Intracellular flow cytometry permits quantitation of diverse molecular targets at the single-cell level. However, limitations in detection sensitivity inherently restrict the method, sometimes resulting in the inability to measure proteins of very low abundance or to differentiate cells expressing subtly different protein concentrations. To improve these measurements, an enzymatic amplification approach called tyramide signal amplification (TSA) was optimized for assessment of intracellular kinase cascades. First, Pacific Blue, Pacific Orange, and Alexa Fluor 488 tyramide reporters were shown to exhibit low non-specific binding in permeabilized cells. Next, the effects of antibody concentration, tyramide concentration, and reaction time on assay resolution were characterized. Use of optimized TSA resulted in a 10-fold or greater improvement in measurement resolution of endogenous Erk and Stat cell signaling pathways relative to standard, non-amplified detection. TSA also enhanced assay sensitivity and, in conjunction with fluorescent cell barcoding, improved assay performance according to a metric used to evaluate high-throughput drug screens. TSA was used to profile Stat1 phosphorylation in primary immune system cells, which revealed heterogeneity in various populations, including CD4+ FoxP3+ regulatory T cells. We anticipate the approach will be broadly applicable to intracellular flow cytometry assays with low signal-to-noise ratios.

Summary

Increasing evidence suggests tumors are maintained by cancer stem cells; however, their nature remains controversial. In a HoxA9-Meis1 (H9M) model of acute myeloid leukemia (AML), we found that tumor-initiating activity existed in three, immunophenotypically distinct compartments, corresponding to disparate lineages on the normal hematopoietic hierarchy—stem/progenitor cells (Lin−kit+), and committed progenitors of the myeloid (Gr1+kit+) and lymphoid lineages (Lym+kit+). These distinct tumor-initiating cells (TIC) clonally recapitulated the immunophenotypic spectrum of the original tumor in vivo (including cells with a less-differentiated immunophenotype) and shared signaling networks, such that in vivo pharmacologic targeting of conserved TIC survival pathways (DNA methyltransferase and MEK phosphorylation) significantly increased survival. Collectively, H9M AML is organized as an atypical hierarchy that defies the strict lineage marker boundaries and unidirectional differentiation of normal hematopoiesis. Moreover, this suggests that in certain malignancies tumor-initiation activity (or “cancer-stemness”) can represent a cellular state that exists independently of distinct immunophenotypic definition.

Abstract

Flow cytometry has emerged as a powerful tool for quantitative, single-cell analysis of both surface markers and intracellular antigens, including phosphoproteins and kinase signaling cascades, with the flexibility to process hundreds of samples in multiwell plate format. Quantitative flow cytometric analysis is being applied in many areas of biology, from the study of immunology in animal models or human patients to high-content drug screening of pharmacologically active compounds. However, these experiments generate thousands of data points per sample, each with multiple measured parameters, leading to data management and analysis challenges. We developed WebFlow (http://webflow.stanford.edu), a web server-based software package to manage, analyze, and visualize data from flow cytometry experiments. WebFlow is accessible via standard web browsers and does not require users to install software on their personal computers. The software enables plate-based annotation of large data sets, which provides the basis for exploratory data analysis tools and rapid visualization of multiple different parameters. These tools include custom user-defined statistics to normalize data to other wells or other channels, as well as interactive, user-selectable heat maps for viewing the underlying single-cell data. The web-based approach of WebFlow allows for sharing of data with collaborators or the general public. WebFlow provides a novel platform for quantitative analysis of flow cytometric data from high-throughput drug screening or disease profiling experiments.

To determine critical host factors involved in HIV-1 replication, a dominant effector genetics approach was developed to reveal signaling pathways on which HIV-1 depends for replication. A large library of short peptide aptamers was expressed via retroviral delivery in T cells. Peptides that interfered with T cell activation-dependent processes that might support HIV-1 replication were identified. One of the selected peptides altered signaling, lead to a difference in T cell activation status, and inhibited HIV-1 replication. The target of the peptide was JAB1/CSN5, a component of the signalosome complex. JAB1 expression overcame the inhibition of HIV-1 replication in the presence of peptide and also promoted HIV-1 replication in activated primary CD4+ T cells. This peptide blocked physiological release of JAB1 from the accessory T cell surface protein LFA-1, downstream AP-1 dependent events, NFAT activation, and HIV-1 replication. Thus, genetic selection for intracellular aptamer inhibitors of host cell processes proximal to signals at the immunological synapse of T cells can define unique mechanisms important to HIV-1 replication.

Multiparametric single-cell analysis is critical for understanding cellular heterogeneity. Despite recent technological advances in single-cell measurements, methods for analyzing high-dimensional single-cell data are often subjective, labor intensive and require prior knowledge of the biological system under investigation. To objectively uncover cellular heterogeneity from single-cell measurements, we present a novel computational approach, Spanning-tree Progression Analysis of Density-normalized Events (SPADE). We applied SPADE to cytometry data of mouse and human bone marrow. In both cases, SPADE organized cells in a hierarchy of related phenotypes that partially recapitulated well-described patterns of hematopoiesis. In addition, SPADE produced a map of intracellular signal activation across the landscape of human hematopoietic development. SPADE revealed a functionally distinct cell population, natural killer (NK) cells, without using any NK-specific parameters. SPADE is a versatile method that facilitates the analysis of cellular heterogeneity, the identification of cell types, and comparison of functional markers in response to perturbations.

Summary

Type I Interferons (IFNs) are important cytokines for innate immunity against viruses and cancer. Sixteen human IFN variants signal through the same cell surface receptors, IFNAR1 and IFNAR2, yet they can evoke markedly different physiological effects. The crystal structures of two human type I IFN ternary signaling complexes containing IFNα2 and IFNω reveal recognition modes and heterotrimeric architectures that are unique amongst the cytokine receptor superfamily, but conserved between different type I IFNs. Receptor-ligand cross-reactivity is enabled by conserved receptor-ligand "anchor-points" interspersed amongst ligand-specific interactions that ‘tune’ the relative IFN binding affinities, in an apparent extracellular ‘ligand proofreading’ mechanism that modulates biological activity. Functional differences between IFNs are linked to their respective receptor recognition chemistries, in concert with a ligand-induced conformational change in IFNAR1, that collectively control signal initiation and complex stability, ultimately regulating differential STAT phosphorylation profiles, receptor internalization rates, and downstream gene expression patterns.

Flow cytometry is an essential tool for dissecting the functional complexity of hematopoiesis. We used single-cell “mass cytometry” to examine healthy human bone marrow, measuring 34 parameters simultaneously in single cells (binding of 31 antibodies, viability, DNA content, and relative cell size). The signaling behavior of cell subsets spanning a defined hematopoietic hierarchy was monitored with 18 simultaneous markers of functional signaling states perturbed by a set of ex vivo stimuli and inhibitors. The data set allowed for an algorithmically driven assembly of related cell types defined by surface antigen expression, providing a superimposable map of cell signaling responses in combination with drug inhibition. Visualized in this manner, the analysis revealed previously unappreciated instances of both precise signaling responses that were bounded within conventionally defined cell subsets and more continuous phosphorylation responses that crossed cell population boundaries in unexpected manners yet tracked closely with cellular phenotype. Collectively, such single-cell analyses provide system-wide views of immune signaling in healthy human hematopoiesis, against which drug action and disease can be compared for mechanistic studies and pharmacologic intervention.

Fluorescent Cell Barcoding (FCB) enables high throughput, i.e. high content flow cytometry by multiplexing samples prior to staining and acquisition on the cytometer. Individual cell samples are barcoded, or labeled, with unique signatures of fluorescent dyes so that they can be mixed together, stained, and analyzed as a single sample. By mixing samples prior to staining, antibody consumption is typically reduced 10 to 100-fold. In addition, data robustness is increased through the combination of control and treated samples, which minimizes pipetting error, staining variation, and the need for normalization. Finally, speed of acquisition is enhanced, enabling large profiling experiments to be run with standard cytometer hardware. In this unit, we outline the steps necessary to apply the FCB method to cell lines as well as primary peripheral blood samples. Important technical considerations such as choice of barcoding dyes, concentrations, labeling buffers, compensation, and software analysis are discussed.

Summary

Dendritic cells (DCs) comprise distinct functional subsets including CD8− and CD8+ classical DCs (cDCs) and interferon-secreting plasmacytoid DCs (pDCs). The cytokine Flt3 ligand (Flt3L) controls the development of DCs, and is particularly important for the pDC, CD8+ cDC and their CD103+ tissue counterparts. We report that mammalian target of rapamycin (mTOR) inhibitor rapamycin impaired Flt3L-driven DC development in vitro, with the pDCs and CD8+-like cDCs most profoundly affected. Conversely, deletion of the phosphoinositide 3-kinase (PI3K)-mTOR negative regulator Pten facilitated Flt3L-driven DC development in culture. DC-specific Pten targeting in vivo caused the expansion of CD8+ and CD103+ cDC numbers, which was reversible by rapamycin. The increased CD8+ cDC numbers caused by Pten deletion correlated with increased susceptibility to the intracellular pathogen Listeria. Thus, PI3K-mTOR signaling downstream of Flt3L controls DC development, and its restriction by Pten ensures optimal DC pool size and subset composition.

Large variations in cell size and shape can undermine traditional gating methods for analyzing flow cytometry data. Correcting for these effects enables analysis of high-throughput data sets, including >5000 yeast samples with diverse cell morphologies.

The regression model approach corrects for the effects of cell morphology on fluorescence, as well as an extremely small and restrictive gate, but without removing any of the cells.In contrast to traditional gating, this approach enables the quantitative analysis of high-throughput flow cytometry experiments, since the regression model can compare between biological samples that show no or little overlap in terms of the morphology of the cells.The analysis of a high-throughput yeast flow cytometry data set consisting of >5000 biological samples identified key proteins that affect the time and intensity of the bifurcation event that happens after the carbon source transition from glucose to fatty acids. Here, some yeast cells undergo major structural changes, while others do not.

Flow cytometry is a widely used technique that enables the measurement of different optical properties of individual cells within large populations of cells in a fast and automated manner. For example, by targeting cell-specific markers with fluorescent probes, flow cytometry is used to identify (and isolate) cell types within complex mixtures of cells. In addition, fluorescence reporters can be used in conjunction with flow cytometry to measure protein, RNA or DNA concentration within single cells of a population.

One of the biggest advantages of this technique is that it provides information of how each cell behaves instead of just measuring the population average. This can be essential when analyzing complex samples that consist of diverse cell types or when measuring cellular responses to stimuli. For example, there is an important difference between a 50% expression increase of all cells in a population after stimulation and a 100% increase in only half of the cells, while the other half remains unresponsive. Another important advantage of flow cytometry is automation, which enables high-throughput studies with thousands of samples and conditions. However, current methods are confounded by populations of cells that are non-uniform in terms of size and granularity. Such variability affects the emitted fluorescence of the cell and adds undesired variability when estimating population fluorescence. This effect also frustrates a sensible comparison between conditions, where not only fluorescence but also cell size and granularity may be affected.

Traditionally, this problem has been addressed by using ‘gates' that restrict the analysis to cells with similar morphological properties (i.e. cell size and cell granularity). Because cells inside the gate are morphologically similar to one another, they will show a smaller variability in their response within the population. Moreover, applying the same gate in all samples assures that observed differences between these samples are not due to differential cell morphologies.

Gating, however, comes with costs. First, since only a subgroup of cells is selected, the final number of cells analyzed can be significantly reduced. This means that in order to have sufficient statistical power, more cells have to be acquired, which, if even possible in the first place, increases the time and cost of the experiment. Second, finding a good gate for all samples and conditions can be challenging if not impossible, especially in cases where cellular morphology changes dramatically between conditions. Finally, gating is a very user-dependent process, where both the size and shape of the gate are determined by the researcher and will affect the outcome, introducing subjectivity in the analysis that complicates reproducibility.

In this paper, we present an alternative method to gating that addresses the issues stated above. The method is based on a regression model containing linear and non-linear terms that estimates and corrects for the effect of cell size and granularity on the observed fluorescence of each cell in a sample. The corrected fluorescence thus becomes ‘free' of the morphological effects.

Because the model uses all cells in the sample, it assures that the corrected fluorescence is an accurate representation of the sample. In addition, the regression model can predict the expected fluorescence of a sample in areas where there are no cells. This makes it possible to compare between samples that have little overlap with good confidence. Furthermore, because the regression model is automated, it is fully reproducible between labs and conditions. Finally, it allows for a rapid analysis of big data sets containing thousands of samples.

To probe the validity of the model, we performed several experiments. We show how the regression model is able to remove the morphological-associated variability as well as an extremely small and restrictive gate, but without the caveat of removing cells. We test the method in different organisms (yeast and human) and applications (protein level detection, separation of mixed subpopulations). We then apply this method to unveil new biological insights in the mechanistic processes involved in transcriptional noise.

Gene transcription is a process subjected to the randomness intrinsic to any molecular event. Although such randomness may seem to be undesirable for the cell, since it prevents consistent behavior, there are situations where some degree of randomness is beneficial (e.g. bet hedging). For this reason, each gene is tuned to exhibit different levels of randomness or noise depending on its functions. For core and essential genes, the cell has developed mechanisms to lower the level of noise, while for genes involved in the response to stress, the variability is greater.

This gene transcription tuning can be determined at many levels, from the architecture of the transcriptional network, to epigenetic regulation. In our study, we analyze the latter using the response of yeast to the presence of fatty acid in the environment. Fatty acid can be used as energy by yeast, but it requires major structural changes and commitments. We have observed that at the population level, there is a bifurcation event whereby some cells undergo these changes and others do not. We have analyzed this bifurcation event in mutants for all the non-essential epigenetic regulators in yeast and identified key proteins that affect the time and intensity of this bifurcation. Even though fatty acid triggers major morphological changes in the cell, the regression model still makes it possible to analyze the over 5000 flow cytometry samples in this data set in an automated manner, whereas a traditional gating approach would be impossible.

Cells exposed to stimuli exhibit a wide range of responses ensuring phenotypic variability across the population. Such single cell behavior is often examined by flow cytometry; however, gating procedures typically employed to select a small subpopulation of cells with similar morphological characteristics make it difficult, even impossible, to quantitatively compare cells across a large variety of experimental conditions because these conditions can lead to profound morphological variations. To overcome these limitations, we developed a regression approach to correct for variability in fluorescence intensity due to differences in cell size and granularity without discarding any of the cells, which gating ipso facto does. This approach enables quantitative studies of cellular heterogeneity and transcriptional noise in high-throughput experiments involving thousands of samples. We used this approach to analyze a library of yeast knockout strains and reveal genes required for the population to establish a bimodal response to oleic acid induction. We identify a group of epigenetic regulators and nucleoporins that, by maintaining an ‘unresponsive population,' may provide the population with the advantage of diversified bet hedging.

Summary

Although vaccinia virus infection results in induction of a robust immunizing response, many closely related poxviruses such as variola (smallpox) and ectromelia (mousepox) are highly pathogenic in their natural hosts. We developed a strategy to map the activation of key signaling networks in vivo and applied this approach to define and compare the earliest signaling events elicited by poxvirus infections in mice. Vaccinia induced rapid TLR2-dependent responses leading to IL-6 production, which then initiated STAT3 signaling in dendritic cells and T cells. In contrast, ectromelia did not induce TLR2 activation and profound mouse strain-dependent responses were observed. In resistant C57BL/6 mice, the STAT1 and STAT3 pathways were rapidly activated, whereas in susceptible BALB/c mice, IL-6-dependent STAT3 activation did not occur. These results indicate that vaccination with vaccinia is dependent on rapid TLR2 and IL-6 driven responses and link the earliest immune signaling events to the outcome of infection.

Today we can generate hundreds of gigabases of DNA and RNA sequencing data in a week for less than US$5,000. The astonishing rate of data generation by these low-cost, high-throughput technologies in genomics is being matched by that of other technologies, such as real-time imaging and mass spectrometry-based flow cytometry. Success in the life sciences will depend on our ability to properly interpret the large-scale, high-dimensional data sets that are generated by these technologies, which in turn requires us to adopt advances in informatics. Here we discuss how we can master the different types of computational environments that exist — such as cloud and heterogeneous computing — to successfully tackle our big data problems.

Characterization of patient-specific disease features at a molecular level is an important emerging field. Patients may be characterized by differences in the level and activity of relevant biomolecules in diseased cells. When high throughput, high dimensional data is available, it becomes possible to characterize differences not only in the level of the biomolecules, but also in the molecular interactions among them. We propose here a novel approach to characterize patient specific signaling, which augments high throughput single cell data with state nodes corresponding to patient and disease states, and learns a Bayesian network based on this data. Features distinguishing individual patients emerge as downstream nodes in the network. We illustrate this approach with a six phospho-protein, 30,000 cell-per-patient dataset characterizing three comparably diagnosed follicular lymphoma, and show that our approach elucidates signaling differences among them.

The phosphorylation and dephosphorylation of proteins by kinases and phosphatases constitute an essential regulatory network in eukaryotic cells. This network supports the flow of information from sensors through signaling systems to effector molecules, and ultimately drives the phenotype and function of cells, tissues, and organisms. Dysregulation of this process has severe consequences and is one of the main factors in the emergence and progression of diseases, including cancer. Thus, major efforts have been invested in developing specific inhibitors that modulate the activity of individual kinases or phosphatases; however, it has been difficult to assess how such pharmacological interventions would affect the cellular signaling network as a whole. Here, we used label-free, quantitative phosphoproteomics in a systematically perturbed model organism (Saccharomyces cerevisiae) to determine the relationships between 97 kinases, 27 phosphatases, and more than 1000 phosphoproteins. We identified 8814 regulated phosphorylation events, describing the first system-wide protein phosphorylation network in vivo. Our results show that, at steady state, inactivation of most kinases and phosphatases affected large parts of the phosphorylation-modulated signal transduction machinery, and not only the immediate downstream targets. The observed cellular growth phenotype was often well maintained despite the perturbations, arguing for considerable robustness in the system. Our results serve to constrain future models of cellular signaling and reinforce the idea that simple linear representations of signaling pathways might be insufficient for drug development and for describing organismal homeostasis.

Background

Single cell network profiling (SCNP) utilizing flow cytometry measures alterations in intracellular signaling responses. Here SCNP was used to characterize Acute Myeloid Leukemia (AML) disease subtypes based on survival, DNA damage response and apoptosis pathways.

Methodology and Principal Findings

Thirty four diagnostic non-M3 AML samples from patients with known clinical outcome were treated with a panel of myeloid growth factors and cytokines, as well as with apoptosis-inducing agents. Analysis of induced Jak/Stat and PI3K pathway responses in blasts from individual patient samples identified subgroups with distinct signaling profiles that were not seen in the absence of a modulator. In vitro exposure of patient samples to etoposide, a DNA damaging agent, revealed three distinct “DNA damage response (DDR)/apoptosis” profiles: 1) AML blasts with a defective DDR and failure to undergo apoptosis; 2) AML blasts with proficient DDR and failure to undergo apoptosis; 3) AML blasts with proficiency in both DDR and apoptosis pathways. Notably, AML samples from clinical responders fell within the “DDR/apoptosis” proficient profile and, as well, had low PI3K and Jak/Stat signaling responses. In contrast, samples from clinical non responders had variable signaling profiles often with in vitro apoptotic failure and elevated PI3K pathway activity. Individual patient samples often harbored multiple, distinct, leukemia-associated cell populations identifiable by their surface marker expression, functional performance of signaling pathway in the face of cytokine or growth factor stimulation, as well as their response to apoptosis-inducing agents.

Conclusions and Significance

Characterizing and tracking changes in intracellular pathway profiles in cell subpopulations both at baseline and under therapeutic pressure will likely have important clinical applications, potentially informing the selection of beneficial targeted agents, used either alone or in combination with chemotherapy.

An early reaction of CD4+ T lymphocytes to antigen is the production of cytokines, notably IL-2. In order to detect cytokine dependent responses, naive antigen-specific T cells were stimulated in vivo and the presence of phosphorylated STAT5 molecules was used to identify the cell populations responding to IL-2. Within hours of T-cell priming, IL-2-dependent STAT5 phosphorylation occurred primarily in Foxp3+ regulatory T cells. In contrast, the antigen-specific T cells received STAT5 signals only after repeated antigen exposure or memory differentiation. Regulatory T cells receiving IL-2 signals proliferated and developed enhanced suppressive activity. These results indicate that one of the earliest events in a T cell response is the activation of endogenous regulatory cells, potentially to prevent autoimmunity.

Abstract

Flow cytometric measurement of signaling protein abundances has proved particularly useful for elucidation of signaling pathway structure. The single cell nature of the data ensures a very large dataset size, providing a statistically robust dataset for structure learning. Moreover, the approach is easily scaled to many conditions in high throughput. However, the technology suffers from a dimensionality constraint: at the cutting edge, only about 12 protein species can be measured per cell, far from sufficient for most signaling pathways. Because the structure learning algorithm (in practice) requires that all variables be measured together simultaneously, this restricts structure learning to the number of variables that constitute the flow cytometer's upper dimensionality limit. To address this problem, we present here an algorithm that enables structure learning for sparsely distributed data, allowing structure learning beyond the measurement technology's upper dimensionality limit for simultaneously measurable variables. The algorithm assesses pairwise (or n-wise) dependencies, constructs “Markov neighborhoods” for each variable based on these dependencies, measures each variable in the context of its neighborhood, and performs structure learning using a constrained search.

We demonstrate the use of electron microscopy as a powerful characterization tool to identify and locate antibody-conjugated composite organic-inorganic (COINs) surface enhanced Raman scattering (SERS) nanoparticles on cells. U937 leukemia cells labeled with antibody CD54-conjugated COINs were characterized in their native, hydrated state using wet Scanning Electron Microscopy (SEM) and in their dehydrated state using high-resolution SEM. In both cases, the backscattered electron detector (BSE) was used to detect and identify the silver constituents in COINs due to its high sensitivity to atomic number variations within a specimen. The imaging and analytical capabilities in the SEM were further complemented by higher resolution Transmission Electron Microscope (TEM) images and Scanning Auger Electron Spectroscopy (AES) data to give reliable and high-resolution information about nanoparticles and their binding to cell surface antigens.

Summary

Progress in understanding the molecular pathogenesis of human myeloproliferative disorders (MPDs) has led to guidelines incorporating genetic assays with histopathology during diagnosis. Advances in flow cytometry have made it possible to simultaneously measure cell type and signaling abnormalities arising as a consequence of genetic pathologies. Using flow cytometry, we observed a specific evoked STAT5 signaling signature in a subset of samples from patients suspected of having juvenile myelomonocytic leukemia (JMML), an aggressive MPD with a challenging clinical presentation during active disease. This signature was a specific feature involving JAK-STAT signaling, suggesting a critical role of this pathway in the biological mechanism of this disorder and indicating potential targets for future therapies.

Significance

Recent advances have enabled simultaneous measurement of cell type and cell signals in primary populations using flow cytometry. This technique enables the question, "Can we track oncogenic cell populations from diagnosis through disease evolution via signaling?" Doing so in an era of using specific inhibitors against components of key signal transduction pathways will be necessary to assess treatment effects in human patients and adapt as cancer cells alter their signaling in response to these treatments. This work uses such an approach to follow patients over time and shows that disease status in juvenile myelomonocytic leukemia (JMML) -- at diagnosis, remission, relapse, and transformation -- is indicated by a subset of cells with an abnormal signaling profile.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease of unknown etiology that involves multiple interacting cell types driven by numerous cytokines and autoimmune epitopes. Although the initiating events leading to SLE pathology are not understood, there is a growing realization that dysregulated cytokine action on immune cells plays an important role in promoting the inflammatory autoimmune state. We applied phospho-specific flow cytometry to characterize the extent to which regulation of cytokine signal transduction through the STAT family of transcription factors is disturbed during the progression of SLE. Using a panel of 10 cytokines thought to have causal roles in the disease, we measured signaling responses at the single-cell level in five immune cell types from the MRLlpr murine model. This generated a highly multiplexed view of how cytokine stimuli are processed by intracellular signaling networks in adaptive and innate immune cells during different stages of SLE pathogenesis. We report that robust changes in cytokine signal transduction occur during the progression of SLE in multiple immune cell subtypes including increased T cell responsiveness to IL-10 and ablation of Stat1 responses to IFNα, IFNγ, IL-6, and IL-21, Stat3 responses to IL-6, Stat5 responses to IL-15, and Stat6 responses to IL-4. We found increased intracellular expression of Suppressor of Cytokine Signaling 1 protein correlated with negative regulation of Stat1 responses to inflammatory cytokines. The results provide evidence of negative feedback regulation opposing inflammatory cytokines that have self-sustaining activities and suggest a cytokine-driven oscillator circuit may drive the periodic disease activity observed in many SLE patients.

Despite extensive evidence of cell signaling alterations induced by human immunodeficiency virus type 1 (HIV-1) in vitro, the relevance of these changes to the clinical and/or immunologic status of HIV-1-infected individuals is often unclear. As such, mapping the details of cell type-specific degradation of immune function as a consequence of changes to signaling network responses has not been readily accessible. We used a flow cytometric-based assay of signaling to determine Janus kinase/signal transducers and activators of transcription (Jak/STAT) signaling changes at the single-cell level within distinct cell subsets from the primary immune cells of HIV-1-infected donors. We identified a specific defect in granulocyte-macrophage colony-stimulating factor (GM-CSF)-driven Stat5 phosphorylation in the monocytes of HIV-1+ donors. This inhibition was statistically significant in a cohort of treated and untreated individuals. Ex vivo Stat5 phosphorylation levels varied among HIV-1+ donors but did not correlate with CD4+ T-cell counts or HIV-1 plasma viral load. Low Stat5 activation occurred in HIV-1-infected donors despite normal GM-CSF receptor levels. Investigation of mitogen-activated protein kinase (MAPK) pathways, also stimulated by GM-CSF, led to the observation that lipopolysaccharide-stimulated extracellular signal-regulated kinase phosphorylation is enhanced in monocytes. Thus, we have identified a specific, imbalanced monocyte signaling profile, with inhibition of STAT and enhancement of MAPK signaling, associated with HIV-1 infection. This understanding of altered monocyte signaling responses that contribute to defective antigen presentation during HIV-1 infection could lead to immunotherapeutic approaches that compensate for the deficiency.

Ebola virus is a highly lethal pathogen that causes hemorrhagic fever in humans and nonhuman primates. Among the seven known viral gene products, the envelope glycoprotein (GP) alone induces cell rounding and detachment that ultimately leads to cell death. Cellular cytoxicity is not seen with comparable levels of expression of a mutant form of GP lacking a mucin-like domain (GPΔmuc). GP-induced cell death is nonapoptotic and is preceded by downmodulation of cell surface molecules involved in signaling pathways, including certain integrins and epidermal growth factor receptor. To investigate the mechanism of GP-induced cellular toxicity, we analyzed the activation of several signal transduction pathways involved in cell growth and survival. The active form of extracellular signal-regulated kinases types 1 and 2 (ERK1/2), phospho-ERK1/2, was reduced in cells expressing GP compared to those expressing GPΔmuc as determined by flow cytometry, in contrast to the case for several other signaling proteins. Subsequent analysis of the activation states and kinase activities of related kinases revealed a more pronounced effect on the ERK2 kinase isoform. Disruption of ERK2 activity by a dominant negative ERK or by small interfering RNA-mediated ERK2 knockdown potentiated the decrease in αV integrin expression associated with toxicity. Conversely, activation of the pathway through the expression of a constitutively active form of ERK2 significantly protected against this effect. These results indicate that the ERK signaling cascade mediates GP-mediated cytotoxicity and plays a role in pathogenicity induced by this gene product.

Efforts to construct therapeutically useful models of biological systems require large and diverse sets of data on functional connections between their components. Here we show that cellular responses to combinations of chemicals reveal how their biological targets are connected. Simulations of pathways with pairs of inhibitors at varying doses predict distinct response surface shapes that are reproduced in a yeast experiment, with further support from a larger screen using human tumour cells. The response morphology yields detailed connectivity constraints between nearby targets, and synergy profiles across many combinations show relatedness between targets in the whole network. Constraints from chemical combinations complement genetic studies, because they probe different cellular components and can be applied to disease models that are not amenable to mutagenesis. Chemical probes also offer increased flexibility, as they can be continuously dosed, temporally controlled, and readily combined. After extending this initial study to cover a wider range of combination effects and pathway topologies, chemical combinations may be used to refine network models or to identify novel targets. This response surface methodology may even apply to non-biological systems where responses to targeted perturbations can be measured.

Background

Leukocyte Function Antigen-1 (LFA-1) is a primary adhesion molecule that plays important roles in T cell activation, leukocyte recirculation, and trans-endothelial migration. By applying a multivariate intracellular phospho-proteomic analysis, we demonstrate that LFA-1 differentially activates signaling molecules.

Results

Signal intensity was dependent on both ICAM ligand and LFA-1 concentration. In the presence of CD3 and CD28 stimulation, ICAM-2 and ICAM-3 decreased TGFβ1 production more than ICAM-1. In long-term differentiation experiments, stimulation with ICAM-3, CD3, and CD28 generated IFNγ producing CD4+CD45RO+CD62L-CD11aBrightCD27- cells that had increased expression of intracellular BCL2, displayed distinct chemokine receptor profiles, and exhibited distinct migratory characteristics. Only CD3/CD28 with ICAM-3 generated CD4+CD45RO+CD62L-CD11aBrightCD27- cells that were functionally responsive to chemotaxis and exhibited higher frequencies of cells that signaled to JNK and ERK1/2 upon stimulation with MIP3α. Furthermore, these reports identify that the LFA-1 receptor, when presented with multiple ligands, can result in distinct T cell differentiation states and suggest that the combinatorial integration of ICAM ligand interactions with LFA-1 have functional consequences for T cell biology.

Conclusion

Thus, the ICAM ligands, differentially modulate LFA-1 signaling in T cells and potentiate the development of memory human T cells in vitro. These findings are of importance in a mechanistic understanding of memory cell differentiation and ex vivo generation of memory cell subsets for therapeutic applications.

The pathogenesis of varicella-zoster virus (VZV) involves a cell-associated viremia during which infectious virus is carried from sites of respiratory mucosal inoculation to the skin. We now demonstrate that VZV infection of T cells is associated with robust virion production and modulation of the apoptosis and interferon pathways within these cells. The VZV serine/threonine protein kinase encoded by ORF66 is essential for the efficient replication of VZV in T cells. Preventing ORF66 protein expression by stop codon insertion (pOka66S) impaired the growth of the parent Oka (pOka) strain in T cells in SCID-hu T-cell xenografts in vivo and reduced formation of VZV virions. The lack of ORF66 protein also increased the susceptibility of infected T cells to apoptosis and reduced the capacity of the virus to interfere with induction of the interferon (IFN) signaling pathway following exposure to IFN-γ. However, preventing ORF66 protein expression only slightly reduced growth in melanoma cells in culture and did not diminish virion formation in these cells. The pOka66S virus showed only a slight defect in growth in SCID-hu skin implants compared with intact pOka. These observations suggest that the ORF66 kinase plays a unique role during infection of T cells and supports VZV T-cell tropism by contributing to immune evasion and enhancing survival of infected T cells.