Single cell transcriptome profiling of retinal ganglion cells identifies cellular subtypes

Abstract

Retinal ganglion cells (RGCs) convey the major output of information collected from the eye to the brain. Thirty subtypes of RGCs have been identified to date. Here, we analyze 6225 RGCs (average of 5000 genes per cell) from right and left eyes by single-cell RNA-seq and classify them into 40 subtypes using clustering algorithms. We identify additional subtypes and markers, as well as transcription factors predicted to cooperate in specifying RGC subtypes. Zic1, a marker of the right eye-enriched subtype, is validated by immunostaining in situ. Runx1 and Fst, the markers of other subtypes, are validated in purified RGCs by fluorescent in situ hybridization (FISH) and immunostaining. We show the extent of gene expression variability needed for subtype segregation, and we show a hierarchy in diversification from a cell-type population to subtypes. Finally, we present a website for comparing the gene expression of RGC subtypes.

Retina Development in Vertebrates: Systems Biology Approaches to Understanding Genetic Programs

“…Description of rare cell‐types is particularly thorough when purified cell populations are used as starting material for single‐cell sequencing. Thus, the analysis of mice RGCs isolated by Thy1 immuno‐panning identified 40 different cell subtypes . Interestingly, this number coincides with the number of categories identified by electrophysiological recordings in mice RGCs, suggesting an exhaustive identification of cellular subtypes through transcriptomic profiling .…”

Emergence of neuronal diversity during vertebrate brain development

“…4d, Sup Fig.12). For example, RGC specification trajectories confirmed several known differentiation regulators including sox11a, sox11b, sox6, irx4a, and pou4f2 28 . Similarly, known regulators of photoreceptor differentiation, such as isl2a 29 , prdm1a 30 , otx5 31 , and crx 32 are expressed early in our photoreceptor trajectories, while known regulators of cone versus rod fate, such as six7 33 , nr2f1b 34 , and nr2e3 35 are expressed as those trajectories diverge.…”

smiFISH and embryo segmentation for single-cell multi-gene RNA quantification in arthropods

“…The smiFISH panels in Figure 5b show eve-expressing cells from an early germband Parhyale embryo, and highlight how a single cell can have a markedly different expression level from its immediately adjoining neighbours. Such single-cell variability within a population has been shown to have important biological relevance, for example in fate determination 11 , cell behaviour 9 , and disease 12 .…”

“…These techniques usually provide only relative expression levels rather than actual RNA numbers, across a pool of cells, so a wealth of information concerning cell to cell variability is lost [2][3][4][5][6] . More recently, single-cell versions of these techniques have been developed, allowing for the first-time quantitation of cell differences in gene expression 7,8 , known to be critical in influencing single-cell behaviours 9, 10 , differentiation 11 and disease 12 . However, the spatial context of the cells with respect to both their neighbouring cells, and to the larger tissue or embryo is still lost 13 .…”