Unified classification of mouse retinal ganglion cells using function, morphology, and gene expression

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.