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Reference Standardization for Quantification and Harmonization of Large-Scale Metabolomics.
Liu, Ken H; Nellis, Mary; Uppal, Karan; Ma, Chunyu; Tran, ViLinh; Liang, Yongliang; Walker, Douglas I; Jones, Dean P.
Afiliação
  • Liu KH; Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, Georgia 30322, United States.
  • Nellis M; Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, Georgia 30322, United States.
  • Uppal K; Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, Georgia 30322, United States.
  • Ma C; Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, Georgia 30322, United States.
  • Tran V; Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, Georgia 30322, United States.
  • Liang Y; Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, Georgia 30322, United States.
  • Walker DI; Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States.
  • Jones DP; Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, Georgia 30322, United States.
Anal Chem ; 92(13): 8836-8844, 2020 07 07.
Article em En | MEDLINE | ID: mdl-32490663
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Metaboloma / Metabolômica Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Metaboloma / Metabolômica Limite: Humans Idioma: En Ano de publicação: 2020 Tipo de documento: Article