Dataset Information

An automated data extraction and classification pipeline to identify a novel type of neuron within the dorsal striatum based on single-cell patch clamp and confocal imaging data.

ABSTRACT: We employed electrophysiological and fluorescence imaging techniques to describe the characteristics of a novel type of neuron discovered in the mouse dorsal striatum. Transgenic mice that express YFP-tagged channelrhodopsin-2 (ChR2) in neurons driven by the promoter for tyrosine hydroxylase (TH) were used and the intrinsic electrical properties of YFP-positive neurons in the dorsal striatum of these mice were characterized using whole-cell patch clamping in acute brain slices. Passive membrane properties - such as membrane capacitance, resting membrane potential and input resistance -and action potential properties- such as amplitude, kinetics and adaptation - were extracted from raw data files. Filling these neurons with neurobiotin enabled visualization of neuronal morphology via immunohistochemical labeling with streptavidin-conjugated fluorophore. Subsequent two-photon imaging allowed analyses of morphological properties such as somaticsize, dendritic branching (Sholl analysis) and density of dendritic spines. Unbiased analyses and hierarchical clustering of both morphological and functional data allowed us to identify a previously undescribed type of striatal neuron with unique properties. To facilitate identification of this new cell type, an end-to-end automated electrophysiology pipeline was developed that extracts relevant parameters and determines striatal neuron identity using neural-network based classifiers. These data and the software tool will permit other investigators to identify this novel type of neuron in their studiesand thereby better understand theroles thatthese neuronsplay in dorsal striatum circuitry.

SUBMITTER: Mao M

PROVIDER: S-EPMC7452657 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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An automated data extraction and classification pipeline to identify a novel type of neuron within the dorsal striatum based on single-cell patch clamp and confocal imaging data.

Mao Miaomiao M Nair Aditya A Augustine George J GJ

Data in brief 20200807

We employed electrophysiological and fluorescence imaging techniques to describe the characteristics of a novel type of neuron discovered in the mouse dorsal striatum. Transgenic mice that express YFP-tagged channelrhodopsin-2 (ChR2) in neurons driven by the promoter for tyrosine hydroxylase (TH) were used and the intrinsic electrical properties of YFP-positive neurons in the dorsal striatum of these mice were characterized using whole-cell patch clamping in acute brain slices. Passive membrane ...[more]

PMID: 32904362

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Project description:Experimental records of single molecules or ion channels from fluorescence microscopy and patch-clamp electrophysiology often include high-frequency noise and baseline fluctuations that are not generated by the system under investigation and have to be removed. Moreover, multiple channels or conductance levels can be present at a time in the data that need to be quantified to accurately understand the behavior of the system. Manual procedures for removing these fluctuations and extracting conducting states or multiple channels are laborious, prone to subjective bias, and likely to hinder the processing of often very large data sets. We introduce a maximal likelihood formalism for separating signal from a noisy and drifting background such as fluorescence traces from imaging of elementary Ca2+ release events called puffs arising from clusters of channels, and patch-clamp recordings of ion channels. Parameters such as the number of open channels or conducting states, noise level, and background signal can all be optimized using the expectation-maximization algorithm. We implement our algorithm following the Baum-Welch approach to expectation-maximization in the portable Java language with a user-friendly graphical interface and test the algorithm on both synthetic and experimental data from the patch-clamp electrophysiology of Ca2+ channels and fluorescence microscopy of a cluster of Ca2+ channels and Ca2+ channels with multiple conductance levels. The resulting software is accurate, fast, and provides detailed information usually not available through manual analysis. Options for visual inspection of the raw and processed data with key parameters are provided, in addition to a range of statistics such as the mean open probabilities, mean open times, mean close times, dwell-time distributions for different number of channels open or conductance levels, amplitude distribution of all opening events, and number of transitions between different number of open channels or conducting levels in asci format with a single click.

Dataset Information

An automated data extraction and classification pipeline to identify a novel type of neuron within the dorsal striatum based on single-cell patch clamp and confocal imaging data.

Publications

An automated data extraction and classification pipeline to identify a novel type of neuron within the dorsal striatum based on single-cell patch clamp and confocal imaging data.

Similar Datasets

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