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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: Mar 8, 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

Effective detection of variation in single-cell transcriptomes using MATQ-seq.

Kuanwei Sheng^1,2, Wenjian Cao¹, Yichi Niu¹

¹Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.

|January 17, 2017

Summary

This summary is machine-generated.

We developed multiple annealing and dC-tailing-based quantitative single-cell RNA-seq (MATQ-seq), a sensitive method for single-cell total RNA sequencing. MATQ-seq accurately captures true biological variation by minimizing technical noise in transcriptional analysis.

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Last Updated: Mar 8, 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

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards

Published on: February 25, 2017

Area of Science:

Molecular Biology
Genomics
Biotechnology

Background:

Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity.
Existing scRNA-seq methods suffer from low sensitivity and high technical noise, limiting accurate quantification of transcriptional variation.
This hinders the study of subtle biological differences within cell populations.

Purpose of the Study:

To develop a highly sensitive and quantitative single-cell RNA sequencing method.
To accurately measure transcriptional variation within single cells and cell populations.
To overcome the limitations of current scRNA-seq techniques.

Main Methods:

Development of multiple annealing and dC-tailing-based quantitative single-cell RNA sequencing (MATQ-seq).
Systematic characterization and quantification of technical noise in the assay.
Application of MATQ-seq to profile whole transcriptomes of single cells.

Main Results:

MATQ-seq demonstrates significantly higher sensitivity compared to conventional methods.
The method effectively reduces technical noise, allowing for more accurate measurements.
MATQ-seq successfully captures genuine biological variation in whole transcriptomes of single cells.

Conclusions:

MATQ-seq provides a robust and accurate approach for single-cell transcriptional profiling.
This advancement enables more reliable studies of cellular heterogeneity and gene expression.
MATQ-seq is a valuable tool for single-cell genomics research.