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Quantitative mapping of the cellular small RNA landscape with AQRNA-seq.
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.
  • Hu JF; Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Yim D; Bristol Myers Squibb, Seattle, WA, USA.
  • Ma D; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Huber SM; A*STAR Genome Institute of Singapore, Singapore, Singapore.
  • Davis N; BioMicro Center, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Bacusmo JM; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Vermeulen S; Laboratory of Toxicology, ETH Zürich, Zürich, Switzerland.
  • Zhou J; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Begley TJ; Theon Therapeutics, Cambridge, MA, USA.
  • DeMott MS; Department of Microbiology & Cell Science, University of Florida, Gainesville, FL, USA.
  • Levine SS; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • de Crécy-Lagard V; KK Research Center, KK Women's and ChildrenBristol Myers Squibb's Hospital, Singapore, Singapore.
  • Dedon PC; The RNA Institute and Department of Biology, University at Albany, Albany, NY, USA.
  • Cao B; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Nat Biotechnol ; 39(8): 978-988, 2021 08.
Article en En | MEDLINE | ID: mdl-33859402
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.
Asunto(s)

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Alineación de Secuencia / Análisis de Secuencia de ARN / MicroARNs Límite: Humans Idioma: En Año: 2021 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Alineación de Secuencia / Análisis de Secuencia de ARN / MicroARNs Límite: Humans Idioma: En Año: 2021 Tipo del documento: Article