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Cellular barcoding: lineage tracing, screening and beyond

Nature Methodsvolume 15pages871879 (2018) | Download Citation


Cellular barcoding is a technique in which individual cells are labeled with unique nucleic acid sequences, termed barcodes, so that they can be tracked through space and time. Cellular barcoding can be used to track millions of cells in parallel, and thus is an efficient approach for investigating heterogeneous populations of cells. Over the past 25 years, cellular barcoding has been used for fate mapping, lineage tracing and high-throughput screening, and has led to important insights into developmental biology and gene function. Driven by plummeting sequencing costs and the power of synthetic biology, barcoding is now expanding beyond traditional applications and into diverse fields such as neuroanatomy and the recording of cellular activity. In this review, we discuss the fundamental principles of cellular barcoding, including the underlying mathematics, and its applications in both new and established fields.

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  1. 1.

    McKenna, A. et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016). Development and application of CRISPR–Cas9-generated evolving barcodes for lineage tracing in zebrafish. See also refs. 2–4.

  2. 2.

    Kalhor, R., Mali, P. & Church, G. M. Rapidly evolving homing CRISPR barcodes. Nat. Methods 14, 195–200 (2017).

  3. 3.

    Junker, J. P. et al. Massively parallel clonal analysis using CRISPR/Cas9 induced genetic scars. bioRxiv Preprint at (2017).

  4. 4.

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

  5. 5.

    Zador, A. M. et al. Sequencing the connectome. PLoS Biol. 10, e1001411 (2012).

  6. 6.

    Peikon, I. D., Gizatullina, D. I. & Zador, A. M. In vivo generation of DNA sequence diversity for cellular barcoding. Nucleic Acids Res. 42, e127 (2014).

  7. 7.

    Frumkin, D., Wasserstrom, A., Kaplan, S., Feige, U. & Shapiro, E. Genomic variability within an organism exposes its cell lineage tree. PLoS Comput. Biol. 1, e50 (2005).

  8. 8.

    Walsh, C. & Cepko, C. L. Widespread dispersion of neuronal clones across functional regions of the cerebral cortex. Science 255, 434–440 (1992). First use of barcodes to track cells.

  9. 9.

    Schepers, K. et al. Dissecting T cell lineage relationships by cellular barcoding. J. Exp. Med. 205, 2309–2318 (2008).

  10. 10.

    Levy, S. F. et al. Quantitative evolutionary dynamics using high-resolution lineage tracking. Nature 519, 181–186 (2015).

  11. 11.

    Perli, S. D., Cui, C. H. & Lu, T. K. Continuous genetic recording with self-targeting CRISPR-Cas in human cells. Science 353, aag0511 (2016). Use of barcode evolution to record the duration and intensity of stimuli.

  12. 12.

    Chruch, G. & Shendure, J. Nucleic acid memory device. US patent application US20030228611A1 (2003).

  13. 13.

    Kebschull, J. M. et al. High-throughput mapping of single-neuron projections by sequencing of barcoded RNA. Neuron 91, 975–987 (2016). Use of barcodes to map axonal projections at single-cell resolution.

  14. 14.

    Han, Y. et al. The logic of single-cell projections from visual cortex. Nature 556, 51–56 (2018).

  15. 15.

    Peikon, I. D. et al. Using high-throughput barcode sequencing to efficiently map connectomes. Nucleic Acids Res. 45, e115 (2017).

  16. 16.

    Kinde, I., Wu, J., Papadopoulos, N., Kinzler, K. W. & Vogelstein, B. Detection and quantification of rare mutations with massively parallel sequencing. Proc. Natl. Acad. Sci. USA 108, 9530–9535 (2011).

  17. 17.

    Shiroguchi, K., Jia, T. Z., Sims, P. A. & Xie, X. S. Digital RNA sequencing minimizes sequence-dependent bias and amplification noise with optimized single-molecule barcodes. Proc. Natl. Acad. Sci. USA 109, 1347–1352 (2012).

  18. 18.

    Kivioja, T. et al. Counting absolute numbers of molecules using unique molecular identifiers. Nat. Methods 9, 72–74 (2011).

  19. 19.

    Fu, G. K., Hu, J., Wang, P. H. & Fodor, S. P. A. Counting individual DNA molecules by the stochastic attachment of diverse labels. Proc. Natl. Acad. Sci. USA 108, 9026–9031 (2011).

  20. 20.

    Casbon, J. A., Osborne, R. J., Brenner, S. & Lichtenstein, C. P. A method for counting PCR template molecules with application to next-generation sequencing. Nucleic Acids Res. 39, e81 (2011).

  21. 21.

    Miner, B. E., Stöger, R. J., Burden, A. F., Laird, C. D. & Hansen, R. S. Molecular barcodes detect redundancy and contamination in hairpin-bisulfite PCR. Nucleic Acids Res. 32, e135 (2004).

  22. 22.

    Brenner, S. & Macevicz, S. C. Molecular counting. WO patent application WO2007087312A3 (2007).

  23. 23.

    Brenner, S. Simultaneous sequencing of tagged polynucleotides. US patent US5763175A (1995).

  24. 24.

    Craig, D. W. et al. Identification of genetic variants using bar-coded multiplexed sequencing. Nat. Methods 5, 887–893 (2008).

  25. 25.

    Cusanovich, D. A. et al. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).

  26. 26.

    Valentini, A., Pompanon, F. & Taberlet, P. DNA barcoding for ecologists. Trends Ecol. Evol. 24, 110–117 (2009).

  27. 27.

    Naik, S. H. et al. Diverse and heritable lineage imprinting of early haematopoietic progenitors. Nature 496, 229–232 (2013).

  28. 28.

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

  29. 29.

    Winzeler, E. A. Functional characterization of the S. cerevisiae genome by gene deletion and parallel analysis. Science 285, 901–906 (1999).

  30. 30.

    Giaever, G. et al. Functional profiling of the Saccharomyces cerevisiae genome. Nature 418, 387–391 (2002).

  31. 31.

    Yu, C. et al. High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines. Nat. Biotechnol. 34, 419–423 (2016).

  32. 32.

    Oyibo, H. et al. A computational framework for converting high-throughput DNA sequencing data into neural circuit connectivity. bioRxiv Preprint at (2018).

  33. 33.

    Wagner, D. E. et al. Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo. Science 360, 981–987 (2018).

  34. 34.

    Loulier, K. et al. Multiplex cell and lineage tracking with combinatorial labels. Neuron 81, 505–520 (2014).

  35. 35.

    Bhang, H. E. et al. Studying clonal dynamics in response to cancer therapy using high-complexity barcoding. Nat. Med. 21, 440–448 (2015).

  36. 36.

    Livet, J. et al. Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system. Nature 450, 56–62 (2007).

  37. 37.

    Cai, D., Cohen, K. B., Luo, T., Lichtman, J. W. & Sanes, J. R. Improved tools for the Brainbow toolbox. Nat. Methods 10, 540–547 (2013).

  38. 38.

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

  39. 39.

    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). Combination of evolving Cas9-generated barcodes and single-cell sequencing to read out both lineage and single-cell transcriptional states of individual cells. See also refs. 40,54.

  40. 40.

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

  41. 41.

    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).

  42. 42.

    Kalhor, R. et al. A homing CRISPR mouse resource for barcoding and lineage tracing. bioRxiv Preprint at (2018).

  43. 43.

    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).

  44. 44.

    van Heijst, J. W. J. et al. Recruitment of antigen-specific CD8+ T cells in response to infection is markedly efficient. Science 325, 1265–1269 (2009).

  45. 45.

    Gerrits, A. et al. Cellular barcoding tool for clonal analysis in the hematopoietic system. Blood 115, 2610–2618 (2010).

  46. 46.

    Golden, J. A., Fields-Berry, S. C. & Cepko, C. L. Construction and characterization of a highly complex retroviral library for lineage analysis. Proc. Natl. Acad. Sci. USA 92, 5704–5708 (1995).

  47. 47.

    Lu, R., Neff, N. F., Quake, S. R. & Weissman, I. L. Tracking single hematopoietic stem cells in vivo using high-throughput sequencing in conjunction with viral genetic barcoding. Nat. Biotechnol. 29, 928–933 (2011). First use of high-throughput sequencing for reading out cellular barcodes in the hematopoietic lineage.

  48. 48.

    Dixit, A. et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866 (2016).

  49. 49.

    Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882 (2016).

  50. 50.

    Jaitin, D. A. et al. dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167, 1883–1896 (2016).

  51. 51.

    Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).

  52. 52.

    Xie, S., Duan, J., Li, B., Zhou, P. & Hon, G. C. Multiplexed engineering and analysis of combinatorial enhancer activity in single cells. Mol. Cell 66, 285–299 (2017).

  53. 53.

    Hill, A. J. et al. On the design of CRISPR-based single-cell molecular screens. Nat. Methods 15, 271–274 (2018).

  54. 54.

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

  55. 55.

    Klingler, E. et al. Single-cell molecular connectomics of intracortically-projecting neurons. bioRxiv Preprint at (2018).

  56. 56.

    Lubeck, E., Coskun, A. F., Zhiyentayev, T., Ahmad, M. & Cai, L. Single-cell in situ RNA profiling by sequential hybridization. Nat. Methods 11, 360–361 (2014).

  57. 57.

    Emanuel, G., Moffitt, J. R. & Zhuang, X. High-throughput, image-based screening of pooled genetic-variant libraries. Nat. Methods 14, 1159–1162 (2017).

  58. 58.

    Lawson, M. J. et al. In situ genotyping of a pooled strain library after characterizing complex phenotypes. Mol. Syst. Biol. 13, 947 (2017).

  59. 59.

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

  60. 60.

    Ke, R. et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods 10, 857–860 (2013).

  61. 61.

    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).

  62. 62.

    Nilsson, M. et al. Padlock probes: circularizing oligonucleotides for localized DNA detection. Science 265, 2085–2088 (1994).

  63. 63.

    Schirmer, M. et al. Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform. Nucleic Acids Res. 43, e37 (2015).

  64. 64.

    Kebschull, J. M. & Zador, A. M. Sources of PCR-induced distortions in high-throughput sequencing data sets. Nucleic Acids Res. 43, e143 (2015).

  65. 65.

    Potapov, V. & Ong, J. L. Examining sources of error in PCR by single-molecule sequencing. PLoS One 12, e0169774 (2017).

  66. 66.

    Pääbo, S., Irwin, D. M. & Wilson, A. C. DNA damage promotes jumping between templates during enzymatic amplification. J. Biol. Chem. 265, 4718–4721 (1990).

  67. 67.

    Schirmer, M., D’Amore, R., Ijaz, U. Z., Hall, N. & Quince, C. Illumina error profiles: resolving fine-scale variation in metagenomic sequencing data. BMC Bioinformatics 17, 125 (2016).

  68. 68.

    Manley, L. J., Ma, D. & Levine, S. S. Monitoring error rates in Illumina sequencing. J. Biomol. Tech. 27, 125–128 (2016).

  69. 69.

    Sanjuán, R., Nebot, M. R., Chirico, N., Mansky, L. M. & Belshaw, R. Viral mutation rates. J. Virol. 84, 9733–9748 (2010).

  70. 70.

    Smith, T., Heger, A. & Sudbery, I. UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy. Genome Res. 27, 491–499 (2017).

  71. 71.

    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).

  72. 72.

    Ma, J., Shen, Z., Yu, Y.-C. & Shi, S.-H. Neural lineage tracing in the mammalian brain. Curr. Opin. Neurobiol. 50, 7–16 (2018).

  73. 73.

    Kretzschmar, K. & Watt, F. M. Lineage tracing. Cell 148, 33–45 (2012).

  74. 74.

    Turner, D. L. & Cepko, C. L. A common progenitor for neurons and glia persists in rat retina late in development. Nature 328, 131–136 (1987).

  75. 75.

    Frank, E. & Sanes, J. R. Lineage of neurons and glia in chick dorsal root ganglia: analysis in vivo with a recombinant retrovirus. Development 111, 895–908 (1991).

  76. 76.

    Walsh, C. & Cepko, C. L. Clonal dispersion in proliferative layers of developing cerebral cortex. Nature 362, 632–635 (1993).

  77. 77.

    Kirkwood, T., Price, J. & Grove, E. The dispersion of neuronal clones across the cerebral cortex. Science 258, 317–320 (1992).

  78. 78.

    Walsh, C., Cepko, C. L., Ryder, E. F., Church, G. M. & Tabin, C. Response. Science 258, 317–320 (1992).

  79. 79.

    Wagenblast, E. et al. A model of breast cancer heterogeneity reveals vascular mimicry as a driver of metastasis. Nature 520, 358–362 (2015).

  80. 80.

    Schmidt, M. et al. Clonality analysis after retroviral-mediated gene transfer to CD34+ cells from the cord blood of ADA-deficient SCID neonates. Nat. Med 9, 463–468 (2003).

  81. 81.

    Sun, J. et al. Clonal dynamics of native haematopoiesis. Nature 514, 322–327 (2014).

  82. 82.

    Rodriguez-Fraticelli, A. E. et al. Clonal analysis of lineage fate in native haematopoiesis. Nature 553, 212–216 (2018).

  83. 83.

    Evrony, G. D. et al. Cell lineage analysis in human brain using endogenous retroelements. Neuron 85, 49–59 (2015).

  84. 84.

    Sulston, J. E. & Horvitz, H. R. Post-embryonic cell lineages of the nematode, Caenorhabditis elegans. Dev. Biol. 56, 110–156 (1977).

  85. 85.

    Kamath, R. S. et al. Systematic functional analysis of the Caenorhabditis elegans genome using RNAi. Nature 421, 231–237 (2003).

  86. 86.

    Smith, A. M. et al. Quantitative phenotyping via deep barcode sequencing. Genome Res. 19, 1836–1842 (2009).

  87. 87.

    Giaever, G. & Nislow, C. The yeast deletion collection: a decade of functional genomics. Genetics 197, 451–465 (2014).

  88. 88.

    Paddison, P. J. et al. A resource for large-scale RNA-interference-based screens in mammals. Nature 428, 427–431 (2004).

  89. 89.

    Berns, K. et al. A large-scale RNAi screen in human cells identifies new components of the p53 pathway. Nature 428, 431–437 (2004).

  90. 90.

    Silva, J. M. et al. Profiling essential genes in human mammary cells by multiplex RNAi screening. Science 319, 617–620 (2008).

  91. 91.

    Shalem, O. et al. Genome-scale CRISPR-Cas9 knockout screening in human cells. Science 343, 84–87 (2014).

  92. 92.

    Zhou, Y. et al. High-throughput screening of a CRISPR/Cas9 library for functional genomics in human cells. Nature 509, 487–491 (2014).

  93. 93.

    Sanjana, N. E., Shalem, O. & Zhang, F. Improved vectors and genome-wide libraries for CRISPR screening. Nat. Methods 11, 783–784 (2014).

  94. 94.

    Oh, S. W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014).

  95. 95.

    Zingg, B. et al. Neural networks of the mouse neocortex. Cell 156, 1096–1111 (2014).

  96. 96.

    Ghosh, S. et al. Sensory maps in the olfactory cortex defined by long-range viral tracing of single neurons. Nature 472, 217–220 (2011).

  97. 97.

    Briggman, K. L., Helmstaedter, M. & Denk, W. Wiring specificity in the direction-selectivity circuit of the retina. Nature 471, 183–188 (2011).

  98. 98.

    Chen, X., Kebschull, J. M., Zhan, H., Sun, Y.-C. & Zador, A. M. High-throughput mapping of long-range neuronal projection using in situ sequencing. bioRxiv Preprint at (2018).

  99. 99.

    Glaser, J. I. et al. Statistical analysis of molecular signal recording. PLoS Comput. Biol. 9, e1003145 (2013).

  100. 100.

    Marblestone, A. H. et al. Rosetta brains: a strategy for molecularly-annotated connectomics. arXiv Preprint at (2014).

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We thank B. Cazakoff for comments on the manuscript. This work was supported by the US National Institutes of Health (5RO1NS073129 and 5RO1DA036913 to A.M.Z.), the Brain Research Foundation (BRF-SIA-2014-03 to A.M.Z.), IARPA (MICrONS D16PC0008 to A.M.Z.), the Simons Foundation (382793/SIMONS to A.M.Z.), a Paul Allen Distinguished Investigator Award (to A.M.Z.), the Boehringer Ingelheim Fonds (PhD fellowship to J.M.K.), and the Genentech Foundation (PhD fellowship to J.M.K.).

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  1. Watson School of Biological Sciences, Cold Spring Harbor, NY, USA

    • Justus M. Kebschull
  2. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA

    • Justus M. Kebschull
    •  & Anthony M. Zador


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Competing interests

A.M.Z. is a founder of MAPneuro.

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Correspondence to Anthony M. Zador.

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