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Updated: Mar 28, 2026
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Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
Published on: November 29, 2024
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Metabolomics in transfusion medicine
Travis Nemkov1, Kirk C Hansen1, Larry J Dumont2
1Department of Biochemistry and Molecular Genetics, University of Colorado Denver-Anschutz Medical Campus, Aurora, Colorado.
Transfusion
|December 15, 2015
Summary
Advancements in metabolomics and systems biology offer new ways to improve blood product storage. Integrating these approaches can enhance the quality and safety of red blood cells and platelets for transfusions.
Area of Science:
- Transfusion Medicine
- Metabolomics
- Systems Biology
Background:
- Biochemical studies of red blood cell (RBC) and platelet (PLT) metabolism have driven progress in transfusion medicine for a century.
- Current storage of RBCs and PLT concentrates is a vital practice, but improvements are needed in storage solutions, processing, and quality testing.
Purpose of the Study:
- To review metabolomics technologies and workflows relevant to transfusion medicine.
- To guide transfusion medicine experts in utilizing metabolomics data.
- To highlight the need for integrating omics data with clinical variables for mechanistic insights.
Main Methods:
- Review of mass spectrometry-based metabolomics technologies.
- Description of routine workflows for transfusion medicine studies.
- Discussion of bioinformatics integration of omics data and systems biology models.
Main Results:
- Metabolomics has provided initial descriptive data in transfusion medicine.
- There is a need to correlate metabolic variables with transfusion outcomes (e.g., RBC recovery).
- Systems biology integration can enable in silico prediction of metabolic phenotypes.
Conclusions:
- Metabolomics and systems biology are crucial for developing innovative blood product storage strategies.
- Integrating omics data with clinical expertise is essential for understanding and improving blood product quality and safety.
- Future research should focus on mechanistic relationships and predictive modeling for optimized transfusion medicine practices.
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