June 2015
Volume 56, Issue 7
ARVO Annual Meeting Abstract  |   June 2015
Defining cell classes in the neural retina
Author Affiliations & Notes
  • Dustin Thad Whitaker
    National Eye Institute, Bethesda, MD
    Neuroscience, Texas A&M University, College Station, TX
  • Jung-Woong Kim
    National Eye Institute, Bethesda, MD
  • Matthew Brooks
    National Eye Institute, Bethesda, MD
  • Anand Swaroop
    National Eye Institute, Bethesda, MD
  • Footnotes
    Commercial Relationships Dustin Whitaker, None; Jung-Woong Kim, None; Matthew Brooks, None; Anand Swaroop, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 5491. doi:
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      Dustin Thad Whitaker, Jung-Woong Kim, Matthew Brooks, Anand Swaroop; Defining cell classes in the neural retina. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):5491.

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

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Purpose: The mammalian neural retina is a heterogeneous mixture of cell classes and subclasses, many of which have been studied none to little due to their ultra low cell number. We hypothesized that by sequencing the transcriptome of many single cells in the mature retina, we would be able to distinguish and cluster cell classes within the retina.

Methods: Retinas of mature one month old Nrlp-GFP mice, where rod photoreceptors fluoresce green, were dissociated into single cell suspensions before harvesting individual cells or pools of small numbers of rodphotorecptors for reverse transcription and cDNA amplification using the SMART-seq application protocol. Following successful amplification, libraries were prepared using the Nextera XT tagmentation reaction. RNA-sequencing was performed on the Illumina HiSeq 2500. We are still in the process of collecting and sequencing the transcriptional profile and hope to sequence 100s of individual cells in addition to low levels of rod photoreceptor populations.

Results: We have successfully amplified cDNA from both single and small populations of rod photoreceptors as well as single non-rod photoreceptor cells. Amplification is initially validated by GAPDH qPCR and some cell-specific genes are also tested prior to library preparation. Tagmented sequencing libraries have been produced and confirmed by both qPCR of sequencing-specific primers and by bioanalyzer. We have just begun our initial sequencing run with single and pooled cell samples and are aiming to obtain approximately five million transcript reads for each sample.

Conclusions: We believe that the RNA-sequencing transcriptome will permit automatic clustering of our rod photoreceptors and other non-rod cells into distinct classes based on their global gene expression profile. In addition, we will be able to define any natural variation in rod photoreceptors at the single cell level from an assumed homogenous population. We will confirm all transcriptome results with independent methods. Knowing the transcriptional profile of all cell classes of this very heterogenous tissue should allow specific and focused physiological, molecular, and genetic analyses.


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