June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
Characterizing molecular signatures of induced pluripotent stem cell derived retinal ganglion cells using single cell sequencing
Author Affiliations & Notes
  • Harini V Gudiseva
    Scheie Eye Institute, Univ of Pennsylvania, Philadelphia, Pennsylvania, United States
  • Naqi Haider
    Scheie Eye Institute, Univ of Pennsylvania, Philadelphia, Pennsylvania, United States
  • Jason Mills
    Scheie Eye Institute, Univ of Pennsylvania, Philadelphia, Pennsylvania, United States
  • Venkata R M Chavali
    Scheie Eye Institute, Univ of Pennsylvania, Philadelphia, Pennsylvania, United States
  • Joan M O'Brien
    Scheie Eye Institute, Univ of Pennsylvania, Philadelphia, Pennsylvania, United States
  • Footnotes
    Commercial Relationships   Harini Gudiseva, None; Naqi Haider, None; Jason Mills, None; Venkata Chavali, None; Joan O'Brien, None
  • Footnotes
    Support  NIH Grant EY023557-01
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 2516. doi:
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    • Get Citation

      Harini V Gudiseva, Naqi Haider, Jason Mills, Venkata R M Chavali, Joan M O'Brien; Characterizing molecular signatures of induced pluripotent stem cell derived retinal ganglion cells using single cell sequencing. Invest. Ophthalmol. Vis. Sci. 2020;61(7):2516.

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

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Abstract

Purpose : To identify molecular makers, differential gene expression and retinal ganglion cell (RGC) subtypes in human induced pluripotent stem cell (iPSC) derived retinal ganglion cells.

Methods : Human pluripotent stem cells were differentiated into RGCs using a step-wise differentiation protocol consisting of small molecules and proteins that was developed in our laboratory. We harvested the mature and functional iPSC-RGCs, consisting of large somas connected by elongated axonal processes, on day 35. Single cell sequencing was performed on these mature iPSC-RGCs using 10X Genomics Single Cell 3' Chip following the Chromium Single Cell 3' V2 protocols. The normalized sequencing libraries were run on Illumina HiSeq 4000 to generate 150 PE reads. The demultiplex FASTQ files were mapped to the hg38 ref genome using STAR and cluster analysis were performed with cell ranger software. The quality control analysis was performed by removing the reads which corresponded to ribosomal and mitochondrial genes, as well as cells that had a mean absolute deviation (MAD) lower than 1X. We identified sub-populations of single cell clusters with varying resolution using unsupervised clustering. We performed differential expression gene (DEG) analysis of the identified clusters to characterize subsets of RGCs and RGC markers in our iPSC-RGCs.

Results : The total number of cells that passed filtering for mitochondrial reads above 20% and ribosomal reads above 50% were 1461. Cells were separated into 6 different clusters based on the gene expression normalization via PCA and TSNE analysis in the Seurat tool. Several previously known RGC marker genes like MAP2, SOX4, TUBB3, SCNG, PAX6, NRN1 were found to be expressed in iPSC-RGC clusters. Differential expression analysis between the separate clusters identified (average log fold change > -0.25 and < 0.25) significant DEG transcripts associated with cell cycle, transcription factor neuron regulatory networks, protein kinases, calcium signaling, growth factor hormones and homeobox transcription factors. This analysis demonstrated the presence of different subpopulations of RGCs within and between the clusters.

Conclusions : We have identified both known and novel marker genes to distinguish subsets of RGCs in our iPSC-RGC clusters. The differentially expressed genes can be used as biomarkers for RGC subtype classification within the human retina.

This is a 2020 ARVO Annual Meeting abstract.

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