Abstract:Background: α-Synuclein (α-syn) is the predominant protein in Lewy-body inclusions, which are pathological hallmarks of α- synucleinopathies, such as Parkinson’s disease (PD) and multiple system atrophy (MSA). Other hallmarks include activation of microglia, elevation of pro-inflammatory cytokines, as well as the activation of T and B cells. These immune changes point towards a dysregulation of both the innate and the adaptive immune system. T cells have been shown to recognize epitopes derived from α-syn and … Show more
“…To determine if the microglia detection model can be effectively adapted to quantify microglial activation across labs, histopathological slides from a different mouse model of αSyn aggregation were used. This model, based on the intrastriatal injection of αSyn PFFs, is characterized by changes in microglia morphology with minimal effects on the total number of cells, a finding that is consistent with mouse models using intrastraital injections of AAVs to produce αSyn aggregation [18,24].…”
Section: Resultssupporting
confidence: 76%
“…To evaluate how well our new microglia detection model quantifies microglia activation we compared area/perimeter ratios between our model and a custom semi-automated object-segmentation MATLAB script that has been previously published[18,24]. Using histological slides from the αSyn induced olfactory dysfunction model, we analyzed 3 brain regions across 4 mice and compared the values of all cells.…”
Section: Resultsmentioning
confidence: 99%
“…Previous studies have demonstrated the area/perimeter ratio of microglia as a reliable means for identifying microglia activation and the quantification of the area/perimeter ratio using semi-automated methods[18,24]. Hence, the area/perimeter ratio was included for the comparison of morphology assessment methods, MATLAB and Aiforia®.…”
Section: Methodsmentioning
confidence: 99%
“…We utilized 7- to 8-week-old wild type (WT) C57BL/6J mice for the model of αSyn induced olfactory dysfunction; 20- to 22-week-old WT C57BL/6N mice for the model of viral infection; 10- to 12-week-old C57BL/6J and NSG mice for the adaptive transfer dataset[24] 20- to 22-week-old wild typeTrim28 heterozygous mice on a FVBN background[25] for the model of striatal α-synuclein (αSyn) aggregation. All animals were bred in the vivarium at Van Andel Institute.…”
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® 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 Parkinson’s 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® platform and are available for study-specific adaptation and validation.
“…To determine if the microglia detection model can be effectively adapted to quantify microglial activation across labs, histopathological slides from a different mouse model of αSyn aggregation were used. This model, based on the intrastriatal injection of αSyn PFFs, is characterized by changes in microglia morphology with minimal effects on the total number of cells, a finding that is consistent with mouse models using intrastraital injections of AAVs to produce αSyn aggregation [18,24].…”
Section: Resultssupporting
confidence: 76%
“…To evaluate how well our new microglia detection model quantifies microglia activation we compared area/perimeter ratios between our model and a custom semi-automated object-segmentation MATLAB script that has been previously published[18,24]. Using histological slides from the αSyn induced olfactory dysfunction model, we analyzed 3 brain regions across 4 mice and compared the values of all cells.…”
Section: Resultsmentioning
confidence: 99%
“…Previous studies have demonstrated the area/perimeter ratio of microglia as a reliable means for identifying microglia activation and the quantification of the area/perimeter ratio using semi-automated methods[18,24]. Hence, the area/perimeter ratio was included for the comparison of morphology assessment methods, MATLAB and Aiforia®.…”
Section: Methodsmentioning
confidence: 99%
“…We utilized 7- to 8-week-old wild type (WT) C57BL/6J mice for the model of αSyn induced olfactory dysfunction; 20- to 22-week-old WT C57BL/6N mice for the model of viral infection; 10- to 12-week-old C57BL/6J and NSG mice for the adaptive transfer dataset[24] 20- to 22-week-old wild typeTrim28 heterozygous mice on a FVBN background[25] for the model of striatal α-synuclein (αSyn) aggregation. All animals were bred in the vivarium at Van Andel Institute.…”
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® 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 Parkinson’s 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® platform and are available for study-specific adaptation and validation.
“…Interestingly, despite most studies indicate that T-cell mediated autoimmune response to αSyn plays a detrimental role in the physiopathology of Parkinson's disease, there is also evidence indicating that T-cell response might be beneficial under some circumstances. For instance, it has recently been shown that the transfer of T-cells into immunodeficient mice (NOD SCID gamma-chain deficient, NSG) reduces the deposits of phospho-S129-αSyn, one of the pathogenic forms of αSyn, in the striatum and frontal cortex in a model of Parkinson's disease induced by the injection of hαSyn preformed fibrils (PFF) into the striatum [29].…”
Section: Role Of T-cells In the Development Of Neuroinflammation And Neurodegeneration Involved In Parkinson's Diseasementioning
Current evidence indicates that neurodegeneration of dopaminergic neurons of the substantia nigra associated to Parkinson’s disease is a consequence of a neuroinflammatory process in which microglial cells play a central role. The initial activation of microglial cells is triggered by pathogenic protein inclusions, which are mainly composed by α-synuclein. Importantly, these pathogenic forms of α-synuclein subsequently induce a T-cell-mediated autoimmune response to dopaminergic neurons. Depending on their functional phenotype, these autoreactive T-cells might shape the functional features of activated microglia. T-cells bearing pro-inflammatory phenotypes such as T-helper (Th)1 or Th17 promote a chronic inflammatory behaviour on microglia, whilst anti-inflammatory T-cells, such as regulatory T-cells (Treg) favour the acquisition of neuroprotective features by microglia. Thus, T-cells play a fundamental role in the development of neuroinflammation and neurodegeneration involved in Parkinson’s disease. This review summarizes the evidence indicating that not only CD4+ T-cells, but also CD8+ T-cells play an important role in the physiopathology of Parkinson’s disease. Next, this review analyses the different T-cell epitopes derived from the pathogenic forms of α-synuclein involved in the autoimmune response associated to Parkinson’s disease in animal models and humans. It also summarizes the requirement of specific alleles of major histocompatibility complexes (MHC) class I and class II necessaries for the presentation of CD8+ and CD4+ T-cell epitopes from the pathogenic forms of α-synuclein in both humans and animal models. Finally, this work summarizes and discusses a number of experimental immunotherapies that aim to strengthen the Treg response or to dampen the inflammatory T-cell response as a therapeutic approach in animal models of Parkinson’s disease.
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