ReTimeML: a retention time predictor that supports the LC-MS/MS analysis of sphingolipids
The analysis of ceramide (Cer) and sphingomyelin (SM) lipid species using liquid chromatography-tandem mass spectrometry (LC-MS/MS) continues to present challenges as their precursor mass and fragmentation can correspond to multiple molecular arrangements. To address this constraint, we developed Re...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2024
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| In: |
Scientific reports
Year: 2024, Volume: 14, Pages: 1-18 |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-024-53860-0 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41598-024-53860-0 Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41598-024-53860-0 
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| Author Notes: | Michael Allwright, Boris Guennewig, Anna E. Hoffmann, Cathrin Rohleder, Beverly Jieu, Long H. Chung, Yingxin C. Jiang, Bruno F. Lemos Wimmer, Yanfei Qi, Anthony S. Don, F. Markus Leweke, Timothy A. Couttas |
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| 245 | 1 | 0 | |a ReTimeML |b a retention time predictor that supports the LC-MS/MS analysis of sphingolipids |c Michael Allwright, Boris Guennewig, Anna E. Hoffmann, Cathrin Rohleder, Beverly Jieu, Long H. Chung, Yingxin C. Jiang, Bruno F. Lemos Wimmer, Yanfei Qi, Anthony S. Don, F. Markus Leweke, Timothy A. Couttas |
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| 520 | |a The analysis of ceramide (Cer) and sphingomyelin (SM) lipid species using liquid chromatography-tandem mass spectrometry (LC-MS/MS) continues to present challenges as their precursor mass and fragmentation can correspond to multiple molecular arrangements. To address this constraint, we developed ReTimeML, a freeware that automates the expected retention times (RTs) for Cer and SM lipid profiles from complex chromatograms. ReTimeML works on the principle that LC-MS/MS experiments have pre-determined RTs from internal standards, calibrators or quality controls used throughout the analysis. Employed as reference RTs, ReTimeML subsequently extrapolates the RTs of unknowns using its machine-learned regression library of mass-to-charge (m/z) versus RT profiles, which does not require model retraining for adaptability on different LC-MS/MS pipelines. We validated ReTimeML RT estimations for various Cer and SM structures across different biologicals, tissues and LC-MS/MS setups, exhibiting a mean variance between 0.23 and 2.43% compared to user annotations. ReTimeML also aided the disambiguation of SM identities from isobar distributions in paired serum-cerebrospinal fluid from healthy volunteers, allowing us to identify a series of non-canonical SMs associated between the two biofluids comprised of a polyunsaturated structure that confers increased stability against catabolic clearance. | ||
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| 700 | 1 | |a Hoffmann, Anna E. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Rohleder, Cathrin |e VerfasserIn |0 (DE-588)1023876469 |0 (DE-627)718655214 |0 (DE-576)367576775 |4 aut | |
| 700 | 1 | |a Jieu, Beverly |e VerfasserIn |4 aut | |
| 700 | 1 | |a Chung, Long H. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Jiang, Yingxin C. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Lemos Wimmer, Bruno F. |e VerfasserIn |4 aut | |
| 700 | 1 | |a Qi, Yanfei |e VerfasserIn |4 aut | |
| 700 | 1 | |a Don, Anthony S. |e VerfasserIn |4 aut | |
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| 700 | 1 | |a Couttas, Timothy A. |e VerfasserIn |4 aut | |
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