Abstract
In mammals, the cerebellum plays an important role in movement control. Cellular research reveals that the cerebellum involves a variety of sub-cell types, including Golgi, granule, interneuron, and unipolar brush cells. The functional characteristics of cerebellar cells exhibit considerable differences among diverse mammalian species, reflecting a potential development and evolution of nervous system. In this study, we aimed to recognize the transcriptional differences between human and mouse cerebellum in four cerebellar sub-cell types by using single-cell sequencing data and machine learning methods. A total of 321,387 single-cell sequencing data were used. The 321,387 cells included 4 cell types, i.e., Golgi (5,048, 1.57%), granule (250,307, 77.88%), interneuron (60,526, 18.83%), and unipolar brush (5,506, 1.72%) cells. Our results showed that by using gene expression profiles as features, the optimal classification model could achieve very high even perfect performance for Golgi, granule, interneuron, and unipolar brush cells, respectively, suggesting a remarkable difference between the genomic profiles of human and mouse. Furthermore, a group of related genes and rules contributing to the classification was identified, which might provide helpful information for deepening the understanding of cerebellar cell heterogeneity and evolution.
Highlights
The cerebellum is like a big regulator and works by affecting the functions of brain, brainstem, and spinal cord at different levels (D’Angelo, 2018)
321,387 cerebellum cell samples were collected. These single-cell samples could be divided into four categories, i.e., Golgi cell (5,048, 1.57%), GC (250,307, 77.88%), interneuron cell (60,526, 18.83%), and UBC (5,506, 1.72%)
The Boruta filtering method was first used to do the optimal compression of features for each of 4 cell datasets
Summary
The cerebellum is like a big regulator and works by affecting the functions of brain, brainstem, and spinal cord at different levels (D’Angelo, 2018). An abnormal cerebellum is linked to some neurological diseases, such as autism, schizophrenia, and depression (D’Angelo, 2010; D’Angelo and Casali, 2012). Cellular research reveals the electrophysiological properties of neurons and synapses in the cerebellum and the mechanism of cerebellar synaptic plasticity (Hansel et al, 2001; D’Angelo and De Zeeuw, 2009; D’Angelo, 2011). The heterogeneity of cerebellum cells among different mammalian species presents a species-specific functional pattern of cerebellum which may be linked to evolution. The physiological function of the cerebellum is crucial and it is essential to explore the gene expression of various cells in the cerebellum for understanding its development, evolution, and working mechanism
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