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CellTagging: combinatorial indexing to simultaneously map lineage and identity at single-cell resolution

Nature Protocols volume 15, pages 750–772 (2020)Cite this article

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Abstract

Single-cell technologies are offering unparalleled insight into complex biology, revealing the behavior of rare cell populations that are masked in bulk population analyses. One current limitation of single-cell approaches is that lineage relationships are typically lost as a result of cell processing. We recently established a method, CellTagging, permitting the parallel capture of lineage information and cell identity via a combinatorial cell indexing approach. CellTagging integrates with high-throughput single-cell RNA sequencing, where sequential rounds of cell labeling enable the construction of multi-level lineage trees. Here, we provide a detailed protocol to (i) generate complex plasmid and lentivirus CellTag libraries for labeling of cells; (ii) sequentially CellTag cells over the course of a biological process; (iii) profile single-cell transcriptomes via high-throughput droplet-based platforms; and (iv) generate a CellTag expression matrix, followed by clone calling and lineage reconstruction. This lentiviral-labeling approach can be deployed in any organism or in vitro culture system that is amenable to viral transduction to simultaneously profile lineage and identity at single-cell resolution.

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Fig. 1: The CellTag workflow for parallel capture of lineage and identity.
Fig. 2: CellTagging applied to a cell reprogramming time course.
Fig. 3: CellTag signatures uniquely label cells across independent biological replicates.
Fig. 4: CellTag filtering and error correction.
Fig. 5
Fig. 6: Expression of CellTags.
Fig. 7: Processing CellTag data.
Fig. 8: Expected CellTag expression metrics.
Fig. 9: Clonal dynamics over a reprogramming time course experiment.
Fig. 10: Lineages reconstructed from a reprogramming time course.

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Data availability

CellTagging of fibroblast to iEP lineage reprogramming42 data are available via GEO: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE99915. The clones and lineages reconstructed from this dataset can be interactively explored via http://celltag.org/, along with our simulator to support CellTag experimental design. CellTagging constructs are available from Addgene: https://www.addgene.org/pooled-library/morris-lab-celltag/. Updates to this protocol will be provided at https://www.protocols.io/view/single-cell-mapping-of-lineage-and-identity-via-ce-yxifxke.

Code availability

Our R package, CellTagR, code and analysis tutorials are available via GitHub: https://github.com/morris-lab/CellTagR.

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Acknowledgements

We thank members of the Morris laboratory for critical discussions and J. Dick (University of Toronto) for the gift of the pSMAL backbone. This work was funded by National Institutes of Health grants R01-GM126112, R21-HG009750 and P30-DK052574; Silicon Valley Community Foundation, Chan Zuckerberg Initiative Grants HCA-A-1704-01646 and HCA2-A-1708-02799; The Children’s Discovery Institute of Washington University and St. Louis Children’s Hospital grant MI-II-2016-544. S.A.M. is supported by a Vallee Scholar Award; B.A.B. is supported by NIH-T32HG000045-18; and K.K. is supported by a Japan Society for the Promotion of Science Postdoctoral Fellowship.

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Authors and Affiliations

  1. Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA

    Wenjun Kong, Brent A. Biddy, Kenji Kamimoto, Junedh M. Amrute, Emily G. Butka & Samantha A. Morris

  2. Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA

    Wenjun Kong, Brent A. Biddy, Kenji Kamimoto, Junedh M. Amrute, Emily G. Butka & Samantha A. Morris

  3. Center of Regenerative Medicine, Washington University School of Medicine, St. Louis, MO, USA

    Wenjun Kong, Brent A. Biddy, Kenji Kamimoto, Junedh M. Amrute, Emily G. Butka & Samantha A. Morris

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Contributions

S.A.M., W.K. and B.A.B. developed and optimized the CellTagging protocols and analyzed the data. K.K. developed CellTag lineage tree reconstuction. J.M.A. and E.G.B. developed the CellTag simulator. W.K. and S.A.M. wrote the manuscript.

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Correspondence to Samantha A. Morris.

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The authors declare no competing interests.

Additional information

Peer review information Nature Protocols thanks Jennifer Adair, James Gagnon 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

Biddy, B. A. et al. Nature 564, 219–224 (2018): https://doi.org/10.1038/s41586-018-0744-4

Guo, C. et al. Genome Biol. 20, 90 (2019): https://doi.org/10.1186/s13059-019-1699-y

Supplementary information

Supplementary Manual 1

A step-by-step tutorial for CellTag data processing using the CellTagR package.

Reporting Summary

Supplementary Software 1

12 scripts comprising the CellTagR package

Supplementary Software 2

Script for the CellTag simulation tool

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Kong, W., Biddy, B.A., Kamimoto, K. et al. CellTagging: combinatorial indexing to simultaneously map lineage and identity at single-cell resolution. Nat Protoc 15, 750–772 (2020). https://doi.org/10.1038/s41596-019-0247-2

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