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Single-cell mapping of lineage and identity in direct reprogramming

Author

Listed:
  • Brent A. Biddy

    (Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis)

  • Wenjun Kong

    (Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis)

  • Kenji Kamimoto

    (Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis)

  • Chuner Guo

    (Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis)

  • Sarah E. Waye

    (Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis)

  • Tao Sun

    (Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis
    Cedars–Sinai Medical Center)

  • Samantha A. Morris

    (Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis
    Washington University School of Medicine in St Louis)

Abstract

Direct lineage reprogramming involves the conversion of cellular identity. Single-cell technologies are useful for deconstructing the considerable heterogeneity that emerges during lineage conversion. However, lineage relationships are typically lost during cell processing, complicating trajectory reconstruction. Here we present ‘CellTagging’, a combinatorial cell-indexing methodology that enables parallel capture of clonal history and cell identity, in which sequential rounds of cell labelling enable the construction of multi-level lineage trees. CellTagging and longitudinal tracking of fibroblast to induced endoderm progenitor reprogramming reveals two distinct trajectories: one leading to successfully reprogrammed cells, and one leading to a ‘dead-end’ state, paths determined in the earliest stages of lineage conversion. We find that expression of a putative methyltransferase, Mettl7a1, is associated with the successful reprogramming trajectory; adding Mettl7a1 to the reprogramming cocktail increases the yield of induced endoderm progenitors. Together, these results demonstrate the utility of our lineage-tracing method for revealing the dynamics of direct reprogramming.

Suggested Citation

  • Brent A. Biddy & Wenjun Kong & Kenji Kamimoto & Chuner Guo & Sarah E. Waye & Tao Sun & Samantha A. Morris, 2018. "Single-cell mapping of lineage and identity in direct reprogramming," Nature, Nature, vol. 564(7735), pages 219-224, December.
  • Handle: RePEc:nat:nature:v:564:y:2018:i:7735:d:10.1038_s41586-018-0744-4
    DOI: 10.1038/s41586-018-0744-4
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    Cited by:

    1. Guillaume Harmange & Raúl A. Reyes Hueros & Dylan L. Schaff & Benjamin Emert & Michael Saint-Antoine & Laura C. Kim & Zijian Niu & Shivani Nellore & Mitchell E. Fane & Gretchen M. Alicea & Ashani T. W, 2023. "Disrupting cellular memory to overcome drug resistance," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    2. Jun Dai & Shuyu Zheng & Matías M. Falco & Jie Bao & Johanna Eriksson & Sanna Pikkusaari & Sofia Forstén & Jing Jiang & Wenyu Wang & Luping Gao & Fernando Perez-Villatoro & Olli Dufva & Khalid Saeed & , 2024. "Tracing back primed resistance in cancer via sister cells," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    3. Humberto Contreras-Trujillo & Jiya Eerdeng & Samir Akre & Du Jiang & Jorge Contreras & Basia Gala & Mary C. Vergel-Rodriguez & Yeachan Lee & Aparna Jorapur & Areen Andreasian & Lisa Harton & Charles S, 2021. "Deciphering intratumoral heterogeneity using integrated clonal tracking and single-cell transcriptome analyses," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    4. F. Nadalin & M. J. Marzi & M. Pirra Piscazzi & P. Fuentes-Bravo & S. Procaccia & M. Climent & P. Bonetti & C. Rubolino & B. Giuliani & I. Papatheodorou & J. C. Marioni & F. Nicassio, 2024. "Multi-omic lineage tracing predicts the transcriptional, epigenetic and genetic determinants of cancer evolution," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
    5. Yelyzaveta Shlyakhtina & Bianca Bloechl & Maximiliano M. Portal, 2023. "BdLT-Seq as a barcode decay-based method to unravel lineage-linked transcriptome plasticity," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    6. A. S. Eisele & M. Tarbier & A. A. Dormann & V. Pelechano & D. M. Suter, 2024. "Gene-expression memory-based prediction of cell lineages from scRNA-seq datasets," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    7. Arpiar Saunders & Kee Wui Huang & Cassandra Vondrak & Christina Hughes & Karina Smolyar & Harsha Sen & Adrienne C. Philson & James Nemesh & Alec Wysoker & Seva Kashin & Bernardo L. Sabatini & Steven A, 2022. "Ascertaining cells’ synaptic connections and RNA expression simultaneously with barcoded rabies virus libraries," Nature Communications, Nature, vol. 13(1), pages 1-18, December.

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