4.6 Article

Single Cell Sequencing of Induced Pluripotent Stem Cell Derived Retinal Ganglion Cells (iPSC-RGC) Reveals Distinct Molecular Signatures and RGC Subtypes

Journal

GENES
Volume 12, Issue 12, Pages -

Publisher

MDPI
DOI: 10.3390/genes12122015

Keywords

retinal ganglion cells; transcriptome; single cell sequencing; iPSC-RGCs; iPSCs; RGC subtypes; FACS analysis; marker genes; clustering; glaucoma

Funding

  1. National Eye Institute, Bethesda, Maryland [1RO1EY023557-01]
  2. Perelman School of Medicine, Department of Ophthalmology, University of Pennsylvania, Philadelphia, PA
  3. Lisa Dean Moseley Foundation, Vision Research Core Grant [P30 EY001583]
  4. F.M. Kirby Foundation
  5. Research to Prevent Blindness
  6. UPenn Hospital Board of Women Visitors
  7. Paul and Evanina Bell Mackall Foundation Trust
  8. National Eye Institute, National Institutes of Health, Department of Health and Human Services [HHSN260220700001C, HHSN263201200001C]

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This study aimed to identify marker genes with differential gene expression and RGC subtypes in cultures of human-induced pluripotent stem cell (iPSC)-derived retinal ganglion cells. Single-cell sequencing was performed and cluster analyses were conducted to determine the RGC diversity and subtype specification within iPSC-RGCs. DEG analysis revealed subsets of RGCs with specific markers and significant differential expression associated with various biological processes and pathways.
We intend to identify marker genes with differential gene expression (DEG) and RGC subtypes in cultures of human-induced pluripotent stem cell (iPSC)-derived retinal ganglion cells. Single-cell sequencing was performed on mature and functional iPSC-RGCs at day 40 using Chromium Single Cell 3' V3 protocols (10X Genomics). Sequencing libraries were run on Illumina Novaseq to generate 150 PE reads. Demultiplexed FASTQ files were mapped to the hg38 reference genome using the STAR package, and cluster analyses were performed using a cell ranger and BBrowser2 software. QC analysis was performed by removing the reads corresponding to ribosomal and mitochondrial genes, as well as cells that had less than 1X mean absolute deviation (MAD), resulting in 4705 cells that were used for further analyses. Cells were separated into clusters based on the gene expression normalization via PCA and TSNE analyses using the Seurat tool and/or Louvain clustering when using BBrowser2 software. DEG analysis identified subsets of RGCs with markers like MAP2, RBPMS, TUJ1, BRN3A, SOX4, TUBB3, SNCG, PAX6 and NRN1 in iPSC-RGCs. Differential expression analysis between separate clusters identified significant DEG transcripts associated with cell cycle, neuron regulatory networks, protein kinases, calcium signaling, growth factor hormones, and homeobox transcription factors. Further cluster refinement identified RGC diversity and subtype specification within iPSC-RGCs. DEGs can be used as biomarkers for RGC subtype classification, which will allow screening model systems that represent a spectrum of diseases with RGC pathology.

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