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Deep learning with digital holographic microscopy discriminates apoptosis and necroptosis

Joost Verduijn (UGent) , Louis Van der Meeren (UGent) , Dmitri Krysko (UGent) and Andre Skirtach (UGent)
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Abstract
Regulated cell death modalities such as apoptosis and necroptosis play an important role in regulating different cellular processes. Currently, regulated cell death is identified using the golden standard techniques such as fluorescence microscopy and flow cytometry. However, they require fluorescent labels, which are potentially phototoxic. Therefore, there is a need for the development of new label-free methods. In this work, we apply Digital Holographic Microscopy (DHM) coupled with a deep learning algorithm to distinguish between alive, apoptotic and necroptotic cells in murine cancer cells. This method is solely based on label-free quantitative phase images, where the phase delay of light by cells is quantified and is used to calculate their topography. We show that a combination of label-free DHM in a high-throughput set-up (similar to 10,000 cells per condition) can discriminate between apoptosis, necroptosis and alive cells in the L929sAhFas cell line with a precision of over 85%. To the best of our knowledge, this is the first time deep learning in the form of convolutional neural networks is applied to distinguish-with a high accuracy-apoptosis and necroptosis and alive cancer cells from each other in a label-free manner. It is expected that the approach described here will have a profound impact on research in regulated cell death, biomedicine and the field of (cancer) cell biology in general.
Keywords
Cancer Research, Cell Biology, Cellular and Molecular Neuroscience, Immunology, CELL-DEATH, L929 CELLS, LIVING CELLS, NECROSIS, MECHANISMS, INTERNALIZATION, CLASSIFICATION, PHOTOTOXICITY, LEVEL, GENE

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MLA
Verduijn, Joost, et al. “Deep Learning with Digital Holographic Microscopy Discriminates Apoptosis and Necroptosis.” CELL DEATH DISCOVERY, vol. 7, no. 1, 2021, doi:10.1038/s41420-021-00616-8.
APA
Verduijn, J., Van der Meeren, L., Krysko, D., & Skirtach, A. (2021). Deep learning with digital holographic microscopy discriminates apoptosis and necroptosis. CELL DEATH DISCOVERY, 7(1). https://doi.org/10.1038/s41420-021-00616-8
Chicago author-date
Verduijn, Joost, Louis Van der Meeren, Dmitri Krysko, and Andre Skirtach. 2021. “Deep Learning with Digital Holographic Microscopy Discriminates Apoptosis and Necroptosis.” CELL DEATH DISCOVERY 7 (1). https://doi.org/10.1038/s41420-021-00616-8.
Chicago author-date (all authors)
Verduijn, Joost, Louis Van der Meeren, Dmitri Krysko, and Andre Skirtach. 2021. “Deep Learning with Digital Holographic Microscopy Discriminates Apoptosis and Necroptosis.” CELL DEATH DISCOVERY 7 (1). doi:10.1038/s41420-021-00616-8.
Vancouver
1.
Verduijn J, Van der Meeren L, Krysko D, Skirtach A. Deep learning with digital holographic microscopy discriminates apoptosis and necroptosis. CELL DEATH DISCOVERY. 2021;7(1).
IEEE
[1]
J. Verduijn, L. Van der Meeren, D. Krysko, and A. Skirtach, “Deep learning with digital holographic microscopy discriminates apoptosis and necroptosis,” CELL DEATH DISCOVERY, vol. 7, no. 1, 2021.
@article{8719794,
  abstract     = {{Regulated cell death modalities such as apoptosis and necroptosis play an important role in regulating different cellular processes. Currently, regulated cell death is identified using the golden standard techniques such as fluorescence microscopy and flow cytometry. However, they require fluorescent labels, which are potentially phototoxic. Therefore, there is a need for the development of new label-free methods. In this work, we apply Digital Holographic Microscopy (DHM) coupled with a deep learning algorithm to distinguish between alive, apoptotic and necroptotic cells in murine cancer cells. This method is solely based on label-free quantitative phase images, where the phase delay of light by cells is quantified and is used to calculate their topography. We show that a combination of label-free DHM in a high-throughput set-up (similar to 10,000 cells per condition) can discriminate between apoptosis, necroptosis and alive cells in the L929sAhFas cell line with a precision of over 85%. To the best of our knowledge, this is the first time deep learning in the form of convolutional neural networks is applied to distinguish-with a high accuracy-apoptosis and necroptosis and alive cancer cells from each other in a label-free manner. It is expected that the approach described here will have a profound impact on research in regulated cell death, biomedicine and the field of (cancer) cell biology in general.}},
  articleno    = {{229}},
  author       = {{Verduijn, Joost and Van der Meeren, Louis and Krysko, Dmitri and Skirtach, Andre}},
  issn         = {{2058-7716}},
  journal      = {{CELL DEATH DISCOVERY}},
  keywords     = {{Cancer Research,Cell Biology,Cellular and Molecular Neuroscience,Immunology,CELL-DEATH,L929 CELLS,LIVING CELLS,NECROSIS,MECHANISMS,INTERNALIZATION,CLASSIFICATION,PHOTOTOXICITY,LEVEL,GENE}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{10}},
  title        = {{Deep learning with digital holographic microscopy discriminates apoptosis and necroptosis}},
  url          = {{http://dx.doi.org/10.1038/s41420-021-00616-8}},
  volume       = {{7}},
  year         = {{2021}},
}

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