Combinatorial programming of human neuronal progenitors using magnetically-guided stoichiometric mRNA delivery
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
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
- Sacha B Nelson, Brandeis University, United States
Version history
- Received: September 26, 2017
- Accepted: April 30, 2018
- Accepted Manuscript published: May 1, 2018 (version 1)
- 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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