Temporal perturbation of ERK dynamics reveals network architecture of FGF2/MAPK signaling

Time-varying stimuli that prolong IKK activation promote nuclear remodeling and mechanistic switching of NF-κB dynamics

Temporal properties of molecules within signaling networks, such as sub-cellular changes in protein abundance, encode information that mediate cellular responses to stimuli. How dynamic signals relay and process information is a critical gap in understanding cellular behaviors. In this work, we investigate transmission of information about changing extracellular cytokine concentrations from receptor-level supramolecular assemblies of IκB kinases (IKK) downstream to the nuclear factor κB (NF-κB) transcription factor (TF). In a custom robot-controlled microfluidic cell culture, we simultaneously measure input-output (I/O) encoding of IKK-NF-κB in dual fluorescent-reporter cells. When compared with single cytokine pulses, dose-conserving pulse trains prolong IKK assemblies and lead to disproportionately enhanced retention of nuclear NF-κB. Using particle swarm optimization, we demonstrate that a mechanistic model does not recapitulate this emergent property. By contrast, invoking mechanisms for NF-κB-dependent chromatin remodeling to the model recapitulates experiments, showing how temporal dosing that prolongs IKK assemblies facilitates switching to permissive chromatin that sequesters nuclear NF-κB. Remarkably, using simulations to resolve single-cell receptor data accurately predicts same-cell NF-κB time courses for more than 80% of our single cell trajectories. Our data and simulations therefore suggest that cell-to-cell heterogeneity in cytokine responses are predominantly due to mechanisms at the level receptor-associated protein complexes.

Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways

The mitogen-activated protein kinase (MAPK) pathway is an important intracellular signaling cascade that plays a key role in various cellular processes. Understanding the regulatory mechanisms of this pathway is essential for developing effective interventions and targeted therapies for related diseases. Recent advances in single-cell proteomic technologies have provided unprecedented opportunities to investigate the heterogeneity and noise within complex, multi-signaling networks across diverse cells and cell types. Mathematical modeling has become a powerful interdisciplinary tool that bridges mathematics and experimental biology, providing valuable insights into these intricate cellular processes. In addition, statistical methods have been developed to infer pathway topologies and estimate unknown parameters within dynamic models. This review presents a comprehensive analysis of how mathematical modeling of the MAPK pathway deepens our understanding of its regulatory mechanisms, enhances the prediction of system behavior, and informs experimental research, with a particular focus on recent advances in modeling and inference using single-cell proteomic data.

The mitogen-activated protein kinase network, wired to dynamically function at multiple scales

The mitogen-activated protein kinase (MAPK)/extracellular signal-regulated kinase (ERK) signaling network is a key transducer of signals from various receptors, including receptor tyrosine kinases (RTKs). It controls cell-cycle entry, survival, motility, differentiation, as well as other fates. After four decades of studying this pathway with biochemical methods, the use of fluorescent biosensors has revealed dynamic behaviors such as ERK pulsing, oscillations, and amplitude-modulated activity. Different RTKs equip the MAPK network with specific feedback mechanisms to encode these different ERK dynamics, which are then subsequently decoded into cytoskeletal events and transcriptional programs, actuating cellular fates. Recently, collective ERK wave behaviors have been observed in multiple systems to coordinate cytoskeletal dynamics with fate decisions within cell collectives. This emphasizes that a correct understanding of this pathway requires studying it at multiple scales.

Engineering global and local signal generators for probing temporal and spatial cellular signaling dynamics

Cells constantly encounter a wide range of environmental signals and rely on their signaling pathways to initiate reliable responses. Understanding the underlying signaling mechanisms and cellular behaviors requires signal generators capable of providing diverse input signals to deliver to cell systems. Current research efforts are primarily focused on exploring cellular responses to global or local signals, which enable us to understand cellular signaling and behavior in distinct dimensions. This review presents recent advancements in global and local signal generators, highlighting their applications in studying temporal and spatial signaling activity. Global signals can be generated using microfluidic or photochemical approaches. Local signal sources can be created using living or artificial cells in combination with different control methods. We also address the strengths and limitations of each signal generator type, discussing challenges and potential extensions for future research. These approaches are expected to continue to facilitate on-going research to discover novel and intriguing cellular signaling mechanisms.

Tonic repression of Collagen I by the Bradykinin receptor 2 in skin fibroblasts

Imbalance of collagen I expression results in severe pathologies. Apart from activation by the TGFβ-receptor/Smad pathway, control of collagen I expression remains poorly understood. Here, we used human dermal fibroblasts expressing a mCherry fluorescent protein driven by endogenous COL1A1 promoter to functionally screen the kinome and phosphatome. We identify 8 negative regulators, revealing that collagen is under tonic repression. The cell surface receptor BDKRB2 represses collagen I and other pro-fibrotic genes. Interestingly, it also promotes other basal membrane ECM genes. This function is independent of the natural ligand, bradykinin, and of SMAD2/3 factors, instead requiring constant ERK1/2 repression. TGFβ stimulation induces rapid BDKRB2 transcriptional downregulation. Human fibrotic fibroblasts have reduced BDKRB2 levels and enhancing its expression in keloid fibroblasts represses COL1A1. We propose that tonic signalling by BDKRB2 prevents collagen overproduction in skin fibroblasts.

Relating individual cell division events to single-cell ERK and Akt activity time courses

Biochemical correlates of stochastic single-cell fates have been elusive, even for the well-studied mammalian cell cycle. We monitored single-cell dynamics of the ERK and Akt pathways, critical cell cycle progression hubs and anti-cancer drug targets, and paired them to division events in the same single cells using the non-transformed MCF10A epithelial line. Following growth factor treatment, in cells that divide both ERK and Akt activities are significantly higher within the S-G2 time window (~ 8.5-40 h). Such differences were much smaller in the pre-S-phase, restriction point window which is traditionally associated with ERK and Akt activity dependence, suggesting unappreciated roles for ERK and Akt in S through G2. Simple metrics of central tendency in this time window are associated with subsequent cell division fates. ERK activity was more strongly associated with division fates than Akt activity, suggesting Akt activity dynamics may contribute less to the decision driving cell division in this context. We also find that ERK and Akt activities are less correlated with each other in cells that divide. Network reconstruction experiments demonstrated that this correlation behavior was likely not due to crosstalk, as ERK and Akt do not interact in this context, in contrast to other transformed cell types. Overall, our findings support roles for ERK and Akt activity throughout the cell cycle as opposed to just before the restriction point, and suggest ERK activity dynamics may be more important than Akt activity dynamics for driving cell division in this non-transformed context.

Identification of cell type-specific correlations between ERK activity and cell viability upon treatment with ERK1/2 inhibitors

Increased MAPK signaling is a hallmark of various cancers and is a central regulator of cell survival. Direct ERK1/2 inhibition is considered a promising approach to avoid ERK1/2 reactivation caused by upstream kinases BRAF, MEK1/2, and KRAS, as well as by receptor tyrosine kinase inhibitors, but the dynamics and selectivity of ERK1/2 inhibitors are much less studied compared with BRAF or MEK inhibitors. Using ERK1/2 and downstream kinase ELK1 reporter cell lines of lung cancer (H1299; NRASQ61K), colon cancer (HCT-116; KRASG13D), neuroblastoma (SH-SY5Y), and leukemia (U937), we examined the relationship between ERK inhibition and drug-induced toxicity for five ERK inhibitors: SCH772984, ravoxertinib, LY3214996, ulixertinib, and VX-11e, as well as one MEK inhibitor, PD0325901. Comparing cell viability and ERK inhibition revealed different ERK dependencies for these cell lines. We identify several drugs, such as SCH772984 and VX-11e, which induce excessive toxicity not directly related to ERK1/2 inhibition in specific cell lines. We also show that PD0325901, LY3214996, and ulixertinib are prone to ERK1/2 reactivation over time. We distinguished two types of ERK1/2 reactivation: the first could be reversed by adding a fresh dose of inhibitors, while the second persists even after additional treatments. We also showed that cells that became resistant to the MEK1/2 inhibitor PD0325901 due to ERK1/2 reactivation remained sensitive to ERK1/2 inhibitor ulixertinib. Our data indicate that correlation of ERK inhibition with drug-induced toxicity in multiple cell lines may help to find more selective and effective ERK1/2 inhibitors.

Optogenetic actuator - ERK biosensor circuits identify MAPK network nodes that shape ERK dynamics

Combining single-cell measurements of ERK activity dynamics with perturbations provides insights into the MAPK network topology. We built circuits consisting of an optogenetic actuator to activate MAPK signaling and an ERK biosensor to measure single-cell ERK dynamics. This allowed us to conduct RNAi screens to investigate the role of 50 MAPK proteins in ERK dynamics. We found that the MAPK network is robust against most node perturbations. We observed that the ERK-RAF and the ERK-RSK2-SOS negative feedback operate simultaneously to regulate ERK dynamics. Bypassing the RSK2-mediated feedback, either by direct optogenetic activation of RAS, or by RSK2 perturbation, sensitized ERK dynamics to further perturbations. Similarly, targeting this feedback in a human ErbB2-dependent oncogenic signaling model increased the efficiency of a MEK inhibitor. The RSK2-mediated feedback is thus important for the ability of the MAPK network to produce consistent ERK outputs, and its perturbation can enhance the efficiency of MAPK inhibitors.

A kinase translocation reporter reveals real-time dynamics of ERK activity in Drosophila

Extracellular signal-regulated kinase (ERK) lies downstream of a core signalling cascade that controls all aspects of development and adult homeostasis. Recent developments have led to new tools to image and manipulate the pathway. However, visualising ERK activity in vivo with high temporal resolution remains a challenge in Drosophila. We adapted a kinase translocation reporter (KTR) for use in Drosophila, which shuttles out of the nucleus when phosphorylated by ERK. We show that ERK-KTR faithfully reports endogenous ERK signalling activity in developing and adult tissues, and that it responds to genetic perturbations upstream of ERK. Using ERK-KTR in time-lapse imaging, we made two novel observations: firstly, sustained hyperactivation of ERK by expression of dominant-active epidermal growth factor receptor raised the overall level but did not alter the kinetics of ERK activity; secondly, the direction of migration of retinal basal glia correlated with their ERK activity levels, suggesting an explanation for the heterogeneity in ERK activity observed in fixed tissue. Our results show that KTR technology can be applied in Drosophila to monitor ERK activity in real-time and suggest that this modular tool can be further adapted to study other kinases. This article has an associated First Person interview with the first author of the paper.

Signalling dynamics, cell decisions, and homeostatic control in health and disease

Cell signalling engenders cells with the capability to receive and process information from the intracellular and extracellular environments, trigger and execute biological responses, and communicate with each other. Ultimately, cell signalling is responsible for maintaining homeostasis at the cellular, tissue and systemic level. For this reason, cell signalling is a topic of intense research efforts aimed to elucidate how cells coordinate transitions between states in developing and adult organisms in physiological and pathological conditions. Here, we review current knowledge of how cell signalling operates at multiple spatial and temporal scales, focusing on how single-cell analytical techniques reveal mechanisms underpinning cell-to-cell variability, signalling plasticity, and collective cellular responses.

Intermittent ERK oscillations downstream of FGF in mouse embryonic stem cells

Signal transduction networks generate characteristic dynamic activities to process extracellular signals and guide cell fate decisions such as to divide or differentiate. The differentiation of pluripotent cells is controlled by FGF/ERK signaling. However, only a few studies have addressed the dynamic activity of the FGF/ERK signaling network in pluripotent cells at high time resolution. Here, we use live cell sensors in wild-type and Fgf4-mutant mouse embryonic stem cells to measure dynamic ERK activity in single cells, for defined ligand concentrations and differentiation states. These sensors reveal pulses of ERK activity. Pulsing patterns are heterogeneous between individual cells. Consecutive pulse sequences occur more frequently than expected from simple stochastic models. Sequences become more prevalent with higher ligand concentration, but are rarer in more differentiated cells. Our results suggest that FGF/ERK signaling operates in the vicinity of a transition point between oscillatory and non-oscillatory dynamics in embryonic stem cells. The resulting heterogeneous dynamic signaling activities add a new dimension to cellular heterogeneity that may be linked to divergent fate decisions in stem cell cultures.

Single cell imaging of ERK and Akt activation dynamics and heterogeneity induced by G protein-coupled receptors

Kinases play key roles in signaling networks that are activated by G-protein-coupled receptors (GPCRs). Kinase activities are generally inferred from cell lysates, hiding cell-to-cell variability. To study the dynamics and heterogeneity of ERK and Akt proteins, we employed high-content biosensor imaging with kinase translocation reporters. The kinases were activated with GPCR ligands. We observed ligand concentration-dependent response kinetics to histamine, α2-adrenergic and S1P receptor stimulation. By using G-protein inhibitors, we observed that Gq mediated the ERK and Akt responses to histamine. In contrast, Gi was necessary for ERK and Akt activation in response to α2-adrenergic receptor activation. ERK and Akt were also strongly activated by S1P, showing high heterogeneity at the single-cell level, especially for ERK. Cluster analysis of time series derived from 68,000 cells obtained under the different conditions revealed several distinct populations of cells that display similar response dynamics. ERK response dynamics to S1P showed high heterogeneity, which was reduced by the inhibition of Gi. To conclude, we have set up an imaging and analysis strategy that reveals substantial cell-to-cell heterogeneity in kinase activity driven by GPCRs.

Summary: High-content biosensor imaging with kinase translocation reporters reveals substantial cell-to-cell heterogeneity in kinase activity driven by G-protein-coupled receptors.

MAPK/ERK Pathway as a Central Regulator in Vertebrate Organ Regeneration

Damage to organs by trauma, infection, diseases, congenital defects, aging, and other injuries causes organ malfunction and is life-threatening under serious conditions. Some of the lower order vertebrates such as zebrafish, salamanders, and chicks possess superior organ regenerative capacity over mammals. The extracellular signal-regulated kinases 1 and 2 (ERK1/2), as key members of the mitogen-activated protein kinase (MAPK) family, are serine/threonine protein kinases that are phylogenetically conserved among vertebrate taxa. MAPK/ERK signaling is an irreplaceable player participating in diverse biological activities through phosphorylating a broad variety of substrates in the cytoplasm as well as inside the nucleus. Current evidence supports a central role of the MAPK/ERK pathway during organ regeneration processes. MAPK/ERK signaling is rapidly excited in response to injury stimuli and coordinates essential pro-regenerative cellular events including cell survival, cell fate turnover, migration, proliferation, growth, and transcriptional and translational activities. In this literature review, we recapitulated the multifaceted MAPK/ERK signaling regulations, its dynamic spatio-temporal activities, and the profound roles during multiple organ regeneration, including appendages, heart, liver, eye, and peripheral/central nervous system, illuminating the possibility of MAPK/ERK signaling as a critical mechanism underlying the vastly differential regenerative capacities among vertebrate species, as well as its potential applications in tissue engineering and regenerative medicine.

Using Microfluidics and Live Cell Reporters to Dissect the Dynamics of TGF-β Signaling in Mouse Embryonic Stem Cells

The TGF-β pathway is known to behave as a classical morphogen, meaning that it can dictate cell fate decisions in a dose-dependent manner. Recent observations however showed that in addition to the absolute value of morphogen concentration, cells could also extract information from its temporal variations. In the present article we describe how to use automated microfluidics cell culture to stimulate cells with precisely defined temporal profiles of morphogens and how to engineer mouse embryonic stem cells with fluorescent reporters of pathway activity to record in real time their response to the applied stimulations. The combination of automated cell culture and of live cell reporter provides a complete toolbox to study how cells encode the information carried by time-varying TGF-β signals.

Mining of Single-Cell Signaling Time-Series for Dynamic Phenotypes with Clustering

Fluorescent live cell time-lapse microscopy is steadily contributing to our better understanding of the relationship between cell signaling and fate. However, large volumes of time-series data generated in these experiments and the heterogenous nature of signaling responses due to cell-cell variability hinder the exploration of such datasets. The population averages insufficiently describe the dynamics, yet finding prototypic dynamic patterns that relate to different cell fates is difficult when mining thousands of time-series. Here we demonstrate a protocol where we identify such dynamic phenotypes in a population of PC-12 cells that respond to a range of sustained growth factor perturbations. We use Time-Course Inspector, a free R/Shiny web application to explore and cluster single-cell time-series.

MECHANISTIC AND DATA-DRIVEN MODELS OF CELL SIGNALING: TOOLS FOR FUNDAMENTAL DISCOVERY AND RATIONAL DESIGN OF THERAPY

A full understanding of cell signaling processes requires knowledge of protein structure/function relationships, protein-protein interactions, and the abilities of pathways to control phenotypes. Computational models offer a valuable framework for integrating that knowledge to predict the effects of system perturbations and interventions in health and disease. Whereas mechanistic models are well suited for understanding the biophysical basis for signal transduction and principles of therapeutic design, data-driven models are particularly suited to distill complex signaling relationships among samples and between multivariate signaling changes and phenotypes. Both approaches have limitations and provide incomplete representations of signaling biology, but their careful implementation and integration can provide new understanding for how manipulating system variables impacts cellular decisions.

Design and implementation of a microfluidic device capable of temporal growth factor delivery reveal filtering capabilities of the EGFR/ERK pathway

Utilizing microfluidics to mimic the dynamic temporal changes of growth factor and cytokine concentrations in vivo has greatly increased our understanding of how signal transduction pathways are structured to encode extracellular stimuli. To date, these devices have focused on delivering pulses of varying frequency, and there are limited cell culture models for delivering slowly increasing concentrations of stimuli that cells may experience in vivo. To examine this setting, we developed and validated a microfluidic device that can deliver increasing concentrations of growth factor over periods ranging from 6 to 24 h. Using this device and a fluorescent biosensor of extracellular-regulated kinase (ERK) activity, we delivered a slowly increasing concentration of epidermal growth factor (EGF) to human mammary epithelial cells and surprisingly observed minimal ERK activation, even at concentrations that stimulate robust activity in bolus delivery. The cells remained unresponsive to subsequent challenges with EGF, and immunocytochemistry suggested that the loss of an epidermal growth factor receptor was responsible. Cells were then challenged with faster rates of change of EGF, revealing an increased ERK activity as a function of rate of change. Specifically, both the fraction of cells that responded and the length of ERK activation time increased with the rate of change. This microfluidic device fills a gap in the current repertoire of in vitro microfluidic devices and demonstrates that slower, more physiological changes in growth factor presentation can reveal new regulatory mechanisms for how signal transduction pathways encode changes in the extracellular growth factor milieu.

Cell geometry, signal dampening, and a bimodal transcriptional response underlie the spatial precision of an ERK-mediated embryonic induction

Precise control of lineage segregation is critical for the development of multicellular organisms, but our quantitative understanding of how variable signaling inputs are integrated to activate lineage-specific gene programs remains limited. Here, we show how precisely two out of eight ectoderm cells adopt neural fates in response to ephrin and FGF signals during ascidian neural induction. In each ectoderm cell, FGF signals activate ERK to a level that mirrors its cell contact surface with FGF-expressing mesendoderm cells. This gradual interpretation of FGF inputs is followed by a bimodal transcriptional response of the immediate early gene, Otx, resulting in its activation specifically in the neural precursors. At low levels of ERK, Otx is repressed by an ETS family transcriptional repressor, ERF2. Ephrin signals are critical for dampening ERK activation levels across ectoderm cells so that only neural precursors exhibit above-threshold levels, evade ERF repression, and "switch on" Otx transcription.

High-throughput injection molded microfluidic device for single-cell analysis of spatiotemporal dynamics

Single-cell level analysis of various cellular behaviors has been aided by recent developments in microfluidic technology. Polydimethylsiloxane (PDMS)-based microfluidic devices have been widely used to elucidate cell differentiation and migration under spatiotemporal stimulation. However, microfluidic devices fabricated with PDMS have inherent limitations due to material issues and non-scalable fabrication process. In this study, we designed and fabricated an injection molded microfluidic device that enables real-time chemical profile control. This device is made of polystyrene (PS), engineered with channel dimensions optimized for injection molding to achieve functionality and compatibility with single cell observation. We demonstrated the spatiotemporal dynamics in the device with computational simulation and experiments. In temporal dynamics, we observed extracellular signal-regulated kinase (ERK) activation of PC12 cells by stimulating the cells with growth factors (GFs). Also, we confirmed yes-associated protein (YAP) phase separation of HEK293 cells under stimulation using sorbitol. In spatial dynamics, we observed the migration of NIH 3T3 cells (transfected with Lifeact-GFP) under different spatiotemporal stimulations of PDGF. Using the injection molded plastic devices, we obtained comprehensive data more easily than before while using less time compared to previous PDMS models. This easy-to-use plastic microfluidic device promises to open a new approach for investigating the mechanisms of cell behavior at the single-cell level.

From complex datasets to predictive models of embryonic development

Modern studies of embryogenesis are increasingly quantitative, powered by rapid advances in imaging, sequencing and genome manipulation technologies. Deriving mechanistic insights from the complex datasets generated by these new tools requires systematic approaches for data-driven analysis of the underlying developmental processes. Here, we use data from our work on signal-dependent gene repression in the Drosophila embryo to illustrate how computational models can compactly summarize quantitative results of live imaging, chromatin immunoprecipitation and optogenetic perturbation experiments. The presented computational approach is ideally suited for integrating rapidly accumulating quantitative data and for guiding future studies of embryogenesis.

Computational methods for characterizing and learning from heterogeneous cell signaling data

Heterogeneity in cell signaling pathways is increasingly appreciated as a fundamental feature of cell biology and a driver of clinically relevant disease phenotypes. Understanding the causes of heterogeneity, the cellular mechanisms used to control heterogeneity, and the downstream effects of heterogeneity in single cells are all key obstacles for manipulating cellular populations and treating disease. Recent advances in genetic engineering, including multiplexed fluorescent reporters, have provided unprecedented measurements of signaling heterogeneity, but these vast data sets are often difficult to interpret, necessitating the use of computational techniques to extract meaning from the data. Here, we review recent advances in computational methods for extracting meaning from these novel data streams. In particular, we evaluate how machine learning methods related to dimensionality reduction and classification can identify structure in complex, dynamic datasets, simplifying interpretation. We also discuss how mechanistic models can be merged with heterogeneous data to understand the underlying differences between cells in a population. These methods are still being developed, but the work reviewed here offers useful applications of specific analysis techniques that could enable the translation of single-cell signaling data to actionable biological understanding.

Cytokine combinations for human blood stem cell expansion induce cell type- and cytokine-specific signaling dynamics

How hematopoietic stem cells (HSCs) integrate signals from their environment to make fate decisions remains incompletely understood. Current knowledge is based on either averages of heterogeneous populations or snapshot analyses, both missing important information about the dynamics of intracellular signaling activity. By combining fluorescent biosensors with time-lapse imaging and microfluidics, we measured the activity of the extracellular signal-regulated kinase (ERK) pathway over time (i.e. dynamics) in live single human umbilical cord blood HSCs and multipotent progenitor cells (MPPs). In single cells, ERK signaling dynamics were highly heterogeneous and depended on the cytokines, their combinations, and cell types. ERK signaling was activated by SCF and FLT3L in HSCs, but by SCF, IL3 and GCSF in MPPs. Different cytokines and their combinations led to distinct ERK signaling dynamics frequencies, and ERK dynamics in HSCs were more transient than those in MPPs. A combination of 5 cytokines recently shown to maintain HSCs in long-term culture, had a more-than-additive effect in eliciting sustained ERK dynamics in HSCs. ERK signaling dynamics also predicted future cell fates. E.g. CD45RA expression increased more in HSC daughters with intermediate than with transient or sustained ERK signaling. We demonstrate heterogeneous, cytokine- and cell type- specific ERK signaling dynamics, illustrating their relevance in regulating HSPC fates.

CODEX, a neural network approach to explore signaling dynamics landscapes

Current studies of cell signaling dynamics that use live cell fluorescent biosensors routinely yield thousands of single-cell, heterogeneous, multi-dimensional trajectories. Typically, the extraction of relevant information from time series data relies on predefined, human-interpretable features. Without a priori knowledge of the system, the predefined features may fail to cover the entire spectrum of dynamics. Here we present CODEX, a data-driven approach based on convolutional neural networks (CNNs) that identifies patterns in time series. It does not require a priori information about the biological system and the insights into the data are built through explanations of the CNNs' predictions. CODEX provides several views of the data: visualization of all the single-cell trajectories in a low-dimensional space, identification of prototypic trajectories, and extraction of distinctive motifs. We demonstrate how CODEX can provide new insights into ERK and Akt signaling in response to various growth factors, and we recapitulate findings in p53 and TGFβ-SMAD2 signaling.

A predictive model of gene expression reveals the role of network motifs in the mating response of yeast

Cells use signaling pathways to receive and process information about their environment. These nonlinear systems rely on feedback and feedforward regulation to respond appropriately to changing environmental conditions. Mathematical models describing signaling pathways often lack predictive power because they are not trained on data that encompass the diverse time scales on which these regulatory mechanisms operate. We addressed this limitation by measuring transcriptional changes induced by the mating response in Saccharomyces cerevisiae exposed to different dynamic patterns of pheromone. We found that pheromone-induced transcription persisted after pheromone removal and showed long-term adaptation upon sustained pheromone exposure. We developed a model of the regulatory network that captured both characteristics of the mating response. We fit this model to experimental data with an evolutionary algorithm and used the parameterized model to predict scenarios for which it was not trained, including different temporal stimulus profiles and genetic perturbations to pathway components. Our model allowed us to establish the role of four architectural elements of the network in regulating gene expression. These network motifs are incoherent feedforward, positive feedback, negative feedback, and repressor binding. Experimental and computational perturbations to these network motifs established a specific role for each in coordinating the mating response to persistent and dynamic stimulation.

Processing Temporal Growth Factor Patterns by an Epidermal Growth Factor Receptor Network Dynamically Established in Space

The proto-oncogenic epidermal growth factor (EGF) receptor (EGFR) is a tyrosine kinase whose sensitivity and response to growth factor signals that vary over time and space determine cellular behavior within a developing tissue. The molecular reorganization of the receptors on the plasma membrane and the enzyme-kinetic mechanisms of phosphorylation are key determinants that couple growth factor binding to EGFR signaling. To enable signal initiation and termination while simultaneously accounting for suppression of aberrant signaling, a coordinated coupling of EGFR kinase and protein tyrosine phosphatase activity is established through space by vesicular dynamics. The dynamical operation mode of this network enables not only time-varying growth factor sensing but also adaptation of the response depending on cellular context. By connecting spatially coupled enzymatic kinase/phosphatase processes and the corresponding dynamical systems description of the EGFR network, we elaborate on the general principles necessary for processing complex growth factor signals.

PlotTwist: A web app for plotting and annotating continuous data

Experimental data can broadly be divided in discrete or continuous data. Continuous data are obtained from measurements that are performed as a function of another quantitative variable, e.g., time, length, concentration, or wavelength. The results from these types of experiments are often used to generate plots that visualize the measured variable on a continuous, quantitative scale. To simplify state-of-the-art data visualization and annotation of data from such experiments, an open-source tool was created with R/shiny that does not require coding skills to operate it. The freely available web app accepts wide (spreadsheet) and tidy data and offers a range of options to normalize the data. The data from individual objects can be shown in 3 different ways: (1) lines with unique colors, (2) small multiples, and (3) heatmap-style display. Next to this, the mean can be displayed with a 95% confidence interval for the visual comparison of different conditions. Several color-blind-friendly palettes are available to label the data and/or statistics. The plots can be annotated with graphical features and/or text to indicate any perturbations that are relevant. All user-defined settings can be stored for reproducibility of the data visualization. The app is dubbed PlotTwist and runs locally or online: https://huygens.science.uva.nl/PlotTwist.

Mining single-cell time-series datasets with Time Course Inspector

Summary

Thanks to recent advances in live cell imaging of biosensors, microscopy experiments can generate thousands of single-cell time-series. To identify sub-populations with distinct temporal behaviours that correspond to different cell fates, we developed Time Course Inspector (TCI)—a unique tool written in R/Shiny to combine time-series analysis with clustering. With TCI it is convenient to inspect time-series, plot different data views and remove outliers. TCI facilitates interactive exploration of various hierarchical clustering and cluster validation methods. We showcase TCI by analysing a single-cell signalling time-series dataset acquired using a fluorescent biosensor.

Availability and implementation

https://github.com/pertzlab/shiny-timecourse-inspector.

Supplementary information

Supplementary data are available at Bioinformatics online.