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Vincenzo Costanzo1, Ludovica Altieri1,2, Matteo Marzi2, Silvia Gasparini2, Carlo Brighi3, Cecilia Mannironi1,2, Patrizia Lavia1,2 ((1)IBPM- Italian National Research Council (CNR), Rome, Italy; (2) Dept of Biology and Biotechnology “Charles Darwin”, Sapienza University of Rome, Italy; 3) CrestOptics S.p.A. Rome, Italy)
Neuronal differentiation is a highly complex multistep process that regulates major molecular and cellular changes during neuron determination and specification. Here we present an artificial intelligence (AI)-based videorecording analysis developed with the goal to extract information on crucial windows of early neuronal commitment in living pluripotent cells in real time. We first characterized the commitment to neuronal fate in differentiating cell models exposed to classical inducers (Retinoic Acid or Neurobasal Medium). Immunofluorescence experiments were performed to depict cell shape changes as well as the appearance of specific neuronal markers. We next recorded the process in living cells and developed two novel AI-based algorithms to perform high-throughput analyses of videorecorded cultures and extract information on phenotypic changes elicited during differentiation. Our preliminary results suggest that the AI-based protocols developed here can identify critical temporal windows during which the lack or mutation of specific genes can impact the process, and can also facilitate screening projects for rescuing genes or for novel therapeutic strategies.