Alport syndrome is a rare kidney disease typically more severe in males due to its X-linked inheritance. However, female patients with heterozygous X-linked Alport syndrome (XLAS) can develop renal failure over time, necessitating accurate pathological assessment for effective therapy. A key pathological finding in female XLAS patients is the mosaic pattern of partial loss of α5 chains of type IV collagen. This study, using a mouse model of XLAS with a nonsense mutation (R471*) in the Col4a5 gene, analogous to human XLAS, aimed to examine the consistency of this pattern with the glomerular basement membrane (GBM) structure. A modified periodic acid-methenamine silver (PAMS) staining method was developed for clearer GBM visualization. The integrated images from COL4α5-stained fluorescence, PAMS, and low-vacuum scanning electron microscopy (LVSEM) into a single-slide section and applied supervised deep learning to predict GBM lesions. Results showed significant individual variability in urinary protein levels and histological lesions. Pathological parameters, including crescent formation, focal segmental glomerulosclerosis, and the COL4α5/α2 ratio, correlated with clinical parameters like urinary protein and plasma creatinine levels. Integrated LVSEM analysis revealed dense GBM regions corresponded to areas where COL4α5 was preserved, while coarse GBM (basket-weave lesions) occurred in COL4α5-deficient regions. These advanced techniques can enhance biopsy-based diagnosis of Alport syndrome and aid in developing AI diagnostic tools for diseases involving basement membrane lesions.
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