Biology subjects

Verbeeck, N.

Publications and source records attributed to Verbeeck, N..

6 recordsLinked to original sources

bioRxiv2026

Quantitative Imaging and Characterization of Ganglioside Diversity in Mouse and Human Brain Tissues

Our understanding of how ganglioside species are spatially and quantitatively distributed within regions in mammalian remains limited. Studies are typically translated from rodents, assuming that gangliosides species contents within brain regions reflect the human condition. Herein, we provide a rich spatial ganglioside brain atlas describing the content, compositional differences, and concentrations of 48 ganglioside species across different regions in mouse and humans with no clinically known neurodegeneration. Our quantitative mass spectrometry imaging (MSI) approach allows non-rigid co-registration of mass spectrometry and microscopy images to allow precise spatial alignment and extraction of normalised ganglioside concentration data to reference mouse brain tissue anatomy and neuropathological annotated tissues for flexible downstream lipidomics analysis. Considerable differences and similarities are observed permitting region-specific ganglioside maps across twelve brain regions in wild type mouse. Gangliosides in mouse brain tissue generally preferred Cer 36:1;O2 configurations whereas human gangliosides in corresponding anatomically-annotated regions tended to favour Cer 38:1;O2 and longer backbones. Notably, this observation is more pronounced in gray matter compared to white matter. Collectively, this study defines the precise and quantitative ganglioside atlases across mice and human brains to guide and accelerate the discovery of biomarkers and therapeutic targets for brain diseases.

neuroscience↗

bioRxiv2025

Multi-omics integration and batch correction using a modality-agnostic deep learning framework

State-of-the-art biotechnologies allow the detection of different molecular species on the same biological sample, generating complex highly-dimensional multi-modal datasets. Gaining a holistic understanding of biological phenomena, such as oncogenesis or aging, requires integrating these diverse modalities into low-dimensional data representations while correcting for technical artifacts. Here we present MIMA, a modular, unsupervised AI framework for multi-omics data integration and batch correction. Applied to complex spatial and single-cell datasets, MIMA effectively removes batch effects, while preserving biologically relevant information, and learns representations predictive of expert pathologist annotations. Additionally, it enables cross-modal translation, uncovers molecular patterns not captured by manual annotations, and despite being modality-agnostic performs on par with specialized state-of-the-art tools. MIMAs flexibility and scalability make it a powerful tool for multimodal data analysis. MIMA provides a foundation for AI-based, multi-omics augmented digital pathology frameworks, offering new opportunities for improved patient stratification and precision medicine through the comprehensive integration of high-dimensional molecular data and histopathological imaging.

bioinformatics↗

bioRxiv2024

Integrating Ambient Mass Spectrometry Imaging and Spatial Transcriptomics on the Same Cancer Tissues to Identify Gene-Metabolite Correlations

The combination of spatial metabolomics and spatial transcriptomics can reveal powerful connections between gene expression and metabolism in heterogeneous tissues, but section-to-section variability can convolute data integration. We present a novel method combining Desorption Electrospray Ionization Mass Spectrometry Imaging spatial metabolomics and spatial transcriptomics on the same tissue sections. We demonstrate this workflow on human breast and lung cancer tissues identifying correlations between metabolites and cancer-related RNA transcripts.

cancer biology↗

bioRxiv2023

Integration of Multiple Spatial-Omics Modalities Reveals Unique Insights into Molecular Heterogeneity of Prostate Cancer

Recent advances in spatial omics methods are revolutionising biomedical research by enabling detailed molecular analyses of cells and their interactions in their native state. As most technologies capture only a specific type of molecules, there is an unmet need to enable integration of multiple spatial-omics datasets. This, however, presents several challenges as these analyses typically operate on separate tissue sections at disparate spatial resolutions. Here, we established a spatial multi-omics integration pipeline enabling co-registration and granularity matching, and applied it to integrate spatial transcriptomics, mass spectrometry-based lipidomics, single nucleus RNA-seq and histomorphological information from human prostate cancer patient samples. This approach revealed unique correlations between lipids and gene expression profiles that are linked to distinct cell populations and histopathological disease states and uncovered molecularly different subregions not discernible by morphology alone. By its ability to correlate datasets that span across the biomolecular and spatial scale, the application of this novel spatial multi-omics integration pipeline provides unprecedented insight into the intricate interplay between different classes of molecules in a tissue context. In addition, it has unique hypothesis-generating potential, and holds promise for applications in molecular pathology, biomarker and target discovery and other tissue-based research fields.

cancer biology↗

bioRxiv2023

Omics Scale Quantitative Mass Spectrometry Imaging of Lipids in Brain Tissue using a Multi-Class Internal Standard Mixture

Mass spectrometry imaging (MSI) has accelerated the understanding of lipid metabolism and spatial distribution in tissues and cells. However, few MSI studies have approached lipid imaging quantitatively and those that have focus on a single lipid class. Herein, we overcome limitation of quantitative MSI (Q-MSI) by using a multi-class internal standard lipid mixture that is sprayed homogenously over the tissue surface with analytical concentrations that reflects endogenous brain lipid levels. Using this approach we have performed Q-MSI for 13 lipid classes representing >200 sum-composition lipid species. This was carried out using both MALDI (negative ion mode) and MALDI-2 (positive ion mode) and pixel-wise normalisation of each lipid species signal to the corresponding class-specific IS an approach analogous to that widely used for shotgun lipidomics from biological extracts. This approach allows pixel concentrations of lipids to be reported in pmol/mm2. Q-MSI of lipids covered 3 orders of magnitude in dynamic range and revealed subtle change sin in distribution compared to conventional total-ion-current normalisation approaches. The robustness of the method was evaluated by repeating experiments in two laboratories on biological replicates using both timsTOF and Orbitrap mass spectrometers operated with a ~4-fold difference in mass resolution power. There was a strong overall correlation in the Q-MSI result obtained using the two approaches with outliers mostly rationalised by isobaric interferences that are only resolved with the Orbitrap system or the higher sensitivity of one instrument for particular lipid species, particularly for lipids detected at low intensity. These data provide insight into how mass resolving power can affect Q-MSI data. This approach opens up the possibility of performing large-scale Q-MSI studies across numerous lipid classes and reveal how absolute lipid concentrations vary throughout and between biological tissues.

biochemistry↗

bioRxiv2020

Spatially-Aware Clustering of Ion Images in Mass Spectrometry Imaging Data Using Deep Learning

Computational analysis is crucial to capitalize on the wealth of spatio-molecular information generated by mass spectrometry imaging (MSI) experiments. Currently, the spatial information available in MSI data is often under-utilized, due to the challenges of in-depth spatial pattern extraction. The advent of deep learning has greatly facilitated such complex spatial analysis. In this work, we use a pre-trained neural network to extract high-level features from ion images in MSI data, and test whether this improves downstream data analysis. The resulting neural network interpretation of ion images, coined neural ion images, are used to cluster ion images based on spatial expressions. We evaluate the impact of neural ion images on two ion image clustering pipelines, namely DBSCAN clustering, combined with UMAP-based dimensionality reduction, and k-means clustering. In both pipelines, we compare regular and neural ion images from two different MSI datasets. All tested pipelines could extract underlying spatial patterns, but the neural network-based pipelines provided better assignment of ion images, with more fine-grained clusters, and greater consistency in the spatial structures assigned to individual clusters. Additionally, we introduce the Relative Isotope Ratio metric to quantitatively evaluate clustering quality. The resulting scores show that isotopical m/z values are more often clustered together in the neural network-based pipeline, indicating improved clustering outcomes. The usefulness of neural ion images extends beyond clustering towards a generic framework to incorporate spatial information into any MSI-focused machine learning pipeline, both supervised and unsupervised.

bioinformatics↗