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Deep learning-based predictive identification of neural stem cell differentiation

Author

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  • Yanjing Zhu

    (Tongji Hospital, Tongji University School of Medicine, School of Life Science and Technology, Tongji University
    Tongji University, Ministry of Education)

  • Ruiqi Huang

    (Tongji Hospital, Tongji University School of Medicine, School of Life Science and Technology, Tongji University
    Tongji University, Ministry of Education)

  • Zhourui Wu

    (Tongji Hospital, Tongji University School of Medicine, School of Life Science and Technology, Tongji University
    Tongji University, Ministry of Education)

  • Simin Song

    (Tongji Hospital, Tongji University School of Medicine, School of Life Science and Technology, Tongji University
    Tongji University, Ministry of Education)

  • Liming Cheng

    (Tongji Hospital, Tongji University School of Medicine, School of Life Science and Technology, Tongji University
    Tongji University, Ministry of Education)

  • Rongrong Zhu

    (Tongji Hospital, Tongji University School of Medicine, School of Life Science and Technology, Tongji University
    Tongji University, Ministry of Education)

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.

Suggested Citation

  • Yanjing Zhu & Ruiqi Huang & Zhourui Wu & Simin Song & Liming Cheng & Rongrong Zhu, 2021. "Deep learning-based predictive identification of neural stem cell differentiation," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22758-0
    DOI: 10.1038/s41467-021-22758-0
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    1. Kavitha Rani Balmuri & Srinivas Konda & Wen-Cheng Lai & Parameshachari Bidare Divakarachari & Kavitha Malali Vishveshwarappa Gowda & Hemalatha Kivudujogappa Lingappa, 2022. "A Long Short-Term Memory Network-Based Radio Resource Management for 5G Network," Future Internet, MDPI, vol. 14(6), pages 1-20, June.
    2. Liang Wang & Jingyi Du & Qilu Liu & Dongshuang Wang & Wenhan Wang & Ming Lei & Keyi Li & Yiwei Li & Aijun Hao & Yuanhua Sang & Fan Yi & Wenjuan Zhou & Hong Liu & Chuanbin Mao & Jichuan Qiu, 2024. "Wrapping stem cells with wireless electrical nanopatches for traumatic brain injury therapy," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    3. Qi, Yufei & Sajadi, S. Mohammad & Baghaei, S. & Rezaei, R. & Li, Wei, 2024. "Digital technologies in sports: Opportunities, challenges, and strategies for safeguarding athlete wellbeing and competitive integrity in the digital era," Technology in Society, Elsevier, vol. 77(C).

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