Microfluidic design for in-vitro liver zonation-a numerical analysis using COMSOL Multiphysics.

The liver is one of the most important organs, with a complex physiology. Current in-vitro approaches are not accurate for disease modeling and drug toxicity research. One of those features is liver zonation, where cells display different physiological states due to different levels of oxygen and nutrient supplements. Organ-on-a-chip technology employs microfluidic platforms that enable a controlled environment for in-vitro cell culture. In this study, we propose a microfluidic design embedding a gas channel (of ambient air), creating an oxygen gradient. We numerically simulate different flow rates and cell densities with the COMSOL Multiphysics package considering cell-specific consumption rates of oxygen and glucose. We establish the cell density and flow rate for optimum oxygen and glucose distribution in the cell culture chamber. Furthermore, we show that a physiologically relevant concentration of oxygen and glucose in the chip is reached after 24 h and 30 min, respectively. The proposed microfluidic design and optimal parameters we identify in this paper provide a tool for in-vitro liver zonation studies. However, the microfluidic design is not exclusively for liver cell experiments but is foreseen to be applicable in cell studies where different gas concentration gradients are critical, e.g., studying hypoxia or toxic gas impact.

Fast and Accurate Nanoparticle Characterization Using Deep-Learning-Enhanced Off-Axis Holography.

Characterization of suspended nanoparticles in their native environment plays a central role in a wide range of fields, from medical diagnostics and nanoparticle-enhanced drug delivery to nanosafety and environmental nanopollution assessment. Standard optical approaches for nanoparticle sizing assess the size via the diffusion constant and, as a consequence, require long trajectories and that the medium has a known and uniform viscosity. However, in most biological applications, only short trajectories are available, while simultaneously, the medium viscosity is unknown and tends to display spatiotemporal variations. In this work, we demonstrate a label-free method to quantify not only size but also refractive index of individual subwavelength particles using 2 orders of magnitude shorter trajectories than required by standard methods and without prior knowledge about the physicochemical properties of the medium. We achieved this by developing a weighted average convolutional neural network to analyze holographic images of single particles, which was successfully applied to distinguish and quantify both size and refractive index of subwavelength silica and polystyrene particles without prior knowledge of solute viscosity or refractive index. We further demonstrate how these features make it possible to temporally resolve aggregation dynamics of 31 nm polystyrene nanoparticles, revealing previously unobserved time-resolved dynamics of the monomer number and fractal dimension of individual subwavelength aggregates.

A Nonlinear Mixed Effects Approach for Modeling the Cell-To-Cell Variability of Mig1 Dynamics in Yeast.

The last decade has seen a rapid development of experimental techniques that allow data collection from individual cells. These techniques have enabled the discovery and characterization of variability within a population of genetically identical cells. Nonlinear mixed effects (NLME) modeling is an established framework for studying variability between individuals in a population, frequently used in pharmacokinetics and pharmacodynamics, but its potential for studies of cell-to-cell variability in molecular cell biology is yet to be exploited. Here we take advantage of this novel application of NLME modeling to study cell-to-cell variability in the dynamic behavior of the yeast transcription repressor Mig1. In particular, we investigate a recently discovered phenomenon where Mig1 during a short and transient period exits the nucleus when cells experience a shift from high to intermediate levels of extracellular glucose. A phenomenological model based on ordinary differential equations describing the transient dynamics of nuclear Mig1 is introduced, and according to the NLME methodology the parameters of this model are in turn modeled by a multivariate probability distribution. Using time-lapse microscopy data from nearly 200 cells, we estimate this parameter distribution according to the approach of maximizing the population likelihood. Based on the estimated distribution, parameter values for individual cells are furthermore characterized and the resulting Mig1 dynamics are compared to the single cell times-series data. The proposed NLME framework is also compared to the intuitive but limited standard two-stage (STS) approach. We demonstrate that the latter may overestimate variabilities by up to almost five fold. Finally, Monte Carlo simulations of the inferred population model are used to predict the distribution of key characteristics of the Mig1 transient response. We find that with decreasing levels of post-shift glucose, the transient response of Mig1 tend to be faster, more extended, and displays an increased cell-to-cell variability.

Osmostress-induced cell volume loss delays yeast Hog1 signaling by limiting diffusion processes and by Hog1-specific effects.

Signal transmission progresses via a series of transient protein-protein interactions and protein movements, which require diffusion within a cell packed with different molecules. Yeast Hog1, the effector protein kinase of the High Osmolarity Glycerol pathway, translocates transiently from the cytosol to the nucleus during adaptation to high external osmolarity. We followed the dynamics of osmostress-induced cell volume loss and Hog1 nuclear accumulation upon exposure of cells to different NaCl concentrations. While Hog1 nuclear accumulation peaked within five minutes following mild osmotic shock it was delayed up to six-fold under severe stress. The timing of Hog1 nuclear accumulation correlated with the degree of cell volume loss and the cells capacity to recover. Also the nuclear translocation of Msn2, the transcription factor of the general stress response pathway, is delayed upon severe osmotic stress suggesting a general phenomenon. We show by direct measurements that the general diffusion rate of Hog1 in the cytoplasm as well as its rate of nuclear transport are dramatically reduced following severe volume reduction. However, neither Hog1 phosphorylation nor Msn2 nuclear translocation were as much delayed as Hog1 nuclear translocation. Our data provide direct evidence that signaling slows down during cell volume compression, probably as a consequence of molecular crowding. Hence one purpose of osmotic adaptation is to restore optimal diffusion rates for biochemical and cell biological processes. In addition, there may be mechanisms slowing down especially Hog1 nuclear translocation under severe stress in order to prioritize Hog1 cytosolic targets.