Atomic force microscopy reveals the dynamic morphology of fenestrations in live liver sinusoidal endothelial cells

Here, we report an atomic force microscopy (AFM)-based imaging method for resolving the fine nanostructures (e.g., fenestrations) in the membranes of live primary murine liver sinusoidal endothelial cells (LSECs). From data on topographical and nanomechanical properties of the selected cell areas collected within 1 min, we traced the dynamic rearrangement of the cell actin cytoskeleton connected with the formation or closing of cell fenestrations, both in non-stimulated LSECs as well as in response to cytochalasin B and antimycin A. In conclusion, AFM-based imaging permitted the near real-time measurements of dynamic changes in fenestrations in live LSECs

Space dependent mean field approximation modelling

C. Zerafa and R. Cauchi acknowledge the support of the Strategic Educational Pathways Scholarship Scheme (Malta). These STEPS scholarships are part-financed by the European Union European Social Fund. B. Zapotoczny thanks for the PhD grant under Sub-Action 8.2.2 Regional Innovation Strategies, Activity 8.2 Know-How Transfer, Priority VIII Regional Business Personnel of the Human Capital Operational Programme, co-funded from the EU resources within the European Social Fund as well as the state budget and the Lubuskie Voivodship.It is shown that the self-consistency condition which is the basic equation for calculating the mean-field order parameter of any mean-field model Hamiltonian can be replaced by the standard Metropolis Monte Carlo scheme. The advantage of this method is its ease of implementation for both the homogeneous mean-field order parameter and the heterogeneous one. To be specific, the mean-field version of the Ising model spin system is discussed in detail and the resulting magnetization is the same as in the case of solving the respective mean-field self-consistency equation. In addition, it is shown that if a high temperature phase of such system is quenched below critical temperature then the mean field experienced by spins develops into a network of domains in analogous way as it happens with the spins in the case of the exact many-body Hamiltonian system and the coarsening processes start to take place. To show that the introduced Metropolis Monte Carlo method works also in case of the continuous variables the order parameter for the Maier-Saupe model for nematic liquid crystals has been calculated.peer-reviewe

Quantification of fenestrations in liver sinusoidal endothelial cells by atomic force microscopy

Liver sinusoidal endothelial cells present unique morphology characterized by the presence of transmembrane pores called fenestrations. The size and number of fenestrations in live cells change dynamically in response to variety of chemical and physical factors. Although scanning electron microscopy is a well-established method for investigation of fixed liver sinusoidal endothelial cells morphology, atomic force microscopy is the interesting alternative providing detailed 3D topographical information. Moreover, simple sample preparation, only by wet-fixation, minimizing sample preparation artifacts enable high-resolution atomic force microscopy-based measurements. In this work, we apply imaging methods based on atomic force microscopy, to describe characteristic features of glutaraldehyde-fixed primary murine liver sinusoidal endothelial cells, namely: mean fenestration diameter, porosity, and fenestrations frequency. We also investigate the effect of different tip apex radius on evaluation of single fenestration diameter. By quantitative description of fenestrations, we demonstrate that atomic force microscopy became a well competing tool for nondestructive quantitative investigation of the liver sinusoidal endothelial cell morphology

AFM image analysis of porous structures by means of neural networks

In this work, we presented a method of finding and characterising transmembrane porous structures, called fenestrations, with the help of convolutional neural networks. Case studies are performed on high resolution AFM images of murine liver sinusoidal endothelial cells (LSECs). At first, we evaluated different kinds of noise occurring in the LSEC AFM measurements. Next, we proposed a schematic structure of the neural network suitable for our purpose. We examined different loss functions, optimising the accuracy of fenestration detection. Finally, we presented the method of rough calculation of fenestration size distributions. We demonstrated that the accuracy of this method surpasses 90%. Furthermore, it is fast, not sensitive to the chosen image contrast and fully deterministic. The simple scheme can be easily modified to different objects of interest, which promotes the use of neural networks as a universal tool for the analysis of various kinds of microscopy images

Tracking fenestrae dynamics in live murine liver sinusoidal endothelial cells

The fenestrae of liver sinusoidal endothelial cells (LSECs) allow passive transport of solutes, macromolecules, and particulate material between the sinusoidal lumen and the liver parenchymal cells. Until recently, fenestrae and fenestrae-associated structures were mainly investigated using electron microscopy on chemically fixed LSECs. Hence, the knowledge about their dynamic properties has remained to date largely elusive. Recent progress in atomic force microscopy (AFM) has allowed the study of live cells in three dimensions (X, Y, and Z) over a prolonged time (t) and this at unprecedented speeds and resolving power. Hence, we employed the latest advances in AFM imaging on living LSECs. As a result, we were able to monitor the position, size, and number of fenestrae and sieve plates using four-dimensional AFM (X, Y, Z, and t) on intact LSECs in vitro. During these time-lapse experiments, dynamic data were collected on the origin and morphofunctional properties of the filtration apparatus of LSECs. We present structural evidence on single laying and grouped fenestrae, thereby elucidating their dynamic nature from formation to disappearance. We also collected data on the life span of fenestrae. More especially, the formation and closing of entire sieve plates were observed, and how the continuous rearrangement of sieve plates affects the structure of fenestrae within them was recorded. We observed also the dawn and rise of fenestrae-forming centers and defenestration centers in LSECs under different experimental conditions. Conclusion: Utilizing a multimodal biomedical high-resolution imaging technique we collected fine structural information on the life span, formation, and disappearance of LSEC fenestrae; by doing so, we also gathered evidence on three different pathways implemented in the loss of fenestrae that result in defenestrated LSECs