The mammalian brain consists of diverse neuron types with different functions. Recent single-cell RNA sequencing approaches led to a whole brain taxonomy of transcriptomically-defined cell types. Patch-seq experiments augment these cell type descriptions by linking transcriptomic profiles with local morphological and electrophysiological properties. However, linking transcriptomic identities to long-range axonal projections and detailed synaptic connectomes remains a major unresolved challenge. To address this, we collected coordinated data sets in mouse brain that consist of Patch-seq neurons, with morphological, electrophysiological, and transcriptomic data collected from the same cell, whole neuron morphologies (WNMs) with both local and long-range axon, and electron microscopy (EM), with detailed local morphology and synaptic connectivity. From the Patch-seq data, we defined integrated morpho-electric-transcriptomic (MET)-types (e.g., 17 MET-types in visual cortex) and built a multi-step classifier to integrate cell type assignments with WNM, and/or EM connectivity, and interrogate cross-modality relationships. We find that in many cases, transcriptomic variations within and across MET-types correspond with morphological and electrophysiological phenotypes. In addition, this variation, along with the anatomical location of the cell, can be used to predict projection targets of individual neurons. We also identify novel cell type-specific circuits, including interhemispheric projections. With this approach, we create a system for high-dimensional cell type classification that can be extended to the whole brain and potentially across species.

Learning Objectives:

1. Describe different approaches used to study cell types and circuits.

2. Identify the neuronal properties that are used to describe cell types.

3. Explain how data sets can be integrated to create a unified cell type description.