Integrating Genetics, Organoid Modeling, and AI-driven Diagnostics in Glomerular Disease Research

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Integrating Genetics, Organoid Modeling, and AI-driven Diagnostics in Glomerular Disease Research

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Co-author 1

karim ssensamba ugrocketmail@gmail.com London school of hygiene and tropical medicine/ LSHTM tropical medicine Kampala Uganda *

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Introduction

Glomerular diseases are a leading cause of chronic kidney disease (CKD) globally, marked by complex genetic and immune-mediated mechanisms. Current diagnostic methods remain invasive and lack precision. Integrating genetic profiling, bioengineered renal organoids, and artificial intelligence (AI)-based analytics can transform disease modeling and early detection. This study developed a “Smart Kidney” platform to enhance understanding and clinical management of glomerular pathology.

Methods

Patient-derived epithelial cells were reprogrammed into induced pluripotent stem cells (iPSCs) and differentiated into renal organoids mimicking glomerular-tubular networks. Whole-exome sequencing (WES) identified pathogenic variants in genes implicated in podocytopathies and complement-mediated nephritis. Organoid morphology and transcriptomic signatures were analyzed using deep-learning algorithms trained on histopathological datasets from biopsy-proven cases of minimal change disease (MCD), focal segmental glomerulosclerosis (FSGS), and IgA nephropathy (IgAN). All procedures adhered to institutional ethical approvals and the Declaration of Istanbul.

Results

The Smart Kidney system identified 47 deleterious variants across 32 genes linked to glomerular injury, including NPHS1, COL4A3, and CFH. Organoids demonstrated structural and immunohistochemical changes mirroring patient phenotypes. AI-assisted analytics achieved 92% concordance with renal biopsy findings and reduced diagnostic time by 45%. Integrated genomic-transcriptomic mapping revealed early dysregulation in collagen, cytokine, and immune pathways preceding histologic damage. Data-driven clustering enabled molecular risk profiling, supporting targeted treatment prediction.

Conclusion

The Smart Kidney framework combines genomic insight, organoid modeling, and AI diagnostics to redefine glomerular disease research and personalized nephrology. By bridging laboratory innovation and clinical care, it enhances early detection, mechanistic understanding, and therapeutic precision. Future research will expand into kidney-on-chip systems and global data collaboration to advance renal precision medicine.

Kewords