NGN2 mmRNA-Based Transcriptional Programming in Microfluidic Guides hiPSCs Toward Neural Fate With Multiple Identities

基于NGN2 mmRNA的微流控转录编程引导hiPSCs向具有多种身份的神经命运分化

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作者:Anna Maria Tolomeo ,Cecilia Laterza ,Eleonora Grespan ,Federica Michielin ,Isaac Canals ,Zaal Kokaia ,Maurizio Muraca ,Onelia Gagliano ,Nicola Elvassore

Abstract

Recent advancements in cell engineering have succeeded in manipulating cell identity with the targeted overexpression of specific cell fate determining transcription factors in a process named transcriptional programming. Neurogenin2 (NGN2) is sufficient to instruct pluripotent stem cells (PSCs) to acquire a neuronal identity when delivered with an integrating system, which arises some safety concerns for clinical applications. A non-integrating system based on modified messenger RNA (mmRNA) delivery method, represents a valuable alternative to lentiviral-based approaches. The ability of NGN2 mmRNA to instruct PSC fate change has not been thoroughly investigated yet. Here we aimed at understanding whether the use of an NGN2 mmRNA-based approach combined with a miniaturized system, which allows a higher transfection efficiency in a cost-effective system, is able to drive human induced PSCs (hiPSCs) toward the neuronal lineage. We show that NGN2 mRNA alone is able to induce cell fate conversion. Surprisingly, the outcome cell population accounts for multiple phenotypes along the neural development trajectory. We found that this mixed population is mainly constituted by neural stem cells (45% ± 18 PAX6 positive cells) and neurons (38% ± 8 βIIITUBULIN positive cells) only when NGN2 is delivered as mmRNA. On the other hand, when the delivery system is lentiviral-based, both providing a constant expression of NGN2 or only a transient pulse, the outcome differentiated population is formed by a clear majority of neurons (88% ± 1 βIIITUBULIN positive cells). Altogether, our data confirm the ability of NGN2 to induce neuralization in hiPSCs and opens a new point of view in respect to the delivery system method when it comes to transcriptional programming applications.

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