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Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets
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Strand-Specific Transcriptome Sequencing Using SMART Technology.

Magnolia Bostick1, Nathalie Bolduc1, Alisa Lehman1

  • 1Takara Bio USA, Inc. (formerly Clontech Laboratories, Inc.), Mountain View, California.

Current Protocols in Molecular Biology
|October 11, 2016
PubMed
Summary
This summary is machine-generated.

Streamlined stranded RNA sequencing (RNA-seq) methods enable rapid, sensitive transcriptome-wide expression analysis from minimal samples. These protocols efficiently remove ribosomal RNA (rRNA), preserving strand information for accurate gene quantification.

Keywords:
degraded RNAnon-coding RNArRNA removalstranded RNA-seq

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Area of Science:

  • Molecular Biology
  • Genomics
  • Transcriptomics

Background:

  • Next-generation sequencing (NGS) enhances biological understanding through high-sensitivity transcriptome-wide RNA expression analysis.
  • Stranded RNA sequencing (RNA-seq) is crucial for distinguishing overlapping genes and quantifying long non-coding RNAs.
  • Existing stranded RNA-seq library preparation methods are often complex, time-consuming, and inefficient, especially with limited starting material and high ribosomal RNA (rRNA) content.

Purpose of the Study:

  • To present streamlined workflows for generating strand-specific RNA sequencing libraries.
  • To enable efficient RNA expression analysis from low input amounts (10 ng to 1 µg total RNA) of biological samples.
  • To overcome challenges associated with ribosomal RNA (rRNA) depletion and library preparation time.

Main Methods:

  • Utilized a combination of RNase H-based RiboGone rRNA removal and SMARTer Stranded RNA-seq technology.
  • Developed workflows for processing 10 ng to 1 µg of total RNA, suitable for as few as 1000 cells.
  • Achieved rRNA depletion of over 95% in mammalian samples and direct production of Illumina-ready libraries.

Main Results:

  • Generated strand-specific RNA-seq libraries in under 7 hours with minimal rRNA carryover.
  • Enabled comprehensive transcriptome analysis, including non-polyadenylated long non-coding RNAs, from limited sample inputs.
  • Demonstrated efficient rRNA depletion and preservation of strand-specific information for accurate quantification.

Conclusions:

  • The presented streamlined workflows significantly improve the efficiency and sensitivity of stranded RNA-seq library preparation.
  • These methods facilitate robust gene expression analysis, even from challenging samples like those with low RNA input or degraded RNA.
  • The optimized protocols support deeper biological insights by enabling accurate quantification of the entire transcriptome.