Journal: Nature Communications

Article Title: Myeloid lineage C3 induces reactive gliosis and neuronal stress during CNS inflammation

doi: 10.1038/s41467-025-58708-3

Figure Lengend Snippet: a Diagram of experimental paradigm showing how eyes were either evaluated for synaptic pathology (left) via cross-sections or retinal ganglion cell (RGC) loss via flat mounts (right) on EAE d16 mice. b Representative image of flat-mounted retina with 12 regions selected for Brn3a + RGC quantification. Scale bar in full retina image = 500 μm, in high magnification counting fields scale bar = 20 μm. c Quantification of Brn3a + RGCs at peak EAE. N = 4 per experimental group. d Quantification of the integrated density of markers Syn1 (left) and PSD95 (right) in the inner plexiform layer of CFA vs GFAP-Cre animals at peak EAE. Signal intensity values are normalized to CFA control, with AU representing “arbitrary units”. N = 4 CFA, 10 GFAP-Cre − and 7 GFAP-Cre + mice. e Representative immunofluorescent staining of Syn1 (red) and PSD95 (green) in the inner and outer plexiform layers across GFAP-Cre experimental groups. Scale bar = 50 um. f Same as in e but evaluating synaptic markers in the inner plexiform layer of LysM-Cre + and LysM-Cre − mice. Values are normalized to the CFA only animals utilized in Fig. 5d. N = 4 CFA, 6 LysM-Cre − and 8 LysM-Cre + mice. g Representative immunofluorescent staining of Syn1 and PSD95 in LysM-Cre − vs LysM-Cre + mice. Scale bar = 50 um. All bar graphs presented as mean +/- SEM, with one-way ANOVAs adjusted for multiple comparisons. Source data are provided as a source data file. Figure 5a created in BioRender. Smith, M. (2025) https://BioRender.com/j21s335 .

Article Snippet: Our MATLAB-based semi-automated RGC counting algorithm was used to determine RGC number in each mouse .

Techniques: Control, Staining