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bioRxiv · 10.1101/2022.11.01.514762

Quartet metabolite reference materials for assessing inter-laboratory reliability and data integration of metabolomic profiling

Zhang, N.·Zhang, P.·Chen, Q.·Zhou, K.·Liu, Y.·Wang, H.·Xie, Y.·Ren, L.·Hou, W.·Yang, J.·Yu, Y.·Zheng, Y.·Shi, L.
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Abstract

Various laboratory-developed metabolomic methods lead to big challenges in inter-laboratory comparability and effective integration of diverse datasets. As part of the Quartet Project, we established a publicly available suite of four metabolite reference materials derived from B-lymphoblastoid cell lines from a family quartet of parents and monozygotic twin daughters. We generated comprehensive LC-MS based metabolomic data from the Quartet reference materials using targeted and untargeted strategies in different laboratories. High variabilities in the qualitative and quantitative metabolomic measurements were observed across laboratories. Moreover, the Quartet multi-sample-based quality metrics were developed for objectively assessing the reliability of metabolomic profiling in detecting intrinsic biological differences among difference groups of samples. Importantly, the ratio-based metabolomic profiling, by scaling the absolute values of a study sample relative to those of a universal reference sample, enables data integration in long-term measurements across difference laboratories or platforms. Thus, we constructed the ratio-based high-confidence reference datasets between two reference samples, providing "ground truth" for inter-laboratory proficiency test, which enables objective assessment of various metabolomic methods. Our study provided the community with rich resources and best practice for objective assessment of inter-laboratory measurements and data integration, ensuring reliable large-scale and longitudinal metabolomic profiling.

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Zhang, N., Zhang, P., Chen, Q., Zhou, K., Liu, Y., Wang, H., Xie, Y., Ren, L., Hou, W., Yang, J., Yu, Y., Zheng, Y., Shi, L.. 2022-11-03. Quartet metabolite reference materials for assessing inter-laboratory reliability and data integration of metabolomic profiling. https://doi.org/10.1101/2022.11.01.514762

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