Yeast cell responses and survival during periodic osmotic stress are controlled by glucose availability.

Natural environments of living organisms are often dynamic and multifactorial, with multiple parameters fluctuating over time. To better understand how cells respond to dynamically interacting factors, we quantified the effects of dual fluctuations of osmotic stress and glucose deprivation on yeast cells using microfluidics and time-lapse microscopy. Strikingly, we observed that cell proliferation, survival, and signaling depend on the phasing of the two periodic stresses. Cells divided faster, survived longer, and showed decreased transcriptional response when fluctuations of hyperosmotic stress and glucose deprivation occurred in phase than when the two stresses occurred alternatively. Therefore, glucose availability regulates yeast responses to dynamic osmotic stress, showcasing the key role of metabolic fluctuations in cellular responses to dynamic stress. We also found that mutants with impaired osmotic stress response were better adapted to alternating stresses than wild-type cells, showing that genetic mechanisms of adaptation to a persistent stress factor can be detrimental under dynamically interacting conditions.

The Central Role of Small Vertical Substrates for the Origin of Grasping in Early Primates.

The manual and pedal grasping abilities of primates, characterized by an opposable hallux, flat nails, and elongated digits, constitute a unique combination of features that likely promoted their characteristic use of arboreal habitats. These hand and foot specificities are central for understanding the origins and early evolution of primates and have long been associated with foraging in a fine-branch milieu. However, other arboreal mammals occupy similar niches, and it remains unclear how substrate type may have exerted a selective pressure on the acquisition of nails and a divergent pollex/hallux in primates or in what sequential order these traits evolved. Here, we video-recorded 14,564 grasps during arboreal locomotion in 11 primate species (6 strepsirrhines and 5 platyrrhines) and 11 non-primate arboreal species (1 scandentian, 3 rodents, 3 carnivorans, and 4 marsupials). We quantified our observations with 19 variables to analyze the effect of substrate orientation and diameter on hand and foot postural repertoire. We found that hand and foot postures correlate with phylogeny. Also, primates exhibited high repertoire diversity, with a strong capability for postural adjustment compared to the other studied groups. Surprisingly, nails do not confer an advantage in negotiating small substrates unless the animal is large, but the possession of a grasping pollex and hallux is crucial for climbing small vertical substrates. We propose that the divergent hallux and pollex may have resulted from a frequent use of vertical plants in early primate ecological scenarios, although nails may not have resulted from a fundamental adaptation to arboreal locomotion.

Measuring Force Intensity and Direction with a Spatially Resolved Soft Sensor for Biomechanics and Robotic Haptic Capability.

Possessing a sense of touch is fundamental for robots to operate outside controlled environments. Nevertheless, pressure and force-sensing technologies are still less mature than vision or proprioception solutions in commercial robots. In this study we present a novel spatially resolved force sensor that allows dynamic measurement of both the intensity and the direction of forces exerted on a custom-shaped surface. Originally designed for biomechanics of arboreal primates, this sensor meets several challenges in engineering robotic skin. Of importance, its ability to measure tangential forces would be instrumental for robotic hands to grasp deformable and unknown objects. Based on optical measurements of deformations, this array sensor presents a soft, biocompatible, weather resistant body, immune to electromagnetic interferences. Central to the cost-effectiveness of this solution is an architecture where a single image sensor handles hundreds of force measurement points simultaneously. We demonstrate the performance of this sensor in reconstructing normal and slantwise forces on a flat prototype adapted to forces under 3 N. Finally, we discuss the broad range of possible customizations and extensions for applications in biomechanics and robotics.

Long-term tracking of budding yeast cells in brightfield microscopy: CellStar and the Evaluation Platform.

With the continuous expansion of single cell biology, the observation of the behaviour of individual cells over extended durations and with high accuracy has become a problem of central importance. Surprisingly, even for yeast cells that have relatively regular shapes, no solution has been proposed that reaches the high quality required for long-term experiments for segmentation and tracking (S&T) based on brightfield images. Here, we present CellStar, a tool chain designed to achieve good performance in long-term experiments. The key features are the use of a new variant of parametrized active rays for segmentation, a neighbourhood-preserving criterion for tracking, and the use of an iterative approach that incrementally improves S&T quality. A graphical user interface enables manual corrections of S&T errors and their use for the automated correction of other, related errors and for parameter learning. We created a benchmark dataset with manually analysed images and compared CellStar with six other tools, showing its high performance, notably in long-term tracking. As a community effort, we set up a website, the Yeast Image Toolkit, with the benchmark and the Evaluation Platform to gather this and additional information provided by others.

What Population Reveals about Individual Cell Identity: Single-Cell Parameter Estimation of Models of Gene Expression in Yeast.

Significant cell-to-cell heterogeneity is ubiquitously observed in isogenic cell populations. Consequently, parameters of models of intracellular processes, usually fitted to population-averaged data, should rather be fitted to individual cells to obtain a population of models of similar but non-identical individuals. Here, we propose a quantitative modeling framework that attributes specific parameter values to single cells for a standard model of gene expression. We combine high quality single-cell measurements of the response of yeast cells to repeated hyperosmotic shocks and state-of-the-art statistical inference approaches for mixed-effects models to infer multidimensional parameter distributions describing the population, and then derive specific parameters for individual cells. The analysis of single-cell parameters shows that single-cell identity (e.g. gene expression dynamics, cell size, growth rate, mother-daughter relationships) is, at least partially, captured by the parameter values of gene expression models (e.g. rates of transcription, translation and degradation). Our approach shows how to use the rich information contained into longitudinal single-cell data to infer parameters that can faithfully represent single-cell identity.