Label-free metabolic optical biomarkers track stem cell fate transition in real time.
Publication Year:
2024
PubMed ID:
38718114
Funding Grants:
Public Summary:
Understanding how stem cells develop and differentiate is vital for research and biomanufacturing, but existing methods can’t track this process in real-time. To address this, researchers developed a technique using fluorescence lifetime imaging (FLIM) with machine learning to monitor stem cell changes as they happen. They identified 56 key markers that reveal shifts in hematopoietic stem cell (HSC) development, including choices in their growth paths. The method also created a “metabolic stemness” score to assess stem cell quality, providing insights into how to improve stem cell maintenance and production outside the body.
Scientific Abstract:
Tracking stem cell fate transition is crucial for understanding their development and optimizing biomanufacturing. Destructive single-cell methods provide a pseudotemporal landscape of stem cell differentiation but cannot monitor stem cell fate in real time. We established a metabolic optical metric using label-free fluorescence lifetime imaging microscopy (FLIM), feature extraction and machine learning-assisted analysis, for real-time cell fate tracking. From a library of 205 metabolic optical biomarker (MOB) features, we identified 56 associated with hematopoietic stem cell (HSC) differentiation. These features collectively describe HSC fate transition and detect its bifurcate lineage choice. We further derived a MOB score measuring the "metabolic stemness" of single cells and distinguishing their division patterns. This score reveals a distinct role of asymmetric division in rescuing stem cells with compromised metabolic stemness and a unique mechanism of PI3K inhibition in promoting ex vivo HSC maintenance. MOB profiling is a powerful tool for tracking stem cell fate transition and improving their biomanufacturing from a single-cell perspective.
