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

Abstract

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.

54 pages, 18 figures; typos corrected

References in corpus (5)

• A Survey on Deep Learning in Medical Image Analysis
• Deep learning-based virtual histology staining using auto-fluorescence of label-free tissue
• PhaseStain: Digital staining of label-free quantitative phase microscopy images using deep learning
• Learning to see through multimode fibers
• HoloStain: Holographic virtual staining of individual biological cells

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