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.

中文翻译：

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ALL-tRNAseq 可对组织样本进行稳健的 tRNA 分析

转移 RNA (tRNA) 是 mRNA 翻译所必需的小接头 RNA。细胞 tRNA 群体的改变可以直接影响癌症发生和进展过程中的 mRNA 解码率和翻译效率。为了评估 tRNA 池组成的变化，已经开发了多种测序方法来克服由这些分子的稳定结构及其大量碱基修饰引起的逆转录阻断。然而，目前尚不清楚当前的测序方案是否忠实地捕获细胞或组织中存在的 tRNA。这对于经常呈现可变 RNA 质量的临床组织样本来说尤其具有挑战性。为此，我们开发了 ALL-tRNAseq，它结合了高度持续的 MarathonRT 和 RNA 去甲基化，以对 tRNA 表达进行稳健评估，并在逆转录之前采用随机接头连接策略，以评估细胞系和组织中的 tRNA 碎片水平。tRNA 片段的掺入不仅可以了解样品的完整性，还可以显着改善组织样品的 tRNA 分析。我们的数据表明，我们的分析策略有效地改进了胶质母细胞瘤和弥漫性大 B 细胞淋巴瘤组织中致癌特征的分类，特别是对于呈现较高水平 RNA 碎片的样本，进一步凸显了 ALL-tRNAseq 在转化研究中的实用性。