Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

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dc.contributor.author Laboissonniere, Lauren
dc.contributor.author Trimarchi, Jeffrey
dc.contributor.author Sonoda, Takuma
dc.contributor.author Lee, Seul Ki
dc.contributor.author Trimarchi, Jeffrey
dc.contributor.author Schmidt, Tiffany
dc.contributor.department Genetics, Development and Cell Biology
dc.date 2018-02-22T22:16:09.000
dc.date.accessioned 2020-06-30T04:01:41Z
dc.date.available 2020-06-30T04:01:41Z
dc.date.copyright Sun Jan 01 00:00:00 UTC 2017
dc.date.embargo 2019-05-22
dc.date.issued 2017-05-22
dc.description.abstract <p>The discovery of cell type-specific markers can provide insight into cellular function and the origins of cellular heterogeneity. With a recent push for the improved understanding of neuronal diversity, it is important to identify genes whose expression defines various subpopulations of cells. The retina serves as an excellent model for the study of central nervous system diversity, as it is composed of multiple major cell types. The study of each major class of cells has yielded genetic markers that facilitate the identification of these populations. However, multiple subtypes of cells exist within each of these major retinal cell classes, and few of these subtypes have known genetic markers, although many have been characterized by morphology or function. A knowledge of genetic markers for individual retinal subtypes would allow for the study and mapping of brain targets related to specific visual functions and may also lend insight into the gene networks that maintain cellular diversity. Current avenues used to identify the genetic markers of subtypes possess drawbacks, such as the classification of cell types following sequencing. This presents a challenge for data analysis and requires rigorous validation methods to ensure that clusters contain cells of the same function. We propose a technique for identifying the morphology and functionality of a cell prior to isolation and sequencing, which will allow for the easier identification of subtype-specific markers. This technique may be extended to non-neuronal cell types, as well as to rare populations of cells with minor variations. This protocol yields excellent-quality data, as many of the libraries have provided read depths greater than 20 million reads for single cells. This methodology overcomes many of the hurdles presented by Single-cell RNA-Seq and may be suitable for researchers aiming to profile cell types in a straightforward and highly efficient manner.</p>
dc.description.comments <p>This article is published as Laboissonniere, L.A., Sonoda, T., Lee, S.K., Trimarchi, J.M., Schmidt, T.M. Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells. J. Vis. Exp. (123), e55229, doi: <a href="http://dx.doi.org/10.3791/55229" target="_blank">10.3791/55229</a> (2017). Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/gdcb_las_pubs/184/
dc.identifier.articleid 1188
dc.identifier.contextkey 11623715
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath gdcb_las_pubs/184
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/37855
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/gdcb_las_pubs/184/2017_Trimarchi_SingleCellRNA.pdf|||Fri Jan 14 21:41:28 UTC 2022
dc.source.uri 10.3791/55229
dc.subject.disciplines Cell and Developmental Biology
dc.subject.disciplines Genetics
dc.subject.disciplines Molecular Genetics
dc.subject.keywords Neuroscience
dc.subject.keywords Issue 123
dc.subject.keywords Intrinsically photosensitive retinal ganglion cells
dc.subject.keywords electrophysiology
dc.subject.keywords RNA sequencing
dc.subject.keywords single-cell isolation
dc.subject.keywords low input RNA sequencing
dc.subject.keywords neurons
dc.title Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 6e9f21d0-82ad-4661-a8f7-7634953c036b
relation.isOrgUnitOfPublication 9e603b30-6443-4b8e-aff5-57de4a7e4cb2
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