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Stetzik, L.

Publications and source records attributed to Stetzik, L..

2 recordsLinked to original sources

Neuroinflammation is an early pathological process in a mouse model of prodromal Parkinsons disease

Approximately 90% of Parkinson's disease (PD) patients exhibit olfactory deficits, which often precedes motor symptoms by years, and are a robust predictor for PD and subsequent cognitive decline. Pathological changes in the PD olfactory system, particularly synuclein (Syn) pathology in the olfactory bulb (OB) and severe involvement of the anterior olfactory nucleus (AON), are distinct from normal aging. However, the causal link between Syn aggregation and functional impairment, potentially involving cytotoxicity and neuroinflammation, remains elusive. To investigate these early pathological events, we utilized a prodromal PD model that we have previously characterized, involving the unilateral OB injection of Syn preformed fibrils (PFF) in wild-type mice. This triggers the transneuronal spread of pathogenic Syn, and olfactory dysfunction starting at 1-month post-injections. Here we report on neuropathological and inflammatory events 1-3 month following injection of PFFs into the OB. Following PFF injection, Syn pSer129 pathology progressively accumulated bilaterally in the AON and ipsilateral in the piriform cortex (PCX) over three months. Stereological quantification revealed no significant neuronal loss in the AON or PCX during this 1-3 month interval, indicating that olfactory impairment precedes overt neurodegeneration. Instead, we identified the upregulation of pro-inflammatory genes (Il-1{beta} ; and Il-6), significant microglial reactivity, and sustained astrogliosis within olfactory structures. These findings identify neuroinflammation as a key early mediator linking Syn pathology to olfactory dysfunction in the absence of neuronal loss. This suggests a critical window for therapeutic intervention and highlights neuroinflammatory markers as potential biomarkers for the prodromal phase of PD.

neuroscience↗

A novel automated morphological analysis of microglia activation using a deep learning assisted model

There is growing evidence for the key role of microglial activation in brain pathophysiology. Consequently, there is a need for efficient automated methods to measure the morphological changes distinctive of microglia functional states in research settings. Currently, many commonly used automated methods can be subject to sample representation bias, time consuming imaging, specific hardware requirements, and difficulty in maintaining an accurate comparison across research environments. To overcome these issues, we use commercially available deep learning tools (Aiforia(R) Cloud (Aifoira Inc., Cambridge, United States) to quantify microglial morphology and cell counts from histopathological slides of Iba1 stained tissue sections. We provide evidence for the effective application of this method across a range of independently collected datasets in mouse models of viral infection and Parkinsons disease. Additionally, we provide a comprehensive workflow with training details and annotation strategies by feature layer that can be used as a guide to generate new models. In addition, all models described in this work are shared within the Aiforia(R) platform and are available for study-specific adaptation and validation.

neuroscience↗