Biology subjects

Didziokas, M.

Publications and source records attributed to Didziokas, M..

4 recordsLinked to original sources

bioRxiv2026

Unique mineralization pattern revealed in TBCK syndrome mouse model

TBCK syndrome is a severe degenerative leukoencephalopathy with multisystem involvement. Neurodevelopmental, craniofacial, and pulmonary challenges are among the topmost effects on these children. TBCK has been implicated in endo-lysosomal regulation, RNA transport, and mTOR-associated pathways, all of which are critical for the development of mineralized tissue. Although craniofacial abnormalities can be clinically apparent, conventional imaging approaches may overlook subtle defects in mineral quality. Here, we apply our multimodal framework to investigate the mineralization of enamel, dentin, and alveolar bone in a Tbck knockout mouse model. This is the first time our multimodal framework will be applied to a genetic condition. Using micro-computed tomography (microCT), histology, nanoindentation, energy-dispersive spectroscopy, and Raman spectroscopy, we identify tissue- and stage-dependent mineral effects undetected by microCT alone. Tbck loss resulted in differences in enamel and dentin element compositions as early as secretory and transition stages, while mechanical properties remained undetected until maturation stage. Notably, Tbck knockout enamel exhibited reduced calcium and phosphorus content, along with increased carbon content during early mineralization, consistent with the retained organic matrix. Additionally, marked and opposing alterations in magnesium and iron levels began at the secretory stage. Together, these findings define a previously unrecognized mineralization signature associated with TBCK deficiency and establish multimodal hard-tissue analysis as a sensitive approach for detecting early craniofacial phenotypes in rare genetic disorders.

developmental biology↗

bioRxiv2025

Resolving near-micron scale features within a whole sheep head using Hierarchical Phase-Contrast Tomography

BackgroundHierarchical Phase-Contrast Tomography (HiP-CT) was developed to image ex vivo intact human soft-tissue organs with local near-micron scale resolution. PurposeWe demonstrate the application of HiP-CT in combination with the recently developed Eikonal Phase Retrieval (EPR) for resolving anatomical features in large hard and soft tissue structures on a sheep head, and show the applicability for zooming to resolve anatomical features with near-micron scale resolution. Materials and MethodsWe imaged an entire skinned sheep head prepared in 70 % ethanol with HiP-CT at an isotropic voxel size of 16.5 {micro}m using the recently developed Eikonal Phase Retrieval (EPR) to reduce artefacts from bones. Local tomography zooms were taken in regions of interest: the eye (4.23 {micro}m voxels) and the coronal suture (2.20 {micro}m voxels). To compare to clinical imaging, we acquired T2-weighted MRI and CT of the sheep head and evaluated the contrast-to-noise ratio for differentiation of white and grey matter in the brain for all modalities. Further evaluations on HiP-CT images include structure tensor analysis for structural orientation in the brain as well as fibre tracking in the coronal suture. ResultsHiP-CT combined with EPR achieved high contrast for both hard and soft tissue. Comparison to MRI showed similar soft-tissue contrast, but much higher spatial resolution. Structure tensor analysis in the brain revealed the orientation of the major white matter bundles. In the eye, near-micron scale features such as retinal layers and the bundles in the optic nerve were visualized. Fiber tracking allowed analysis of the orientation of collagen fiber bundles in the coronal suture. ConclusionThis work highlights the potential of HiP-CT to image a complete sheep head, ex vivo, and hierarchically zoom without sectioning to resolve few-microns features locally, enabling comprehensive three-dimensional visualization of intricate cranial structures and their spatial interrelations. Summary statementTechnical developments in HiP-CT enable ex vivo X-ray imaging of an intact sheep head with local resolution to near-micron scale, demonstrating future viability on a human head. Key resultsO_LIA whole sheep head was imaged with synchrotron-based hierarchical phase-contrast tomography coupled with Eikonal Phase-retrieval with 16.5 {micro}m isotropic voxels. C_LIO_LIThe high contrast for both hard and soft tissue enabled differentiation of brain white and grey matter, the optic nerve and bone features within the skull. C_LIO_LILocal tomography zoom scans in an eye (4.23 {micro}m voxels) and coronal suture (2.20 {micro}m voxels) allowed visualization and analysis of near-micron sized features. C_LI

bioengineering↗

bioRxiv2024

Introducing SPROUT (Semi-automated Parcellation of Region Outputs Using Thresholding): an adaptable computer vision tool to generate 3D segmentations

The segmentation of fine-grained and complex structures from volumetric data, such as 3D biomedical images, is a manually intensive process, with performance hindered by limited training data and the difficulty of adapting AI models for specialised datasets. Here, we introduce SPROUT, a user-friendly and interpretable segmentation framework that leverages domain-specific priors, enabling experts to translate their knowledge into reproducible, high-quality segmentations across diverse imaging modalities without the need for training data. Its adaptive design facilitates parameter transferability and improves generalisation across similar datasets. Implemented as scripts and a napari plugin, it supports interactive editing and scalable batch processing, lowering the technical barrier for domain experts. We applied SPROUT to datasets spanning different imaging modalities, anatomical complexities, postures, and target structures, producing high-quality segmentations across 2D and 3D tasks. Quantitative comparisons with other methods on a representative dataset showed SPROUT achieved results comparable to expert-corrected interpolation while requiring substantially less manual input. In scenarios where SPROUT achieved high-quality results, supervised models often struggled to reach similar accuracy, highlighting the challenge of deep learning methods in complex domains. We also explored integration with foundation models to accelerate segmentation in high-contrast datasets, illustrating potential for hybrid workflows.

evolutionary biology↗

bioRxiv2024

Multi-modal characterization of rodent tooth development

Craniofacial tissues undergo hard tissue development through mineralization and changes in physicochemical properties. This study investigates the mechanical and chemical properties of developing enamel, dentin, and bone in the mouse mandible. We employ a multi-modal, multi-scale analysis of the developing incisor and first molar at postnatal day 12 by integrating micro-computed tomography (microCT), nanoindentation (NI), energy dispersive spectroscopy (EDS), and Raman spectroscopy. Our findings demonstrate distinct patterns of mechanical, elemental, and chemical changes across mineralized tissues. These results suggest that mineral composition drives mechanical properties across different craniofacial hard tissues. Integrating multi-modal characterization of mineralized tissues opens new opportunities for investigating structure-function relationships in craniofacial biology and genetics.

developmental biology↗