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DNA-based memory devices for recording cellular events

Abstract

Measuring biological data across time and space is critical for understanding complex biological processes and for various biosurveillance applications. However, such data are often inaccessible or difficult to directly obtain. Less invasive, more robust and higher-throughput biological recording tools are needed to profile cells and their environments. DNA-based cellular recording is an emerging and powerful framework for tracking intracellular and extracellular biological events over time across living cells and populations. Here, we review and assess DNA recorders that utilize CRISPR nucleases, integrases and base-editing strategies, as well as recombinase and polymerase-based methods. Quantitative characterization, modelling and evaluation of these DNA-recording modalities can guide their design and implementation for specific application areas.

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Fig. 1: Components of cellular memory.
Fig. 2: Examples of DNA-recording devices.
Fig. 3: Applications of DNA-based biological recorders.

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References

  1. Antebi, Y. E., Nandagopal, N. & Elowitz, M. B. An operational view of intercellular signaling pathways. Curr. Opin. Syst. Biol. 1, 16–24 (2017).

    PubMed  PubMed Central  Google Scholar 

  2. Masel, J. & Siegal, M. L. Robustness: mechanisms and consequences. Trends Genet. 25, 395–403 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Purvis, J. E. & Lahav, G. Encoding and decoding cellular information through signaling dynamics. Cell 152, 945–956 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  5. Goldman, N. et al. Towards practical, high-capacity, low-maintenance information storage in synthesized DNA. Nature 494, 77–80 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Erlich, Y. & Zielinski, D. DNA Fountain enables a robust and efficient storage architecture. Science 355, 950–954 (2017).

    CAS  PubMed  Google Scholar 

  7. Grass, R. N., Heckel, R., Puddu, M., Paunescu, D. & Stark, W. J. Robust chemical preservation of digital information on DNA in silica with error-correcting codes. Angew. Chem. Int. Ed. 54, 2552–2555 (2015).

    CAS  Google Scholar 

  8. van der Woude, M. W. & Baumler, A. J. Phase and antigenic variation in bacteria. Clin. Microbiol. Rev. 17, 581–611 (2004).

    PubMed  PubMed Central  Google Scholar 

  9. Marraffini, L. A. CRISPR-Cas immunity in prokaryotes. Nature 526, 55–61 (2015).

    CAS  PubMed  Google Scholar 

  10. Nemazee, D. Receptor editing in lymphocyte development and central tolerance. Nat. Rev. Immunol. 6, 728–740 (2006).

    CAS  PubMed  Google Scholar 

  11. Medhekar, B. & Miller, J. F. Diversity-generating retroelements. Curr. Opin. Microbiol. 10, 388–395 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Haselkorn, R. Developmentally regulated gene rearrangements in prokaryotes. Annu. Rev. Genet. 26, 113–130 (1992).

    CAS  PubMed  Google Scholar 

  13. Nowacki, M., Shetty, K. & Landweber, L. F. RNA-mediated epigenetic programming of genome rearrangements. Annu. Rev. Genomics Hum. Genet. 12, 367–389 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Shendure, J. et al. DNA sequencing at 40: past, present and future. Nature 550, 345–353 (2017).

    CAS  PubMed  Google Scholar 

  15. Kosuri, S. & Church, G. M. Large-scale de novo DNA synthesis: technologies and applications. Nat. Methods 11, 499–507 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Keung, A. J., Joung, J. K., Khalil, A. S. & Collins, J. J. Chromatin regulation at the frontier of synthetic biology. Nat. Rev. Genet. 16, 159–171 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Burrill, D. R. & Silver, P. A. Making cellular memories. Cell 140, 13–18 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Newby, G. A. et al. A genetic tool to track protein aggregates and control prion inheritance. Cell 171, 966–979 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Woodworth, M. B., Girskis, K. M. & Walsh, C. A. Building a lineage from single cells: genetic techniques for cell lineage tracking. Nat. Rev. Genet. 18, 230–244 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Camilli, A. & Mekalanos, J. J. Use of recombinase gene fusions to identify Vibrio cholerae genes induced during infection. Mol. Microbiol. 18, 671–683 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Ceroni, F. et al. Burden-driven feedback control of gene expression. Nat. Methods 15, 387–393 (2018).

    CAS  PubMed  Google Scholar 

  22. Roybal, K. T. et al. Engineering T cells with customized therapeutic response programs using synthetic Notch receptors. Cell 167, 419–432 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Ostrov, N. et al. A modular yeast biosensor for low-cost point-of-care pathogen detection. Sci. Adv. 3, e1603221 (2017).

    PubMed  PubMed Central  Google Scholar 

  24. Taylor, N. D. et al. Engineering an allosteric transcription factor to respond to new ligands. Nat. Methods 13, 177–183 (2016).

    CAS  PubMed  Google Scholar 

  25. Schmidl, S. R., Sheth, R. U., Wu, A. & Tabor, J. J. Refactoring and optimization of light-switchable Escherichia coli two-component systems. ACS Synth. Biol. 3, 820–831 (2014).

    CAS  PubMed  Google Scholar 

  26. Stock, A. M., Robinson, V. L. & Goudreau, P. N. Two-component signal transduction. Annu. Rev. Biochem. 69, 183–215 (2000).

    CAS  PubMed  Google Scholar 

  27. Lim, W. A. Designing customized cell signalling circuits. Nat. Rev. Mol. Cell Biol. 11, 393–403 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Isaacs, F. J., Dwyer, D. J. & Collins, J. J. RNA synthetic biology. Nat. Biotechnol. 24, 545–554 (2006).

    CAS  PubMed  Google Scholar 

  29. Green, A. A., Silver, P. A., Collins, J. J. & Yin, P. Toehold switches: de-novo-designed regulators of gene expression. Cell 159, 925–939 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Wroblewska, L. et al. Mammalian synthetic circuits with RNA binding proteins for RNA-only delivery. Nat. Biotechnol. 33, 839–841 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 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). By utilizing a copy-number-inducible plasmid, the CRISPR–Cas integrase system is utilized to record and reconstruct temporally changing biological signals.

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Landry, B. P., Palanki, R., Dyulgyarov, N., Hartsough, L. A. & Tabor, J. J. Phosphatase activity tunes two-component system sensor detection threshold. Nat. Commun. 9, 1433 (2018).

    PubMed  PubMed Central  Google Scholar 

  33. Brophy, J. A. & Voigt, C. A. Principles of genetic circuit design. Nat. Methods 11, 508–520 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Daniel, R., Rubens, J. R., Sarpeshkar, R. & Lu, T. K. Synthetic analog computation in living cells. Nature 497, 619–623 (2013).

    CAS  PubMed  Google Scholar 

  35. Rubens, J. R., Selvaggio, G. & Lu, T. K. Synthetic mixed-signal computation in living cells. Nat. Commun. 7, 11658 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Bashor, C. J., Helman, N. C., Yan, S. & Lim, W. A. Using engineered scaffold interactions to reshape MAP kinase pathway signaling dynamics. Science 319, 1539–1543 (2008).

    CAS  PubMed  Google Scholar 

  37. Liu, Y. et al. Directing cellular information flow via CRISPR signal conductors. Nat. Methods 13, 938–944 (2016).

    CAS  PubMed  Google Scholar 

  38. Nielsen, A. A. K. et al. Genetic circuit design automation. Science 352, aac7341 (2016).

    PubMed  Google Scholar 

  39. Olson, E. J. & Tabor, J. J. Post-translational tools expand the scope of synthetic biology. Curr. Opin. Chem. Biol. 16, 300–306 (2012).

    CAS  PubMed  Google Scholar 

  40. Stanton, B. Z., Chory, E. J. & Crabtree, G. R. Chemically induced proximity in biology and medicine. Science 359, eaao5902 (2018).

    PubMed  PubMed Central  Google Scholar 

  41. Deribe, Y. L., Pawson, T. & Dikic, I. Post-translational modifications in signal integration. Nat. Struct. Mol. Biol. 17, 666–672 (2010).

    CAS  PubMed  Google Scholar 

  42. Pham, T. M. et al. A single-molecule approach to DNA replication in Escherichia coli cells demonstrated that DNA polymerase III is a major determinant of fork speed. Mol. Microbiol. 90, 584–596 (2013).

    CAS  PubMed  Google Scholar 

  43. Doudna, J. A. & Charpentier, E. The new frontier of genome engineering with CRISPR-Cas9. Science 346, 1258096 (2014).

    PubMed  Google Scholar 

  44. Kim, H. & Kim, J.-S. A guide to genome engineering with programmable nucleases. Nat. Rev. Genet. 15, 321–334 (2014).

    CAS  PubMed  Google Scholar 

  45. Wirth, D. et al. Road to precision: recombinase-based targeting technologies for genome engineering. Curr. Opin. Biotechnol. 18, 411–419 (2007).

    CAS  PubMed  Google Scholar 

  46. Grindley, N. D. F., Whiteson, K. L. & Rice, P. A. Mechanisms of site-specific recombination. Annu. Rev. Biochem. 75, 567–605 (2006).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Mimee, M., Tucker, A. C., Voigt, C. A. & Lu, T. K. Programming a human commensal bacterium, Bacteroides thetaiotaomicron, to sense and respond to stimuli in the murine gut microbiota. Cell Syst. 1, 62–71 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Fernandez-Rodriguez, J., Yang, L., Gorochowski, T. E., Gordon, D. B. & Voigt, C. A. Memory and combinatorial logic based on DNA inversions: dynamics and evolutionary stability. ACS Synth. Biol. 4, 1361–1372 (2015).

    CAS  PubMed  Google Scholar 

  51. Friedland, A. E. et al. Synthetic gene networks that count. Science 324, 1199–1202 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Courbet, A., Endy, D., Renard, E., Molina, F. & Bonnet, J. Detection of pathological biomarkers in human clinical samples via amplifying genetic switches and logic gates. Sci. Transl Med. 7, 289ra83 (2015).

    PubMed  Google Scholar 

  53. Bonnet, J., Yin, P., Ortiz, M. E., Subsoontorn, P. & Endy, D. Amplifying genetic logic gates. Science 340, 599–603 (2013).

    CAS  PubMed  Google Scholar 

  54. Siuti, P., Yazbek, J. & Lu, T. K. Synthetic circuits integrating logic and memory in living cells. Nat. Biotechnol. 31, 448–452 (2013).

    CAS  PubMed  Google Scholar 

  55. 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). Recombinase-based genetic circuits are formalized in a computer science state machine framework, enabling the design of synthetic circuits that discriminate the ordering of chemical inputs.

    PubMed  Google Scholar 

  56. Hsiao, V., Hori, Y., Rothemund, P. W. & Murray, R. M. A population-based temporal logic gate for timing and recording chemical events. Mol. Syst. Biol. 12, 869–814 (2016).

    PubMed  PubMed Central  Google Scholar 

  57. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Farzadfard, F. & Lu, T. K. Genomically encoded analog memory with precise in vivo DNA writing in living cell populations. Science 346, 1256272 (2014). A framework for writing genomic addresses utilizing ssDNA recombination is demonstrated, enabling recording of input signal intensity and duration and interfacing with host responses in E. coli.

    PubMed  PubMed Central  Google Scholar 

  59. Tang, W. & Liu, D. R. Rewritable multi-event analog recording in bacterial and mammalian cells. Science 360, eaap8992 (2018). The authors develop base-editing approaches for cellular recording applications in both E. coli and mammalian cells.

    PubMed  PubMed Central  Google Scholar 

  60. Komor, A. C., Kim, Y. B., Packer, M. S., Zuris, J. A. & Liu, D. R. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature 533, 420–424 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Farzadfard, F. et al. Single-nucleotide-resolution computing and memory in living cells. Preprint at bioRxiv https://www.biorxiv.org/content/early/2018/02/16/263657 (2018).

  62. Gaudelli, N. M. et al. Programmable base editing of A·T to G·C in genomic DNA without DNA cleavage. Nature 551, 464–471 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–821 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Mali, P. et al. RNA-guided human genome engineering via Cas9. Science 339, 823–826 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Jinek, M. et al. RNA-programmed genome editing in human cells. eLife 2, e00471 (2013).

    PubMed  PubMed Central  Google Scholar 

  66. Cong, L. et al. Multiplex genome engineering using CRISPR/Cas systems. Science 339, 819–823 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Lieber, M. R. The mechanism of human nonhomologous DNA end joining. J. Biol. Chem. 283, 1–5 (2008).

    CAS  PubMed  Google Scholar 

  68. McKenna, A. et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016). Cas9-nuclease-based stochastic editing of target arrays is utilized to reconstruct the lineage of cells and zebrafish embryos.

    PubMed  PubMed Central  Google Scholar 

  69. Schmidt, S. T., Zimmerman, S. M., Wang, J., Kim, S. K. & Quake, S. R. Quantitative analysis of synthetic cell lineage tracing using nuclease barcoding. ACS Synth. Biol. 6, 936–942 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Frieda, K. L. et al. Synthetic recording and in situ readout of lineage information in single cells. Nature 541, 107–111 (2017). Cas9-nuclease-based stochastic editing of target arrays is combined with smFISH spatial readouts to reconstruct spatial lineage and could be applied to reconstruct spatiotemporal gene expression.

    CAS  PubMed  Google Scholar 

  71. Zetsche, B. et al. Cpf1 is a single RNA-guided endonuclease of a class 2 CRISPR-Cas system. Cell 163, 759–771 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Kim, Y. G., Cha, J. & Chandrasegaran, S. Hybrid restriction enzymes: zinc finger fusions to Fok I cleavage domain. Proc. Natl Acad. Sci. USA 93, 1156–1160 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Bibikova, M., Beumer, K., Trautman, J. K. & Carroll, D. Enhancing gene targeting with designed zinc finger nucleases. Science 300, 764 (2003).

    CAS  PubMed  Google Scholar 

  74. Miller, J. C. et al. An improved zinc-finger nuclease architecture for highly specific genome editing. Nat. Biotechnol. 25, 778–785 (2007).

    CAS  PubMed  Google Scholar 

  75. Boch, J. et al. Breaking the code of DNA binding specificity of TAL-type III effectors. Science 326, 1509–1512 (2009).

    CAS  PubMed  Google Scholar 

  76. Moscou, M. J. & Bogdanove, A. J. A simple cipher governs DNA recognition by TAL effectors. Science 326, 1501 (2009).

    CAS  PubMed  Google Scholar 

  77. Christian, M. et al. Targeting DNA double-strand breaks with TAL effector nucleases. Genetics 186, 757–761 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Kalhor, R., Mali, P. & Church, G. M. Rapidly evolving homing CRISPR barcodes. Nat. Methods 14, 195–200 (2016). The authors couple recursive editing of single-guide RNA sequences to an in situ sequencing readout for spatial lineage tracing applications.

    PubMed  PubMed Central  Google Scholar 

  79. Perli, S. D., Cui, C. H. & Lu, T. K. Continuous genetic recording with self-targeting CRISPR-Cas in human cells. Science 353, aag0511 (2016). The authors demonstrate recursive editing of single-guide RNA sequences, allowing for recording of signal intensity and duration in mammalian cells.

    PubMed  Google Scholar 

  80. Glaser, J. I. et al. Statistical analysis of molecular signal recording. PLOS Comput. Biol. 9, e1003145 (2013). The authors propose a statistical framework for temporal recording of ion concentration utilizing polymerase directional writing.

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  83. Jackson, S. A. et al. CRISPR-Cas: adapting to change. Science 356, eaal5056 (2017).

    PubMed  Google Scholar 

  84. Sternberg, S. H., Richter, H., Charpentier, E. & Qimron, U. Adaptation in CRISPR-Cas systems. Mol. Cell 61, 797–808 (2016).

    CAS  PubMed  Google Scholar 

  85. Shipman, S. L., Nivala, J., Macklis, J. D. & Church, G. M. Molecular recordings by directed CRISPR spacer acquisition. Science 353, aaf1175 (2016). In this work, the CRISPR–Cas integrase system is utilized to record the temporal ordering of oligonucleotide sequences electroporated into cell populations.

    PubMed  PubMed Central  Google Scholar 

  86. 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). CRISPR–Cas-integrase-based oligonucleotide recordings are scaled to store an animated frame in the genomes of living bacteria.

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Shur, A. & Murray, R. M. Proof of concept continuous event logging in living cells. Preprint at bioRxiv https://www.biorxiv.org/content/early/2018/03/08/225151 (2018).

  88. Kluesner, M. et al. EditR: a novel base editing quantification software using Sanger sequencing. Preprint at bioRxiv https://www.biorxiv.org/content/early/2017/11/05/213496 (2017).

  89. Bentley, D. R. et al. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456, 53–59 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Pei, W. et al. Polylox barcoding reveals haematopoietic stem cell fates realized in vivo. Nature 548, 456–460 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Quick, J. et al. Real-time, portable genome sequencing for Ebola surveillance. Nature 530, 228–232 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Gaudet, M., Fara, A.-G., Beritognolo, I. & Sabatti, M. Allele-specific PCR in SNP genotyping. Methods Mol. Biol. 578, 415–424 (2009).

    CAS  PubMed  Google Scholar 

  93. Didenko, V. V. DNA probes using fluorescence resonance energy transfer (FRET): designs and applications. Biotechniques 31, 1106–1116 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Lee, J.-H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Chen, X., Sun, Y.-C., Church, G. M., Lee, J.-H. & Zador, A. M. Efficient in situ barcode sequencing using padlock probe-based BaristaSeq. Nucleic Acids Res. 46, e22 (2018).

    PubMed  Google Scholar 

  96. Chen, F., Tillberg, P. W. & Boyden, E. S. Expansion microscopy. Science 347, 543–548 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Kunkel, T. A. & Bebenek, R. DNA replication fidelity. Annu. Rev. Biochem. 69, 497–529 (2000).

    CAS  PubMed  Google Scholar 

  98. Deveau, H. et al. Phage response to CRISPR-encoded resistance in Streptococcus thermophilus. J. Bacteriol. 190, 1390–1400 (2008).

    CAS  PubMed  Google Scholar 

  99. Gudbergsdottir, S. et al. Dynamic properties of the Sulfolobus CRISPR/Cas and CRISPR/Cmr systems when challenged with vector-borne viral and plasmid genes and protospacers. Mol. Microbiol. 79, 35–49 (2010).

    PubMed  Google Scholar 

  100. Weller, G. R. et al. Identification of a DNA nonhomologous end-joining complex in bacteria. Science 297, 1686–1689 (2002).

    CAS  PubMed  Google Scholar 

  101. Pitcher, R. S., Wilson, T. E. & Doherty, A. J. New insights into NHEJ repair processes in prokaryotes. Cell Cycle 4, 675–678 (2005).

    CAS  PubMed  Google Scholar 

  102. Nuñez, J. K., Bai, L., Harrington, L. B., Hinder, T. L. & Doudna, J. A. CRISPR immunological memory requires a host factor for specificity. Mol. Cell 62, 824–833 (2016).

    PubMed  Google Scholar 

  103. Pattanayak, V. et al. High-throughput profiling of off-target DNA cleavage reveals RNA-programmed Cas9 nuclease specificity. Nat. Biotechnol. 31, 839–837 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Fu, Y. et al. High-frequency off-target mutagenesis induced by CRISPR-Cas nucleases in human cells. Nat. Biotechnol. 31, 822–826 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  105. Nivala, J., Shipman, S. L. & Church, G. M. Spontaneous CRISPR loci generation in vivo by non-canonical spacer integration. Nat. Microbiol. 3, 310–318 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  106. Raj, B. et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol. 40, 181–115 (2018).

    Google Scholar 

  107. Alemany, A., Florescu, M., Baron, C. S., Peterson-Maduro, J. & van Oudenaarden, A. Whole-organism clone tracing using single-cell sequencing. Nature 556, 108–112 (2018).

    CAS  PubMed  Google Scholar 

  108. Spanjaard, B. et al. Simultaneous lineage tracing and cell-type identification using CRISPR–Cas9-induced genetic scars. Nat. Biotechnol. 36, 469–473 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Sender, R., Fuchs, S. & Milo, R. Revised estimates for the number of human and bacteria cells in the body. PLOS Biol. 14, e1002533 (2016).

    PubMed  PubMed Central  Google Scholar 

  110. Abel, S. et al. Sequence tag–based analysis of microbial population dynamics. Nat. Methods 12, 223–226 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Nicholson, J. K. et al. Host-gut microbiota metabolic interactions. Science 336, 1262–1267 (2012).

    CAS  PubMed  Google Scholar 

  112. Smillie, C. S. et al. Ecology drives a global network of gene exchange connecting the human microbiome. Nature 480, 241–244 (2011).

    CAS  PubMed  Google Scholar 

  113. Kording, K. P. Of toasters and molecular ticker tapes. PLOS Comput. Biol. 7, e1002291 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Marblestone, A. H. et al. Physical principles for scalable neural recording. Front. Comput. Neurosci. 7, 137 (2013).

    PubMed  PubMed Central  Google Scholar 

  115. Lim, W. A. & June, C. H. The principles of engineering immune cells to treat cancer. Cell 168, 724–740 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Eldar, A. & Elowitz, M. B. Functional roles for noise in genetic circuits. Nature 467, 167–173 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  117. Balázsi, G., van Oudenaarden, A. & Collins, J. J. Cellular decision making and biological noise: from microbes to mammals. Cell 144, 910–925 (2011).

    PubMed  PubMed Central  Google Scholar 

  118. Fisher, R. A., Gollan, B. & Helaine, S. Persistent bacterial infections and persister cells. Nat. Rev. Microbiol. 15, 453–464 (2017).

    CAS  PubMed  Google Scholar 

  119. Leonard, S. P. et al. Genetic engineering of bee gut microbiome bacteria with a toolkit for modular assembly of broad-host-range plasmids. ACS Synth. Biol. 7, 1279–1290 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. Gupta, S., Bram, E. E. & Weiss, R. Genetically programmable pathogen sense and destroy. ACS Synth. Biol. 2, 715–723 (2013).

    CAS  PubMed  Google Scholar 

  121. Hwang, I. Y. et al. Reprogramming microbes to be pathogen-seeking killers. ACS Synth. Biol. 3, 228–237 (2014).

    CAS  PubMed  Google Scholar 

  122. Tauriainen, S., Karp, M., Chang, W. & Virta, M. Luminescent bacterial sensor for cadmium and lead. Biosens. Bioelectron. 13, 931–938 (1998).

    CAS  PubMed  Google Scholar 

  123. Stocker, J. et al. Development of a set of simple bacterial biosensors for quantitative and rapid measurements of arsenite and arsenate in potable water. Environ. Sci. Technol. 37, 4743–4750 (2003).

    CAS  PubMed  Google Scholar 

  124. Antunes, M. S. et al. Programmable ligand detection system in plants through a synthetic signal transduction pathway. PLOS ONE 6, e16292 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. Belkin, S. et al. Remote detection of buried landmines using a bacterial sensor. Nat. Biotechnol. 35, 308–310 (2017).

    CAS  PubMed  Google Scholar 

  126. Gooch, J., Daniel, B., Abbate, V. & Frascione, N. Taggant materials in forensic science: a review. Trends Analyt. Chem. 83, 49–54 (2016).

    CAS  Google Scholar 

  127. Hwang, I. Y. et al. Engineered probiotic Escherichia coli can eliminate and prevent Pseudomonas aeruginosa gut infection in animal models. Nat. Commun. 8, 15028 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  128. Danino, T. et al. Programmable probiotics for detection of cancer in urine. Sci. Transl Med. 7, 289ra84 (2015).

    PubMed  PubMed Central  Google Scholar 

  129. Daeffler, K. N. M. et al. Engineering bacterial thiosulfate and tetrathionate sensors for detecting gut inflammation. Mol. Systems Biol. 13, 923 (2017).

    Google Scholar 

  130. Riglar, D. T. et al. Engineered bacteria can function in the mammalian gut long-term as live diagnostics of inflammation. Nat. Biotechnol. 35, 653–658 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  131. Landry, B. P. & Tabor, J. J. Engineering diagnostic and therapeutic gut bacteria. Microbiol. Spectr. https://doi.org/10.1128/microbiolspec.BAD-0020-2017 (2017).

    Article  PubMed  Google Scholar 

  132. Riglar, D. T. & Silver, P. A. Engineering bacteria for diagnostic and therapeutic applications. Nat. Rev. Microbiol. 16, 214–225 (2018).

    CAS  PubMed  Google Scholar 

  133. Din, M. O. et al. Synchronized cycles of bacterial lysis for in vivo delivery. Nature 536, 81–85 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. Tschirhart, T. et al. Electronic control of gene expression and cell behaviour in Escherichia coli through redox signalling. Nat. Commun. 8, 14030 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  135. Ghadessy, F. J. et al. Generic expansion of the substrate spectrum of a DNA polymerase by directed evolution. Nat. Biotechnol. 22, 755–759 (2004).

    CAS  PubMed  Google Scholar 

  136. Heler, R. et al. Mutations in Cas9 enhance the rate of acquisition of viral spacer sequences during the CRISPR-Cas immune response. Mol. Cell 64, 168–175 (2016).

    Google Scholar 

  137. Hu, J. H. et al. Evolved Cas9 variants with broad PAM compatibility and high DNA specificity. Nature 556, 57–63 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  138. Kleinstiver, B. P. et al. Engineered CRISPR-Cas9 nucleases with altered PAM specificities. Nature 523, 481–485 (2015).

    PubMed  PubMed Central  Google Scholar 

  139. Kleinstiver, B. P. et al. High-fidelity CRISPR–Cas9 nucleases with no detectable genome-wide off-target effects. Nature 529, 490–495 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  141. Clark, J. M. Novel non-templated nucleotide addition reactions catalyzed by procaryotic and eucaryotic DNA polymerases. Nucleic Acids Res. 16, 9677–9686 (1988).

    CAS  PubMed  PubMed Central  Google Scholar 

  142. Zyrina, N. V., Antipova, V. N. & Zheleznaya, L. A. Ab initiosynthesis by DNA polymerases. FEMS Microbiol. Lett. 351, 1–6 (2014).

    CAS  Google Scholar 

  143. Lee, H. H. et al. Enzymatic DNA synthesis for digital information storage. Preprint at bioRxiv https://www.biorxiv.org/content/early/2018/06/16/348987 (2018).

  144. Palluk, S. et al. De novo DNA synthesis using polymerase-nucleotide conjugates. Nat. Biotechnol. 36, 645–650 (2018).

    CAS  PubMed  Google Scholar 

  145. Zhang, Y. et al. A semi-synthetic organism that stores and retrieves increased genetic information. Nature 551, 644–647 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors apologize to colleagues whose work could not be cited owing to space limitations. H.H.W. acknowledges funding from the US National Institutes of Health (1R01AI132403-01), the US Office of Naval Research (N00014-17-1-2353, N00014-15-1-2704), the US National Science Foundation (NSF; MCB-1453219) and the Burroughs Wellcome Fund Pathogenesis of Infectious Disease (PATH; 1016691). R.U.S. is supported by a Fannie and John Hertz Foundation Fellowship and an NSF Graduate Research Fellowship (DGE-11-44155).

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Nature Reviews Genetics thanks T. Fulga, Y. Liu and Y. Michaels for their contribution to the peer review of this work.

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Glossary

Cas1–Cas2 CRISPR integrase

Conserved machinery in CRISPR immune systems mediating integration of short spacers from intracellular DNA sources into genomic arrays in a directional manner.

Site-specific recombinase systems

Systems composed of a recombinase enzyme and flanking target recognition sites around a target sequence. These systems enable inversion, excision or integration of the target sequence on the basis of the orientation of recognition sites.

Recombinase state machine

(RSM). A fixed-address writer encompassing a formalized architecture of genetic programmes created from combinations of three orthogonal recombinase systems.

Synthetic cellular recorder integrating biological events

(SCRIBE). A single-stranded DNA (ssDNA)-recombination-based flexible writing approach.

Retron

A bacterial reverse transcriptase system that produces a molecule that is a hybrid of RNA and single-stranded DNA (ssDNA) called multicopy ssDNA (msDNA).

mSCRIBE

(mammalian SCRIBE). A Cas9-nuclease-based stochastic writing approach.

CRISPR-mediated analog multi-event recording apparatus

(CAMERA). A base-editing-based flexible writing approach.

Base editing

A Cas9-based genome engineering approach in which a catalytically dead Cas9 (dCas9) with no nuclease activity is linked to a deaminase (dCas9-BE), enabling single-base-pair genomic mutation at desired locations.

Catalytically dead Cas9

(dCas9). A modified version of Cas9 that lacks endonuclease activity via engineered point mutations. It can be linked to other effector domains for diverse sequence-specific genome engineering applications.

Cas9

CRISPR-associated protein 9; a genome engineering nuclease tool enabling cleavage of desired genomic sites specified by a single-guide RNA (sgRNA).

Non-homologous end joining

(NHEJ). An endogenous pathway enabling repair of double-strand breaks (DSBs).

Self-targeting gRNA

(stgRNA). A single-guide RNA (sgRNA) that is targeted to its own sequence, which enables stochastic sequence evolution over time.

Directional writers

DNA writing relying on directional addition of single or multiple base pairs.

DNA polymerase

A type of enzyme that replicates DNA polymers on the basis of an existing template DNA by serial addition of individual nucleotides.

Temporal recording in arrays by CRISPR expansion

(TRACE). A Cas1–Cas2-based CRISPR spacer acquisition system to record biological signals over time.

Fluorescence resonance energy transfer

(FRET). A biochemical mechanism of energy transfer between two chromophores that can be utilized for sequence-specific DNA detection applications.

Memory by engineered mutagenesis with optical in situ readout

(MEMOIR). A Cas9-nuclease-based stochastic writing approach with spatial readout by single-molecule RNA fluorescence in situ hybridization (smFISH).

Genome editing of synthetic target arrays for lineage tracing

(GESTALT). A Cas9-nuclease-based stochastic writing approach enabling large-scale lineage tracing applications.

Terminal deoxynucleotidyl transferases

(TdTs). DNA polymerases that can add nucleotides to DNA without a template.

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Sheth, R.U., Wang, H.H. DNA-based memory devices for recording cellular events. Nat Rev Genet 19, 718–732 (2018). https://doi.org/10.1038/s41576-018-0052-8

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