scholarly journals Single Cell RNA Sequencing of stem cell-derived retinal ganglion cells

2017 ◽  
Author(s):  
Maciej Daniszewski ◽  
Anne Senabouth ◽  
Quan Nguyen ◽  
Duncan E. Crombie ◽  
Samuel W. Lukowski ◽  
...  
Keyword(s):  
Stem Cell ◽  
Embryonic Stem Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Retinal Ganglion Cells ◽  
Human Embryonic Stem Cell ◽  
Ganglion Cells ◽  
Embryonic Stem ◽  
Retinal Ganglion ◽  
Single Cell Rna Sequencing

ABSTRACTWe used human embryonic stem cell-derived retinal ganglion cells (RGCs) to characterize the transcriptome of 1,174 cells at the single cell level. The human embryonic stem cell line BRN3B-mCherry A81-H7 was differentiated to RGCs using a guided differentiation approach. Cells were harvested at day 36 and subsequently prepared for single cell RNA sequencing. Our data indicates the presence of three distinct subpopulations of cells, with various degrees of maturity. One cluster of 288 cells upregulated genes involved in axon guidance together with semaphorin interactions, cell-extracellular matrix interactions and ECM proteoglycans, suggestive of a more mature phenotype.

scholarly journals Single cell RNA sequencing of stem cell-derived retinal ganglion cells

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Maciej Daniszewski ◽  
Anne Senabouth ◽  
Quan H. Nguyen ◽  
Duncan E. Crombie ◽  
Samuel W. Lukowski ◽  
...  
Keyword(s):  
Stem Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Retinal Ganglion Cells ◽  
Ganglion Cells ◽  
Retinal Ganglion ◽  
Single Cell Rna Sequencing

scholarly journals Laser capture microdissection-based single-cell RNA sequencing reveals optic nerve crush-related early mRNA alterations in retinal ganglion cells

2020 ◽  
Author(s):  
Dongyan Pan ◽  
Mengqiao Xu ◽  
Xin Chang ◽  
Mao Xia ◽  
Yibin Fang ◽  
...  
Keyword(s):  
Optic Nerve ◽  
Single Cell ◽  
Rna Sequencing ◽  
Retinal Ganglion Cells ◽  
Ganglion Cells ◽  
Optic Nerve Crush ◽  
Retinal Ganglion ◽  
Nerve Crush ◽  
Single Cell Rna Sequencing ◽  
Laser Capture

AbstractRetinal ganglion cells (RGC) are the primary cell type injured in a variety of diseases of the optic nerve, and the early changes of RGC’s RNA profiling may be important to understand the mechanism of optic nerve injury and axon regeneration. Here we employed the optic nerve crush (ONC) model to explore early mRNA alterations in RGCs using laser capture microdissection (LCM) and single-cell RNA sequencing. We successfully established an optimal LCM protocol using 30 μm-thick retinal tissue sections mounted on glass slides and laser pressure catapulting (LPC) to collect RGCs and obtain high-quality RNA for single-cell sequencing. Based on our protocol, we identified 8744 differentially expressed genes that were involved in ONC-related early mRNA alterations in RGCs. Candidate genes included Atf3, Lgals3, LOC102551701, Plaur, Tmem140 and Maml1. The LCM-based single-cell RNA sequencing allowed new insights into the early mRNA changes in RGCs, highlighting new molecules associated with ONC.

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

Genes ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 2015
Author(s):  
Harini V. Gudiseva ◽  
Vrathasha Vrathasha ◽  
Jie He ◽  
Devesh Bungatavula ◽  
Joan M. O’Brien ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cell ◽  
Single Cell ◽  
Retinal Ganglion Cells ◽  
Pluripotent Stem Cell ◽  
Ganglion Cells ◽  
Induced Pluripotent Stem Cell ◽  
Retinal Ganglion ◽  
Single Cell Sequencing ◽  
Induced Pluripotent

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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