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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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. |
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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 |
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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. |
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School of Electrical and Electronic Engineering |
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School of Electrical and Electronic Engineering Chan, Kok Ken Shang, Linwei Qiao, Zhen Liao, Yikai Kim, Munho Chen, Yu-Cheng |
format |
Article |
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Chan, Kok Ken Shang, Linwei Qiao, Zhen Liao, Yikai Kim, Munho Chen, Yu-Cheng |
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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 |
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2023 |
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https://hdl.handle.net/10356/170535 |
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1779156681532375040 |