Perisomatic ultrastructure efficiently classifies cells in mouse cortex

Abstract

Mammalian neocortex contains a highly diverse set of cell types. These cell types have been mapped systematically using a variety of molecular, electrophysiological and morphological approaches1–4. Each modality offers new perspectives on the variation of biological processes underlying cell-type specialization. Cellular-scale electron microscopy provides dense ultrastructural examination and an unbiased perspective on the subcellular organization of brain cells, including their synaptic connectivity and nanometre-scale morphology. In data that contain tens of thousands of neurons, most of which have incomplete reconstructions, identifying cell types becomes a clear challenge for analysis5. Here, to address this challenge, we present a systematic survey of the somatic region of all cells in a cubic millimetre of cortex using quantitative features obtained from electron microscopy. This analysis demonstrates that the perisomatic region is sufficient to identify cell types, including types defined primarily on the basis of their connectivity patterns. We then describe how this classification facilitates cell-type-specific connectivity characterization and locating cells with rare connectivity patterns in the dataset. An analysis demonstrates that quantitative measurements of perisomatic ultrastructure features of neurons can be used to categorize them into cell types.