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Deep learning with digital holographic microscopy discriminates apoptosis and necroptosis

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 fluorescen...

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Autores principales: Verduijn, Joost, Van der Meeren, Louis, Krysko, Dmitri V., Skirtach, André G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8413278/
https://www.ncbi.nlm.nih.gov/pubmed/34475384
http://dx.doi.org/10.1038/s41420-021-00616-8
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author Verduijn, Joost
Van der Meeren, Louis
Krysko, Dmitri V.
Skirtach, André G.
author_facet Verduijn, Joost
Van der Meeren, Louis
Krysko, Dmitri V.
Skirtach, André G.
author_sort Verduijn, Joost
collection PubMed
description 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 (~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.
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spelling pubmed-84132782021-09-22 Deep learning with digital holographic microscopy discriminates apoptosis and necroptosis Verduijn, Joost Van der Meeren, Louis Krysko, Dmitri V. Skirtach, André G. Cell Death Discov Article 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 (~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. Nature Publishing Group UK 2021-09-02 /pmc/articles/PMC8413278/ /pubmed/34475384 http://dx.doi.org/10.1038/s41420-021-00616-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Verduijn, Joost
Van der Meeren, Louis
Krysko, Dmitri V.
Skirtach, André G.
Deep learning with digital holographic microscopy discriminates apoptosis and necroptosis
title Deep learning with digital holographic microscopy discriminates apoptosis and necroptosis
title_full Deep learning with digital holographic microscopy discriminates apoptosis and necroptosis
title_fullStr Deep learning with digital holographic microscopy discriminates apoptosis and necroptosis
title_full_unstemmed Deep learning with digital holographic microscopy discriminates apoptosis and necroptosis
title_short Deep learning with digital holographic microscopy discriminates apoptosis and necroptosis
title_sort deep learning with digital holographic microscopy discriminates apoptosis and necroptosis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8413278/
https://www.ncbi.nlm.nih.gov/pubmed/34475384
http://dx.doi.org/10.1038/s41420-021-00616-8
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