June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
Integrative analysis of single-cell multi-omics data of the human retina
Author Affiliations & Notes
  • Qingnan Liang
    HGSC, Department of Human and Molecular Genetics, Baylor College of Medicine, Houston, Texas, United States
    Verna and Marrs McLean Department of Biochemistry and Molecular BiologyBiochemistry, Baylor College of Medicine, Houston, Texas, United States
  • Xuesen Cheng
    HGSC, Department of Human and Molecular Genetics, Baylor College of Medicine, Houston, Texas, United States
  • Leah Owen
    Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, Utah, United States
    Department of Population Health Sciences, University of Utah, Salt Lake City, Utah, United States
  • Akbar Shakoor
    Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, Utah, United States
  • Albert T Vitale
    Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, Utah, United States
  • Nadine Husami
    Department of Ophthalmology, Jacobs School of Medicine and Biomedical Engineering,, University at Buffalo SUNY, Buffalo, New York, United States
    VA Western New York Healthcare System Buffalo VA Medical Center, Buffalo, New York, United States
  • Denise Morgan
    Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, Utah, United States
  • Michael H Farkas
    Department of Ophthalmology, Jacobs School of Medicine and Biomedical Engineering,, University at Buffalo SUNY, Buffalo, New York, United States
    VA Western New York Healthcare System Buffalo VA Medical Center, Buffalo, New York, United States
  • Ivana K Kim
    Retina Service, Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts, United States
  • Yumei Li
    HGSC, Department of Human and Molecular Genetics, Baylor College of Medicine, Houston, Texas, United States
  • Margaret M DeAngelis
    Department of Ophthalmology, Jacobs School of Medicine and Biomedical Engineering,, University at Buffalo SUNY, Buffalo, New York, United States
    VA Western New York Healthcare System Buffalo VA Medical Center, Buffalo, New York, United States
  • Rui Chen
    HGSC, Department of Human and Molecular Genetics, Baylor College of Medicine, Houston, Texas, United States
    Verna and Marrs McLean Department of Biochemistry and Molecular BiologyBiochemistry, Baylor College of Medicine, Houston, Texas, United States
  • Footnotes
    Commercial Relationships   Qingnan Liang, None; Xuesen Cheng, None; Leah Owen, None; Akbar Shakoor, None; Albert Vitale, None; Nadine Husami, None; Denise Morgan, None; Michael Farkas, None; Ivana Kim, None; Yumei Li, None; Margaret DeAngelis, None; Rui Chen, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 1555. doi:
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    • Get Citation

      Qingnan Liang, Xuesen Cheng, Leah Owen, Akbar Shakoor, Albert T Vitale, Nadine Husami, Denise Morgan, Michael H Farkas, Ivana K Kim, Yumei Li, Margaret M DeAngelis, Rui Chen; Integrative analysis of single-cell multi-omics data of the human retina. Invest. Ophthalmol. Vis. Sci. 2021;62(8):1555.

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

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Abstract

Purpose : The human retina is composed of different neuronal and non-neuronal cell types. Significant cell heterogeneity is observed within many neural retina cell types, with their composition in the tissue ranging anywhere, from 75% to less than 0.05%. However, the number of retinal cell subtypes and their gene expression signature remains largely undiscovered, especially for rarer cell types. Additionally, open chromatin profiles for the human retina at the single-cell level has not yet been reported. Therefore, we are generating the first version of human neural retinal cell atlas reference by characterizing both the transcriptome and open chromatin profile for all cell types in the human retina.

Methods : Single-nuclei RNA-seq and single-nuclei ATAC-seq were carried out to profile well-characterized normal human retina from over twenty donors (average age over 70). Each donor retina was dissected into three geographic regions: the fovea, macula, and peripheral retina and flash-frozen afterward. A fractionation protocol was developed to enrich nuclei from rare neuron cell types, including bipolar cells, amacrine cells, and retinal ganglion cells. Integrative data analysis was performed to identify cell subtypes, marker genes, chromatin signature, and transcription factors and modules. Experimental approaches including immunofluorescence staining and RNA in situ hybridization are performed to validate novel cell populations.

Results : A transcriptome profile was generated for over 250K nuclei, leading to the identification of over 60 cell types in the human retina. Through comparison among the human, and previously reported monkey/mouse datasets, we have identified conserved and primate-specific subtypes. In parallel, single-cell open chromatin profiles were generated using single-nuclei ATAC-seq from 160k nuclei. The integration of the single-nuclei RNA-seq and single-nuclei ATAC-seq data allows us to obtain the open chromatin profiles of each subtype. Key transcription factors and transcription modules were identified at both major- and sub- cell-type levels.

Conclusions : This study represents the most comprehensive single-cell transcriptome and chromatin accessibility profiling for the human neural retina to date. Over 400K single nuclei were profiled for their transcriptome or chromatin accessibility, with over 60 cell types. This well-characterized dataset serves as the first version of a human retina cell atlas reference.

This is a 2021 ARVO Annual Meeting abstract.

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