Deep learning-based predictive identification of neural stem cell differentiation

Yanjing Zhu, Ruiqi Huang, Zhourui Wu, Simin Song, Liming Cheng () and Rongrong Zhu ()
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Yanjing Zhu: Tongji Hospital, Tongji University School of Medicine, School of Life Science and Technology, Tongji University
Ruiqi Huang: Tongji Hospital, Tongji University School of Medicine, School of Life Science and Technology, Tongji University
Zhourui Wu: Tongji Hospital, Tongji University School of Medicine, School of Life Science and Technology, Tongji University
Simin Song: Tongji Hospital, Tongji University School of Medicine, School of Life Science and Technology, Tongji University
Liming Cheng: Tongji Hospital, Tongji University School of Medicine, School of Life Science and Technology, Tongji University
Rongrong Zhu: Tongji Hospital, Tongji University School of Medicine, School of Life Science and Technology, Tongji University

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: Abstract 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.

Date: 2021
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DOI: 10.1038/s41467-021-22758-0

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