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Classification of GABAergic interneurons by leading neuroscientists.

ABSTRACT: There is currently no unique catalog of cortical GABAergic interneuron types. In 2013, we asked 48 leading neuroscientists to classify 320 interneurons by inspecting images of their morphology. That study was the first to quantify the degree of agreement among neuroscientists in morphology-based interneuron classification, showing high agreement for the chandelier and Martinotti types, yet low agreement for most of the remaining types considered. Here we present the dataset containing the classification choices by the neuroscientists according to interneuron type as well as to five prominent morphological features. These data can be used as crisp or soft training labels for learning supervised machine learning interneuron classifiers, while further analyses can try to pinpoint anatomical characteristics that make an interneuron especially difficult or especially easy to classify.

SUBMITTER: Mihaljevic B

PROVIDER: S-EPMC6805952 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Classification of GABAergic interneurons by leading neuroscientists.

Mihaljević Bojan B Benavides-Piccione Ruth R Bielza Concha C Larrañaga Pedro P DeFelipe Javier J

Scientific data 20191022 1

There is currently no unique catalog of cortical GABAergic interneuron types. In 2013, we asked 48 leading neuroscientists to classify 320 interneurons by inspecting images of their morphology. That study was the first to quantify the degree of agreement among neuroscientists in morphology-based interneuron classification, showing high agreement for the chandelier and Martinotti types, yet low agreement for most of the remaining types considered. Here we present the dataset containing the classi ...[more]

PMID: 31641131

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Project description:Fast-spiking (FS) interneurons are important elements of neocortical circuitry that constitute the primary source of synaptic inhibition in adult cortex and impart temporal organization on ongoing cortical activity. The highly specialized intrinsic membrane and firing properties that allow cortical FS interneurons to perform these functions are attributable to equally specialized gene expression, which is ultimately coordinated by cell-type-specific transcriptional regulation. Although embryonic transcriptional events govern the initial steps of cell-type specification in most cortical interneurons, including FS cells, the electrophysiological properties that distinguish adult cortical cell types emerge relatively late in postnatal development, and the transcriptional events that drive this maturational process are not known. To address this, we used mouse whole-genome microarrays and whole-cell patch clamp to characterize the transcriptional and electrophysiological maturation of cortical FS interneurons between postnatal day 7 (P7) and P40. We found that the intrinsic and synaptic physiology of FS cells undergoes profound regulation over the first 4 postnatal weeks and that these changes are correlated with primarily monotonic but bidirectional transcriptional regulation of thousands of genes belonging to multiple functional classes. Using our microarray screen as a guide, we discovered that upregulation of two-pore K leak channels between P10 and P25 contributes to one of the major differences between the intrinsic membrane properties of immature and adult FS cells and found a number of other candidate genes that likely confer cell-type specificity on mature FS cells. Experiment Overall Design: We characterized the transcriptional maturation of genetically labeled FS interneurons in mouse S1 cortex using whole-genome microarrays.mRNA was harvested from manually sorted GFPexpressing neurons dissociated from acutely prepared brain slices at a range of developmental time points (P7, P10, P15, P25, and P40) and subsequently reverse transcribed, amplified, labeled, and hybridized to Affymetrix 430 2.0 whole-genome microarrays (three microarrays from three different animals per condition). We used the G42 transgenic mice described by Chattopadhyaya et al. (2004) for all experiments.

Dataset Information

Classification of GABAergic interneurons by leading neuroscientists.

Publications

Classification of GABAergic interneurons by leading neuroscientists.

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