Serial Block Face Scanning Electron Microscopy Studies of Diabetic Kidney Disease
Diabetic kidney disease (DKD) is by far the most common cause of end stage kidney disease in the US. Novel insights about the evolution of DKD lesions may help identify new targets for treatment. Most of what we currently know about the structural changes of DKD comes from the studies performed by two-dimensional (2D) transmission electron microscopy (TEM); however, complexity of some of the cellular and subcellular structures demands for 3D studies. Here, we propose to apply serial block face scanning EM (SBF-SEM), a relatively novel technique that allows for 3D EM studies, to study DKD in human and mice. We will optimize SBFSEM tissue preparation protocols, will generate 3D models to better understand the spatial relationships between the glomerular structures. Adding quantitative approached to the volumetric data from SBF-SEM will make this much more powerful. We will develop quantitative approaches to study mitochondrial number and fission/fusion and subpodocyte space volume. Accomplishment of these goals may help finding structural evidence of pathogenetic processes that may be amenable to interventions. These could lead to proof of concept studies where interventions altering these structural variables influence outcomes.

Automated quantification of ultrastructural pathology of diabetic nephropathy using deep learning
Diabetic nephropathy is by far the most common cause of end stage kidney disease. The combination of morphometric approaches and electron microscopy have provided major contributions to the current understanding of the progression of diabetic nephropathy. Structural changes when properly quantified are not only regarded as robust biomarker of progression and severity of diabetic nephropathy, but also correlate with renal function and can predict progression of diabetic nephropathy. Application of these methods has been limited to research studies, largely because currently automated approaches are not available. Deep learning is a form of machine learning methods based on artificial neural networks which has been proven to be a powerful tool for image analysis. Here, we aim to develop deep learning algorithms to automate segmentation and quantification of key glomerular structural parameters that are relevant to diabetic nephropathy, including glomerular basement membrane thickness, podocyte foot process width and expansion of mesangium and mesangial matrix. We will train deep learning algorithms on a large collection of electron microscopy images from kidney biopsies obtained from patients with type 1 and type 2 diabetes with a wide spectrum of diabetic nephropathy severity, as well as kidney biopsies from normal control subjects and will validate the method using multiple approaches.

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