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

Mikhail E. Kandel, Yuchen R. He, Young Jae Lee, Taylor Hsuan-Yu Chen, Kathryn Michele Sullivan, Onur Aydin, M. Taher A. Saif, Hyunjoon Kong, Nahil Sobh () and Gabriel Popescu ()
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Mikhail E. Kandel: University of Illinois at Urbana-Champaign
Yuchen R. He: University of Illinois at Urbana-Champaign
Young Jae Lee: University of Illinois at Urbana-Champaign
Taylor Hsuan-Yu Chen: University of Illinois at Urbana-Champaign
Kathryn Michele Sullivan: University of Illinois at Urbana-Champaign
Onur Aydin: University of Illinois at Urbana-Champaign
M. Taher A. Saif: University of Illinois at Urbana-Champaign
Hyunjoon Kong: University of Illinois at Urbana-Champaign
Nahil Sobh: University of Illinois at Urbana-Champaign
Gabriel Popescu: University of Illinois at Urbana-Champaign

Nature Communications, 2020, vol. 11, issue 1, 1-10

Abstract: Abstract 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.

Date: 2020
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DOI: 10.1038/s41467-020-20062-x

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