Signal amplification by combining two advanced immunohistochemical techniques
AbstractThe immunohistochemical techniques known as EnVision™+ System (EVS) and Mirror Image Complementary Antibody (MICA) were recently introduced into laboratory practice because of their high sensitivity. In this paper these techniques were compared and their sequences combined to obtain a new method possibly more sensitive than the original ones. The immunohistochemical staining employing the avidin-biotin complex (ABC), largely used as routine, was adopted as a term of comparison. Samples from the small and large intestine of pigs and sheep were fixed in Bouin and embedded in Paraplast. The primary antibodies utilized were directed against the neuronal nitric oxide synthase (nNOS), vasoactive intestinal polypeptide (VIP) and chromogranin A (Cr A). Targets of these antibodies were nerve structures of the intestinal wall, as well as endocrine cells scattered in the mucosa of the bowel, defined neuroendocrine cells or paraneurons. The EVS method appeared as slightly superior to the MICA method regarding sensitivity of detection. The EVS/MICA (combined) method resulted four/eight times more effective than the original techniques regarding sensitivity of detection and staining intensity, both at low and high dilutions of the primary antibodies. Of these latter, immunopositive structures were still clearly identifiable, at a dilution of 1:256,000. Such efficiency could be explained by the high number of revealing molecules of peroxidase contained in the new sequence. The application of the combined method is recommended when a small quantity of tissue antigens needs to be detected immunohistochemically.
PlumX Metrics provide insights into the ways people interact with individual pieces of research output (articles, conference proceedings, book chapters, and many more) in the online environment. Examples include, when research is mentioned in the news or is tweeted about. Collectively known as PlumX Metrics, these metrics are divided into five categories to help make sense of the huge amounts of data involved and to enable analysis by comparing like with like.
Copyright (c) 2009 D Russo, A Ambrosino, A Vittoria, A Cecio
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.