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Related Experiment Video

Updated: Aug 4, 2025

Large-scale Three-dimensional Imaging of Cellular Organization in the Mouse Neocortex

Large-scale Three-dimensional Imaging of Cellular Organization in the Mouse Neocortex

Published on: September 5, 2018

Transcriptomic cell type structures in vivo neuronal activity across multiple timescales.

Aidan Schneider¹, Mehdi Azabou², Louis McDougall-Vigier¹

¹Department of Biology, Washington University in St. Louis, St. Louis, MO 63130, USA.

|March 30, 2023

Summary

Neuron activity timing reflects transcriptomic cell type, even across different stimuli. This finding suggests cell type is imprinted in neural activity patterns, impacting circuit function.

Keywords:

CP: Neuroscience cell types deep learning electrophysiology multihead attention optophysiology transcriptomics visual cortex

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Last Updated: Aug 4, 2025

Large-scale Three-dimensional Imaging of Cellular Organization in the Mouse Neocortex

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Published on: September 5, 2018

Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing

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Published on: October 12, 2018

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Published on: October 26, 2018

Area of Science:

Neuroscience
Computational Neuroscience
Systems Neuroscience

Background:

Cell type is a fundamental property influencing neuronal function within neural circuits.
Understanding how cell type relates to dynamic neural activity is crucial for deciphering brain function.

Purpose of the Study:

To investigate if transcriptomic cell type influences the precise timing of single neuron activity.
To explore the potential for using activity timing to classify neuron types.

Main Methods:

Development of a deep-learning architecture to analyze inter-event intervals across various timescales.
Application of the model to calcium imaging and electrophysiology data from behaving animals.
Validation using a bio-realistic model of the visual cortex.

Main Results:

Transcriptomic cell-class information is demonstrably embedded in single neuron activity timing.
Classification accuracy improves when considering cortical layer and projection class alongside cell type.
Computational 'fingerprints' of cell types are generalizable across different types of stimuli.

Conclusions:

Single neuron activity timing contains information about transcriptomic cell type.
This relationship holds true in vivo, in silico, and across diverse stimuli.
Transcriptomic identity may be fundamentally imprinted in the temporal dynamics of neural activity.