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Updated: May 25, 2026
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Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
Published on: November 29, 2024
Assessing the metabolomics "dark matter" by a detectable khipu model.
Yuanye Chi1, Joshua M Mitchell1, Shujian Zheng1
1The Jackson Laboratory for Genomic Medicine, 10 Discovery Drive, Farmington, CT, 06032, USA.
Summary
Untargeted metabolomics data contains many unknown compounds. A new model shows most abundant features are real, but identifying these unknowns remains the biggest challenge in LC-MS metabolomics.
Area of Science:
- Metabolomics
- Biomedical Research
- Analytical Chemistry
Background:
- Untargeted metabolomics often yields numerous unknown features, termed "dark matter".
- Interpreting these unknowns is crucial for data analysis and advancing the field.
- Distinguishing real compounds from artifacts is a key challenge.
Purpose of the Study:
- To investigate the nature of unknown features in untargeted metabolomics.
- To develop a model for understanding feature patterns based on compound abundance.
- To assess the contribution of artifacts like in-source fragments.
Main Methods:
- Proposed a "detectable khipu" model linking ion patterns to compound abundance.
- Systematically analyzed 61 public blood liquid chromatography-mass spectrometry (LC-MS) metabolomics datasets.
- Evaluated feature characteristics across diverse biomedical studies.
Main Results:
- Abundant features predominantly exhibit identifiable ion patterns.
- In-source fragments constitute less than 10% of detected features.
- Each dataset identified 1,000–2,000 high-confidence compounds, with over half remaining unknown.
Conclusions:
- The primary obstacle in LC-MS metabolomics is not ion grouping or fragment analysis.
- The major knowledge gap lies in the identification of unknown compounds.
- Focus should shift towards developing methods for unknown compound identification.
