Jacob J. Hughey

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

Cells operate in dynamic environments using extraordinary communication capabilities that emerge from the interactions of genetic circuitry. The mammalian immune response is a striking example of the coordination of different cell types. Cell-to-cell communication is primarily mediated by signalling molecules that form spatiotemporal concentration gradients, requiring cells to respond to a wide range of signal intensities. Here we use high-throughput microfluidic cell culture and fluorescence microscopy, quantitative gene expression analysis and mathematical modelling to investigate how single mammalian cells respond to different concentrations of the signalling molecule tumour-necrosis factor (TNF)-α, and relay information to the gene expression programs by means of the transcription factor nuclear factor (NF)-κB. We measured NF-κB activity in thousands of live cells under TNF-α doses covering four orders of magnitude. We find, in contrast to population-level studies with bulk assays, that the activation is heterogeneous and is a digital process at the single-cell level with fewer cells responding at lower doses. Cells also encode a subtle set of analogue parameters to modulate the outcome; these parameters include NF-κB peak intensity, response time and number of oscillations. We developed a stochastic mathematical model that reproduces both the digital and analogue dynamics as well as most gene expression profiles at all measured conditions, constituting a broadly applicable model for TNF-α-induced NF-κB signalling in various types of cells. These results highlight the value of high-throughput quantitative measurements with single-cell resolution in understanding how biological systems operate.

Single-cell NF-[kgr]B dynamics reveal digital activation and analogue information processing

Cells operate in dynamic environments using extraordinary communication capabilities that emerge from the interactions of genetic circuitry. The mammalian immune response is a striking example of the coordination of different cell types. Cell-to-cell communication is primarily mediated by signalling molecules that form spatiotemporal concentration gradients, requiring cells to respond to a wide range of signal intensities. Here we use high-throughput microfluidic cell culture and fluorescence microscopy, quantitative gene expression analysis and mathematical modelling to investigate how single mammalian cells respond to different concentrations of the signalling molecule tumour-necrosis factor (TNF)-α, and relay information to the gene expression programs by means of the transcription factor nuclear factor (NF)-κB. We measured NF-κB activity in thousands of live cells under TNF-α doses covering four orders of magnitude. We find, in contrast to population-level studies with bulk assays, that the activation is heterogeneous and is a digital process at the single-cell level with fewer cells responding at lower doses. Cells also encode a subtle set of analogue parameters to modulate the outcome; these parameters include NF-κB peak intensity, response time and number of oscillations. We developed a stochastic mathematical model that reproduces both the digital and analogue dynamics as well as most gene expression profiles at all measured conditions, constituting a broadly applicable model for TNF-α-induced NF-κB signalling in various types of cells. These results highlight the value of high-throughput quantitative measurements with single-cell resolution in understanding how biological systems operate.

Microfluidic platform for real-time signaling analysis of multiple single T cells in parallel.

Deciphering the signaling pathways that govern stimulation of naïve CD4+ T helper cells by antigen-presenting cells via formation of the immunological synapse is key to a fundamental understanding of the progression of successful adaptive immune response. The study of T cell-APC interactions in vitro is challenging, however, due to the difficulty of tracking individual, non-adherent cell pairs over time. Studying single cell dynamics over time reveals rare, but critical, signaling events that might be averaged out in bulk experiments, but these less common events are undoubtedly important for an integrated understanding of a cellular response to its microenvironment. We describe a novel application of microfluidic technology that overcomes many limitations of conventional cell culture and enables the study of hundreds of passively sequestered hematopoietic cells for extended periods of time. This microfluidic cell trap device consists of 440 18 micromx18 micromx10 microm PDMS, bucket-like structures opposing the direction of flow which serve as corrals for cells as they pass through the cell trap region. Cell viability analysis revealed that more than 70% of naïve CD4+ T cells (TN), held in place using only hydrodynamic forces, subsequently remain viable for 24 hours. Cytosolic calcium transients were successfully induced in TN cells following introduction of chemical, antibody, or cellular forms of stimulation. Statistical analysis of TN cells from a single stimulation experiment reveals the power of this platform to distinguish different calcium response patterns, an ability that might be utilized to characterize T cell signaling states in a given population. Finally, we investigate in real time contact- and non-contact-based interactions between primary T cells and dendritic cells, two main participants in the formation of the immunological synapse. Utilizing the microfluidic traps in a daisy-chain configuration allowed us to observe calcium transients in TN cells exposed only to media conditioned by secretions of lipopolysaccharide-matured dendritic cells, an event which is easily missed in conventional cell culture where large media-to-cell ratios dilute cellular products. Further investigation into this intercellular signaling event indicated that LPS-matured dendritic cells, in the absence of antigenic stimulation, secrete chemical signals that induce calcium transients in T(N) cells. While the stimulating factor(s) produced by the mature dendritic cells remains to be identified, this report illustrates the utility of these microfluidic cell traps for analyzing arrays of individual suspension cells over time and probing both contact-based and intercellular signaling events between one or more cell populations.

High-Sensitivity Measurements of Multiple Kinase Activities in Live Single Cells

Increasing evidence has shown that population dynamics are qualitatively different from single-cell behaviors. Reporters to probe dynamic, single-cell behaviors are desirable yet relatively scarce. Here, we describe an easy-to-implement and generalizable technology to generate reporters of kinase activity for individual cells. Our technology converts phosphorylation into a nucleocytoplasmic shuttling event that can be measured by epifluorescence microscopy. Our reporters reproduce kinase activity for multiple types of kinases and allow for calculation of active kinase concentrations via a mathematical model. Using this technology, we made several experimental observations that had previously been technicallyunfeasible, including stimulus-dependent patterns of c-Jun N-terminal kinase (JNK) and nuclear factor kappa B (NF-κB) activation. We also measured JNK, p38, and ERK activities simultaneously, finding that p38 regulates the peak number, but not the intensity, of ERK fluctuations. Our approach opens the possibility of analyzing a wide range of kinase-mediated processes in individual cells.

A Noisy Paracrine Signal Determines the Cellular NF-κB Response to Lipopolysaccharide

A low-concentration paracrine TNF-α signal contributes to the variability in NF-κB activation dynamics in the response to lipopolysaccharide. Prolonging NF-κB Activation Regulation of the activity of the transcription factor NF-κB, which plays key roles in immune responses, exhibits complicated cellular dynamics. Tumor necrosis factor–α (TNF-α), a proinflammatory cytokine that activates the death-domain receptor TNFR, and lipopolysaccharide (LPS), a pathogen-derived molecule that activates the Toll-like receptor TLR4, both activate NF-κB. Lee et al. provide a mechanism by which cells respond to these two ligands with different kinetics. Cells responding to TNF-α exhibit an oscillating translocation of NF-κB in and out of the nucleus, with all cells responding similarly. In contrast, cells responding to LPS showed two distinct modes, with one population exhibiting transient nuclear localization of NF-κB and a second exhibiting persistent nuclear localization. Lee et al. modified an existing computational model of the pathways that activate NF-κB and found that cells responding to LPS produce TNF-α in concentrations that are low enough that only a subset of neighboring cells responds. This paracrine TNF-α signal produces the population of LPS-responsive cells with persistent prolonged NF-κB activation. Nearly identical cells can exhibit substantially different responses to the same stimulus. We monitored the nuclear localization dynamics of nuclear factor κB (NF-κB) in single cells stimulated with tumor necrosis factor–α (TNF-α) and lipopolysaccharide (LPS). Cells stimulated with TNF-α have quantitative differences in NF-κB nuclear localization, whereas LPS-stimulated cells can be clustered into transient or persistent responders, representing two qualitatively different groups based on the NF-κB response. These distinct behaviors can be linked to a secondary paracrine signal secreted at low concentrations, such that not all cells undergo a second round of NF-κB activation. From our single-cell data, we built a computational model that captures cell variability, as well as population behaviors. Our findings show that mammalian cells can create “noisy” environments to produce diversified responses to stimuli.

Computational modeling of mammalian signaling networks

One of the most exciting developments in signal transduction research has been the proliferation of studies in which a biological discovery was initiated by computational modeling. In this study, we review the major efforts that enable such studies. First, we describe the experimental technologies that are generally used to identify the molecular components and interactions in, and dynamic behavior exhibited by, a network of interest. Next, we review the mathematical approaches that are used to model signaling network behavior. Finally, we focus on three specific instances of ‘model‐driven discovery’: cases in which computational modeling of a signaling network has led to new insights that have been verified experimentally. Copyright

Robust meta-analysis of gene expression using the elastic net

Meta-analysis of gene expression has enabled numerous insights into biological systems, but current methods have several limitations. We developed a method to perform a meta-analysis using the elastic net, a powerful and versatile approach for classification and regression. To demonstrate the utility of our method, we conducted a meta-analysis of lung cancer gene expression based on publicly available data. Using 629 samples from five data sets, we trained a multinomial classifier to distinguish between four lung cancer subtypes. Our meta-analysis-derived classifier included 58 genes and achieved 91% accuracy on leave-one-study-out cross-validation and on three independent data sets. Our method makes meta-analysis of gene expression more systematic and expands the range of questions that a meta-analysis can be used to address. As the amount of publicly available gene expression data continues to grow, our method will be an effective tool to help distill these data into knowledge.

ZeitZeiger: supervised learning for high-dimensional data from an oscillatory system

Numerous biological systems oscillate over time or space. Despite these oscillators’ importance, data from an oscillatory system is problematic for existing methods of regularized supervised learning. We present ZeitZeiger, a method to predict a periodic variable (e.g. time of day) from a high-dimensional observation. ZeitZeiger learns a sparse representation of the variation associated with the periodic variable in the training observations, then uses maximum-likelihood to make a prediction for a test observation. We applied ZeitZeiger to a comprehensive dataset of genome-wide gene expression from the mammalian circadian oscillator. Using the expression of 13 genes, ZeitZeiger predicted circadian time (internal time of day) in each of 12 mouse organs to within ∼1 h, resulting in a multi-organ predictor of circadian time. Compared to the state-of-the-art approach, ZeitZeiger was faster, more accurate and used fewer genes. We then validated the multi-organ predictor on 20 additional datasets comprising nearly 800 samples. Our results suggest that ZeitZeiger not only makes accurate predictions, but also gives insight into the behavior and structure of the oscillator from which the data originated. As our ability to collect high-dimensional data from various biological oscillators increases, ZeitZeiger should enhance efforts to convert these data to knowledge.

Single-cell variation leads to population invariance in NF-κB signaling dynamics

Most features of NF-κB activation dynamics vary significantly with respect to ligand type and concentration. The distribution of the time between two nuclear entries is an invariant feature in populations but not individual cells, suggesting an additional level of control, which regulates the overall distribution of translocation timing.

Single-Cell and Population NF-κB Dynamic Responses Depend on Lipopolysaccharide Preparation

Background Lipopolysaccharide (LPS), found in the outer membrane of gram-negative bacteria, elicits a strong response from the transcription factor family Nuclear factor (NF)-κB via Toll-like receptor (TLR) 4. The cellular response to lipopolysaccharide varies depending on the source and preparation of the ligand, however. Our goal was to compare single-cell NF-κB dynamics across multiple sources and concentrations of LPS. Methodology/Principal Findings Using live-cell fluorescence microscopy, we determined the NF-κB activation dynamics of hundreds of single cells expressing a p65-dsRed fusion protein. We used computational image analysis to measure the nuclear localization of the fusion protein in the cells over time. The concentration range spanned up to nine orders of magnitude for three E. coli LPS preparations. We find that the LPS preparations induce markedly different responses, even accounting for potency differences. We also find that the ability of soluble TNF receptor to affect NF-κB dynamics varies strikingly across the three preparations. Conclusions/Significance Our work strongly suggests that the cellular response to LPS is highly sensitive to the source and preparation of the ligand. We therefore caution that conclusions drawn from experiments using one preparation may not be applicable to LPS in general.