RESUMEN
The mammalian cortex is comprised of cells with different morphological, physiological, and molecular properties that can be classified according to shared properties into cell types. Defining the contribution of each cell type to the computational and cognitive processes that are guided by the cortex is essential for understanding its function in health and disease. We use transcriptomic and epigenomic cortical cell type taxonomies from mice and humans to define marker genes and enhancers, and to build genetic tools for cortical cell types. Here, we present a large toolkit for selective targeting of cortical populations, including mouse transgenic lines and recombinant adeno-associated virus (AAV) vectors containing genomic enhancers. We report evaluation of fifteen new transgenic driver lines and over 680 different enhancer AAVs covering all major subclasses of cortical cells, with many achieving a high degree of specificity, comparable with existing transgenic lines. We find that the transgenic lines based on marker genes can provide exceptional specificity and completeness of cell type labeling, but frequently require generation of a triple-transgenic cross for best usability/specificity. On the other hand, enhancer AAVs are easy to screen and use, and can be easily modified to express diverse cargo, such as recombinases. However, their use depends on many factors, such as viral titer and route of administration. The tools reported here as well as the scaled process of tool creation provide an unprecedented resource that should enable diverse experimental strategies towards understanding mammalian cortex and brain function.
RESUMEN
The mammalian brain is composed of diverse neuron types that play different functional roles. Recent single-cell RNA sequencing approaches have led to a whole brain taxonomy of transcriptomically-defined cell types, yet cell type definitions that include multiple cellular properties can offer additional insights into a neuron's role in brain circuits. While the Patch-seq method can investigate how transcriptomic properties relate to the local morphological and electrophysiological properties of cell types, linking transcriptomic identities to long-range projections is a major unresolved challenge. To address this, we collected coordinated Patch-seq and whole brain morphology data sets of excitatory neurons in mouse visual cortex. From the Patch-seq data, we defined 16 integrated morpho-electric-transcriptomic (MET)-types; in parallel, we reconstructed the complete morphologies of 300 neurons. We unified the two data sets with a multi-step classifier, to integrate cell type assignments and interrogate cross-modality relationships. We find that 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 the projection targets of individual neurons. We also shed new light on infragranular cell types and circuits, including cell-type-specific, interhemispheric projections. With this approach, we establish a comprehensive, integrated taxonomy of excitatory neuron types in mouse visual cortex and create a system for integrated, high-dimensional cell type classification that can be extended to the whole brain and potentially across species.