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<p>Cellular morphology, a critical manifestation of biological characteristics, is linked to functions. In traditional cell detection, invasive labeling and detection methods not only compromise cellular viability but also entail labor-intensive workflows. Here we presented a non-invasive artificial intelligence framework that integrated deep learning (DL) and machine learning (ML) to predict the immunomodulatory capacity of mesenchymal stem cells (MSCs) through morphological profiling. The improved PreAct-ResNet50 encoder-decoder architecture was used to achieve high-accuracy instance segmentation of cells and nuclei, enabling quantification of morphological features. A LightGBM-based predictive model was subsequently employed to predict MSCs immunomodulatory biomarkers through morphological features. This dual-model system demonstrated satisfactory cell segmentation and biological characteristics prediction capabilities through performance testing. Our method provided an efficient, non- invasive tool for real-time MSCs potency assessment, which could enhance quality controls in cell therapy manufacturing.</p>

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Deep learning-based in silico labeling for analyzing morphological features of MSCs to predict immunomodulatory capacity

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月10日

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  • Zhiyu Liu,
  • Gang An,
  • Xiao Liang,
  • Xumin Wu,
  • Junyuan Hu,
  • Haijun Wang,
  • Jingfeng Ou,
  • Xiuping Zeng,
  • Zhiliang Xia,
  • Kaixiang Hou,
  • Wanglong Chu,
  • Jianbin Ye,
  • Cui Liao,
  • Zhengmian Zhang,
  • Muyun Liu

摘要

Cellular morphology, a critical manifestation of biological characteristics, is linked to functions. In traditional cell detection, invasive labeling and detection methods not only compromise cellular viability but also entail labor-intensive workflows. Here we presented a non-invasive artificial intelligence framework that integrated deep learning (DL) and machine learning (ML) to predict the immunomodulatory capacity of mesenchymal stem cells (MSCs) through morphological profiling. The improved PreAct-ResNet50 encoder-decoder architecture was used to achieve high-accuracy instance segmentation of cells and nuclei, enabling quantification of morphological features. A LightGBM-based predictive model was subsequently employed to predict MSCs immunomodulatory biomarkers through morphological features. This dual-model system demonstrated satisfactory cell segmentation and biological characteristics prediction capabilities through performance testing. Our method provided an efficient, non- invasive tool for real-time MSCs potency assessment, which could enhance quality controls in cell therapy manufacturing.