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Recording transcriptional histories using Record-seq

Nature Protocols volume 15, pages 513–539 (2020)Cite this article

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Abstract

It is difficult to elucidate the transcriptional history of a cell using current experimental approaches, as they are destructive in nature and therefore describe only a moment in time. To overcome these limitations, we recently established Record-seq, a technology that enables transcriptional recording by CRISPR spacer acquisition from RNA. The recorded transcriptomes are recovered by SENECA, a method that selectively amplifies expanded CRISPR arrays, followed by deep sequencing. The resulting CRISPR spacers are aligned to the host genome, thereby enabling transcript quantification and associated analyses. Here, we describe the experimental procedures of the Record-seq workflow as well as subsequent data analysis. Beginning with the experimental design, Record-seq data can be obtained and analyzed within 1–2 weeks.

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Fig. 1: Transcriptional recording by CRISPR spacer acquisition from RNA.
Fig. 2: Record-seq experimental and computational workflows.
Fig. 3: Illustrative output plots generated by the recoRdseq R package, with samples color-coded by experimental group specified in the design matrix.

Data availability

Deep sequencing data are available in the National Center for Biotechnology Information Sequence Read Archive (PRJNA510019). An example counts dataset can be accessed from https://github.com/plattlab/Transcriptional-Recording/tree/master/example-data.

Code availability

The python code is available in the Supplementary Data. The latest versions of the code can be accessed at the Platt lab github (https://github.com/plattlab/Transcriptional-Recording). The code in this protocol has been peer reviewed.

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Acknowledgements

We thank C. Beisel, E. Burcklen, K. Eschbach, T. Schär, and I. Nissen from the Genomics Facility Basel for assistance in Illumina sequencing. F.S, M.Y.C, T.T., and R.J.P. are supported in part by funds from the Swiss National Science Foundation, ETH domain Personalized Health and Related Technologies, Brain and Behavior Research Foundation, and the National Centres of Competence–Molecular Systems Engineering. Plasmid reagents are available through Addgene. Correspondence and requests for materials should be addressed to R.J.P. (rplatt@ethz.ch).

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Author notes

  1. These authors contributed equally: Tanmay Tanna, Florian Schmidt.

Authors and Affiliations

  1. Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland

    Tanmay Tanna, Florian Schmidt, Mariia Y. Cherepkova & Randall J. Platt

  2. Scientific IT Services, ETH Zurich, Zurich, Switzerland

    Michal Okoniewski

  3. Department of Chemistry, University of Basel, Basel, Switzerland

    Randall J. Platt

Authors
  1. Tanmay Tanna
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  2. Florian Schmidt
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  3. Mariia Y. Cherepkova
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  4. Michal Okoniewski
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  5. Randall J. Platt
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Contributions

F.S., M.Y.C., T.T., and R.J.P. designed the study. F.S. performed experiments, T.T. and F.S. analyzed data, T.T. created the python pipeline for primary analysis, T.T. and M.O created the recoRdseq library for secondary analysis, F.S., T.T, M.Y.C., and R.J.P. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Randall J. Platt.

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Competing interests

R.J.P. and F.S. are inventors on a patent application filed by ETH Zurich relating to work in this article. All other authors have no competing interests.

Additional information

Peer review information Nature Protocols thanks Stan J. J. Brouns, Simon Jackson and Malcolm F. White for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Key references using this protocol

Schmidt, F. et al. Nature 562, 380–385 (2018): https://doi.org/10.1038/s41586-018-0569-1

Integrated supplementary information

Supplementary Figure 1 Additional illustrative output plots generated by the recoRdseq R package, with samples color-coded by experimental group specified in the design matrix.

a. Mean untransformed genome-aligning spacer counts by group (error bars represent standard error of the mean). b. PCA plots for rlog transformed gene-aligning spacer counts for the top 50 variable genes with clusters defined by k-means clustering (k=2). c. Venn diagram showing overlap in significant DE genes (padj < 0.05) detected by the three DE tools (DESeq2, edgeR and baySeq). d. Heatmap showing unsupervised hierarchical clustering of samples based on rlog transformed genome-aligning spacer counts for high-confidence DE genes detected by the three DE tools. For all panels, n=6 independent biological replicates.

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Tanna, T., Schmidt, F., Cherepkova, M.Y. et al. Recording transcriptional histories using Record-seq. Nat Protoc 15, 513–539 (2020). https://doi.org/10.1038/s41596-019-0253-4

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