Dataset Information

Classification of neocortical interneurons using affinity propagation.

ABSTRACT: In spite of over a century of research on cortical circuits, it is still unknown how many classes of cortical neurons exist. In fact, neuronal classification is a difficult problem because it is unclear how to designate a neuronal cell class and what are the best characteristics to define them. Recently, unsupervised classifications using cluster analysis based on morphological, physiological, or molecular characteristics, have provided quantitative and unbiased identification of distinct neuronal subtypes, when applied to selected datasets. However, better and more robust classification methods are needed for increasingly complex and larger datasets. Here, we explored the use of affinity propagation, a recently developed unsupervised classification algorithm imported from machine learning, which gives a representative example or exemplar for each cluster. As a case study, we applied affinity propagation to a test dataset of 337 interneurons belonging to four subtypes, previously identified based on morphological and physiological characteristics. We found that affinity propagation correctly classified most of the neurons in a blind, non-supervised manner. Affinity propagation outperformed Ward's method, a current standard clustering approach, in classifying the neurons into 4 subtypes. Affinity propagation could therefore be used in future studies to validly classify neurons, as a first step to help reverse engineer neural circuits.

SUBMITTER: Santana R

PROVIDER: S-EPMC3847556 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Classification of neocortical interneurons using affinity propagation.

Santana Roberto R McGarry Laura M LM Bielza Concha C Larrañaga Pedro P Yuste Rafael R

Frontiers in neural circuits 20131203

In spite of over a century of research on cortical circuits, it is still unknown how many classes of cortical neurons exist. In fact, neuronal classification is a difficult problem because it is unclear how to designate a neuronal cell class and what are the best characteristics to define them. Recently, unsupervised classifications using cluster analysis based on morphological, physiological, or molecular characteristics, have provided quantitative and unbiased identification of distinct neuron ...[more]

PMID: 24348339

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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 neocortical interneurons using affinity propagation.

Publications

Classification of neocortical interneurons using affinity propagation.

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