Automated gold particle quantification of immunogold labeled micrographs

Abstract

BackgroundImmunogold cytochemistry is the method of choice for precise localization of antigens on a subcellular scale. The process of immunogold quantification in electron micrographs is laborious, especially for proteins with a dense distribution pattern. New methodsHere I present a MATLAB based toolbox that is optimized for a typical immunogold analysis workflow. It combines automatic detection of gold particles through a multi-threshold algorithm with manual segmentation of cell membranes and regions of interests. ResultsThe automated particle detection algorithm was applied to a typical immunogold dataset of neural tissue, and was able to detect particles with a high degree of precision. Without manual correction, the algorithm detected 97% of all gold particles, with merely a 0.1% false-positive rate. Comparisons with existing method(s)To my knowledge, this is the first free and publicly available software custom made for immunogold analyses. The proposed particle detection method compares favorably to previously published algorithms. ConclusionsThe software presented here will be valuable tool for researchers in neuroscience working with immunogold cytochemistry.