Biology subjects

Sporring, J.

Publications and source records attributed to Sporring, J..

6 recordsLinked to original sources

bioRxiv2026

Hierarchical Phase-Contrast Tomography Imaging: Applicability in biomedical research

ObjectivesHierarchical Phase-Contrast Tomography (HiP-CT) enables non-destructive, multi-scale imaging of whole human organs. We describe how HiP-CT is utilized for biomedical research within the Human Organ Atlas Hub through three case studies: mapping the enteric nervous system (ENS) of the human colon, analysing myocardial and AV conduction architecture in Tetralogy of Fallot (TOF), and characterizing ductal organization in breast carcinoma. The challenges we faced with this novel biomedical data are discussed. MethodsWhole-organ and region-of-interest scans of three types of human organs were acquired at the European Synchrotron Radiation Facility (ESRF) with isotropic voxel sizes ranging from 20 {micro}m to 0.8 {micro}m. For the colon, voxel binning and RootPainter were employed to tackle data size to segment the ENS. For the heart, voxel-wise myocyte orientation mapping was calculated in terabyte-scale datasets with a high-performance computational framework (Cardiotensor). Breast carcinoma samples were correlated with histopathology for structure validation. ResultsHiP-CT revealed the large-scale organization of the ENS in the colon, enabling visualisation of the 3D structures of the ENS across the colon In TOF hearts, analysis uncovered abnormal myocardial structure and heterogeneous conduction system morphology. In breast carcinoma, HiP-CT resolved the full hierarchy of ductal structures and vascular relationships within tumour and peritumoral regions. ConclusionsHiP-CT provides unprecedented, hierarchical insight into intact human organ structure, bridging the gap between histology and radiology. Advances in knowledgeHiP-CT establishes a new ex vivo radiological modality capable of linking microscale pathology to whole-organ context, advancing translational research in neurogastroenterology, cardiology, and oncology

biophysics↗

bioRxiv2025

In-depth 3D Exploration of Autosomal Dominant Polycystic Kidney Disease Through Light Sheet Fluorescence Microscopy

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most prevalent genetic kidney disorder. Animal preclinical studies are one of the main tools to study this disease, often through either 2D histology imaging for high-resolution analysis or CT or MRI for full kidney segmentation. As an alternative to these modalities, we propose the use of Light Sheet Fluorescence Microscopy (LSFM) for high-resolution 3D imaging of healthy and ADPKD-induced mouse kidneys, enabling a detailed volumetric morphological analysis of the diseases effects. In a mouse ADPKD model, ex vivo imaging of the kidneys was performed through LSFM, after which a combination of machine learning and other processing techniques allowed us to perform an in-depth image analysis. This includes the segmentation of key structures, such as the full kidney volume and, within it, its internal cavities, cortex, glomeruli, and cysts, complemented by texture analysis of tubular structures in the cortical area. Pathological kidneys exhibited significant volume enlargement and increased internal cavities due to cystogenesis. While glomerular count remained stable, their spatial distribution was altered, showing increased interglomerular distances and show-casing the deformations produced by the disease. The texture analysis of tubules from the cortex region identified Local Binary Pattern (LBP) uniformity and porosity as key biomarkers of tissue deformation, which could be used as markers to further evaluate the development of the disease. These findings underscore the potential of LSFM imaging as a powerful tool for detailed ADPKD characterization and treatment assessment.

bioengineering↗

bioRxiv2023

A Practical Approach to Estimating the Effect of Synchrotron Radiation on Sperm Motility and Viability

Studying the dynamics of sperm tail beating in 3D is important for understanding decreasing fertility trends related to motility. Syn-chrotron X-ray tomography (SXRT) shows promising results for imaging live biological samples in 3D and time, making it a candidate for imaging sperm tail beating patterns. However, the dose of ionizing radiation (IR) that the cells would receive during image acquisition is of concern as this is likely to affect essential cellular functions. The effect of IR on motility at the dose rates encountered in a synchrotron has never been assessed. Here, results are presented of analyzing the movement and viability of sperm cells exposed at varying durations at the TOMCAT beamline (Swiss Light Source (SLS)) which has the field of view and spatial resolution needed to reconstruct sperm cells coupled with sub-second exposure times. The results indicate that motility is affected long before viability and that sperm cell dynamics are affected very quickly by the beam.

biophysics↗

bioRxiv2022

Astrocytic engagement of the corticostriatal synaptic cleft is disrupted in a mouse model of Huntington disease

Astroglial dysfunction contributes to the pathogenesis of Huntingtons disease (HD), and glial replacement can ameliorate disease course. To establish the topographic relationship of diseased astrocytes to medium spiny neuron (MSN) synapses in HD, we used 2-photon imaging to map the relationship of tRFP-tagged striatal astrocytes and rabies-traced, EGFP-tagged coupled neuronal pairs, in R6/2 HD and wild-type (WT) mice. The tagged, prospectively-identified corticostriatal synapses were then studied by correlated light electron microscopy followed by serial block-face scanning EM, allowing nm scale assessment of synaptic structure in 3D. By this means, we compared the astrocytic engagement of single striatal synapses in HD and WT brains. R6/2 HD astrocytes exhibited constricted domains, with significantly less coverage of mature dendritic spines than WT astrocytes, despite enhanced engagement of immature, thin spines. These data suggest that disease-dependent changes in astroglial engagement and sequestration of MSN synapses enable the high synaptic and extrasynaptic levels of glutamate and K+ that underlie the striatal hyperexcitability of HD. As such, these data suggest that astrocytic structural pathology may causally contribute to the synaptic dysfunction and disease phenotype of those neurodegenerative disorders characterized by network overexcitation. Significance StatementAstrocytic physiological dysfunction contributes to development of the neurodegenerative phenotype in Huntingtons disease (HD), but the structural correlates to this dysfunction are unclear. Here, we used a combination of viral tracing, phenotype-specific tagging, and ultrastructural modalities to reconstruct and study HD synapses at nm scale, in the neostriata of HD mice. We discovered significant impairment in the glial engagement of mature striatal synapses. In light of the known deficiencies in glutamate and potassium uptake by HD astrocytes, these findings suggest the potential for leakage of excitatory synaptic contents during neurotransmission, and hance a structural basis for neuronal hyperexcitability in HD. More broadly, our data suggest that astrocytic structural pathology may causally contribute to those neurodegenerative disorders associated with central hyperexcitability.

neuroscience↗

bioRxiv2022

Extracting Mitochondrial Cristae Characteristics from 3D Focused Ion Beam Scanning Electron Microscopy Data

1Mitochondria are the main suppliers of energy for cells and their bioenergetic function is regulated by mitochondrial dynamics: the constant changes in mitochondria size, shape, and cristae morphology to secure cell homeostasis. Although mitochondrial dysfunction is implicated in a wide range of diseases, our understanding of mitochondrial function remains limited by the complexity of inferring these spatial features from 2D electron microscopical (EM) images of intact tissue. Here, we present a semi-automatic method for segmentation and 3D reconstruction of mitochondria, cristae, and intracristal spaces based on 2D EM images of the murine hippocampus. We show that our method provides a more accurate characterization of mitochondrial ultrastructure in 3D than common 2D approaches and propose an operational index of mitochondrias internal organization. We speculate that this tool may help increase our understanding of mitochondrial dynamics in health and disease.

neuroscience↗

bioRxiv2020

Comparison of manual and machine learning image processing approaches to determine fungiform papillae on the tongue

Human taste perception is associated with the papillae on the tongue as they contain a large proportion of chemoreceptors for basic tastes and other chemosensation. Especially the density of fungiform papillae (FP) is considered as an index for responsiveness to oral chemosensory stimuli. The standard procedure for FP counting involves visual identification and manual counting of specific parts of the tongue by trained operators. This is a tedious task and automated image analysis methods are desirable. In this paper a machine learning image processing method based on a convolutional neural network is presented. This automated method was compared with three standard manual FP counting procedures using tongue pictures from 132 subjects. Automated FP counts, within the selected areas and the whole tongue, significantly correlated with the manual counting methods (all {rho}s [≥] 0.76). When comparing the images for gender and PROP status, the density of FP predicted from automated analysis was in good agreement with data from the manual counting methods, especially in the case of gender. Moreover, the present results reinforce the idea that caution should be applied in considering the relationship between FP density and PROP responsiveness since this relationship can be an oversimplification of the complexity of phenomena arising at the central and peripherical levels. Indeed, no significant correlations were found between FP and PROP bitterness ratings using the automated method for selected areas or the whole tongue. Besides providing estimates of the number of FP, the machine learning approach used a tongue coordinate system that normalizes the size and shape of an individual tongue and generated a heat map of the FP position and normalized area they cover. The present study demonstrated that the machine learning approach could provide similar estimates of FP on the tongue as compared to manual counting methods and provide estimates of more difficult-to-measure parameters, such as the papillaes areas and shape.

physiology↗