Neuronal cell-type classification: challenges, opportunities and the path forward

Key Points Classification of neurons into types enables their reproducible identification across times, laboratories and conditions. Classification also facilitates genetic access for functional studies, as well as analyses of development, evolution and disease. Neuronal cell types must be defined by multiple criteria related to their morphological, physiological, molecular and connectional properties. Past efforts at neuronal classification were hindered by severe biases and under-sampling, but newly developed high-throughput techniques allow this limitation to be circumvented. For some regions of the central nervous system, particularly the retina and cerebral cortex, a complete cell census appears within reach. Principles derived from the well-developed field of species taxonomy (systematics) provide common-sense guidelines for cell-type classification. Attempts to group the cells of the nervous system into classes or types face technical and conceptual barriers. Zeng and Sanes consider the current approaches to classification and propose a strategy and set of principles to guide future classification efforts. Neurons have diverse molecular, morphological, connectional and functional properties. We believe that the only realistic way to manage this complexity — and thereby pave the way for understanding the structure, function and development of brain circuits — is to group neurons into types, which can then be analysed systematically and reproducibly. However, neuronal classification has been challenging both technically and conceptually. New high-throughput methods have created opportunities to address the technical challenges associated with neuronal classification by collecting comprehensive information about individual cells. Nonetheless, conceptual difficulties persist. Borrowing from the field of species taxonomy, we propose principles to be followed in the cell-type classification effort, including the incorporation of multiple, quantitative features as criteria, the use of discontinuous variation to define types and the creation of a hierarchical system to represent relationships between cells. We review the progress of classifying cell types in the retina and cerebral cortex and propose a staged approach for moving forward with a systematic cell-type classification in the nervous system.