期刊
MICRON
卷 150, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.micron.2021.103121
关键词
Quantitative analysis of LSEC porosity; Machine learning; Fenestrations; Liver sinusoidal endothelial cells; Atomic force microscopy; Super-resolution microscopy
类别
资金
- European Union [766181]
- Research Council of Norway [288565]
- Polish National Science Centre under the SYMFONIA 3 project [UMO-2015/16/W/NZ4/00070]
- Marie Curie Actions (MSCA) [766181] Funding Source: Marie Curie Actions (MSCA)
This study utilized three super resolution techniques to obtain images of liver sinusoidal endothelial cell fenestrations and compared three different methods of fenestration image analysis. In addition to comparing analysis methods, user bias was also studied through comparison of data obtained by different users.
Liver Sinusoidal Endothelial Cells (LSEC) line the hepatic vasculature providing blood filtration via trans-membrane 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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