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Related Experiment Video

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Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
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Label-free data standardization for clinical metabolomics.

Petr G Lokhov1, Dmitri L Maslov1, Oleg N Kharibin1

  • 1Institute of Biomedical Chemistry, Pogodinskaya st.10, 119121 Moscow, Russia.

Biodata Mining
|March 7, 2017
PubMed
Summary

A new label-free standardization algorithm, SantaOmics, uses stable internal standards in blood plasma to convert metabolomics data into a comparable scale, improving laboratory diagnostics.

Keywords:
Blood plasmaClinical metabolomicsData standardizationMass spectrometry

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Area of Science:

  • Biochemistry
  • Analytical Chemistry
  • Bioinformatics

Background:

  • Metabolomics enables detection of thousands of substances per assay, offering promise for improved laboratory diagnostics.
  • Current metabolomics data often uses conditional units, hindering direct comparison and requiring conversion to actual concentrations.
  • Generating individual calibration curves for numerous substances is a significant challenge in metabolomics data analysis.

Purpose of the Study:

  • To develop a label-free standardization algorithm for metabolomics data.
  • To overcome the challenge of converting conditional metabolomics units to actual concentrations.
  • To enable direct comparability of metabolomics data with reference standards.

Main Methods:

  • Discovery of stable internal standards within blood plasma.
  • Development of the SantaOmics (Standardization algorithm for nonlinearly transformed arrays in Omics) algorithm.
  • Utilizing the 'knee point' for data standardization.

Main Results:

  • Identification of stable internal standards in blood plasma.
  • Successful implementation of the SantaOmics algorithm for label-free standardization.
  • Demonstration that SantaOmics converts metabolomics data to a standardized scale, substituting actual concentration measurements.
  • Metabolomics data becomes directly comparable with each other and with reference data.

Conclusions:

  • The developed SantaOmics algorithm significantly simplifies the use of metabolomics data in laboratory diagnostics.
  • Standardized metabolomics data enhances diagnostic capabilities.
  • The algorithm facilitates more reliable interpretation of complex metabolomics datasets.