Lateralized deficits after unilateral AAV-vector based overexpression of alpha-synuclein in the midbrain of rats on drug-free behavioural tests

"Advanced" Parkinson's disease: A review

There is no consensus driven definition of "advanced" Parkinson's disease (APD) currently. APD has been described in terms of emergence of specific clinical features and clinical milestones of the disease e.g., motor fluctuations, time to increasing falls, emergence of cognitive decline, etc. The pathological burden of disease has been used to characterize various stages of the disease. Imaging markers have been associated with various motor and nonmotor symptoms of advancing disease. In this review, we present an overview of clinical, pathologic, and imaging markers of APD. We also propose a model of disease definition involving longitudinal assessments of these markers as well as quality of life metrics to better understand and predict disease progression in those with Parkinson's disease.

Knockout or inhibition of USP30 protects dopaminergic neurons in a Parkinson's disease mouse model

Mutations in SNCA, the gene encoding α-synuclein (αSyn), cause familial Parkinson's disease (PD) and aberrant αSyn is a key pathological hallmark of idiopathic PD. This α-synucleinopathy leads to mitochondrial dysfunction, which may drive dopaminergic neurodegeneration. PARKIN and PINK1, mutated in autosomal recessive PD, regulate the preferential autophagic clearance of dysfunctional mitochondria ("mitophagy") by inducing ubiquitylation of mitochondrial proteins, a process counteracted by deubiquitylation via USP30. Here we show that loss of USP30 in Usp30 knockout mice protects against behavioral deficits and leads to increased mitophagy, decreased phospho-S129 αSyn, and attenuation of SN dopaminergic neuronal loss induced by αSyn. These observations were recapitulated with a potent, selective, brain-penetrant USP30 inhibitor, MTX115325, with good drug-like properties. These data strongly support further study of USP30 inhibition as a potential disease-modifying therapy for PD.

A comparison of machine learning approaches for the quantification of microglial cells in the brain of mice, rats and non-human primates

Microglial cells are brain-specific macrophages that swiftly react to disruptive events in the brain. Microglial activation leads to specific modifications, including proliferation, morphological changes, migration to the site of insult, and changes in gene expression profiles. A change in inflammatory status has been linked to many neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease. For this reason, the investigation and quantification of microglial cells is essential for better understanding their role in disease progression as well as for evaluating the cytocompatibility of novel therapeutic approaches for such conditions. In the following study we implemented a machine learning-based approach for the fast and automatized quantification of microglial cells; this tool was compared with manual quantification (ground truth), and with alternative free-ware such as the threshold-based ImageJ and the machine learning-based Ilastik. We first trained the algorithms on brain tissue obtained from rats and non-human primate immunohistochemically labelled for microglia. Subsequently we validated the accuracy of the trained algorithms in a preclinical rodent model of Parkinson's disease and demonstrated the robustness of the algorithms on tissue obtained from mice, as well as from images provided by three collaborating laboratories. Our results indicate that machine learning algorithms can detect and quantify microglial cells in all the three mammalian species in a precise manner, equipotent to the one observed following manual counting. Using this tool, we were able to detect and quantify small changes between the hemispheres, suggesting the power and reliability of the algorithm. Such a tool will be very useful for investigation of microglial response in disease development, as well as in the investigation of compatible novel therapeutics targeting the brain. As all network weights and labelled training data are made available, together with our step-by-step user guide, we anticipate that many laboratories will implement machine learning-based quantification of microglial cells in their research.

Automated quantification of neuronal swellings in a preclinical rodent model of Parkinson's disease detects region-specific changes in pathology

BACKGROUND

The development of axonal pathology is a key characteristic of many neurodegenerative disease such as Parkinson's disease and Alzheimer's disease. With advanced disease progression, affected axons do display several signs of pathology such as swelling and fragmentation. In the AAV vector-mediated alpha-synuclein overexpression model of Parkinson's disease, large (> 20 µm²) pathological swellings are prominent characteristics in cortical and subcortical structures.

NEW METHOD

This report describes a novel, macro-based workflow to quantify axonal pathology in the form of axonal swellings in the AAV vector-based alpha-synuclein overexpression model. Specifically, the approach is using background correction and thresholding before quantification of structures in 3D throughout a tissue stack.

RESULTS

The method was used to quantify TH and aSYN axonal swellings in the prefrontal cortex, striatum, and hippocampus. Regional differences in volume and number of axonal swellings were observed for both in TH and aSYN, with the striatum displaying the greatest signs of pathology.

COMPARISON WITH EXISTING METHODS

Existing methods for the quantification of axonal pathology do either rely on proprietary software or are based on manual quantification. The ImageJ workflow described here provides a method to objectively quantify axonal swellings both in volume and number.

CONCLUSION

The method described can readily assess axonal pathology in preclinical rodent models of Parkinson's disease and can be easily adapted to other model systems and/or markers.