Published: Vol 9, Iss 24, Dec 20, 2019 DOI: 10.21769/BioProtoc.3454 Views: 3797
Reviewed by: Ehsan KheradpezhouhGeoffrey C. Y. LauAnonymous reviewer(s)
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Abstract
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.
Keywords: Peripheral nervous systemBackground
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 (Frykman et al., 1979; Auer, 1994). 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 ImageJ using a basic fluorescence microscopy set-up. The working principle of the macros is demonstrated in Figure 1.
Usually, tyrosine hydroxylase (TH) is used as a marker of sympathetic fibers. TH catalyzes the conversion from L-tyrosine to L-3,4-dihydroxyphenylalanine (L-DOPA), which represents the rate-limiting step of catecholamine synthesis (Nagatsu et al., 1964; Hufton et al., 1995). TH is ubiquitously expressed in sympathetic nervous fibers as well as in a multitude of other cells throughout most mammalian tissues. Due to the fact that TH is not exclusively expressed in nervous fibers, we decided to introduce a counterstain to improve the distinction between TH-positive sympathetic fibers and TH-positive cells. The neurofilament peripherin is ubiquitously and exclusively expressed in nervous fibers and is therefore an excellent candidate for double stainings (Portier et al., 1983). We hypothesized, that with peripherin and TH co-staining, TH-positive fibers will be distinguished from TH-positive cells. Fibers will be discernible by the co-localization of peripherin and TH, while all other TH signals originate from TH-positive cells. If high-end technology required for automated counting processes is not available, fiber density is usually determined by manually counting visible TH-positive fibers in 17 high power fields (HPF), according to published methodology (Straub et al., 2008). However, this is a time-consuming and observer-dependent process. We present in this work a simple, high-throughput, automated screening method of sympathetic fiber density in tissues.
Figure 1. Demonstration of the automated counting process. A first macro creates a Region of interest (ROI) by automatically setting a threshold for TH positive areas (A). A second macro then lays the ROI over the corresponding peripherin stained image and creates masks wherever TH and peripherin staining are co-located (B, C). These masks are then counted and a list is displayed, showing the counts for each image that was analysed. A merge image is shown to illustrate the co-localization (D). White arrows indicate fibers that can be identified by eye, green arrows indicate those identified by co-localization only.
Materials and Reagents
Equipment
Software
Procedure
Recipes
Acknowledgments
This protocol was established for the original research paper “Introduction and validation of a new semi-automated method to determine sympathetic fiber density in target tissues” (Bleck et al., 2019).
Ethics
C57B/6J mice used to establish this protocol were bred and sacrificed for organ extraction at the animal experiment facility of the University of Düsseldorf (ZETT). All methods were carried out in accordance with the approved guidelines under the project ID O 57/15.
References
Article Information
Publication history
Accepted: Nov 21, 2019
Published: Dec 20, 2019
Copyright
© 2019 The Authors; exclusive licensee Bio-protocol LLC.
How to cite
Bleck, D., Erdene-Byambadoo, L., Brinks, R., Schneider, M. and Pongratz, G. (2019). Semi-automated Model to Accurately Counting Sympathetic Nervous Fibers. Bio-protocol 9(24): e3454. DOI: 10.21769/BioProtoc.3454.
Category
Neuroscience > Behavioral neuroscience > Animal model
Neuroscience > Nervous system disorders > Animal model
Neuroscience > Sensory and motor systems > Animal model
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