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Related Experiment Video

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A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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Reference Standardization for Quantification and Harmonization of Large-Scale Metabolomics.

Ken H Liu1, Mary Nellis1, Karan Uppal1

  • 1Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, Georgia 30322, United States.

Analytical Chemistry
|June 4, 2020
PubMed
Summary
This summary is machine-generated.

Reference standardization offers a robust method for harmonizing high-resolution metabolomics data across diverse studies. This approach enables accurate quantification and batch correction for large-scale metabolomics datasets.

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Area of Science:

  • Metabolomics
  • Analytical Chemistry
  • Biostatistics

Background:

  • High-resolution metabolomics (HRM) data present quantification and harmonization challenges.
  • Variability across studies and analytical methods hinders data integration.
  • Standardized methods are crucial for reliable large-scale metabolomics analysis.

Purpose of the Study:

  • To introduce and validate reference standardization for HRM data.
  • To enable accurate quantification and batch correction of metabolomics data.
  • To support the harmonization of large-scale human metabolomics datasets.

Main Methods:

  • Concurrent analysis of calibrated pooled reference samples.
  • Application of reference standardization for single-step batch correction and quantification.
  • Quantitative measurement of approximately 200 metabolites in three reference materials (Qstd3, CHEAR, NIST1950).

Main Results:

  • Quantitative measures for ~200 metabolites in three reference materials were provided.
  • Reference standardization successfully harmonized metabolomics data from 3677 human samples across 17 studies.
  • Data harmonization was achieved over a 17-month period using two complementary HRM methods.

Conclusions:

  • Reference standardization is a suitable method for harmonizing large-scale metabolomics data.
  • This approach enhances the capability to quantify numerous known and unidentified metabolites.
  • The method is effective for high-resolution mass spectrometry-based metabolomics.