Andrew Kneynsberg1, Timothy J Collier2, Fredric P Manfredsson3, Nicholas M Kanaan4. 1. Neuroscience Program, Michigan State University, East Lansing, MI, USA; Department of Translational Science and Molecular Medicine, College of Human Medicine, Michigan State University, Grand Rapids, MI, USA. 2. Neuroscience Program, Michigan State University, East Lansing, MI, USA; Department of Translational Science and Molecular Medicine, College of Human Medicine, Michigan State University, Grand Rapids, MI, USA; Morris K. Udall Center of Excellence for Parkinson's Disease Research at Michigan State University, USA; Hauenstein Neuroscience Center, Mercy Health Saint Mary's, Grand Rapids, MI, USA. 3. Neuroscience Program, Michigan State University, East Lansing, MI, USA; Department of Translational Science and Molecular Medicine, College of Human Medicine, Michigan State University, Grand Rapids, MI, USA; Hauenstein Neuroscience Center, Mercy Health Saint Mary's, Grand Rapids, MI, USA. 4. Neuroscience Program, Michigan State University, East Lansing, MI, USA; Department of Translational Science and Molecular Medicine, College of Human Medicine, Michigan State University, Grand Rapids, MI, USA; Morris K. Udall Center of Excellence for Parkinson's Disease Research at Michigan State University, USA; Hauenstein Neuroscience Center, Mercy Health Saint Mary's, Grand Rapids, MI, USA. Electronic address: nicholas.kanaan@hc.msu.edu.
Abstract
BACKGROUND: Axon viability is critical for maintaining neural connectivity, which is central to neural functionality. Many neurodegenerative diseases (e.g., Parkinson's disease (PD) and Alzheimer's disease) appear to involve extensive axonal degeneration that often precedes somatic loss in affected neural populations. Axonal degeneration involves a number of intracellular pathways and characteristic changes in axon morphology (i.e., swelling, fragmentation, and loss). NEW METHOD: We describe a relatively simple set of methods to quantify the axonal degeneration using the 6-hydroxydopamine neurotoxin model of PD in rats and a colchicine-induced model in primary rat neurons. Specifically, approaches are described that use the spaceballs stereological probe for tissue sections and petrimetrics stereological probe for cultured neurons, and image analysis techniques in both tissue sections and cultured neurons. RESULTS: These methods provide a mechanism for obtaining quantitative and semi-quantitative data to track the extent of axonal degeneration and may prove useful as outcome measures in studies aimed at preventing or slowing axonal degeneration in disease models. COMPARISON WITH EXISTING METHODS: Existing methods of quantification of axonal degeneration use densitometry and manual counts of axonal projections, but they do not utilize the random, unbiased systematic sampling approaches that are characteristic of stereological methods. The ImageJ thresholding analyses described here provide a descriptive method for quantifying the state of axonal degeneration. CONCLUSIONS: These methods provide an efficient and effective means to quantify the extent and state of axonal degeneration in animal tissue and cultured neurons and can be used in other models for the same purposes.
BACKGROUND: Axon viability is critical for maintaining neural connectivity, which is central to neural functionality. Many neurodegenerative diseases (e.g., Parkinson's disease (PD) and Alzheimer's disease) appear to involve extensive axonal degeneration that often precedes somatic loss in affected neural populations. Axonal degeneration involves a number of intracellular pathways and characteristic changes in axon morphology (i.e., swelling, fragmentation, and loss). NEW METHOD: We describe a relatively simple set of methods to quantify the axonal degeneration using the 6-hydroxydopamine neurotoxin model of PD in rats and a colchicine-induced model in primary rat neurons. Specifically, approaches are described that use the spaceballs stereological probe for tissue sections and petrimetrics stereological probe for cultured neurons, and image analysis techniques in both tissue sections and cultured neurons. RESULTS: These methods provide a mechanism for obtaining quantitative and semi-quantitative data to track the extent of axonal degeneration and may prove useful as outcome measures in studies aimed at preventing or slowing axonal degeneration in disease models. COMPARISON WITH EXISTING METHODS: Existing methods of quantification of axonal degeneration use densitometry and manual counts of axonal projections, but they do not utilize the random, unbiased systematic sampling approaches that are characteristic of stereological methods. The ImageJ thresholding analyses described here provide a descriptive method for quantifying the state of axonal degeneration. CONCLUSIONS: These methods provide an efficient and effective means to quantify the extent and state of axonal degeneration in animal tissue and cultured neurons and can be used in other models for the same purposes.
Authors: Nicholas M Kanaan; Timothy J Collier; Deanna M Marchionini; Susan O McGuire; Matthew F Fleming; Caryl E Sortwell Journal: Brain Res Date: 2005-12-20 Impact factor: 3.252
Authors: Katya J Park; Carlos Ayala Grosso; Isabelle Aubert; David R Kaplan; Freda D Miller Journal: Nat Neurosci Date: 2010-03-28 Impact factor: 24.884
Authors: Bogdan Beirowski; Livia Berek; Robert Adalbert; Diana Wagner; Daniela S Grumme; Klaus Addicks; Richard R Ribchester; Michael P Coleman Journal: J Neurosci Methods Date: 2004-03-15 Impact factor: 2.390
Authors: Margaret A Hamner; Ashley McDonough; Davin C Gong; Levi J Todd; German Rojas; Sibylle Hodecker; Christopher B Ransom; Thomas A Reh; Bruce R Ransom; Jonathan R Weinstein Journal: Glia Date: 2021-12-23 Impact factor: 7.452
Authors: Marina Y Khodanovich; Alena A Kisel; Andrey E Akulov; Dmitriy N Atochin; Marina S Kudabaeva; Valentina Y Glazacheva; Michael V Svetlik; Yana A Medvednikova; Lilia R Mustafina; Vasily L Yarnykh Journal: J Cereb Blood Flow Metab Date: 2018-01-26 Impact factor: 6.200
Authors: Andrew Kneynsberg; Benjamin Combs; Kyle Christensen; Gerardo Morfini; Nicholas M Kanaan Journal: Front Neurosci Date: 2017-10-17 Impact factor: 4.677
Authors: Mitchell J Bartlett; Andrew J Flores; Tony Ye; Saskia I Smidt; Hannah K Dollish; Jennifer A Stancati; Drew C Farrell; Kate L Parent; Kristian P Doyle; David G Besselsen; Michael L Heien; Stephen L Cowen; Kathy Steece-Collier; Scott J Sherman; Torsten Falk Journal: Exp Neurol Date: 2020-07-25 Impact factor: 5.330
Authors: Lara Marrone; Paolo M Marchi; Christopher P Webster; Raffaele Marroccella; Ian Coldicott; Steven Reynolds; João Alves-Cruzeiro; Zih-Liang Yang; Adrian Higginbottom; Mukhran Khundadze; Pamela J Shaw; Christian A Hübner; Matthew R Livesey; Mimoun Azzouz Journal: Hum Mol Genet Date: 2022-08-23 Impact factor: 5.121
Authors: Marina Yu Khodanovich; Ilya L Gubskiy; Marina S Kudabaeva; Darya D Namestnikova; Alena A Kisel; Tatyana V Anan'ina; Yana A Tumentceva; Lilia R Mustafina; Vasily L Yarnykh Journal: J Cereb Blood Flow Metab Date: 2021-06-09 Impact factor: 6.960