Single-cell NF-kappaB dynamics reveal digital activation and analogue information processing

Constructing network topologies for multiple signal-encoding functions

Background

Cells use signaling protein networks to sense their environment and mediate specific responses. Information about environmental stress is usually encoded in the dynamics of the signaling molecules, and qualitatively distinct dynamics of the same signaling molecule can lead to dramatically different cell fates. Exploring the design principles of networks with multiple signal-encoding functions is important for understanding how distinct dynamic patterns are shaped and integrated by real cellular networks, and for building cells with targeted sensing–response functions via synthetic biology.

Results

In this paper, we investigate multi-node enzymatic regulatory networks with three signal-encoding functions, i.e., dynamic responses of oscillation, transient activation, and sustained activation upon step stimulation by three different inducers, respectively. Taking into account competition effects of the substrates for the same enzyme in the enzymatic reactions, we searched for robust subnetworks for each signal-encoding function by three-node-network enumeration and then integrated the three subnetworks together via node-merging. The obtained tri-functional networks consisted of four to six nodes, and the core structures of these networks were hybrids of the motifs for the subfunctions.

Conclusions

The simplest but relatively robust tri-functional networks demonstrated that the three functions were compatible within a simple negative feedback loop. Depending on the network structure, the competition effects of the substrates for the same enzyme within the networks could promote or hamper the target functions, and can create implicit functional motifs. Overall, the networks we obtained could in principle be synthesized to construct dynamic control circuits with multiple target functions.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0676-5) contains supplementary material, which is available to authorized users.

Analysis of Cell Lineage Trees by Exact Bayesian Inference Identifies Negative Autoregulation of Nanog in Mouse Embryonic Stem Cells

Many cellular effectors of pluripotency are dynamically regulated. In principle, regulatory mechanisms can be inferred from single-cell observations of effector activity across time. However, rigorous inference techniques suitable for noisy, incomplete, and heterogeneous data are lacking. Here, we introduce stochastic inference on lineage trees (STILT), an algorithm capable of identifying stochastic models that accurately describe the quantitative behavior of cell fate markers observed using time-lapse microscopy data collected from proliferating cell populations. STILT performs exact Bayesian parameter inference and stochastic model selection using a particle-filter-based algorithm. We use STILT to investigate the autoregulation of Nanog, a heterogeneously expressed core pluripotency factor, in mouse embryonic stem cells. STILT rejects the possibility of positive Nanog autoregulation with high confidence; instead, model predictions indicate weak negative feedback. We use STILT for rational experimental design and validate model predictions using novel experimental data. STILT is available for download as an open source framework from http://www.imsb.ethz.ch/research/claassen/Software/stilt---stochastic-inference-on-lineage-trees.html.

Qualitative modelling of the interplay of inflammatory status and butyrate in the human gut: a hypotheses about robust bi-stability

Background

Gut microbiota interacts with the human gut in multiple ways. Microbiota composition is altered in inflamed gut conditions. Likewise, certain microbial fermentation products as well as the lipopolysaccharides of the outer membrane are examples of microbial products with opposing influences on gut epithelium inflammation status. This system of intricate interactions is known to play a core role in human gut inflammatory diseases. Here, we present and analyse a simplified model of bidirectional interaction between the microbiota and the host: in focus is butyrate as an example for a bacterial fermentation product with anti-inflammatory properties.

Results

We build a dynamical model based on an existing model of inflammatory regulation in gut epithelial cells. Our model introduces both butyrate as a bacterial product which counteracts inflammation, as well as bacterial LPS as a pro-inflammatory bacterial product. Moreover, we propose an extension of this model that also includes a feedback interaction towards bacterial composition. The analysis of these dynamical models shows robust bi-stability driven by butyrate concentrations in the gut. The extended model hints towards a further possible enforcement of the observed bi-stability via alteration of gut bacterial composition. A theoretical perspective on the stability of the described switch-like character is discussed.

Conclusions

Interpreting the results of this qualitative model allows formulating hypotheses about the switch-like character of inflammatory regulation in the gut epithelium, involving bacterial products as constitutive parts of the system. We also speculate about possible explanations for observed bimodal distributions in bacterial compositions in the human gut. The switch-like behaviour of the system proved to be mostly independent of parameter choices. Further implications of the qualitative character of our modeling approach for the robustness of the proposed hypotheses are discussed, as well as the pronounced role of butyrate compared to other inflammatory regulators, especially LPS, NF- κB and cytokines.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0667-6) contains supplementary material, which is available to authorized users.

Signal Distortion: How Intracellular Pathogens Alter Host Cell Fate by Modulating NF-κB Dynamics

By uncovering complex dynamics in the expression or localization of transcriptional regulators in single cells that were otherwise hidden at the population level, live cell imaging has transformed our understanding of how cells sense and orchestrate appropriate responses to changes in their internal state or extracellular environment. This has proved particularly true for the nuclear factor-kappaB (NF-κB) family of transcription factors, key regulators of the inflammatory response and innate immune function, which are capable of encoding information about the mode and intensity of stimuli in the dynamics of NF-κB nuclear accumulation and loss. While live cell imaging continues to serve as a useful tool in ongoing efforts to characterize the feedbacks that shape these dynamics and to connect dynamics to downstream gene expression, it is also proving invaluable for recent studies that seek to determine how intracellular pathogens subvert NF-κB signaling to survive and replicate within host cells by providing quantitative information about the pathogen and changes in NF-κB activity during different stages of an infection. Here, we provide a brief overview of NF-κB signaling in innate immune cells and review recent literature that uses live imaging to investigate the mechanisms by which bacterial and yeast pathogens modulate NF-κB in a variety of different host cell types to evade destruction or maintain the viability of an intracellular growth niche.

Combinatorial processing of bacterial and host-derived innate immune stimuli at the single-cell level

During the course of a bacterial infection, cells are exposed simultaneously to a range of bacterial and host factors, which converge on the central transcription factor nuclear factor (NF)-κB. How do single cells integrate and process these converging stimuli? Here we tackle the question of how cells process combinatorial signals by making quantitative single-cell measurements of the NF-κB response to combinations of bacterial lipopolysaccharide and the stress cytokine tumor necrosis factor. We found that cells encode the presence of both stimuli via the dynamics of NF-κB nuclear translocation in individual cells, suggesting the integration of NF-κB activity for these stimuli occurs at the molecular and pathway level. However, the gene expression and cytokine secretion response to combinatorial stimuli were more complex, suggesting that other factors in addition to NF-κB contribute to signal integration at downstream layers of the response. Taken together, our results support the theory that during innate immune threat assessment, a pathogen recognized as both foreign and harmful will recruit an enhanced immune response. Our work highlights the remarkable capacity of individual cells to process multiple input signals and suggests that a deeper understanding of signal integration mechanisms will facilitate efforts to control dysregulated immune responses.

Viable cell culture in PDMS-based microfluidic devices

Microfluidics has played a vital role in developing novel methods to investigate biological phenomena at the molecular and cellular level during the last two decades. Microscale engineering of cellular systems is nevertheless a nascent field marked inherently by frequent disruptive advancements in technology such as PDMS-based soft lithography. Viable culture and manipulation of cells in microfluidic devices requires knowledge across multiple disciplines including molecular and cellular biology, chemistry, physics, and engineering. There has been numerous excellent reviews in the past 15 years on applications of microfluidics for molecular and cellular biology including microfluidic cell culture (Berthier et al., 2012; El-Ali, Sorger, & Jensen, 2006; Halldorsson et al., 2015; Kim et al., 2007; Mehling & Tay, 2014; Sackmann et al., 2014; Whitesides, 2006; Young & Beebe, 2010), cell culture models (Gupta et al., 2016; Inamdar & Borenstein, 2011; Meyvantsson & Beebe, 2008), cell secretion (Schrell et al., 2016), chemotaxis (Kim & Wu, 2012; Wu et al., 2013), neuron culture (Millet & Gillette, 2012a, 2012b), drug screening (Dittrich & Manz, 2006; Eribol, Uguz, & Ulgen, 2016; Wu, Huang, & Lee, 2010), cell sorting (Autebert et al., 2012; Bhagat et al., 2010; Gossett et al., 2010; Wyatt Shields Iv, Reyes, & López, 2015), single cell studies (Lecault et al., 2012; Reece et al., 2016; Yin & Marshall, 2012), stem cell biology (Burdick & Vunjak-Novakovic, 2009; Wu et al., 2011; Zhang & Austin, 2012), cell differentiation (Zhang et al., 2017a), systems biology (Breslauer, Lee, & Lee, 2006), 3D cell culture (Huh et al., 2011; Li et al., 2012; van Duinen et al., 2015), spheroids and organoids (Lee et al., 2016; Montanez-Sauri, Beebe, & Sung, 2015; Morimoto & Takeuchi, 2013; Skardal et al., 2016; Young, 2013), organ-on-chip (Bhatia & Ingber, 2014; Esch, Bahinski, & Huh, 2015; Huh et al., 2011; van der Meer & van den Berg, 2012), and tissue engineering (Andersson & Van Den Berg, 2004; Choi et al., 2007; Hasan et al., 2014). In this chapter, we provide an overview of PDMS-based microdevices for microfluidic cell culture. We discuss the advantages and challenges of using PDMS-based soft lithography for microfluidic cell culture and highlight recent progress and future directions in this area.

An information-theoretic framework for deciphering pleiotropic and noisy biochemical signaling

Many components of signaling pathways are functionally pleiotropic, and signaling responses are marked with substantial cell-to-cell heterogeneity. Therefore, biochemical descriptions of signaling require quantitative support to explain how complex stimuli (inputs) are encoded in distinct activities of pathways effectors (outputs). A unique perspective of information theory cannot be fully utilized due to lack of modeling tools that account for the complexity of biochemical signaling, specifically for multiple inputs and outputs. Here, we develop a modeling framework of information theory that allows for efficient analysis of models with multiple inputs and outputs; accounts for temporal dynamics of signaling; enables analysis of how signals flow through shared network components; and is not restricted by limited variability of responses. The framework allows us to explain how identity and quantity of type I and type III interferon variants could be recognized by cells despite activating the same signaling effectors.

Experimental and engineering approaches to intracellular communication

Communication between and within cells is essential for multicellular life. While intracellular signal transduction pathways are often specified in molecular terms, the information content they transmit remains poorly defined. Here, we review research efforts to merge biological experimentation with concepts of communication that emerge from the engineering disciplines of signal processing and control theory. We discuss the challenges of performing experiments that quantitate information transfer at the molecular level, and we highlight recent studies that have advanced toward a clearer definition of the information content carried by signaling molecules. Across these studies, we emphasize a theme of increasingly well-matched experimental and theoretical approaches to decode the data streams directing cellular behavior.

Protein and RNA quantification of multiple genes in single cells

Single-cell analysis overcomes the problems of cellular heterogeneity by revealing the individual differences between cells in tissue. The current tools used to profile gene expression at the single-cell level are arduous and often require specialized equipment. We have previously developed a technique to quantify protein expression levels in single living cells. Here, we combine quantification of protein expression with absolute measurement of mRNA amounts of the same gene in the same cell, to profile the expression of genes at the transcriptional and translational levels. We show that high heterogeneity exists at both the mRNA and protein levels for multiple genes, even among monoclonal cells. We demonstrate a rapid, straightforward approach to single-cell profiling of RNA and protein production.

Integrating Immunology and Microfluidics for Single Immune Cell Analysis

The field of immunoengineering aims to develop novel therapies and modern vaccines to manipulate and modulate the immune system and applies innovative technologies toward improved understanding of the immune system in health and disease. Microfluidics has proven to be an excellent technology for analytics in biology and chemistry. From simple microsystem chips to complex microfluidic designs, these platforms have witnessed an immense growth over the last decades with frequent emergence of new designs. Microfluidics provides a highly robust and precise tool which led to its widespread application in single-cell analysis of immune cells. Single-cell analysis allows scientists to account for the heterogeneous behavior of immune cells which often gets overshadowed when conventional bulk study methods are used. Application of single-cell analysis using microfluidics has facilitated the identification of several novel functional immune cell subsets, quantification of signaling molecules, and understanding of cellular communication and signaling pathways. Single-cell analysis research in combination with microfluidics has paved the way for the development of novel therapies, point-of-care diagnostics, and even more complex microfluidic platforms that aid in creating in vitro cellular microenvironments for applications in drug and toxicity screening. In this review, we provide a comprehensive overview on the integration of microsystems and microfluidics with immunology and focus on different designs developed to decode single immune cell behavior and cellular communication. We have categorized the microfluidic designs in three specific categories: microfluidic chips with cell traps, valve-based microfluidics, and droplet microfluidics that have facilitated the ongoing research in the field of immunology at single-cell level.

Automated microfluidic droplet sampling with integrated, mix-and-read immunoassays to resolve endocrine tissue secretion dynamics

A fully automated droplet generation and analysis device based on pressure driven push-up valves for precise pumping of fluid and volumetric metering has been developed for high resolution hormone secretion sampling and measurement. The device consists of a 3D-printer templated reservoir for single cells or single tissue culturing, a Y-shaped channel for reagents and sample mixing, a T-junction channel for droplet formation, a reference channel to overcome drifts in fluorescence signal, and a long droplet storage channel allowing incubation for homogeneous immunoassays. The droplets were made by alternating peristaltic pumping of aqueous and oil phases. Device operation was automated, giving precise control over several droplet parameters such as size, oil spacing, and ratio of sample and reference droplets. By integrating an antibody-oligonucleotide based homogeneous immunoassay on-chip, high resolution temporal sampling into droplets was combined with separation-free quantification of insulin secretion from single islets of Langerhans using direct optical readout from the droplets. Quantitative assays of glucose-stimulated insulin secretion were demonstrated at 15 second temporal resolution while detecting as low as 10 amol per droplet, revealing fast insulin oscillations that mirror well-known intracellular calcium signals. This droplet sampling and direct optical analysis approach effectively digitizes the secretory time record from cells into droplets, and the system should be generalizable to a variety of cells and tissue types.

Immune signalling by supramolecular assemblies

Formation of supramolecular assemblies appears to be a general mechanism in immune signalling pathways. These supramolecular assemblies appear to form through a nucleated polymerization mechanism. This review examines selected immune signalling pathways that involve supramolecular assemblies, describes the concepts of protein polymerization, and discusses how those concepts of protein polymerization implicate new elegant ways for signal amplification, setting threshold and noise reduction in these pathways.

Challenges of Decoding Transcription Factor Dynamics in Terms of Gene Regulation

Technological advances are continually improving our ability to obtain more accurate views about the inner workings of biological systems. One such rapidly evolving area is single cell biology, and in particular gene expression and its regulation by transcription factors in response to intrinsic and extrinsic factors. Regarding the study of transcription factors, we discuss some of the promises and pitfalls associated with investigating how individual cells regulate gene expression through modulation of transcription factor activities. Specifically, we discuss four leading experimental approaches, the data that can be obtained from each, and important considerations that investigators should be aware of when drawing conclusions from such data.

Multisite phosphorylation provides a reliable mechanism for making decisions in noisy environments

The ability to make decisions at the cellular level is absolutely critical for the survival of organisms. Eukaryotic cells are constantly making binary decisions in response to internal and environmental signals. Among the most notable transducers of information are protein kinases. The regulation of these signaling proteins often relies on the activity of other protein kinases located upstream in the signaling cascade. However, these signaling systems are by their own nature an important source of molecular noise. Herein, we have assessed the role of multisite phosphorylation in detecting signals in the face of molecular noise. To address this issue, we have conceptually envisioned the biochemical transduction machinery as a classifier model that can lead to four possible outputs: true positives and negatives, and false positives and negatives. In this probabilistic framework, we show that multisite phosphorylation represents a mechanism to filter noise during the decision-making process. We present results showing that nonessential phosphorylation sites contribute to increase the rate of true positives while, at the same time, they can lessen the rate of false positives. This simultaneous increase in sensitivity and specificity, makes multisite phosphorylation a valuable and easily implemented mechanism to reliably transduce information in noisy contexts.

Modeling Procedures

Being concerned by the understanding of the mechanism underlying chronic degenerative diseases , we presented in the previous chapter the medical systems biology conceptual framework that we present for that purpose in this volume. More specifically, we argued there the clear advantages offered by a state-space perspective when applied to the systems-level description of the biomolecular machinery that regulates complex degenerative diseases. We also discussed the importance of the dynamical interplay between the risk factors and the network of interdependencies that characterizes the biochemical, cellular, and tissue-level biomolecular reactions that underlie the physiological processes in health and disease. As we pointed out in the previous chapter, the understanding of this interplay (articulated around cellular phenotypic plasticity properties, regulated by specific kinds of gene regulatory networks) is necessary if prevention is chosen as the human-health improvement strategy (potentially involving the modulation of the patient's lifestyle). In this chapter we provide the medical systems biology mathematical and computational modeling tools required for this task.

Automated Microfluidic System for Dynamic Stimulation and Tracking of Single Cells

Dynamic environments determine cell fate decisions and function. Understanding the relationship between extrinsic signals on cellular responses and cell fate requires the ability to dynamically change environmental inputs in vitro, while continuously observing individual cells over extended periods of time. This is challenging for nonadherent cells, such as hematopoietic stem and progenitor cells, because media flow displaces and disturbs such cells, preventing culture and tracking of single cells. Here, we present a programmable microfluidic system designed for the long-term culture and time-lapse imaging of nonadherent cells in dynamically changing cell culture conditions without losing track of individual cells. The dynamic, valve-controlled design permits targeted seeding of cells in up to 48 independently controlled culture chambers, each providing sufficient space for long-term cell colony expansion. Diffusion-based media exchange occurs rapidly and minimizes displacement of cells and eliminates shear stress. The chip was successfully tested with long-term culture and tracking of primary hematopoietic stem and progenitor cells, and murine embryonic stem cells. This system will have important applications to analyze dynamic signaling inputs controlling fate choices.

An acoustofluidic trap and transfer approach for organizing a high density single cell array

We demonstrate a hybrid microfluidic system that combines fluidic trapping and acoustic switching to organize an array of single cells at high density. The fluidic trapping step is achieved by balancing the hydrodynamic resistances of three parallel channel segments forming a microfluidic trifurcation, the purpose of which was to capture single cells in a high-density array. Next, the cells were transferred into adjacent larger compartments by generating an array of streaming micro-vortices to move the cells to the desired streamlines in a massively parallel format. This approach can compartmentalize single cells with efficiencies of ≈67% in compartments that have diameters on the order of ∼100 um, which is an appropriate size for single cell proliferation studies and other single cell biochemical measurements.

Gene expression distribution deconvolution in single-cell RNA sequencing

We developed deconvolution of single-cell expression distribution (DESCEND), a method to recover cross-cell distribution of the true gene expression level from observed counts in single-cell RNA sequencing, allowing adjustment of known confounding cell-level factors. With the recovered distribution, DESCEND provides reliable estimates of distribution-based measurements, such as the dispersion of true gene expression and the probability that true gene expression is positive. This is important, as with better estimates of these measurements, DESCEND clarifies and improves many downstream analyses including finding differentially expressed genes, identifying cell types, and selecting differentiation markers. Another contribution is that we verified using nine public datasets a simple “Poisson-alpha” noise model for the technical noise of unique molecular identifier-based single-cell RNA-sequencing data, clarifying the current intense debate on this issue.

Single-cell RNA sequencing (scRNA-seq) enables the quantification of each gene’s expression distribution across cells, thus allowing the assessment of the dispersion, nonzero fraction, and other aspects of its distribution beyond the mean. These statistical characterizations of the gene expression distribution are critical for understanding expression variation and for selecting marker genes for population heterogeneity. However, scRNA-seq data are noisy, with each cell typically sequenced at low coverage, thus making it difficult to infer properties of the gene expression distribution from raw counts. Based on a reexamination of nine public datasets, we propose a simple technical noise model for scRNA-seq data with unique molecular identifiers (UMI). We develop deconvolution of single-cell expression distribution (DESCEND), a method that deconvolves the true cross-cell gene expression distribution from observed scRNA-seq counts, leading to improved estimates of properties of the distribution such as dispersion and nonzero fraction. DESCEND can adjust for cell-level covariates such as cell size, cell cycle, and batch effects. DESCEND’s noise model and estimation accuracy are further evaluated through comparisons to RNA FISH data, through data splitting and simulations and through its effectiveness in removing known batch effects. We demonstrate how DESCEND can clarify and improve downstream analyses such as finding differentially expressed genes, identifying cell types, and selecting differentiation markers.

Unsupervised clustering and epigenetic classification of single cells

Characterizing epigenetic heterogeneity at the cellular level is a critical problem in the modern genomics era. Assays such as single cell ATAC-seq (scATAC-seq) offer an opportunity to interrogate cellular level epigenetic heterogeneity through patterns of variability in open chromatin. However, these assays exhibit technical variability that complicates clear classification and cell type identification in heterogeneous populations. We present scABC, an R package for the unsupervised clustering of single-cell epigenetic data, to classify scATAC-seq data and discover regions of open chromatin specific to cell identity.

Dual Roles for Ikaros in Regulation of Macrophage Chromatin State and Inflammatory Gene Expression

Macrophage activation by bacterial LPS leads to induction of a complex inflammatory gene program dependent on numerous transcription factor families. The transcription factor Ikaros has been shown to play a critical role in lymphoid cell development and differentiation; however, its function in myeloid cells and innate immune responses is less appreciated. Using comprehensive genomic analysis of Ikaros-dependent transcription, DNA binding, and chromatin accessibility, we describe unexpected dual repressor and activator functions for Ikaros in the LPS response of murine macrophages. Consistent with the described function of Ikaros as transcriptional repressor, Ikzf1^-/- macrophages showed enhanced induction for select responses. In contrast, we observed a dramatic defect in expression of many delayed LPS response genes, and chromatin immunoprecipitation sequencing analyses support a key role for Ikaros in sustained NF-κB chromatin binding. Decreased Ikaros expression in Ikzf1^+/- mice and human cells dampens these Ikaros-enhanced inflammatory responses, highlighting the importance of quantitative control of Ikaros protein level for its activator function. In the absence of Ikaros, a constitutively open chromatin state was coincident with dysregulation of LPS-induced chromatin remodeling, gene expression, and cytokine responses. Together, our data suggest a central role for Ikaros in coordinating the complex macrophage transcriptional program in response to pathogen challenge.

Live Imaging Reveals that the First Division of Differentiating Human Embryonic Stem Cells Often Yields Asymmetric Fates

How do stem cells respond to signals to initiate differentiation? Here, we show that, despite uniform exposure to differentiation-inducing extracellular signals, individual human embryonic stem cells (hESCs) respond heterogeneously. To track how hESCs incipiently exit pluripotency, we established a system to differentiate hESCs as single cells and conducted live imaging to track their very first cell division. We followed the fate of their earliest daughters as they remained undifferentiated or differentiated toward the primitive streak (the earliest descendants of pluripotent cells). About 30%-50% of the time, hESCs divided to yield one primitive streak and one undifferentiated daughter. The undifferentiated daughter cell was innately resistant to WNT signaling and could not respond to this primitive-streak-specifying differentiation signal. Hence, the first division of differentiating hESCs sometimes yields daughters with diverging fates, with implications for the efficiency of directed differentiation protocols and the underlying rules of lineage commitment.

Encoding Growth Factor Identity in the Temporal Dynamics of FOXO3 under the Combinatorial Control of ERK and AKT Kinases

Extracellular growth factors signal to transcription factors via a limited number of cytoplasmic kinase cascades. It remains unclear how such cascades encode ligand identities and concentrations. In this paper, we use live-cell imaging and statistical modeling to study FOXO3, a transcription factor regulating diverse aspects of cellular physiology that is under combinatorial control. We show that FOXO3 nuclear-to-cytosolic translocation has two temporally distinct phases varying in magnitude with growth factor identity and cell type. These phases comprise synchronous translocation soon after ligand addition followed by an extended back-and-forth shuttling; this shuttling is pulsatile and does not have a characteristic frequency, unlike a simple oscillator. Early and late dynamics are differentially regulated by Akt and ERK and have low mutual information, potentially allowing the two phases to encode different information. In cancer cells in which ERK and Akt are dysregulated by oncogenic mutation, the diversity of states is lower.

The BH3-only protein BAD mediates TNFα cytotoxicity despite concurrent activation of IKK and NF-κB in septic shock

The inflammatory cytokine TNFα plays a crucial role in the pathology of many inflammatory and infectious diseases. However, the mechanism underlying TNFα cytotoxicity in these diseases is incompletely understood. Here we report that the pro-apoptotic BCL-2 family member BAD mediates TNFα cytotoxicity despite concurrent activation of IKK and NF-κB in vitro by inducing apoptosis in cultured cells and in vivo by eliciting tissue damage of multiple organs and contributing to mortality in septic shock. At high doses, TNFα significantly inactivates RhoA through activation of the Src-p190GAP pathway, resulting in massive actin stress fiber destabilization, followed by substantial BAD release from the cytoskeleton to the cytosol. Under this condition, activated IKK fails to phosphorylate all cytosolic BAD, allowing translocation of non-phosphorylated BAD to mitochondria to trigger apoptosis. Polymicrobial infection utilizes the same mechanism as high-dose TNFα to elicit apoptosis-associated tissue damage of multiple organs. Consequently, loss of Bad or elimination of BAD pro-apoptotic activity protects mice from tissue damage of multiple organs and reduces mortality rates. Our results support a model in which BAD mediates TNFα cytotoxicity despite concurrent activation of the IKK-NF-κB pathway in cultured mammalian cells and in septic shock.

A model for the integration of conflicting exogenous and endogenous signals by dendritic cells

Cells of the immune system are confronted with opposing pro- and anti-inflammatory signals. Dendritic cells (DC) integrate these cues to make informed decisions whether to initiate an immune response. Confronted with exogenous microbial stimuli, DC endogenously produce both anti- (IL-10) and pro-inflammatory (TNFα) cues whose joint integration controls the cell's final decision. Backed by experimental measurements we present a theoretical model to quantitatively describe the integration mode of these opposing signals. We propose a two step integration model that modulates the effect of the two types of signals: an initial bottleneck integrates both signals (IL-10 and TNFα), the output of which is later modulated by the anti-inflammatory signal. We show that the anti-inflammatory IL-10 signaling is long ranged, as opposed to the short-ranged pro-inflammatory TNFα signaling. The model suggests that the population averaging and modulation of the pro-inflammatory response by the anti-inflammatory signal is a safety guard against excessive immune responses.

Temperature regulates NF-κB dynamics and function through timing of A20 transcription

NF-κB signaling plays a pivotal role in control of the inflammatory response. We investigated how the dynamics and function of NF-κB were affected by temperature within the mammalian physiological range (34 °C to 40 °C). An increase in temperature led to an increase in NF-κB nuclear/cytoplasmic oscillation frequency following Tumor Necrosis Factor alpha (TNFα) stimulation. Mathematical modeling suggested that this temperature sensitivity might be due to an A20-dependent mechanism, and A20 silencing removed the sensitivity to increased temperature. The timing of the early response of a key set of NF-κB target genes showed strong temperature dependence. The cytokine-induced expression of many (but not all) later genes was insensitive to temperature change (suggesting that they might be functionally temperature-compensated). Moreover, a set of temperature- and TNFα-regulated genes were implicated in NF-κB cross-talk with key cell-fate-controlling pathways. In conclusion, NF-κB dynamics and target gene expression are modulated by temperature and can accurately transmit multidimensional information to control inflammation.

Inferring a nonlinear biochemical network model from a heterogeneous single-cell time course data

Mathematical modeling and analysis of biochemical reaction networks are key routines in computational systems biology and biophysics; however, it remains difficult to choose the most valid model. Here, we propose a computational framework for data-driven and systematic inference of a nonlinear biochemical network model. The framework is based on the expectation-maximization algorithm combined with particle smoother and sparse regularization techniques. In this method, a “redundant” model consisting of an excessive number of nodes and regulatory paths is iteratively updated by eliminating unnecessary paths, resulting in an inference of the most likely model. Using artificial single-cell time-course data showing heterogeneous oscillatory behaviors, we demonstrated that this algorithm successfully inferred the true network without any prior knowledge of network topology or parameter values. Furthermore, we showed that both the regulatory paths among nodes and the optimal number of nodes in the network could be systematically determined. The method presented in this study provides a general framework for inferring a nonlinear biochemical network model from heterogeneous single-cell time-course data.

Microenvironmental Signals and Biochemical Information Processing: Cooperative Determinants of Intratumoral Plasticity and Heterogeneity

Intra-tumor cellular heterogeneity is a major challenge in cancer therapy. Tumors are composed of multiple phenotypic subpopulations that vary in their ability to initiate metastatic tumors and in their sensitivity to chemotherapy. In many cases, cells can transition between these subpopulations, not by genetic mutation, but instead through reversible changes in signal transduction or gene expression programs. This plasticity begins at the level of the microenvironment where local autocrine and paracrine signals, exosomes, tumor–stroma interactions, and extracellular matrix (ECM) composition create a signaling landscape that varies over space and time. The integration of this complex array of signals engages signaling pathways that control gene expression. The resulting modulation of gene expression programs causes individual cells to sample a wide array of phenotypic states that support tumor growth, dissemination, and therapeutic resistance. In this review, we discuss how information flows dynamically within the microenvironmental landscape to inform cell state decisions and to create intra-tumoral heterogeneity. We address the role of plasticity in the acquisition of transient and prolonged drug resistant states and discuss how targeted pharmacological modification of the signaling landscape may be able to constrain phenotypic plasticity, leading to improved treatment responses.

NF-κB, the Importance of Being Dynamic: Role and Insights in Cancer

In this review, we aim at describing the results obtained in the past years on dynamics features defining NF-κB regulatory functions, as we believe that these developments might have a transformative effect on the way in which NF-κB involvement in cancer is studied. We will also describe technical aspects of the studies performed in this context, including the use of different cellular models, culture conditions, microscopy approaches and quantification of the imaging data, balancing their strengths and limitations and pointing out to common features and to some open questions. Our emphasis in the methodology will allow a critical overview of literature and will show how these cutting-edge approaches can contribute to shed light on the involvement of NF-κB deregulation in tumour onset and progression. We hypothesize that this “dynamic point of view” can be fruitfully applied to untangle the complex relationship between NF-κB and cancer and to find new targets to restrain cancer growth.

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.

Quantitative analysis reveals crosstalk mechanisms of heat shock-induced attenuation of NF-κB signaling at the single cell level

Elevated temperature induces the heat shock (HS) response, which modulates cell proliferation, apoptosis, the immune and inflammatory responses. However, specific mechanisms linking the HS response pathways to major cellular signaling systems are not fully understood. Here we used integrated computational and experimental approaches to quantitatively analyze the crosstalk mechanisms between the HS-response and a master regulator of inflammation, cell proliferation, and apoptosis the Nuclear Factor κB (NF-κB) system. We found that populations of human osteosarcoma cells, exposed to a clinically relevant 43°C HS had an attenuated NF-κB p65 response to Tumor Necrosis Factor α (TNFα) treatment. The degree of inhibition of the NF-κB response depended on the HS exposure time. Mathematical modeling of single cells indicated that individual crosstalk mechanisms differentially encode HS-mediated NF-κB responses while being consistent with the observed population-level responses. In particular "all-or-nothing" encoding mechanisms were involved in the HS-dependent regulation of the IKK activity and IκBα phosphorylation, while others involving transport were "analogue". In order to discriminate between these mechanisms, we used live-cell imaging of nuclear translocations of the NF-κB p65 subunit. The single cell responses exhibited "all-or-nothing" encoding. While most cells did not respond to TNFα stimulation after a 60 min HS, 27% showed responses similar to those not receiving HS. We further demonstrated experimentally and theoretically that the predicted inhibition of IKK activity was consistent with the observed HS-dependent depletion of the IKKα and IKKβ subunits in whole cell lysates. However, a combination of "all-or-nothing" crosstalk mechanisms was required to completely recapitulate the single cell data. We postulate therefore that the heterogeneity of the single cell responses might be explained by the cell-intrinsic variability of HS-modulated IKK signaling. In summary, we show that high temperature modulates NF-κB responses in single cells in a complex and unintuitive manner, which needs to be considered in hyperthermia-based treatment strategies.

Modeling Cell-to-Cell Communication Networks Using Response-Time Distributions

Cell-to-cell communication networks have critical roles in coordinating diverse organismal processes, such as tissue development or immune cell response. However, compared with intracellular signal transduction networks, the function and engineering principles of cell-to-cell communication networks are far less understood. Major complications include: cells are themselves regulated by complex intracellular signaling networks; individual cells are heterogeneous; and output of any one cell can recursively become an additional input signal to other cells. Here, we make use of a framework that treats intracellular signal transduction networks as "black boxes" with characterized input-to-output response relationships. We study simple cell-to-cell communication circuit motifs and find conditions that generate bimodal responses in time, as well as mechanisms for independently controlling synchronization and delay of cell-population responses. We apply our modeling approach to explain otherwise puzzling data on cytokine secretion onset times in T cells. Our approach can be used to predict communication network structure using experimentally accessible input-to-output measurements and without detailed knowledge of intermediate steps.

Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding

Transcription factors (TFs) are primary regulators of gene expression in cells, where they bind specific genomic target sites to control transcription. Quantitative measurements of TF-DNA binding energies can improve the accuracy of predictions of TF occupancy and downstream gene expression in vivo and shed light on how transcriptional networks are rewired throughout evolution. Here, we present a sequencing-based TF binding assay and analysis pipeline (BET-seq, for Binding Energy Topography by sequencing) capable of providing quantitative estimates of binding energies for more than one million DNA sequences in parallel at high energetic resolution. Using this platform, we measured the binding energies associated with all possible combinations of 10 nucleotides flanking the known consensus DNA target interacting with two model yeast TFs, Pho4 and Cbf1. A large fraction of these flanking mutations change overall binding energies by an amount equal to or greater than consensus site mutations, suggesting that current definitions of TF binding sites may be too restrictive. By systematically comparing estimates of binding energies output by deep neural networks (NNs) and biophysical models trained on these data, we establish that dinucleotide (DN) specificities are sufficient to explain essentially all variance in observed binding behavior, with Cbf1 binding exhibiting significantly more nonadditivity than Pho4. NN-derived binding energies agree with orthogonal biochemical measurements and reveal that dynamically occupied sites in vivo are both energetically and mutationally distant from the highest affinity sites.

A membrane-anchored aptamer sensor for probing IFNγ secretion by single cells

Insight into the behavior of individual immune cells, in particular cytokine secretion, will contribute to a more fundamental understanding of the immune system. In this work, we have developed a cell membrane-anchored sensor for the detection of cytokines secreted by single cells using a combination of aptamer-based sensors and droplet microfluidics.

Noninvasive detection of macrophage activation with single-cell resolution through machine learning

We present a method enabling the noninvasive study of minute cellular changes in response to stimuli, based on the acquisition of multiple parameters through label-free microscopy. The retrieved parameters are related to different attributes of the cell. Morphological variables are extracted from quantitative phase microscopy and autofluorescence images, while molecular indicators are retrieved via Raman spectroscopy. We show that these independent parameters can be used to build a multivariate statistical model based on logistic regression, which we apply to the detection at the single-cell level of macrophage activation induced by lipopolysaccharide (LPS) exposure and compare their respective performance in assessing the individual cellular state. The models generated from either morphology or Raman can reliably and independently detect the activation state of macrophage cells, which is validated by comparison with their cytokine secretion and intracellular expression of molecules related to the immune response. The independent models agree on the degree of activation, showing that the features provide insight into the cellular response heterogeneity. We found that morphological indicators are linked to the phenotype, which is mostly related to downstream effects, making the results obtained with these variables dose-dependent. On the other hand, Raman indicators are representative of upstream intracellular molecular changes related to specific activation pathways. By partially inhibiting the LPS-induced activation using progesterone, we could identify several subpopulations, showing the ability of our approach to identify the effect of LPS activation, specific inhibition of LPS, and also the effect of progesterone alone on macrophage cells.

Digital signaling network drives the assembly of the AIM2-ASC inflammasome

The AIM2-ASC inflammasome is a filamentous signaling platform essential for mounting host defense against cytoplasmic dsDNA arising not only from invading pathogens but also from damaged organelles. Currently, the design principles of its underlying signaling network remain poorly understood at the molecular level. We show here that longer dsDNA is more effective in inducing AIM2 assembly, its self-propagation, and downstream ASC polymerization. This observation is related to the increased probability of forming the base of AIM2 filaments, and indicates that the assembly discerns small dsDNA as noise at each signaling step. Filaments assembled by receptor AIM2, downstream ASC, and their joint complex all persist regardless of dsDNA, consequently generating sustained signal amplification and hysteresis. Furthermore, multiple positive feedback loops reinforce the assembly, as AIM2 and ASC filaments accelerate the assembly of nascent AIM2 with or without dsDNA. Together with a quantitative model of the assembly, our results indicate that an ultrasensitive digital circuit drives the assembly of the AIM2-ASC inflammasome.

Cell fate in antiviral response arises in the crosstalk of IRF, NF-κB and JAK/STAT pathways

The innate immune system processes pathogen-induced signals into cell fate decisions. How information is turned to decision remains unknown. By combining stochastic mathematical modelling and experimentation, we demonstrate that feedback interactions between the IRF3, NF-κB and STAT pathways lead to switch-like responses to a viral analogue, poly(I:C), in contrast to pulse-like responses to bacterial LPS. Poly(I:C) activates both IRF3 and NF-κB, a requirement for induction of IFNβ expression. Autocrine IFNβ initiates a JAK/STAT-mediated positive-feedback stabilising nuclear IRF3 and NF-κB in first responder cells. Paracrine IFNβ, in turn, sensitises second responder cells through a JAK/STAT-mediated positive feedforward pathway that upregulates the positive-feedback components: RIG-I, PKR and OAS1A. In these sensitised cells, the 'live-or-die' decision phase following poly(I:C) exposure is shorter-they rapidly produce antiviral responses and commit to apoptosis. The interlinked positive feedback and feedforward signalling is key for coordinating cell fate decisions in cellular populations restricting pathogen spread.

Understanding non-linear effects from Hill-type dynamics with application to decoding of p53 signaling

Analytical equations are derived depicting four possible scenarios resulting from pulsed signaling of a system subject to Hill-type dynamics. Pulsed Hill-type dynamics involves the binding of multiple signal molecules to a receptor and occurs e.g., when transcription factor p53 orchestrates cancer prevention, during calcium signaling, and during circadian rhythms. The scenarios involve: (i) enhancement of high-affinity binders compared to low-affinity ones, (ii) slowing reactions involving high-affinity binders, (iii) transfer of the clocking of low-affinity binders from the signal molecule to the products, and (iv) a unique clocking process that produces incremental increases in the activity of high-affinity binders with each signal pulse. In principle, these mostly non-linear effects could control cellular outcomes. An applications to p53 signaling is developed, with binding to most gene promoters identified as category (iii) responses. However, currently unexplained enhancement of high-affinity promoters such as CDKN1a (p21) by pulsed signaling could be an example of (i). In general, provision for all possible scenarios is required in the design of mathematical models incorporating pulsed Hill-type signaling as some aspect.

Cell-specific responses to the cytokine TGFβ are determined by variability in protein levels

The cytokine TGFβ provides important information during embryonic development, adult tissue homeostasis, and regeneration. Alterations in the cellular response to TGFβ are involved in severe human diseases. To understand how cells encode the extracellular input and transmit its information to elicit appropriate responses, we acquired quantitative time-resolved measurements of pathway activation at the single-cell level. We established dynamic time warping to quantitatively compare signaling dynamics of thousands of individual cells and described heterogeneous single-cell responses by mathematical modeling. Our combined experimental and theoretical study revealed that the response to a given dose of TGFβ is determined cell specifically by the levels of defined signaling proteins. This heterogeneity in signaling protein expression leads to decomposition of cells into classes with qualitatively distinct signaling dynamics and phenotypic outcome. Negative feedback regulators promote heterogeneous signaling, as a SMAD7 knock-out specifically affected the signal duration in a subpopulation of cells. Taken together, we propose a quantitative framework that allows predicting and testing sources of cellular signaling heterogeneity.

Intelligent image-based in situ single-cell isolation

Quantifying heterogeneities within cell populations is important for many fields including cancer research and neurobiology; however, techniques to isolate individual cells are limited. Here, we describe a high-throughput, non-disruptive, and cost-effective isolation method that is capable of capturing individually targeted cells using widely available techniques. Using high-resolution microscopy, laser microcapture microscopy, image analysis, and machine learning, our technology enables scalable molecular genetic analysis of single cells, targetable by morphology or location within the sample.

The isolation of single cells while retaining context is important for quantifying cellular heterogeneity but technically challenging. Here, the authors develop a high-throughput, scalable workflow for microscopy-based single cell isolation using machine-learning, high-throughput microscopy and laser capture microdissection.

Measurement of NF-κB Activation in TLR-Activated Macrophages

Nuclear factor kappa-B (NF-κB) is a key transcription factor in the regulation of the innate immune inflammatory response in activated macrophages. NF-κB functions as a homo- or hetero-dimer derived from one or more of the five members of the NF-κB family, and is activated through a well-studied process of stimulus-dependent inhibitor degradation, post-translational modification, nuclear translocation, and chromatin binding. Its activity is subject to multiple levels of feedback control through both inhibitor protein activity and direct regulation of NF-κB components. Many methods have been developed to measure and quantify NF-κB activation. In this chapter, we summarize available methods and present a protocol for image-based measurement of NF-κB activation in macrophages activated with microbial stimuli. Using either a stably expressed GFP-tagged fusion of the RelA NF-κB protein, or direct detection of endogenous RelA by immunocytochemistry, we describe data collection and analysis to quantify NF-κB cytosol to nuclear translocation in single cells using fluorescence microscopy.

Live-cell measurements of kinase activity in single cells using translocation reporters

Although kinases are important regulators of many cellular processes, measuring their activity in live cells remains challenging. We have developed kinase translocation reporters (KTRs), which enable multiplexed measurements of the dynamics of kinase activity at a single-cell level. These KTRs are composed of an engineered construct in which a kinase substrate is fused to a bipartite nuclear localization signal (bNLS) and nuclear export signal (NES), as well as to a fluorescent protein for microscopy-based detection of its localization. The negative charge introduced by phosphorylation of the substrate is used to directly modulate nuclear import and export, thereby regulating the reporter's distribution between the cytoplasm and nucleus. The relative cytoplasmic versus nuclear fluorescence of the KTR construct (the C/N ratio) is used as a proxy for the kinase activity in living, single cells. Multiple KTRs can be studied in the same cell by fusing them to different fluorescent proteins. Here, we present a protocol to execute and analyze live-cell microscopy experiments using KTRs. We describe strategies for development of new KTRs and procedures for lentiviral expression of KTRs in a cell line of choice. Cells are then plated in a 96-well plate, from which multichannel fluorescent images are acquired with automated time-lapse microscopy. We provide detailed guidance for a computational analysis and parameterization pipeline. The entire procedure, from virus production to data analysis, can be completed in ∼10 d.

Cellular Decision Making by Non-Integrative Processing of TLR Inputs

Cells receive a multitude of signals from the environment, but how they process simultaneous signaling inputs is not well understood. Response to infection, for example, involves parallel activation of multiple Toll-like receptors (TLRs) that converge on the nuclear factor κB (NF-κB) pathway. Although we increasingly understand inflammatory responses for isolated signals, it is not clear how cells process multiple signals that co-occur in physiological settings. We therefore examined a bacterial infection scenario involving co-stimulation of TLR4 and TLR2. Independent stimulation of these receptors induced distinct NF-κB dynamic profiles, although surprisingly, under co-stimulation, single cells continued to show ligand-specific dynamic responses characteristic of TLR2 or TLR4 signaling rather than a mixed response, comprising a cellular decision that we term "non-integrative" processing. Iterating modeling and microfluidic experiments revealed that non-integrative processing occurred through interaction of switch-like NF-κB activation, receptor-specific processing timescales, cell-to-cell variability, and TLR cross-tolerance mediated by multilayer negative feedback.