Investigative Ophthalmology & Visual Science Cover Image for Volume 65, Issue 7
June 2024
Volume 65, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2024
Digital eye: single-cell multiomics simulation to model and study cell-cell-interactions during development, regeneration and in the pathogenesis of retinal and optic nerve diseases
Author Affiliations & Notes
  • Emil Kriukov
    Massachusetts Eye and Ear, Boston, Massachusetts, United States
    Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts, United States
  • Petr Y Baranov
    Massachusetts Eye and Ear, Boston, Massachusetts, United States
    Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts, United States
  • Footnotes
    Commercial Relationships   Emil Kriukov None; Petr Baranov None
  • Footnotes
    Support  Gilbert Family Foundation G00
Investigative Ophthalmology & Visual Science June 2024, Vol.65, 408. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Emil Kriukov, Petr Y Baranov; Digital eye: single-cell multiomics simulation to model and study cell-cell-interactions during development, regeneration and in the pathogenesis of retinal and optic nerve diseases. Invest. Ophthalmol. Vis. Sci. 2024;65(7):408.

      Download citation file:


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

      ×
  • Supplements
Abstract

Purpose : Retina and optic nerve are complex heterogenous systems. The latest approaches in transcriptomics brought the analysis to the single-cell resolution, allowing to identify novel molecules and pathways. However, the way cells interact with each outher within the organ ecosystem usually remains unknown. Here, we propose a new method of analyzing transcriptomics data by creating a “digital eye” that allows to computationally introduce diseased cells into healthy tissue and vice versa. It allows to simulate stages of disease progression and study and model the way cells interact at the ligand-receptor level with the true single-cell resolution.

Methods : We start with the scRNA-seq data for human adult healthy and age macular degenerated (AMD) retinas. Our pipeline includes the methods of data processing (Harmony, SCTransform, ForceAtlas2) and cell-cell interactions analysis (CellChat, Scriabin). We focus on combining the datasets of healthy and AMD retinas to generate the following conditions: 1) healthy, reference 2) healthy with AMD microglia 3) healthy with AMD endothelia 4) healthy with AMD RPE 5) healthy with AMD glia 6) AMD 7) AMD with healthy RGC, Control.

Results : We identify cell-cell interactions that profile the disease staging. For healthy condition: ApoE, TAFA, GALECTIN, GDNF, ANNEXIN, IL16, SELPG. For healthy with AMD microglia: IFN-II, GH, VWF, 2-AG, MMP, LHB, SEMA5, ADGRG, CRH, TIGIT, CD200, PACAP, CX3C, ESAM, PVR, ENHO. For AMD: IFN-I, Ach, MIF, IL16, CSF3, LHB, NMU. The upregulated interactions in healthy with AMD microglia condition are CD45, PCDH, SLITRK, NT, PSAP, IL2, L1CAM, THY1, IGFBP, Chemerin, NPVF, CD96, with their upregulation in addition to new interactions of BTLA, CLDN, LIGHT, SEMA4, CypA, PDGF, FLRT in the AMD condition. We demonstrate the gradual increase of PCDH interactions in the AMD progression, first affecting ON-bipolar and horizontal cells. We show 95% similarity of cell-cell interactions between the AMD and AMD Control conditions.

Conclusions : We describe a new approach of single-cell data analysis for recapitulating the disease progression staging based on comparison of cell populations and microenvironment in retina in the mix of healthy and diseased cells. Our pipelines pinpoint multiple ligands and receptors that can be modulated at the early stage of AMD progression.

This abstract was presented at the 2024 ARVO Annual Meeting, held in Seattle, WA, May 5-9, 2024.

 

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×