ArticleFrontiers in molecular neuroscience2010
Grouping and classifying electrophysiologically-defined classes of neocortical neurons by single cell, whole-genome expression profiling.
Article in Frontiers in molecular neuroscience, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 35 citations in OpenAlex.
- Nicotinic Modulation of Fast-Spiking Neurons in Rat Somatosensory Cortex across Development.eNeuro · 2025Article
- Disease-causing Slack potassium channel mutations produce opposite effects on excitability of excitatory and inhibitory neurons.Cell reports · 2024Article
- Patch-seq: Multimodal Profiling of Single-Cell Morphology, Electrophysiology, and Gene Expression.Methods in molecular biology (Clifton, N.J.) · 2024Article
- The emerging landscape of spatial profiling technologies.Nature reviews. Genetics · 2022Review
- Illuminating Neural Circuits in Alzheimer's Disease.Neuroscience bulletin · 2021Review
- Patch-seq: Past, Present, and Future.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Review
- Molecular expression profiles of morphologically defined hippocampal neuron types: Empirical evidence and relational inferences.Hippocampus · 2020Article
- Subthreshold Activity Underlying the Diversity and Selectivity of the Primary Auditory Cortex Studied by Intracellular Recordings in Awake Marmosets.Cerebral cortex (New York, N.Y. : 1991) · 2019Article
- Kv4.2 channel activity controls intrinsic firing dynamics of arcuate kisspeptin neurons.The Journal of physiology · 2018Article
- Patch-Seq Protocol to Analyze the Electrophysiology, Morphology and Transcriptome of Whole Single Neurons Derived From Human Pluripotent Stem Cells.Frontiers in molecular neuroscience · 2018Article
- Assessing Transcriptome Quality in Patch-Seq Datasets.Frontiers in molecular neuroscience · 2018Article
- Multimodal profiling of single-cell morphology, electrophysiology, and gene expression using Patch-seq.Nature protocols · 2017Article
- Q&A: using Patch-seq to profile single cells.BMC biology · 2017Article
- Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells.Journal of visualized experiments : JoVE · 2017Article
- Predicting the functional states of human iPSC-derived neurons with single-cell RNA-seq and electrophysiology.Molecular psychiatry · 2016Article
- Cell type-specific transcriptome profiling in mammalian brains.Frontiers in bioscience (Landmark edition) · 2016Review
- Integration of electrophysiological recordings with single-cell RNA-seq data identifies neuronal subtypes.Nature biotechnology · 2016Article
- Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq.Nature biotechnology · 2016Article
- Combinatorial expression rules of ion channel genes in juvenile rat (Rattus norvegicus) neocortical neurons.PloS one · 2012Article
- Cell type-specific transcriptomics in the brain.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2011Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The diversity of neuronal cell types and how to classify them are perennial questions in neuroscience. The advent of global gene expression analysis raised the possibility that comprehensive transcription profiling will resolve neuronal cell types into groups that reflect some or all aspects of their phenotype. This approach has been successfully used to compare gene expression between groups of neurons defined by a common property. Here we extend this approach to ask whether single neuron gene expression profiling can prospectively resolve neuronal subtypes into groups, independent of any phenotypic information, and whether those groups reflect meaningful biological properties of those neurons. We applied methods we have developed to compare gene expression among single neural stem cells to study global gene expression in 18 randomly picked neurons from layer II/III of the early postnatal mouse neocortex. Cells were selected by morphology and by firing characteristics and electrical properties, enabling the definition of each cell as either fast- or regular-spiking, corresponding to a class of inhibitory interneurons or excitatory pyramidal cells. Unsupervised clustering of young neurons by global gene expression resolved the cells into two groups and those broadly corresponded with the two groups of fast- and regular-spiking neurons. Clustering of the entire, diverse group of 18 neurons of different developmental stages also successfully grouped neurons in accordance with the electrophysiological phenotypes, but with more cells misassigned among groups. Genes specifically enriched in regular spiking neurons were identified from the young neuron expression dataset. These results provide a proof of principle that single-cell gene expression profiling may be used to group and classify neurons in a manner reflecting their known biological properties and may be used to identify cell-specific transcripts.
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