Monitoring amyloidogenesis with a 3D deep-learning-guided biolaser imaging array

Amyloidogenesis is a critical hallmark for many neurodegenerative diseases and drug screening; however, identifying intermediate states of protein aggregates at an earlier stage remains challenging. Herein, we developed a peptide-encapsulated droplet microlaser to monitor the amyloidogenesis process...

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Main Authors: Chan, Kok Ken, Shang, Linwei, Qiao, Zhen, Liao, Yikai, Kim, Munho, Chen, Yu-Cheng
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/170535
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1705352023-09-19T01:23:38Z Monitoring amyloidogenesis with a 3D deep-learning-guided biolaser imaging array Chan, Kok Ken Shang, Linwei Qiao, Zhen Liao, Yikai Kim, Munho Chen, Yu-Cheng School of Electrical and Electronic Engineering School of Chemical and Biomedical Engineering Engineering::Electrical and electronic engineering Engineering::Bioengineering Amyloidogenesis Biolaser Amyloidogenesis is a critical hallmark for many neurodegenerative diseases and drug screening; however, identifying intermediate states of protein aggregates at an earlier stage remains challenging. Herein, we developed a peptide-encapsulated droplet microlaser to monitor the amyloidogenesis process and evaluate the efficacy of anti-amyloid drugs. The lasing wavelength changes accordingly with the amyloid peptide folding behaviors and nanostructure conformations in the droplet resonator. A 3D deep-learning strategy was developed to directly image minute spectral shifts through a far-field camera. By extracting 1D color information and 2D features from the laser images, the progression of the amyloidogenesis process could be monitored using arrays of laser images from microdroplets. The training set, validation set, and test set of the multimodal learning model achieved outstanding classification accuracies of over 95%. This study shows the great potential of deep-learning-empowered peptide microlaser yields for protein misfolding studies and paves the way for new possibilities for high-throughput imaging of cavity biosensing. Agency for Science, Technology and Research (A*STAR) Ministry of Education (MOE) Y.-C.C. would especially like to thank the financial support from A*STAR Singapore under its AME YIRG-Grant (Project No. A2084c0063). This project is also supported by Institute for Digital Molecular Analytics and Science under the Research Centres of Excellence scheme of MOE. M.K. thanks the financial support from the Ministry of Education under grant MOE-T2EP50120-0001. 2023-09-19T01:23:38Z 2023-09-19T01:23:38Z 2022 Journal Article Chan, K. K., Shang, L., Qiao, Z., Liao, Y., Kim, M. & Chen, Y. (2022). Monitoring amyloidogenesis with a 3D deep-learning-guided biolaser imaging array. Nano Letters, 22(22), 8949-8956. https://dx.doi.org/10.1021/acs.nanolett.2c03148 1530-6984 https://hdl.handle.net/10356/170535 10.1021/acs.nanolett.2c03148 36367840 2-s2.0-85141976394 22 22 8949 8956 en A2084c0063 MOE-T2EP50120-0001 Nano Letters © 2022 American Chemical Society. 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::Electrical and electronic engineering
Engineering::Bioengineering
Amyloidogenesis
Biolaser
spellingShingle Engineering::Electrical and electronic engineering
Engineering::Bioengineering
Amyloidogenesis
Biolaser
Chan, Kok Ken
Shang, Linwei
Qiao, Zhen
Liao, Yikai
Kim, Munho
Chen, Yu-Cheng
Monitoring amyloidogenesis with a 3D deep-learning-guided biolaser imaging array
description Amyloidogenesis is a critical hallmark for many neurodegenerative diseases and drug screening; however, identifying intermediate states of protein aggregates at an earlier stage remains challenging. Herein, we developed a peptide-encapsulated droplet microlaser to monitor the amyloidogenesis process and evaluate the efficacy of anti-amyloid drugs. The lasing wavelength changes accordingly with the amyloid peptide folding behaviors and nanostructure conformations in the droplet resonator. A 3D deep-learning strategy was developed to directly image minute spectral shifts through a far-field camera. By extracting 1D color information and 2D features from the laser images, the progression of the amyloidogenesis process could be monitored using arrays of laser images from microdroplets. The training set, validation set, and test set of the multimodal learning model achieved outstanding classification accuracies of over 95%. This study shows the great potential of deep-learning-empowered peptide microlaser yields for protein misfolding studies and paves the way for new possibilities for high-throughput imaging of cavity biosensing.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Chan, Kok Ken
Shang, Linwei
Qiao, Zhen
Liao, Yikai
Kim, Munho
Chen, Yu-Cheng
format Article
author Chan, Kok Ken
Shang, Linwei
Qiao, Zhen
Liao, Yikai
Kim, Munho
Chen, Yu-Cheng
author_sort Chan, Kok Ken
title Monitoring amyloidogenesis with a 3D deep-learning-guided biolaser imaging array
title_short Monitoring amyloidogenesis with a 3D deep-learning-guided biolaser imaging array
title_full Monitoring amyloidogenesis with a 3D deep-learning-guided biolaser imaging array
title_fullStr Monitoring amyloidogenesis with a 3D deep-learning-guided biolaser imaging array
title_full_unstemmed Monitoring amyloidogenesis with a 3D deep-learning-guided biolaser imaging array
title_sort monitoring amyloidogenesis with a 3d deep-learning-guided biolaser imaging array
publishDate 2023
url https://hdl.handle.net/10356/170535
_version_ 1779156681532375040