ALL-tRNAseq enables robust tRNA profiling in tissue samples

• Published in: Genes & development Vol. 37; no. 5-6; pp. 243 - 257
• Main Authors: Scheepbouwer, Chantal, Aparicio-Puerta, Ernesto, Gomez-Martin, Cristina, Verschueren, Heleen, van Eijndhoven, Monique, Wedekind, Laurine E, Giannoukakos, Stavros, Hijmering, Nathalie, Gasparotto, Lisa, van der Galien, Hilde T, van Rijn, Roos S, Aronica, Eleonora, Kibbelaar, Robby, Heine, Vivi M, Wesseling, Pieter, Noske, David P, Vandertop, W Peter, de Jong, Daphne, Pegtel, D Michiel, Hackenberg, Michael, Wurdinger, Tom, Gerber, Alan, Koppers-Lalic, Danijela
• Format: Journal Article
• Language: English
• Published: United States Cold Spring Harbor Laboratory Press 01.03.2023
• Subjects: High-Throughput Nucleotide Sequencing - methods; Protein Biosynthesis; Resource/Methodology; RNA, Messenger - metabolism; RNA, Transfer - genetics; RNA, Transfer - metabolism; Sequence Analysis, RNA - methods; cancer; transfer RNA; tissue samples; high-throughput sequencing
• ISSN: 0890-9369; 1549-5477
• DOI: 10.1101/gad.350233.122

Transfer RNAs (tRNAs) are small adaptor RNAs essential for mRNA translation. Alterations in the cellular tRNA population can directly affect mRNA decoding rates and translational efficiency during cancer development and progression. To evaluate changes in the composition of the tRNA pool, multiple sequencing approaches have been developed to overcome reverse transcription blocks caused by the stable structures of these molecules and their numerous base modifications. However, it remains unclear whether current sequencing protocols faithfully capture tRNAs existing in cells or tissues. This is specifically challenging for clinical tissue samples that often present variable RNA qualities. For this reason, we developed ALL-tRNAseq, which combines the highly processive MarathonRT and RNA demethylation for the robust assessment of tRNA expression, together with a randomized adapter ligation strategy prior to reverse transcription to assess tRNA fragmentation levels in both cell lines and tissues. Incorporation of tRNA fragments not only informed on sample integrity but also significantly improved tRNA profiling of tissue samples. Our data showed that our profiling strategy effectively improves classification of oncogenic signatures in glioblastoma and diffuse large B-cell lymphoma tissues, particularly for samples presenting higher levels of RNA fragmentation, further highlighting the utility of ALL-tRNAseq for translational research.