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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: Dec 18, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Benchmarking single-cell RNA-sequencing protocols for cell atlas projects.

Elisabetta Mereu¹, Atefeh Lafzi¹, Catia Moutinho¹

¹CNAG-CRG, Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain.

Nature Biotechnology

|June 11, 2020

Summary

This summary is machine-generated.

Choosing the right single-cell RNA sequencing (scRNA-seq) protocol is crucial for accurate cell atlases. Our study benchmarks 13 protocols, revealing significant performance differences impacting cell type and state characterization.

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Last Updated: Dec 18, 2025

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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

Published on: March 7, 2018

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for transcriptome analysis at the individual cell level.
Current scRNA-seq protocols vary significantly in efficiency, bias, scalability, and cost, leading to uncertainty in protocol selection.
The development of comprehensive cell atlases necessitates a clear understanding of protocol performance.

Purpose of the Study:

To systematically evaluate and compare the performance of commonly used scRNA-seq and single-nucleus RNA-seq protocols.
To assess the ability of different protocols to comprehensively characterize cell types and states.
To provide data-driven guidance for protocol selection in research and large-scale atlas projects.

Main Methods:

A multicenter study was conducted comparing 13 distinct scRNA-seq and single-nucleus RNA-seq protocols.
A heterogeneous reference sample resource was utilized for standardized protocol application.
Benchmark datasets were generated for systematic comparative analysis of protocol performance.

Main Results:

Significant variations in RNA capture efficiency, library complexity, and bias were observed across the evaluated protocols.
Protocol performance directly impacted the detection of cell-type specific markers.
Differences in protocol performance influenced the predictive accuracy and suitability for integration into reference atlases.

Conclusions:

Protocol choice critically affects the quality and interpretability of scRNA-seq data.
The study highlights marked differences in the ability of protocols to comprehensively describe cellular heterogeneity.
Findings offer essential guidance for researchers and consortia, including the Human Cell Atlas initiative, in selecting optimal scRNA-seq methodologies.