Biology subjects

Schwaiger-Haber, M.

Publications and source records attributed to Schwaiger-Haber, M..

4 recordsLinked to original sources

bioRxiv2026

Using XCiMS to Quantify the Benefits of Ion Mobility Spectrometry for Untargeted Metabolomics

Integrating ion mobility spectrometry (IMS) with liquid chromatography-mass spectrometry (LC-MS) has the potential to improve untargeted metabolomics workflows, but assessing the benefits of IMS has been challenging due to a paucity of software solutions to process raw LC-IMS-MS data in a manner that enables direct comparison with LC-MS data. We developed the XCiMS software to collapse 4D data into pseudo-3D data. Peak detection is then performed in the pseudo-3D space, followed by targeted extraction of 4D signals from 3D-guided regions of interest. Here, we applied XCiMS to estimate the potential of IMS for untargeted metabolomics when analyzing human plasma. First, we assessed the number of metabolites that are uniquely resolved by LC-IMS-MS but not LC-MS. Although as many as 50% of 3D peaks produce multiple 4D peaks when using high-resolution IMS, few represent newly resolved structural isomers. We found that the increase in features due to IMS was mostly caused by charge isomers and ion clusters. Second, we investigated the impact of IMS on metabolite identification. When using a 16-minute chromatographic separation, we found that IMS can decrease the frequency of chimeric MS/MS by about half. We conclude that while IMS adds considerable complexity to untargeted metabolomics data without resolving a large number of additional structural isomers relative to a standard LC-MS experiment, it does facilitate metabolite identification by decreasing chimeric spectra and providing CCS values for library searching.

bioinformatics↗

bioRxiv2026

Cell-specific isotope labeling identifies myo-inositol transfer between neurons and oligodendroglia to support myelin repair

Neurons and glial cells are biochemically coupled through the exchange of nutrients, but our knowledge of which metabolites are transferred between them remains limited due to technical challenges. Here, we introduce a strategy to label specific cell types with isotopic tracers so that metabolite transfer can be measured directly in the intact brain. By engineering neurons in mice to metabolize 13C-labeled cellobiose, a glucose dimer that wild-type cells cannot catabolize, we selectively track neuron-derived metabolites by using mass spectrometry-based metabolomics. Applying this approach enabled us to identify myo-inositol as a critical metabolite synthesized by neurons and transferred to oligodendrocyte progenitor cells (OPCs) via the SLC5A3 transporter. The transfer of myo-inositol from neurons to OPCs promotes OPC proliferation and differentiation by enhancing phosphatidylinositol synthesis and upregulating expression of myelin-associated genes. During demyelination, deficient nutrient transfer can be rescued by dietary supplementation of myo-inositol, which accelerates myelin repair. These findings establish a generalizable technology for tracing intercellular metabolite transfer in vivo and identify a previously unrecognized mechanism of myo-inositol transfer from neurons to glial cells in support of CNS regeneration, revealing a potential metabolic target for therapeutic intervention in neurodegenerative disease.

biochemistry↗

bioRxiv2025

Construction of Multi-Modal Transcriptome-Small Molecule Interaction Networks from High-Throughput Measurements to Study Human Complex Traits

We present Gene-Embedded Multi-modal Networks (GEM-Net), a semi-supervised framework for constructing multi-modal networks centered on genes. GEM-Net uses gene-level modules and selectively incorporates heterogeneous omics profiles using a correlated meta-analysis strategy that accounts for scale imbalance, missingness, and intra-modular correlation. Prior to network inference, we developed a harmonized data processing protocol that adjusts each omic layer independently through a shared mathematical workflow involving transformation, dimensionality reduction, and regression-based covariate adjustment. GEM-Net modules were inferred and benchmarked against unsupervised methods using transcriptomic, metabolomic, and lipidomic data from the Long Life Family Study (LLFS), a unique cohort enriched for exceptional familial longevity and health. GEM-Net modules were more diverse and biologically interpretable, with stronger support from protein- protein interactions, transcriptional regulation, and metabolic annotations. Applying GEM-Net to metabolic health in LLFS revealed an axis between the microbiome-derived metabolite N-acetylglycine and immune genes (FCER1A, HDC, CPA3, MS4A2) associated with improved insulin sensitivity and reduced inflammation in healthy older individuals. GEM-Nets offer a reusable reference from a long-lived population and a generalizable framework for multi-omics discovery. https://doi.org/10.5281/zenodo.15003731.

systems biology↗

bioRxiv2021

HERMES: a molecular formula-oriented method to target the metabolome

Comprehensive metabolome analyses are hampered by low identification rates of metabolites due to suboptimal strategies in MS and MS2 acquisition, and data analysis. Here we present a molecular formula-oriented and peak detection-free method, HERMES, that improves sensitivity and selectivity for metabolite profiling in MS and structural annotation in MS2. An analysis of environmental water, E. coli, and human plasma extracts by HERMES showed increased biological specificity of MS2 scans, leading to improved mass spectral similarity scoring and identification rates when compared to iterative data-dependent acquisition (DDA). HERMES is available as an R package with a user-friendly graphical interface to allow data analysis and interactive tracking of compound annotations.

bioinformatics↗