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Related Experiment Video

Updated: Jun 21, 2026

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response

Measuring mRNA Levels Over Time During the Yeast S. cerevisiae Hypoxic Response

Published on: August 10, 2017

Quantification of the yeast transcriptome by single-molecule sequencing.

Doron Lipson¹, Tal Raz, Alix Kieu

¹Helicos Biosciences Corporation, Cambridge, Massachusetts, USA.

Nature Biotechnology

|July 8, 2009

Summary

This summary is machine-generated.

We developed single-molecule sequencing digital gene expression (smsDGE), an amplification-free method for precise RNA quantification. This high-throughput technique offers an efficient alternative for gene expression profiling.

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Area of Science:

Molecular Biology
Genomics
Biotechnology

Background:

Accurate quantification of cellular RNA transcripts is crucial for understanding gene expression.
Existing methods often involve amplification, which can introduce bias.
There is a need for high-throughput, amplification-free RNA sequencing methods.

Purpose of the Study:

To introduce and validate single-molecule sequencing digital gene expression (smsDGE).
To demonstrate smsDGE as a high-throughput, amplification-free method for accurate RNA quantification.
To assess the performance of smsDGE for transcriptome analysis.

Main Methods:

Developed smsDGE using a Helicos Genetic Analysis system.
Employed a reverse-transcription and polyA-tailing sample preparation.
Sequencing generates a single read per transcript, enabling direct counting.

Main Results:

Applied smsDGE to Saccharomyces cerevisiae, generating millions of aligned reads per channel.
Achieved accurate quantitative measurements over four orders of magnitude using spiked-in RNA.
Demonstrated high correlation across independent runs and sample preparations.

Conclusions:

smsDGE provides accurate quantification of polyadenylated RNA transcripts without amplification.
The method is highly efficient for transcript counting due to single-read representation.
smsDGE offers a viable, high-throughput alternative for gene expression profiling.