What does this strategy enable?
Credentialing creates an experimental ground truth for whether untargeted LC-MS features arise from biological metabolites. Natural-abundance and uniformly 13C-labeled material are mixed at defined ratios; expected isotope spacing and intensity relationships are used to label credible biological features. The number and quality of credentialed features can then be used to optimize extraction, chromatography, acquisition or feature-processing parameters.
Transferable workflow
Workflow
- 1
Generate matched unlabeled and uniformly 13C-labeled biological material
Grow a model biological system in natural-abundance and uniformly 13C-enriched media.
- 2
Prepare defined isotope mixtures
Mix labeled and unlabeled extracts at known ratios so true biological features have predictable isotope spacing and intensity relationships.
- 3
Run the candidate untargeted workflow
Process the mixtures using the extraction, chromatography, instrument and feature-detection workflow to be benchmarked.
- 4
Credential biological features
Identify feature pairs that satisfy the expected mass shifts, retention-time agreement and isotope-intensity ratios.
- 5
Optimize the workflow
Compare the number and behavior of credentialed features rather than total feature count when tuning experimental or computational parameters.
Strategy profile
Components
Sample preparation, Data acquisition, Preprocessing and feature extraction, Quality control, Statistical analysis
Sample types
Microbial culture or biomass
Biological context
Microbial, Broadly applicable
Scope and considerations
Designed for
- Benchmarking and optimizing untargeted metabolomics pipelines using experimentally defined biological features.
Important requirements
- A biological model that can be grown with uniform stable-isotope labeling.
- Accurate-mass data capable of resolving the expected isotope relationships.
Considerations
- Optimization in a model system does not guarantee identical performance in every biological matrix.
- The strategy distinguishes biological origin from artifact but does not identify metabolite structures.
- The source establishes credentialing using labeled and unlabeled E. coli extracts; adaptation to other matrices or platforms requires validation.
Reusable resources
Publication
Mahieu NG, Huang X, Chen Y-J, Patti GJ. Credentialing Features: A Platform to Benchmark and Optimize Untargeted Metabolomic Methods. Analytical Chemistry 86, 9583–9589 (2014).