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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Aug 27, 2025

Isolation of Region-specific Microglia from One Adult Mouse Brain Hemisphere for Deep Single-cell RNA Sequencing

Isolation of Region-specific Microglia from One Adult Mouse Brain Hemisphere for Deep Single-cell RNA Sequencing

Published on: December 3, 2019

Decoding brain memory formation by single-cell RNA sequencing.

Atlas M Sardoo¹, Shaoqiang Zhang², Thomas N Ferraro³

¹Department of Biological & Biomedical Sciences, Rowan University, Glassboro, NJ 08028, USA.

Briefings in Bioinformatics

|September 26, 2022

Summary

This summary is machine-generated.

New sequencing techniques reveal how specific brain cells, known as engram cells, change their gene expression to form and store memories. This research aids understanding of memory mechanisms in health and disease.

Keywords:

Alzheimer disease memory formation scRNA-seq snRNA-seq

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Last Updated: Aug 27, 2025

Isolation of Region-specific Microglia from One Adult Mouse Brain Hemisphere for Deep Single-cell RNA Sequencing

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

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

Published on: October 12, 2018

Area of Science:

Neuroscience
Computational Biology
Genomics

Background:

Understanding memory formation and storage is a fundamental neuroscience challenge.
Engram cells are key neuronal populations representing memory traces.
Gene expression dynamics in engram cells alter neuronal function and connectivity.

Purpose of the Study:

To review experimental and computational methods of single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq).
To discuss recent studies applying sc/snRNA-seq to memory mechanisms in human diseases and mouse models.
To highlight computational challenges in deciphering memory regulatory networks.

Main Methods:

Single-cell RNA sequencing (scRNA-seq) for gene expression profiling.
Single-nucleus RNA sequencing (snRNA-seq) for gene expression profiling.
Analysis of dynamic gene expression patterns in engram cells.

Main Results:

sc/snRNA-seq enables delineation of dynamic expression profiles in neuronal subsets.
These techniques facilitate identification of memory-specific genes and regulatory networks.
Applications reveal molecular mechanisms of memory in disease contexts.

Conclusions:

sc/snRNA-seq are powerful tools for studying molecular underpinnings of memory.
Understanding gene expression dynamics is crucial for deciphering long-term memory formation.
Computational approaches are vital for analyzing complex regulatory mechanisms.