Optimization of large-scale pseudotargeted metabolomics method based on liquid chromatography–mass spectrometry

• A comprehensive strategy was developed based on pseudotargeted analysis for large-scale metabolomics study.
• Improved repeatability by including blank wash, pooled QC and post-calibration.
• The proposed strategy can be used to combine metabolomics data from different batches.
• The stability and maximal capability of a single analytical batch could be extended to at least 282 injections (about 110 h).
• The improved reproducibility provides a reliable protocol for large-scale clinical and epidemiological metabolomics studies.

Liquid chromatography–mass spectrometry (LC–MS) is now a main stream technique for large-scale metabolic phenotyping to obtain a better understanding of genomic functions. However, repeatability is still an essential issue for the LC–MS based methods, and convincing strategies for long time analysis are urgently required. Our former reported pseudotargeted method which combines nontargeted and targeted analyses, is proved to be a practical approach with high-quality and information-rich data. In this study, we developed a comprehensive strategy based on the pseudotargeted analysis by integrating blank-wash, pooled quality control (QC) sample, and post-calibration for the large-scale metabolomics study. The performance of strategy was optimized from both pre- and post-acquisition sections including the selection of QC samples, insertion frequency of QC samples, and post-calibration methods. These results imply that the pseudotargeted method is rather stable and suitable for large-scale study of metabolic profiling. As a proof of concept, the proposed strategy was applied to the combination of 3 independent batches within a time span of 5 weeks, and generated about 54% of the features with coefficient of variations (CV) below 15%. Moreover, the stability and maximal capability of a single analytical batch could be extended to at least 282 injections (about 110 h) while still providing excellent stability, the CV of 63% metabolic features was less than 15%. Taken together, the improved repeatability of our strategy provides a reliable protocol for large-scale metabolomics studies.