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Mammalian NET-seq analysis defines nascent RNA profiles and associated RNA processing genome-wide

Nature Protocols volume 11, pages 413–428 (2016)Cite this article

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Abstract

The transcription cycle of RNA polymerase II (Pol II) correlates with changes to the phosphorylation state of its large subunit C-terminal domain (CTD). We recently developed Native Elongation Transcript sequencing using mammalian cells (mNET-seq), which generates single-nucleotide–resolution genome-wide profiles of nascent RNA and co-transcriptional RNA processing that are associated with different CTD phosphorylation states. Here we provide a detailed protocol for mNET-seq. First, Pol II elongation complexes are isolated with specific phospho-CTD antibodies from chromatin solubilized by micrococcal nuclease digestion. Next, RNA derived from within the Pol II complex is size fractionated and Illumina sequenced. Using mNET-seq, we have previously shown that Pol II pauses at both ends of protein-coding genes but with different CTD phosphorylation patterns, and we have also detected phosphorylation at serine 5 (Ser5-P) CTD-specific splicing intermediates and Pol II accumulation over co-transcriptionally spliced exons. With moderate biochemical and bioinformatic skills, mNET-seq can be completed in ∼6 d, not including sequencing and data analysis.

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Figure 1: Overview of the mNET-seq procedure.
Figure 2: Examples of gel purification steps in the mNET-seq method.
Figure 3: Example of a mNET-seq profile for the SIK1 gene.

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Acknowledgements

We thank S. Murphy for critically testing this protocol, and A.R. Fialho Grosso for advice on bioinformatics analysis. This work was supported by funding to N.J.P. (Wellcome Trust Programme no. 091805/Z/10/Z and European Research Council (ERC) Advanced grant no. 339270) and to M.C-F. (Fundação Ciência e Tecnologia, Portugal).

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Authors and Affiliations

  1. Sir William Dunn School of Pathology, University of Oxford, Oxford, UK

    Takayuki Nojima & Nicholas J Proudfoot

  2. Instituto de Medicina Molecular, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal

    Tomás Gomes & Maria Carmo-Fonseca

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  1. Takayuki Nojima
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  2. Tomás Gomes
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  3. Maria Carmo-Fonseca
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  4. Nicholas J Proudfoot
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Contributions

T.N., M.C.-F. and N.J.P. designed the protocol. All authors wrote the paper. T.N. developed the protocol and performed all experiments. T.G. bioinfomatically analyzed the data.

Corresponding authors

Correspondence to Takayuki Nojima or Nicholas J Proudfoot.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 mNET-seq vs GRO-seq

Correlation between log2 (RPM) of Total Pol II mNET-seq and GRO-seq data for genome windows of 500kb. Both samples show very good correlation (Pearson's Correlation Coeficient (PCC) = 0.93, p-value < 2.2e-16). GRO-seq data (GEO accession number GSM1518913) and RNA-seq data used to determine gene expression (GEO accession number GSM1155630) were from previously published studies (Andersson, R. et al. Nat. Commun. 5, 5336 (2014); Lacoste, N. et al. Mol. Cell 53, 631-644 (2014)).

Supplementary Figure 2 mNET-seq Pol II specificity

Boxplot distribution comparison of log2 reads per base (Rpb) values for Total RNA Pol II mNET-seq in expressed protein coding, tRNA and rRNA genes. For protein coding genes, only the region [TSS, TSS+100] is used, to consider a region without reads corresponding to splicing intermediates (which appear at the end of exons), and also to use a region with a size similar to that of tRNA and rRNA genes to avoid any size bias when normalizing.

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Supplementary Figures 1 and 2 (PDF 237 kb)

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Nojima, T., Gomes, T., Carmo-Fonseca, M. et al. Mammalian NET-seq analysis defines nascent RNA profiles and associated RNA processing genome-wide. Nat Protoc 11, 413–428 (2016). https://doi.org/10.1038/nprot.2016.012

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