A research team from the Institute of Medical Science at The University of Tokyo has published significant findings in the journal Nature Communications, successfully developing a hematopoietic stem cell quality prediction system that combines quantitative phase imaging with deep learning. This innovative technology overcomes the limitations of traditional “snapshot-style” analysis methods, bringing a major breakthrough to the field of stem cell therapy.
The team utilized quantitative phase imaging (QPI) technology to achieve label-free continuous observation of individual hematopoietic stem cells for up to 96 hours. Project leader Assistant Professor Takao Yogo explained: “For the first time, we discovered that even hematopoietic stem cell populations that appear identical under traditional analysis actually exhibit significant differences in proliferation rate, morphological changes, and motility.”
The study revealed the following key findings:
Uncovered previously unrecognized “infinite diversity” within hematopoietic stem cell populations
Established correlations between cellular dynamic features and Hlf gene expression
Developed a deep learning-based prediction model whose accuracy improves with increasing temporal information
Provided a new standard for dynamic monitoring in stem cell quality assessment
The deep learning system developed by the team can predict the therapeutic potential of cells based on their behavioral characteristics. Dr. Yogo emphasized: “This technology elevates stem cell quality control to an entirely new level, which is critical for ensuring the safety of gene therapy and regenerative medicine.” Currently, the team is optimizing system performance and exploring its practical application in clinical treatments.
This research not only opens new avenues for stem cell biology studies but also lays a technical foundation for developing safer and more effective cell therapy solutions. With further development, this approach holds promise for application to other types of stem cells, driving overall progress in the field of regenerative medicine.
