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. Remarkably, 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. Moreover, our approach showcases superior precision and robustness in designed independent test scenarios involving various inducers, including neurotrophins, hormones, small molecule compounds and even nanoparticles, suggesting excellent generalizability and applicability. We anticipate that our accurate and robust deep learning-based platform for NSCs differentiation identification will accelerate the progress of NSCs applications.

提出将神经干细胞（NSC）分化为神经元对于设计潜在的基于细胞的中枢神经系统（CNS）疾病治疗策略至关重要，但是，分化的确定和预测非常复杂，尤其是尚不明确在早期。我们假设深度学习可以从大规模数据集中提取细节，并提出了一种可预测的NSC命运可靠可靠识别的深度神经网络模型。值得注意的是，仅使用明场图像而不进行人工标记，即使在培养1天之初，我们的模型仍可有效地识别分化的细胞类型。此外，我们的方法在涉及各种诱导物（包括神经营养蛋白，激素，小分子化合物，甚至是纳米颗粒，都具有出色的通用性和适用性。我们期望我们基于NSCs识别的准确而强大的基于深度学习的平台将加速NSCs应用程序的进步。