Classification of amyloidosis by model‐assisted mass spectrometry‐based proteomics

Detalhes bibliográficos
Autor(a) principal: Palstrøm, Nicolai Bjødstrup
Data de Publicação: 2022
Outros Autores: Rojek, Aleksandra M., Møller, Hanne E.H., Hansen, Charlotte Toftmann, Matthiesen, Rune, Rasmussen, Lars Melholt, Abildgaard, Niels, Beck, Hans Christian
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/130615
Resumo: Funding Information: Funding: This research was partly funded by a “Center of Clinical Excellence” research grant from the Health Region of Southern Denmark to Odense Amyloidosis Center (AmyC). Publisher Copyright: © 2021 by the authors. Li-censee MDPI, Basel, Switzerland.
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spelling Classification of amyloidosis by model‐assisted mass spectrometry‐based proteomicsAmyloidosisLaser microdissectionMachine learningMass spectrometryProteomicsCatalysisMolecular BiologySpectroscopyComputer Science ApplicationsPhysical and Theoretical ChemistryOrganic ChemistryInorganic ChemistryFunding Information: Funding: This research was partly funded by a “Center of Clinical Excellence” research grant from the Health Region of Southern Denmark to Odense Amyloidosis Center (AmyC). Publisher Copyright: © 2021 by the authors. Li-censee MDPI, Basel, Switzerland.Amyloidosis is a rare disease caused by the misfolding and extracellular aggregation of proteins as insoluble fibrillary deposits localized either in specific organs or systemically through-out the body. The organ targeted and the disease progression and outcome is highly dependent on the specific fibril‐forming protein, and its accurate identification is essential to the choice of treat-ment. Mass spectrometry‐based proteomics has become the method of choice for the identification of the amyloidogenic protein. Regrettably, this identification relies on manual and subjective inter-pretation of mass spectrometry data by an expert, which is undesirable and may bias diagnosis. To circumvent this, we developed a statistical model‐assisted method for the unbiased identification of amyloid‐containing biopsies and amyloidosis subtyping. Based on data from mass spectrometric analysis of amyloid‐containing biopsies and corresponding controls. A Boruta method applied on a random forest classifier was applied to proteomics data obtained from the mass spectrometric analysis of 75 laser dissected Congo Red positive amyloid‐containing biopsies and 78 Congo Red negative biopsies to identify novel “amyloid signature” proteins that included clusterin, fibulin‐1, vitronectin complement component C9 and also three collagen proteins, as well as the well‐known amyloid signature proteins apolipoprotein E, apolipoprotein A4, and serum amyloid P. A SVM learning algorithm were trained on the mass spectrometry data from the analysis of the 75 amyloid-containing biopsies and 78 amyloid‐negative control biopsies. The trained algorithm performed su-perior in the discrimination of amyloid‐containing biopsies from controls, with an accuracy of 1.0 when applied to a blinded mass spectrometry validation data set of 103 prospectively collected am-yloid‐containing biopsies. Moreover, our method successfully classified amyloidosis patients ac-cording to the subtype in 102 out of 103 blinded cases. Collectively, our model‐assisted approach identified novel amyloid‐associated proteins and demonstrated the use of mass spectrometry‐based data in clinical diagnostics of disease by the unbiased and reliable model‐assisted classification of amyloid deposits and of the specific amyloid subtype.NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)Centro de Estudos de Doenças Crónicas (CEDOC)RUNPalstrøm, Nicolai BjødstrupRojek, Aleksandra M.Møller, Hanne E.H.Hansen, Charlotte ToftmannMatthiesen, RuneRasmussen, Lars MelholtAbildgaard, NielsBeck, Hans Christian2022-01-11T03:18:25Z2022-01-012022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/130615eng1661-6596PURE: 35865963https://doi.org/10.3390/ijms23010319info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:09:13Zoai:run.unl.pt:10362/130615Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:46:49.384598Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Classification of amyloidosis by model‐assisted mass spectrometry‐based proteomics
title Classification of amyloidosis by model‐assisted mass spectrometry‐based proteomics
spellingShingle Classification of amyloidosis by model‐assisted mass spectrometry‐based proteomics
Palstrøm, Nicolai Bjødstrup
Amyloidosis
Laser microdissection
Machine learning
Mass spectrometry
Proteomics
Catalysis
Molecular Biology
Spectroscopy
Computer Science Applications
Physical and Theoretical Chemistry
Organic Chemistry
Inorganic Chemistry
title_short Classification of amyloidosis by model‐assisted mass spectrometry‐based proteomics
title_full Classification of amyloidosis by model‐assisted mass spectrometry‐based proteomics
title_fullStr Classification of amyloidosis by model‐assisted mass spectrometry‐based proteomics
title_full_unstemmed Classification of amyloidosis by model‐assisted mass spectrometry‐based proteomics
title_sort Classification of amyloidosis by model‐assisted mass spectrometry‐based proteomics
author Palstrøm, Nicolai Bjødstrup
author_facet Palstrøm, Nicolai Bjødstrup
Rojek, Aleksandra M.
Møller, Hanne E.H.
Hansen, Charlotte Toftmann
Matthiesen, Rune
Rasmussen, Lars Melholt
Abildgaard, Niels
Beck, Hans Christian
author_role author
author2 Rojek, Aleksandra M.
Møller, Hanne E.H.
Hansen, Charlotte Toftmann
Matthiesen, Rune
Rasmussen, Lars Melholt
Abildgaard, Niels
Beck, Hans Christian
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)
Centro de Estudos de Doenças Crónicas (CEDOC)
RUN
dc.contributor.author.fl_str_mv Palstrøm, Nicolai Bjødstrup
Rojek, Aleksandra M.
Møller, Hanne E.H.
Hansen, Charlotte Toftmann
Matthiesen, Rune
Rasmussen, Lars Melholt
Abildgaard, Niels
Beck, Hans Christian
dc.subject.por.fl_str_mv Amyloidosis
Laser microdissection
Machine learning
Mass spectrometry
Proteomics
Catalysis
Molecular Biology
Spectroscopy
Computer Science Applications
Physical and Theoretical Chemistry
Organic Chemistry
Inorganic Chemistry
topic Amyloidosis
Laser microdissection
Machine learning
Mass spectrometry
Proteomics
Catalysis
Molecular Biology
Spectroscopy
Computer Science Applications
Physical and Theoretical Chemistry
Organic Chemistry
Inorganic Chemistry
description Funding Information: Funding: This research was partly funded by a “Center of Clinical Excellence” research grant from the Health Region of Southern Denmark to Odense Amyloidosis Center (AmyC). Publisher Copyright: © 2021 by the authors. Li-censee MDPI, Basel, Switzerland.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-11T03:18:25Z
2022-01-01
2022-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/130615
url http://hdl.handle.net/10362/130615
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 1661-6596
PURE: 35865963
https://doi.org/10.3390/ijms23010319
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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