ArticleJournal of natural products2021
Interlaboratory Comparison of Untargeted Mass Spectrometry Data Uncovers Underlying Causes for Variability.
Article in Journal of natural products, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
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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.
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.
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Who cites it
37 citing papers in PubMed.
- Multi-Platform Metabolomics for Fraud Detection in Spices and Botanical Food Supplements: From Fingerprint Acquisition to Decision-Grade Evidence.Molecules (Basel, Switzerland) · 2026Review
- Mass spectrometry-based metabolomics approaches to interrogate host-microbiome interactions in mammalian systems.Natural product reports · 2026Review
- Cultivar to chemotype: characterizing complex botanicals with mass spectrometry metabolomics.Natural product reports · 2026Review
- Combined multi-omics and multi-spectral profiling of plasma extracellular vesicles reveals liquid biopsy biomarkers for glioma diagnosis.Cell reports. Medicine · 2026Article
- Comparability of Liquid Chromatography Tandem Mass Spectrometry Analysis of Dissolved Organic Matter across Laboratories.Environmental science & technology · 2026Article
- Discovery of pyridomycin derivatives as InhA inhibitors from actinomycetes through molecular networking and an In-House tandem mass library.Natural products and bioprospecting · 2026Article
- Review
- From Retention Time to Functional Group Assignment: A Chemical Database-Driven Approach for High-Resolution Mass Data of Marine Dissolved Organic Matter.Rapid communications in mass spectrometry : RCM · 2025Article
- To Fly, or Not to Fly, That Is the Question: A Deep Learning Model for Peptide Detectability Prediction in Mass Spectrometry.Journal of proteome research · 2025Article
- Identifying novel metabolites in children with attention-deficit hyperactivity disorder through metabolome profiling.Translational psychiatry · 2025Article
- Library Enabling Annotation of Botanical Natural Products (LEAFBot): An Open-Access Library of Mass Spectrometry Fragmentation Spectra for Plant Metabolites.Journal of the American Society for Mass Spectrometry · 2025Article
- MS1FA: Shiny app for the annotation of redundant features in untargeted metabolomics datasets.Bioinformatics (Oxford, England) · 2025Article
- Harmonization of FT-ICR-MS Instruments for Interoperable Multi-Laboratory Comprehensive Compositional Profiling.Analytical chemistry · 2025Article
- Article
- Effect of different pooled qc samples on data quality during an inter-batch experiment in untargeted UHPLC-HRMS analysis on two different MS platforms.Analytical and bioanalytical chemistry · 2025Article
- Soil microbiome characterization and its future directions with biosensing.Journal of biological engineering · 2024Article
- Stable Isotope Tracing Analysis in Cancer Research: Advancements and Challenges in Identifying Dysregulated Cancer Metabolism and Treatment Strategies.Metabolites · 2024Review
- metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics.Metabolites · 2024Article
- Integrating High-Resolution Mass Spectral Data, Bioassays and Computational Models to Annotate Bioactives in Botanical Extracts: Case Study Analysis ofMolecules (Basel, Switzerland) · 2024Article
- Liquid chromatography-mass spectrometry quantification of phytochemicals inFrontiers in chemistry · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Despite the value of mass spectrometry in modern natural products discovery workflows, it remains very difficult to compare data sets between laboratories. In this study we compared mass spectrometry data for the same sample set from two different laboratories (quadrupole time-of-flight and quadrupole-Orbitrap) and evaluated the similarity between these two data sets in terms of both mass spectrometry features and their ability to describe the chemical composition of the sample set. Somewhat surprisingly, the two data sets, collected with appropriate controls and replication, had very low feature overlap (25.7% of Laboratory A features overlapping 21.8% of Laboratory B features). Our data clearly demonstrate that differences in fragmentation, charge state, and adduct formation in the ionization source are a major underlying cause for these differences. Consistent with other recent literature, these findings challenge the conventional wisdom that electrospray ionization mass spectrometry (ESI-MS) yields a simple one-to-one correspondence between analytes in solution and features in the data set. Importantly, despite low overlap in feature lists, principal component analysis (PCA) generated qualitatively similar PCA plots. Overall, our findings demonstrate that comparing untargeted metabolomics data between laboratories is challenging, but that data sets with low feature overlap can yield the same qualitative description of a sample set using PCA.
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Registered trials
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.