Dissecting genealogy and cell cycle as sources of cell-to-cell variability in MAPK signaling using high-throughput lineage tracking

Exploring carbon source related localization and phosphorylation in the Snf1/Mig1 network using population and single cell-based approaches

The AMPK/SNF1 pathway governs energy balance in eukaryotic cells, notably influencing glucose de-repression. In S. cerevisiae, Snf1 is phosphorylated and hence activated upon glucose depletion. This activation is required but is not sufficient for mediating glucose de-repression, indicating further glucose-dependent regulation mechanisms. Employing fluorescence recovery after photobleaching (FRAP) in conjunction with non-linear mixed effects modelling, we explore the spatial dynamics of Snf1 as well as the relationship between Snf1 phosphorylation and its target Mig1 controlled by hexose sugars. Our results suggest that inactivation of Snf1 modulates Mig1 localization and that the kinetic of Snf1 localization to the nucleus is modulated by the presence of non-fermentable carbon sources. Our data offer insight into the true complexity of regulation of this central signaling pathway in orchestrating cellular responses to fluctuating environmental cues. These insights not only expand our understanding of glucose homeostasis but also pave the way for further studies evaluating the importance of Snf1 localization in relation to its phosphorylation state and regulation of downstream targets.

Distinct, age-dependent TLR7/8 signaling responses in porcine gamma-delta T cells

Gamma-Delta T cells are a prominent subset of T cells in pigs. However, developmental changes, antigen recognition, cell migration, and their contributions to pathogen clearance remain largely unknown. We have recently shown that porcine γδ T cells express Toll-like receptors (TLRs), and that TLR7/8 stimulation can function as a co-stimulatory signal that complements cytokine-induced signals to enhance INFγ production. Nonetheless, the signaling pathways behind this increased cytokine responsiveness remained unclear. Here, we analyzed the signaling pathways by measuring cellular kinase activity and selective inhibition, confirming that the TLR7/8 expression by γδ T cells is indeed functional. Moreover, TLR downstream signaling responses showed a distinct age-dependency, emphasizing the importance of age in immune function. While the TLR7/8 co-stimulation depended on activation of IRAK1/4, p38 and JNK in adult-derived γδ T cells, γδ T cells from young pigs utilized only p38, indicating the existence of an alternative signaling pathway in young pigs. Overall, this data suggests that porcine γδ T cells could be able to recognize viral RNA through TLR7/8 and subsequently support the survival and activation of the adaptive immune response by cytokine production.

Cell region fingerprints enable highly precise single-cell tracking and lineage reconstruction

Experimental studies of cell growth, inheritance and their associated processes by microscopy require accurate single-cell observations of sufficient duration to reconstruct the genealogy. However, cell tracking-assigning identical cells on consecutive images to a track-is often challenging, resulting in laborious manual verification. Here, we propose fingerprints to identify problematic assignments rapidly. A fingerprint distance compares the structural information contained in the low frequencies of a Fourier transform to measure the similarity between cells in two consecutive images. We show that fingerprints are broadly applicable across cell types and image modalities, provided the image has sufficient structural information. Our tracker (Trac^X) uses fingerprints to reject unlikely assignments, thereby increasing tracking performance on published and newly generated long-term data sets. For Saccharomyces cerevisiae, we propose a comprehensive model for cell size control at the single-cell and population level centered on the Whi5 regulator, demonstrating how precise tracking can help uncover previously undescribed single-cell biology.

Fungi-on-a-Chip: microfluidic platforms for single-cell studies on fungi

This review highlights new advances in the emerging field of 'Fungi-on-a-Chip' microfluidics for single-cell studies on fungi and discusses several future frontiers, where we envisage microfluidic technology development to be instrumental in aiding our understanding of fungal biology. Fungi, with their enormous diversity, bear essential roles both in nature and our everyday lives. They inhabit a range of ecosystems, such as soil, where they are involved in organic matter degradation and bioremediation processes. More recently, fungi have been recognized as key components of the microbiome in other eukaryotes, such as humans, where they play a fundamental role not only in human pathogenesis, but also likely as commensals. In the food sector, fungi are used either directly or as fermenting agents and are often key players in the biotechnological industry, where they are responsible for the production of both bulk chemicals and antibiotics. Although the macroscopic fruiting bodies are immediately recognizable by most observers, the structure, function, and interactions of fungi with other microbes at the microscopic scale still remain largely hidden. Herein, we shed light on new advances in the emerging field of Fungi-on-a-Chip microfluidic technologies for single-cell studies on fungi. We discuss the development and application of microfluidic tools in the fields of medicine and biotechnology, as well as in-depth biological studies having significance for ecology and general natural processes. Finally, a future perspective is provided, highlighting new frontiers in which microfluidic technology can benefit this field.

Scalable and flexible inference framework for stochastic dynamic single-cell models

Understanding the inherited nature of how biological processes dynamically change over time and exhibit intra- and inter-individual variability, due to the different responses to environmental stimuli and when interacting with other processes, has been a major focus of systems biology. The rise of single-cell fluorescent microscopy has enabled the study of those phenomena. The analysis of single-cell data with mechanistic models offers an invaluable tool to describe dynamic cellular processes and to rationalise cell-to-cell variability within the population. However, extracting mechanistic information from single-cell data has proven difficult. This requires statistical methods to infer unknown model parameters from dynamic, multi-individual data accounting for heterogeneity caused by both intrinsic (e.g. variations in chemical reactions) and extrinsic (e.g. variability in protein concentrations) noise. Although several inference methods exist, the availability of efficient, general and accessible methods that facilitate modelling of single-cell data, remains lacking. Here we present a scalable and flexible framework for Bayesian inference in state-space mixed-effects single-cell models with stochastic dynamic. Our approach infers model parameters when intrinsic noise is modelled by either exact or approximate stochastic simulators, and when extrinsic noise is modelled by either time-varying, or time-constant parameters that vary between cells. We demonstrate the relevance of our approach by studying how cell-to-cell variation in carbon source utilisation affects heterogeneity in the budding yeast Saccharomyces cerevisiae SNF1 nutrient sensing pathway. We identify hexokinase activity as a source of extrinsic noise and deduce that sugar availability dictates cell-to-cell variability.

Single B cell technologies for monoclonal antibody discovery

Monoclonal antibodies (mAbs) are often selected from antigen-specific single B cells derived from different hosts, which are notably short-lived in ex vivo culture conditions and hence, arduous to interrogate. The development of several new techniques and protocols has facilitated the isolation and retrieval of antibody-coding sequences of antigen-specific B cells by also leveraging miniaturization of reaction volumes. Alternatively, mAbs can be generated independently of antigen-specific B cells, comprising display technologies and, more recently, artificial intelligence-driven algorithms. Consequently, a considerable variety of techniques are used, raising the demand for better consolidation. In this review, we present and discuss the major techniques available to interrogate antigen-specific single B cells to isolate antigen-specific mAbs, including their main advantages and disadvantages.

Rapid prototyping and design of cybergenetic single-cell controllers

The design and implementation of synthetic circuits that operate robustly in the cellular context is fundamental for the advancement of synthetic biology. However, their practical implementation presents challenges due to low predictability of synthetic circuit design and time-intensive troubleshooting. Here, we present the Cyberloop, a testing framework to accelerate the design process and implementation of biomolecular controllers. Cellular fluorescence measurements are sent in real-time to a computer simulating candidate stochastic controllers, which in turn compute the control inputs and feed them back to the controlled cells via light stimulation. Applying this framework to yeast cells engineered with optogenetic tools, we examine and characterize different biomolecular controllers, test the impact of non-ideal circuit behaviors such as dilution on their operation, and qualitatively demonstrate improvements in controller function with certain network modifications. From this analysis, we derive conditions for desirable biomolecular controller performance, thereby avoiding pitfalls during its biological implementation.

YeastNet: Deep-Learning-Enabled Accurate Segmentation of Budding Yeast Cells in Bright-Field Microscopy

Accurate and efficient segmentation of live-cell images is critical in maximizing data extraction and knowledge generation from high-throughput biology experiments. Despite recent development of deep-learning tools for biomedical imaging applications, great demand for automated segmentation tools for high-resolution live-cell microscopy images remains in order to accelerate the analysis. YeastNet dramatically improves the performance of the non-trainable classic algorithm, and performs considerably better than the current state-of-the-art yeast-cell segmentation tools. We have designed and trained a U-Net convolutional network (named YeastNet) to conduct semantic segmentation on bright-field microscopy images and generate segmentation masks for cell labeling and tracking. YeastNet enables accurate automatic segmentation and tracking of yeast cells in biomedical applications. YeastNet is freely provided with model weights as a Python package on GitHub.

Cross-compartment signal propagation in the mitotic exit network

In budding yeast, the mitotic exit network (MEN), a GTPase signaling cascade, integrates spatial and temporal cues to promote exit from mitosis. This signal integration requires transmission of a signal generated on the cytoplasmic face of spindle pole bodies (SPBs; yeast equivalent of centrosomes) to the nucleolus, where the MEN effector protein Cdc14 resides. Here, we show that the MEN activating signal at SPBs is relayed to Cdc14 in the nucleolus through the dynamic localization of its terminal kinase complex Dbf2-Mob1. Cdc15, the protein kinase that activates Dbf2-Mob1 at SPBs, also regulates its nuclear access. Once in the nucleus, priming phosphorylation of Cfi1/Net1, the nucleolar anchor of Cdc14, by the Polo-like kinase Cdc5 targets Dbf2-Mob1 to the nucleolus. Nucleolar Dbf2-Mob1 then phosphorylates Cfi1/Net1 and Cdc14, activating Cdc14. The kinase-primed transmission of the MEN signal from the cytoplasm to the nucleolus exemplifies how signaling cascades can bridge distant inputs and responses.

Mig1 localization exhibits biphasic behavior which is controlled by both metabolic and regulatory roles of the sugar kinases

Glucose, fructose and mannose are the preferred carbon/energy sources for the yeast Saccharomyces cerevisiae. Absence of preferred energy sources activates glucose derepression, which is regulated by the kinase Snf1. Snf1 phosphorylates the transcriptional repressor Mig1, which results in its exit from the nucleus and subsequent derepression of genes. In contrast, Snf1 is inactive when preferred carbon sources are available, which leads to dephosphorylation of Mig1 and its translocation to the nucleus where Mig1 acts as a transcription repressor. Here we revisit the role of the three hexose kinases, Hxk1, Hxk2 and Glk1, in glucose de/repression. We demonstrate that all three sugar kinases initially affect Mig1 nuclear localization upon addition of glucose, fructose and mannose. This initial import of Mig1 into the nucleus was temporary; for continuous nucleocytoplasmic shuttling of Mig1, Hxk2 is required in the presence of glucose and mannose and in the presence of fructose Hxk2 or Hxk1 is required. Our data suggest that Mig1 import following exposure to preferred energy sources is controlled via two different pathways, where (1) the initial import is regulated by signals derived from metabolism and (2) continuous shuttling is regulated by the Hxk2 and Hxk1 proteins. Mig1 nucleocytoplasmic shuttling appears to be important for the maintenance of the repressed state in which Hxk1/2 seems to play an essential role.

Cell-in-the-loop pattern formation with optogenetically emulated cell-to-cell signaling

Designing and implementing synthetic biological pattern formation remains challenging due to underlying theoretical complexity as well as the difficulty of engineering multicellular networks biochemically. Here, we introduce a cell-in-the-loop approach where living cells interact through in silico signaling, establishing a new testbed to interrogate theoretical principles when internal cell dynamics are incorporated rather than modeled. We present an easy-to-use theoretical test to predict the emergence of contrasting patterns in gene expression among laterally inhibiting cells. Guided by the theory, we experimentally demonstrate spontaneous checkerboard patterning in an optogenetic setup, where cell-to-cell signaling is emulated with light inputs calculated in silico from real-time gene expression measurements. The scheme successfully produces spontaneous, persistent checkerboard patterns for systems of sixteen patches, in quantitative agreement with theoretical predictions. Our research highlights how tools from dynamical systems theory may inform our understanding of patterning, and illustrates the potential of cell-in-the-loop for engineering synthetic multicellular systems.

An Optogenetic Platform for Real-Time, Single-Cell Interrogation of Stochastic Transcriptional Regulation

Transcription is a highly regulated and inherently stochastic process. The complexity of signal transduction and gene regulation makes it challenging to analyze how the dynamic activity of transcriptional regulators affects stochastic transcription. By combining a fast-acting, photo-regulatable transcription factor with nascent RNA quantification in live cells and an experimental setup for precise spatiotemporal delivery of light inputs, we constructed a platform for the real-time, single-cell interrogation of transcription in Saccharomyces cerevisiae. We show that transcriptional activation and deactivation are fast and memoryless. By analyzing the temporal activity of individual cells, we found that transcription occurs in bursts, whose duration and timing are modulated by transcription factor activity. Using our platform, we regulated transcription via light-driven feedback loops at the single-cell level. Feedback markedly reduced cell-to-cell variability and led to qualitative differences in cellular transcriptional dynamics. Our platform establishes a flexible method for studying transcriptional dynamics in single cells.

A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics

Single-cell time-lapse data provide the means for disentangling sources of cell-to-cell and intra-cellular variability, a key step for understanding heterogeneity in cell populations. However, single-cell analysis with dynamic models is a challenging open problem: current inference methods address only single-gene expression or neglect parameter correlations. We report on a simple, flexible, and scalable method for estimating cell-specific and population-average parameters of non-linear mixed-effects models of cellular networks, demonstrating its accuracy with a published model and dataset. We also propose sensitivity analysis for identifying which biological sub-processes quantitatively and dynamically contribute to cell-to-cell variability. Our application to endocytosis in yeast demonstrates that dynamic models of realistic size can be developed for the analysis of single-cell data and that shifting the focus from single reactions or parameters to nuanced and time-dependent contributions of sub-processes helps biological interpretation. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics.

Single-cell profiling screen identifies microtubule-dependent reduction of variability in signaling

Populations of isogenic cells often respond coherently to signals, despite differences in protein abundance and cell state. Previously, we uncovered processes in the Saccharomyces cerevisiae pheromone response system (PRS) that reduced cell-to-cell variability in signal strength and cellular response. Here, we screened 1,141 non-essential genes to identify 50 "variability genes". Most had distinct, separable effects on strength and variability of the PRS, defining these quantities as genetically distinct "axes" of system behavior. Three genes affected cytoplasmic microtubule function: BIM1, GIM2, and GIM4 We used genetic and chemical perturbations to show that, without microtubules, PRS output is reduced but variability is unaffected, while, when microtubules are present but their function is perturbed, output is sometimes lowered, but its variability is always high. The increased variability caused by microtubule perturbations required the PRS MAP kinase Fus3 and a process at or upstream of Ste5, the membrane-localized scaffold to which Fus3 must bind to be activated. Visualization of Ste5 localization dynamics demonstrated that perturbing microtubules destabilized Ste5 at the membrane signaling site. The fact that such microtubule perturbations cause aberrant fate and polarity decisions in mammals suggests that microtubule-dependent signal stabilization might also operate throughout metazoans.

Heritable stress response dynamics revealed by single-cell genealogy

Cells often respond to environmental stimuli by activating specific transcription factors. Upon exposure to glucose limitation stress, it is known that yeast Saccharomyces cerevisiae cells dephosphorylate the general stress response factor Msn2, leading to its nuclear localization, which in turn activates the expression of many genes. However, the precise dynamics of Msn2 nucleocytoplasmic translocations and whether they are inherited over multiple generations in a stress-dependent manner are not well understood. Tracking Msn2 localization events in yeast lineages grown on a microfluidic chip, here we report how cells modulate the amplitude, duration, frequency, and dynamic pattern of the localization events in response to glucose limitation stress. Single yeast cells were found to modulate the amplitude and frequency of Msn2 nuclear localization, but not its duration. Moreover, the Msn2 localization frequency was epigenetically inherited in descendants of mother cells, leading to a decrease in cell-to-cell variation in localization frequency. An analysis of the time dynamic patterns of nuclear localizations between genealogically related cell pairs using an information theory approach found that the magnitude of pattern similarity increased with stress intensity and was strongly inherited by the descendant cells at the highest stress level. By dissecting how general stress response dynamics is contributed by different modulation schemes over long time scales, our work provides insight into which scheme evolution might have acted on to optimize fitness in stressful environments.

NuA4 Lysine Acetyltransferase Complex Contributes to Phospholipid Homeostasis in Saccharomyces cerevisiae

Actively proliferating cells constantly monitor and readjust their metabolic pathways to ensure the replenishment of phospholipids necessary for membrane biogenesis and intracellular trafficking. In Saccharomyces cerevisiae, multiple studies have suggested that the lysine acetyltransferase complex NuA4 plays a role in phospholipid homeostasis. For one, NuA4 mutants induce the expression of the inositol-3-phosphate synthase gene, INO1, which leads to excessive accumulation of inositol, a key metabolite used for phospholipid biosynthesis. Additionally, NuA4 mutants also display negative genetic interactions with sec14-1^ts, a mutant of a lipid-binding gene responsible for phospholipid remodeling of the Golgi. Here, using a combination of genetics and transcriptional profiling, we explore the connections between NuA4, inositol, and Sec14. Surprisingly, we found that NuA4 mutants did not suppress but rather exacerbated the growth defects of sec14-1^ts under inositol-depleted conditions. Transcriptome studies reveal that while loss of the NuA4 subunit EAF1 in sec14-1^ts does derepress INO1 expression, it does not derepress all inositol/choline-responsive phospholipid genes, suggesting that the impact of Eaf1 on phospholipid homeostasis extends beyond inositol biosynthesis. In fact, we find that NuA4 mutants have impaired lipid droplet levels and through genetic and chemical approaches, we determine that the genetic interaction between sec14-1^ts and NuA4 mutants potentially reflects a role for NuA4 in fatty acid biosynthesis. Altogether, our work identifies a new role for NuA4 in phospholipid homeostasis.

A living vector field reveals constraints on galactose network induction in yeast

When a cell encounters a new environment, its transcriptional response can be constrained by its history. For example, yeast cells in galactose induce GAL genes with a speed and unanimity that depends on previous nutrient conditions. Cellular memory of long-term glucose exposure delays GAL induction and makes it highly variable with in a cell population, while other nutrient histories lead to rapid, uniform responses. To investigate how cell-level gene expression dynamics produce population-level phenotypes, we built living vector fields from thousands of single-cell time courses of the proteins Gal3p and Gal1p as cells switched to galactose from various nutrient histories. We show that, after sustained glucose exposure, the lack of these GAL transducers leads to induction delays that are long but also variable; that cellular resources constrain induction; and that bimodally distributed expression levels arise from lineage selection-a subpopulation of cells induces more quickly and outcompetes the rest. Our results illuminate cellular memory in this important model system and illustrate how resources and randomness interact to shape the response of a population to a new environment.

Microfluidic Platforms for Yeast-Based Aging Studies

The budding yeast Saccharomyces cerevisiae has been a powerful model for the study of aging and has enabled significant contributions to our understanding of basic mechanisms of aging in eukaryotic cells. However, the laborious low-throughput nature of conventional methods of performing aging assays limits the pace of discoveries in this field. Some of the technical challenges of conventional aging assay methods can be overcome by use of microfluidic systems coupled to time-lapse microscopy. One of the major advantages is the ability of a microfluidic system to perform long-term cell culture under well-defined environmental conditions while tracking individual yeast. Here, recent advancements in microfluidic platforms for various yeast-based studies including replicative lifespan assay, long-term culture and imaging, gene expression, and cell signaling are discussed. In addition, emerging problems and limitations of current microfluidic approaches are examined and perspectives on the future development of this dynamic field are presented.

Single-cell dynamics and variability of MAPK activity in a yeast differentiation pathway

In response to pheromones, yeast cells activate a MAPK pathway to direct processes important for mating, including gene induction, cell-cycle arrest, and polarized cell growth. Although a variety of assays have been able to elucidate signaling activities at multiple steps in the pathway, measurements of MAPK activity during the pheromone response have remained elusive, and our understanding of single-cell signaling behavior is incomplete. Using a yeast-optimized FRET-based mammalian Erk-activity reporter to monitor Fus3 and Kss1 activity in live yeast cells, we demonstrate that overall mating MAPK activity exhibits distinct temporal dynamics, rapid reversibility, and a graded dose dependence around the K_D of the receptor, where phenotypic transitions occur. The complex dose response was found to be largely a consequence of two feedbacks involving cyclin-mediated scaffold phosphorylation and Fus3 autoregulation. Distinct cell cycle-dependent response patterns comprised a large portion of the cell-to-cell variability at each dose, constituting the major source of extrinsic noise in coupling activity to downstream gene-expression responses. Additionally, we found diverse spatial MAPK activity patterns to emerge over time in cells undergoing default, gradient, and true mating responses. Furthermore, ramping up and rapid loss of activity were closely associated with zygote formation in mating-cell pairs, supporting a role for elevated MAPK activity in successful cell fusion and morphogenic reorganization. Altogether, these findings present a detailed view of spatiotemporal MAPK activity during the pheromone response, elucidating its role in mediating complex long-term developmental fates in a unicellular differentiation system.

Versatile, simple-to-use microfluidic cell-culturing chip for long-term, high-resolution, time-lapse imaging

Optical long-term observation of individual cells, combined with modern data analysis tools, allows for a detailed study of cell-to-cell variability, heredity, and differentiation. We developed a microfluidic device featuring facile cell loading, simple and robust operation, and which is amenable to high-resolution life-cell imaging. Different cell strains can be grown in parallel in the device under constant or changing media perfusion without cross-talk between the cell ensembles. The culturing chamber has been optimized for use with nonadherent cells, such as Saccharomyces cerevisiae, and enables controlled colony growth over multiple generations under aerobic or anaerobic conditions. Small changes in the layout will make the device also useable with bacteria or mammalian cells. The platform can be readily set up in every laboratory with minimal additional requirements and can be operated without technology training.

Stationary nanoliter droplet array with a substrate of choice for single adherent/nonadherent cell incubation and analysis

Microfluidic water-in-oil droplets that serve as separate, chemically isolated compartments can be applied for single-cell analysis; however, to investigate encapsulated cells effectively over prolonged time periods, an array of droplets must remain stationary on a versatile substrate for optimal cell compatibility. We present here a platform of unique geometry and substrate versatility that generates a stationary nanodroplet array by using wells branching off a main microfluidic channel. These droplets are confined by multiple sides of a nanowell and are in direct contact with a biocompatible substrate of choice. The device is operated by a unique and reversed loading procedure that eliminates the need for fine pressure control or external tubing. Fluorocarbon oil isolates the droplets and provides soluble oxygen for the cells. By using this approach, the metabolic activity of single adherent cells was monitored continuously over time, and the concentration of viable pathogens in blood-derived samples was determined directly by measuring the number of colony-formed droplets. The method is simple to operate, requires a few microliters of reagent volume, is portable, is reusable, and allows for cell retrieval. This technology may be particularly useful for multiplexed assays for which prolonged and simultaneous visual inspection of many isolated single adherent or nonadherent cells is required.

Frontier microfluidic techniques for short and long-term single cell analysis

Here, we review the frontier microfluidic techniques for single cell analysis (SCA), which is important for research of many biological systems. Microfluidics provides high-throughput, high-resolution experiments at low cost and reagent use, making it especially useful for single cell analysis. Recent advancements in the field have made SCA more feasible, improving device throughput and resolution, adding capabilities, and combining different functions to bring forth new assays. Developments in incubation have allowed for long-term cell tracking assays to be performed with single cell resolution. The ability of systems to provide chemical isolation or prolonged growth of adherent cells is also discussed.

A microfluidic system for studying ageing and dynamic single-cell responses in budding yeast

Recognition of the importance of cell-to-cell variability in cellular decision-making and a growing interest in stochastic modeling of cellular processes has led to an increased demand for high density, reproducible, single-cell measurements in time-varying surroundings. We present ALCATRAS (A Long-term Culturing And TRApping System), a microfluidic device that can quantitatively monitor up to 1000 cells of budding yeast in a well-defined and controlled environment. Daughter cells are removed by fluid flow to avoid crowding allowing experiments to run for over 60 hours, and the extracellular media may be changed repeatedly and in seconds. We illustrate use of the device by measuring ageing through replicative life span curves, following the dynamics of the cell cycle, and examining history-dependent behaviour in the general stress response.

Cellular noise suppression by the regulator of G protein signaling Sst2

G proteins and their associated receptors process information from a variety of environmental stimuli to induce appropriate cellular responses. Generally speaking, each cell in a population responds within defined limits, despite large variation in the expression of protein signaling components. Therefore, we postulated that noise suppression is encoded within the signaling system. Using the yeast mating pathway as a model, we evaluated the ability of a regulator of G protein signaling (RGS) protein to suppress noise. We found that the RGS protein Sst2 limits variability in transcription and morphogenesis in response to pheromone stimulation. While signal suppression is a result of both the GAP (GTPase accelerating) and receptor binding functions of Sst2, noise suppression requires only the GAP activity. Taken together, our findings reveal a hitherto overlooked role of RGS proteins as noise suppressors and demonstrate an ability to uncouple signal and noise in a prototypical stimulus-response pathway.

Pump-free multi-well-based microfluidic system for high-throughput analysis of size-control relative genes in budding yeast

Time-lapse single cell imaging by microscopy can provide precise cell information such as the cell size, the cell cycle duration, protein localization and protein expression level. Usually, a microfluidic system is needed for these measurements in order to provide a constant culture environment and confine the cells so that they grow in a monolayer. However, complex connections are required between the channels inside the chip and the outside media, and a complex procedure is needed for loading of cells, thereby making this type of system unsuitable for application in high-throughput single cell scanning experiments. Here we provide a novel and easily operated pump-free multi-well-based microfluidic system which enables the high-throughput loading of many different budding yeast strains into monolayer growth conditions just by use of a multi-channel pipette. Wild type budding yeast (Saccharomyces cerevisiae) and 62 different budding yeast size control relative gene deletion strains were chosen for scanning. We obtained normalized statistical results for the mother cell doubling time, daughter cell doubling time, mother cell size and daughter cell size of different gene deletion strains relative to the corresponding parameters of the wild type cells. Meanwhile, we compared the typical cell morphology of different strains and analyzed the relationship between the cell genotype and phenotype. This method which can be easily used in a normal biology lab may help researchers who need to carry out the high-throughput scanning of cell morphology and growth.

Long-term single cell analysis of S. pombe on a microfluidic microchemostat array

Although Schyzosaccharomyces pombe is one of the principal model organisms for studying the cell cycle, surprisingly few methods have characterized S. pombe growth on the single cell level, and no methods exist capable of analyzing thousands of cells and tens of thousands of cell division events. We developed an automated microfluidic platform permitting S. pombe to be grown on-chip for several days under defined and changeable conditions. We developed an image processing pipeline to extract and quantitate several physiological parameters including cell length, time to division, and elongation rate without requiring synchronization of the culture. Over a period of 50 hours our platform analyzed over 100000 cell division events and reconstructed single cell lineages up to 10 generations in length. We characterized cell lengths and division times in a temperature shift experiment in which cells were initially grown at 30°C and transitioned to 25°C. Although cell length was identical at both temperatures at steady-state, we observed transient changes in cell length if the temperature shift took place during a critical phase of the cell cycle. We further show that cells born with normal length do divide over a wide range of cell lengths and that cell length appears to be controlled in the second generation, were large newly born cells have a tendency to divide more rapidly and thus at a normalized cell size. The platform is thus applicable to measure fine-details in cell cycle dynamics, should be a useful tool to decipher the molecular mechanism underlying size homeostasis, and will be generally applicable to study processes on the single cell level that require large numbers of precision measurements and single cell lineages.

Increasing the Content of High-Content Screening: An Overview

Target-based high-throughput screening (HTS) has recently been critiqued for its relatively poor yield compared to phenotypic screening approaches. One type of phenotypic screening, image-based high-content screening (HCS), has been seen as particularly promising.

In this article, we assess whether HCS is as high content as it can be. We analyze HCS publications and find that although the number of HCS experiments published each year continues to grow steadily, the information content lags behind. We find that a majority of high-content screens published so far (60−80%) made use of only one or two image-based features measured from each sample and disregarded the distribution of those features among each cell population. We discuss several potential explanations, focusing on the hypothesis that data analysis traditions are to blame. This includes practical problems related to managing large and multidimensional HCS data sets as well as the adoption of assay quality statistics from HTS to HCS. Both may have led to the simplification or systematic rejection of assays carrying complex and valuable phenotypic information.

We predict that advanced data analysis methods that enable full multiparametric data to be harvested for entire cell populations will enable HCS to finally reach its potential.

Research highlights: microfluidics meets big data

In this issue we highlight a collection of recent work in which microfluidic parallelization and automation have been employed to address the increasing need for large amounts of quantitative data concerning cellular function--from correlating microRNA levels to protein expression, increasing the throughput and reducing the noise when studying protein dynamics in single-cells, and understanding how signal dynamics encodes information. The painstaking dissection of cellular pathways one protein at a time appears to be coming to an end, leading to more rapid discoveries which will inevitably translate to better cellular control--in producing useful gene products and treating disease at the individual cell level. From these studies it is also clear that development of large scale mutant or fusion libraries, automation of microscopy, image analysis, and data extraction will be key components as microfluidics contributes its strengths to aid systems biology moving forward.

From cradle to grave: high-throughput studies of aging in model organisms

Aging-the progressive decline of biological functions-is a universal fact of life. Decades of intense research in unicellular and metazoan model organisms have highlighted that aging manifests at all levels of biological organization - from the decline of individual cells, to tissue and organism degeneration. To better understand the aging process, we must first aim to integrate quantitative biological understanding on the systems and cellular levels. A second key challenge is to then understand the many heterogeneous outcomes that may result in aging cells, and to connect cellular aging to organism-wide degeneration. Addressing these challenges requires the development of high-throughput aging and longevity assays. In this review, we highlight the emergence of high-throughput aging approaches in the most commonly used model organisms. We conclude with a discussion of the critical questions that can be addressed with these new methods.

Determinants of cell-to-cell variability in protein kinase signaling

Cells reliably sense environmental changes despite internal and external fluctuations, but the mechanisms underlying robustness remain unclear. We analyzed how fluctuations in signaling protein concentrations give rise to cell-to-cell variability in protein kinase signaling using analytical theory and numerical simulations. We characterized the dose-response behavior of signaling cascades by calculating the stimulus level at which a pathway responds (‘pathway sensitivity’) and the maximal activation level upon strong stimulation. Minimal kinase cascades with gradual dose-response behavior show strong variability, because the pathway sensitivity and the maximal activation level cannot be simultaneously invariant. Negative feedback regulation resolves this trade-off and coordinately reduces fluctuations in the pathway sensitivity and maximal activation. Feedbacks acting at different levels in the cascade control different aspects of the dose-response curve, thereby synergistically reducing the variability. We also investigated more complex, ultrasensitive signaling cascades capable of switch-like decision making, and found that these can be inherently robust to protein concentration fluctuations. We describe how the cell-to-cell variability of ultrasensitive signaling systems can be actively regulated, e.g., by altering the expression of phosphatase(s) or by feedback/feedforward loops. Our calculations reveal that slow transcriptional negative feedback loops allow for variability suppression while maintaining switch-like decision making. Taken together, we describe design principles of signaling cascades that promote robustness. Our results may explain why certain signaling cascades like the yeast pheromone pathway show switch-like decision making with little cell-to-cell variability.

Cells sense their surroundings and respond to soluble factors in the extracellular space. Extracellular factors frequently induce heterogeneous responses, thereby restricting the biological outcome to a fraction of the cell population. However, the question arises how such cell-to-cell variability can be controlled, because some cellular systems show a very homogenous response at a defined level of an extracellular stimulus. We derived an analytical framework to systematically characterize the cell-to-cell variability of intracellular signaling pathways which transduce external signals. We analyzed how heterogeneity arises from fluctuations in the total concentrations of signaling proteins because this is the main source of variability in eukaryotic systems. We find that signaling pathways can be highly variable or inherently invariant, depending on the kinetic parameters and the structural features of the cascade. Our results indicate that the cell-to-cell variability can be reduced by negative feedback in the cascade or by signaling crosstalk between parallel pathways. We precisely define the role of negative feedback loops in variability suppression, and show that different aspects of the dose-response curve can be controlled, depending on the feedback kinetics and site of action in the cascade. This work constitutes a first step towards a systematic understanding of cell-to-cell variability in signal transduction.