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

Area of Science:

• Nephrology
• Immunology
• Genomics

Background:

• Minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS) are primary nephrotic syndromes with distinct clinical trajectories.
• Both conditions involve podocyte injury and immune system dysregulation, necessitating deeper molecular understanding.
• Identifying novel biomarkers and therapeutic targets is crucial for managing disease progression and improving patient outcomes.

Purpose of the Study:

• To investigate cellular interactions and gene expression patterns in MCD and FSGS using single-cell transcriptomics.
• To identify key genes associated with disease progression and macrophage activation.
• To develop predictive models for transitioning from MCD to FSGS.

Main Methods:

• Analysis of single-cell transcriptomic datasets (GSE213030, GSE176465).
• Application of machine learning algorithms for diagnostic modeling and immune subtype identification.
• Validation of key genes using qRT-PCR and immunohistochemistry in a mouse model, focusing on M1 macrophage activation.

Main Results:

• Six hub genes (PTPRC, ACTR2, MYO1F, UBB, CSF1R, LYN) were identified as central to macrophage activation.
• These genes showed strong correlations with M1 macrophage activation in transcriptomic and spatial co-expression analyses in rat models.
• Machine learning models confirmed the predictive value of these genes for MCD-to-FSGS progression.

Conclusions:

• The identified six hub genes serve as potential biomarkers for predicting progression from MCD to FSGS.
• These genes' roles in macrophage activation suggest them as promising therapeutic targets for personalized treatment strategies.
• This research offers insights into molecular mechanisms underlying nephrotic syndrome progression and potential avenues for intervention.