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Related Experiment Video

Updated: May 16, 2025

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

A practical guide for single-cell transcriptome data analysis in neuroscience.

Yoshinori Hayakawa¹, Haruka Ozaki²

¹Bioinformatics Laboratory, Institute of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan.

Neuroscience Research

|March 31, 2025

Summary

This summary is machine-generated.

This tutorial guides neuroscience researchers through single-cell RNA sequencing (scRNA-seq) data analysis. It covers essential workflows for robust analysis, enhancing the study of brain function and disease.

Keywords:

Data analysis workflow Neuroscience Practical guide Single-cell RNA sequencing

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Published on: April 25, 2011

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Last Updated: May 16, 2025

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

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

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Published on: March 29, 2024

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Area of Science:

Neuroscience
Genomics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) offers deep insights into cellular heterogeneity and dynamic processes.
Its application in neuroscience aids in identifying brain cell types, developmental pathways, and disease mechanisms.

Purpose of the Study:

To provide a practical guide for scRNA-seq data analysis in neuroscience.
To demonstrate essential workflows and theoretical foundations for reliable and reproducible studies.

Main Methods:

Quality control and data preprocessing.
Data integration, cell clustering, and differential expression analysis using the Seurat R package.
Comparative analysis for identifying differentially expressed genes and biological interpretation.

Main Results:

Demonstration of a comparative analysis approach for gene expression differences.
Emphasis on robust analysis methods to address scRNA-seq data challenges.

Conclusions:

Enhanced reliability and reproducibility of scRNA-seq studies in neuroscience.
Support for exploring cellular mechanisms and advancing brain function and disease research.