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.

BAR-Seq clonal tracking of gene-edited cells

Abstract

Gene editing by engineered nucleases has revolutionized the field of gene therapy by enabling targeted and precise modification of the genome. However, the limited availability of methods for clonal tracking of edited cells has resulted in a paucity of information on the diversity, abundance and behavior of engineered clones. Here we detail the wet laboratory and bioinformatic BAR-Seq pipeline, a strategy for clonal tracking of cells harboring homology-directed targeted integration of a barcoding cassette. We present the BAR-Seq web application, an online, freely available and easy-to-use software that allows performing clonal tracking analyses on raw sequencing data without any computational resources or advanced bioinformatic skills. BAR-Seq can be applied to most editing strategies, and we describe its use to investigate the clonal dynamics of human edited hematopoietic stem/progenitor cells in xenotransplanted hosts. Notably, BAR-Seq may be applied in both basic and translational research contexts to investigate the biology of edited cells and stringently compare editing protocols at a clonal level. Our BAR-Seq pipeline allows library preparation and validation in a few days and clonal analyses of edited cell populations in 1 week.

Your institute does not have access to this article

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: BAR-Seq pipeline for clonal tracking of edited cells.
Fig. 2: Alternative configurations and generation of the BAR-Seq HDR template library.
Fig. 3: BAR-Seq bioinformatic pipeline.
Fig. 4: Web application for BAR-Seq bioinformatic analyses.
Fig. 5: Anticipated results of the BAR-Seq pipeline.

Data availability

The BAR-Seq software with some example datasets is provided as a zip file (Supplementary Software) in the Supplementary Information. These datasets, which are part of the study originally described in ref. 9 and available in Gene Expression Omnibus with the accession code GSE144340, have been analyzed with the BAR-Seq computational pipeline to generate the example results presented in Fig. 4.

Code availability

The scripts for BAR-Seq analysis are freely available at https://bitbucket.org/bereste/bar-seq under the terms of the GNU General Public License version 3 (GPLv3). The BAR-Seq webtool is freely available at http://www.bioinfotiget.it/barseq.

References

  1. Naldini, L. Gene therapy returns to centre stage. Nature 526, 351–360 (2015).

    CAS  PubMed  Google Scholar 

  2. Bramlett, C. et al. Clonal tracking using embedded viral barcoding and high-throughput sequencing. Nat. Protoc. 15, 1436–1458 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Cavazzana-Calvo, M. et al. Gene therapy of human severe combined immunodeficiency (SCID)-X1 disease. Science 288, 669–672 (2000).

    CAS  PubMed  Google Scholar 

  4. Biffi, A. et al. Lentiviral hematopoietic stem cell gene therapy benefits metachromatic leukodystrophy. Science 341, 1233158 (2013).

    PubMed  Google Scholar 

  5. Aiuti, A. et al. Lentiviral hematopoietic stem cell gene therapy in patients with Wiskott-Aldrich syndrome. Science 341, 1233151 (2013).

    PubMed  PubMed Central  Google Scholar 

  6. Scala, S. et al. Dynamics of genetically engineered hematopoietic stem and progenitor cells after autologous transplantation in humans. Nat. Med. 24, 1683–1690 (2018).

    CAS  PubMed  Google Scholar 

  7. Wu, C. et al. Clonal tracking of rhesus macaque hematopoiesis highlights a distinct lineage origin for natural killer cells. Cell Stem Cell 14, 486–499 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Doudna, J. A. The promise and challenge of therapeutic genome editing. Nature 578, 229–236 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Ferrari, S. et al. Efficient gene editing of human long-term hematopoietic stem cells validated by clonal tracking. Nat. Biotechnol. 38, 1298–1308 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Kebschull, J. M. & Zador, A. M. Cellular barcoding: lineage tracing, screening and beyond. Nat. Methods 15, 871–879 (2018).

    CAS  PubMed  Google Scholar 

  11. 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).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. San Filippo, J., Sung, P. & Klein, H. Mechanism of eukaryotic homologous recombination. Annu. Rev. Biochem. 77, 229–257 (2008).

    CAS  PubMed  Google Scholar 

  13. Yeh, C. D., Richardson, C. D. & Corn, J. E. Advances in genome editing through control of DNA repair pathways. Nat. Cell Biol. 21, 1468–1478 (2019).

    CAS  PubMed  Google Scholar 

  14. Wang, J. et al. Homology-driven genome editing in hematopoietic stem and progenitor cells using ZFN mRNA and AAV6 donors. Nat. Biotechnol. 33, 1256–1263 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Weinreb, C., Rodriguez-Fraticelli, A., Camargo, F. D. & Klein, A. M. Lineage tracing on transcriptional landscapes links state to fate during differentiation. Science 367, eaaw3381 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Wagenblast, E. et al. Functional profiling of single CRISPR/Cas9-edited human long-term hematopoietic stem cells. Nat. Commun. 10, 4730 (2019).

    PubMed  PubMed Central  Google Scholar 

  17. Bai, T. et al. Expansion of primitive human hematopoietic stem cells by culture in a zwitterionic hydrogel. Nat. Med. 25, 1566–1575 (2019).

    CAS  PubMed  Google Scholar 

  18. Genovese, P. et al. Targeted genome editing in human repopulating haematopoietic stem cells. Nature 510, 235–240 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Dever, D. P. et al. CRISPR/Cas9 β-globin gene targeting in human haematopoietic stem cells. Nature 539, 384–389 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  21. Kalhor, R. et al. Developmental barcoding of whole mouse via homing CRISPR. Science 361, eaat9804 (2018).

    PubMed  PubMed Central  Google Scholar 

  22. van Overbeek, M. et al. DNA repair profiling reveals nonrandom outcomes at Cas9-mediated breaks. Mol. Cell 63, 633–646 (2016).

    PubMed  Google Scholar 

  23. Kosicki, M., Tomberg, K. & Bradley, A. Repair of double-strand breaks induced by CRISPR–Cas9 leads to large deletions and complex rearrangements. Nat. Biotechnol. 36, 765–771 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Rago, C., Vogelstein, B. & Bunz, F. Genetic knockouts and knockins in human somatic cells. Nat. Protoc. 2, 2734–2746 (2007).

    CAS  PubMed  Google Scholar 

  25. Khan, I. F., Hirata, R. K. & Russell, D. W. AAV-mediated gene targeting methods for human cells. Nat. Protoc. 6, 482–501 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Bak, R. O., Dever, D. P. & Porteus, M. H. CRISPR/Cas9 genome editing in human hematopoietic stem cells. Nat. Protoc. 13, 358–376 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Naik, S. H., Schumacher, T. N. & Perié, L. Cellular barcoding: a technical appraisal. Exp. Hematol. 42, 598–608 (2014).

    PubMed  Google Scholar 

  28. Bystrykh, L. V. & Belderbos, M. E. Clonal analysis of cells with cellular barcoding: when numbers and sizes matter. in Stem Cell Heterogeneity (ed. Turksen, K.) 57–89 (Humana Press, 2016).

  29. Karlin, S. & Altschul, S. F. Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes. Proc. Natl Acad. Sci. USA 87, 2264–2268 (1990).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Eddy, S. R. A probabilistic model of local sequence alignment that simplifies statistical significance estimation. PLoS Comput. Biol. 4, e1000069 (2008).

    PubMed  PubMed Central  Google Scholar 

  31. Stinson, D. R. Cryptography: Theory and Practice (CRC Press, 1995). .

  32. Gibson, D. G. et al. Enzymatic assembly of DNA molecules up to several hundred kilobases. Nat. Methods 6, 343–345 (2009).

    CAS  PubMed  Google Scholar 

  33. Roth, T. L. et al. Reprogramming human T cell function and specificity with non-viral genome targeting. Nature 559, 405–409 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Gurumurthy, C. B. et al. Creation of CRISPR-based germline-genome-engineered mice without ex vivo handling of zygotes by i-GONAD. Nat. Protoc. 14, 2452–2482 (2019).

    CAS  PubMed  Google Scholar 

  35. Tiscornia, G., Singer, O. & Verma, I. M. Production and purification of lentiviral vectors. Nat. Protoc. 1, 241–245 (2006).

    CAS  PubMed  Google Scholar 

  36. Galibert, L. & Merten, O.-W. Latest developments in the large-scale production of adeno-associated virus vectors in insect cells toward the treatment of neuromuscular diseases. J. Invertebr. Pathol. 107, S80–S93 (2011).

    CAS  PubMed  Google Scholar 

  37. Grieger, J. C., Choi, V. W. & Samulski, R. J. Production and characterization of adeno-associated viral vectors. Nat. Protoc. 1, 1412–1428 (2006).

    CAS  PubMed  Google Scholar 

  38. Doria, M., Ferrara, A. & Auricchio, A. AAV2/8 vectors purified from culture medium with a simple and rapid protocol transduce murine liver, muscle, and retina efficiently. Hum. Gene Ther. Methods 24, 392–398 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Ran, F. A. et al. Genome engineering using the CRISPR-Cas9 system. Nat. Protoc. 8, 2281–2308 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Boitano, A. E. et al. Aryl hydrocarbon receptor antagonists promote the expansion of human hematopoietic stem cells. Science 329, 1345–1348 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Fares, I. et al. Pyrimidoindole derivatives are agonists of human hematopoietic stem cell self-renewal. Science 345, 1509–1512 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Schiroli, G. et al. Preclinical modeling highlights the therapeutic potential of hematopoietic stem cell gene editing for correction of SCID-X1. Sci. Transl. Med. 9, eaan0820 (2017).

    PubMed  Google Scholar 

  43. Lux, C. T. et al. TALEN-mediated gene editing of HBG in human hematopoietic stem cells leads to therapeutic fetal hemoglobin induction. Mol. Ther. Methods Clin. Dev. 12, 175–183 (2019).

    CAS  PubMed  Google Scholar 

  44. Cromer, M. K. et al. Global transcriptional response to CRISPR/Cas9-AAV6-based genome editing in CD34+ hematopoietic stem and progenitor cells. Mol. Ther. 26, 2431–2442 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Schiroli, G. et al. Precise gene editing preserves hematopoietic stem cell function following transient p53-mediated DNA damage response. Cell Stem Cell 24, 551–565.e8 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. De Ravin, S. S. et al. CRISPR-Cas9 gene repair of hematopoietic stem cells from patients with X-linked chronic granulomatous disease. Sci. Transl. Med. 9, eaah3480 (2017).

    PubMed  Google Scholar 

  47. Petrillo, C. et al. Cyclosporine H overcomes innate immune restrictions to improve lentiviral transduction and gene editing in human hematopoietic stem cells. Cell Stem Cell 23, 820–832.e9 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Clement, K. et al. CRISPResso2 provides accurate and rapid genome editing sequence analysis. Nat. Biotechnol. 37, 224–226 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Brinkman, E. K., Chen, T., Amendola, M. & Van Steensel, B. Easy quantitative assessment of genome editing by sequence trace decomposition. Nucleic Acids Res. 42, e168 (2014).

    PubMed  PubMed Central  Google Scholar 

  50. Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).

    PubMed  PubMed Central  Google Scholar 

  51. Lassmann, T. TagDust2: a generic method to extract reads from sequencing data. BMC Bioinformatics 16, 24 (2015).

    PubMed  PubMed Central  Google Scholar 

  52. Thielecke, L., Cornils, K. & Glauche, I. GenBaRcode: a comprehensive R-package for genetic barcode analysis. Bioinformatics 36, 2189–2194 (2020).

    CAS  PubMed  Google Scholar 

  53. Cornish-Bowden, A. Nomenclature for incompletely specified bases in nucleic acid sequences: recommendations 1984. Nucleic Acids Res. 13, 3021–3030 (1985).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Del Core Luca, M. E. & Di Serio Clelia, C. A. Dealing with data evolution and data integration: an approach using rarefaction. in 49th Scientific Meeting of the Italian Statistical Society Palermo, Italy (20–22 June, 2018).

Download references

Acknowledgements

We thank all members of L.N.’s laboratory for discussion, the IRCCS San Raffaele Hospital Flow Cytometry facility (FRACTAL), the IRCCS San Raffaele Center for Omics Sciences (COSR), A. Auricchio and M. Doria (Telethon Institute of GEnetics and Medicine, the TIGEM Vector Core, Pozzuoli, Italy) for providing AAV6 vectors, L. Periè (Institute Curie, Paris, France) and J. Urbanus (the Netherlands Cancer Institute, Amsterdam, the Netherlands) for advice on the BAR cloning strategy, G. Schiroli (SR-Tiget) for initial help with the design of the BAR-Seq strategy, D. Lazarevic (COSR) for help with BAR-Seq amplicon sequencing and A. Calabria (SR-Tiget) for help with richness estimation. This work was supported by grants to: L.N. from Telethon (TIGET grant E4), the Italian Ministry of Health (PE-2016-02363691; E-Rare-3 JTC 2017), the Italian Ministry of University and Research (PRIN 2017 Prot. 20175XHBPN), the EU Horizon 2020 Program (UPGRADE) and the Louis-Jeantet Foundation through the 2019 Jeantet-Collen Prize for Translational Medicine; P.G. from Telethon (TIGET grant E3) and the Italian Ministry of Health (GR-2013-02358956). S.F. conducted this study as partial fulfillment of his Ph.D. in Molecular Medicine, International Ph.D. School, Vita-Salute San Raffaele University (Milan, Italy). A.J. conducted this study as partial fulfillment of his Ph.D. in Translational and Molecular Medicine (DIMET), Milano-Bicocca University (Monza, Italy).

Author information

Authors and Affiliations

Authors

Contributions

S.F., P.G. and L.N conceived and developed the protocol. S.B., D.C. and I.M. developed the BAR-Seq bioinformatic pipeline. S.B. and I.M. developed the online BAR-Seq tool. S.F., A.J., L.A. and P.G. developed and optimized the gene-editing protocol. S.F., S.B., A.J., P.G., I.M. and L.N. wrote the manuscript. I.M., L.N. and P.G. supervised the work and share senior authorship.

Corresponding author

Correspondence to Samuele Ferrari.

Ethics declarations

Competing interests

L.N. and P.G. are inventors of patents on applications of gene editing in HSPCs owned and managed by the San Raffaele Scientific Institute and the Telethon Foundation, including a patent application on improved gene editing filed by S.F., A.J., P.G. and L.N. L.N. is founder and quota holder and P.G. is quota holder of GeneSpire, a startup company aiming to develop ex vivo gene editing in genetic diseases. All other authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks Cynthia Dunbar, Diego A. Espinoza and Alejo E. Rodriguez-Fraticelli 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.

Related links

Key reference using this protocol

Ferrari, S. et al. Nat. Biotechnol. 38, 1298–1308 (2020): https://doi.org/10.1038/s41587-020-0551-y

Supplementary information

Supplementary Information

Supplementary Fig. 1.

Supplementary Software

BAR-Seq software and example datasets

Supplementary Tables 1–3

Tables of probability collisions and list of BAR-Seq primers

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ferrari, S., Beretta, S., Jacob, A. et al. BAR-Seq clonal tracking of gene-edited cells. Nat Protoc 16, 2991–3025 (2021). https://doi.org/10.1038/s41596-021-00529-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41596-021-00529-x

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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