In-depth 3D exploration of autosomal dominant polycystic kidney disease through light sheet fluorescence microscopy.
Authors
Affiliations (12)
Affiliations (12)
- Bioengineering Department, Universidad Carlos III de Madrid (UC3M), Leganes, Spain.
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain.
- Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, Madrid, Spain.
- Advanced Digital Microscopy, Institute for Research in Bioimedicine (IRB Barcelona), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.
- Consejo Superior de Investigaciones Científicas - Centro de Neurociencias Cajal (CSIC - CNC), Alcalá de Henares, Spain.
- Institute for Genetics and Cancer, University of Edinburgh (UoE), Edinburgh, UK.
- Computer Science Department, Københavns Universitet (KU), Copenhagen, Denmark.
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain.
- High Performance Research Group in Physiopathology and Pharmacology of the Digestive System (NeuGut), Universidad Rey Juan Carlos (URJC), Alcorcon, Spain.
- Centro de Investigación Médica Aplicada (CIMA), Universidad de Navarra (UNAV), Pamplona, Spain.
- Bioengineering Department, Universidad Carlos III de Madrid (UC3M), Leganes, Spain. [email protected].
- Instituto de Investigación Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain. [email protected].
Abstract
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 disease's 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 showcasing 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.