Статья

Quantitative analysis methods for studying fenestrations in liver sinusoidal endothelial cells. A comparative study

Karolina SzafranskaDepartment of Medical Biology, Vascular Biology Research Group, University of Tromsø (UiT), The Arctic University of Norway, Norway; Centre for Nanometer-Scale Science and Advanced Materials, NANOSAM, Faculty of Physics, Astronomy, and Applied Computer Science, Jagiellonian University, Krakow, Poland. Electronic address: [email protected]Christopher Florian HolteDepartment of Medical Biology, Vascular Biology Research Group, University of Tromsø (UiT), The Arctic University of Norway, NorwayLarissa D. KruseDepartment of Medical Biology, Vascular Biology Research Group, University of Tromsø (UiT), The Arctic University of Norway, NorwayHongli MaoDepartment of Medical Biology, Vascular Biology Research Group, University of Tromsø (UiT), The Arctic University of Norway, NorwayCristina Ionica ØieDepartment of Medical Biology, Vascular Biology Research Group, University of Tromsø (UiT), The Arctic University of Norway, NorwayMarek SzymońskiCentre for Nanometer-Scale Science and Advanced Materials, NANOSAM, Faculty of Physics, Astronomy, and Applied Computer Science, Jagiellonian University, Krakow, PolandBartłomiej ZapotocznyDepartment of Medical Biology, Vascular Biology Research Group, University of Tromsø (UiT), The Arctic University of Norway, Norway; Institute of Nuclear Physics, Polish Academy of Sciences, 31-342, Krakow, PolandPeter McCourtDepartment of Medical Biology, Vascular Biology Research Group, University of Tromsø (UiT), The Arctic University of Norway, Norway

2021en

ABI

Аннотация

Liver Sinusoidal Endothelial Cells (LSEC) line the hepatic vasculature providing blood filtration via transmembrane nanopores called fenestrations. These structures are 50-300 nm in diameter, which is below the resolution limit of a conventional light microscopy. To date, there is no standardized method of fenestration image analysis. With this study, we provide and compare three different approaches: manual measurements, a semi-automatic (threshold-based) method, and an automatic method based on user-friendly open source machine learning software. Images were obtained using three super resolution techniques - atomic force microscopy (AFM), scanning electron microscopy (SEM), and structured illumination microscopy (SIM). Parameters describing fenestrations such as diameter, area, roundness, frequency, and porosity were measured. Finally, we studied the user bias by comparison of the data obtained by five different users applying provided analysis methods.

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Идентификаторы

DOI: 10.1016/j.micron.2021.103121

Цитирования и источники

Цитирований: 2Использованных источников: 0

Показатели — AkademScholar