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

Gubinelli, Francesco; Cazzolla, G; Negrini, Matilde; Kulacz, I; Mehrdadian, A; Tomasello, Giuseppe; Venuti, Chiara; Sarauskyte, Livija; Jacobs, F; Manfredsson, Fredric P.; Davidsson, Marcus; Heuer, Andreas

doi:10.1016/j.bbr.2022.113887

Cited by 9 publications

(4 citation statements)

References 119 publications

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“…Various imaging [ 93 , 94 ] and post-mortem [ 95 – 99 ] studies performed on pre-clinical models of PD as well as on humans samples showed the clear link between inflammation and the disease. In this work, AAV-9 mediated overexpression of h-aSYN produced an increase of IBA1 + cells in the ipsilateral midbrain as compared with the contralateral hemisphere; these results are in accordance with our previously published studies [ 39 , 47 , 100 ]. Interestingly, we observed a slight increase of IBA1 + cells in the ipsilateral midbrain of the AAV-noTG rats compared with the contralateral midbrain receiving no injection.…”

Section: Discussionsupporting

confidence: 94%

“…Timewise, single image AI quantification was achieved within seconds, while the human user needed minutes, making this tool time and cost-efficient. On a biological level, the obtained results are in accordance with what is expected from these models and with previous published literature [ 39 , 47 , 85 – 87 ], confirming the reliability and sensitivity of the tool in the three species. Recent clinical and genetical studies have enfolded the role of microglial cells as an essential contributor in late onset Alzheimer’s disease [ 88 , 89 ].…”

Section: Discussionsupporting

confidence: 91%

“…The preclinical rodent model of PD used in the present study is based on adeno-associated viral vector (AAV9) overexpression of human WT alpha synuclein (h-aSYN) as previously described [ 47 ]. As control, we used an identical construct lacking the transgene (noTG), as described previously [ 47 ]. Prior to AAV delivery, animals were anesthetised with a mixture of 5% isoflurane and oxygen and placed into a stereotactic frame.…”

Section: Methodsmentioning

confidence: 99%

“…All brains were sectioned into 40 μm coronal 1:12 series using a freezing microtome (SM200R, Leica) and stored in antifreeze solution at -20° C until use. DAB-immunohistochemistry (DAB-IHC) was performed as described previously [ 47 ]. Briefly, the sections were removed from the anti-freeze solution and washed 3 times in PBS (pH = 7.4), and subsequently incubated for 15 minutes in PBS with 3% H 2 O 2 and 10% methanol to quench endogenous peroxidase activity.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2023

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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.

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