Logo image
Back
Quantitative measurement of retinal ganglion cell populations via histology-based random forest classification
Journal article   Peer reviewed

Quantitative measurement of retinal ganglion cell populations via histology-based random forest classification

Adam Hedberg-Buenz, Mark A Christopher, Carly J Lewis, Kimberly A Fernandes, Laura M Dutca, Kai Wang, Todd E Scheetz, Michael D Abràmoff, Richard T Libby, Mona K Garvin, …
Experimental Eye Research, Vol.146, pp.370-385
05/2016
DOI: 10.1016/j.exer.2015.09.011
PMCID: PMC4841761
PMID: 26474494

View Online

Abstract

The inner surface of the retina contains a complex mixture of neurons, glia, and vasculature, including retinal ganglion cells (RGCs), the final output neurons of the retina and primary neurons that are damaged in several blinding diseases. The goal of the current work was two-fold: to assess the feasibility of using computer-assisted detection of nuclei and random forest classification to automate the quantification of RGCs in hematoxylin/eosin (H&E)-stained retinal whole-mounts; and if possible, to use the approach to examine how nuclear size influences disease susceptibility among RGC populations. To achieve this, data from RetFM-J, a semi-automated ImageJ-based module that detects, counts, and collects quantitative data on nuclei of H&E-stained whole-mounted retinas, were used in conjunction with a manually curated set of images to train a random forest classifier. To test performance, computer-derived outputs were compared to previously published features of several well-characterized mouse models of ophthalmic disease and their controls: normal C57BL/6J mice; Jun-sufficient and Jun-deficient mice subjected to controlled optic nerve crush (CONC); and DBA/2J mice with naturally occurring glaucoma. The result of these efforts was development of RetFM-Class, a command-line-based tool that uses data output from RetFM-J to perform random forest classification of cell type. Comparative testing revealed that manual and automated classifications by RetFM-Class correlated well, with 83.2% classification accuracy for RGCs. Automated characterization of C57BL/6J retinas predicted 54,642 RGCs per normal retina, and identified a 48.3% Jun-dependent loss of cells at 35 days post CONC and a 71.2% loss of RGCs among 16-month-old DBA/2J mice with glaucoma. Output from automated analyses was used to compare nuclear area among large numbers of RGCs from DBA/2J mice (n = 127,361). In aged DBA/2J mice with glaucoma, RetFM-Class detected a decrease in median and mean nucleus size of cells classified into the RGC category, as did an independent confirmation study using manual measurements of nuclear area demarcated by BRN3A-immunoreactivity. In conclusion, we have demonstrated that histology-based random forest classification is feasible and can be utilized to study RGCs in a high-throughput fashion. Despite having some limitations, this approach demonstrated a significant association between the size of the RGC nucleus and the DBA/2J form of glaucoma. •H&E-stained retinal whole-mounts can be used with a random forest classifier to discern cell-type.•No loss of displaced amacrine cells in glaucomatous DBA/2J mice was detected.•Glaucomatous DBA/2J mice exhibit non-random changes in nuclear size of RGCs.
 Glaucoma Retinal ganglion cells Image analysis Jun Random forest-based classification DBA/2J Quantitative analysis Nucleus size

Details

Metrics

22 Record Views
27 Times Cited - Web of Science
Logo image