Application of the Physical Disector Principle for Quantification of Dopaminergic Neuronal Loss in a Rat 6-Hydroxydopamine Nigral Lesion Model of Parkinson's Disease

Stereological analysis is the optimal tool for quantitative assessment of brain morphological and cellular changes induced by neurotoxic lesions or treatment interventions. Stereological methods based on random sampling techniques yield unbiased estimates of particle counts within a defined volume,...

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Veröffentlicht in:Frontiers in neuroanatomy 2017-12, Vol.11, p.109
Hauptverfasser: Fabricius, Katrine, Barkholt, Pernille, Jelsing, Jacob, Hansen, Henrik H
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Sprache:eng
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Zusammenfassung:Stereological analysis is the optimal tool for quantitative assessment of brain morphological and cellular changes induced by neurotoxic lesions or treatment interventions. Stereological methods based on random sampling techniques yield unbiased estimates of particle counts within a defined volume, thereby providing a true quantitative estimate of the target cell population. Neurodegenerative diseases involve loss of specific neuron types, such as the midbrain tyrosine hydroxylase-positive dopamine neurons in Parkinson's disease and in animal models of nigrostriatal degeneration. Therefore, we applied an established automated physical disector principle in a fractionator design for efficient stereological quantitative analysis of tyrosine hydroxylase (TH)-positive dopamine neurons in the substantia nigra pars compacta of hemiparkinsonian rats with unilateral 6-hydroxydopamine (6-OHDA) lesions. We obtained reliable estimates of dopamine neuron numbers, and established the relationship between behavioral asymmetry and dopamine neuron loss on the lesioned side. In conclusion, the automated physical disector principle provided a useful and efficient tool for unbiased estimation of TH-positive neurons in rat midbrain, and should prove valuable for investigating neuroprotective strategies in 6-OHDA model of parkinsonism, while generalizing to other immunohistochemically-defined cell populations.
ISSN:1662-5129
1662-5129
DOI:10.3389/fnana.2017.00109