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Related Experiment Video

Updated: Jul 4, 2025

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data.

Ethan Bahl^1,2, Snehajyoti Chatterjee^3,4, Utsav Mukherjee^3,4,5

¹Department of Psychiatry, University of Iowa, Iowa City, IA, USA.

Nature Communications

|January 26, 2024

Summary

This summary is machine-generated.

We developed NEUROeSTIMator, a deep learning model that uses gene expression to measure neuronal activation. This tool accurately estimates brain activity across species and cell types, aiding neuroscience research.

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Last Updated: Jul 4, 2025

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

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

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Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Published on: March 3, 2023

Area of Science:

Neuroscience
Computational Biology
Genomics

Background:

Neuronal activity-driven gene transcription is crucial for synaptic plasticity, brain development, and memory formation.
Single-cell RNA sequencing (scRNAseq) enables studying gene expression at cellular resolution.
Existing methods lack robustness across diverse biological contexts.

Purpose of the Study:

To introduce NEUROeSTIMator, a deep learning model for estimating neuronal activation from transcriptomic data.
To validate NEUROeSTIMator's accuracy and generalizability across species, cell types, and brain regions.
To apply NEUROeSTIMator to spatial transcriptomics for mapping learning-induced activity patterns.

Main Methods:

Developed a deep learning model (NEUROeSTIMator) integrating transcriptomic signals.
Validated the model against Patch-seq electrophysiological data.
Applied the model to existing scRNAseq datasets and a spatial transcriptomic study in male mice.

Main Results:

NEUROeSTIMator accurately estimates neuronal activation, correlating with electrophysiological features.
The model demonstrates robustness across different species, cell types, and brain regions.
Identified unique patterns of learning-induced neuronal activity in specific brain regions using spatial transcriptomics.

Conclusions:

NEUROeSTIMator is a powerful and versatile tool for quantifying neuronal activation.
The model enhances the analysis of activity-dependent transcription in neuroscience.
NEUROeSTIMator facilitates the study of brain function and behavior at a molecular level.