Abstract

RNA sequencing (RNA-Seq) is a powerful tool for analyzing the identity of cellular RNAs but is often limited by the amount of material available for analysis. In spite of extensive efforts employing existing protocols, we observed that it was not possible to obtain useful sequencing libraries from nuclear RNA derived from cultured human cells after crosslinking and immunoprecipitation (CLIP). Here, we report a method for obtaining strand-specific small RNA libraries for RNA sequencing that requires picograms of RNA. We employ an intramolecular circularization step that increases the efficiency of library preparation and avoids the need for intermolecular ligations of adaptor sequences. Other key features include random priming for full-length cDNA synthesis and gel-free library purification. Using our method, we generated CLIP-Seq libraries from nuclear RNA that had been UV-crosslinked and immunoprecipitated with anti-Argonaute 2 (Ago2) antibody. Computational protocols were developed to enable analysis of raw sequencing data and we observe substantial differences between recognition by Ago2 of RNA species in the nucleus relative to the cytoplasm. This RNA self-circularization approach to RNA sequencing (RC-Seq) allows data to be obtained using small amounts of input RNA that cannot be sequenced by standard methods.

Highlights

  • RNA sequencing (RNA-Seq) has become a widely used tool for investigating gene expression [1]

  • All synthetic RNAs, primers for generating complementary DNA (cDNA) and PCR primers were obtained from Integrated DNA Technologies (IDT) and purified by polyacrylamide gel electrophoresis (PAGE)

  • Single-stranded RNA will compete with circular RNA during each step of library preparation and sequencing, reducing sequencing depth

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Summary

Introduction

RNA sequencing (RNA-Seq) has become a widely used tool for investigating gene expression [1]. The usefulness of RNA-Seq, is often limited by the amount of input RNA needed to yield meaningful data. RNA-Seq can be used to analyze both long RNA and small RNAs. For the sequencing of long RNA fragments (>200 bases), the most sensitive methods may allow researchers to study single cell transcriptome and require as little as 10–100 pg of total RNA as input [2,3,4,5]. Standard long RNA sequencing methods often use random priming to generate reads across the entire length of all transcripts under study [6,7]. Random priming is not an option for sequencing small RNAs because they are unlikely to yield DNA sequences of sufficient length to be mapped uniquely within a genome. To sequence small RNA (

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