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Combinatorial programming of human neuronal progenitors using magnetically-guided stoichiometric mRNA delivery

  1. Sayyed M Azimi
  2. Steven D Sheridan
  3. Mostafa Ghannad-Rezaie
  4. Peter M Eimon
  5. Mehmet Fatih Yanik  Is a corresponding author
  1. Massachusetts Institute of Technology, United States
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Cite this article as: eLife 2018;7:e31922 doi: 10.7554/eLife.31922

Abstract

Identification of optimal transcription-factor expression patterns to direct cellular differentiation along a desired pathway presents significant challenges. We demonstrate massively combinatorial screening of temporally-varying mRNA transcription factors to direct differentiation of neural progenitor cells using a dynamically-reconfigurable magnetically-guided spotting technology for localizing mRNA, enabling experiments on millimetre size spots. In addition, we present a time-interleaved delivery method that dramatically reduces fluctuations in the delivered transcription-factor copy-numbers per cell. We screened combinatorial and temporal delivery of a pool of midbrain-specific transcription factors to augment the generation of dopaminergic neurons. We show that the combinatorial delivery of LMX1A, FOXA2 and PITX3 is highly effective in generating dopaminergic neurons from midbrain progenitors. We show that LMX1A significantly increases TH-expression levels when delivered to neural progenitor cells either during proliferation or after induction of neural differentiation, while FOXA2 and PITX3 increase expression only when delivered prior to induction, demonstrating temporal dependence of factor addition.

Data availability

All data generated or analyzed during this study are included in the manuscript and supporting files.

Article and author information

Author details

  1. Sayyed M Azimi

    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Steven D Sheridan

    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Mostafa Ghannad-Rezaie

    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Peter M Eimon

    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0447-517X
  5. Mehmet Fatih Yanik

    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, United States
    For correspondence
    yanik@ethz.ch
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8963-2893

Funding

NIH Office of the Director

  • Mehmet Fatih Yanik

David and Lucile Packard Foundation

  • Mehmet Fatih Yanik

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Sacha B Nelson, Brandeis University, United States

Publication history

  1. Received: September 26, 2017
  2. Accepted: April 30, 2018
  3. Accepted Manuscript published: May 1, 2018 (version 1)
  4. Version of Record published: May 18, 2018 (version 2)

Copyright

© 2018, Azimi et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

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