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Interrogation of clonal tracking data using barcodetrackR

Nature Computational Science volume 1, pages 280–289 (2021)Cite this article

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Abstract

Clonal tracking methods provide quantitative insights into the cellular output of genetically labeled progenitor cells across time and cellular compartments. In the context of gene and cell therapies, clonal tracking methods have enabled the tracking of progenitor cell output both in humans receiving therapies and in corresponding animal models, providing valuable insight into lineage reconstitution, clonal dynamics and vector genotoxicity. However, the absence of a toolbox for analysis of clonal tracking data has precluded the development of standardized analytical frameworks within the field. Thus, we developed barcodetrackR, an R package and accompanying Shiny app containing diverse tools for the analysis and visualization of clonal tracking data. We demonstrate the utility of barcodetrackR in exploring longitudinal clonal patterns and lineage relationships in a number of clonal tracking studies of hematopoietic stem and progenitor cells (HSPCs) in humans receiving HSPC gene therapy and in animals receiving lentivirally transduced HSPC transplants or tumor cells.

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Fig. 1: Clonal tracking experimental design and barcodetrackR analysis.
Fig. 2: Global clonal distributions.
Fig. 3: Measures of clonal diversity.
Fig. 4: Longitudinal clonal patterns.
Fig. 5: Lineage bias.

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Data availability

All clonal tracking datasets analyzed in this study are publicly available with accession instructions outlined in Supplementary Table 1. The Six dataset7 was downloaded from https://github.com/BushmanLab/HSC_diversity/tree/master/data. The Belderbos dataset31 was downloaded from its manuscript’s supplementary material. The Elder dataset32 was downloaded from GEO accession GSE149170. The Espinoza dataset30 was downloaded from GEO accession GSE153130. The Wu dataset11 was downloaded from its manuscript’s supplementary material. The Koelle dataset9 was downloaded from https://github.com/dunbarlabNIH/R-code-and-tabular-data. The Clarke dataset33 was downloaded from ref. 51. Data were pre-processed in R to create tabular data files amenable for use with barcodetrackR. Source data are provided with this paper.

Code availability

The barcodetrackR package is freely available from GitHub under a Creative Commons 0 license and can be accessed at https://github.com/dunbarlabNIH/barcodetrackR. Additionally, the package is available through the Bioconductor repository (https://www.bioconductor.org/). A frozen version of the package at the time of publication is available on Zenodo52. A frozen and interactive version of the package at the time of publication is available on Code Ocean53, allowing readers to reproduce all figures and the full barcodetrackR vignette within a pre-specified computational environment.

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Acknowledgements

We thank D. Allan of the NHLBI Intramural Research Program for his contributions to approaches for statistical testing of genetic tag abundances. We thank members of the Dunbar lab for helpful feedback in revision of this manuscript. D.A.E. was supported by NIH Medical Scientist Training Program T32 GM07170 and T32 G000046. R.D.M., S.J.K., C.W. and C.E.D. were supported by the Division of Intramural Research at the National Heart, Lung and Blood Institute.

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Author notes

  1. These authors contributed equally: Diego A. Espinoza, Ryland D. Mortlock.

Authors and Affiliations

  1. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Diego A. Espinoza

  2. Translational Stem Cell Biology Branch, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA

    Diego A. Espinoza, Ryland D. Mortlock, Samson J. Koelle, Chuanfeng Wu & Cynthia E. Dunbar

  3. Department of Statistics, University of Washington, Seattle, WA, USA

    Samson J. Koelle

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Contributions

D.A.E. and R.D.M. wrote the manuscript. D.A.E. and R.D.M. developed code and performed analysis of existing datasets. S.J.K. and C.W. aided with development of visualizations. C.E.D. supervised the project and edited the manuscript.

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Correspondence to Cynthia E. Dunbar.

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The authors declare no competing interests.

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Peer review information Nature Computational Science thanks Jennifer E. Adair, Mark Enstrom, Ingmar Glauche and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Fernando Chirigati was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary information

Supplementary Information

Supplementary Section 1, Table 1 and Figs. 1–7.

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Source Data Fig. 2

Numerical data underlying all panels of Fig. 2. Data for each panel is in a separate tab of the spreadsheet.

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Numerical data underlying all panels of Fig. 3. Data for each panel is in a separate tab of the spreadsheet.

Source Data Fig. 4

Numerical data underlying all panels of Fig. 4. Data for each panel is in a separate tab of the spreadsheet.

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Numerical data underlying all panels of Fig. 5. Data for each panel is in a separate tab of the spreadsheet.

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Espinoza, D.A., Mortlock, R.D., Koelle, S.J. et al. Interrogation of clonal tracking data using barcodetrackR. Nat Comput Sci 1, 280–289 (2021). https://doi.org/10.1038/s43588-021-00057-4

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