Transcription of two long noncoding RNAs mediates mating-type control of gametogenesis in budding yeast.

The cell-fate decision leading to gametogenesis is essential for sexual reproduction. In S. cerevisiae, only diploid MATa/α but not haploid MATa or MATα cells undergo gametogenesis, known as sporulation. We find that transcription of two long noncoding RNAs (lncRNAs) mediates mating-type control of sporulation. In MATa or MATα haploids, expression of IME1, the central inducer of gametogenesis, is inhibited in cis by transcription of the lncRNA IRT1, located in the IME1 promoter. IRT1 transcription recruits the Set2 histone methyltransferase and the Set3 histone deacetylase complex to establish repressive chromatin at the IME1 promoter. Inhibiting expression of IRT1 and an antisense transcript that antagonizes the expression of the meiotic regulator IME4 allows cells expressing the haploid mating type to sporulate with kinetics that are indistinguishable from that of MATa/α diploids. Conversely, expression of the two lncRNAs abolishes sporulation in MATa/α diploids. Thus, transcription of two lncRNAs governs mating-type control of gametogenesis in yeast.

A rate threshold mechanism regulates MAPK stress signaling and survival.

Cells are exposed to changes in extracellular stimulus concentration that vary as a function of rate. However, how cells integrate information conveyed from stimulation rate along with concentration remains poorly understood. Here, we examined how varying the rate of stress application alters budding yeast mitogen-activated protein kinase (MAPK) signaling and cell behavior at the single-cell level. We show that signaling depends on a rate threshold that operates in conjunction with stimulus concentration to determine the timing of MAPK signaling during rate-varying stimulus treatments. We also discovered that the stimulation rate threshold and stimulation rate-dependent cell survival are sensitive to changes in the expression levels of the Ptp2 phosphatase, but not of another phosphatase that similarly regulates osmostress signaling during switch-like treatments. Our results demonstrate that stimulation rate is a regulated determinant of cell behavior and provide a paradigm to guide the dissection of major stimulation rate dependent mechanisms in other systems.

Distribution shapes govern the discovery of predictive models for gene regulation.

Despite substantial experimental and computational efforts, mechanistic modeling remains more predictive in engineering than in systems biology. The reason for this discrepancy is not fully understood. One might argue that the randomness and complexity of biological systems are the main barriers to predictive understanding, but these issues are not unique to biology. Instead, we hypothesize that the specific shapes of rare single-molecule event distributions produce substantial yet overlooked challenges for biological models. We demonstrate why modern statistical tools to disentangle complexity and stochasticity, which assume normally distributed fluctuations or enormous datasets, do not apply to the discrete, positive, and nonsymmetric distributions that characterize mRNA fluctuations in single cells. As an example, we integrate single-molecule measurements and advanced computational analyses to explore mitogen-activated protein kinase induction of multiple stress response genes. Through systematic analyses of different metrics to compare the same model to the same data, we elucidate why standard modeling approaches yield nonpredictive models for single-cell gene regulation. We further explain how advanced tools recover precise, reproducible, and predictive understanding of transcription regulation mechanisms, including gene activation, polymerase initiation, elongation, mRNA accumulation, spatial transport, and decay.

Single-cell analysis reveals that noncoding RNAs contribute to clonal heterogeneity by modulating transcription factor recruitment.

Mechanisms through which long intergenic noncoding RNAs (ncRNAs) exert regulatory effects on eukaryotic biological processes remain largely elusive. Most studies of these phenomena rely on methods that measure average behaviors in cell populations, lacking resolution to observe the effects of ncRNA transcription on gene expression in a single cell. Here, we combine quantitative single-molecule RNA FISH experiments with yeast genetics and computational modeling to gain mechanistic insights into the regulation of the Saccharomyces cerevisiae protein-coding gene FLO11 by two intergenic ncRNAs, ICR1 and PWR1. Direct detection of FLO11 mRNA and these ncRNAs in thousands of individual cells revealed alternative expression states and provides evidence that ICR1 and PWR1 contribute to FLO11's variegated transcription, resulting in Flo11-dependent phenotypic heterogeneity in clonal cell populations by modulating recruitment of key transcription factors to the FLO11 promoter.

Integrating single-molecule experiments and discrete stochastic models to understand heterogeneous gene transcription dynamics.

The production and degradation of RNA transcripts is inherently subject to biological noise that arises from small gene copy numbers in individual cells. As a result, cellular RNA levels can exhibit large fluctuations over time and from one cell to the next. This article presents a range of precise single-molecule experimental techniques, based upon RNA fluorescence in situ hybridization, which can be used to measure the fluctuations of RNA at the single-cell level. A class of models for gene activation and deactivation is postulated in order to capture complex stochastic effects of chromatin modifications or transcription factor interactions. A computational tool, known as the finite state projection approach, is introduced to accurately and efficiently analyze these models in order to predict how probability distributions of RNA change over time in response to changing environmental conditions. These single-molecule experiments, discrete stochastic models, and computational analyses are systematically integrated to identify models of gene regulation dynamics. To illustrate the power and generality of our integrated experimental and computational approach, we explore cases that include different models for three different RNA types (sRNA, mRNA and nascent RNA), three different experimental techniques and three different biological species (bacteria, yeast and human cells).

Finite state projection based bounds to compare chemical master equation models using single-cell data.

Emerging techniques now allow for precise quantification of distributions of biological molecules in single cells. These rapidly advancing experimental methods have created a need for more rigorous and efficient modeling tools. Here, we derive new bounds on the likelihood that observations of single-cell, single-molecule responses come from a discrete stochastic model, posed in the form of the chemical master equation. These strict upper and lower bounds are based on a finite state projection approach, and they converge monotonically to the exact likelihood value. These bounds allow one to discriminate rigorously between models and with a minimum level of computational effort. In practice, these bounds can be incorporated into stochastic model identification and parameter inference routines, which improve the accuracy and efficiency of endeavors to analyze and predict single-cell behavior. We demonstrate the applicability of our approach using simulated data for three example models as well as for experimental measurements of a time-varying stochastic transcriptional response in yeast.

From analog to digital models of gene regulation.

Recently, major progress has been made to develop computational models to predict and explain the mechanisms and behaviors of gene regulation. Here, we review progress on how these mechanisms and behaviors have been interpreted with analog models, where cell properties continuously modulate transcription, and digital models, where gene modulation involves discrete activation and inactivation events. We introduce recent experimental approaches, which measure these gene regulatory behaviors at single-cell and single-molecule resolution, and we discuss the integration of these approaches with computational models to reveal biophysical insight. By analyzing simple toy models in the context of existing experimental capabilities, we discuss the interplay between different experiments and different models to measure and interpret gene regulatory behaviors. Finally, we review recent successes in the development of predictive computational models for the control of gene regulation behaviors.

Rate thresholds in cell signaling have functional and phenotypic consequences in non-linear time-dependent environments.

All cells employ signal transduction pathways to respond to physiologically relevant extracellular cytokines, stressors, nutrient levels, hormones, morphogens, and other stimuli that vary in concentration and rate in healthy and diseased states. A central unsolved fundamental question in cell signaling is whether and how cells sense and integrate information conveyed by changes in the rate of extracellular stimuli concentrations, in addition to the absolute difference in concentration. We propose that different environmental changes over time influence cell behavior in addition to different signaling molecules or different genetic backgrounds. However, most current biomedical research focuses on acute environmental changes and does not consider how cells respond to environments that change slowly over time. As an example of such environmental change, we review cell sensitivity to environmental rate changes, including the novel mechanism of rate threshold. A rate threshold is defined as a threshold in the rate of change in the environment in which a rate value below the threshold does not activate signaling and a rate value above the threshold leads to signal activation. We reviewed p38/Hog1 osmotic stress signaling in yeast, chemotaxis and stress response in bacteria, cyclic adenosine monophosphate signaling in Amoebae, growth factors signaling in mammalian cells, morphogen dynamics during development, temporal dynamics of glucose and insulin signaling, and spatio-temproral stressors in the kidney. These reviewed examples from the literature indicate that rate thresholds are widespread and an underappreciated fundamental property of cell signaling. Finally, by studying cells in non-linear environments, we outline future directions to understand cell physiology better in normal and pathophysiological conditions.

Phenotypic consequences of logarithmic signaling in MAPK stress response.

How cells respond to dynamic environmental changes is crucial for understanding fundamental biological processes and cell physiology. In this study, we developed an experimental and quantitative analytical framework to explore how dynamic stress gradients that change over time regulate cellular volume, signaling activation, and growth phenotypes. Our findings reveal that gradual stress conditions substantially enhance cell growth compared to conventional acute stress. This growth advantage correlates with a minimal reduction in cell volume dependent on the dynamic of stress. We explain the growth phenotype with our finding of a logarithmic signal transduction mechanism in the yeast Mitogen-Activated Protein Kinase (MAPK) osmotic stress response pathway. These insights into the interplay between gradual environments, cell volume change, dynamic cell signaling, and growth, advance our understanding of fundamental cellular processes in gradual stress environments.

Kinetics of osmotic stress regulate a cell fate switch of cell survival.

Exposure of cells to diverse types of stressful environments differentially regulates cell fate. Although many types of stresses causing this differential regulation are known, it is unknown how changes over time of the same stressor regulate cell fate. Changes in extracellular osmolarity are critically involved in physiological and pathophysiological processes in several tissues. We observe that human cells survive gradual but not acute hyperosmotic stress. We find that stress, caspase, and apoptosis signaling do not activate during gradual stress in contrast to acute treatments. Contrary to the current paradigm, we see a substantial accumulation of proline in cells treated with gradual but not acute stresses. We show that proline can protect cells from hyperosmotic stress similar to the osmoprotection in plants and bacteria. Our studies found a cell fate switch that enables cells to survive gradually changing stress environments by preventing caspase activation and protect cells through proline accumulation.

Building predictive signaling models by perturbing yeast cells with time-varying stimulations resulting in distinct signaling responses.

This protocol provides a step-by-step approach to perturb single cells with time-varying stimulation profiles, collect distinct signaling responses, and use these to infer a system of ordinary differential equations to capture and predict dynamics of protein-protein regulation in signal transduction pathways. The models are validated by predicting the signaling activation upon new cell stimulation conditions. In comparison to using standard step-like stimulations, application of diverse time-varying cell stimulations results in better inference of model parameters and substantially improves model predictions. For complete details on the use and results of this protocol, please refer to Jashnsaz et al. (2020).

Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments.

Modern biological experiments are becoming increasingly complex, and designing these experiments to yield the greatest possible quantitative insight is an open challenge. Increasingly, computational models of complex stochastic biological systems are being used to understand and predict biological behaviors or to infer biological parameters. Such quantitative analyses can also help to improve experiment designs for particular goals, such as to learn more about specific model mechanisms or to reduce prediction errors in certain situations. A classic approach to experiment design is to use the Fisher information matrix (FIM), which quantifies the expected information a particular experiment will reveal about model parameters. The Finite State Projection based FIM (FSP-FIM) was recently developed to compute the FIM for discrete stochastic gene regulatory systems, whose complex response distributions do not satisfy standard assumptions of Gaussian variations. In this work, we develop the FSP-FIM analysis for a stochastic model of stress response genes in S. cerevisae under time-varying MAPK induction. We verify this FSP-FIM analysis and use it to optimize the number of cells that should be quantified at particular times to learn as much as possible about the model parameters. We then extend the FSP-FIM approach to explore how different measurement times or genetic modifications help to minimize uncertainty in the sensing of extracellular environments, and we experimentally validate the FSP-FIM to rank single-cell experiments for their abilities to minimize estimation uncertainty of NaCl concentrations during yeast osmotic shock. This work demonstrates the potential of quantitative models to not only make sense of modern biological data sets, but to close the loop between quantitative modeling and experimental data collection.

Diverse Cell Stimulation Kinetics Identify Predictive Signal Transduction Models.

Computationally understanding the molecular mechanisms that give rise to cell signaling responses upon different environmental, chemical, and genetic perturbations is a long-standing challenge that requires models that fit and predict quantitative responses for new biological conditions. Overcoming this challenge depends not only on good models and detailed experimental data but also on the rigorous integration of both. We propose a quantitative framework to perturb and model generic signaling networks using multiple and diverse changing environments (hereafter "kinetic stimulations") resulting in distinct pathway activation dynamics. We demonstrate that utilizing multiple diverse kinetic stimulations better constrains model parameters and enables predictions of signaling dynamics that would be impossible using traditional dose-response or individual kinetic stimulations. To demonstrate our approach, we use experimentally identified models to predict signaling dynamics in normal, mutated, and drug-treated conditions upon multitudes of kinetic stimulations and quantify which proteins and reaction rates are most sensitive to which extracellular stimulations.

Multiplex RNA single molecule FISH of inducible mRNAs in single yeast cells.

Transcript levels powerfully influence cell behavior and phenotype and are carefully regulated at several steps. Recently developed single cell approaches such as RNA single molecule fluorescence in-situ hybridization (smFISH) have produced advances in our understanding of how these steps work within the cell. In comparison to single-cell sequencing, smFISH provides more accurate quantification of RNA levels. Additionally, transcript subcellular localization is directly visualized, enabling the analysis of transcription (initiation and elongation), RNA export and degradation. As part of our efforts to investigate how this type of analysis can generate improved models of gene expression, we used smFISH to quantify the kinetic expression of STL1 and CTT1 mRNAs in single Saccharomyces cerevisiae cells upon 0.2 and 0.4 M NaCl osmotic stress. In this Data Descriptor, we outline our procedure along with our data in the form of raw images and processed mRNA counts. We discuss how these data can be used to develop single cell modelling approaches, to study fundamental processes in transcription regulation and develop single cell image processing approaches.

Automated cell boundary and 3D nuclear segmentation of cells in suspension.

To characterize cell types, cellular functions and intracellular processes, an understanding of the differences between individual cells is required. Although microscopy approaches have made tremendous progress in imaging cells in different contexts, the analysis of these imaging data sets is a long-standing, unsolved problem. The few robust cell segmentation approaches that exist often rely on multiple cellular markers and complex time-consuming image analysis. Recently developed deep learning approaches can address some of these challenges, but they require tremendous amounts of data and well-curated reference data sets for algorithm training. We propose an alternative experimental and computational approach, called CellDissect, in which we first optimize specimen preparation and data acquisition prior to image processing to generate high quality images that are easier to analyze computationally. By focusing on fixed suspension and dissociated adherent cells, CellDissect relies only on widefield images to identify cell boundaries and nuclear staining to automatically segment cells in two dimensions and nuclei in three dimensions. This segmentation can be performed on a desktop computer or a computing cluster for higher throughput. We compare and evaluate the accuracy of different nuclear segmentation approaches against manual expert cell segmentation for different cell lines acquired with different imaging modalities.

Generating kinetic environments to study dynamic cellular processes in single cells.

Cells of any organism are consistently exposed to changes over time in their environment. The kinetics by which these changes occur are critical for the cellular response and fate decision. It is therefore important to control the temporal changes of extracellular stimuli precisely to understand biological mechanisms in a quantitative manner. Most current cell culture and biochemical studies focus on instant changes in the environment and therefore neglect the importance of kinetic environments. To address these shortcomings, we developed two experimental methodologies to precisely control the environment of single cells. These methodologies are compatible with standard biochemistry, molecular, cell and quantitative biology assays. We demonstrate applicability by obtaining time series and time point measurements in both live and fixed cells. We demonstrate the feasibility of the methodology in yeast and mammalian cell culture in combination with widely used assays such as flow cytometry, time-lapse microscopy and single-molecule RNA Fluorescent in-situ Hybridization (smFISH). Our experimental methodologies are easy to implement in most laboratory settings and allows the study of kinetic environments in a wide range of assays and different cell culture conditions.

Molecular force balance measurements reveal that double-stranded DNA unbinds under force in rate-dependent pathways.

Strand separation of double-stranded DNA is a crucial step for essential cellular processes such as recombination and transcription. By means of a molecular force balance, we have analyzed the impact of different pulling directions and different force-loading rates on the unbinding process of short double-stranded DNA. At loading rates above 9 x 10(5) pN/s, we found a marked difference in rupture probability for pulling the duplex in 3'-3' direction compared to a 5'-5' direction, indicating different unbinding pathways. We propose a mechanism by which unbinding at low loading rates is dominated by nondirectional thermal fluctuations, whereas mechanical properties of the DNA become more important at high loading rates and reveal the asymmetry of the phosphoribose backbone. Our model explains the difference of 3'-3' and 5'-5' unbinding as a kinetic process, where the loading rate exceeds the relaxation time of DNA melting bubbles.

Systematic identification of signal-activated stochastic gene regulation.

Although much has been done to elucidate the biochemistry of signal transduction and gene regulatory pathways, it remains difficult to understand or predict quantitative responses. We integrate single-cell experiments with stochastic analyses, to identify predictive models of transcriptional dynamics for the osmotic stress response pathway in Saccharomyces cerevisiae. We generate models with varying complexity and use parameter estimation and cross-validation analyses to select the most predictive model. This model yields insight into several dynamical features, including multistep regulation and switchlike activation for several osmosensitive genes. Furthermore, the model correctly predicts the transcriptional dynamics of cells in response to different environmental and genetic perturbations. Because our approach is general, it should facilitate a predictive understanding for signal-activated transcription of other genes in other pathways or organisms.

Using gene expression noise to understand gene regulation.

Phenotypic variation is ubiquitous in biology and is often traceable to underlying genetic and environmental variation. However, even genetically identical cells in identical environments display variable phenotypes. Stochastic gene expression, or gene expression "noise," has been suggested as a major source of this variability, and its physiological consequences have been topics of intense research for the last decade. Several recent studies have measured variability in protein and messenger RNA levels, and they have discovered strong connections between noise and gene regulation mechanisms. When integrated with discrete stochastic models, measurements of cell-to-cell variability provide a sensitive "fingerprint" with which to explore fundamental questions of gene regulation. In this review, we highlight several studies that used gene expression variability to develop a quantitative understanding of the mechanisms and dynamics of gene regulation.