Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Lovergne, Lila, Ghosh, Dhruba, Schuck, Renaud, Polyzos, Aris A., Chen, Andrew D., Martin, Michael C., Barnard, Edward S., Brown, James B., McMurray, Cynthia T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
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
_version_ 1783732466259853312
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
work_keys_str_mv AT lovergnelila aninfraredspectralbiomarkeraccuratelypredictsneurodegenerativediseaseclassintheabsenceofovertsymptoms
AT ghoshdhruba aninfraredspectralbiomarkeraccuratelypredictsneurodegenerativediseaseclassintheabsenceofovertsymptoms
AT schuckrenaud aninfraredspectralbiomarkeraccuratelypredictsneurodegenerativediseaseclassintheabsenceofovertsymptoms
AT polyzosarisa aninfraredspectralbiomarkeraccuratelypredictsneurodegenerativediseaseclassintheabsenceofovertsymptoms
AT chenandrewd aninfraredspectralbiomarkeraccuratelypredictsneurodegenerativediseaseclassintheabsenceofovertsymptoms
AT martinmichaelc aninfraredspectralbiomarkeraccuratelypredictsneurodegenerativediseaseclassintheabsenceofovertsymptoms
AT barnardedwards aninfraredspectralbiomarkeraccuratelypredictsneurodegenerativediseaseclassintheabsenceofovertsymptoms
AT brownjamesb aninfraredspectralbiomarkeraccuratelypredictsneurodegenerativediseaseclassintheabsenceofovertsymptoms
AT mcmurraycynthiat aninfraredspectralbiomarkeraccuratelypredictsneurodegenerativediseaseclassintheabsenceofovertsymptoms
AT lovergnelila infraredspectralbiomarkeraccuratelypredictsneurodegenerativediseaseclassintheabsenceofovertsymptoms
AT ghoshdhruba infraredspectralbiomarkeraccuratelypredictsneurodegenerativediseaseclassintheabsenceofovertsymptoms
AT schuckrenaud infraredspectralbiomarkeraccuratelypredictsneurodegenerativediseaseclassintheabsenceofovertsymptoms
AT polyzosarisa infraredspectralbiomarkeraccuratelypredictsneurodegenerativediseaseclassintheabsenceofovertsymptoms
AT chenandrewd infraredspectralbiomarkeraccuratelypredictsneurodegenerativediseaseclassintheabsenceofovertsymptoms
AT martinmichaelc infraredspectralbiomarkeraccuratelypredictsneurodegenerativediseaseclassintheabsenceofovertsymptoms
AT barnardedwards infraredspectralbiomarkeraccuratelypredictsneurodegenerativediseaseclassintheabsenceofovertsymptoms
AT brownjamesb infraredspectralbiomarkeraccuratelypredictsneurodegenerativediseaseclassintheabsenceofovertsymptoms
AT mcmurraycynthiat infraredspectralbiomarkeraccuratelypredictsneurodegenerativediseaseclassintheabsenceofovertsymptoms