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Artificial intelligence–coupled plasmonic infrared sensor for detection of structural protein biomarkers in neurodegenerative diseases
Diagnosis of neurodegenerative disorders (NDDs) including Parkinson’s disease and Alzheimer’s disease is challenging owing to the lack of tools to detect preclinical biomarkers. The misfolding of proteins into oligomeric and fibrillar aggregates plays an important role in the development and progres...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337894/ https://www.ncbi.nlm.nih.gov/pubmed/37436975 http://dx.doi.org/10.1126/sciadv.adg9644 |
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author | Kavungal, Deepthy Magalhães, Pedro Kumar, Senthil T. Kolla, Rajasekhar Lashuel, Hilal A. Altug, Hatice |
author_facet | Kavungal, Deepthy Magalhães, Pedro Kumar, Senthil T. Kolla, Rajasekhar Lashuel, Hilal A. Altug, Hatice |
author_sort | Kavungal, Deepthy |
collection | PubMed |
description | Diagnosis of neurodegenerative disorders (NDDs) including Parkinson’s disease and Alzheimer’s disease is challenging owing to the lack of tools to detect preclinical biomarkers. The misfolding of proteins into oligomeric and fibrillar aggregates plays an important role in the development and progression of NDDs, thus underscoring the need for structural biomarker–based diagnostics. We developed an immunoassay-coupled nanoplasmonic infrared metasurface sensor that detects proteins linked to NDDs, such as alpha-synuclein, with specificity and differentiates the distinct structural species using their unique absorption signatures. We augmented the sensor with an artificial neural network enabling unprecedented quantitative prediction of oligomeric and fibrillar protein aggregates in their mixture. The microfluidic integrated sensor can retrieve time-resolved absorbance fingerprints in the presence of a complex biomatrix and is capable of multiplexing for the simultaneous monitoring of multiple pathology-associated biomarkers. Thus, our sensor is a promising candidate for the clinical diagnosis of NDDs, disease monitoring, and evaluation of novel therapies. |
format | Online Article Text |
id | pubmed-10337894 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-103378942023-07-13 Artificial intelligence–coupled plasmonic infrared sensor for detection of structural protein biomarkers in neurodegenerative diseases Kavungal, Deepthy Magalhães, Pedro Kumar, Senthil T. Kolla, Rajasekhar Lashuel, Hilal A. Altug, Hatice Sci Adv Physical and Materials Sciences Diagnosis of neurodegenerative disorders (NDDs) including Parkinson’s disease and Alzheimer’s disease is challenging owing to the lack of tools to detect preclinical biomarkers. The misfolding of proteins into oligomeric and fibrillar aggregates plays an important role in the development and progression of NDDs, thus underscoring the need for structural biomarker–based diagnostics. We developed an immunoassay-coupled nanoplasmonic infrared metasurface sensor that detects proteins linked to NDDs, such as alpha-synuclein, with specificity and differentiates the distinct structural species using their unique absorption signatures. We augmented the sensor with an artificial neural network enabling unprecedented quantitative prediction of oligomeric and fibrillar protein aggregates in their mixture. The microfluidic integrated sensor can retrieve time-resolved absorbance fingerprints in the presence of a complex biomatrix and is capable of multiplexing for the simultaneous monitoring of multiple pathology-associated biomarkers. Thus, our sensor is a promising candidate for the clinical diagnosis of NDDs, disease monitoring, and evaluation of novel therapies. American Association for the Advancement of Science 2023-07-12 /pmc/articles/PMC10337894/ /pubmed/37436975 http://dx.doi.org/10.1126/sciadv.adg9644 Text en Copyright © 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited. |
spellingShingle | Physical and Materials Sciences Kavungal, Deepthy Magalhães, Pedro Kumar, Senthil T. Kolla, Rajasekhar Lashuel, Hilal A. Altug, Hatice Artificial intelligence–coupled plasmonic infrared sensor for detection of structural protein biomarkers in neurodegenerative diseases |
title | Artificial intelligence–coupled plasmonic infrared sensor for detection of structural protein biomarkers in neurodegenerative diseases |
title_full | Artificial intelligence–coupled plasmonic infrared sensor for detection of structural protein biomarkers in neurodegenerative diseases |
title_fullStr | Artificial intelligence–coupled plasmonic infrared sensor for detection of structural protein biomarkers in neurodegenerative diseases |
title_full_unstemmed | Artificial intelligence–coupled plasmonic infrared sensor for detection of structural protein biomarkers in neurodegenerative diseases |
title_short | Artificial intelligence–coupled plasmonic infrared sensor for detection of structural protein biomarkers in neurodegenerative diseases |
title_sort | artificial intelligence–coupled plasmonic infrared sensor for detection of structural protein biomarkers in neurodegenerative diseases |
topic | Physical and Materials Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10337894/ https://www.ncbi.nlm.nih.gov/pubmed/37436975 http://dx.doi.org/10.1126/sciadv.adg9644 |
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