July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Complement Landscape of the Mouse Retina Based on Single-cell Transcriptomics
Author Affiliations & Notes
  • Divyansh Agarwal
    Genomics and Computational Biology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
    Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, United States
  • Mingyao Li
    Biostatistics, Epidemiology and Informatics , University of Pennsylvania, Philadelphia, Pennsylvania, United States
  • Nancy Zhang
    Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, United States
    Genomics and Computational Biology, University of Pennsylvania, Philadelphia, Pennsylvania, United States
  • Dwight Stambolian
    Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States
    Penn Presbyterian Medical Center, Scheie Eye Institute, Philadelphia, Pennsylvania, United States
  • Footnotes
    Commercial Relationships   Divyansh Agarwal, None; Mingyao Li, None; Nancy Zhang, None; Dwight Stambolian, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 3940. doi:https://doi.org/
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      Divyansh Agarwal, Mingyao Li, Nancy Zhang, Dwight Stambolian; Complement Landscape of the Mouse Retina Based on Single-cell Transcriptomics. Invest. Ophthalmol. Vis. Sci. 2018;59(9):3940. doi: https://doi.org/.

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      © ARVO (1962-2015); The Authors (2016-present)

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  • Supplements
Abstract

Purpose : Complement contributes substantially to ocular homeostasis and also mediates synaptic refinement in the developing retinogeniculate system. Indeed, its dysregulation is a feature of several inflammatory conditions, including uveoretinitis, diabetic retinopathy, glaucoma, age-related macular degeneration. Since the retina is an immune-privileged organ, understanding its local complement system becomes all the more critical. As a prelude to making inferences about human retina, we performed single-cell sequencing of the mouse retina.

Methods : To understand which retinal cells are the source of complement, we map mouse retinal complement expression at a single-cell resolution. The expression of complement components of the classical pathway, mannose-binding lectin (MBL) pathway and alternative pathway in the retina was determined by single-cell RNA-sequencing. We used graph-based clustering to classify ~24,000 mouse retinal cells into major cell types (Fig. 1), and subsequently determined the “hotspots” of complement expression. We examined and identified inflammatory genes that exhibit transcriptional co-bursting with complement, as well as confirmed at a transcriptome-level, known protein-level associations between cytokines and complement.

Results : Low-levels of complements C1qa, C1qb, C1qc, C1ra, C1s1, C2 and MBL-associated serine protease (MASP)-2 were expressed by most retinal cell types. Interestingly, we observe C4B isoform expression localized to Muller cells, CFH expression exclusively in Muller and microglial cells, and CFI expression specific to rod bipolar cells. Furthermore, in retinal microglia, we identified transcription factors (TFs) potentially associated with CFH expression based on TF-co-expression and predicted putative direct-binding targets. We found that Klf11, Etv5 and Elk1 were significantly associated with CFH expression (Bonferroni corrected p-values: 5x10-4; 6x10-3 and 8x10-4, respectively). These three transcription factors have been implicated in retinal angiogenic sprouting and neurogenesis, suggesting a pathway through Factor H.

Conclusions : Overall, we provide a comprehensive map of complement expression in specific cell types within a normal mouse retina, and identify novel regulators which modulate that expression.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.

 

Fig. 1: t-SNE analysis of ~24,000 mouse retinal cells. Eleven major cell types were identified based on known cell markers.

Fig. 1: t-SNE analysis of ~24,000 mouse retinal cells. Eleven major cell types were identified based on known cell markers.

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