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Quartet metabolite reference materials for inter-laboratory proficiency test and data integration of metabolomics
Naixin Zhang1, Qiaochu Chen1, Peipei Zhang1
1State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Shanghai Cancer Center, Fudan University, Shanghai, China.
Genome Biology
|January 24, 2024
Summary
This study introduces metabolite reference materials for improved metabolomics data comparability. Ratio-based profiling enables accurate cross-laboratory data integration for reliable studies.
Area of Science:
- Biochemistry
- Genomics
- Analytical Chemistry
Background:
- Laboratory-developed metabolomic methods present significant challenges in inter-laboratory comparability.
- Integrating diverse metabolomic datasets is difficult due to variations in experimental protocols and data processing.
- Lack of standardized reference materials hinders the reliability of metabolomics studies.
Purpose of the Study:
- To establish a suite of metabolite reference materials for the Quartet Project.
- To assess the reliability and comparability of metabolomics data across different laboratories.
- To develop a method for effective integration of diverse metabolomic datasets.
Main Methods:
- Establishment of four metabolite reference materials from B lymphoblastoid cell lines.
- Generation of comprehensive liquid chromatography-mass spectrometry (LC-MS) metabolomic data using targeted and untargeted strategies.
- Application of ratio-based metabolomics profiling for cross-laboratory quantitative data integration.
Main Results:
- Significant variations in metabolomics profiling reliability were identified across laboratories.
- Ratio-based metabolomics profiling enables accurate cross-laboratory quantitative data integration.
- High-confidence reference datasets were constructed for inter-laboratory accuracy assessment.
Conclusions:
- The study provides valuable resources and best practices for inter-laboratory proficiency testing.
- The developed methods ensure the reliability of large-scale and longitudinal metabolomic studies.
- Standardized reference materials and data integration strategies are crucial for advancing metabolomics research.

