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Plekhova, V.

Publications and source records attributed to Plekhova, V..

2 recordsLinked to original sources
bioRxiv2026

Ion Mobility-Enhanced LA-REIMS Improves Molecular Resolution in Ambient Biofluid Metabolomics

Ambient metabolomics techniques such as laser-assisted rapid evaporative ionization mass spectrometry (LA-REIMS) enable fast, preparation-free fingerprinting of biological samples but are inherently limited by spectral congestion in the absence of chromatographic separation. While ion mobility spectrometry provides additional gas-phase separation, maintaining ion transmission under the transient signals characteristic of laser desorption, remains analytically challenging. Here, we define operating conditions for cyclic traveling-wave ion mobility spectrometry (cIMS) that preserve transmission under LA-REIMS duty-cycle constraints and systematically evaluate how cIMS integration reshapes biofluid fingerprints and enhances chemical specificity in chromatography-free metabolomics analysis. Under optimized single-pass conditions, cIMS separation reorganized LA-REIMS spectra into structured mass/mobility feature domains, enabling selective mobility-based filtering of matrix-derived salt cluster ions. This reduced non-biological background contributions by up to 35% of total spectral intensity while preserving over 90% of detected untargeted features. Although cIMS operation introduced a sensitivity penalty relative to time-of-flight-only acquisition, approximately 80% of the total ion current was recovered under optimized conditions. Mobility-resolved data revealed coherent homologous series and class-specific structural trends, particularly for lipids, supporting class-level annotation. Analysis of 101 metabolite and lipid standards covering a broad physicochemical range (logP -5.30 to 19.40) demonstrated comprehensive molecular coverage, high mass accuracy (mean 2.4 ppm), and good agreement with reference CCS values (mean deviation 4.0%), with isomer separation observed for biologically important secondary bile acids in extended separation cycles. Collectively, these results establish LA-REIMS-cIMS as a practical analytical strategy for enhancing chemical specificity and spectral interpretability in support of high-throughput large-scale metabolic fingerprinting. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=147 SRC="FIGDIR/small/709786v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@18a2dfdorg.highwire.dtl.DTLVardef@d165d6org.highwire.dtl.DTLVardef@1750291org.highwire.dtl.DTLVardef@fbbce9_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO Ion mobility spectrometry adds an orthogonal gas-phase separation to LA-REIMS, reorganizing complex biofluid spectra into distinct mass-mobility feature bands and improving molecular resolution in rapid ambient ionization metabolomics. C_FIG

biochemistry↗
bioRxiv2025

Robust metabolomics data normalization across scales and experimental designs

Metabolomics studies employing liquid chromatography-mass spectrometry are affected by signal drift and batch effects, introducing technical variance that impedes biological knowledge discovery. Quality control (QC) sample-based normalization strategies are widely implemented but remain vulnerable to outliers, thereby reducing normalization performance. We introduce rLOESS, rGAM and tGAM, three robust normalization methods that improve resistance to outliers by downweighting or accommodating them. Leveraging additive models, the rGAM and tGAM methods allow flexible non-linear modeling, differential sample weighting, and data-driven QC representativeness evaluation. Implementations of these methods are gathered in the Metanorm R package, integrating robust normalization with visualization for performance verification, while supporting efficient parallel processing. In in silico and/or experimental datasets, the robust methods, relative to several popular existing strategies, improved replicate concordance, and reduced drift and batch effects. The robust methods, with improved recovery of the underlying signal demonstrated in simulation, produced distinct differential abundance results, highlighting the impact of normalization on downstream statistical inference. Overall, tGAM-based normalization suggested the best performance across scenarios and is proposed as default choice. Metanorm is versatile, supporting normalization in metabolomics studies across scales and experimental setups.

bioinformatics↗