Deep learning-based predictive identification of neural stem cell differentiation

The differentiation of neural stem cells (NSCs) into neurons is proposed to be critical in devising potential cell-based therapeutic strategies for central nervous system (CNS) diseases, however, the determination and prediction of differentiation is complex and not yet clearly established, especially at the early stage. We hypothesize that deep learning could extract minutiae from large-scale datasets, and present a deep neural network model for predictable reliable identification of NSCs fate. Using only bright field images without artificial labelling, our model is surprisingly effective at identifying the differentiated cell types, even as early as 1 day of culture.

• 1
  A deep learning-based model can accurately identify differentiated cell types from brightfield images of NSCs as early as 1 day of culture.
• 2
  The brightfield model demonstrates strong generalizability and robustness across various inducers, including neurotrophins, hormones, small molecules, and nanoparticles.
• 3
  The deep learning model can predict NSC fate within 1 day, offering a valuable approach for increasing efficiency and reducing costs in molecular screening.