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Recording transcriptional histories using Record-seq


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.

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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

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 ( The code in this protocol has been peer reviewed.


  1. Schmidt, F. & Platt, R. J. Applications of CRISPR-Cas for synthetic biology and genetic recording. Curr. Opin. Syst. Biol. 5, 9–15 (2017).

    Article  Google Scholar 

  2. Farzadfard, F. & Lu, T. K. Emerging applications for DNA writers and molecular recorders. Science 361, 870–875 (2018).

    Article  CAS  Google Scholar 

  3. Esvelt, K. M. & Wang, H. H. Genome-scale engineering for systems and synthetic biology. Mol. Syst. Biol. 9, 641 (2013).

    Article  Google Scholar 

  4. Farzadfard, F. & Lu, T. K. Synthetic biology. Genomically encoded analog memory with precise in vivo DNA writing in living cell populations. Science 346, 1256272 (2014).

    Article  Google Scholar 

  5. Roquet, N., Soleimany, A. P., Ferris, A. C., Aaronson, S. & Lu, T. K. Synthetic recombinase-based state machines in living cells. Science 353, aad8559 (2016).

    Article  Google Scholar 

  6. Weinberg, B. H. et al. Large-scale design of robust genetic circuits with multiple inputs and outputs for mammalian cells. Nat. Biotechnol. 35, 453–462 (2017).

    Article  CAS  Google Scholar 

  7. Zamft, B. M. et al. Measuring cation dependent DNA polymerase fidelity landscapes by deep sequencing. PloS ONE 7, e43876 (2012).

    Article  CAS  Google Scholar 

  8. McKenna, A. et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016).

    Article  Google Scholar 

  9. Raj, B. et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol. 36, 442–450 (2018).

    Article  CAS  Google Scholar 

  10. Frieda, K. L. et al. Synthetic recording and in situ readout of lineage information in single cells. Nature 541, 107–111 (2017).

    Article  CAS  Google Scholar 

  11. Perli, S. D., Cui, C. H. & Lu, T. K. Continuous genetic recording with self-targeting CRISPR-Cas in human cells. Science 353, (2016).

    Article  Google Scholar 

  12. Tang, W. & Liu, D. R. Rewritable multi-event analog recording in bacterial and mammalian cells. Science 360, (2018).

    Article  Google Scholar 

  13. Sheth, R. U., Yim, S. S., Wu, F. L. & Wang, H. H. Multiplex recording of cellular events over time on CRISPR biological tape. Science 358, 1457–1461 (2017).

    Article  CAS  Google Scholar 

  14. Silas, S. et al. Direct CRISPR spacer acquisition from RNA by a natural reverse transcriptase-Cas1 fusion protein. Science 351, aad4234 (2016).

    Article  Google Scholar 

  15. Shipman, S. L., Nivala, J., Macklis, J. D. & Church, G. M. Molecular recordings by directed CRISPR spacer acquisition. Science 353, aaf1175 (2016).

    Article  Google Scholar 

  16. Shipman, S. L., Nivala, J., Macklis, J. D. & Church, G. M. CRISPR-Cas encoding of a digital movie into the genomes of a population of living bacteria. Nature 547, 345–349 (2017).

    Article  CAS  Google Scholar 

  17. Schmidt, F., Cherepkova, M. Y. & Platt, R. J. Transcriptional recording by CRISPR spacer acquisition from RNA. Nature 562, 380–385 (2018).

    Article  CAS  Google Scholar 

  18. Nuñez, J. K. et al. Cas1–Cas2 complex formation mediates spacer acquisition during CRISPR–Cas adaptive immunity. Nat. Struct. Mol. Biol. 21, 528–534 (2014).

    Article  Google Scholar 

  19. Jackson, S. A. et al. CRISPR-Cas: adapting to change. Science 356, (2017).

    Article  Google Scholar 

  20. Yosef, I., Goren, M. G. & Qimron, U. Proteins and DNA elements essential for the CRISPR adaptation process in Escherichia coli. Nucleic Acids Res. 40, 5569–5576 (2012).

    Article  CAS  Google Scholar 

  21. Koster, J. & Rahmann, S. Snakemake—a scalable bioinformatics workflow engine. Bioinformatics 28, 2520–2522 (2012).

    Article  Google Scholar 

  22. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  Google Scholar 

  23. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  Google Scholar 

  24. Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

  25. Liao, Y., Smyth, G. K. & Shi, W. The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 41, e108 (2013).

    Article  Google Scholar 

  26. Leinonen, R., Sugawara, H. & Shumway, M., International Nucleotide Sequence Database, C. The sequence read archive. Nucleic Acids Res. 39, D19–21 (2011).

    Article  CAS  Google Scholar 

  27. Stead, M. B. et al. RNAsnap: a rapid, quantitative and inexpensive, method for isolating total RNA from bacteria. Nucleic Acids Res. 40, e156 (2012).

    Article  CAS  Google Scholar 

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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. (

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Authors and Affiliations



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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Schmidt, F. et al. Nature 562, 380–385 (2018):

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).

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