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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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Related Experiment Video

Updated: Nov 26, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2.

Robert R Stickels^1,2,3, Evan Murray¹, Pawan Kumar¹

¹Broad Institute of Harvard and MIT, Cambridge, MA, USA.

Nature Biotechnology

|December 8, 2020

Summary

This summary is machine-generated.

Slide-seqV2 enhances spatial transcriptomics by improving RNA capture efficiency tenfold over Slide-seq. This advanced technology maps mRNA locations in neurons and reveals spatiotemporal development in the mouse neocortex.

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Related Experiment Videos

Last Updated: Nov 26, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

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Published on: May 16, 2025

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

Area of Science:

Molecular Biology
Genomics
Neuroscience

Background:

Understanding tissue formation and function requires precise measurement of molecular locations within tissues.
Previous Slide-seq technology offered transcriptome-wide RNA detection at 10 μm spatial resolution.

Purpose of the Study:

To introduce Slide-seqV2, an improved spatial transcriptomics technology.
To enhance RNA capture efficiency and spatial resolution for molecular localization studies.
To apply Slide-seqV2 to investigate neuronal mRNA localization and developmental processes.

Main Methods:

Improvements in library generation, bead synthesis, and array indexing.
Achieving approximately 50% of single-cell RNA-seq capture efficiency, a ~10-fold increase over Slide-seq.
Integration of spatial data with single-cell trajectory analysis tools.

Main Results:

Identified dendritically localized mRNAs in mouse hippocampal neurons.
Characterized spatiotemporal development of the mouse neocortex with improved sampling of genetic programs.
Demonstrated significantly higher transcript detection efficiency compared to the original Slide-seq.

Conclusions:

Slide-seqV2 offers near-cellular resolution and high transcript detection efficiency.
The technology is versatile and applicable to diverse experimental contexts in spatial biology.
Slide-seqV2 advances the study of molecular localization in complex biological systems.