NGN2 mmRNA-Based Transcriptional Programming in Microfluidic Guides hiPSCs Toward Neural Fate With Multiple Identities - Tolomeo, Anna Maria; Laterza, Cecilia; Grespan, Eleonora; Michielin, Federica; Canals, Isaac; Kokaia, Zaal; Muraca, Maurizio; Gagliano, Onelia; Elvassore, Nicola (Frontiers in Cellular Neuroscience, 2021)
Recent advancements in cell engineering has 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 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 pluripotent stem cell fate change has not been thoroughly investigated yet. Here we aimed at understanding whether the use of an NGN2 mRNA-based approach combined with a miniaturized system, which allows a higher transfection efficiency in a cost-effective system, is able to drive pluripotent stem cells 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.