Brain cell laser powered by deep-learning-enhanced laser modes

Single cellular lasers have recently attracted tremendous research due to their outstanding lasing characteristics for cell sensing and tracking. Thanks to enhanced light−cell interactions in Fabry–Pérot microcavities, transverse laser modes from cellular lasers are highly correlated to the spatial...

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Main Authors: Qiao, Zhen, Sun, Wen, Zhang, Na, Ang, Randall, Wang, Wenjie, Chew, Sing Yian, Chen, Yu-Cheng
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/159910
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1599102022-07-05T07:53:22Z Brain cell laser powered by deep-learning-enhanced laser modes Qiao, Zhen Sun, Wen Zhang, Na Ang, Randall Wang, Wenjie Chew, Sing Yian Chen, Yu-Cheng School of Electrical and Electronic Engineering Lee Kong Chian School of Medicine (LKCMedicine) School of Chemical and Biomedical Engineering Engineering::Bioengineering Biological Lasers Cell Phenotyping Single cellular lasers have recently attracted tremendous research due to their outstanding lasing characteristics for cell sensing and tracking. Thanks to enhanced light−cell interactions in Fabry–Pérot microcavities, transverse laser modes from cellular lasers are highly correlated to the spatial biophysical properties of cells. However, the huge complexity and randomness of laser modes set a critical challenge towards practical applications in cell analysis. In this study, deep learning is applied to unravel the complex laser modes generated from single-cell lasers by establishing the correlation between laser modes and cellular physical properties. Primary cells extracted from rat brains and cell-like droplets are investigated and trained through a convolutional neuron network based on laser mode images. Detailed simulations and experiments are conducted to study the effect of cell size on laser modes. Predictions of cell diameters with a sub-micron accuracy are achieved with deep learning. Finally, the potential application of using deep-learning-enhanced laser modes for cell classification is demonstrated. Neuron and glial cells extracted from rat brains are classified through hyperspectral images of laser modes. The results demonstrate that deep learning has the potential to enable laser modes with biological significance and functions, offering new possibilities for biophotonic applications. Agency for Science, Technology and Research (A*STAR) Ministry of Education (MOE) This research was supported by A*STAR under its AME YIRG Grant (Project No. A2084c0063). Funding support by the Ministry of Education Academic Research Tier 1 grant (RG38/19) was acknowledged by S.Y.C. and N.Z. 2022-07-05T07:53:21Z 2022-07-05T07:53:21Z 2021 Journal Article Qiao, Z., Sun, W., Zhang, N., Ang, R., Wang, W., Chew, S. Y. & Chen, Y. (2021). Brain cell laser powered by deep-learning-enhanced laser modes. Advanced Optical Materials, 9(22), 2101421-. https://dx.doi.org/10.1002/adom.202101421 2195-1071 https://hdl.handle.net/10356/159910 10.1002/adom.202101421 2-s2.0-85114108406 22 9 2101421 en A2084c0063 RG38/19 Advanced Optical Materials © 2021 Wiley-VCH GmbH. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Bioengineering
Biological Lasers
Cell Phenotyping
spellingShingle Engineering::Bioengineering
Biological Lasers
Cell Phenotyping
Qiao, Zhen
Sun, Wen
Zhang, Na
Ang, Randall
Wang, Wenjie
Chew, Sing Yian
Chen, Yu-Cheng
Brain cell laser powered by deep-learning-enhanced laser modes
description Single cellular lasers have recently attracted tremendous research due to their outstanding lasing characteristics for cell sensing and tracking. Thanks to enhanced light−cell interactions in Fabry–Pérot microcavities, transverse laser modes from cellular lasers are highly correlated to the spatial biophysical properties of cells. However, the huge complexity and randomness of laser modes set a critical challenge towards practical applications in cell analysis. In this study, deep learning is applied to unravel the complex laser modes generated from single-cell lasers by establishing the correlation between laser modes and cellular physical properties. Primary cells extracted from rat brains and cell-like droplets are investigated and trained through a convolutional neuron network based on laser mode images. Detailed simulations and experiments are conducted to study the effect of cell size on laser modes. Predictions of cell diameters with a sub-micron accuracy are achieved with deep learning. Finally, the potential application of using deep-learning-enhanced laser modes for cell classification is demonstrated. Neuron and glial cells extracted from rat brains are classified through hyperspectral images of laser modes. The results demonstrate that deep learning has the potential to enable laser modes with biological significance and functions, offering new possibilities for biophotonic applications.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Qiao, Zhen
Sun, Wen
Zhang, Na
Ang, Randall
Wang, Wenjie
Chew, Sing Yian
Chen, Yu-Cheng
format Article
author Qiao, Zhen
Sun, Wen
Zhang, Na
Ang, Randall
Wang, Wenjie
Chew, Sing Yian
Chen, Yu-Cheng
author_sort Qiao, Zhen
title Brain cell laser powered by deep-learning-enhanced laser modes
title_short Brain cell laser powered by deep-learning-enhanced laser modes
title_full Brain cell laser powered by deep-learning-enhanced laser modes
title_fullStr Brain cell laser powered by deep-learning-enhanced laser modes
title_full_unstemmed Brain cell laser powered by deep-learning-enhanced laser modes
title_sort brain cell laser powered by deep-learning-enhanced laser modes
publishDate 2022
url https://hdl.handle.net/10356/159910
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