ArticleAnalytical chemistry2021
Modifying Chromatography Conditions for Improved Unknown Feature Identification in Untargeted Metabolomics.
Article in Analytical chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Article
- Multi-omic responses to acute exercise in abdominal subcutaneous adipose tissue of sedentary adults: findings from MoTrPAC.bioRxiv : the preprint server for biology · 2026Article
- Integrative Multi-omics Analysis of the Human Skeletal Muscle Response to Endurance or Resistance Exercise: Findings from the Molecular Transducers of Physical Activity Consortium (MoTrPAC).bioRxiv : the preprint server for biology · 2026Article
- Modern Analytical Techniques in Epilepsy Research.International journal of molecular sciences · 2026Review
- Multi-Omic, Multi-Tissue Responses to Acute Exercise in Sedentary Adults: Findings from the Molecular Transducers of Physical Activity Consortium.bioRxiv : the preprint server for biology · 2026Article
- Improving the Detection of Analyte Degeneracies in Untargeted Liquid Chromatography-Tandem Mass Spectrometry Data.Analytical chemistry · 2025Article
- Do experimental projection methods outcompete retention time prediction models in non-target screening? A case study on LC/HRMS interlaboratory comparison data.The Analyst · 2025Article
- Predicting Vancomycin Clearance in Neonates and Infants by Integrating Machine Learning and Metabolomics With Population Pharmacokinetics.Clinical and translational science · 2025Article
- A Spatial Metabolomics Annotation Workflow Leveraging Cyclic Ion Mobility and Machine Learning-Predicted Collision Cross Sections.Journal of the American Society for Mass Spectrometry · 2025Article
- In-Depth Chemical Analysis of the Brain Extracellular Space UsingAnalytical chemistry · 2024Article
- Offline Two-Dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome Following Fecal Microbiota Transplantation.Journal of proteome research · 2024Article
- Online and Offline Prioritization of Chemicals of Interest in Suspect Screening and Non-targeted Screening with High-Resolution Mass Spectrometry.Analytical chemistry · 2024Review
- Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics.Handbook of experimental pharmacology · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Untargeted metabolomics is an essential component of systems biology research, but it is plagued by a high proportion of detectable features not identified with a chemical structure. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments produce spectra that can be searched against databases to help identify or classify these unknowns, but many features do not generate spectra of sufficient quality to enable successful annotation. Here, we explore alterations to gradient length, mass loading, and rolling precursor ion exclusion parameters for reversed phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) that improve compound identification performance for human plasma samples. A manual review of spectral matches from the HILIC data set was used to determine reasonable thresholds for search score and other metrics to enable semi-automated MS/MS data analysis. Compared to typical LC-MS/MS conditions, methods adapted for compound identification increased the total number of unique metabolites that could be matched to a spectral database from 214 to 2052. Following data alignment, 68.0% of newly identified features from the modified conditions could be detected and quantitated using a routine 20-min LC-MS run. Finally, a localized machine learning model was developed to classify the remaining unknowns and select a subset that shared spectral characteristics with successfully identified features. A total of 576 and 749 unidentified features in the HILIC and RPLC data sets were classified by the model as high-priority unknowns or higher-importance targets for follow-up analysis. Overall, our study presents a simple strategy to more deeply annotate untargeted metabolomics data for a modest additional investment of time and sample.
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