Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline
Abstract
:1. Introduction
2. Pipeline Design and Architecture
2.1. Storage Tool
2.2. Workflow Starter
2.3. Workflow Engine
3. Data Interpretation
- A list of measured patient samples available to the current session;
- A comprehensive per sample feature table;
- ○
- Concatenating positive and negative ion mode results;
- ○
- Including mass spectrometry data, statistical metrics, and links to third party metabolite and pathway databases;
- ○
- With unmodifiable result columns, to safeguard result integrity;
- ○
- Users can add per-feature annotations describing diagnostic relevance based on assigned user roles (i.e., data analyst, clinical laboratory specialist) and the changes are logged in an audit trail;
- Bar plot-based visualization for comparing the detected sample features against quality control, validation, or other patient samples;
- A collaborative review and approval process of each sample for patient diagnosis based on configurable user roles; and
- Real-time updates to each sample’s status as users collaboratively browse, annotate and review features.
4. Validation and Quality Control
4.1. Quality Control of Data
- Repeatability of retention time;
- Repeatability of response;
- Mass accuracy.
4.2. Validation of Data Processing
4.3. Validation of Pipeline Releases
5. Case Study: Very Long-Chain acyl-CoA Dehydrogenase Deficiency in an Adult Patient
6. Discussion
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Name | Feature Mass | Retention Time Delta % | Mean Intensity Patient | ESI− | ESI+ |
---|---|---|---|---|---|
Dihydrouracil | 115.0501 | 4.716981 | 6,865,418 | - | ↑ |
Ornithine | 133.0973 | 0.990099 | 3,652,369.5 | - | ↑ |
Xanthine | 153.0407 | 6.122449 | 14,056,124.5 | - | ↑ |
Ornithine | 155.079 | 0.990099 | 426,197.5 | - | ↑ |
Pimelic acid | 161.0808 | 2.523659 | 1,699,250.5 | - | ↑ |
L-Phenylalanine | 167.0892 | 2.506964 | 25,365,509 | - | ↑ |
Xanthine | 175.0227 | 6.122449 | 616,580 | - | ↑ |
L-Tyrosine | 183.0842 | 3.317535 | 8,101,318.5 | - | ↑ |
Pimelic acid | 184.0662 | 2.523659 | 1,089,222 | - | ↑ |
L-Phenylalanine | 188.0678 | 2.506964 | 2,532,136.5 | - | ↑ |
L-Phenylalanine | 189.0713 | 2.506964 | 268,255 | - | ↑ |
N-Acetylmannosamine | 244.0796 | 1.470588 | 9,803,499 | - | ↑ |
gamma-Glutamylphenylalanine | 296.1308 | 2.763385 | 20,148,682.5 | - | ↑ |
L-Palmitoylcarnitine | 400.3423 | 0 | 19,211,216.5 | - | ↑ |
L-Palmitoylcarnitine | 401.3458 | 0 | 5,606,300 | - | ↑ |
L-Palmitoylcarnitine | 422.3247 | 0 | 270,923.5 | - | ↑ |
Mesaconic acid | 129.0194 | 6.329114 | 19,645,695.5 | ↑ | - |
Xanthine | 151.026 | 3.255814 | 22,174,103 | ↑ | - |
Xanthine | 152.0289 | 3.255814 | 1,288,583 | ↑ | - |
Pimelic acid | 159.0661 | 0.770416 | 28,452,947 | ↑ | - |
Pimelic acid | 160.0697 | 0.770416 | 7,346,168 | ↑ | - |
L-Phenylalanine | 164.0714 | 6.376812 | 4,407,907 | ↑ | - |
L-Phenylalanine | 165.0751 | 2.608696 | 9,234,293.5 | ↑ | - |
L-Tyrosine | 180.0664 | 5.294118 | 33,613,076 | ↑ | - |
L-Tyrosine | 180.0661 | 8.823529 | 462,782.5 | ↑ | - |
N-Acetylmannosamine | 256.0597 | 1.470588 | 5,667,181.5 | ↑ | - |
gamma-Glutamylphenylalanine | 293.1141 | 3.971119 | 47,200,428 | ↑ | - |
gamma-Glutamylphenylalanine | 294.1174 | 3.971119 | 7,670,664 | ↑ | - |
Bar Plot | Feature Name | m/z | Retention Time (min) | Adduct | HMDB ID | Fold Change |
---|---|---|---|---|---|---|
(a) | Tetradecenoyl/C14:1 | 370.295 | 13.37 | M + H | HMDB0002014 | 407.936 |
(b) | Dodecanoyl/C12:0 | 367.264 | 12.24 | M + Na | HMDB0002250 | 51.387 |
(c) | Tetradecadienyl/C14:2 | 368.279 | 12.82 | M + H | HMDB0013331 | 156.804 |
(d) | Tetradecenoyl/C14:0 | 372.311 | 13.82 | M + H | HMDB0005066 | 354.890 |
(e) | Hexadecenoyl/C16:1 | 398.326 | 14.08 | M + H | HMDB0013207 | 1227.147 |
(f) | Palmitoyl/C16:0 | 401.345 | 14.52 | M + H | HMDB0000222 | 59.362 |
(g) | Linoleyl/C18:2 | 424.342 | 14.32 | M + H | HMDB0006469 | 66.594 |
(h) | Octadecenyl/C18:1 | 426.357 | 14.69 | M + H | HMDB0013338 | 74.586 |
(i) | Stearoyl/C18:0 | 428.373 | 15.08 | M + H | HMDB0000848 | 29.269 |
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Hoegen, B.; Zammit, A.; Gerritsen, A.; Engelke, U.F.H.; Castelein, S.; van de Vorst, M.; Kluijtmans, L.A.J.; Huigen, M.C.D.G.; Wevers, R.A.; van Gool, A.J.; et al. Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline. Metabolites 2021, 11, 568. https://doi.org/10.3390/metabo11090568
Hoegen B, Zammit A, Gerritsen A, Engelke UFH, Castelein S, van de Vorst M, Kluijtmans LAJ, Huigen MCDG, Wevers RA, van Gool AJ, et al. Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline. Metabolites. 2021; 11(9):568. https://doi.org/10.3390/metabo11090568
Chicago/Turabian StyleHoegen, Brechtje, Alan Zammit, Albert Gerritsen, Udo F. H. Engelke, Steven Castelein, Maartje van de Vorst, Leo A. J. Kluijtmans, Marleen C. D. G. Huigen, Ron A. Wevers, Alain J. van Gool, and et al. 2021. "Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline" Metabolites 11, no. 9: 568. https://doi.org/10.3390/metabo11090568
APA StyleHoegen, B., Zammit, A., Gerritsen, A., Engelke, U. F. H., Castelein, S., van de Vorst, M., Kluijtmans, L. A. J., Huigen, M. C. D. G., Wevers, R. A., van Gool, A. J., Gilissen, C., Coene, K. L. M., & Kulkarni, P. (2021). Metabolomics-Based Screening of Inborn Errors of Metabolism: Enhancing Clinical Application with a Robust Computational Pipeline. Metabolites, 11(9), 568. https://doi.org/10.3390/metabo11090568