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Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double <em>In Vitro</em> Transcription with Absolute Counts Sequencing (DIVA-Seq)

Single-cell RNA sequencing (RNA-seq) is now a widely implemented tool for assaying gene expression. Commercially available single-cell RNA-sequencing ...

Cold Spring Harbor Laboratory

The assembly of neural circuits involves multiple sequential steps such as the specification of cell-types, their migration to proper brain locations, morphological and physiological differentiation, and the formation and maturation of synaptic connections. This intricate and often prolonged process is guided by elaborate genetic mechanisms that regulate each step. Evidence from numerous systems suggests that each cell-type, once specified, is endowed with a genetic program that unfolds in response to, and is regulated by, extrinsic signals, including cell-cell and synaptic interactions. To a large extent, the execution of this intrinsic program is achieved by the expression of specific sets of genes that support distinct developmental processes. Therefore, a comprehensive analysis of the developmental progression of gene expression in synaptic partners of neurons may provide a basis for exploring the genetic mechanisms regulating circuit assembly. Here we examined the developmental gene expression profiles of well-defined cell-types in a stereotyped microcircuit of the cerebellar cortex. We found that the transcriptomes of Purkinje cell and stellate/basket cells are highly dynamic throughout postnatal development. We revealed "phasic expression" of transcription factors, ion channels, receptors, cell adhesion molecules, gap junction proteins, and identified distinct molecular pathways that might contribute to sequential steps of cerebellar inhibitory circuit formation. We further revealed a correlation between genomic clustering and developmental co-expression of hundreds of transcripts, suggesting the involvement of chromatin level gene regulation during circuit formation.

Developmental Coordination of Gene Expression between Synaptic Partners During GABAergic Circuit Assembly in Cerebellar Cortex.

Co-expression networks have been a useful tool for functional genomics, providing important clues about the cellular and biochemical mechanisms that are active in normal and disease processes. However, co-expression analysis is often treated as a black box with results being hard to trace to their basis in the data. Here, we use both published and novel single-cell RNA sequencing (RNA-seq) data to understand fundamental drivers of gene-gene connectivity and replicability in co-expression networks.

Exploiting single-cell expression to characterize co-expression replicability.

Understanding the organizational logic of neural circuits requires deciphering the biological basis of neuronal diversity and identity, but there is no consensus on how neuron types should be defined. We analyzed single-cell transcriptomes of a set of anatomically and physiologically characterized cortical GABAergic neurons and conducted a computational genomic screen for transcriptional profiles that distinguish them from one another. We discovered that cardinal GABAergic neuron types are delineated by a transcriptional architecture that encodes their synaptic communication patterns. This architecture comprises 6 categories of ∼40 gene families, including cell-adhesion molecules, transmitter-modulator receptors, ion channels, signaling proteins, neuropeptides and vesicular release components, and transcription factors. Combinatorial expression of select members across families shapes a multi-layered molecular scaffold along the cell membrane that may customize synaptic connectivity patterns and input-output signaling properties. This molecular genetic framework of neuronal identity integrates cell phenotypes along multiple axes and provides a foundation for discovering and classifying neuron types.

Transcriptional Architecture of Synaptic Communication Delineates GABAergic Neuron Identity.

Human mutations in CNTNAP2 are associated with an array of neuropsychiatric and neurological syndromes, including speech and language disorders, epilepsy, and autism spectrum disorder (ASD). We examined Cntnap2's expression and function in GABAergic cortical interneurons (CINs), where its RNA is present at highest levels in chandelier neurons, PV+ neurons and VIP+ neurons. In vivo functions were studied using both constitutive Cntnap2 null mice and a transplantation assay, the latter to assess cell autonomous phenotypes of medial ganglionic eminence (MGE)-derived CINs. We found that Cntnap2 constitutive null mutants had normal numbers of MGE-derived CINs, but had reduced PV+ CINs. Transplantation assays showed that Cntnap2 cell autonomously regulated the physiology of parvalbumin (PV)+, fast-spiking CINs; no phenotypes were observed in somatostatin+, regular spiking, CINs. We also tested the effects of 4 human CNTNAP2 ASD missense mutations in vivo, and found that they impaired PV+ CIN development. Together, these data reveal that reduced CNTNAP2 function impairs PV+ CINs, a cell type with important roles in regulating cortical circuits.

Mouse Cntnap2 and Human CNTNAP2 ASD Alleles Cell Autonomously Regulate PV+ Cortical Interneurons.

Single-cell RNA-sequencing (scRNA-seq) technology provides a new avenue to discover and characterize cell types; however, the experiment-specific technical biases and analytic variability inherent to current pipelines may undermine its replicability. Meta-analysis is further hampered by the use of ad hoc naming conventions. Here we demonstrate our replication framework, MetaNeighbor, that quantifies the degree to which cell types replicate across datasets, and enables rapid identification of clusters with high similarity. We first measure the replicability of neuronal identity, comparing results across eight technically and biologically diverse datasets to define best practices for more complex assessments. We then apply this to novel interneuron subtypes, finding that 24/45 subtypes have evidence of replication, which enables the identification of robust candidate marker genes. Across tasks we find that large sets of variably expressed genes can identify replicable cell types with high accuracy, suggesting a general route forward for large-scale evaluation of scRNA-seq data.

Anirban Paul

Cold Spring Harbor Laboratory

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing (DIVA-Seq)