Biology subjects

Soltwisch, J.

Publications and source records attributed to Soltwisch, J..

3 recordsLinked to original sources

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↗

bioRxiv2024

msiFlow: Automated Workflows for Reproducible and Scalable Multimodal Mass Spectrometry Imaging and Immunofluorescence Microscopy Data Processing and Analysis

Multimodal imaging by matrix-assisted laser desorption ionisation mass spectrometry imaging (MALDI MSI) and immunofluorescence microscopy holds great potential for understanding pathological mechanisms by mapping molecular signatures from the tissue microenvironment to specific cell populations. However, existing open-source software solutions for analysis of MALDI MSI data are incomplete, require programming skills and contain laborious manual steps, hindering broadly applicable, reproducible, and high-throughput analysis to generate impactful biological discoveries across interdisciplinary research fields. Here we present msiFlow, an accessible open-source, platform-independent and vendor-neutral software for end-to-end, high-throughput, transparent and reproducible analysis of multimodal imaging data. msiFlow integrates all necessary steps from import and pre-processing of raw MALDI MSI data to visual analysis output, as well as registration, along with state-of-the-art and newly developed algorithms, into automated workflows. Using msiFlow, we unravel the molecular heterogeneity of leukocytes in infected tissues by spatial regulation of ether-linked phospholipids containing arachidonic acid. We anticipate that msiFlow will facilitate the broad applicability of MSI in the emerging field of multimodal imaging to uncover context-dependent cellular regulations in disease states.

bioinformatics↗

bioRxiv2024

Large-Scale Evaluation of Spatial Metabolomics Protocols and Technologies

Spatial metabolomics using imaging mass spectrometry (MS) enables untargeted and label-free metabolite mapping in biological samples. Despite the range of available imaging MS protocols and technologies, our understanding of metabolite detection under specific conditions is limited due to sparse empirical data and predictive theories. Consequently, challenges persist in designing new experiments, and accurately annotating and interpreting data. In this study, we systematically measured the detectability of 172 biologically-relevant metabolites across common imaging MS protocols using custom reference samples. We evaluated 24 MALDI-imaging MS protocols for untargeted metabolomics, and demonstrated the applicability of our findings to complex biological samples through comparison with animal tissue data. We showcased the potential for extending our results to further analytes by predicting metabolite detectability based on molecular properties. Additionally, our interlaboratory comparison of 10 imaging MS technologies, including MALDI, DESI, and IR-MALDESI, showed extensive metabolite coverage and comparable results, underscoring the broad applicability of our findings within the imaging MS community. We share our results and data through a new interactive web application integrated with METASPACE. This resource offers an extensive catalogue of detectable metabolite ions, facilitating protocol selection, supporting data annotation, and benefiting future untargeted spatial metabolomics studies.

systems biology↗