转录组
计算生物学
线粒体DNA
生物
RNA序列
基因
遗传学
基因表达
作者
Zhenni Wang,Kaixiang Zhou,Qing Yuan,Dongbo Chen,Xie Hu,Fanfan Xie,Liu Yang,Jinliang Xing
标识
DOI:10.1021/acs.analchem.3c03741
摘要
The transcription of the mitochondrial genome is pivotal for maintenance of mitochondrial functions, and the deregulated mitochondrial transcriptome contributes to various pathological changes. Despite substantial progress having been achieved in uncovering the transcriptional complexity of the nuclear transcriptome, many unknowns and controversies remain for the mitochondrial transcriptome, partially owing to the lack of a highly efficient mitochondrial RNA (mtRNA) sequencing and analysis approach. Here, we first comprehensively evaluated the influence of essential experimental protocols, including strand-specific library construction, two RNA enrichment strategies, and optimal rRNA depletion, on accurately profiling mitochondrial transcriptome in whole-transcriptome sequencing (WTS) data. Based on these insights, we developed a highly efficient approach specifically suitable for targeted sequencing of whole mitochondrial transcriptome, termed capture-based mtRNA seq (CAP), in which strand-specific library construction and optimal rRNA depletion were applied. Compared with WTS, CAP has a great decrease of required data volume without affecting the sensitivity and accuracy of detection. In addition, CAP also characterized the unannotated mt-tRNA transcripts whose expression levels are below the detection limits of conventional WTS. As a proof-of-concept characterization of mtRNAs, the transcription initiation sites and mtRNA cleavage ratio were accurately identified in CAP data. Moreover, CAP had very reliable performance in plasma and single-cell samples, highlighting its wide application. Altogether, the present study has established a highly efficient pipeline for targeted sequencing of mtRNAs, which may pave the way toward functional annotation of mtRNAs and mtRNA-based diagnostic and therapeutic strategies in various diseases.
科研通智能强力驱动
Strongly Powered by AbleSci AI