Yogo Takao, Iwamoto Yuichiro, Becker Hans Jiro, Kimura Takaharu, Ishida Reiko, Sugiyama-Finnis Ayano, Yokomizo Tomomasa, Suda Toshio, Ota Sadao, Yamazaki Satoshi
Division of Cell Regulation, Center for Experimental Medicine and Systems Biology, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.
Nat Commun. 2025 Jul 14;16(1):6496. doi: 10.1038/s41467-025-61846-3.
Innovative identification technologies for hematopoietic stem cells (HSCs) have expanded the scope of stem cell biology. Clinically, the functional quality of HSCs critically influences the safety and therapeutic efficacy of stem cell therapies. However, most analytical techniques capture only a single snapshot, disregarding the temporal context. A comprehensive understanding of the temporal heterogeneity of HSCs necessitates live-cell, real-time and non-invasive analysis. Here, we developed a prediction system for HSC diversity by integrating single-HSC ex vivo expansion technology with quantitative phase imaging (QPI)-driven machine learning. By analyzing the cellular kinetics of individual HSCs, we discovered previously undetectable diversity that snapshot analysis cannot resolve. The QPI-driven algorithm quantitatively evaluates stemness at the single-cell level and leverages temporal information to significantly improve prediction accuracy. This platform advances the field from snapshot-based identification of HSCs to dynamic, time-resolved prediction of their functional quality based on past cellular kinetics.
造血干细胞（HSCs）的创新识别技术拓展了干细胞生物学的研究范畴。在临床上，造血干细胞的功能质量对干细胞治疗的安全性和疗效至关重要。然而，大多数分析技术仅捕捉单一时刻的情况，忽略了时间背景。要全面了解造血干细胞的时间异质性，就需要对活细胞进行实时、非侵入性分析。在此，我们通过将单个造血干细胞体外扩增技术与定量相成像（QPI）驱动的机器学习相结合，开发了一种造血干细胞多样性预测系统。通过分析单个造血干细胞的细胞动力学，我们发现了快照分析无法解析的、先前未被检测到的多样性。QPI驱动的算法在单细胞水平上定量评估干性，并利用时间信息显著提高预测准确性。该平台将造血干细胞领域从基于快照的识别推进到基于过去细胞动力学对其功能质量进行动态、时间分辨的预测。
