ArticleAnalytical and bioanalytical chemistry2026
Performance evaluation of polarity switching for plasma untargeted metabolomics using Orbitrap mass spectrometer.
Article in Analytical and bioanalytical chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Untargeted metabolomics often requires separate positive and negative ionization mode injections to maximize metabolome coverage, increasing analytical time, analysis cost, and complexity. Polarity switching (PSW) represents a promising alternative, yet its adoption in untargeted metabolomics remains limited, likely due to the lack of systematic performance evaluations as well as lack of polarity-switching capability on some mass spectrometers. In this study, we compared the performance of the PSW method to that of separate polarity injections by assessing the total number of detected features, the total number of metabolite annotations, Level 1 and 2 annotations, statistical analysis outcomes, and functional enrichment analysis results. Overall, separate polarity acquisition yielded the highest number of total detected features and annotations. Despite these differences, a strong overlap was observed at higher annotation confidence levels, with 70-76% overlap in Level 1 and Level 2 identified metabolites between the two methods. Notably, metabolites exhibiting significant fold changes showed consistent directional changes across both acquisition approaches, indicating that both methods captured similar biological trends. Statistical analyses using approaches such as t-tests and linear mixed models (LMM) showed that 50-60% of significant metabolites overlapped between methods, with similarity increasing when adjusted p-values were applied. Functional analysis further demonstrated strong agreement between the two approaches, with the most significant pathways consistently identified by both approaches. Overall, while separate polarity acquisition detects a larger number of features and annotations, PSW acquisition captures largely comparable biological information. These findings support PSW for untargeted metabolomics, particularly in exploratory, high-throughput studies prioritizing global metabolic trends with limited resources, providing practical guidance for strategy selection based on study goals and analytical priorities.
Indexed as
Identifiers
42905136What 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.