19
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      A simple and fast densitometric method for the analysis of tyrosine hydroxylase immunoreactivity in the substantia nigra pars compacta and in the ventral tegmental area.

      Brain research. Brain research protocols
      Animals, Calibration, Cell Count, Densitometry, Image Processing, Computer-Assisted, Immunohistochemistry, Indicators and Reagents, Oxidopamine, Rats, Rats, Wistar, Substantia Nigra, drug effects, enzymology, Sympathectomy, Chemical, Sympatholytics, Tissue Fixation, Tyrosine 3-Monooxygenase, metabolism, Ventral Tegmental Area

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Parkinson's disease is a progressive dyskinetic disorder caused by degeneration of mesencephalic dopaminergic neurons in the substantia nigra pars compacta (SNpc) and, to a lesser extent, in the ventral tegmental area (VTA). Tyrosine hydroxylase (TH) is a rate-limiting enzyme for dopamine synthesis, therefore immunohistochemistry for TH can be used as an important marker of dopaminergic cell loss in these regions. Traditionally, immunohistochemical experiments are analyzed qualitatively by optical microscopic observation or more rarely semi-quantitatively evaluated by densitometry. A common problem with such papers is the lack of a clear explanation of the algorithms and macros employed in the semi-quantitative approaches. In this paper, we describe, in detail, an easy, fast and precise protocol for the analysis of TH immunoreactivity in SNpc and VTA using one of the most popular image analysis software packages (Image Pro-Plus). We believe that this protocol will facilitate the evaluation of mesencephalic TH immunoreactivity in various available animal models of Parkinson's disease.

          Related collections

          Author and article information

          Comments

          Comment on this article