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.

The mammalian cortex is comprised of cells classified into types according to shared properties. Defining the contribution of each cell type to the processes guided by the cortex is essential for understanding its function in health and disease. We use transcriptomic and epigenomic cortical cell-type taxonomies from mouse and human to define marker genes and putative enhancers and create a large toolkit of transgenic lines and enhancer adeno-associated viruses (AAVs) for selective targeting of cortical cell populations. We report creation and evaluation of fifteen transgenic driver lines, two reporter lines, and >1,000 different enhancer AAV vectors covering most subclasses of cortical cells. The tools reported here have been made publicly available, and along with the scaled process of tool creation, evaluation, and modification, they will enable diverse experimental strategies toward understanding mammalian cortex and brain function.

The mammalian cortex is comprised of cells classified into types according to shared properties. Defining the contribution of each cell type to the processes guided by the cortex is essential for understanding its function in health and disease. We used transcriptomic and epigenomic cortical cell type taxonomies from mouse and human to define marker genes and putative enhancers and created a large toolkit of transgenic lines and enhancer AAVs for selective targeting of cortical cell populations. We report evaluation of fifteen new transgenic driver lines, two new reporter lines, and >800 different enhancer AAVs covering most subclasses of cortical cells. The tools reported here as well as the scaled process of tool creation and modification enable diverse experimental strategies towards understanding mammalian cortex and brain function.

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.

We present a unique, extensive, and open synaptic physiology analysis platform and dataset. Through its application, we reveal principles that relate cell type to synaptic properties and intralaminar circuit organization in the mouse and human cortex. The dynamics of excitatory synapses align with the postsynaptic cell subclass, whereas inhibitory synapse dynamics partly align with presynaptic cell subclass but with considerable overlap. Synaptic properties are heterogeneous in most subclass-to-subclass connections. The two main axes of heterogeneity are strength and variability. Cell subclasses divide along the variability axis, whereas the strength axis accounts for substantial heterogeneity within the subclass. In the human cortex, excitatory-to-excitatory synaptic dynamics are distinct from those in the mouse cortex and vary with depth across layers 2 and 3.

Rapid cell type identification by new genomic single-cell analysis methods has not been met with efficient experimental access to these cell types. To facilitate access to specific neural populations in mouse cortex, we collected chromatin accessibility data from individual cells and identified enhancers specific for cell subclasses and types. When cloned into recombinant adeno-associated viruses (AAVs) and delivered to the brain, these enhancers drive transgene expression in specific cortical cell subclasses. We extensively characterized several enhancer AAVs to show that they label different projection neuron subclasses as well as a homologous neuron subclass in human cortical slices. We also show how coupling enhancer viruses expressing recombinases to a newly generated transgenic mouse, Ai213, enables strong labeling of three different neuronal classes/subclasses in the brain of a single transgenic animal. This approach combines unprecedented flexibility with specificity for investigation of cell types in the mouse brain and beyond.

Labeling and perturbation of specific cell types in multicellular systems has transformed our ability to understand them. The rapid pace of cell type identification by new single-cell analysis methods has not been met with efficient access to these newly discovered types. To enable access to specific neural populations in the mouse cortex, we have collected single cell chromatin accessibility data from select cell types. We clustered the single cell data and mapped them to single cell transcriptomics to identify highly specific enhancers for cell subclasses. These enhancers, when cloned into AAVs and delivered to the brain by retro-orbital injections, enable transgene expression in specific cell subclasses throughout the mouse brain. This approach will enable functional investigation of cell types in the mouse cortex and beyond. One sentence Summary Combining scATAC-seq and scRNA-seq identifies subclass-specific enhancers to label cells in mouse cortex at high resolution.

Modern genetic approaches are powerful in providing access to diverse cell types in the brain and facilitating the study of their function. Here, we report a large set of driver and reporter transgenic mouse lines, including 23 new driver lines targeting a variety of cortical and subcortical cell populations and 26 new reporter lines expressing an array of molecular tools. In particular, we describe the TIGRE2.0 transgenic platform and introduce Cre-dependent reporter lines that enable optical physiology, optogenetics, and sparse labeling of genetically defined cell populations. TIGRE2.0 reporters broke the barrier in transgene expression level of single-copy targeted-insertion transgenesis in a wide range of neuronal types, along with additional advantage of a simplified breeding strategy compared to our first-generation TIGRE lines. These novel transgenic lines greatly expand the repertoire of high-precision genetic tools available to effectively identify, monitor, and manipulate distinct cell types in the mouse brain.