D. Buelow, R. | Search bioRxiv

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↗

bioRxiv2022

Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT)

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell and pathomics data to find sample level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics and pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell and pathomics data. Moreover, PILOT provides a statistical approach to delineate non-linear changes in cell populations, gene expression, and tissue structures related to the disease trajectories supporting interpretation of predictions.

bioinformatics↗