Tag Archives: glaucoma

Modeling Complex Age-Related Eye Disease

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We have a new Progress in Retinal and Eye Research manuscript out in collaboration with my colleagues here at the Moran Eye Center.

Authors: Silke Becker, Zia L’Ecuyer, Bryan W Jones, Moussa A Zouache, Fiona S McDonnell, Frans Vinberg.

Abstract: Modeling complex eye diseases like age-related macular degeneration (AMD) and glaucoma poses significant challenges, since these conditions depend highly on age-related changes that occur over several decades, with many contributing factors remaining unknown. Although both diseases exhibit a relatively high heritability of >50%, a large proportion of individuals carrying AMD- or glaucoma-associated genetic risk variants will never develop these diseases. Furthermore, several environmental and lifestyle factors contribute to and modulate the pathogenesis and progression of AMD and glaucoma.

Several strategies replicate the impact of genetic risk variants, pathobiological pathways and environmental and lifestyle factors in AMD and glaucoma in mice and other species. In this review we will mostly discuss the most commonly available mouse models, which have and will likely continue to improve our understanding of the pathobiology of age-related eye diseases. Uncertainties persist whether small animal models can truly recapitulate disease progression and vision loss in patients, raising doubts regarding their usefulness when testing novel gene or drug therapies. We will elaborate on concerns that relate to shorter lifespan, body size and allometries, lack of macula and a true lamina cribrosa, as well as absence and sequence disparities of certain genes and differences in their chromosomal location in mice.

Since biological, rather than chronological, age likely predisposes an organism for both glaucoma and AMD, more rapidly aging organisms like small rodents may open up possibilities that will make research of these diseases more timely and financially feasible. On the other hand, due to the above-mentioned anatomical and physiological features, as well as pharmacokinetic and -dynamic differences small animal models are not ideal to study the natural progression of vision loss or the efficacy and safety of novel therapies. In this context, we will also discuss the advantages and pitfalls of alternative models that include larger species, such as non-human primates and rabbits, patient-derived retinal organoids, and human organ donor eyes.

Impact of Glaucoma On Retinal Ganglion Cell Subtypes: A Single-Cell RNA-seq Analysis of the DBA/2J Mouse

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This abstract was presented today, May 1st at the 2018 Association for Research in Vision and Opthalmology (ARVO) meetings in Honolulu, Hawaii by Siamak Yousefi, Hao Chen, Jesse Ingels, Sumana R. Chintalapudi, Megan Mulligan, Bryan W. Jones, Vanessa Marie Morales-Tirado, Pete Williams, Simon W. John, Felix Struebing, Eldon E. Geisert, Monica Jablonski, Lu Lu, Robert Williams

Purpose
We are developing methods to define molecular signatures of cellular stress during early stages of glaucoma for major subtypes of retinal ganglion cells (RGCs). Our first aim is to develop reliable mRNA biomarkers for RGC subtypes in the DBA/2J (D2) mouse model prior to disease onset. Our second objective is to quantify cellular stress in RGC subtypes at early stages of disease using known sets of stress-responsive transcripts (e.g. Struebing et al, 2016 PMID:27733864; Williams et al. 2017, PMID:28209901; Lu et al, ARVO 2018).

Methods
Whole retinas from D2 or D2.Cg-Tg(Thy1-CFP)23Jrs/SjJ at 130 to 150 days-of-age were dissociated gently and size selected (>10 µm). RGCs were enriched using THY1 antibody-coated beads. Fluidigm HT microfluidics plates were used to isolate and generate scRNA-seq libraries of full length polyA-positive mRNAs using SMART-Seq v4. Libraries were sequenced using HiSeq3000, PE151. Following alignment using STAR, expression was normalized to log2(FPKM+1) across ~25,000 unique transcript models. Cells with fewer than 1000 detected genes and genes expressed in fewer than 1% of RGCs were excluded. Sets of genes with high variance and/or high expression were used for principal component analysis (PCA). Twenty PCs were used for graph-based unsupervised clustering and visualized using t-distributed stochastic neighbor embedding (tSNE). Gene specificity was computed for all transcripts across all clusters. The top transcripts per cluster with expression >1 in 1% or more of cells, were used to diagnose cellular identify of clusters. The top 30 genes per cluster were searched in PubMed against a panel of cell and tissue specific terms using Chilibot.

Results
The scRNA-seq protocol generates 150,000 – 200,000 uniquely mapped mRNA reads/cell and ~5000 genes/cells. We currently have 1600 cells, of which over half are RGCs. Around 75% of cells are positive for two or more of the following RGC markers: Thy1, Rbpms, Rbpms2, Jam2, G3bp1, and Ywhaz. This set of cells and different subsets of genes are now being used for RGC clustering. We have identified at least 17 clusters in initial datasets using these protocols and are now linking clusters to major classes of RGCs.

Conclusions
Molecular signatures of cellular stress and RGC subtypes in early stage of glaucoma should now be identifiable using unsupervised learning techniques.