Evidence map›Paper›PMID 38388524›Full record

ArticleScientific reports2024

ReTimeML: a retention time predictor that supports the LC-MS/MS analysis of sphingolipids.

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 and 2 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.2field-weighted citation impact, top 24% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 5 citations in OpenAlex.

  1. Characterization of novel and recurrentJournal of neuromuscular diseases · 2026
    Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 4 institutions in 2 countries.

Michael Allwright *ForeFront, Brain and Mind Centre, The University of Sydney, Sydney, Australia.
Boris Guennewig *ForeFront, Brain and Mind Centre, The University of Sydney, Sydney, Australia.
Anna E HoffmannTranslational Research Collective, Brain and Mind Centre, The University of Sydney, Sydney, NSW, 2006, Australia.
Cathrin RohlederTranslational Research Collective, Brain and Mind Centre, The University of Sydney, Sydney, NSW, 2006, Australia.
Beverly JieuTranslational Research Collective, Brain and Mind Centre, The University of Sydney, Sydney, NSW, 2006, Australia.
Long H ChungCentenary Institute, The University of Sydney, Sydney, Australia.
Yingxin C JiangCentenary Institute, The University of Sydney, Sydney, Australia.
Bruno F Lemos WimmerTranslational Research Collective, Brain and Mind Centre, The University of Sydney, Sydney, NSW, 2006, Australia.
Yanfei QiCentenary Institute, The University of Sydney, Sydney, Australia.
Anthony S DonSchool of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, Australia.
F Markus LewekeTranslational Research Collective, Brain and Mind Centre, The University of Sydney, Sydney, NSW, 2006, Australia.
Timothy A CouttasTranslational Research Collective, Brain and Mind Centre, The University of Sydney, Sydney, NSW, 2006, Australia. timothy.couttas@sydney.edu.au.
University of Sydney · AUCentenary Institute · AUCentral Institute of Mental Health · DEHeidelberg University · DE

Funding

Medical Research Futures Fund GA268519National Health and Medical Research Council GNT1162545
6 · The paper itself

Abstract

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.

Indexed as

SphingolipidsTandem Mass SpectrometryCeramidesChromatography, LiquidHumansLiquid Chromatography-Mass SpectrometrySphingomyelinsCeramidesSphingolipidsSphingomyelinsCeramideCerebrospinal fluidLassoLC–MS/MSPythonRegression modellingRetention timeRidgeSerumSphingomyelinStreamlit

Identifiers

PMID38388524
PMCPMC10883992
OpenAlexW4392079116

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.