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Simultaneous lineage tracing and cell-type identification using CRISPR–Cas9-induced genetic scars

Abstract

A key goal of developmental biology is to understand how a single cell is transformed into a full-grown organism comprising many different cell types. Single-cell RNA-sequencing (scRNA-seq) is commonly used to identify cell types in a tissue or organ1. However, organizing the resulting taxonomy of cell types into lineage trees to understand the developmental origin of cells remains challenging. Here we present LINNAEUS (lineage tracing by nuclease-activated editing of ubiquitous sequences)—a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA-seq with computational analysis of lineage barcodes, generated by genome editing of transgenic reporter genes, we reconstruct developmental lineage trees in zebrafish larvae, and in heart, liver, pancreas, and telencephalon of adult fish. LINNAEUS provides a systematic approach for tracing the origin of novel cell types, or known cell types under different conditions.

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Figure 1: Using the CRISPR–Cas9 system for massively parallel single-cell lineage tracing.
Figure 2: Computational reconstruction of lineage trees on the single-cell level.
Figure 3: Single-cell lineage analysis of adult organs reveals hierarchies of cell fate decisions.

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References

  1. Grün, D. & van Oudenaarden, A. Design and analysis of single-cell sequencing experiments. Cell 163, 799–810 (2015).

    Article  PubMed  Google Scholar 

  2. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Spanjaard, B. & Junker, J.P. Methods for lineage tracing on the organism-wide level. Curr. Opin. Cell Biol. 49, 16–21 (2017).

    Article  CAS  PubMed  Google Scholar 

  4. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Setty, M. et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34, 637–645 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Haghverdi, L., Büttner, M., Wolf, F.A., Buettner, F. & Theis, F.J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).

    Article  CAS  PubMed  Google Scholar 

  7. Barker, N. et al. Identification of stem cells in small intestine and colon by marker gene Lgr5. Nature 449, 1003–1007 (2007).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  9. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 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).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ju, Y.S. et al. Somatic mutations reveal asymmetric cellular dynamics in the early human embryo. Nature 543, 714–718 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

  19. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Pan, Y.A. et al. Zebrabow: multispectral cell labeling for cell tracing and lineage analysis in zebrafish. Development 140, 2835–2846 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Klein, A.M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Satija, R., Farrell, J.A., Gennert, D., Schier, A.F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Villarreal, D.D. et al. Microhomology directs diverse DNA break repair pathways and chromosomal translocations. PLoS Genet. 8, e1003026 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Schier, A.F. & Talbot, W.S. Molecular genetics of axis formation in zebrafish. Annu. Rev. Genet. 39, 561–613 (2005).

    Article  CAS  PubMed  Google Scholar 

  25. Wiens, J.J. Missing data and the design of phylogenetic analyses. J. Biomed. Inform. 39, 34–42 (2006).

    Article  CAS  PubMed  Google Scholar 

  26. Jagannathan-Bogdan, M. & Zon, L.I. Hematopoiesis. Development 140, 2463–2467 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kimmel, C.B. & Warga, R.M. Indeterminate cell lineage of the zebrafish embryo. Dev. Biol. 124, 269–280 (1987).

    Article  CAS  PubMed  Google Scholar 

  28. Keegan, B.R., Meyer, D. & Yelon, D. Organization of cardiac chamber progenitors in the zebrafish blastula. Development 131, 3081–3091 (2004).

    Article  CAS  PubMed  Google Scholar 

  29. Alemany, A. et al. Whole-organism clone tracing using single-cell sequencing. Nature doi:10.1038/nature25969 (2018).

  30. Raj, B. et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol. doi:10.1038/nbt.4103 (2018).

  31. Kivioja, T. et al. Counting absolute numbers of molecules using unique molecular identifiers. Nat. Methods 9, 72–74 (2012).

    Article  CAS  Google Scholar 

  32. Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).

  33. Wang, G.C. & Wang, Y. Frequency of formation of chimeric molecules as a consequence of PCR coamplification of 16S rRNA genes from mixed bacterial genomes. Appl. Environ. Microbiol. 63, 4645–4650 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Waltman, L. & van Eck, N.J. A smart local moving algorithm for large-scale modularity-based community detection. Eur. Phys. J. B 86, 471 (2013).

    Article  Google Scholar 

  35. van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  36. Amir, A.D. et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545–552 (2013).

    Article  CAS  PubMed Central  Google Scholar 

  37. McDavid, A. et al. Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments. Bioinformatics 29, 461–467 (2013).

    Article  CAS  PubMed  Google Scholar 

  38. Howe, D.G. et al. ZFIN, the Zebrafish Model Organism Database: increased support for mutants and transgenics. Nucleic Acids Res. 41, D854–D860 (2013).

    Article  CAS  PubMed  Google Scholar 

  39. Kane, D.A. & Kimmel, C.B. The zebrafish midblastula transition. Development 119, 447–456 (1993).

    CAS  PubMed  Google Scholar 

  40. Kobitski, A.Y. et al. An ensemble-averaged, cell density-based digital model of zebrafish embryo development derived from light-sheet microscopy data with single-cell resolution. Sci. Rep. 5, 8601–8610 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank R. Opitz, M. Guedes Simoes, D. Panakova, T. Durovic, and J. Ninkovic for help with cell-type identification. We also acknowledge support by MDC/BIMSB core facilities (zebrafish, genomics, bioinformatics), and we thank J. Richter for help with zebrafish experiments. Work in J.P.J.'s laboratory was funded by a European Research Council Starting Grant (ERC-StG 715361 SPACEVAR), a Fondation Leducq Transatlantic Networks Grant (16CVD03), and a Helmholtz Incubator grant (Sparse2Big ZT-I-0007). B.H. was supported by a PhD fellowship from Studienstiftung des deutschen Volkes.

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Authors and Affiliations

Authors

Contributions

J.P.J., B.S. and B.H. conceived and designed the project. B.H., N.M. and S.J. optimized dissection and dissociation protocols. B.H. developed the experimental approach for combined scar and transcriptome detection, and B.H. and N.M. performed experiments. B.S. developed computational methods and analyzed the data, with support by P.O.-C. J.P.J. and N.N. guided experiments, and J.P.J. guided analysis. J.P.J. and B.S. wrote the manuscript, with input from all other authors. All authors discussed and interpreted results.

Corresponding author

Correspondence to Jan Philipp Junker.

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Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–20, Supplementary Table 1 and Supplementary Note 1 (PDF 13066 kb)

Life Sciences Reporting Summary (PDF 170 kb)

Supplementary Dataset 1

Technical information for all sequenced libraries (XLSX 10 kb)

Supplementary Dataset 2

Cell type information for 5 dpf larvae (XLSX 40 kb)

Supplementary Dataset 3

Scar detection statistics for 5 dpf larvae (XLSX 14 kb)

Supplementary Dataset 4

Cell type information for adult organs (XLSX 40 kb)

Supplementary Dataset 5

Scar detection statistics for adult organs (XLSX 13 kb)

Supplementary Dataset 6

Statistics connection enrichment analysis (XLSX 146 kb)

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Spanjaard, B., Hu, B., Mitic, N. et al. Simultaneous lineage tracing and cell-type identification using CRISPR–Cas9-induced genetic scars. Nat Biotechnol 36, 469–473 (2018). https://doi.org/10.1038/nbt.4124

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