ArticleMetabolomics : Official journal of the Metabolomic Society2019
Impact of matrix effects and ionization efficiency in non-quantitative untargeted metabolomics.
Article in Metabolomics : Official journal of the Metabolomic Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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.
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.
Who cites it
30 citing papers in PubMed.
- Multi-omics integration of proteomics and metabolomics in pediatric health and disease.Communications medicine · 2026Review
- Toward Metabolomics Analyses With Combined Capillary Vibrating Sharp-Edge Spray Ionization and Atmospheric Pressure Chemical Ionization.Rapid communications in mass spectrometry : RCM · 2026Article
- Graded selenium supplementation restructures yak colostrum composition and metabolomic organization under high-altitude conditions.Food chemistry: X · 2026Article
- Dried blood spots capture a wide range of metabolic pathways and biological changes associated with exercise.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- Ionization efficiency prediction of electrospray ionization mass spectrometry analytes based on molecular fingerprints and cumulative neutral losses.Journal of cheminformatics · 2025Article
- Accuracy, linearity, and statistical differences in comparative quantification in untargeted plant metabolomics using LC-ESI-Orbitrap-MS.Analytical and bioanalytical chemistry · 2025Article
- PIK3CA mutation fortifies molecular determinants for immune signaling in vascular cancers.Cancer gene therapy · 2025Article
- Impact of Biotransformation on Internal Concentrations and Specificity Classification of Organic Chemicals in the Zebrafish Embryo (Environmental science & technology · 2024Article
- The salivary metabolome of children and parental caregivers in a large-scale family environment study.npj metabolic health and disease · 2024Article
- Metabolomic signatures of carfilzomib-related cardiotoxicity in patients with multiple myeloma.Clinical and translational science · 2024Article
- MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics.Nature communications · 2024Article
- Multiplatform untargeted metabolomics.Magnetic resonance in chemistry : MRC · 2023Review
- Signal Response Evaluation Applied to Untargeted Mass Spectrometry Data to Improve Data Interpretability.Journal of the American Society for Mass Spectrometry · 2023Article
- Untargeted Metabolomic Analysis of Lactation-Stage-Matched Human and Bovine Milk Samples at 2 Weeks Postnatal.Nutrients · 2023Article
- Spatial Metabolomics Reveals the Multifaceted Nature of Lamprey Buccal Gland and Its Diverse Mechanisms for Blood-Feeding.Communications biology · 2023Article
- A Proposed Methodology to Deal with the Impact of In Vitro Cellular Matrix on the Analytical Performances of a Targeted Metabolomic LC-HRMS Method.International journal of molecular sciences · 2023Article
- Balancing Trade-Offs Imposed by Growth Media and Mass Spectrometry for Bacterial Exometabolomics.Applied and environmental microbiology · 2022Article
- Development of cVSSI-APCI for the Improvement of Ion Suppression and Matrix Effects in Complex Mixtures.Analytical chemistry · 2022Article
- A Novel Propidium Monoazide-Based PCR Assay Can Measure Viable UropathogenicFrontiers in cellular and infection microbiology · 2022Article
- An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics.Frontiers in molecular biosciences · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
introductionLC-MS-based untargeted metabolomics has become increasingly popular due to the vast amount of information gained in a single analysis. Many studies utilize metabolomics to profile metabolic changes in various representative biofluids, tissues, or other sample types. Most analyses are performed measuring changes in the metabolic pool of a single biological matrix due to an altered phenotype, such as disease versus normal. Measurements in such experiments are typically highly reproducible with little variation due to analytical and technological advancements in mass spectrometry. With the expanded application of metabolomics into various non-analytical scientific disciplines, the emergence of studies comparing the signal intensities of specific analytes across different biological matrices (e.g. plasma vs. urine) is becoming more common, but the matrix effect between sample types is often neglected. Additionally, the practice of comparing the signal intensities of different analytes and correlating to relative abundance is also increasingly prevalent, but the response ratio between analytes due to differences in ionization efficiency is not always accounted for in data analysis. This report serves to communicate and raise awareness of these two well-recognized issues to prevent improper data interpretation in the field of metabolomics.
objectivesWe demonstrate the impact of matrix effects and ionization efficiency with labeled analytical standards in human plasma, serum, and urine and describe how the direct comparison of non-quantitative signal intensities between biofluids, as well as between different analytes in the same biofluid, in untargeted metabolomics is inaccurate without proper response corrections.
methodsHuman plasma, serum, and urine (n = 4 technical replicates per biofluid) were spiked with a panel of labeled internal standards all at identical concentrations, simultaneously extracted, and analyzed by UHPLC-HRMS. Signal intensities were compared for demonstration of the impact of matrix effects in untargeted metabolomics. A neat mixture of two co-eluting, structurally-similar labeled standards at the same concentration was also analyzed to demonstrate the effect of ionization efficiency on signal intensity.
resultsDespite being spiked at identical concentrations, labeled standards we examined in this study showed significant differences in their signal intensities between biofluids, as well as from each other in the same biofluid, due to matrix effects. Co-eluting standards were also found to yield significantly different signal intensities at identical concentrations due to differences in ionization efficiency.
conclusionsDue to the presence of matrix effects in untargeted, non-quantitative metabolomics, the signal intensity of any single analyte cannot be directly compared to the signal intensity of that same analyte (or any other analyte) between any two different matrices. Due to differences in ionization efficiency, the signal intensity of any single analyte cannot be directly compared to the signal intensity of any other analyte, even in the same matrix.
Indexed as
Identifiers
31584114What OpenQuestion holds
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.