Morphology-based deep learning enables accurate detection of senescence in mesenchymal stem cell cultures.

Journal: BMC biology
Published Date: Jan 2, 2024

Abstract

BACKGROUND: Cell senescence is a sign of aging and plays a significant role in the pathogenesis of age-related disorders. For cell therapy, senescence may compromise the quality and efficacy of cells, posing potential safety risks. Mesenchymal stem cells (MSCs) are currently undergoing extensive research for cell therapy, thus necessitating the development of effective methods to evaluate senescence. Senescent MSCs exhibit distinctive morphology that can be used for detection. However, morphological assessment during MSC production is often subjective and uncertain. New tools are required for the reliable evaluation of senescent single cells on a large scale in live imaging of MSCs.

Authors

  • Liangge He
    Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, 1066 Xueyuan Avenue, Shenzhen, 518060, China.
  • Mingzhu Li
    Department of Thyroid and Breast Surgery, East Branch of Quanzhou First Hospital, Fujian 362000, China.
  • Xinglie Wang
    Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China.
  • Xiaoyan Wu
    Department of Cardiology, Lishui Central Hospital and the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui, China.
  • Guanghui Yue
    National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
  • Tianfu Wang
    School of Biomedical Engineering, Shenzhen University Health Sciences Center, Shenzhen, Guangdong 518060, P.R.China.
  • Yan Zhou
    Department of Computer Science, University of Texas at Dallas, Richardson, Texas 75080, United States.
  • Baiying Lei
  • Guangqian Zhou
    Department of Medical Cell Biology and Genetics, Shenzhen Key Laboratory of Anti-Aging and Regenerative Medicine, Shenzhen Engineering Laboratory of Regenerative Technologies for Orthopedic Diseases, Shenzhen University Medical School, Shenzhen, 518060, China. gqzhou@szu.edu.cn.

Keywords

External Resources

Popular Topics
Recent Journals