Connecting single-cell transcriptomes to projectomes in mouse visual cortex.

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.

Human neocortical expansion involves glutamatergic neuron diversification.

The neocortex is disproportionately expanded in human compared with mouse1,2, both in its total volume relative to subcortical structures and in the proportion occupied by supragranular layers composed of neurons that selectively make connections within the neocortex and with other telencephalic structures. Single-cell transcriptomic analyses of human and mouse neocortex show an increased diversity of glutamatergic neuron types in supragranular layers in human neocortex and pronounced gradients as a function of cortical depth3. Here, to probe the functional and anatomical correlates of this transcriptomic diversity, we developed a robust platform combining patch clamp recording, biocytin staining and single-cell RNA-sequencing (Patch-seq) to examine neurosurgically resected human tissues. We demonstrate a strong correspondence between morphological, physiological and transcriptomic phenotypes of five human glutamatergic supragranular neuron types. These were enriched in but not restricted to layers, with one type varying continuously in all phenotypes across layers 2 and 3. The deep portion of layer 3 contained highly distinctive cell types, two of which express a neurofilament protein that labels long-range projection neurons in primates that are selectively depleted in Alzheimer's disease4,5. Together, these results demonstrate the explanatory power of transcriptomic cell-type classification, provide a structural underpinning for increased complexity of cortical function in humans, and implicate discrete transcriptomic neuron types as selectively vulnerable in disease.

Scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization.

The Patch-seq approach is a powerful variation of the patch-clamp technique that allows for the combined electrophysiological, morphological, and transcriptomic characterization of individual neurons. To generate Patch-seq datasets at scale, we identified and refined key factors that contribute to the efficient collection of high-quality data. We developed patch-clamp electrophysiology software with analysis functions specifically designed to automate acquisition with online quality control. We recognized the importance of extracting the nucleus for transcriptomic success and maximizing membrane integrity during nucleus extraction for morphology success. The protocol is generalizable to different species and brain regions, as demonstrated by capturing multimodal data from human and macaque brain slices. The protocol, analysis and acquisition software are compiled at https://githubcom/AllenInstitute/patchseqtools. This resource can be used by individual labs to generate data across diverse mammalian species and that is compatible with large publicly available Patch-seq datasets.

Consistent cross-modal identification of cortical neurons with coupled autoencoders.

Consistent identification of neurons in different experimental modalities is a key problem in neuroscience. Although methods to perform multimodal measurements in the same set of single neurons have become available, parsing complex relationships across different modalities to uncover neuronal identity is a growing challenge. Here we present an optimization framework to learn coordinated representations of multimodal data and apply it to a large multimodal dataset profiling mouse cortical interneurons. Our approach reveals strong alignment between transcriptomic and electrophysiological characterizations, enables accurate cross-modal data prediction, and identifies cell types that are consistent across modalities.

Integrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells.

Neurons are frequently classified into distinct types on the basis of structural, physiological, or genetic attributes. To better constrain the definition of neuronal cell types, we characterized the transcriptomes and intrinsic physiological properties of over 4,200 mouse visual cortical GABAergic interneurons and reconstructed the local morphologies of 517 of those neurons. We find that most transcriptomic types (t-types) occupy specific laminar positions within visual cortex, and, for most types, the cells mapping to a t-type exhibit consistent electrophysiological and morphological properties. These properties display both discrete and continuous variation among t-types. Through multimodal integrated analysis, we define 28 met-types that have congruent morphological, electrophysiological, and transcriptomic properties and robust mutual predictability. We identify layer-specific axon innervation pattern as a defining feature distinguishing different met-types. These met-types represent a unified definition of cortical GABAergic interneuron types, providing a systematic framework to capture existing knowledge and bridge future analyses across different modalities.

Classification of electrophysiological and morphological neuron types in the mouse visual cortex.

Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To systematically profile morpho-electric properties of mammalian neurons, we established a single-cell characterization pipeline using standardized patch-clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly accessible online database, the Allen Cell Types Database, to display these datasets. Intrinsic physiological properties were measured from 1,938 neurons from the adult laboratory mouse visual cortex, morphological properties were measured from 461 reconstructed neurons, and 452 neurons had both measurements available. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We established a taxonomy of morphologically and electrophysiologically defined cell types for this region of the cortex, with 17 electrophysiological types, 38 morphological types and 46 morpho-electric types. There was good correspondence with previously defined transcriptomic cell types and subclasses using the same transgenic mouse lines.

Dopaminergic modulation of basal ganglia output through coupled excitation-inhibition.

Learning and maintenance of skilled movements require exploration of motor space and selection of appropriate actions. Vocal learning and social context-dependent plasticity in songbirds depend on a basal ganglia circuit, which actively generates vocal variability. Dopamine in the basal ganglia reduces trial-to-trial neural variability when the bird engages in courtship song. Here, we present evidence for a unique, tonically active, excitatory interneuron in the songbird basal ganglia that makes strong synaptic connections onto output pallidal neurons, often linked in time with inhibitory events. Dopamine receptor activity modulates the coupling of these excitatory and inhibitory events in vitro, which results in a dynamic change in the synchrony of a modeled population of basal ganglia output neurons receiving excitatory and inhibitory inputs. The excitatory interneuron thus serves as one biophysical mechanism for the introduction or modulation of neural variability in this circuit.

Network analysis of microRNA and mRNA seasonal dynamics in a highly plastic sensorimotor neural circuit.

BACKGROUND: Adult neurogenesis and the incorporation of adult-born neurons into functional circuits requires precise spatiotemporal coordination across molecular networks regulating a wide array of processes, including cell proliferation, apoptosis, neurotrophin signaling, and electrical activity. MicroRNAs (miRs) - short, non-coding RNA sequences that alter gene expression by post-transcriptional inhibition or degradation of mRNA sequences - may be involved in the global coordination of such diverse biological processes. To test the hypothesis that miRs related to adult neurogenesis and related cellular processes are functionally regulated in the nuclei of the avian song control circuit, we used microarray analyses to quantify changes in expression of miRs and predicted target mRNAs in the telencephalic nuclei HVC, the robust nucleus of arcopallium (RA), and the basal ganglia homologue Area X in breeding and nonbreeding Gambel's white-crowned sparrows (Zonotrichia leucophrys gambelli). RESULTS: We identified 46 different miRs that were differentially expressed across seasons in the song nuclei. miR-132 and miR-210 showed the highest differential expression in HVC and Area X, respectively. Analyzing predicted mRNA targets of miR-132 identified 33 candidate target genes that regulate processes including cell cycle control, calcium signaling, and neuregulin signaling in HVC. Likewise, miR-210 was predicted to target 14 mRNAs differentially expressed across seasons that regulate serotonin, GABA, and dopamine receptor signaling and inflammation. CONCLUSIONS: Our results identify potential miR-mRNA regulatory networks related to adult neurogenesis and provide opportunities to discover novel genetic control of the diverse biological processes and factors related to the functional incorporation of new neurons to the adult brain.

A promoter in the coding region of the calcium channel gene CACNA1C generates the transcription factor CCAT.

The C-terminus of the voltage-gated calcium channel Cav1.2 encodes a transcription factor, the calcium channel associated transcriptional regulator (CCAT), that regulates neurite extension and inhibits Cav1.2 expression. The mechanisms by which CCAT is generated in neurons and myocytes are poorly understood. Here we show that CCAT is produced by activation of a cryptic promoter in exon 46 of CACNA1C, the gene that encodes CaV1.2. Expression of CCAT is independent of Cav1.2 expression in neuroblastoma cells, in mice, and in human neurons derived from induced pluripotent stem cells (iPSCs), providing strong evidence that CCAT is not generated by cleavage of CaV1.2. Analysis of the transcriptional start sites in CACNA1C and immune-blotting for channel proteins indicate that multiple proteins are generated from the 3' end of the CACNA1C gene. This study provides new insights into the regulation of CACNA1C, and provides an example of how exonic promoters contribute to the complexity of mammalian genomes.