Abstract
RNA structure determination is essential to understand how RNA carries out its diverse biological functions. In cells, RNA isoforms are readily expressed with partial variations within their sequences due, for example, to alternative splicing, heterogeneity in the transcription start site, RNA processing or differential termination/polyadenylation. Nanopore dimethyl sulfate mutational profiling (Nano-DMS-MaP) is a method for in situ isoform-specific RNA structure determination. Unlike similar methods that rely on short sequencing reads, Nano-DMS-MaP employs nanopore sequencing to resolve the structures of long and highly similar RNA molecules to reveal their previously hidden structural differences. This Protocol describes the development and applications of Nano-DMS-MaP and outlines the main considerations for designing and implementing a successful experiment: from bench to data analysis. In cell probing experiments can be carried out by an experienced molecular biologist in 3–4 d. Data analysis requires good knowledge of command line tools and Python scripts and requires a further 3–5 d.
Key points
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Nano-DMS-MaP is a method for in situ isoform-specific RNA structure determination. It employs nanopore sequencing to resolve the structures of long and highly similar RNA molecules, revealing previously hidden structural differences.
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Compared with short-read sequencing, in which it is difficult to uniquely map individual reads to highly similar transcript isoforms, Nano-DMS-MaP uses long-read Nanopore sequencing, enabling unambiguous assignment of reads to transcript isoforms.
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Data availability
The data used to generate the anticipated results were originally published in ref. 22. All sequencing data are available at Sequence Read Archive (SRP424422, Bioproject ID PRJNA938445).
Code availability
Code used for the Nano-DMS-MaP analysis is accessible via the Smyth lab Github (https://github.com/smyth-lab/Nano-DMS-MaP) and is available for reuse under the Massachusetts Institute of Technology (MIT) License.
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Acknowledgements
This study was funded by the Helmholtz Association (VH-NG-1347 to R.P.S.) and the National Institutes of Health Center for HIV RNA Studies (SUBK00019361 to R.P.S). A.-S.G.-B. was supported with a fellowship from the Peter und Traudl Engelhorn Stiftung and a Post Doc Plus funding (Graduate School of Life Sciences, University of Würzburg).
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Bohn, P. et al. Nat. Methods 20, 849–859 (2023): https://doi.org/10.1038/s41592-023-01862-7
Extended data
Extended Data Fig. 1 Flowchart of bioinformatic analysis.
Each major stage of the analysis is shown highlighted in colored boxes and QC steps are shown on the right side. Files generated during the different steps of the analysis are depicted in boxes. The tools/analysis steps are shown as triangular arrows, with options highlighted in hexagons.
Extended Data Fig. 2 Effect of subsampling on Pearson correlation coefficient between DMS reactivities of different isoforms of both replicates.
(a) Pearson correlation coefficient within multiple subsample iterations at the same subsampling depth reveal isoforms where all variation has been sufficiently sampled (correlation coefficient >0.9 at subsampling rate of 50%), and those where the underlying diversity is not yet fully sampled (correlation coefficient <0.9 at subsampling rate of 50%). (b) Pearson correlation coefficient between replicates 1 and 2 of each sample and isoform at different subsampling depths as a quality control measure of reproducibility of DMS probing data. Plateauing of the correlation coefficient below 0.9 with increasing coverage may indicate that stochastic effects during reverse transcription due to low number of fully reverse transcribed molecules may have resulted in divergent cDNA mutation pools.
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Source Data Fig. 2
Raw data for Fig. 2c.
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Gribling-Burrer, AS., Bohn, P. & Smyth, R.P. Isoform-specific RNA structure determination using Nano-DMS-MaP. Nat Protoc (2024). https://doi.org/10.1038/s41596-024-00959-3
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DOI: https://doi.org/10.1038/s41596-024-00959-3
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