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Kandel ME, He YR, Lee YJ, Chen THY, Sullivan KM, Aydin O, Saif MTA, Kong H, Sobh N, Popescu G. Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments. Nat Commun 2020; 11:6256. [PMID: 33288761 PMCID: PMC7721808 DOI: 10.1038/s41467-020-20062-x] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 10/28/2020] [Indexed: 12/28/2022] Open
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.
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Affiliation(s)
- Mikhail E Kandel
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Yuchen R He
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Young Jae Lee
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Taylor Hsuan-Yu Chen
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | - Onur Aydin
- Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - M Taher A Saif
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Hyunjoon Kong
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Carl Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Nahil Sobh
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
| | - Gabriel Popescu
- Beckman Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
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