TY - JOUR

T1 - Deep learning reveals 3D atherosclerotic plaque distribution and composition

AU - Jurtz, Vanessa Isabell

AU - Skovbjerg, Grethe

AU - Salinas, Casper Gravesen

AU - Roostalu, Urmas

AU - Pedersen, Louise

AU - Hecksher-Sørensen, Jacob

AU - Rolin, Bidda

AU - Nyberg, Michael

AU - van de Bunt, Martijn

AU - Ingvorsen, Camilla

N1 - Publisher Copyright: © 2020, The Author(s).

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

U2 - 10.1038/s41598-020-78632-4

DO - 10.1038/s41598-020-78632-4

M3 - Journal article

C2 - 33299076

AN - SCOPUS:85097371003

VL - 10

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

M1 - 21523

ER -