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Recording gene expression order in DNA by CRISPR addition of retron barcodes

Nature volume 608, pages 217–225 (2022)Cite this article

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Abstract

Biological processes depend on the differential expression of genes over time, but methods to make physical recordings of these processes are limited. Here we report a molecular system for making time-ordered recordings of transcriptional events into living genomes. We do this through engineered RNA barcodes, based on prokaryotic retrons1, that are reverse transcribed into DNA and integrated into the genome using the CRISPR–Cas system2. The unidirectional integration of barcodes by CRISPR integrases enables reconstruction of transcriptional event timing based on a physical record through simple, logical rules rather than relying on pretrained classifiers or post hoc inferential methods. For disambiguation in the field, we will refer to this system as a Retro-Cascorder.

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Fig. 1: Cas1–Cas2 integrates retron RT-DNA.
Fig. 2: Diversification of retron-based barcodes.
Fig. 3: Mechanism of RT-DNA spacer acquisition.
Fig. 4: Temporal recordings of gene expression.
Fig. 5: Modelling the limits of retron recording.

Data availability

All data supporting the findings of this study are available within the article and its Supplementary Information or will be made available from the authors on request. Sequencing data associated with this study are available in the NCBI Sequence Read Archive (PRJNA838025).

Code availability

Custom code to process and analyse data from this study is available on GitHub (https://github.com/Shipman-Lab/Spacer-Seq).

References

  1. Simon, A. J., Ellington, A. D. & Finkelstein, I. J. Retrons and their applications in genome engineering. Nucleic Acids Res. 47, 11007–11019 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Barrangou, R. et al. CRISPR provides acquired resistance against viruses in prokaryotes. Science 315, 1709–1712 (2007).

    Article  ADS  CAS  PubMed  Google Scholar 

  3. Church, G. M., Gao, Y. & Kosuri, S. Next-generation digital information storage in DNA. Science 337, 1628–1628 (2012).

    Article  ADS  CAS  PubMed  Google Scholar 

  4. 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  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  5. Yim, S. S. et al. Robust direct digital-to-biological data storage in living cells. Nat. Chem. Biol. 17, 246–253 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ceze, L., Nivala, J. & Strauss, K. Molecular digital data storage using DNA. Nat. Rev. Genet. 20, 456–466 (2019).

    Article  CAS  PubMed  Google Scholar 

  7. 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  PubMed  Google Scholar 

  8. 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  ADS  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  ADS  CAS  PubMed  Google Scholar 

  10. Wagner, D. E. & Klein, A. M. Lineage tracing meets single-cell omics: opportunities and challenges. Nat. Rev. Genet. 21, 410–427 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Perli, S. D., Cui, C. H. & Lu, T. K. Continuous genetic recording with self-targeting CRISPR–Cas in human cells. Science 353, aag0511 (2016).

    Article  PubMed  Google Scholar 

  13. Park, J. et al. Recording of elapsed time and temporal information about biological events using Cas9. Cell 184, 1047–1063 (2021).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  15. Simon, A. J., Morrow, B. R. & Ellington, A. D. Retroelement-based genome editing and evolution. ACS Synth. Biol. 7, 2600–2611 (2018).

    Article  CAS  PubMed  Google Scholar 

  16. Sharon, E. et al. Functional genetic variants revealed by massively parallel precise genome editing. Cell 175, 544–557 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Schubert, M. G. et al. High-throughput functional variant screens via in vivo production of single-stranded DNA. Proc. Natl Acad. Sci. USA 118, e2018181118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Lopez, S. C., Crawford, K. D., Lear, S. K., Bhattarai-Kline, S. & Shipman, S. L. Precise genome editing across kingdoms of life using retron-derived DNA. Nat. Chem. Biol. 18, 199–206 (2022).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  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  PubMed  PubMed Central  Google Scholar 

  21. 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  PubMed  PubMed Central  Google Scholar 

  22. Wang, J. et al. Structural and mechanistic basis of PAM-dependent spacer acquisition in CRISPR–Cas systems. Cell 163, 840–853 (2015).

    Article  CAS  PubMed  Google Scholar 

  23. Millman, A. et al. Bacterial retrons function in anti-phage defense. Cell 183, 1551–1561 (2020).

    Article  CAS  PubMed  Google Scholar 

  24. Bobonis, J. et al. Bacterial retrons encode tripartite toxin/antitoxin systems. Preprint at bioRxiv https://doi.org/10.1101/2020.06.22.160168 (2020).

  25. Lampson, B. C. et al. Reverse transcriptase in a clinical strain of Escherichia coli: production of branched RNA-linked msDNA. Science 243, 1033–1038 (1989).

    Article  ADS  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  27. Bonnet, J., Subsoontorn, P. & Endy, D. Rewritable digital data storage in live cells via engineered control of recombination directionality. Proc. Natl Acad. Sci. USA 109, 8884–8889 (2012).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  28. Kim, S. et al. Selective loading and processing of prespacers for precise CRISPR adaptation. Nature 579, 141–145 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  29. Ramachandran, A., Summerville, L., Learn, B. A., DeBell, L. & Bailey, S. Processing and integration of functionally oriented prespacers in the Escherichia coli CRISPR system depends on bacterial host exonucleases. J. Biol. Chem. 295, 3403–3414 (2020).

    Article  CAS  PubMed  Google Scholar 

  30. Chapman, K. B. & Boeke, J. D. Isolation and characterization of the gene encoding yeast debranching enzyme. Cell 65, 483–492 (1991).

    Article  CAS  PubMed  Google Scholar 

  31. Lim, D. Structure and biosynthesis of unbranched multicopy single-stranded DNA by reverse transcriptase in a clinical Eschehchia coli isolate. Mol. Microbiol. 6, 3531–3542 (1992).

    Article  CAS  PubMed  Google Scholar 

  32. Jung, H., Liang, J., Jung, Y. & Lim, D. Characterization of cell death in Escherichia coli mediated by XseA, a large subunit of exonuclease VII. J. Microbiol. 53, 820–828 (2015).

    Article  CAS  PubMed  Google Scholar 

  33. Han, E. S. et al. RecJ exonuclease: substrates, products and interaction with SSB. Nucleic Acids Res. 34, 1084–1091 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Meyer, A. J., Segall-Shapiro, T. H., Glassey, E., Zhang, J. & Voigt, C. A. Escherichia coli “Marionette” strains with 12 highly optimized small-molecule sensors. Nat. Chem. Biol. 15, 196–204 (2019).

    Article  CAS  PubMed  Google Scholar 

  35. Grubbs, F. E. Procedures for detecting outlying observations in samples. Technometrics 11, 1–21 (1969).

    Article  Google Scholar 

  36. Stefansky, W. Rejecting outliers in factorial designs. Technometrics 14, 469–479 (1972).

    Article  MATH  Google Scholar 

  37. Hayflick, L. & Moorhead, P. S. The serial cultivation of human diploid cell strains. Exp. Cell. Res. 25, 585–621 (1961).

    Article  CAS  PubMed  Google Scholar 

  38. Yang, L. et al. Permanent genetic memory with >1-byte capacity. Nat. Methods 11, 1261–1266 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Yehl, K. & Lu, T. Scaling computation and memory in living cells. Curr. Opin. Biomed. Eng. 4, 143–151 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Mosberg, J. A., Gregg, C. J., Lajoie, M. J., Wang, H. H. & Church, G. M. Improving lambda Red genome engineering in Escherichia coli via rational removal of endogenous nucleases. PLoS ONE 7, e44638 (2012).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  41. Moore, S. D. In Strain Engineering: Methods and Protocols (ed. Williams, J. A.) 155–169 (Humana Press, 2011).

  42. Datsenko, K. A. & Wanner, B. L. One-step inactivation of chromosomal genes in Escherichia coli K-12 using PCR products. Proc. Natl Acad. Sci. USA 97, 6640–6645 (2000).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  43. Rogers, J. K. et al. Synthetic biosensors for precise gene control and real-time monitoring of metabolites. Nucleic Acids Res. 43, 7648–7660 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Work was supported by funding from the Simons Foundation Autism Research Initiative (SFARI) Bridge to Independence Award Program, the Pew Biomedical Scholars Program, the NIH/NIGMS (1DP2GM140917-01) and the UCSF Program for Breakthrough Biomedical Research. S.L.S. is a Chan Zuckerberg Biohub investigator and acknowledges additional funding support from the L.K. Whittier Foundation. S.K.L. was supported by an NSF Graduate Research Fellowship (2034836). S.C.L. was supported by a Berkeley Fellowship for Graduate Study. We thank K. Claiborn for editorial assistance.

Author information

Authors and Affiliations

  1. Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, USA

    Santi Bhattarai-Kline, Sierra K. Lear, Chloe B. Fishman, Santiago C. Lopez & Seth L. Shipman

  2. Graduate Program in Bioengineering, University of California, San Francisco and Berkeley, San Francisco, CA, USA

    Sierra K. Lear & Santiago C. Lopez

  3. Department of Neurobiology, Duke University Medical Center, Durham, NC, USA

    Elana R. Lockshin

  4. Department of Genetics, Harvard Medical School, Boston, MA, USA

    Max G. Schubert & George M. Church

  5. Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA

    Max G. Schubert & George M. Church

  6. Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA

    Jeff Nivala

  7. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA

    Seth L. Shipman

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  1. Santi Bhattarai-Kline
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Contributions

S.L.S. conceived the study with J.N. and G.M.C. contributing. S.B.-K. and S.L.S. designed experiments and analysed all data. Contributions to data collection were made by S.K.L. (Extended Data Fig. 4), C.B.F. (Extended Data Fig. 5) and S.L.S. (Figs. 1c,e and 3b–e). S.B.-K. collected all other data. E.R.L., M.G.S. and S.C.L. performed preliminary experiments not included in the figures. S.B.-K. performed the computational modelling of recordings. S.B.-K. wrote the manuscript with input from all co-authors.

Corresponding author

Correspondence to Seth L. Shipman.

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Competing interests

S.L.S., G.M.C., M.G.S. and J.N. are named inventors on a patent application assigned to Harvard College, “Method of recording multiplexed biological information into a CRISPR array using a retron” (US20200115706A1).

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Nature thanks Channabasavaiah Gurumurthy and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Accompaniment to Figure 1.

a, Hypothetical Eco1 wild-type ncRNA-linked RT-DNA structure. b, Hypothetical Eco1 v32 ncRNA-linked RT-DNA structure and hypothetical duplexed RT-DNA prespacer structure. Nucleotides that are altered from wild-type Eco1 are shown in orange. c, Hypothetical Eco1 v35 ncRNA-linked RT-DNA structure and hypothetical duplexed RT-DNA prespacer structure. Nucleotides that are altered from wild-type Eco1 are shown in green.

Extended Data Fig. 2 Accompaniment to Figure 2.

Hypothetical barcoded Eco1 v35 ncRNA-linked RT-DNA structure and hypothetical duplexed RT-DNA prespacer structure. Bases used to barcode retrons are shown in red.

Extended Data Fig. 3 Accompaniment to Figure 3.

a, Hypothetical wild-type Eco4 ncRNA-linked RT-DNA structure. ExoVII-dependent RT-DNA cleavage site is shown as a red slash. b, Eco4-derived spacer sequences and orientations. Bases are coloured to match Figure 3f. c, Proportion of Eco4-derived spacers in each orientation. Open circles are individual biological replicates.

Extended Data Fig. 4 Change in YFP fluorescence when expressed using inducible promoters.

The y-axis shows fluorescence (in arbitrary units) normalized to culture density (OD600).

Extended Data Fig. 5 Growth curves (upper plot) and max growth rates (lower plot) of E. coli with different combinations of retron recording components and inducers.

In growth curve plots the solid line is the mean OD600 of three biological replicates, with dotted lines showing the standard deviation. In maximum growth rate plots, each symbol is a single biological replicate. Bars show the mean and standard deviation. Statistically significant differences in maximum growth rate, as calculated by Tukey’s multiple comparison’s test, are highlighted. a, Growth kinetics of E. coli with different combinations of retron recording plasmids, all without inducers. b, Growth kinetics of E. coli with recording plasmid pSBK.079, with and without inducers. c, Growth kinetics of E. coli with signal plasmid pSBK.134, with and without inducers. Only one biological replicate is present in condition 'pSBK.134 + aTc' (pink). d, Growth kinetics of E. coli with signal plasmid pSBK.136, with and without inducers. e, Growth kinetics of E. coli with signal plasmid pSBK.134 and recording plasmid pSBK.079, with and without inducers. f, Growth kinetics of E. coli with signal plasmid pSBK.136 and recording plasmid pSBK.079, with and without inducers.

Extended Data Fig. 6 Accompaniment to Figure 4.

a, Ordering rules for pSBK.134 A-before-B replicates. The scores for each rule, and the composite score, are shown for each individual replicate. X-containing boxes indicate that no informative arrays, for that particular rule, were present in that replicate. b, As in panel a, ordering rules for pSBK.134 B-before-A replicates. c, As in panel a, ordering rules for pSBK.136 A-before-B replicates. d, As in panel a, ordering rules for pSBK.136 B-before-A replicates.

Extended Data Fig. 7 Long-term stability of retron-derived recordings in CRISPR arrays.

a, Ordering rules for 24+24-h, A-before-B recordings during post-recording multiday culture. Individual and composite scores are shown for samples taken on days 0, 2, 5, and 9 of culture. Each open circle represents the score, for that rule, from a single biological replicate. A total of 3 biological replicates are shown here. b, Changes in ordering rule scores over time in biological replicate 1. c, Changes in ordering rule scores over time in biological replicate 2. d, Changes in ordering rule scores over time in biological replicate 3.

Supplementary information

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This file contains Supplementary Fig. 1 and Supplementary Tables 1–4.

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Bhattarai-Kline, S., Lear, S.K., Fishman, C.B. et al. Recording gene expression order in DNA by CRISPR addition of retron barcodes. Nature 608, 217–225 (2022). https://doi.org/10.1038/s41586-022-04994-6

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