A freely available semi-automated method for quantifying retinal ganglion cells in entire retinal flatmounts

- The developed ImageJ script counts Brn3a-immunopositive retinal ganglion cells (RGCs) in a retinal flatmount.
- The results of the script completely agree with data from manual counting.
- The script can successfully assess RGC degeneration in different rodent models of glaucoma.
- The script is an easily accessible tool to accurately quantify RGCs in an objective manner.
- Because it only relies on a few parameters, the script can easily be optimized for other labeling methods in various tissues.

Glaucomatous optic neuropathies are characterized by progressive loss of retinal ganglion cells (RGCs), the neurons that connect the eye to the brain. Quantification of these RGCs is a cornerstone in experimental optic neuropathy research and commonly performed via manually quantifying parts of the retina. However, this is a time-consuming process subject to inter- and intra-observer variability. Here we present a freely available ImageJ script to semi-automatically quantify RGCs in entire retinal flatmounts after immunostaining for the RGC-specific transcription factor Brn3a. The blob-like signal of Brn3a-immunopositive RGCs is enhanced via eigenvalues of the Hessian matrix and the resulting local maxima are counted as RGCs. After the user has outlined the retinal flatmount area, the total RGC number and retinal area are reported and an isodensity map, showing the RGC density distribution across the retina, is created. The semi-automated quantification shows a very strong correlation (Pearson's r ≥ 0.99) with manual counts for both widefield and confocal images, thereby validating the data generated via the developed script. Moreover, application of this method in established glaucomatous optic neuropathy models such as N-methyl-D-aspartate-induced excitotoxicity, optic nerve crush and laser-induced ocular hypertension revealed RGC loss conform with literature. Compared to manual counting, the described automated quantification method is faster and shows user-independent consistency. Furthermore, as the script detects the RGC number in entire retinal flatmounts, the method allows detection of regional differences in RGC density. As such, it can help advance research investigating the degenerative mechanisms of glaucomatous optic neuropathies and the effectiveness of new neuroprotective treatments. Because the script is flexible and easy to optimize due to a low number of critical parameters, it can potentially be applied in combination with other tissues or alternative labeling protocols.