The mammalian brain consists of diverse neuron types with various functions. Recent single-cell RNA sequencing approaches have led to a whole-brain taxonomy of transcriptomically defined cell types1. Patch-seq experiments augment these cell-type descriptions by linking transcriptomic profiles with local morphological and electrophysiological properties2–7. However, linking transcriptomic identities to long-range axonal projections remains a major unresolved challenge. Here, to address this, we collected two datasets from the mouse visual cortex consisting of: (1) 1,528 excitatory Patch-seq neurons, with local morphological, electrophysiological and transcriptomic data collected from each cell, and (2) 341 excitatory, whole-neuron morphologies. From the Patch-seq data, we defined 17 morphoelectric–transcriptomic types and built a multistep classifier to integrate cell-type assignments with whole-neuron morphology and interrogate cross-modality relationships. We find that transcriptomic variation within and across morphoelectric–transcriptomic types corresponds with morphological and electrophysiological phenotypes. In addition, these gene expression patterns, along with the anatomical location of the cell, can be used to predict projection targets of individual neurons. We observed novel multimodal cell-type signatures for layer 5 intratelencephalic and extratelencephalic neurons and shed new light on their axonal circuitry, including interhemispheric intratelencephalic projections. With this approach, we establish a comprehensive, integrated taxonomy of cortical, excitatory neuron types, and create a system for high-dimensional cell-type classification that can be extended to the whole brain and potentially across species.
