Semi-automated Model to Accurately Counting Sympathetic Nervous Fibers

In recent years, the role of sympathetic nervous fibers in chronic inflammation has become increasingly evident. At the onset of inflammation, sympathetic activity is increased in the affected tissue. However, sympathetic fibers are largely absent from chronically inflamed tissues. Apparently, there is a very dynamic relationship between sympathetic innervation and the immune system in areas of inflammation, and hence a rapid and easy method for quantification of nerve fiber density of target organs is of great value to answer potential research questions. Sympathetic nerve ends lie in close proximity to immune cells in lymphoid tissues and lymphoid cells are equipped with catecholamine receptors. Catecholamines such as dopamine and adrenaline are secreted by sympathetic nervous fibers and can influence immune cell activity directly. Thereby the sympathetic nervous system immediately participates in the regulation of inflammation. Changes in innervation density could therefore indicate dysregulation of inflammatory processes. Currently, nervous fiber densities are either determined by tedious manual counting, which is not suitable for high throughput approaches, or by expensive automated processes relying on specialized software and high-end microscopy equipment. Usually, tyrosine hydroxylase (TH) is used as the marker for sympathetic fibers. In order to overcome the current quantification bottleneck with a cost-efficient alternative, an automated process was established and compared to the classic manual approach of counting TH-positive sympathetic fibers. Since TH is not exclusively expressed on sympathetic fibers, but also in a number of catecholamine-producing cells, a prerequisite for automated determination of fiber densities is to reliably distinguish between cells and fibers. Therefore, an additional stain using peripherin which is exclusively expressed in nervous fibers as a secondary marker was established. This new and simple method can be used as a high-throughput approach to reliably and quickly estimate sympathetic nervous system (SNS) nerve fiber density in target tissues.