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The microscopic anatomy of the liver is a complex and intricate system that comprises numerous structural units known as liver lobules, each of which is comparable in size to a sesame seed. These hexagonal structures consist of plates of liver cells or hepatocytes, which are characterized by their versatility and abundance of cellular apparatus like rough and smooth ER, Golgi apparatus, peroxisomes, and mitochondria.
Hepatocytes perform a variety of essential functions. They secrete...
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A Standardized Method for the Analysis of Liver Sinusoidal Endothelial Cells and Their Fenestrations by Scanning Electron Microscopy
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Quantitative analysis methods for studying fenestrations in liver sinusoidal endothelial cells. A comparative study.

K Szafranska1, C F Holte2, L D Kruse2

  • 1Department 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.

Micron (Oxford, England : 1993)
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Summary

This study introduces and compares three methods for analyzing liver sinusoidal endothelial cell (LSEC) fenestrations, crucial for blood filtration. Machine learning offers a promising automated approach to overcome current limitations in fenestration image analysis.

Keywords:
Atomic force microscopyFenestrationsLiver sinusoidal endothelial cellsMachine learningQuantitative analysis of LSEC porositySuper-resolution microscopy

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Area of Science:

  • Cell Biology
  • Microscopy
  • Image Analysis

Background:

  • Liver sinusoidal endothelial cells (LSEC) are vital for hepatic blood filtration through fenestrations.
  • Fenestrations, nanopores of 50-300 nm, are below light microscopy resolution.
  • A standardized method for fenestration image analysis is currently lacking.

Purpose of the Study:

  • To develop and compare three distinct methods for analyzing LSEC fenestrations.
  • To evaluate manual, semi-automatic, and automatic (machine learning) image analysis approaches.
  • To assess user bias in fenestration parameter measurements.

Main Methods:

  • Super-resolution microscopy techniques: atomic force microscopy (AFM), scanning electron microscopy (SEM), and structured illumination microscopy (SIM).
  • Image analysis methods: manual measurements, threshold-based semi-automatic analysis, and open-source machine learning software.
  • Measurement of fenestration parameters: diameter, area, roundness, frequency, and porosity.

Main Results:

  • Comparison of quantitative data across the three imaging modalities and analysis methods.
  • Identification of user bias through analysis by five different users.
  • Validation of an automated machine learning approach for fenestration analysis.

Conclusions:

  • The study provides a comparative framework for LSEC fenestration image analysis.
  • Machine learning offers a reproducible and potentially less biased method for fenestration quantification.
  • Standardized analysis methods are essential for advancing research on LSEC function and liver disease.