Molecular signatures of retinal ganglion cells revealed through single cell profiling.
Lauren A LaboissonniereJillian J GoetzGregory M MartinRan BiTerry J S LundLaura EllsonMadison R LynchBailey MooneyHannah WickhamPeng LiuGregory W SchwartzJeffrey M TrimarchiPublished in: Scientific reports (2019)
Retinal ganglion cells can be classified into more than 40 distinct subtypes, whether by functional classification or transcriptomics. The examination of these subtypes in relation to their physiology, projection patterns, and circuitry would be greatly facilitated through the identification of specific molecular identifiers for the generation of transgenic mice. Advances in single cell transcriptomic profiling have enabled the identification of molecular signatures for cellular subtypes that are only rarely found. Therefore, we used single cell profiling combined with hierarchical clustering and correlate analyses to identify genes expressed in distinct populations of Parvalbumin-expressing cells and functionally classified RGCs. RGCs were manually isolated based either upon fluorescence or physiological distinction through cell-attached recordings. Microarray hybridization and RNA-Sequencing were employed for the characterization of transcriptomes and in situ hybridization was utilized to further characterize gene candidate expression. Gene candidates were identified based upon cluster correlation, as well as expression specificity within physiologically distinct classes of RGCs. Further, we identified Prph, Ctxn3, and Prkcq as potential candidates for ipRGC classification in the murine retina. The use of these genes, or one of the other newly identified subset markers, for the generation of a transgenic mouse would enable future studies of RGC-subtype specific function, wiring, and projection.
Keyphrases
- single cell
- rna seq
- induced apoptosis
- genome wide
- high throughput
- cell cycle arrest
- poor prognosis
- bioinformatics analysis
- single molecule
- machine learning
- genome wide identification
- cell death
- copy number
- deep learning
- dna methylation
- oxidative stress
- magnetic resonance imaging
- magnetic resonance
- signaling pathway
- binding protein
- genome wide analysis
- optical coherence tomography
- contrast enhanced