ALL-tRNAseq enables robust tRNA profiling in tissue samples

Area of Science:

• Molecular Biology
• Genomics
• Cancer Research

Background:

• Transfer RNAs (tRNAs) are crucial for protein synthesis, and their altered levels are linked to cancer progression.
• Existing sequencing methods struggle to accurately quantify tRNAs due to their structure and modifications, especially in clinical tissue samples with variable RNA quality.

Purpose of the Study:

• To develop a robust sequencing protocol for accurately assessing tRNA expression and fragmentation in diverse biological samples.
• To evaluate the utility of the new protocol for classifying oncogenic signatures in cancer tissues.

Main Methods:

• Developed ALL-tRNAseq, integrating MarathonRT, RNA demethylation, and a randomized adapter ligation strategy.
• Applied the method to cell lines and clinical tissue samples, including glioblastoma and diffuse large B-cell lymphoma.
• Assessed tRNA expression, fragmentation levels, and their impact on sample integrity and classification.

Main Results:

• ALL-tRNAseq provides a robust assessment of tRNA expression, overcoming challenges posed by RNA structure and modifications.
• Incorporation of tRNA fragment analysis improved tRNA profiling, particularly for fragmented tissue samples.
• The method enhanced the classification of oncogenic signatures in glioblastoma and diffuse large B-cell lymphoma tissues.

Conclusions:

• ALL-tRNAseq offers a reliable method for tRNA profiling in both cell lines and clinical tissues, improving accuracy with fragmented samples.
• This technique is valuable for translational research, aiding in the classification of cancer subtypes and potentially informing therapeutic strategies.