An optimized method for histological detection of dopaminergic neurons in Drosophila melanogaster

Daria Drobysheva, Kristen Ameel, Brandon Welch, Esther Ellison, Khan Chaichana, Bryan Hoang, Shilpy Sharma, Wendi Neckameyer, Irina Srinakevitch, Kelley J. Murphy, Aloisia Schmid

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


Parkinson's disease (PD) affects >1 million Americans and is marked by the loss of dopaminergic neurons in the substantia nigra. PD has been linked to two causative factors: genetic risks (hereditary PD) and environmental toxins (idiopathic PD). In recent years, considerable effort has been devoted to the development of a Drosophila model of human PD that might be useful for examining the cellular mechanisms of PD pathology by genetic screening. In 2000, Feany and Bender reported a Drosophila model of PD in which transgenic flies expressing human mutant α-synuclein exhibited shortened life spans, dopaminergic losses, Parkinsonian behaviors, and Lewy bodies in surviving dopaminergic neurons. Since then, a number of studies have been published that validate the model or build on it; conversely, a number report an inability to replicate the results and suggest that most protocols for dopaminergic histology underreport the actual numbers of dopaminergic neurons in the insect brain. Here we report the optimization of dopaminergic histology in Drosophila and identification of new dopaminergic neurons, show the remarkable dendritic complexity of these neurons, and provide an updated count of these neurons in adult brains. This manuscript contains online supplemental material at Please visit this article online to view these materials.

Original languageEnglish (US)
Pages (from-to)1049-1063
Number of pages15
JournalJournal of Histochemistry and Cytochemistry
Issue number12
StatePublished - Dec 2008
Externally publishedYes


  • Dopamine
  • Drosophila
  • Fixation
  • Histology
  • Parkinson's disease
  • Technique

ASJC Scopus subject areas

  • Anatomy
  • Histology


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