Dataset Information

Single-cell RNAseq reveals cell adhesion molecule profiles in electrophysiologically defined neurons.

ABSTRACT: In brain, signaling mediated by cell adhesion molecules defines the identity and functional properties of synapses. The specificity of presynaptic and postsynaptic interactions that is presumably mediated by cell adhesion molecules suggests that there exists a logic that could explain neuronal connectivity at the molecular level. Despite its importance, however, the nature of such logic is poorly understood, and even basic parameters, such as the number, identity, and single-cell expression profiles of candidate synaptic cell adhesion molecules, are not known. Here, we devised a comprehensive list of genes involved in cell adhesion, and used single-cell RNA sequencing (RNAseq) to analyze their expression in electrophysiologically defined interneurons and projection neurons. We compared the cell type-specific expression of these genes with that of genes involved in transmembrane ion conductances (i.e., channels), exocytosis, and rho/rac signaling, which regulates the actin cytoskeleton. Using these data, we identified two independent, developmentally regulated networks of interacting genes encoding molecules involved in cell adhesion, exocytosis, and signal transduction. Our approach provides a framework for a presumed cell adhesion and signaling code in neurons, enables correlating electrophysiological with molecular properties of neurons, and suggests avenues toward understanding synaptic specificity.

SUBMITTER: Foldy C

PROVIDER: S-EPMC5024636 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Single-cell RNAseq reveals cell adhesion molecule profiles in electrophysiologically defined neurons.

Földy Csaba C Darmanis Spyros S Aoto Jason J Malenka Robert C RC Quake Stephen R SR Südhof Thomas C TC

Proceedings of the National Academy of Sciences of the United States of America 20160816 35

In brain, signaling mediated by cell adhesion molecules defines the identity and functional properties of synapses. The specificity of presynaptic and postsynaptic interactions that is presumably mediated by cell adhesion molecules suggests that there exists a logic that could explain neuronal connectivity at the molecular level. Despite its importance, however, the nature of such logic is poorly understood, and even basic parameters, such as the number, identity, and single-cell expression prof ...[more]

PMID: 27531958

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Project description:Single-cell RNA sequencing (scRNAseq) represents a new kind of microscope that can measure the transcriptome profiles of thousands of individual cells from complex cellular mixtures, such as in a tissue, in a single experiment. This technology is particularly valuable for characterization of tissue heterogeneity because it can be used to identify and classify all cell types in a tissue. This is generally done by clustering the data, based on the assumption that cells of a particular type share similar transcriptomes, distinct from other cell types in the tissue. However, nearly all clustering algorithms have tunable parameters which affect the number of clusters they will identify in data. The R Shiny software tool described here, scClustViz, provides a simple interactive graphical user interface for exploring scRNAseq data and assessing the biological relevance of clustering results. Given that cell types are expected to have distinct gene expression patterns, scClustViz uses differential gene expression between clusters as a metric for assessing the fit of a clustering result to the data at multiple cluster resolution levels. This helps select a clustering parameter for further analysis. scClustViz also provides interactive visualisation of: cluster-specific distributions of technical factors, such as predicted cell cycle stage and other metadata; cluster-wise gene expression statistics to simplify annotation of cell types and identification of cell type specific marker genes; and gene expression distributions over all cells and cell types. scClustViz provides an interactive interface for visualisation, assessment, and biological interpretation of cell-type classifications in scRNAseq experiments that can be easily added to existing analysis pipelines, enabling customization by bioinformaticians while enabling biologists to explore their results without the need for computational expertise. It is available at https://baderlab.github.io/scClustViz/.

Dataset Information

Single-cell RNAseq reveals cell adhesion molecule profiles in electrophysiologically defined neurons.

Publications

Single-cell RNAseq reveals cell adhesion molecule profiles in electrophysiologically defined neurons.

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