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He Y, He S, Kandel ME, Lee YJ, Hu C, Sobh N, Anastasio MA, Popescu G. Cell Cycle Stage Classification Using Phase Imaging with Computational Specificity. ACS Photonics 2022; 9:1264-1273. [PMID: 35480491 PMCID: PMC9026251 DOI: 10.1021/acsphotonics.1c01779] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Indexed: 06/01/2023]
Abstract
Traditional methods for cell cycle stage classification rely heavily on fluorescence microscopy to monitor nuclear dynamics. These methods inevitably face the typical phototoxicity and photobleaching limitations of fluorescence imaging. Here, we present a cell cycle detection workflow using the principle of phase imaging with computational specificity (PICS). The proposed method uses neural networks to extract cell cycle-dependent features from quantitative phase imaging (QPI) measurements directly. Our results indicate that this approach attains very good accuracy in classifying live cells into G1, S, and G2/M stages, respectively. We also demonstrate that the proposed method can be applied to study single-cell dynamics within the cell cycle as well as cell population distribution across different stages of the cell cycle. We envision that the proposed method can become a nondestructive tool to analyze cell cycle progression in fields ranging from cell biology to biopharma applications.
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Affiliation(s)
- Yuchen
R. He
- Department
of Electrical and Computer Engineering, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
- Beckman
Institute for Advanced Science and Technology, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Shenghua He
- Department
of Computer Science & Engineering, Washington
University in St. Louis, St. Louis, Missouri 63130, United States
| | - Mikhail E. Kandel
- Department
of Electrical and Computer Engineering, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
- Beckman
Institute for Advanced Science and Technology, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Young Jae Lee
- Beckman
Institute for Advanced Science and Technology, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
- Neuroscience
Program, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Chenfei Hu
- Department
of Electrical and Computer Engineering, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
- Beckman
Institute for Advanced Science and Technology, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Nahil Sobh
- Beckman
Institute for Advanced Science and Technology, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
- NCSA
Center for Artificial Intelligence Innovation, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Mark A. Anastasio
- Department
of Electrical and Computer Engineering, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
- Beckman
Institute for Advanced Science and Technology, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
- Department
of Bioengineering, University of Illinois
at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Gabriel Popescu
- Department
of Electrical and Computer Engineering, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
- Beckman
Institute for Advanced Science and Technology, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
- Department
of Bioengineering, University of Illinois
at Urbana−Champaign, Urbana, Illinois 61801, United States
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Kandel ME, Rubessa M, He YR, Schreiber S, Meyers S, Matter Naves L, Sermersheim MK, Sell GS, Szewczyk MJ, Sobh N, Wheeler MB, Popescu G. Reproductive outcomes predicted by phase imaging with computational specificity of spermatozoon ultrastructure. Proc Natl Acad Sci U S A 2020; 117:18302-9. [PMID: 32690677 DOI: 10.1073/pnas.2001754117] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
The ability to evaluate sperm at the microscopic level, at high-throughput, would be useful for assisted reproductive technologies (ARTs), as it can allow specific selection of sperm cells for in vitro fertilization (IVF). The tradeoff between intrinsic imaging and external contrast agents is particularly acute in reproductive medicine. The use of fluorescence labels has enabled new cell-sorting strategies and given new insights into developmental biology. Nevertheless, using extrinsic contrast agents is often too invasive for routine clinical operation. Raising questions about cell viability, especially for single-cell selection, clinicians prefer intrinsic contrast in the form of phase-contrast, differential-interference contrast, or Hoffman modulation contrast. While such instruments are nondestructive, the resulting image suffers from a lack of specificity. In this work, we provide a template to circumvent the tradeoff between cell viability and specificity by combining high-sensitivity phase imaging with deep learning. In order to introduce specificity to label-free images, we trained a deep-convolutional neural network to perform semantic segmentation on quantitative phase maps. This approach, a form of phase imaging with computational specificity (PICS), allowed us to efficiently analyze thousands of sperm cells and identify correlations between dry-mass content and artificial-reproduction outcomes. Specifically, we found that the dry-mass content ratios between the head, midpiece, and tail of the cells can predict the percentages of success for zygote cleavage and embryo blastocyst formation.
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