ISAC Award Program Application Abstract

A Glomerular Nanoscale Spatial Atlas
Joshua Vaughan   (Seattle, WA)
We will create a Glomerular Nanoscale Spatial Atlas (GNSA) that will be the first resource of its kind to provide high-resolution (~50 nm) annotated data sets of whole mouse glomeruli for healthy, aged, and diseased tissue as well as a smaller set of whole healthy human glomeruli at ~100 nm spatial resolution. This will be accomplished by combining advanced fluorescence microscopy methods with sophisticated machine learning image analysis. The GNSA will provide detailed 3D models created by digitally segmenting and annotating all major components of whole glomeruli, including cell number and locations and all major “structural” components (Bowman’s capsule, the glomerular basement membrane (GBM), mesangial matrix, and capillary loops). The central goals of the GNSA are to identify previously unknown glomerular phenotypes and to create a digital resource that can be mined for diverse purposes by the nephrology, pathology, and computational communities. The GNSA will be an important and groundbreaking innovation that will solve unmet needs for researchers, clinicians, and engineers as described, below. First, the GNSA will provide novel and valuable quantitated observables. These include capillary network analysis (blood volume, distribution of capillary diameters and lengths, tortuosity, network flatness), GBM surface area, mesangium volume, cell type and location for the four principal glomerular cell types, etc. Uniquely, all observables can be directly related to one another within the same individual glomerulus, rather than using statistical correlations between optical and EM data sets acquired on different tissue specimens. In turn, these observables will be valuable to compare as a function of sex, age, and disease. Second, the high spatial resolution full-glomerulus models will provide invaluable information for quantitative modeling and next-generation technologies. Researchers studying kidney hemodynamics and filtration will use the GNSA data to create detailed models of kidney function, while bioengineers will create templates for designing micro- and meso-scale artificial structures informed by real glomeruli, and medical engineers in the burgeoning field of computer-aided pathology will develop or refine algorithms that may one day revolutionize renal pathology. Third, the GNSA framework will be highly valuable to future projects. Seed funding from the ISAC for the GNSA would pay dividends by establishing a pipeline for future studies that focus on hypothesis-driven biological research or applied studies in pathology.
Data for this report has not yet been released.

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