Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Unravelling cellular relationships during development and regeneration using genetic lineage tracing

Nature Reviews Molecular Cell Biology volume 20, pages 753–765 (2019)Cite this article

Subjects

Abstract

Tracking the progeny of single cells is necessary for building lineage trees that recapitulate processes such as embryonic development and stem cell differentiation. In classical lineage tracing experiments, cells are fluorescently labelled to allow identification by microscopy of a limited number of cell clones. To track a larger number of clones in complex tissues, fluorescent proteins are now replaced by heritable DNA barcodes that are read using next-generation sequencing. In prospective lineage tracing, unique DNA barcodes are introduced into single cells through genetic manipulation (using, for example, Cre-mediated recombination or CRISPR–Cas9-mediated editing) and tracked over time. Alternatively, in retrospective lineage tracing, naturally occurring somatic mutations can be used as endogenous DNA barcodes. Finally, single-cell mRNA-sequencing datasets that capture different cell states within a developmental or differentiation trajectory can be used to recapitulate lineages. In this Review, we discuss methods for prospective or retrospective lineage tracing and demonstrate how trajectory reconstruction algorithms can be applied to single-cell mRNA-sequencing datasets to infer developmental or differentiation tracks. We discuss how these approaches are used to understand cell fate during embryogenesis, cell differentiation and tissue regeneration.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Genetic lineage tracing approaches.
Fig. 2: Prospective genetic lineage tracing to study mouse haematopoiesis.
Fig. 3: Prospective genetic lineage tracing using the CRISPR–Cas9 system.
Fig. 4: Zebrafish development studies using CRISPR–Cas9 genetic lineage tracing.
Fig. 5: Naturally occurring mutations are used for retrospective lineage tracing.

Similar content being viewed by others

References

  1. Sulston, J. E., Schierenberg, E., White, J. G. & Thomson, J. N. The embryonic cell lineage of the nematode caenorhabditis elegans. Dev. Biol. 100, 64–119 (1983).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  3. Shapiro, E., Biezuner, T. & Linnarsson, S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14, 618–630 (2013).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Navin, N. et al. Inferring tumor progression from genomic heterogeneity. Genome Res. 20, 68–80 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Sun, J. et al. Clonal dynamics of native haematopoiesis. Nature 514, 322–327 (2014). This work introduces the Sleeping Beauty transposase system for prospective genetic lineage tracing in the mouse and reveals a limited contribution of HSCs to steady-state blood production.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Till, J. E. & Mc, C. E. A direct measurement of the radiation sensitivity of normal mouse bone marrow cells. Radiat. Res. 14, 213–222 (1961).

    CAS  PubMed  Google Scholar 

  9. Becker, A. J., Mc, C. E. & Till, J. E. Cytological demonstration of the clonal nature of spleen colonies derived from transplanted mouse marrow cells. Nature 197, 452–454 (1963).

    CAS  PubMed  Google Scholar 

  10. Jacobsen, S. E. W. & Nerlov, C. Haematopoiesis in the era of advanced single-cell technologies. Nat. Cell Biol. 21, 2–8 (2019).

    CAS  PubMed  Google Scholar 

  11. Laurenti, E. & Gottgens, B. From haematopoietic stem cells to complex differentiation landscapes. Nature 553, 418–426 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Metzger, D., Clifford, J., Chiba, H. & Chambon, P. Conditional site-specific recombination in mammalian cells using a ligand-dependent chimeric Cre recombinase. Proc. Natl Acad. Sci. USA 92, 6991–6995 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Littlewood, T. D., Hancock, D. C., Danielian, P. S., Parker, M. G. & Evan, G. I. A modified oestrogen receptor ligand-binding domain as an improved switch for the regulation of heterologous proteins. Nucleic Acids Res. 23, 1686–1690 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Feil, R. et al. Ligand-activated site-specific recombination in mice. Proc. Natl Acad. Sci. USA 93, 10887–10890 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Feil, R., Wagner, J., Metzger, D. & Chambon, P. Regulation of Cre recombinase activity by mutated estrogen receptor ligand-binding domains. Biochem. Biophys. Res. Commun. 237, 752–757 (1997).

    CAS  PubMed  Google Scholar 

  17. Rinkevich, Y., Lindau, P., Ueno, H., Longaker, M. T. & Weissman, I. L. Germ-layer and lineage-restricted stem/progenitors regenerate the mouse digit tip. Nature 476, 409–413 (2011).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  19. Snippert, H. J. et al. Intestinal crypt homeostasis results from neutral competition between symmetrically dividing Lgr5 stem cells. Cell 143, 134–144 (2010).

    CAS  PubMed  Google Scholar 

  20. 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 

  21. Jinek, M. et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 337, 816–821 (2012). This work introduces the Cre–loxP-based Polylox system for inducible recombination of DNA barcodes in mice, which is used to study haematopoiesis.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Gasiunas, G., Barrangou, R., Horvath, P. & Siksnys, V. Cas9-crRNA ribonucleoprotein complex mediates specific DNA cleavage for adaptive immunity in bacteria. Proc. Natl Acad. Sci. USA 109, E2579–E2586 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  24. Junker, J. P. et al. Massively parallel clonal analysis using CRISPR/Cas9 induced genetic scars. bioRXiv Preprint at https://doi.org/10.1101/056499 (2017).

  25. 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). This study presents scScarTrace, a method for prospective genetic lineage tracing in zebrafish using CRISPR–Cas9 to study the clonality of blood, brain, eyes and caudal fin.

    CAS  PubMed  Google Scholar 

  26. Raj, B. et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol. 36, 442–450 (2018). This article describes the developmenr of scGESTALT, a method for prospective genetic lineage tracing in zebrafish using CRISPR–Cas9, and the characterization of lineage relationships between cell types in the adult brain.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Spanjaard, B. et al. Simultaneous lineage tracing and cell-type identification using CRISPR-Cas9-induced genetic scars. Nat. Biotechnol. 36, 469–473 (2018). This study introduces LINNAEUS, a method for prospective genetic lineage tracing in zebrafish using CRISPR–Cas9 and uses it to study multiple organs of zebrafish larvae.

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Woo, K. & Fraser, S. E. Order and coherence in the fate map of the zebrafish nervous system. Development 121, 2595–2609 (1995).

    CAS  PubMed  Google Scholar 

  29. Solek, C. M., Feng, S., Perin, S., Weinschutz Mendes, H. & Ekker, M. Lineage tracing of dlx1a/2a and dlx5a/6a expressing cells in the developing zebrafish brain. Dev. Biol. 427, 131–147 (2017).

    CAS  PubMed  Google Scholar 

  30. Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017). This study presents Monocle2, an scRNA-seq-based trajectory reconstruction algorithm that connects single cells on the basis of transcriptional similarity to infer a developmental or differentiation pseudotime.

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Xu, J. et al. Temporal-spatial resolution fate mapping reveals distinct origins for embryonic and adult microglia in zebrafish. Dev. Cell 34, 632–641 (2015).

    CAS  PubMed  Google Scholar 

  32. Henninger, J. et al. Clonal fate mapping quantifies the number of haematopoietic stem cells that arise during development. Nat. Cell Biol. 19, 17–27 (2017).

    CAS  PubMed  Google Scholar 

  33. Kissa, K. & Herbomel, P. Blood stem cells emerge from aortic endothelium by a novel type of cell transition. Nature 464, 112–115 (2010).

    CAS  PubMed  Google Scholar 

  34. Bertrand, J. Y. et al. Haematopoietic stem cells derive directly from aortic endothelium during development. Nature 464, 108–111 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Tu, S. & Johnson, S. L. Fate restriction in the growing and regenerating zebrafish fin. Dev. Cell 20, 725–732 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Lin, X. et al. An ectoderm-derived myeloid-like cell population functions as antigen transporters for Langerhans cells in zebrafish epidermis. Dev. Cell 49, 605–617 (2019).

    CAS  PubMed  Google Scholar 

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

    PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  39. Kalhor, R. et al. Developmental barcoding of whole mouse via homing CRISPR. Science 361, aat9804 (2018). This study reports the generation of a mouse line (MARC1) for prospective genetic lineage tracing using CRISPR–Cas9, which is used to build lineage trees of early embryogenesis.

    Google Scholar 

  40. Chan, M. M. et al. Molecular recording of mammalian embryogenesis. Nature 570, 77–82 (2019). The authors develop a method for prospective genetic lineage tracing using CRISPR–Cas9 in a mouse model to study lineage relationships during early embryogenesis.

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Biasco, L. et al. In vivo tracking of human haematopoiesis reveals patterns of clonal dynamics during early and steady-state reconstitution phases. Cell Stem Cell 19, 107–119 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 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 

  43. Ludwig, L. S. et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell 176, 1325–1339 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Zarrei, M., MacDonald, J. R., Merico, D. & Scherer, S. W. A copy number variation map of the human genome. Nat. Rev. Genet. 16, 172–183 (2015).

    CAS  PubMed  Google Scholar 

  45. Abyzov, A. et al. Somatic copy number mosaicism in human skin revealed by induced pluripotent stem cells. Nature 492, 438–442 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Knouse, K. A., Wu, J. & Amon, A. Assessment of megabase-scale somatic copy number variation using single-cell sequencing. Genome Res. 26, 376–384 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. McConnell, M. J. et al. Mosaic copy number variation in human neurons. Science 342, 632–637 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Cai, X. et al. Single-cell, genome-wide sequencing identifies clonal somatic copy-number variation in the human brain. Cell Rep. 8, 1280–1289 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Wang, Y. et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Casasent, A. K. et al. Multiclonal invasion in breast tumors identified by topographic single cell sequencing. Cell 172, 205–217 e212 (2018). This work demonstrates the capture of CNV by whole-genome amplification in single cells while preserving spatial information using laser-capture microdissection.

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Katsonis, P. et al. Single nucleotide variations: biological impact and theoretical interpretation. Protein Sci. 23, 1650–1666 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Zong, C., Lu, S., Chapman, A. R. & Xie, X. S. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 338, 1622–1626 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Lodato, M. A. et al. Somatic mutation in single human neurons tracks developmental and transcriptional history. Science 350, 94–98 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Leung, M. L. et al. Single-cell DNA sequencing reveals a late-dissemination model in metastatic colorectal cancer. Genome Res. 27, 1287–1299 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Xu, X. et al. Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumour. Cell 148, 886–895 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Ostertag, E. M. & Kazazian, H. H. Jr. Biology of mammalian L1 retrotransposons. Annu. Rev. Genet. 35, 501–538 (2001).

    CAS  PubMed  Google Scholar 

  58. Hwang, B. et al. Lineage tracing using a Cas9-deaminase barcoding system targeting endogenous L1 elements. Nat. Commun. 10, 1234 (2019).

    PubMed  PubMed Central  Google Scholar 

  59. Evrony, G. D. et al. Single-neuron sequencing analysis of L1 retrotransposition and somatic mutation in the human brain. Cell 151, 483–496 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Ellegren, H. Microsatellites: simple sequences with complex evolution. Nat. Rev. Genet. 5, 435–445 (2004).

    CAS  PubMed  Google Scholar 

  62. Reizel, Y. et al. Colon stem cell and crypt dynamics exposed by cell lineage reconstruction. PLOS Genet. 7, e1002192 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. Reizel, Y. et al. Cell lineage analysis of the mammalian female germline. PLOS Genet. 8, e1002477 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Stewart, J. B. & Chinnery, P. F. The dynamics of mitochondrial DNA heteroplasmy: implications for human health and disease. Nat. Rev. Genet. 16, 530–542 (2015). In this article, the authors demonstrate that mitochondrial mutations can be detected by scRNA-seq and scATAC-seq and can be used to reconstruct clonal relationships of haematopoietic and leukaemic cells.

    CAS  PubMed  Google Scholar 

  65. Nam, A. S. et al. Somatic mutations and cell identity linked by genotyping of transcriptomes. Nature 571, 355–360 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Kester, L. & van Oudenaarden, A. Single-cell transcriptomics meets lineage tracing. Cell Stem Cell 23, 166–179 (2018).

    CAS  PubMed  Google Scholar 

  67. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Park, J. et al. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 360, 758–763 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Baron, C. S. et al. Single-cell transcriptomics reveal the dynamic of haematopoietic stem cell production in the aorta. Nat. Commun. 9, 2517 (2018).

    PubMed  PubMed Central  Google Scholar 

  70. Zhong, S. et al. A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex. Nature 555, 524–528 (2018).

    CAS  PubMed  Google Scholar 

  71. Savas, P. et al. Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat. Med. 24, 986–993 (2018).

    CAS  PubMed  Google Scholar 

  72. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018). The authors present RNA velocity, a trajectory reconstruction algorithm that uses the abundance of spliced and unspliced mRNA detected by scRNA-seq to predict the future state of individual cells.

    PubMed  PubMed Central  Google Scholar 

  73. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 6, 377–382 (2009).

    CAS  PubMed  Google Scholar 

  74. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Pijuan-Sala, B. et al. A single-cell molecular map of mouse gastrulation and early organogenesis. Nature 566, 490–495 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  77. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Stahl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    CAS  PubMed  Google Scholar 

  79. Shah, S. et al. Dynamics and spatial genomics of the nascent transcriptome by intron seqFISH. Cell 174, 363–376 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    PubMed  PubMed Central  Google Scholar 

  81. Vickovic, S. et al. High-density spatial transcriptomics arrays for in situ tissue profiling. bioRXiv Preprint at https://doi.org/10.1101/563338 (2018).

  82. Zhang, L. et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature 564, 268–272 (2018).

    CAS  PubMed  Google Scholar 

  83. Mooijman, D., Dey, S. S., Boisset, J. C., Crosetto, N. & van Oudenaarden, A. Single-cell 5hmC sequencing reveals chromosome-wide cell-to-cell variability and enables lineage reconstruction. Nat. Biotechnol. 34, 852–856 (2016).

    CAS  PubMed  Google Scholar 

  84. Houseman, E. A. et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13, 86 (2012).

    PubMed  PubMed Central  Google Scholar 

  85. Accomando, W. P., Wiencke, J. K., Houseman, E. A., Nelson, H. H. & Kelsey, K. T. Quantitative reconstruction of leukocyte subsets using DNA methylation. Genome Biol. 15, R50 (2014).

    PubMed  PubMed Central  Google Scholar 

  86. Salas, L. A. et al. Tracing human stem cell lineage during development using DNA methylation. Genome Res. 28, 1285–1295 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank A. Alemany and J. Yeung for valuable feedback on the manuscript.

Author information

Authors and Affiliations

  1. Oncode Institute, Utrecht, Netherlands

    Chloé S. Baron & Alexander van Oudenaarden

  2. Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences, Utrecht, Netherlands

    Chloé S. Baron & Alexander van Oudenaarden

  3. University Medical Center Utrecht, Utrecht, Netherlands

    Chloé S. Baron & Alexander van Oudenaarden

  4. University Medical Center Utrecht, Cancer Genomics Netherlands, Utrecht, Netherlands

    Chloé S. Baron & Alexander van Oudenaarden

Authors
  1. Chloé S. Baron
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alexander van Oudenaarden
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

C.S.B. researched data and wrote the manuscript. A.v.O. reviewed and edited the manuscript.

Corresponding author

Correspondence to Alexander van Oudenaarden.

Ethics declarations

Competing interest

The authors declare no competing financial interests.

Additional information

Peer review information

Nature Reviews Molecular Cell Biology thanks Bin Zhou and the other, anonymous, reviewer(s) 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.

Glossary

Long-term haematopoietic stem cells

(LT-HSCs). Blood stem cells able to self-renew and differentiate into all types of mature blood cells.

Primed cells

Differentiating cells with a determined cell fate.

Lateral plate mesoderm

A subset of mesodermal tissue found in developing embryos that will form the body walls and circulatory system.

Primitive wave of haematopoiesis

A process by which the embryo generates transient haematopoietic cells, which are necessary for embryonic development.

Definitive wave of haematopoiesis

A process by which definitive adult haematopoietic stem cells are generated during embryogenesis.

Dome stage

A developmental stage of zebrafish embryos that is reached at 4.3 h of development.

Nuc-seq

RNA-sequencing technology for nuclear RNA capture from frozen tissue samples.

Minimum spanning tree

The shortest way to connect all edges of a graph.

Pseudotime

A quantitative measure of biological progression through a process such as cell differentiation.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baron, C.S., van Oudenaarden, A. Unravelling cellular relationships during development and regeneration using genetic lineage tracing. Nat Rev Mol Cell Biol 20, 753–765 (2019). https://doi.org/10.1038/s41580-019-0186-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41580-019-0186-3

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing