Biology subjects

Cuypers, E.

Publications and source records attributed to Cuypers, E..

4 recordsLinked to original sources

bioRxiv2026

Spatial Single Cell Lipid-Transcriptomic Coupling Reveals Metabolic Niches in Glioblastoma

Glioblastoma is characterized by spatial heterogeneity, with tumor-core and invasive-edge regions differing in cellular composition, transcriptional state, and metabolic context. Spatial transcriptomics has improved understanding of glioblastoma tissue organization. However, cellular transcriptional programs and metabolic interpretation remain poorly resolved. Here, single-cell matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) with spatial transcriptomics were integrated on the same tissue sections to map lipid and transcriptomic organization across matched tumor-core and invasive-region samples from 10 glioblastoma patients. Common region-dependent cellular organization along the core - versus invasive regions were identified after accounting for patient specific signatures. Tumor cores were enriched with astrocyte-like malignant and immune cell types, whereas invasive regions showed increased contribution from oligodendrocyte (progenitor cells). Despite these compositional differences, tumor and invasive cells had a shared transcriptional state space, indicating that regional identity is shaped by altered spatial organization of shared cell states. Transcriptional programs associated with proliferation, hypoxia, extracellular matrix remodeling, tumor associated macrophages and microglia (TAMs) phagocytic activity, T-cell infiltration, and lipid synthesis were spatially structured and differed between tumor and invasive compartments. MALDI-MSI revealed broad lipidomic remodeling across these regions. Tumor regions were enriched in membrane and storage lipid classes, whereas invasive regions showed relative enrichment of lipid species associated with membrane turnover. Integrating lipid and transcriptomic layers revealed spatial lipid-gene program coupling, with tumor cores showing stronger and more coherent coupling than invasive regions. TAM phagocytic activity co-localized with cholesteryl ester abundance, while lipid synthesis coupled to phosphatidylcholine-rich astrocyte-like tumor niches. Exploratory analysis indicated that MGMT promoter methylation may be associated with this altered lipid-transcriptional coupling, particularly in TAM-associated lipid-handling programs. Together, these findings imply that spatial coordination between lipid states and transcriptional programs is a key feature of glioblastoma metabolic heterogeneity.

molecular biology↗

bioRxiv2026

Atherosclerotic plaque iron accumulation characterizes a distinct phase of intra-plaque hemorrhage and is associated with inflammation and remodeling

Intraplaque haemorrhage (IPH) is a hallmark of advanced atherosclerosis and a major risk factor for ischemic stroke and myocardial infarction. Current IPH classification focusses on extravascular erythrocyte presence as a proxy of acute bleeding, where iron detection generally indicates older haemorrhages. While often used interchangeably, a comprehensive analysis of transcriptional and metabolic context and impact of iron and erythrocyte deposition on the plaque is still lacking. Here, we investigate iron as a late-stage IPH hallmark in human atherosclerotic plaques. We analysed erythrocyte-rich, iron-rich, and non-IPH regions in human carotid endarterectomy plaques by re-analysing a published transcriptomic dataset of 43 patient samples. In addition, we performed histological and immune phenotyping to define plaque traits associated with iron versus erythrocyte accumulation. Finally, we performed spatial metabolic profiling to functionally define iron-rich regions. Although iron and erythrocyte deposits frequently co-localised, both co-related with different histological traits. While iron- and erythrocyte-rich regions shared transcriptomic features of advanced plaques compared with non-IPH regions, direct comparison showed differences in gene expression profiles. Iron deposition was associated with increased myeloid cell accumulation and a unique spatial metabolic signature distinct from erythrocyte-rich and non-IPH regions. While sharing many characteristics with IPH plaques, the molecular, cellular and metabolic landscape of iron-rich regions is marked by features of plaque remodelling and repair. This makes iron deposition a unique hallmark of late-stage IPH, extending the current erythrocyte-based definition of IPH.

pathology↗

bioRxiv2026

Thyra: Bridging Mass Spectrometry Imaging and SpatialData for Unified Multi-Modal Analysis

Mass Spectrometry Imaging (MSI) is a powerful technique for mapping molecular distributions, and its integration with other imaging modalities is crucial for comprehensive understanding of molecular systems. Fragmented data formats and the limitations of existing standards like imzML, challenge spatial biology centric multi-modal data analysis and adherence to FAIR data principles. This paper introduces Thyra, a modern Python library designed to convert MSI data into the SpatialData framework, a unified and extensible multi-platform file format that crucially integrates MSI into the broader spatial omics ecosystem. Thyras modular architecture, intelligent mass axis resampling, and sparse matrix backend address performance bottlenecks and facilitate seamless integration with tools for advanced spatial statistics and visualization. By adopting SpatialData, Thyra not only improves data interoperability, accessibility, and reusability, but also unlocks new avenues for multi-modal research, empowering scientists to integrate rich chemical information into diverse biological workflows.

bioinformatics↗

bioRxiv2025

One Section, Two Worlds: Single-Cell Integration of MALDI-MSI and Spatial Transcriptomics on the Same Single Tissue Section

Understanding tissue complexity requires spatially resolved multiomic data at single-cell resolution. Here, we present a workflow that integrates high-resolution matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) with Xenium spatial transcriptomics (SPT) on a single tissue section. This one-section strategy ensures exact spatial correspondence between metabolic and transcriptomic features, avoiding the misalignment issues of serial sections, where even minor offsets can result in sampling different cells. We validate compatibility of MALDI-MSI with downstream SPT, preserving transcriptomic quality despite semi-destructive ionization. Using mouse brain and human glioblastoma tissues, we achieve pixel-perfect modality coregistration, enabling per-cell MALDI spectra extraction aligned with gene expression. Integrated clustering reveals enhanced cell-type resolution and identifies metabolic heterogeneity within transcriptionally defined populations. This enables a direct and precise correlation between what a cell is doing and its biochemical state, providing a more holistic and accurate picture of cellular function, heterogeneity, and interaction in health and disease. Our workflow provides a scalable path to multiomic atlases of disease and development, advancing both data integration and translational research.

molecular biology↗