Date Published: May 29, 2019
Publisher: Public Library of Science
Author(s): Dennis Bleck, Li Ma, Lkham Erdene-Bymbadoo, Ralph Brinks, Matthias Schneider, Li Tian, Georg Pongratz, Jörg Hermann Fritz.
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 tissue. 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. 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 distinct between cells and fibers. Therefore, an additional staining using peripherin exclusively expressed in nervous fibers as a secondary marker was established. Using this novel approach, we studied the spleens from a syndecan-3 knockout (SDC3KO) mouse line, and demonstrated equal results on SNS fiber density for both manual and automated counts (Manual counts: wildtype: 22.57 +/- 11.72 fibers per mm2; ko: 31.95 +/- 18.85 fibers per mm2; p = 0.05; Automated counts: wildtype: 31.6 +/- 18.98 fibers per mm2; ko: 45.49 +/- 19.65 fibers per mm2; p = 0.02). In conclusion, this new and simple method can be used as a high-throughput approach to reliably and quickly estimate SNS nerve fiber density in target tissues.
In order to provide less time-consuming alternatives to the tedious process of manually counting nervous fibers in tissues of interest and to stream line quantification and characterization of nervous fibers, automated and semi-automated processes have been developed and deployed as early as 1979. These processes require special equipment, such as array processors or specialized graphics ports and software, which is highly cost-intensive and often adapted to only one particular purpose [1,2]. To allow a more cost-efficient analysis of overall innervation in several target tissues, a semi-automated counting method was established by us. It is based upon several macros programmed for Image J using a basic fluorescence microscopy set up.
The automation of the counting process for sympathetic nerve fibers and TH+ cells, respectively, presents a number of advantages. First of all, it is considerably less time-consuming than counting fibers, or other target structures, by eye. It also eliminates the effect of subjective perception by the experimenter from the process. Another benefit of the automated process is the fact, that the images captured for the analysis are available for future studies or replication of the analysis, while in the previously described manual approach, targets were counted under the microscope without capturing the area of investigation as images . Compared to other automated processes that have been used to count and analyze nervous fibers in tissue sections, this approach does not require any special equipment or software. It is therefore a lot more cost-efficient than other approaches, which are based on three-dimensional analysis [33–35]. Apart from these practical advantages, the automated method offers a number of analytical upsides. With the automated approach, it is possible to count double-positive structures of variable shapes and sizes, whereas the previous manual method only allowed for a discrimination by size and shape, for example by only counting objects that were fiber-shaped and above 50 μm in length determined through a micrometer eyepiece . These discrimination criteria eliminate all fibers running perpendicular to the plane of the section, which will be the largest proportion of fibers, and only the least number of fibers running horizontally to the plane of the section is registered for analysis. Therefore, the number of fibers recorded by the automated approach is increased, due to the fact that all double-positive structures were registered.