Biology subjects

Schwenzfeier, J.

Publications and source records attributed to Schwenzfeier, J..

4 recordsLinked to original sources

bioRxiv2026

Tensile Expansion Mass Spectrometry for single cell metabolomics imaging

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) enables the spatial mapping of endogenous biomolecules within native biological specimens; however, it remains limited in achieving single-cell resolution. While advances in instrument modifications, computational processing methods, and tissue-based sample preparation have facilitated high lateral resolutions and cellular level imaging, resolving metabolic heterogeneity at the single-cell level remains challenging for users without specific expertise or custom instrumentation. Here, we present tensile expansion mass spectrometry (TExMS), a cost-effective approach for single-cell MALDI-MSI that is compatible with commercial MSI instrumentation. TExMS utilizes highly stretchable hydrogels as a substrate for live-cell seeding, attachment, and desiccation, avoiding the need for chemical fixation and enabling the retention of both intracellular and extracellular metabolites, including media-derived components that are lost during fixation and washing. We used TExMS to expand individual cells of a human high-grade serous ovarian cancer (HGSOC) cell line and spatially map their small molecule (<800 Da) production. TExMS enabled [~]4-fold linear expansion of the hydrogel, translating to a [~]1.7-fold increase in average cell area and [~]1.3-fold increase in nuclear area and resulting in improved lateral resolution of metabolite distributions. Benchmarking against other platforms for high resolution MALDI-MSI, TExMS offered comparable spatial resolution to microgrid-enabled MALDI-MSI with 15 to 20-fold shorter acquisition times. We then used TExMS to map numerous intermediates from glycolysis, the tricarboxylic acid (TCA) cycle, and amino acid biosynthesis and probe the effects of serum starvation conditions on metabolic flux through these pathways, demonstrating a powerful use case for single-cell MALDI-MSI through TExMS. Table of Contents (TOC) O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=85 SRC="FIGDIR/small/745024v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@18f8c09org.highwire.dtl.DTLVardef@132bc4aorg.highwire.dtl.DTLVardef@1e7c2caorg.highwire.dtl.DTLVardef@a586cf_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2025

LipidQMap - An Open-Source Tool for Quantitative Mass Spectrometry Imaging of Lipids

Mass spectrometry imaging (MSI) is a powerful tool in both basic and clinical research, enabling spatial visualization of biomolecules and drugs in tissue sections. However, factors influencing mass spectrometric response during imaging are increasingly recognized for their impact on apparent molecular distribution. Quantitative mass spectrometry imaging (qMSI) addresses this variability by incorporating analytical standards that undergo the same processes as analytes. While qMSI sample preparation protocols for omics-scale quantitative lipidomics are actively evolving, software solutions for downstream data processing remain scarce. Here, we introduce LipidQMap, the first open-source platform for processing omics-scale qMSI lipidomics data. LipidQMap applies one-point calibration, normalizing annotated lipid signals against class-specific standards on a pixel-by-pixel basis, and generates concentration heat maps in pmol/mm2. The software supports centroided data import, recalibration, and lipid identification using a built-in, user-modifiable lipid database. LipidQMap resolves Na/H adduct isobaric overlaps by leveraging sodiated-to-protonated adduct ratios of standards, validated across several MSI platforms using mouse brain sections. Type II isobaric overlaps are corrected using predicted isotopic patterns. Extensive validation demonstrates that LipidQMap is robust across MSI platforms and harmonizes qMSI data, enabling more accurate and reproducible spatial lipid quantification.

bioinformatics↗

bioRxiv2025

Single-cell mass spectrometry imaging combined with immunofluorescence reveals neutrophil heterogeneity in inflammation

Multimodal single-cell approaches allow for a holistic analysis of complex biological systems. In this study, we developed a novel single-cell analysis pipeline integrating immunofluorescence-based protein with mass spectrometry imaging-based lipid analysis of circulating human neutrophils. The combination of both modalities identified the emergence of pathogenic neutrophils in liver cirrhosis patients thus epitomizing the potential of this technology to reveal cellular phenotypes in health and disease.

immunology↗

bioRxiv2024

Histology-Guided Single-Cell Mass Spectrometry Imaging using Integrated Bright-field and Fluorescence Microscopy

The rapidly evolving field of spatial biology revolves around the analysis of cells in their native microenvironment. This analysis can include morphological features, the presence of specific antigens or gene expression. To add another layer of information, recent methodological advances in matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) now enable the untargeted analysis of lipids and metabolites at subcellular resolution. The integration of MALDI-MSI at the single-cell level with established optical modalities, however, relies on an accurate yet intricate co-registration. Here, we describe the integration of bright-field and fluorescence microscopy into a prototype ion source of a state-of-the art MALDI-MSI instrument to obtain lipid and fluorescence microscopy-derived information from the same specimen, hence with intrinsic spatial correlation. We demonstrate the potential of the combined mass spectrometric and optical single-cell analysis on three examples. This includes the visualization of intracellular lipid distributions in macrophages, the introduction of pre-MALDI immunofluorescence staining on the example of murine cerebellum, and the heterogeneity of lipid profiles of tumor infiltrating neutrophils correlated to their individual microenvironments. Overall, the achieved tight correlation of single-cell lipid profiles with morphologic features and protein expression patterns constitutes a powerful resource for cell biology.

cell biology↗