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Vierdag, W.-M. A. M.

Publications and source records attributed to Vierdag, W.-M. A. M..

2 recordsLinked to original sources

Conserved cerebellar rhombic lip compartmentalization and Eomes regulatory networks govern unipolar brush cell development

The rhombic lip (RL) gives rise to all cerebellar glutamatergic cell types, including unipolar brush cells (UBCs). Disruptions to UBC development can lead to the neurodevelopmental disorder Dandy-Walker Syndrome and the pediatric brain tumor medulloblastoma, but these diseases have not been adequately modeled in mice. To evaluate conservation of UBC development in mouse and human, we examined UBC localization, lineage decisions, and the underlying molecular mechanisms of UBC differentiation using multiplex immunofluorescence and single-cell RNA-seq of wild-type and conditional knockout animals of the primary UBC transcription factor Eomes. Similar to the human RL, the murine RL is molecularly compartmentalized, cycling EOMES+ UBC progenitors are highly abundant, and persist after birth. Eomes regulates the transcriptional networks important for UBC differentiation and migration, but not UBC fate. Overall, our findings suggest that murine UBC development recapitulates many features of human UBC development, with EOMES playing a central role in UBC maturation.

developmental biology↗

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↗