• DOI:10.1038/s41467-020-20062-x
• Corpus ID: 212725023

Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments

@article{Kandel2020PhaseIW,
  title={Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments},
  author={Mikhail Eugene Kandel and Yuchen R. He and Young Jae Lee and Taylor Hsuan-Yu Chen and Kathryn M. Sullivan and Onur Aydin and M. Taher A. Saif and Hyun Joon Kong and Nahil Atef Sobh and Gabriel Popescu},
  journal={Nature Communications},
  year={2020},
  volume={11}
}

• M. Kandel, Yuchen R. He, +7 authors G. Popescu
• Published 19 February 2020
• Biology
• Nature Communications

Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy’s utility. Recently, it has been shown that artificial intelligence (AI) can transform one form of contrast into another. We present phase imaging with computational specificity (PICS), a combination of quantitative phase imaging and AI, which provides information about unlabeled live cells with high specificity. Our imaging system allows for automatic training, while inference is built into the acquisition software and runs in real-time. Applying the computed fluorescence maps back to the quantitative phase imaging (QPI) data, we measured the growth of both nuclei and cytoplasm independently, over many days, without loss of viability. Using a QPI method that suppresses multiple scattering, we measured the dry mass content of individual cell nuclei within spheroids. In its current implementation, PICS offers a versatile quantitative technique for continuous simultaneous monitoring of individual cellular components in biological applications where long-term label-free imaging is desirable.