Key factor screening in mouse NASH model using single-cell sequencing combined with machine learning.

Aims: To identify and analyze genes closely related to the progression of nonalcoholic steatohepatitis (NASH) by employing a combination of single-cell RNA sequencing and machine-learning algorithms. Main methods: Single-cell RNA sequencing (scRNA-seq) analysis was performed to find the cell population with the most significant differences between the Chow and NASH groups. This approach was used to validate the developmental trajectory of this cell population and investigate changes in cellular communication and important signaling pathways among these cells. Subsequently, high dimensional Weighted Gene Co-expression Network Analysis (hdWGCNA) was used to find the key modules in NASH. Machine learning analyses were performed to further identify core genes. Deep learning techniques were applied to elucidate the correlation between core genes and immune cells. The accuracy of this correlation was further confirmed using deep learning techniques, specifically Convolutional Neural Networks. Key findings: By comparing scRNA-seq data between the Chow and NASH groups, we have observed a notable distinction existing in the Kupffer cell population. Signaling interactions between hepatic macrophages and other cells were significantly heightened in the NASH group. Through subsequent analysis of macrophage subtypes and key modules, we identified 150 genes tightly associated with NASH. Finally, we highlighted the 16 most significant core genes using multiple iterations of machine learning. Furthermore, we pointed out the close relationship between core genes and immune cells. Significances: Using scRNA-seq analysis and machine learning, we can distinguish NASH-related genes from large genetic datasets, providing theoretical support in finding potential targets for the development of novel therapies.