title: Quantitative measurement of retinal ganglion cell populations via histology-based random forest classification.
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.
authors.affiliation: VA Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System, Iowa City, IA, USA; Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, IA 52242, USA.
authors.forename: Adam
authors.initials: A
authors.lastname: Hedberg-Buenz
authors.affiliation: Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA.
authors.forename: Mark A
authors.initials: MA
authors.lastname: Christopher
authors.affiliation: Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, IA 52242, USA.
authors.forename: Carly J
authors.initials: CJ
authors.lastname: Lewis
authors.affiliation: Flaum Eye Institute, University of Rochester Medical Center, Rochester, NY 14642, USA.
authors.forename: Kimberly A
authors.initials: KA
authors.lastname: Fernandes
authors.affiliation: VA Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System, Iowa City, IA, USA; Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA 52242, USA.
authors.forename: Laura M
authors.initials: LM
authors.lastname: Dutca
authors.affiliation: Department of Biostatistics, University of Iowa, Iowa City, IA 52242, USA.
authors.forename: Kai
authors.initials: K
authors.lastname: Wang
authors.affiliation: Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA; Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA 52242, USA.
authors.forename: Todd E
authors.initials: TE
authors.lastname: Scheetz
authors.affiliation: VA Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System, Iowa City, IA, USA; Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA; Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA 52242, USA; Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242, USA.
authors.forename: Michael D
authors.initials: MD
authors.lastname: Abràmoff
authors.affiliation: Flaum Eye Institute, University of Rochester Medical Center, Rochester, NY 14642, USA.
authors.forename: Richard T
authors.initials: RT
authors.lastname: Libby
authors.affiliation: VA Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System, Iowa City, IA, USA; Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242, USA.
authors.forename: Mona K
authors.initials: MK
authors.lastname: Garvin
authors.affiliation: VA Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System, Iowa City, IA, USA; Department of Molecular Physiology and Biophysics, University of Iowa, Iowa City, IA 52242, USA; Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA 52242, USA. Electronic address: michael-g-anderson@uiowa.edu.
authors.forename: Michael G
authors.initials: MG
authors.lastname: Anderson
mesh_terms: D025042:Amacrine Cells; D000818:Animals; D002452:Cell Count / Q000379:methods*; D002467:Cell Nucleus / Q000473:pathology; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D003941:Diagnostic Techniques, Ophthalmological; D004195:Disease Models, Animal; D005240:Feasibility Studies; D005901:Glaucoma / Q000145:classification* / Q000473:pathology; D051379:Mice; D008810:Mice, Inbred C57BL; D008811:Mice, Inbred DBA; D012165:Retinal Ganglion Cells / Q000166:cytology*
keywords: DBA/2J; Glaucoma; Image analysis; Jun; Nucleus size; Quantitative analysis; Random forest-based classification; Retinal ganglion cells