Due to its high modification content tRNAs are notoriously hard to quantify by reverse transcription and RNAseq. Bypassing numerous biases resulting from concatenation of enzymatic treatments, we here report a hybrid approach that harnesses the advantages of hybridization-based and deep sequencing–based approaches. The method renders obsolete any RNAseq related workarounds and correction factors that affect accuracy, sensitivity, and turnaround time. Rather than by reverse transcription, quantitative information on the isoacceptor composition of a tRNA pool is transferred to a cDNA mixture in a single step procedure, thereby omitting all enzymatic conversations except for the subsequent barcoding PCR. As a result, a detailed tRNA composition matrix can be obtained from femtomolar amounts of total tRNA. The method is fast, low in cost, and its bioinformatic data workup surprisingly simple. These properties make the approach amenable to high-throughput investigations including clinical samples, as we have demonstrated by application to a collection of variegated biological questions, each answered with novel findings. These include tRNA pool quantification of polysome-bound tRNA, of tRNA modification knockout strains under stress conditions, and of Alzheimer patients’ brain tissues.

中文翻译：

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DORQ-seq：通过结合 cDNA 杂交和深度测序对 femtomol tRNA 库进行高通量定量


由于其高修饰含量，众所周知，tRNA 很难通过逆转录和 RNAseq 进行定量。绕过酶处理串联导致的许多偏差，我们在这里报告了一种混合方法，该方法利用了基于杂交和基于深度测序的方法的优势。该方法消除了任何影响准确性、灵敏度和周转时间的 RNAseq 相关解决方法和校正因子。不是通过逆转录，而是在一步程序中将 tRNA 库的同受体组成的定量信息转移到 cDNA 混合物中，从而省略了除后续条形码 PCR 之外的所有酶促对话。因此，可以从飞摩尔量的总 tRNA 中获得详细的 tRNA 组成矩阵。该方法速度快，成本低，而且其生物信息学数据处理非常简单。这些特性使该方法适用于包括临床样本在内的高通量研究，正如我们通过应用于一系列多样化的生物学问题所证明的那样，每个问题都以新的发现回答。这些包括多核糖体结合的 tRNA、应激条件下 tRNA 修饰敲除菌株的 tRNA 库定量以及阿尔茨海默病患者脑组织的 tRNA 库定量。