Discover Native tRNA Profiles with High-Resolution Nano tRNA Sequencing

At a glance:

• Introduction
• What is Nano tRNA Sequencing?
• Key Advantages of Our Nano tRNA Sequencing Service
• Applications of Nano tRNA Sequencing
• Technology Overview – How Nano tRNA Sequencing Works
• Bioinformatics Analysis
• Choosing the Right Platform for tRNA Analysis
• Sample Requirements for Nano tRNA Sequencing
• Why Choose CD Genomics
• Case Study: Quantitative Analysis of Native tRNA Modifications Using Nanopore Sequencing
• FAQs
• Demo

What is Nano tRNA Sequencing?

Nano tRNA sequencing is a specialised approach that uses Oxford Nanopore's direct RNA technology to read full-length, native tRNA molecules without cDNA synthesis or PCR. This capability is critical because tRNAs carry more than 150 known chemical modifications, many of which are lost, altered, or masked during reverse transcription in conventional RNA-seq.

In nano tRNA sequencing, individual tRNAs pass through a nanopore, generating electrical signals that reflect both their nucleotide sequence and their modification status. These signals allow researchers to quantify tRNA abundance, distinguish isoacceptors, and identify modification signatures across the tRNAome. Because this method captures each molecule directly, it retains the native biochemical information required for epitranscriptomic studies.

Nano tRNA sequencing is particularly valuable for projects exploring translation dynamics, stress adaptation, metabolic regulation, or drug-response mechanisms where both tRNA expression and modification states influence protein synthesis. For readers interested in nanopore-based RNA chemistry, our Nanopore Direct RNA Sequencing page provides further detail on signal interpretation and model-based modification calling.

Applications of Nano tRNA Sequencing

tRNAome Profiling Across Conditions

• Quantify global tRNA abundance and isoacceptor usage.
• Compare tRNA pools across cell types, stress states, developmental stages, or experimental treatments.
• Investigate codon–anticodon balance and translational efficiency.

Mapping tRNA Modifications

• Detect modification signatures such as Ψ, m¹A, m⁷G, i⁶A, and U34 variants from nanopore signal patterns.
• Study how modification networks respond to nutrient changes, metabolic stress, or pathway perturbation.
• Characterise modification-linked defects in genetic models.

Translation Dynamics and Adaptive Responses

• Explore how tRNA availability influences ribosome speed, decoding accuracy, and protein output.
• Analyse shifts in the tRNAome during stress adaptation, metabolic rewiring, or environmental changes.
• Identify tRNA-based regulatory pathways relevant to non-clinical research.

Drug Mechanism and Toxicology (Research Use Only)

• Profile tRNA changes in response to compounds that target translation, mitochondria, stress signalling, or RNA metabolism.
• Reveal modification-associated responses linked to compound exposure or toxicity.
• Integrate tRNA-level insights with transcriptomic or metabolomic datasets.

Cross-Species and Comparative Studies

• Suitable for bacteria, yeast, plants, model animals, and human-derived samples.
• Enables comparative analysis of tRNA evolution, decoding bias, and modification conservation.

Optional Extension: Ribo-tRNA Sequencing

• Combine nano tRNA sequencing with ribosome pulldown to investigate ribosome-associated tRNAs and decoding events in actively translating ribosomes.
• Useful for studies of translation initiation, elongation dynamics, and ribosome pausing.

Bioinformatics Analysis

Sample Requirements for Nano tRNA Sequencing

Case Study: Quantitative Analysis of Native tRNA Modifications Using Nanopore Sequencing

Lucas, M.C., Pryszcz, L.P., Medina, R. et al. Quantitative analysis of tRNA abundance and modifications by nanopore RNA sequencing. Nature Biotechnology42, 72–86 (2024).

1. Background

tRNAs carry a dense network of chemical modifications that regulate their folding, decoding activity, and stability. Until recently, researchers relied on indirect methods—such as mass spectrometry or cDNA-based RNA-seq—to infer these marks. These approaches either lacked positional information or erased modifications during reverse transcription.

This study tested whether Oxford Nanopore's direct RNA sequencing could deliver a unified view of tRNA abundance, isoacceptor usage, and modification signatures within a single experiment.

This work provides one of the strongest proof-of-concept demonstrations for native tRNA sequencing, offering insights now routinely leveraged in our nano tRNA sequencing service.

2. Methods

Native tRNAs were extracted from Schizosaccharomyces pombe (wild-type and enzyme-knockout strains). The workflow included:

• tRNA deacylation & adapter ligation to linearise tRNAs for sequencing
• ONT direct RNA sequencing to preserve native modifications
• Dorado basecalling models (Ψ, m⁶A, m⁵C, inosine)
• Both on-label (Ψ model detecting Ψ) and off-label (Ψ model detecting other U-modifications) strategies
• Orthogonal validation using JACUSA2 to compare WT and knockout-induced basecalling errors

This approach allowed the authors to characterise modification probabilities and assign modification sites to specific enzymes.

3. Results

The authors demonstrated that nanopore sequencing can distinguish modified from unmodified tRNA positions with high confidence. Using the Ψ model, they visualised clear probability peaks at known pseudouridine sites, while knockout strains displayed corresponding signal loss.

Nanopore-derived probability distribution for Ψ55 in native tRNAs, showing a strong high-probability peak consistent with known pseudouridine modification.

Key Findings

• High-resolution modification detection:
• ONT signals generated near-binary Ψ probabilities at validated sites.
• Novel modification discovery:
• Two previously unreported pseudouridine sites—Ψ8 (Pus7-dependent) and Ψ22 (Pus1-dependent)—were identified.
• Multi-modification mapping:
• Off-label use of the Ψ model detected U34 variants and m⁷G46 by analysing KL-divergence patterns.
• Enzyme-modification networks:
• Integration of knockout data revealed cross-talk, such as Ψ55 influencing m¹A58 formation.
• Accurate tRNA quantification:
• tRNA abundance values matched orthogonal measurements, confirming quantitative reliability.

4. Conclusions

This study confirms that nanopore direct RNA sequencing is a powerful single-molecule approach for analysing both tRNA abundance and modification landscapes. Importantly:

• Native modifications remain intact
• Modification probabilities correlate with known enzymatic dependencies
• Novel tRNA modifications can be identified
• Single-nucleotide resolution is achievable across the tRNAome
• Quantification is reproducible across replicates and conditions

Demo

1. tRNA Abundance Bar Plot (Isoacceptor Profiling)

2. Modification Probability Heatmap

3. Nanopore Signal Trace (Squiggle Plot) for Modification Detection