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An infrared spectral biomarker accurately predicts neurodegenerative disease class in the absence of overt symptoms
Although some neurodegenerative diseases can be identified by behavioral characteristics relatively late in disease progression, we currently lack methods to predict who has developed disease before the onset of symptoms, when onset will occur, or the outcome of therapeutics. New biomarkers are need...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8329289/ https://www.ncbi.nlm.nih.gov/pubmed/34341363 http://dx.doi.org/10.1038/s41598-021-93686-8 |
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author | Lovergne, Lila Ghosh, Dhruba Schuck, Renaud Polyzos, Aris A. Chen, Andrew D. Martin, Michael C. Barnard, Edward S. Brown, James B. McMurray, Cynthia T. |
author_facet | Lovergne, Lila Ghosh, Dhruba Schuck, Renaud Polyzos, Aris A. Chen, Andrew D. Martin, Michael C. Barnard, Edward S. Brown, James B. McMurray, Cynthia T. |
author_sort | Lovergne, Lila |
collection | PubMed |
description | Although some neurodegenerative diseases can be identified by behavioral characteristics relatively late in disease progression, we currently lack methods to predict who has developed disease before the onset of symptoms, when onset will occur, or the outcome of therapeutics. New biomarkers are needed. Here we describe spectral phenotyping, a new kind of biomarker that makes disease predictions based on chemical rather than biological endpoints in cells. Spectral phenotyping uses Fourier Transform Infrared (FTIR) spectromicroscopy to produce an absorbance signature as a rapid physiological indicator of disease state. FTIR spectromicroscopy has over the past been used in differential diagnoses of manifest disease. Here, we report that the unique FTIR chemical signature accurately predicts disease class in mouse with high probability in the absence of brain pathology. In human cells, the FTIR biomarker accurately predicts neurodegenerative disease class using fibroblasts as surrogate cells. |
format | Online Article Text |
id | pubmed-8329289 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-83292892021-08-04 An infrared spectral biomarker accurately predicts neurodegenerative disease class in the absence of overt symptoms Lovergne, Lila Ghosh, Dhruba Schuck, Renaud Polyzos, Aris A. Chen, Andrew D. Martin, Michael C. Barnard, Edward S. Brown, James B. McMurray, Cynthia T. Sci Rep Article Although some neurodegenerative diseases can be identified by behavioral characteristics relatively late in disease progression, we currently lack methods to predict who has developed disease before the onset of symptoms, when onset will occur, or the outcome of therapeutics. New biomarkers are needed. Here we describe spectral phenotyping, a new kind of biomarker that makes disease predictions based on chemical rather than biological endpoints in cells. Spectral phenotyping uses Fourier Transform Infrared (FTIR) spectromicroscopy to produce an absorbance signature as a rapid physiological indicator of disease state. FTIR spectromicroscopy has over the past been used in differential diagnoses of manifest disease. Here, we report that the unique FTIR chemical signature accurately predicts disease class in mouse with high probability in the absence of brain pathology. In human cells, the FTIR biomarker accurately predicts neurodegenerative disease class using fibroblasts as surrogate cells. Nature Publishing Group UK 2021-08-02 /pmc/articles/PMC8329289/ /pubmed/34341363 http://dx.doi.org/10.1038/s41598-021-93686-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Lovergne, Lila Ghosh, Dhruba Schuck, Renaud Polyzos, Aris A. Chen, Andrew D. Martin, Michael C. Barnard, Edward S. Brown, James B. McMurray, Cynthia T. An infrared spectral biomarker accurately predicts neurodegenerative disease class in the absence of overt symptoms |
title | An infrared spectral biomarker accurately predicts neurodegenerative disease class in the absence of overt symptoms |
title_full | An infrared spectral biomarker accurately predicts neurodegenerative disease class in the absence of overt symptoms |
title_fullStr | An infrared spectral biomarker accurately predicts neurodegenerative disease class in the absence of overt symptoms |
title_full_unstemmed | An infrared spectral biomarker accurately predicts neurodegenerative disease class in the absence of overt symptoms |
title_short | An infrared spectral biomarker accurately predicts neurodegenerative disease class in the absence of overt symptoms |
title_sort | infrared spectral biomarker accurately predicts neurodegenerative disease class in the absence of overt symptoms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8329289/ https://www.ncbi.nlm.nih.gov/pubmed/34341363 http://dx.doi.org/10.1038/s41598-021-93686-8 |
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