”