Title: Quantitative mapping of the cellular small RNA landscape with AQRNA-seq.

Authors: Hu, Jennifer F; Yim, Daniel; Ma, Duanduan; Huber, Sabrina M; Davis, Nick; Bacusmo, Jo Marie; Vermeulen, Sidney; Zhou, Jieliang; Begley, Thomas J; DeMott, Michael S; Levine, Stuart S; de Crécy-Lagard, Valérie; Dedon, Peter C; Cao, Bo

Published In Nat Biotechnol, (2021 Aug)

Abstract: Current next-generation RNA-sequencing (RNA-seq) methods do not provide accurate quantification of small RNAs within a sample, due to sequence-dependent biases in capture, ligation and amplification during library preparation. We present a method, absolute quantification RNA-sequencing (AQRNA-seq), that minimizes biases and provides a direct, linear correlation between sequencing read count and copy number for all small RNAs in a sample. Library preparation and data processing were optimized and validated using a 963-member microRNA reference library, oligonucleotide standards of varying length, and RNA blots. Application of AQRNA-seq to a panel of human cancer cells revealed >800 detectable miRNAs that varied during cancer progression, while application to bacterial transfer RNA pools, with the challenges of secondary structure and abundant modifications, revealed 80-fold variation in tRNA isoacceptor levels, stress-induced site-specific tRNA fragmentation, quantitative modification maps, and evidence for stress-induced, tRNA-driven, codon-biased translation. AQRNA-seq thus provides a versatile means to quantitatively map the small RNA landscape in cells.

PubMed ID: 33859402

MeSH Terms: No MeSH terms associated with this publication