Single-cell transcriptomics and machine-learning reveal M1 macrophage-driven progression from minimal change disease to focal segmental glomerulosclerosis.

Abstract

Minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS) are two key nephrotic syndrome types with significant clinical implications. MCD predominantly affects children, while FSGS is more common in adults, often leading to irreversible kidney dysfunction. Despite shared features like podocyte injury and immune dysregulation, their pathological and clinical presentations differ. Understanding gene expression changes in these diseases could reveal new therapeutic targets. Single-cell transcriptomic datasets (GSE213030 and GSE176465) were analyzed to investigate cellular interactions in MCD and FSGS. Machine learning algorithms developed diagnostic models, and immune subtypes were identified for detailed subtype analysis. Key genes were validated using qRT-PCR and immunohistochemical staining in a mouse model, focusing on their association with M1 macrophage activation. Integrated single-cell analysis identified six key genes (,,,,, and) central to macrophage activation. These genes were closely linked to M1 macrophage activation, as confirmed through transcriptomic profiling and spatial co-expression patterns in Sprague-Dawley (SD) rat models. Machine learning models validated their predictive value in disease progression from MCD to FSGS. This study highlights six hub genes as potential biomarkers for predicting MCD-to-FSGS progression. Their roles in macrophage activation suggest these genes may serve as novel therapeutic targets for personalized treatment strategies, particularly for patients at high risk of disease transition.