Quantitative and long-term cell imaging with computational hyperspectral interferometry

Abstract

Quantitative phase imaging (QPI) has quickly emerged as a powerful tool for label-free living cell morphology and metabolism monitoring. However, for current QPI techniques, interference signals from different layers overlay with each other and impede nanoscale optical sectioning. This phenomenon leads to unsatisfactory performances for optically thick or complex scattering biological samples. To address this challenge, we have developed an alternative quantitative phase microscopy with computational hyperspectral interferometry. Nanoscale optical sectioning could be achieved with Fourier domain spectral decomposition. Morphological fluctuations and refractive index distribution could be reconstructed simultaneously with 89.2 nm axial resolution and 1.91 nm optical path difference sensitivity. With this method, we established a label-free cell imaging system for long-term cellular dry mass measurement and in-situ dynamic single cell monitoring. Different intrinsic cell growth characteristics of dry mass between HeLa cells and Human Cervical Epithelial Cells (HCerEpiC) were studied. The dry mass of HeLa cells consistently increased before M phase, whereas that of HCerEpiC increased and then decreased. The maximum growth rate of HeLa cells was 11.7% higher than that of HCerEpiC. We also use the proposed method and system to explore the relationship between cellular dry mass distributions and drug effects for cancer cells. The results show that cells with higher nuclear dry mass and nuclear density standard deviations were more likely to survive the chemotherapy. The presented work shows potential values for cell growth dynamics research, cell health characterization, medication guidance and adjuvant drug development.