Deep learning-based predictive classification of functional subpopulations of hematopoietic stem cells and multipotent progenitors.

Journal: Stem cell research & therapy
PMID: 38475857

Abstract

BACKGROUND: Hematopoietic stem cells (HSCs) and multipotent progenitors (MPPs) play a pivotal role in maintaining lifelong hematopoiesis. The distinction between stem cells and other progenitors, as well as the assessment of their functions, has long been a central focus in stem cell research. In recent years, deep learning has emerged as a powerful tool for cell image analysis and classification/prediction.

Authors

  • Shen Wang
    Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Jianzhong Han
    Coriell Institute for Medical Research, Camden, NJ, USA.
  • Jingru Huang
    Shanghai Key Laboratory of Medical Epigenetics, Laboratory of Cancer Epigenetics, Institutes of Biomedical Sciences, Medical College of Fudan University, Chinese Academy of Medical Sciences, Shanghai, People's Republic of China.
  • Khayrul Islam
    Department of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA, USA.
  • Yuheng Shi
    University of Texas Health Science Center at Houston, Houston, Texas, USA.
  • Yuyuan Zhou
    Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.
  • DongWook Kim
    Soft Robotics Research Center, Seoul National University, Seoul, Korea.
  • Jane Zhou
    Health and Human Biology, Brown University, Providence, RI, USA.
  • Zhaorui Lian
    Coriell Institute for Medical Research, Camden, NJ, USA.
  • Yaling Liu
    Southwestern University of Finance and Economics, Chengdu, Sichuan, China.
  • Jian Huang
    Center for Informational Biology, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, P. R. China.

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