An open-source computational tool to automatically quantify immunolabeled retinal ganglion cells. (June 2016)

• Record Type: Journal Article
• Authors: Dordea, Ana C.; Bray, Mark-Anthony; Allen, Kaitlin; Logan, David J.; Fei, Fei; Malhotra, Rajeev; Gregory, Meredith S.; Carpenter, Anne E.; Buys, Emmanuel S.
• Is Part Of: Experimental eye research. Volume 147(2016:Jun.)
• Page Start: 50
• Page End: 56
• Publication Date: 2016-06
• Subjects: Automated quantification -- Retinal ganglion cell -- CellProfiler -- CellProfiler analyst; Ophthalmology -- Periodicals; Eye -- Periodicals; Œil -- Périodiques; Ophthalmology; Periodicals; Electronic journals; 612.8405
• DOI: 10.1016/j.exer.2016.04.012
• Languages: English
• ISSNs: 0014-4835

Abstract

Abstract: A fully automated and robust method was developed to quantify β-III-tubulin-stained retinal ganglion cells, combining computational recognition of individual cells by CellProfiler and a machine-learning tool to teach phenotypic classification of the retinal ganglion cells by CellProfiler Analyst. In animal models of glaucoma, quantification of immunolabeled retinal ganglion cells is currently performed manually and remains time-consuming. Using this automated method, quantifications of retinal ganglion cell images were accelerated tenfold: 1800 images were counted in 3 h using our automated method, while manual counting of the same images took 72 h. This new method was validated in an established murine model of microbead-induced optic neuropathy. The use of the publicly available software and the method's user-friendly design allows this technique to be easily implemented in any laboratory.