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Additional file 7. Overview of mouse strains used in this study.

Additional file 4. Expression of pro-regenerative genes in cluster MDM7. Gene ontology analysis on the upregulated genes in Mg5 versus Mg2> 20; log2>1) showing the top 20 enriched GO terms for Mg5.

Additional file 5. Nichenet analysis of MDMs against injured RGCs. A Overview of potential receptors on the retinal ganglion cells of the ligands expressed by the different macrophage clusters. The colourrepresents the regulatory potential of the receptors based on the prior model of ligand-receptor interactions.Receptor expression in the different retinal ganglion cell populations is shown with the colourrepresenting the scaled average expression in the corresponding cluster. B Overview of the predicted target genes in the retinal ganglion cells of the ligands expressed by the different macrophage clusters. The colourrepresents the regulatory potential of the target genes based on the prior model of ligand-target gene interactions. C Circle plot of potential ligand-receptor pairs. It shows the links between predicted ligands from the different monocyte-derived macrophage clusters of the regenerating retinawith their associated receptors found on alpha- and intrinsically photosensitive retinal ganglion cells.

Additional file 1. scRNA-seq of CD45+CD11b+ cells from naïve or ONC retinas. A UMAP showing all CD11b+ cells profiled from the healthy and ONC retinas. BAM, border associated macrophage, cDC: conventional dendritic cell, migDC: migratory dendritic cell, MDM: monocyte-derived macrophage, Mg: microglia, MO, monocyte, N: neutrophils, NK: natural killer cell. B UMAPs showing the expression of the indicated genes. Red line highlights the putative macrophage-neutrophil doublets. C Volcano plot displaying differential expression between Mg3 and Mg1. Genes with adjusted p-value 1 are shown in red. D Volcano plot displaying differential expression between Mg4 and Mg1. Genes with adjusted p-value 1 are shown in red. E Quantification of the density and activityof microglia in retina at different timepoints after ONC corresponding with images shown in figure 1E. Data are shown as mean ± SEM. Repeated measures one-way ANOVA followed by Tukey’s multiple comparisons test, statistical significance between different timepoints is indicated using different letters: conditions that share the same letter are not significantly different, while conditions with different letters are significantly different from each other. n=3-4 mice per condition. F UMAPs showing the expression of the indicated genes, corresponding to the dataset shown in S1A.

Additional file 3. Full scRNA-seq dataset of naïve, ONC and ONC+P3C CD11b+CD45+ cells. A UMAP and cluster annotation showing 22081 cells of both healthy, injuredand regeneratingretinas. BAM, border associated macrophage, cDC: conventional dendritic cell, migDC: migratory dendritic cell, MDM: monocyte-derived macrophage, Mg: microglia, MO, monocyte, N: neutrophils, NK: natural killer cell. B,C UMAP showing 6997 cells of retinas at 4dpi ONC+P3Cand 7956 cells of retinas at 8dpi ONC+P3C. Individual pie charts show the distribution of neutrophils, monocytes or alle immune populations. Numbers in the pie chart are percentages of the cells from the corresponding cluster.

Additional file 1: Supplementary figures

AIM: Diabetic retinopathy is a common complication of diabetes and a leading cause of visual impairment and blindness. Despite recent advances, our understanding of its pathophysiology remains incomplete. The aim of this study was to provide deeper insight into the complex network of molecular and cellular changes that underlie diabetic retinopathy by systematically mapping the transcriptional changes that occur in the different cellular compartments of the degenerating diabetic mouse retina. METHODS: Single-cell RNA sequencing was performed on retinal tissue from 12-week-old wild-type and Akimba (Ins2AkitaxVEGF+/-) mice, which are known to replicate features of clinical diabetic retinopathy. This resulted into transcriptome data for 9474 retinal cells, which could be annotated to 8 distinct retinal cell types. Using STRING analysis, we studied differentially expressed gene networks in neuronal, glial and immune cell compartments to create a comprehensive view on the pathological changes that occur in the Akimba retina. Using subclustering analysis, we further characterized macroglial and inflammatory cell subpopulations. Prominent findings were confirmed on protein level using immunohistochemistry, Western blotting and ELISA. RESULTS: At 12 weeks, the Akimba retina was found to display degeneration of rod photoreceptors and presence of inflammatory cells, identified by subclustering analysis as monocyte, macrophage and microglial populations. Analysis of differential expressed genes in the rod, cone, bipolar cell and macroglial compartments indicated changes in cell metabolism and ribosomal gene expression, gliosis, activation of immune system pathways and redox and metal ion dyshomeostasis. Experiments on protein level supported a metabolic shift from glycolysis to oxidative phosphorylation (GAPDH), activation of microglia/macrophages (Isolectin-B4), metal ion and oxidative stress response (metallothionein and heme oxygenase-1) and reactive macroglia (GFAP and S100) in the Akimba retina, as compared to wild-type mice. Our single-cell approach also indicates macroglial subpopulations with distinct fibrotic, inflammatory and gliotic profiles. CONCLUSION: Our study identifies molecular pathways underlying inflammatory, metabolic and oxidative stress-mediated changes in the Akimba mouse model of diabetic retinopathy and distinguishes distinct functional subtypes of inflammatory and macroglial cells. ispartof: Diabetologia vol:63 issue:10 pages:2235-2248 ispartof: location:Germany status: published

Additional file 2. Details on hyperspectral image analysis.

Additional file 2. Details on hyperspectral image analysis.

In this study, we report the results of a comprehensive phenotyping of the retina of the App(NL-G-F) mouse. We demonstrate that soluble A beta accumulation is present in the retina of these mice early in life and progresses to A beta plaque formation by midlife. This rising A beta burden coincides with local microglia reactivity, astrogliosis, and abnormalities in retinal vein morphology. Electrophysiological recordings revealed signs of neuronal dysfunction yet no overt neurodegeneration was observed and visual performance outcomes were unaffected in the App(NL-G-F) mouse. Furthermore, we show that hyperspectral imaging can be used to quantify retinal A beta, underscoring its potential as a biomarker for AD diagnosis and monitoring. These findings suggest that the App(NL-G-F) retina mimics the early, preclinical stages of AD, and, together with retinal imaging techniques, offers unique opportunities for drug discovery and fundamental research into preclinical AD. MV, LV, LuM and LDG are fellows of the Research Foundation Flanders (FWO). This research was supported by the Alzheimer's Research Foundation (SAO-FRA), the European Union Horizon 2020 Research and Innovation Program (2014-2020) (HERALD project, granted by the ATTRAC T consortium). The Centre for Eye Research Australia receives Operational Infrastructure Support from the Victorian Government. XH and PvW acknowledge funding support from the H & L Hecht Trust and the Yulgilbar Alzheimer's Research Program. De Groef, L (corresponding author), Univ Leuven KU Leuven, Dept Biol, Neural Circuit Dev & Regenerat Res Grp, Naamsestr 61,Box 2464, B-3000 Leuven, Belgium. Lies.Degroef@kuleuven.be

Prominin-1/CD133 (Prom-1) is a commonly used marker of neuronal, vascular, hematopoietic and other stem cells, yet little is known about its biological role and importancein vivo. Here, we show that loss of Prom-1 results in progressive degeneration of mature photoreceptors with complete loss of vision. Despite the expression of Prom-1 on endothelial progenitors, photoreceptor degeneration was not attributable to retinal vessel defects, but caused by intrinsic photoreceptor defects in disk formation, outer segment morphogenesis, and associated with visual pigment sorting and phototransduction abnormalities. These findings shed novel insight on how Prom-1 regulates neural retinal development and phototransduction in vertebrates.