Assessment of Dopaminergic Homeostasis in Mice by Use of High-performance Liquid Chromatography Analysis and Synaptosomal Dopamine Uptake

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Dopamine (DA) is a modulatory neurotransmitter controlling motor activity, reward processes and cognitive function. Impairment of dopaminergic (DAergic) neurotransmission is strongly associated with several central nervous system-associated diseases such as Parkinson's disease, attention-deficit-hyperactivity disorder and drug addiction(1)(,)(2)(,)(3)(,)(4). Delineating disease mechanisms involving DA imbalance is critically dependent on animal models to mimic aspects of the diseases, and thus protocols that assess specific parts of the DA homeostasis are important to provide novel insights and possible therapeutic targets for these diseases. Here, we present two useful experimental protocols that when combined provide a functional read-out of the DAergic system in mice. Biochemical and functional parameters on DA homeostasis are obtained through assessment of DA levels and dopamine transporter (DAT) functionality(5). When investigating the DA system, the ability to reliably measure endogenous levels of DA from adult brain is essential. Therefore, we present how to perform high-performance liquid chromatography (HPLC) on brain tissue from mice to determine levels of DA. We perform the experiment on tissue from dorsal striatum (dStr) and nucleus accumbens (NAc), but the method is also suitable for other DA-innervated brain areas. DAT is essential for reuptake of DA into the presynaptic terminal, thereby controlling the temporal and spatial activity of released DA. Knowing the levels and functionality of DAT in the striatum is of major importance when assessing DA homeostasis. Here, we provide a protocol that allows to simultaneously deduce information on surface levels and function using a synaptosomal(6) DA uptake assay. Current methods combined with standard immunoblotting protocols provide the researcher with relevant tools to characterize the DAergic system.

Original languageEnglish
Article numbere56093
JournalJournal of Visualized Experiments
Issue number127
Number of pages10
Publication statusPublished - Sep 2017

    Research areas

  • Journal Article

ID: 185875493