Open AccessReference Standardization for Quantification and Harmonization of Large-Scale Metabolomics
Author(s) -
Ken H. Liu,
Mary M. Nellis,
Karan Uppal,
Chunyu Ma,
ViLinh Tran,
Yongliang Liang,
Douglas I. Walker,
Dean P. Jones
Publication year - 2020
Publication title -
analytical chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.117
H-Index - 332
eISSN - 1520-6882
pISSN - 0003-2700
DOI - 10.1021/acs.analchem.0c00338
Subject(s) - standardization , metabolomics , harmonization , reference values , chemistry , scale (ratio) , resolution (logic) , computational biology , data mining , chromatography , computer science , biology , artificial intelligence , physics , quantum mechanics , acoustics , medicine , operating system
Reference standardization was developed to address quantification and harmonization challenges for high-resolution metabolomics (HRM) data collected across different studies or analytical methods. Reference standardization relies on the concurrent analysis of calibrated pooled reference samples at predefined intervals and enables a single-step batch correction and quantification for high-throughput metabolomics. Here, we provide quantitative measures of approximately 200 metabolites for each of three pooled reference materials (220 metabolites for Qstd3, 211 metabolites for CHEAR, 204 metabolites for NIST1950) and show application of this approach for quantification supports harmonization of metabolomics data collected from 3677 human samples in 17 separate studies analyzed by two complementary HRM methods over a 17-month period. The results establish reference standardization as a method suitable for harmonizing large-scale metabolomics data and extending capabilities to quantify large numbers of known and unidentified metabolites detected by high-resolution mass spectrometry methods.