Abstract
Embedded viral barcoding in combination with high-throughput sequencing is a powerful technology with which to track single-cell clones. It can provide clonal-level insights into cellular proliferation, development, differentiation, migration, and treatment efficacy. Here, we present a detailed protocol for a viral barcoding procedure that includes the creation of barcode libraries, the viral delivery of barcodes, the recovery of barcodes, and the computational analysis of barcode sequencing data. The entire procedure can be completed within a few weeks. This barcoding method requires cells to be susceptible to viral transduction. It provides high sensitivity and throughput, and enables precise quantification of cellular progeny. It is cost efficient and does not require any advanced skills. It can also be easily adapted to many types of applications, including both in vitro and in vivo experiments.
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Data availability
A sample dataset has been deposited in Figshare: https://doi.org/10.35092/yhjc.11374446. This dataset was used to generate Figs. 4 and 5.
Code availability
The Python scripts have been provided in the Supplementary Software of this paper. The code in this paper has been peer-reviewed.
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Acknowledgements
We thank all members of the Lu lab for helping to optimize the protocol and C. Lytal for help editing the text. We thank the USC Stem Cell Flow Cytometry Facility and CHLA Sequencing Core for technical support. This research was funded primarily by a National Institutes of Health (NIH) R00 early investigator grant (NIH-R00-HL113104) and R01 grants (R01HL135292 and R01HL138225). R.L. is a Scholar of the Leukemia & Lymphoma Society and a Richard N. Merkin Assistant Professor. The project described was supported in part by award no. P30CA014089 from the National Cancer Institute. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute or the National Institutes of Health.
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Contributions
R.L. conceived and developed the protocol. C.B., D.J., J.C., and A.N. optimized the barcode extraction protocol. D.J. and J.E. improved the Python code for analyzing high-throughput sequencing data. C.B., D.J., and R.L. prepared the manuscript. J.C. and A.N. provided assistance in manuscript text preparation.
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Peer review information Nature Protocols thanks Leïla Périé and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Key references using this protocol
Lu, R., Neff, N. R., Quake, S. R. & Weissman, I. L. Nat. Biotechnol. 29, 928–933 (2011): https://doi.org/10.1038/nbt.1977
Nguyen, L. et al. EMBO Rep. 19, e45702 (2018): https://doi.org/10.15252/embr.201745702
Brewer, C., Chu, E., Chin, M. & Lu, R. Cell Rep. 15, 1848–1857 (2016): https://doi.org/10.1016/j.celrep.2016.04.061
Supplementary information
Supplementary Software
The software for data analysis (Steps 121–127)
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Bramlett, C., Jiang, D., Nogalska, A. et al. Clonal tracking using embedded viral barcoding and high-throughput sequencing. Nat Protoc 15, 1436–1458 (2020). https://doi.org/10.1038/s41596-019-0290-z
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DOI: https://doi.org/10.1038/s41596-019-0290-z
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