Label-free cell viability assay using phase imaging with computational specificity

Abstract Existing approaches to evaluate cell viability involve cell staining with chemical reagents. However, this step of exogenous staining makes these methods undesirable for rapid, nondestructive and long term investigation. Here, we present instantaneous viability assessment of unlabeled cells using phase imaging with computation specificity (PICS). This new concept utilizes deep learning techniques to compute viability markers associated with the specimen measured by quantitative phase imaging. Demonstrated on HeLa cells culture, the proposed method reports approximately 95% accuracy in identifying injured and dead cells. Further comparison of cell morphology with labeled HeLa cells suggests that potential adverse effect on cell dynamics introduced by the viability reagents can be avoided using the label-free investigation method, which would be valuable for a broad range of biomedical applications.