Enzymes in high-throughput RNA sequencing: Applications and challenges.

Area of Science:

• Molecular Biology
• Genomics
• Biochemistry

Background:

• High-throughput RNA sequencing offers genome-wide insights into cellular RNA dynamics and responses to environmental stimuli.
• Next-generation sequencing, particularly the Illumina platform, provides extensive data output.
• Efficient end-to-end reverse transcription into cDNA is critical for RNA sequencing but is hindered by RNA structures and modifications.

Purpose of the Study:

• To evaluate and compare the efficiency of various reverse transcriptase (RT) enzymes for complete cDNA synthesis.
• To identify optimal RT enzymes for sequencing challenging RNA molecules, using tRNA as a model.
• To provide a generalizable workflow for RNA sequencing applicable to diverse RT enzymes.

Main Methods:

• Utilized transfer RNA (tRNA) as a model RNA due to its complex structure and extensive post-transcriptional modifications.
• Compared the processivity and efficiency of end-to-end cDNA synthesis across several contemporary RT enzymes.
• Developed and validated a general sequencing workflow compatible with multiple RT enzymes.

Main Results:

• Demonstrated significant variations in the efficiency of end-to-end cDNA synthesis among different RT enzymes when sequencing tRNA.
• Identified specific RT enzymes that exhibit superior performance in overcoming RNA structural barriers and modifications.
• Established a robust workflow that enhances the success rate of cDNA synthesis for complex RNA targets.

Conclusions:

• The choice of reverse transcriptase enzyme significantly impacts the success of RNA sequencing, particularly for structured and modified RNAs.
• tRNA presents a challenging yet valuable model for assessing RT performance in next-generation sequencing.
• The presented workflow and comparative analysis guide researchers in selecting appropriate RT enzymes for comprehensive RNA sequencing experiments.