Dataset Information

Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq.

ABSTRACT: Despite the importance of the mammalian neocortex for complex cognitive processes, we still lack a comprehensive description of its cellular components. To improve the classification of neuronal cell types and the functional characterization of single neurons, we present Patch-seq, a method that combines whole-cell electrophysiological patch-clamp recordings, single-cell RNA-sequencing and morphological characterization. Following electrophysiological characterization, cell contents are aspirated through the patch-clamp pipette and prepared for RNA-sequencing. Using this approach, we generate electrophysiological and molecular profiles of 58 neocortical cells and show that gene expression patterns can be used to infer the morphological and physiological properties such as axonal arborization and action potential amplitude of individual neurons. Our results shed light on the molecular underpinnings of neuronal diversity and suggest that Patch-seq can facilitate the classification of cell types in the nervous system.

SUBMITTER: Cadwell CR

PROVIDER: S-EPMC4840019 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq.

Cadwell Cathryn R CR Palasantza Athanasia A Jiang Xiaolong X Berens Philipp P Deng Qiaolin Q Yilmaz Marlene M Reimer Jacob J Shen Shan S Bethge Matthias M Tolias Kimberley F KF Sandberg Rickard R Tolias Andreas S AS

Nature biotechnology 20151221 2

Despite the importance of the mammalian neocortex for complex cognitive processes, we still lack a comprehensive description of its cellular components. To improve the classification of neuronal cell types and the functional characterization of single neurons, we present Patch-seq, a method that combines whole-cell electrophysiological patch-clamp recordings, single-cell RNA-sequencing and morphological characterization. Following electrophysiological characterization, cell contents are aspirate ...[more]

PMID: 26689543

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Project description:Acute regulation of CO2 and pH homeostasis requires sensory feedback from peripheral (carotid body) and central (central) CO2/pH sensitive cells - so called respiratory chemoreceptors. Subsets of brainstem serotonin (5-HT) neurons in the medullary raphe are CO2 sensitive or insensitive based on differences in embryonic origin, suggesting these functionally distinct subpopulations may have unique transcriptional profiles. Here, we used Patch-to-Seq to determine if the CO2 responses in brainstem 5-HT neurons could be correlated to unique transcriptional profiles and/or unique molecular markers and pathways. First, firing rate changes with hypercapnic acidosis were measured in fluorescently labeled 5-HT neurons in acute brainstem slices from transgenic, Dahl SS (SSMcwi) rats expressing T2/ePet-eGFP transgene in Pet-1 expressing (serotonin) neurons (SS ePet1-eGFP rats). Subsequently, the transcriptomic and pathway profiles of CO2 sensitive and insensitive 5-HT neurons were determined and compared by single cell RNA (scRNAseq) and bioinformatic analyses. Low baseline firing rates were a distinguishing feature of CO2 sensitive 5-HT neurons. scRNAseq of these recorded neurons revealed 166 differentially expressed genes among CO2 sensitive and insensitive 5-HT neurons. Pathway analyses yielded novel predicted upstream regulators, including the transcription factor Egr2 and Leptin. Additional bioinformatic analyses identified 6 candidate gene markers of CO2 sensitive 5-HT neurons, and 2 selected candidate genes (CD46 and Iba57) were both expressed in 5-HT neurons determined via in situ mRNA hybridization. Together, these data provide novel insights into the transcriptional control of cellular chemoreception and provide unbiased candidate gene markers of CO2 sensitive 5-HT neurons. Methodologically, these data highlight the utility of the patch-to-seq technique in enabling the linkage of gene expression to specific functions, like CO2 chemoreception, in a single cell to identify potential mechanisms underlying functional differences in otherwise similar cell types.

Project description:Summary A variety of mechanosensory neurons are involved in touch, proprioception, and pain. Many molecular components of the mechanotransduction machinery subserving these sensory modalities remain to be discovered. Here, we combine recordings of mechanosensitive (MS) currents in mechanosensory neurons with single-cell RNA sequencing. Transcriptional profiles are mapped onto previously identified sensory neuron types to identify cell-type correlates between datasets. Correlation of current signatures with single-cell transcriptomes provides a one-to-one correspondence between mechanoelectric properties and transcriptomically defined neuronal populations. Moreover, a gene-expression differential comparison provides a set of candidate genes for mechanotransduction complexes. Piezo2 is expectedly found to be enriched in rapidly adapting MS current-expressing neurons, whereas Tmem120a and Tmem150c, thought to mediate slow-type MS currents, are uniformly expressed in all mechanosensory neuron subtypes. Further knockdown experiments disqualify them as mediating MS currents in sensory neurons. This dataset constitutes an open resource to explore further the cell-type-specific determinants of mechanosensory properties. Graphical abstract Highlights • Patch-seq is performed on mechanically characterized DRG sensory neurons• Transcriptomically defined neuronal classes and current subtypes are correlated• Datasets of specific transcripts of current subtype-expressing neurons are generated• TACAN (Tmem120a) and Tentonin-3 (Tmem150c) do not contribute to DRG mechano-currents Mechanosensation is the least understood sensory modality at the molecular level. Parpaite et al. combine single-cell RNA sequencing with mechanosensitive current recordings in cultured sensory neurons. One-to-one correspondence between mechanoelectric properties and single-cell transcriptomes provides a valuable resource for the identification of molecular factors involved in mechanosensation.

Dataset Information

Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq.

Publications

Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq.

Similar Datasets

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