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.
Bibliographical noteFunding Information:
We are grateful that this work was financially supported by grants from the Swedish Research Council (AH: VR2016-01789 ), Demensförbundet (AH), the (AH), the Crafoord foundation (AH), the Gyllenstiernska Krapperupsstiftelsen (AH), the Thorsten and Elsa Segerfalks Stiftelsen (AH), (AH), the Royal Physiographic Society in Lund (AH), The Swedish Parkinson Foundation the Fredrik och Ingrid Thurings Stiftelsen (AH), the Swedish Society of Medicine Åhlensstiftelsen (AH). MD was supported by a post-doctoral fellowship provided by the Royal Physiographic Society in Lund. LQ and CL were financially supported by grants from the Swedish Research Council (CL and LQ: 2018-02785_VR ) and Swedish Brain Foundation (CL: FO2019-0311 ).
Subject classification (UKÄ)
- Software Engineering
- 3D quantification
- Axonal pathology
- Axonal swellings