Quantitative mapping of the cellular small RNA landscape with AQRNA-seq.
Jennifer F HuGuo Rong Daniel YimDuanduan MaSabrina M HuberNick DavisJo Marie BacusmoSidney VermeulenJieliang ZhouThomas J BegleyMichael S DeMottStuart S LevineValérie de Crécy-LagardPeter C DedonBo CaoPublished in: Nature biotechnology (2021)
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.
Keyphrases
- single cell
- rna seq
- stress induced
- copy number
- high resolution
- nucleic acid
- genome wide
- induced apoptosis
- endothelial cells
- papillary thyroid
- squamous cell carcinoma
- dna methylation
- gene expression
- endoplasmic reticulum stress
- lymph node metastasis
- single molecule
- big data
- signaling pathway
- induced pluripotent stem cells
- label free
- artificial intelligence