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RNA-seq

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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.
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Related Experiment Video

Updated: Mar 24, 2026

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

Single-cell transcriptome sequencing: recent advances and remaining challenges.

Serena Liu¹, Cole Trapnell¹

¹Department of Genome Sciences, University of Washington, Seattle, WA, USA.

|March 8, 2016

Summary

This summary is machine-generated.

Single-cell RNA sequencing offers high-resolution transcriptomic analysis, revealing cellular diversity missed by bulk methods. This powerful technique is advancing our understanding of cell states and dynamics.

Keywords:

Single-cell RNA-sequencing single-cell transcriptomic profiling

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Last Updated: Mar 24, 2026

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

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing

Published on: May 23, 2018

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

Published on: January 7, 2020

Area of Science:

Genomics
Molecular Biology
Bioinformatics

Background:

Traditional bulk RNA sequencing averages gene expression, masking cellular heterogeneity.
Single-cell RNA sequencing (scRNA-seq) overcomes these limitations by analyzing individual cells.
Understanding cell-to-cell variation is crucial in biology and medicine.

Purpose of the Study:

To highlight the capabilities and growing importance of single-cell RNA sequencing.
To discuss the applications of scRNA-seq in deciphering cell states and dynamics.
To outline future directions and potential of scRNA-seq technologies.

Main Methods:

High-throughput, high-resolution transcriptomic analysis at the single-cell level.
Utilizing scRNA-seq to circumvent averaging artifacts inherent in bulk population data.
Employing advanced analytical frameworks for interpreting complex single-cell datasets.

Main Results:

scRNA-seq effectively unravels complex cellular populations.
Developmental trajectories and transcriptional dynamics are accurately reconstructed.
New insights into cellular diversity within seemingly homogeneous populations are generated.

Conclusions:

Single-cell RNA sequencing is a robust and economically viable tool for transcriptomic analysis.
Ongoing improvements in throughput, sensitivity, and analytical methods will expand scRNA-seq applications.
scRNA-seq is revolutionizing the study of cellular heterogeneity and biological processes.