SUMMARY Cone photoreceptors carry out phototransduction in daylight conditions and provide the critical first step in color vision. Despite their importance, little is known about the developmental mechanisms involved in their generation, particularly how they are determined relative to rod photoreceptors, the cells that initiate vision in dim light. Here, we report the identification of a cis-regulatory module (CRM) for the thyroid hormone receptor beta (Thrb) gene, an early cone marker. We found that ThrbCRM1 is active in progenitor cells biased to the production of cones and an interneuronal cell type, the horizontal cell (HC). Molecular analysis of ThrbCRM1 revealed that it is combinatorially regulated by the Otx2 and Onecut1 transcription factors. Onecut1 is sufficient to induce cells with the earliest markers of cones and HCs. Conversely, interference with Onecut1 transcriptional activity leads to precocious rod development, suggesting that Onecut1 is critically important in defining cone versus rod fates.

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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.

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Classification and characterization of neuronal types is critical for understanding their function and dysfunction. Neuronal classification schemes typically rely on measurements of electrophysiological, morphological and molecular features, but aligning these data sets has been challenging. Here, we present a unified classification of retinal ganglion cells (RGCs), the sole retinal output neurons. We used visually-evoked responses to classify 1777 mouse RGCs into 42 types. We also obtained morphological or transcriptomic data from subsets and used these measurements to align the functional classification to publicly available morphological and transcriptomic data sets. We created an online database that allows users to browse or download the data and to classify RGCs from their light responses using a machine-learning algorithm. This work provides a resource for studies of RGCs, their upstream circuits in the retina, and their projections in the brain, and establishes a framework for future efforts in neuronal classification and open data distribution.