Deep learning reveals 3D atherosclerotic plaque distribution and composition

Abstract Complications of atherosclerosis are the leading cause of morbidity and mortality worldwide. Various genetically modified mouse models are used to investigate disease trajectory with classical histology, currently the preferred methodology to elucidate plaque composition. Here, we show the...

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Autores principales:	Vanessa Isabell Jurtz, Grethe Skovbjerg, Casper Gravesen Salinas, Urmas Roostalu, Louise Pedersen, Jacob Hecksher-Sørensen, Bidda Rolin, Michael Nyberg, Martijn van de Bunt, Camilla Ingvorsen
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/307dce623c614724b179921559a4ac86
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spelling	oai:doaj.org-article:307dce623c614724b179921559a4ac862021-12-02T11:43:51ZDeep learning reveals 3D atherosclerotic plaque distribution and composition10.1038/s41598-020-78632-42045-2322https://doaj.org/article/307dce623c614724b179921559a4ac862020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-78632-4https://doaj.org/toc/2045-2322Abstract Complications of atherosclerosis are the leading cause of morbidity and mortality worldwide. Various genetically modified mouse models are used to investigate disease trajectory with classical histology, currently the preferred methodology to elucidate plaque composition. Here, we show the strength of light-sheet fluorescence microscopy combined with deep learning image analysis for characterising and quantifying plaque burden and composition in whole aorta specimens. 3D imaging is a non-destructive method that requires minimal ex vivo handling and can be up-scaled to large sample sizes. Combined with deep learning, atherosclerotic plaque in mice can be identified without any ex vivo staining due to the autofluorescent nature of the tissue. The aorta and its branches can subsequently be segmented to determine how anatomical position affects plaque composition and progression. Here, we find the highest plaque accumulation in the aortic arch and brachiocephalic artery. Simultaneously, aortas can be stained for markers of interest (for example the pan immune cell marker CD45) and quantified. In ApoE−/− mice we observe that levels of CD45 reach a plateau after which increases in plaque volume no longer correlate to immune cell infiltration. All underlying code is made publicly available to ease adaption of the method.Vanessa Isabell JurtzGrethe SkovbjergCasper Gravesen SalinasUrmas RoostaluLouise PedersenJacob Hecksher-SørensenBidda RolinMichael NybergMartijn van de BuntCamilla IngvorsenNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Vanessa Isabell Jurtz Grethe Skovbjerg Casper Gravesen Salinas Urmas Roostalu Louise Pedersen Jacob Hecksher-Sørensen Bidda Rolin Michael Nyberg Martijn van de Bunt Camilla Ingvorsen Deep learning reveals 3D atherosclerotic plaque distribution and composition
description	Abstract Complications of atherosclerosis are the leading cause of morbidity and mortality worldwide. Various genetically modified mouse models are used to investigate disease trajectory with classical histology, currently the preferred methodology to elucidate plaque composition. Here, we show the strength of light-sheet fluorescence microscopy combined with deep learning image analysis for characterising and quantifying plaque burden and composition in whole aorta specimens. 3D imaging is a non-destructive method that requires minimal ex vivo handling and can be up-scaled to large sample sizes. Combined with deep learning, atherosclerotic plaque in mice can be identified without any ex vivo staining due to the autofluorescent nature of the tissue. The aorta and its branches can subsequently be segmented to determine how anatomical position affects plaque composition and progression. Here, we find the highest plaque accumulation in the aortic arch and brachiocephalic artery. Simultaneously, aortas can be stained for markers of interest (for example the pan immune cell marker CD45) and quantified. In ApoE−/− mice we observe that levels of CD45 reach a plateau after which increases in plaque volume no longer correlate to immune cell infiltration. All underlying code is made publicly available to ease adaption of the method.
format	article
author	Vanessa Isabell Jurtz Grethe Skovbjerg Casper Gravesen Salinas Urmas Roostalu Louise Pedersen Jacob Hecksher-Sørensen Bidda Rolin Michael Nyberg Martijn van de Bunt Camilla Ingvorsen
author_facet	Vanessa Isabell Jurtz Grethe Skovbjerg Casper Gravesen Salinas Urmas Roostalu Louise Pedersen Jacob Hecksher-Sørensen Bidda Rolin Michael Nyberg Martijn van de Bunt Camilla Ingvorsen
author_sort	Vanessa Isabell Jurtz
title	Deep learning reveals 3D atherosclerotic plaque distribution and composition
title_short	Deep learning reveals 3D atherosclerotic plaque distribution and composition
title_full	Deep learning reveals 3D atherosclerotic plaque distribution and composition
title_fullStr	Deep learning reveals 3D atherosclerotic plaque distribution and composition
title_full_unstemmed	Deep learning reveals 3D atherosclerotic plaque distribution and composition
title_sort	deep learning reveals 3d atherosclerotic plaque distribution and composition
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/307dce623c614724b179921559a4ac86
work_keys_str_mv	AT vanessaisabelljurtz deeplearningreveals3datheroscleroticplaquedistributionandcomposition AT gretheskovbjerg deeplearningreveals3datheroscleroticplaquedistributionandcomposition AT caspergravesensalinas deeplearningreveals3datheroscleroticplaquedistributionandcomposition AT urmasroostalu deeplearningreveals3datheroscleroticplaquedistributionandcomposition AT louisepedersen deeplearningreveals3datheroscleroticplaquedistributionandcomposition AT jacobheckshersørensen deeplearningreveals3datheroscleroticplaquedistributionandcomposition AT biddarolin deeplearningreveals3datheroscleroticplaquedistributionandcomposition AT michaelnyberg deeplearningreveals3datheroscleroticplaquedistributionandcomposition AT martijnvandebunt deeplearningreveals3datheroscleroticplaquedistributionandcomposition AT camillaingvorsen deeplearningreveals3datheroscleroticplaquedistributionandcomposition
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Deep learning reveals 3D atherosclerotic plaque distribution and composition

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