Quantitative phase imaging with temporal kinetics predicts hematopoietic stem cell diversity

Abstract

Innovative identification technologies for hematopoietic stem cells (HSCs) have advanced the frontiers of stem cell biology. 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 single-cell kinetics with QPI, we discovered previously undetectable diversity among HSCs that snapshot analysis fails to capture. Our QPI-driven algorithm quantitatively evaluates the stemness of individual HSCs and incorporates temporal information to significantly improve prediction accuracy. This platform marks a paradigm shift from "identification" to "prediction", enabling us to forecast HSC status by analyzing their past temporal kinetics.