Biology subjects

Gevaert, T.

Publications and source records attributed to Gevaert, T..

3 recordsLinked to original sources

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↗

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↗

bioRxiv2020

Lipidomic profiling of clinical prostate cancer reveals targetable alterations in lipid composition

Dysregulated lipid metabolism is a prominent feature of prostate cancer that is driven by androgen receptor (AR) signaling. Herein, we used quantitative mass spectrometry to define the "lipidome" in prostate tumors with matched benign tissues (n=21), independent tissues (n=47), and primary prostate explants cultured with a clinical AR antagonist, enzalutamide (n=43). Significant differences in lipid composition were detected and spatially visualized in tumors compared to matched benign samples. Notably, tumors featured higher proportions of monounsaturated lipids overall and elongated fatty acid chains in phosphatidylinositol and phosphatidylserine lipids. Significant associations between lipid profile and malignancy were validated in unmatched samples, and PL composition was characteristically altered in patient tissues that responded to AR inhibition. Importantly, targeting of altered tumor-related lipid features, via inhibition of acetyl CoA carboxylase 1, significantly reduced cellular proliferation in tissue explants (n=13). This first characterization of the prostate cancer lipidome in clinical tissues revealed enhanced fatty acid synthesis, elongation and desaturation as tumor-defining features, with potential for therapeutic targeting.

cancer biology↗