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.

AAV2-Mediated Transduction of the Mouse Retina After Optic Nerve Injury

“…Damaged RGC somas undergo dramatic morphological changes, typified by retraction of the dendritic arbor 19 – 23 with evidence of nuclear and soma shrinkage. 24 , 25 This spectrum of changes makes it uncertain if the necessary cell surface cues required for viral interaction are retained in damaged cells.…”

Software for Quantifying and Batch Processing Images of Brn3a and RBPMS Immunolabelled Retinal Ganglion Cells in Retinal Wholemounts

“…Although there may be a slight over-or under-representation of true RGC counts, automated cell counting using fixed objective parameters is more likely to generate consistent RGC counts upon repeated sampling than using a manual method with considerable potential subjectivity, and, therefore, variability. 31 Our program provides a useful research tool with a number of attractive features: (1) wide spectrum of automation, including both image optimization and RGC quantification; (2) applicability to a variety of different antigens (validated to date for the RGCspecific labels Brn3a and RBPMS) with accuracy comparable to manual counting and the existing literature; (3) interchangeability in handling both naive and injured retinal wholemounts; (4) ability to differentiate clusters or clumps of cells with acceptable accuracy; (5) "batch processing" function with seamless transfer of tabulated results to a spread sheet application for ease of statistical analysis; and (6) automated wholeretinal analysis with integrated retinal isodensity map generation.…”

“…Although there may be a slight over- or under-representation of true RGC counts, automated cell counting using fixed objective parameters is more likely to generate consistent RGC counts upon repeated sampling than using a manual method with considerable potential subjectivity, and, therefore, variability. 31 …”

“…RGCs were identifiable with reasonable accuracy (83.2%) by using random forest classification based on morphological criteria. 31 Similar to our program, this plug-in initially requires the user to manually calibrate the program with a "training" set of images to ensure accurate RGC detection prior to performing automated RGC counts. Despite using high magnification (200×) photomicrographs and manually subtracting artifacts from photomicrographs prior to automated counting, some difficulty was encountered with missed nuclei associated with cell clumps or concealment by the nerve fiber layer.…”

Image-based drug screen identifies HDAC inhibitors as novel Golgi disruptors synergizing with JQ1

“…Classification of phenotypes was done within the KNIME Analytics Platform ( Petri and Meister, 2013 ; Dietz and Berthold, 2016 ) using a random forest algorithm ( Touw et al. , 2013 ; Hedberg-Buenz et al. , 2016 ).…”