STED microscopy: A simplified method for liver sinusoidal endothelial fenestrae analysis

Tuning of Liver Sieve: The Interplay between Actin and Myosin Regulatory Light Chain Regulates Fenestration Size and Number in Murine Liver Sinusoidal Endothelial Cells

Liver sinusoidal endothelial cells (LSECs) facilitate the efficient transport of macromolecules and solutes between the blood and hepatocytes. The efficiency of this transport is realized via transcellular nanopores, called fenestrations. The mean fenestration size is 140 ± 20 nm, with the range from 50 nm to 350 nm being mostly below the limits of diffraction of visible light. The cellular mechanisms controlling fenestrations are still poorly understood. In this study, we tested a hypothesis that both Rho kinase (ROCK) and myosin light chain (MLC) kinase (MLCK)-dependent phosphorylation of MLC regulates fenestrations. We verified the hypothesis using a combination of several molecular inhibitors and by applying two high-resolution microscopy modalities: structured illumination microscopy (SIM) and scanning electron microscopy (SEM). We demonstrated precise, dose-dependent, and reversible regulation of the mean fenestration diameter within a wide range from 120 nm to 220 nm and the fine-tuning of the porosity in a range from ~0% up to 12% using the ROCK pathway. Moreover, our findings indicate that MLCK is involved in the formation of new fenestrations—after inhibiting MLCK, closed fenestrations cannot be reopened with other agents. We, therefore, conclude that the Rho-ROCK pathway is responsible for the control of the fenestration diameter, while the inhibition of MLCK prevents the formation of new fenestrations.

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

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.

Characterizing liver sinusoidal endothelial cell fenestrae on soft substrates upon AFM imaging and deep learning

BACKGROUND

Liver sinusoidal endothelial cells (LSECs) display unique fenestrated morphology. Alterations in the size and number of fenestrae play a crucial role in the progression of various liver diseases. While their features have been visualized using atomic force microscopy (AFM), the in situ imaging methods and off-line analyses are further required for fenestra quantification.

METHODS

Primary mouse LSECs were cultured on a collagen-I-coated culture dish, or a polydimethylsiloxane (PDMS) or polyacrylamide (PA) hydrogel substrate. An AFM contact mode was applied to visualize fenestrae on individual fixed LSECs. Collected images were analyzed using an in-house developed image recognition program based on fully convolutional networks (FCN).

RESULTS

Key scanning parameters were first optimized for visualizing the fenestrae on LSECs on culture dish, which was also applicable for the LSECs cultured on various hydrogels. The intermediate-magnification morphology images of LSECs were used for developing the FCN-based, fenestra recognition program. This program enabled us to recognize the vast majority of fenestrae from AFM images after twice trainings at a typical accuracy of 81.6% on soft substrate and also quantify the statistics of porosity, number of fenestrae and distribution of fenestra diameter.

CONCLUSIONS

Combining AFM imaging with FCN training is able to quantify the morphological distributions of LSEC fenestrae on various substrates.

SIGNIFICANCE

AFM images acquired and analyzed here provided the global information of surface ultramicroscopic structures over an entire cell, which is fundamental in understanding their regulatory mechanisms and pathophysiological relevance in fenestra-like evolution of individual cells on stiffness-varied substrates.

Actin Depolymerization in Dedifferentiated Liver Sinusoidal Endothelial Cells Promotes Fenestrae Re-Formation

Liver sinusoidal endothelial cells (LSECs) possess fenestrae, which are key for the exchange between blood and hepatocytes. Alterations in their number or diameter have important implications for hepatic function in liver diseases. They are lost early in the development of hepatic fibrosis through a process called capillarization. In this study, we aimed to demonstrate whether in vitro dedifferentiated LSECs that have lost fenestrae are able to re-form these structures. Using stimulated emission depletion super-resolution microscopy in combination with transmission electron microscopy, we analyzed fenestrae formation in a model mimicking the capillarization process in vitro. Actin is known to be involved in fenestrae regulation in differentiated LSECs. Using cytochalasin D, an actin-depolymerizing agent, we demonstrated that dedifferentiated LSECs remain capable of forming fenestrae. Conclusion: We provide a new insight into the complex role of actin in fenestrae formation and in the control of their size and show that LSEC fenestrae re-formation is possible, suggesting that this process could be used during fibrosis regression to try to restore exchanges and hepatocyte functions.