Inhibition of MAPK Hog1 Results in Increased Hsp104 Aggregate Formation Probably through Elevated Arsenite Influx into the Cells, an Approach with Numerous Potential Applications

Arsenic is a highly toxic and carcinogenic metalloid widely dispersed in the environment, contaminating water and soil and accumulating in crops. Paradoxically, arsenic is also part of modern therapy and employed in treating numerous ailments and diseases. Hence, inventing strategies to tune cellular arsenic uptake based on purpose is striking. Here, we describe an approach in which the arsenite uptake can be increased using a MAPK inhibitor. Employing microfluidic flow chambers in combination with optical tweezers and fluorescent microscopy, we elevated the influx of arsenite into the yeast Saccharomyces cerevisiae cells following short-term treatment with a Hog1 kinase inhibitor. The increase in arsenite uptake was followed on arsenite triggered redistribution of a reporter protein, Hsp104-GFP, which was imaged over time. The effect was even more pronounced when the yeast mother and daughter cells were analyzed disjointedly, an opportunity provided owing to single-cell analysis. Our data firstly provide a strategy to increase arsenite uptake and secondly show that arsenite triggered aggregates, previously shown to be sites of damaged proteins, are distributed asymmetrically and less accumulated in daughter cells. Inventing approaches to tune arsenite uptake has a great value for its use in environmental as well as medical applications

Design and evaluation of a microfluidic system for inhibition studies of yeast cell signaling

In cell signaling, different perturbations lead to different responses and using traditional biological techniques that result in averaged data may obscure important cell-to-cell variations. The aim of this study was to develop and evaluate a four-inlet microfluidic system that enables single-cell analysis by investigating the effect on Hog1 localization post a selective Hog1 inhibitor treatment during osmotic stress. Optical tweezers was used to position yeast cells in an array of desired size and density inside the microfluidic system. By changing the flow rates through the inlet channels, controlled and rapid introduction of two different perturbations over the cell array was enabled. The placement of the cells was determined by diffusion rates flow simulations. The system was evaluated by monitoring the subcellular localization of a fluorescently tagged kinase of the yeast "High Osmolarity Glycerol" (HOG) pathway, Hog1-GFP. By sequential treatment of the yeast cells with a selective Hog1 kinase inhibitor and sorbitol, the subcellular localization of Hog1-GFP was analysed on a single-cell level. The results showed impaired Hog1-GFP nuclear localization, providing evidence of a congenial design. The setup made it possible to remove and add an agent within 2 seconds, which is valuable for investigating the dynamic signal transduction pathways and cannot be done using traditional methods. We are confident that the features of the four-inlet microfluidic system will be a valuable tool and hence contribute significantly to unravel the mechanisms of the HOG pathway and similar dynamic signal transduction pathways

The yeast osmostress response is carbon source dependent

Adaptation to altered osmotic conditions is a fundamental property of living cells and has been studied in detail in the yeast Saccharomyces cerevisiae. Yeast cells accumulate glycerol as compatible solute, controlled at different levels by the High Osmolarity Glycerol (HOG) response pathway. Up to now, essentially all osmostress studies in yeast have been performed with glucose as carbon and energy source, which is metabolised by glycolysis with glycerol as a by-product. Here we investigated the response of yeast to osmotic stress when yeast is respiring ethanol as carbon and energy source. Remarkably, yeast cells do not accumulate glycerol under these conditions and it appears that trehalose may partly take over the role as compatible solute. The HOG pathway is activated in very much the same way as during growth on glucose and is also required for osmotic adaptation. Slower volume recovery was observed in ethanol-grown cells as compared to glucose-grown cells. Dependence on key regulators as well as the global gene expression profile were similar in many ways to those previously observed in glucose-grown cells. However, there are indications that cells re-arrange redox-metabolism when respiration is hampered under osmostress, a feature that could not be observed in glucose-grown cells

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 nanoparticleenhanced 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 andpolystyrene 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

Roadmap for Optical Tweezers 2023

Optical tweezers are tools made of light that enable contactless pushing, trapping, and manipulation of objects ranging from atoms to space light sails. Since the pioneering work by Arthur Ashkin in the 1970s, optical tweezers have evolved into sophisticated instruments and have been employed in a broad range of applications in life sciences, physics, and engineering. These include accurate force and torque measurement at the femtonewton level, microrheology of complex fluids, single micro- and nanoparticle spectroscopy, single-cell analysis, and statistical-physics experiments. This roadmap provides insights into current investigations involving optical forces and optical tweezers from their theoretical foundations to designs and setups. It also offers perspectives for applications to a wide range of research fields, from biophysics to space exploration

CellStress - open source image analysis program for single-cell analysis

This work describes our image-analysis software, CellStress, which has been developed in Matlab and is issued under a GPL license. CellStress was developed in order to analyze migration of fluorescent proteins inside single cells during changing environmental conditions. CellStress can also be used to score information regarding protein aggregation in single cells over time, which is especially useful when monitoring cell signaling pathways involved in e.g. Alzheimer\u27s or Huntington\u27s disease. Parallel single-cell analysis of large numbers of cells is an important part of the research conducted in systems biology and quantitative biology in order to mathematically describe cellular processes. To quantify properties for single cells, large amounts of data acquired during extended time periods are needed. Manual analyses of such data involve huge efforts and could also include a bias, which complicates the use and comparison of data for further simulations or modeling. Therefore, it is necessary to have an automated and unbiased image analysis procedure, which is the aim of CellStress. CellStress utilizes cell contours detected by CellStat (developed at Fraunhofer-Chalmers Centre), which identifies cell boundaries using bright field images, and thus reduces the fluorescent labeling needed

Hydrodynamic Cell Trapping for High Throughput Single-Cell Applications

The possibility to conduct complete cell assays under a precisely controlled environment while consuming minor amounts of chemicals and precious drugs have made microfluidics an interesting candidate for quantitative single-cell studies. Here, we present an application-specific microfluidic device, cellcomb, capable of conducting high-throughput single-cell experiments. The system employs pure hydrodynamic forces for easy cell trapping and is readily fabricated in polydimethylsiloxane (PDMS) using soft lithography techniques. The cell-trapping array consists of V-shaped pockets designed to accommodate up to six Saccharomyces cerevisiae (yeast cells) with the average diameter of 4 μm. We used this platform to monitor the impact of flow rate modulation on the arsenite (As(III)) uptake in yeast. Redistribution of a green fluorescent protein (GFP)-tagged version of the heat shock protein Hsp104 was followed over time as read out. Results showed a clear reverse correlation between the arsenite uptake and three different adjusted low = 25 nL min−1, moderate = 50 nL min−1, and high = 100 nL min−1 flow rates. We consider the presented device as the first building block of a future integrated application-specific cell-trapping array that can be used to conduct complete single cell experiments on different cell types