Figure 1
A schematic representation of the eye and retina to illustrate where the images required for the plugin originate. (A) The retina is located at the back of the eye and contains multiple cell types. The retinal ganglion cells (RGCs – red) are responsible for communicating information from the eye to the brain via their long axons. (B) A set of retinal wholemounts illustrating individual RGCs (red) or transduced RGCs (green on top of red). Note the viral vector also labels the RGC axons. (C) Fluorescence microscopy images showing RGCs labelled with RBPMS (red – left) or Brn3A (red – right) in addition to fluorescence generated from the viral vector (green – right). White boxes indicate magnified regions for analysis. Retinal transduction was achieved using a rAAV2-hSYN-eGFP viral vector. Images were taken on a Leica DMi8 microscope with a 20x objective (left) or Leica SPE confocal microscope at 40x (right). Schematics were created with BioRender.com.
Figure 2
Architecture of ImageJ plugin ‘RGC Counter’. The user interface, the output in XLSX format, and the image processing steps for this plugin are shown.
Figure 3
Architecture of ImageJ plugin ‘RGC Transduction’. The user interface, the key parts of the output in XLSX format, and the image processing steps for this plugin are shown.
Figure 4
Architecture of ImageJ plugin ‘RGC Batch’. The user interface of ‘RGC Batch’ allows the selection of either ‘RGC Counter’ or ‘RGC Transduction’. The workflow is shown as a schematic diagram, and key outputs in XLSX format are shown.
Figure 5
Assessment of RGC number by the ImageJ plugin ‘RGC Counter’. RGCs in mouse retinal wholemounts were visualised using the immunohistochemical marker RBPMS. (A) Fluorescence microscopy image of an uninjured retinal wholemount (left) and automated quantification of RGCs by ‘RGC Counter’ (right). (B) Comparison between the number of RGCs detected by the ‘RGC Counter’ plugin and manual cell counts by six investigators from images of an uninjured retinal wholemount [Y = 1.10x – 121.0, Pearson’s r = 0.961, R2 = 0.924, p = 0.002, Pearson product-moment correlation]. (C) Fluorescence microscopy image of a retinal wholemount from an animal that underwent an optic nerve crush injury (left) and automated quantification of RGCs by ‘RGC Counter’ (right). (D) Comparison between the number of RGCs detected by the ‘RGC Counter’ plugin and manual cell counts by six investigators from images of an injured retinal wholemount [Y = 1.14x – 33.4, Pearson’s r = 0.961, R2 = 0.923, p = 0.002, Pearson product-moment correlation]. (E– F) Time required to analyse a single image of the uninjured and injured retinal wholemounts, respectively [Uninjured: F (6, 35) = 65.2, p < 0.001 for all investigators compared to RGC counter, ANOVA with Dunnett’s multiple comparisons test; Injured: F (6, 35) = 50.9, p < 0.001 for all investigators compared to RGC Counter, ANOVA with Dunnett’s multiple comparisons test]. The Pearson product-moment correlation and ANOVA data are shown as average ± 95% CI and average ± SEM, respectively. Images were taken on a Leica DMi8 microscope with 20x objective.
Figure 6
Assessment of the transduction efficiency of viral vectors for RGCs by the ImageJ plugin ‘RGC Transduction’. The viral vector AAV2-hSYN-eGFP was delivered to the mouse retina via intravitreal injection (5 × 10E9 viral particles/eye). One month after injection, the retinal wholemount was prepared and RGCs were visualised using the immunohistochemical marker Brn3a. (A) Fluorescence microscopy image of a transduced retinal wholemount (top left) and automated quantification of transduced RGCs by plugin ‘RGC Transduction’ (top right). The merged image consisting of two colours is also shown as split channels for eGFP-positive cells in green (bottom left) and Brn3A-positive cells in red (bottom right). (B–C) Comparison between the results of the ‘RGC Transduction’ plugin and manual quantifications by six investigators for the number of transduced RGCs and total number of RGCs, respectively, in 12 images from one transduced retinal wholemount [Number of transduced RGCs: Y = 0.65x + 18.9, Pearson’s r = 0.718, R2 = 0.516, p = 0.009, Pearson product-moment correlation; Number of total RGCs: Y = 1.09x – 25.3, Pearson’s r = 0.995, R2 = 0.989, p < 0.001, Pearson product-moment correlation]. (D) Transduction efficiency of the viral vector for RGCs in each analysed image [F (6, 77) = 1.33, p = 0.25 for all investigators compared to ‘RGC Transduction’, ANOVA with Dunnett’s multiple comparisons test]. (E) Time required to analyse a single image of a transduced retinal wholemount [F (6, 77) = 92.9, p < 0.001 for all investigators compared to ‘RGC Transduction’, ANOVA with Dunnett’s multiple comparisons test]. The Pearson product-moment correlation and ANOVA data are shown as average ± 95% CI and average ± SEM, respectively. Images were taken on a Leica SPE confocal microscope with 40x objective.
Figure 7
Validation of the ImageJ plugin ‘RGC Batch’. Fluorescence microscopy images were analysed using the same user-set parameters and were either processed in one batch by ‘RGC Batch’ or assessed per individual image by ‘RGC Counter’ or ‘RGC Transduction’, respectively. (A) Comparison between the number of RGCs counted by ImageJ plugins ‘RGC Batch’ and ‘RGC Counter’ from twelve images of injured and uninjured retinal wholemounts. [Y = 1x + 0, Pearson’s r = 1, R2 = 1, Pearson product-moment correlation]. (B) Comparison between the number of transduced RGCs measured by ImageJ plugins ‘RGC Batch’ and ‘RGC Transduction’ in twelve images from one transduced retinal wholemount. [Y = 1x + 0, Pearson’s r = 1, R2 = 1, Pearson product-moment correlation]. Each dot represents a separate image.
Supplementary Figure 1
Variance in results of transgene fluorescence intensity when done by manual quantification. Six investigators independently analysed 12 fluorescence microscopy images originating from one transduced retinal wholemount. The mean fluorescence intensity per transduced RGC was measured by outlining 15 randomly selected RGCs per image in ImageJ. This method of manual quantification led to inconsistent outcomes between the investigators [F (5, 66) = 12.7, p < 0.001, ANOVA], highlighting the need for automated quantification methods such as ‘Simple RGC’.