Optica Open
Browse
arXiv.svg (5.58 kB)

Phase Imaging with Computational Specificity (PICS) for measuring dry mass changes in sub-cellular compartments

Download (5.58 kB)
preprint
posted on 2023-11-30, 19:30 authored by 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, Gabriel Popescu
Due to its specificity, fluorescence microscopy (FM) has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit FM's utility. Recently, it has been shown that artificial intelligence (AI) can transform one form of contrast into another. We present 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 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.

History

Disclaimer

This arXiv metadata record was not reviewed or approved by, nor does it necessarily express or reflect the policies or opinions of, arXiv.

Usage metrics

    Licence

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC