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A computer-guided design tool to increase the efficiency of cellular conversions

Author

Listed:
  • Sascha Jung

    (Bizkaia Technology Park)

  • Evan Appleton

    (Wyss Institute for Biologically Inspired Engineering at Harvard University
    Harvard Medical School)

  • Muhammad Ali

    (University of Luxembourg
    Maastricht University School for Mental Health and Neuroscience (MHeNs), Department of Psychiatry and Neuropsychology, Maastricht University)

  • George M. Church

    (Wyss Institute for Biologically Inspired Engineering at Harvard University
    Harvard Medical School
    GC Therapeutics, Inc)

  • Antonio del Sol

    (Bizkaia Technology Park
    University of Luxembourg
    IKERBASQUE, Basque Foundation for Science
    Moscow Institute of Physics and Technology)

Abstract

Human cell conversion technology has become an important tool for devising new cell transplantation therapies, generating disease models and testing gene therapies. However, while transcription factor over-expression-based methods have shown great promise in generating cell types in vitro, they often endure low conversion efficiency. In this context, great effort has been devoted to increasing the efficiency of current protocols and the development of computational approaches can be of great help in this endeavor. Here we introduce a computer-guided design tool that combines a computational framework for prioritizing more efficient combinations of instructive factors (IFs) of cellular conversions, called IRENE, with a transposon-based genomic integration system for efficient delivery. Particularly, IRENE relies on a stochastic gene regulatory network model that systematically prioritizes more efficient IFs by maximizing the agreement of the transcriptional and epigenetic landscapes between the converted and target cells. Our predictions substantially increased the efficiency of two established iPSC-differentiation protocols (natural killer cells and melanocytes) and established the first protocol for iPSC-derived mammary epithelial cells with high efficiency.

Suggested Citation

  • Sascha Jung & Evan Appleton & Muhammad Ali & George M. Church & Antonio del Sol, 2021. "A computer-guided design tool to increase the efficiency of cellular conversions," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21801-4
    DOI: 10.1038/s41467-021-21801-4
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    Cited by:

    1. Carlos Company & Matthias Jürgen Schmitt & Yuliia Dramaretska & Michela Serresi & Sonia Kertalli & Ben Jiang & Jiang-An Yin & Adriano Aguzzi & Iros Barozzi & Gaetano Gargiulo, 2024. "Logical design of synthetic cis-regulatory DNA for genetic tracing of cell identities and state changes," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    2. Peizhuo Wang & Xiao Wen & Han Li & Peng Lang & Shuya Li & Yipin Lei & Hantao Shu & Lin Gao & Dan Zhao & Jianyang Zeng, 2023. "Deciphering driver regulators of cell fate decisions from single-cell transcriptomics data with CEFCON," Nature Communications, Nature, vol. 14(1), pages 1-16, December.

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