Mapping Drug-Induced Neuropathy through In-Situ Motor Protein Tracking and Machine Learning

Zhigao Yi; Huxin Gao; Xianglin Ji; Xin-Yi Yeo; Suet Yen Chong; Yujie Mao; Baiwen Luo; Chao Shen; Sanyang Han; Jiong-Wei Wang; Sangyong Jung; Peng Shi; Hongliang Ren; Xiaogang Liu

doi:10.1021/jacs.1c07312

Mapping Drug-Induced Neuropathy through In-Situ Motor Protein Tracking and Machine Learning

Zhigao Yi, Huxin Gao, Xianglin Ji, Xin-Yi Yeo, Suet Yen Chong, Yujie Mao, Baiwen Luo, Chao Shen, Sanyang Han, Jiong-Wei Wang, Sangyong Jung, Peng Shi, Hongliang Ren, Xiaogang Liu

Research output: Contribution to journal › Article › peer-review

14 Scopus citations

Abstract

Chemotherapy can induce toxicity in the central and peripheral nervous systems and result in chronic adverse reactions that impede continuous treatment and reduce patient quality of life. There is a current lack of research to predict, identify, and offset drug-induced neurotoxicity. Rapid and accurate assessment of potential neuropathy is crucial for cost-effective diagnosis and treatment. Here we report dynamic near-infrared upconversion imaging that allows intraneuronal transport to be traced in real time with millisecond resolution, but without photobleaching or blinking. Drug-induced neurotoxicity can be screened prior to phenotyping, on the basis of subtle abnormalities of kinetic characteristics in intraneuronal transport. Moreover, we demonstrate that combining the upconverting nanoplatform with machine learning offers a powerful tool for mapping chemotherapy-induced peripheral neuropathy and assessing drug-induced neurotoxicity.

Original language	English (US)
Journal	Journal of the American Chemical Society
DOIs	https://doi.org/10.1021/jacs.1c07312
State	Published - Sep 1 2021
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2021-09-03
Acknowledged KAUST grant number(s): OSR-2018-CRG7-3736
Acknowledgements: This work was supported by the Agency for Science, Technology and Research (A*STAR) (A1983c0038), the National Research Foundation, Prime Minister’s Office, Singapore, under the NRF Investigatorship program (Award No. NRF-NRFI05-2019-0003), the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2018-CRG7-3736, the NUS NANONASH Program (NUHSRO/2020/002/NanoNash/LOA; R143000B43114), the Singapore Ministry of Health’s National Medical Research Council (NMRC/OFYIRG/0081/2018), and the National Natural Science Foundation of China (21771135, 21871071). S.Y.C. would like to thank the generous support from the ESR/TENG GL PhD scholarship program. We thank Akihiro Yamanaka, Angelo All, John Jia En Chua, Yinghua Qu, Liangliang Liang, Yuxia Liu, Sirui Liu, and Di Fu for helpful discussions.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

Biochemistry
Colloid and Surface Chemistry
General Chemistry
Catalysis

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10.1021/jacs.1c07312

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title = "Mapping Drug-Induced Neuropathy through In-Situ Motor Protein Tracking and Machine Learning",

abstract = "Chemotherapy can induce toxicity in the central and peripheral nervous systems and result in chronic adverse reactions that impede continuous treatment and reduce patient quality of life. There is a current lack of research to predict, identify, and offset drug-induced neurotoxicity. Rapid and accurate assessment of potential neuropathy is crucial for cost-effective diagnosis and treatment. Here we report dynamic near-infrared upconversion imaging that allows intraneuronal transport to be traced in real time with millisecond resolution, but without photobleaching or blinking. Drug-induced neurotoxicity can be screened prior to phenotyping, on the basis of subtle abnormalities of kinetic characteristics in intraneuronal transport. Moreover, we demonstrate that combining the upconverting nanoplatform with machine learning offers a powerful tool for mapping chemotherapy-induced peripheral neuropathy and assessing drug-induced neurotoxicity.",

author = "Zhigao Yi and Huxin Gao and Xianglin Ji and Xin-Yi Yeo and Chong, {Suet Yen} and Yujie Mao and Baiwen Luo and Chao Shen and Sanyang Han and Jiong-Wei Wang and Sangyong Jung and Peng Shi and Hongliang Ren and Xiaogang Liu",

note = "KAUST Repository Item: Exported on 2021-09-03 Acknowledged KAUST grant number(s): OSR-2018-CRG7-3736 Acknowledgements: This work was supported by the Agency for Science, Technology and Research (A*STAR) (A1983c0038), the National Research Foundation, Prime Minister{\textquoteright}s Office, Singapore, under the NRF Investigatorship program (Award No. NRF-NRFI05-2019-0003), the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2018-CRG7-3736, the NUS NANONASH Program (NUHSRO/2020/002/NanoNash/LOA; R143000B43114), the Singapore Ministry of Health{\textquoteright}s National Medical Research Council (NMRC/OFYIRG/0081/2018), and the National Natural Science Foundation of China (21771135, 21871071). S.Y.C. would like to thank the generous support from the ESR/TENG GL PhD scholarship program. We thank Akihiro Yamanaka, Angelo All, John Jia En Chua, Yinghua Qu, Liangliang Liang, Yuxia Liu, Sirui Liu, and Di Fu for helpful discussions. This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

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doi = "10.1021/jacs.1c07312",

language = "English (US)",

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AU - Yi, Zhigao

AU - Gao, Huxin

AU - Ji, Xianglin

AU - Yeo, Xin-Yi

AU - Chong, Suet Yen

AU - Mao, Yujie

AU - Luo, Baiwen

AU - Shen, Chao

AU - Han, Sanyang

AU - Wang, Jiong-Wei

AU - Jung, Sangyong

AU - Shi, Peng

AU - Ren, Hongliang

AU - Liu, Xiaogang

N1 - KAUST Repository Item: Exported on 2021-09-03 Acknowledged KAUST grant number(s): OSR-2018-CRG7-3736 Acknowledgements: This work was supported by the Agency for Science, Technology and Research (A*STAR) (A1983c0038), the National Research Foundation, Prime Minister’s Office, Singapore, under the NRF Investigatorship program (Award No. NRF-NRFI05-2019-0003), the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. OSR-2018-CRG7-3736, the NUS NANONASH Program (NUHSRO/2020/002/NanoNash/LOA; R143000B43114), the Singapore Ministry of Health’s National Medical Research Council (NMRC/OFYIRG/0081/2018), and the National Natural Science Foundation of China (21771135, 21871071). S.Y.C. would like to thank the generous support from the ESR/TENG GL PhD scholarship program. We thank Akihiro Yamanaka, Angelo All, John Jia En Chua, Yinghua Qu, Liangliang Liang, Yuxia Liu, Sirui Liu, and Di Fu for helpful discussions. This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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N2 - Chemotherapy can induce toxicity in the central and peripheral nervous systems and result in chronic adverse reactions that impede continuous treatment and reduce patient quality of life. There is a current lack of research to predict, identify, and offset drug-induced neurotoxicity. Rapid and accurate assessment of potential neuropathy is crucial for cost-effective diagnosis and treatment. Here we report dynamic near-infrared upconversion imaging that allows intraneuronal transport to be traced in real time with millisecond resolution, but without photobleaching or blinking. Drug-induced neurotoxicity can be screened prior to phenotyping, on the basis of subtle abnormalities of kinetic characteristics in intraneuronal transport. Moreover, we demonstrate that combining the upconverting nanoplatform with machine learning offers a powerful tool for mapping chemotherapy-induced peripheral neuropathy and assessing drug-induced neurotoxicity.

AB - Chemotherapy can induce toxicity in the central and peripheral nervous systems and result in chronic adverse reactions that impede continuous treatment and reduce patient quality of life. There is a current lack of research to predict, identify, and offset drug-induced neurotoxicity. Rapid and accurate assessment of potential neuropathy is crucial for cost-effective diagnosis and treatment. Here we report dynamic near-infrared upconversion imaging that allows intraneuronal transport to be traced in real time with millisecond resolution, but without photobleaching or blinking. Drug-induced neurotoxicity can be screened prior to phenotyping, on the basis of subtle abnormalities of kinetic characteristics in intraneuronal transport. Moreover, we demonstrate that combining the upconverting nanoplatform with machine learning offers a powerful tool for mapping chemotherapy-induced peripheral neuropathy and assessing drug-induced neurotoxicity.

UR - http://hdl.handle.net/10754/670900

UR - https://pubs.acs.org/doi/10.1021/jacs.1c07312

U2 - 10.1021/jacs.1c07312

DO - 10.1021/jacs.1c07312

M3 - Article

C2 - 34469145

SN - 0002-7863

JO - Journal of the American Chemical Society

JF - Journal of the American Chemical Society

ER -

Mapping Drug-Induced Neuropathy through In-Situ Motor Protein Tracking and Machine Learning

Abstract

Bibliographical note

ASJC Scopus subject areas

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