Machado–Joseph disease (MJD), also known as spinocerebellar ataxia type 3 (SCA3), is an autosomal dominant neurodegenerative disorder (ND). While most research in NDs has been following a neuron-centric point of view, microglia are now recognized as crucial in the brain. Previous work revealed alterations that point to an increased activation state of microglia in the brain of CMVMJD135 mice, a MJD mouse model that replicates the motor symptoms and neuropathology of the human condition. Here, we investigated the extent to which microglia are actively contributing to MJD pathogenesis and symptom progression. For this, we used PLX3397 to reduce the number of microglia in the brain of CMVMJD135 mice. In addition, a set of statistical and machine learning models were further implemented to analyze the impact of PLX3397 on the morphology of the surviving microglia. Then, a battery of behavioral tests was used to evaluate the impact of microglial depletion on the motor phenotype of CMVMJD135 mice. Although PLX3397 treatment substantially reduced microglia density in the affected brain regions, it did not affect the motor deficits seen in CMVMJD135 mice. In addition to reducing the number of microglia, the treatment with PLX3397 induced morphological changes suggestive of activation in the surviving microglia, the microglia of wild-type animals becoming similar to those of CMVMJD135 animals. These results suggest that microglial cells are not key contributors for MJD progression. Furthermore, the impact of PLX3397 on microglial activation should be taken into account in the interpretation of findings of ND modification seen upon treatment with this CSF1R inhibitor.

Microglia have been increasingly implicated in neurodegenerative diseases (NDs), and specific disease associated microglia (DAM) profiles have been defined for several of these NDs. Yet, the microglial profile in Machado–Joseph disease (MJD) remains unexplored. Here, we characterized the profile of microglia in the CMVMJD135 mouse model of MJD. This characterization was performed using primary microglial cultures and microglial cells obtained from disease-relevant brain regions of neonatal and adult CMVMJD135 mice, respectively. Machine learning models were implemented to identify potential clusters of microglia based on their morphological features, and an RNA-sequencing analysis was performed to identify molecular perturbations and potential therapeutic targets. Our findings reveal morphological alterations that point to an increased activation state of microglia in CMVMJD135 mice and a disease-specific transcriptional profile of MJD microglia, encompassing a total of 101 differentially expressed genes, with enrichment in molecular pathways related to oxidative stress, immune response, cell proliferation, cell death, and lipid metabolism. Overall, these results allowed us to define the cellular and molecular profile of MJD-associated microglia and to identify genes and pathways that might represent potential therapeutic targets for this disorder.

Microglial cells are the first line of defense within the central nervous system, with morphological characterization being widely used to define their activation status. Most methods to evaluate microglia status are manual, and, therefore, often biased, inaccurate, and time consuming. In fact, the process to collect morphological data starts with the acquisition of photomicrographs from where images of single cells are extracted. Then, the researcher collects the morphological features that characterize each cell. However, a manual data collection process from single cells can take weeks to complete. This work describes an open-source ImageJ plugin, MorphData , which automatizes the data extraction process of morphological features of single microglial cells. The plugin collects, processes, and organizes features associated with cell complexity and ramification. In a computer with limited computing power, MorphData was able to handle 699 single cells in less than 14 minutes. The same process, if performed manually, would take almost 19 working days. Overall, MorphData significantly reduces the time taken to collect morphological data from microglial cells, which can then be used to study, understand, and characterize microglia behavior in the brain of human patients or of animal models of neurological and psychiatric diseases.