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.

Optogenetic spatial patterning of cooperation in yeast populations.

Microbial communities are shaped by complex metabolic interactions such as cooperation and competition for resources. Methods to control such interactions could lead to major advances in our ability to better engineer microbial consortia for synthetic biology applications. Here, we use optogenetics to control SUC2 invertase production in yeast, thereby shaping spatial assortment of cooperator and cheater cells. Yeast cells behave as cooperators (i.e., transform sucrose into hexose, a public good) upon blue light illumination or cheaters (i.e., consume hexose produced by cooperators to grow) in the dark. We show that cooperators benefit best from the hexoses they produce when their domain size is constrained between two cut-off length-scales. From an engineering point of view, the system behaves as a bandpass filter. The lower limit is the trace of cheaters' competition for hexoses, while the upper limit is defined by cooperators' competition for sucrose. Cooperation mostly occurs at the frontiers with cheater cells, which not only compete for hexoses but also cooperate passively by letting sucrose reach cooperators. We anticipate that this optogenetic method could be applied to shape metabolic interactions in a variety of microbial ecosystems.

A new look at an old classic: implementation of a SERS-based water hardness titration.

The routine use of SERS as an analytical technique has been hindered by practical considerations among which the irreproducibility of its signals and the lack of robustness of its calibration. In the present work, we examine a strategy to perform quantitative SERS without the need for calibration. The method reinvests a colorimetric volumetric titration procedure to determine water hardness but involves monitoring the progression of the titration through the SERS signal of a complexometric indicator. Upon reaching the equivalence between the chelating titrant and the metal analytes, the SERS signal abruptly jumps, which conveniently serves as an end-point marker. Three mineral waters spanning divalent metal concentrations varying by a factor of 25 were successfully titrated in this way, with satisfactory accuracy. Remarkably, the developed procedure can be run in less than an hour, without laboratory-grade carrying capacity and would be relevant for field measurements.

Optogenetic control of beta-carotene bioproduction in yeast across multiple lab-scales.

Optogenetics arises as a valuable tool to precisely control genetic circuits in microbial cell factories. Light control holds the promise of optimizing bioproduction methods and maximizing yields, but its implementation at different steps of the strain development process and at different culture scales remains challenging. In this study, we aim to control beta-carotene bioproduction using optogenetics in Saccharomyces cerevisiae and investigate how its performance translates across culture scales. We built four lab-scale illumination devices, each handling different culture volumes, and each having specific illumination characteristics and cultivating conditions. We evaluated optogenetic activation and beta-carotene production across devices and optimized them both independently. Then, we combined optogenetic induction and beta-carotene production to make a light-inducible beta-carotene producer strain. This was achieved by placing the transcription of the bifunctional lycopene cyclase/phytoene synthase CrtYB under the control of the pC120 optogenetic promoter regulated by the EL222-VP16 light-activated transcription factor, while other carotenogenic enzymes (CrtI, CrtE, tHMG) were expressed constitutively. We show that illumination, culture volume and shaking impact differently optogenetic activation and beta-carotene production across devices. This enabled us to determine the best culture conditions to maximize light-induced beta-carotene production in each of the devices. Our study exemplifies the stakes of scaling up optogenetics in devices of different lab scales and sheds light on the interplays and potential conflicts between optogenetic control and metabolic pathway efficiency. As a general principle, we propose that it is important to first optimize both components of the system independently, before combining them into optogenetic producing strains to avoid extensive troubleshooting. We anticipate that our results can help designing both strains and devices that could eventually lead to larger scale systems in an effort to bring optogenetics to the industrial scale.

CyberSco.Py an open-source software for event-based, conditional microscopy.

Timelapse fluorescence microscopy imaging is routinely used in quantitative cell biology. However, microscopes could become much more powerful investigation systems if they were endowed with simple unsupervised decision-making algorithms to transform them into fully responsive and automated measurement devices. Here, we report CyberSco.Py, Python software for advanced automated timelapse experiments. We provide proof-of-principle of a user-friendly framework that increases the tunability and flexibility when setting up and running fluorescence timelapse microscopy experiments. Importantly, CyberSco.Py combines real-time image analysis with automation capability, which allows users to create conditional, event-based experiments in which the imaging acquisition parameters and the status of various devices can be changed automatically based on the image analysis. We exemplify the relevance of CyberSco.Py to cell biology using several use case experiments with budding yeast. We anticipate that CyberSco.Py could be used to address the growing need for smart microscopy systems to implement more informative quantitative cell biology experiments.

The Promise of Optogenetics for Bioproduction: Dynamic Control Strategies and Scale-Up Instruments.

Progress in metabolic engineering and synthetic and systems biology has made bioproduction an increasingly attractive and competitive strategy for synthesizing biomolecules, recombinant proteins and biofuels from renewable feedstocks. Yet, due to poor productivity, it remains difficult to make a bioproduction process economically viable at large scale. Achieving dynamic control of cellular processes could lead to even better yields by balancing the two characteristic phases of bioproduction, namely, growth versus production, which lie at the heart of a trade-off that substantially impacts productivity. The versatility and controllability offered by light will be a key element in attaining the level of control desired. The popularity of light-mediated control is increasing, with an expanding repertoire of optogenetic systems for novel applications, and many optogenetic devices have been designed to test optogenetic strains at various culture scales for bioproduction objectives. In this review, we aim to highlight the most important advances in this direction. We discuss how optogenetics is currently applied to control metabolism in the context of bioproduction, describe the optogenetic instruments and devices used at the laboratory scale for strain development, and explore how current industrial-scale bioproduction processes could be adapted for optogenetics or could benefit from existing photobioreactor designs. We then draw attention to the steps that must be undertaken to further optimize the control of biological systems in order to take full advantage of the potential offered by microbial factories.

Autonomous and Assisted Control for Synthetic Microbiology.

The control of microbes and microbial consortia to achieve specific functions requires synthetic circuits that can reliably cope with internal and external perturbations. Circuits that naturally evolved to regulate biological functions are frequently robust to alterations in their parameters. As the complexity of synthetic circuits increases, synthetic biologists need to implement such robust control "by design". This is especially true for intercellular signaling circuits for synthetic consortia, where robustness is highly desirable, but its mechanisms remain unclear. Cybergenetics, the interface between synthetic biology and control theory, offers two approaches to this challenge: external (computer-aided) and internal (autonomous) control. Here, we review natural and synthetic microbial systems with robustness, and outline experimental approaches to implement such robust control in microbial consortia through population-level cybergenetics. We propose that harnessing natural intercellular circuit topologies with robust evolved functions can help to achieve similar robust control in synthetic intercellular circuits. A "hybrid biology" approach, where robust synthetic microbes interact with natural consortia and-additionally-with external computers, could become a useful tool for health and environmental applications.

Rational programming of history-dependent logic in cellular populations.

Genetic programs operating in a history-dependent fashion are ubiquitous in nature and govern sophisticated processes such as development and differentiation. The ability to systematically and predictably encode such programs would advance the engineering of synthetic organisms and ecosystems with rich signal processing abilities. Here we implement robust, scalable history-dependent programs by distributing the computational labor across a cellular population. Our design is based on standardized recombinase-driven DNA scaffolds expressing different genes according to the order of occurrence of inputs. These multicellular computing systems are highly modular, do not require cell-cell communication channels, and any program can be built by differential composition of strains containing well-characterized logic scaffolds. We developed automated workflows that researchers can use to streamline program design and optimization. We anticipate that the history-dependent programs presented here will support many applications using cellular populations for material engineering, biomanufacturing and healthcare.

Observing Nutrient Gradients, Gene Expression and Growth Variation Using the "Yeast Machine" Microfluidic Device.

The natural environment of microbial cells like bacteria and yeast is often a complex community in which growth and internal organization reflect morphogenetic processes and interactions that are dependent on spatial position and time. While most of research is performed in simple homogeneous environments (e.g., bulk liquid cultures), which cannot capture full spatiotemporal community dynamics, studying biofilms or colonies is complex and usually does not give access to the spatiotemporal dynamics at single cell level. Here, we detail a protocol for generation of a microfluidic device, the "yeast machine", with arrays of long monolayers of yeast colonies to advance the global understanding of how intercellular metabolic interactions affect the internal structure of colonies within defined and customizable spatial dimensions. With Saccharomyces cerevisiae as a model yeast system we used the "yeast machine" to demonstrate the emergence of glucose gradients by following expression of fluorescently labelled hexose transporters. We further quantified the expression spatial patterns with intra-colony growth rates and expression of other genes regulated by glucose availability. In addition to this, we showed that gradients of amino acids also form within a colony, potentially opening similar approaches to study spatiotemporal formation of gradients of many other nutrients and metabolic waste products. This approach could be used in the future to decipher the interplay between long-range metabolic interactions, cellular development, and morphogenesis in other same species or more complex multi-species systems at single-cell resolution and timescales relevant to ecology and evolution.

Enrichment of persisters enabled by a ß-lactam-induced filamentation method reveals their stochastic single-cell awakening.

When exposed to lethal doses of antibiotics, bacterial populations are most often not completely eradicated. A small number of phenotypic variants, defined as 'persisters', are refractory to antibiotics and survive treatment. Despite their involvement in relapsing infections, processes determining phenotypic switches from and to the persister state largely remain elusive. This is mainly due to the low frequency of persisters and the lack of reliable persistence markers, both hampering studies of persistence at the single-cell level. Here we present a highly effective persister enrichment method involving cephalexin, an antibiotic that induces extensive filamentation of susceptible cells. We used our enrichment method to monitor outgrowth of Escherichia coli persisters at the single-cell level, thereby conclusively demonstrating that persister awakening is a stochastic phenomenon. We anticipate that our approach can have far-reaching consequences in the persistence field, by allowing single-cell studies at a much higher throughput than previously reported.

A microfluidic device for inferring metabolic landscapes in yeast monolayer colonies.

Microbial colonies are fascinating structures in which growth and internal organization reflect complex morphogenetic processes. Here, we generated a microfluidics device with arrays of long monolayer yeast colonies to further global understanding of how intercellular metabolic interactions affect the internal structure of colonies within defined boundary conditions. We observed the emergence of stable glucose gradients using fluorescently labeled hexose transporters and quantified the spatial correlations with intra-colony growth rates and expression of other genes regulated by glucose availability. These landscapes depended on the external glucose concentration as well as secondary gradients, for example amino acid availability. This work demonstrates the regulatory genetic networks governing cellular physiological adaptation are the key to internal structuration of cellular assemblies. This approach could be used in the future to decipher the interplay between long-range metabolic interactions, cellular development and morphogenesis in more complex systems.

Hyperosmotic Stress Response Memory is Modulated by Gene Positioning in Yeast.

Cellular memory is a critical ability that allows microorganisms to adapt to potentially detrimental environmental fluctuations. In the unicellular eukaryote Saccharomyces cerevisiae, cellular memory can take the form of faster or slower responses within the cell population to repeated stresses. Using microfluidics and fluorescence time-lapse microscopy, we studied how yeast responds to short, pulsed hyperosmotic stresses at the single-cell level by analyzing the dynamic behavior of the stress-responsive STL1 promoter (pSTL1) fused to a fluorescent reporter. We established that pSTL1 exhibits variable successive activation patterns following two repeated short stresses. Despite this variability, most cells exhibited a memory of the first stress as decreased pSTL1 activity in response to the second stress. Notably, we showed that genomic location is important for the memory effect, since displacement of the promoter to a pericentromeric chromatin domain decreased the transcriptional strength of pSTL1 and led to a loss of memory. This study provides a quantitative description of a cellular memory that includes single-cell variability and highlights the contribution of chromatin structure to stress memory.

Identification of individual cells from z-stacks of bright-field microscopy images.

Obtaining single cell data from time-lapse microscopy images is critical for quantitative biology, but bottlenecks in cell identification and segmentation must be overcome. We propose a novel, versatile method that uses machine learning classifiers to identify cell morphologies from z-stack bright-field microscopy images. We show that axial information is enough to successfully classify the pixels of an image, without the need to consider in focus morphological features. This fast, robust method can be used to identify different cell morphologies, including the features of E. coli, S. cerevisiae and epithelial cells, even in mixed cultures. Our method demonstrates the potential of acquiring and processing Z-stacks for single-layer, single-cell imaging and segmentation.

Balancing a genetic toggle switch by real-time feedback control and periodic forcing.

Cybergenetics is a novel field of research aiming at remotely pilot cellular processes in real-time with to leverage the biotechnological potential of synthetic biology. Yet, the control of only a small number of genetic circuits has been tested so far. Here we investigate the control of multistable gene regulatory networks, which are ubiquitously found in nature and play critical roles in cell differentiation and decision-making. Using an in silico feedback control loop, we demonstrate that a bistable genetic toggle switch can be dynamically maintained near its unstable equilibrium position for extended periods of time. Importantly, we show that a direct method based on dual periodic forcing is sufficient to simultaneously maintain many cells in this undecided state. These findings pave the way for the control of more complex cell decision-making systems at both the single cell and the population levels, with vast fundamental and biotechnological applications.

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.

Long-range self-organization of cytoskeletal myosin II filament stacks.

Although myosin II filaments are known to exist in non-muscle cells, their dynamics and organization are incompletely understood. Here, we combined structured illumination microscopy with pharmacological and genetic perturbations, to study the process of actomyosin cytoskeleton self-organization into arcs and stress fibres. A striking feature of the myosin II filament organization was their 'registered' alignment into stacks, spanning up to several micrometres in the direction orthogonal to the parallel actin bundles. While turnover of individual myosin II filaments was fast (characteristic half-life time 60 s) and independent of actin filament turnover, the process of stack formation lasted a longer time (in the range of several minutes) and required myosin II contractility, as well as actin filament assembly/disassembly and crosslinking (dependent on formin Fmnl3, cofilin1 and α-actinin-4). Furthermore, myosin filament stack formation involved long-range movements of individual myosin filaments towards each other suggesting the existence of attractive forces between myosin II filaments. These forces, possibly transmitted via mechanical deformations of the intervening actin filament network, may in turn remodel the actomyosin cytoskeleton and drive its self-organization.

Age-Related Changes in Locomotor Performance Reveal a Similar Pattern for Caenorhabditis elegans, Mus domesticus, Canis familiaris, Equus caballus, and Homo sapiens.

Locomotion is one of the major physiological functions for most animals. Previous studies have described aging mechanisms linked to locomotor performance among different species. However, the precise dynamics of these age-related changes, and their interactions with development and senescence, are largely unknown. Here, we use the same conceptual framework to describe locomotor performances in Caenorhabditis elegans, Mus domesticus, Canis familiaris, Equus caballus, and Homo sapiens. We show that locomotion is a consistent biomarker of age-related changes, with an asymmetrical pattern throughout life, regardless of the type of effort or its duration. However, there is variation (i) among species for the same mode of locomotion, (ii) within species for different modes of locomotion, and (iii) among individuals of the same species for the same mode of locomotion. Age-related patterns are modulated by genetic (such as selective breeding) as well as environmental conditions (such as temperature). However, in all cases, the intersection of the rising developmental phase and the declining senescent phase reveals neither a sharp transition nor a plateau, but a smooth transition, emphasizing a crucial moment: the age at peak performance. This transition may define a specific target for future investigations on the dynamics of such biological interactions.

Extracellular Vesicle Production Loaded with Nanoparticles and Drugs in a Trade-off between Loading, Yield and Purity: Towards a Personalized Drug Delivery System.

Extracellular vesicles (EVs) released by cells and circulating in body fluids are recognized as potent vectors of intercellular self-communication. Due to their cellular origin, EVs hold promise as naturally targeted "personalized" drug delivery system insofar as they can be engineered with drugs or theranostic nanoparticles. However, technical hurdles related to their production, drug loading, purification, and characterization restrain the translation of self-derived EVs into a clinical drug delivery system. Herein, different methods are compared to generate and to purify EVs encapsulating iron oxide nanoparticles and a clinical photosensitizer drug (Foscan) as biocamouflaged agents for photodynamic therapy, magnetic resonance imaging, magnetic manipulation, and hyperthermia. Theranostic EVs are produced from drug- and nanoparticle-loaded endothelial cells either by spontaneous release in complete medium, by starvation in serum-free medium or by mechanical stress in a microfluidic chip mimicking vessel shear stress, and purified by ultracentrifugation or magnetic sorting. The impact of the production and purification protocols is investigated on EV yield and size, nanoparticle and drug cargo, and finally on their therapeutic efficacy. EV production by starvation combined with purification by ultracentrifugation may be considered a reasonable trade-off between loading, yield, and purity for biogeneration of theranostic EVs.

Out-of-equilibrium stationary states, percolation, and subcritical instabilities in a fully nonconservative system.

The exploration of the phase diagram of a minimal model for barchan fields leads to the description of three distinct phases for the system: stationary, percolable, and unstable. In the stationary phase the system always reaches an out-of-equilibrium, fluctuating, stationary state, independent of its initial conditions. This state has a large and continuous range of dynamics, from dilute-where dunes do not interact-to dense, where the system exhibits both spatial structuring and collective behavior leading to the selection of a particular size for the dunes. In the percolable phase, the system presents a percolation threshold when the initial density increases. This percolation is unusual, as it happens on a continuous space for moving, interacting, finite lifetime dunes. For extreme parameters, the system exhibits a subcritical instability, where some of the dunes in the field grow without bound. We discuss the nature of the asymptotic states and their relations to well-known models of statistical physics.