Single-cell measurements of IgE-mediated FcεRI signaling using an integrated microfluidic platform

Block Search Stochastic Simulation Algorithm (BlSSSA): A Fast Stochastic Simulation Algorithm for Modeling Large Biochemical Networks

Stochastic simulation algorithms are extensively used for exploring stochastic behavior of biochemical pathways/networks. Computational cost of these algorithms is high in simulating real biochemical systems due to their large size, complex structure and stiffness. In order to reduce the computational cost, several algorithms have been developed. It is observed that these algorithms are basically fast in simulating weakly coupled networks. In case of strongly coupled networks, they become slow as their computational cost become high in maintaining complex data structures. Here, we develop Block Search Stochastic Simulation Algorithm (BlSSSA). BlSSSA is not only fast in simulating weakly coupled networks but also fast in simulating strongly coupled and stiff networks. We compare its performance with other existing algorithms using two hypothetical networks, viz., linear chain and colloidal aggregation network, and three real biochemical networks, viz., B cell receptor signaling network, FceRI signaling network and a stiff 1,3-Butadiene Oxidation network. It has been shown that BlSSSA is faster than other algorithms considered in this study.

Dissecting Particle Uptake Heterogeneity in a Cell Population Using Bayesian Analysis

Individual cells in a solution display variable uptake of nanomaterials, peptides, and nutrients. Such variability reflects their heterogeneity in endocytic capacity. In a recent work, we have shown that the endocytic capacity of a cell depends on its size and surface density of endocytic components (transporters). We also demonstrated that in MDA-MB-231 breast cancer cells, the cell-surface transporter density (n) may decay with cell radius (r) following the power rule n ∼ r^α, where α ≈ -1. In this work, we investigate how n and r may independently contribute to the endocytic heterogeneity of a cell population. Our analysis indicates that the smaller cells display more heterogeneity because of the higher stochastic variations in n. By contrast, the larger cells display a more uniform uptake, reflecting less-stochastic variations in n. We provide analyses of these dependencies by establishing a stochastic model. Our analysis reveals that the exponent α in the above relationship is not a constant; rather, it is a random variable whose distribution depends on cell size r. Using Bayesian analysis, we characterize the cell-size-dependent distributions of α that accurately capture the particle uptake heterogeneity of MDA-MB-231 cells.

Microfluidic methods for precision diagnostics in food allergy

Food allergy has reached epidemic proportions and has become a significant source of healthcare burden. Oral food challenge, the gold standard for food allergy assessment, often is not performed because it places the patient at risk of developing anaphylaxis. However, conventional alternative food allergy tests lack a sufficient predictive value. Therefore, there is a critical need for better diagnostic tests that are both accurate and safe. Microfluidic methods have the potential of helping one to address such needs and to personalize the diagnostics. This article first reviews conventional diagnostic approaches used in food allergy. Second, it reviews recent efforts to develop novel biomarkers and in vitro diagnostics. Third, it summarizes the microfluidic methods developed thus far for food allergy diagnosis. The article concludes with a discussion of future opportunities for using microfluidic methods for achieving precision diagnostics in food allergy, including multiplexing the detection of multiple biomarkers, sampling of tissue-resident cytokines and immune cells, and multi-organ-on-a-chip technology.

Microfluidic Immunoaffinity Basophil Activation Test for Point-of-Care Allergy Diagnosis

BACKGROUND

The flow cytometry-based basophil activation test (BAT) is used for the diagnosis of allergic response. However, flow cytometry is time-consuming, requiring skilled personnel and cumbersome processing, which has limited its use in the clinic. Here, we introduce a novel microfluidic-based immunoaffinity BAT (miBAT) method.

METHODS

The microfluidic device, coated with anti-CD203c, was designed to capture basophils directly from whole blood. The captured basophils are activated by anti-FcεRI antibody followed by optical detection of CD63 expression (degranulation marker). The device was first characterized using a basophil cell line followed by whole blood experiments. We evaluated the device with ex vivo stimulation of basophils in whole blood from healthy controls and patients with allergies and compared it with flow cytometry.

RESULTS

The microfluidic device was capable of capturing basophils directly from whole blood followed by in vitro activation and quantification of CD63 expression. CD63 expression was significantly higher (P = 0.0002) in on-chip activated basophils compared with nonactivated cells. The difference in CD63 expression on anti-FcεRI-activated captured basophils in microfluidic chip was significantly higher (P = 0.03) in patients with allergies compared with healthy controls, and the results were comparable with flow cytometry analysis (P = 0.04). Furthermore, there was no significant difference of CD63% expression in anti-FcεRI-activated captured basophils in microfluidic chip compared with flow cytometry.

CONCLUSIONS

We report on the miBAT. This device is capable of isolating basophils directly from whole blood for on-chip activation and detection. The new miBAT method awaits validation in larger patient populations to assess performance in diagnosis and monitoring of patients with allergies at the point of care.

Efficient anticorrelated variance reduction for stochastic simulation of biochemical reactions

We investigate the computational challenge of improving the accuracy of the stochastic simulation estimation by inducing negative correlation through the anticorrelated variance reduction technique. A direct application of the technique to the stochastic simulation algorithm (SSA), employing the inverse transformation, is not efficient for simulating large networks because its computational cost is similar to the sum of independent simulation runs. We propose in this study a new algorithm that employs the propensity bounds of reactions, introduced recently in their rejection-based SSA, to correlate and synchronise the trajectories during the simulation. The selection of reaction firings by our approach is exact due to the rejection-based mechanism. In addition, by applying the anticorrelated variance technique to select reaction firings, our approach can induce substantial correlation between realisations, hence reducing the variance of the estimator. The computational advantage of our rejection-based approach in comparison with the traditional inverse transformation is that it only needs to maintain a single data structure storing propensity bounds of reactions, which is updated infrequently, hence achieving better performance.

Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor

Receptor tyrosine kinases (RTKs) typically contain multiple autophosphorylation sites in their cytoplasmic domains. Once activated, these autophosphorylation sites can recruit downstream signaling proteins containing Src homology 2 (SH2) and phosphotyrosine-binding (PTB) domains, which recognize phosphotyrosine-containing short linear motifs (SLiMs). These domains and SLiMs have polyspecific or promiscuous binding activities. Thus, multiple signaling proteins may compete for binding to a common SLiM and vice versa. To investigate the effects of competition on RTK signaling, we used a rule-based modeling approach to develop and analyze models for ligand-induced recruitment of SH2/PTB domain-containing proteins to autophosphorylation sites in the insulin-like growth factor 1 (IGF1) receptor (IGF1R). Models were parameterized using published datasets reporting protein copy numbers and site-specific binding affinities. Simulations were facilitated by a novel application of model restructuration, to reduce redundancy in rule-derived equations. We compare predictions obtained via numerical simulation of the model to those obtained through simple prediction methods, such as through an analytical approximation, or ranking by copy number and/or K_D value, and find that the simple methods are unable to recapitulate the predictions of numerical simulations. We created 45 cell line-specific models that demonstrate how early events in IGF1R signaling depend on the protein abundance profile of a cell. Simulations, facilitated by model restructuration, identified pairs of IGF1R binding partners that are recruited in anti-correlated and correlated fashions, despite no inclusion of cooperativity in our models. This work shows that the outcome of competition depends on the physicochemical parameters that characterize pairwise interactions, as well as network properties, including network connectivity and the relative abundances of competitors.

Cells rely on networks of interacting biomolecules to sense and respond to environmental perturbations and signals. However, it is unclear how information is processed to generate appropriate and specific responses to signals, especially given that these networks tend to share many components. For example, receptors that detect distinct ligands and regulate distinct cellular activities commonly interact with overlapping sets of downstream signaling proteins. Here, to investigate the downstream signaling of a well-studied receptor tyrosine kinase (RTK), the insulin-like growth factor 1 (IGF1) receptor (IGF1R), we formulated and analyzed 45 cell line-specific mathematical models, which account for recruitment of 18 different binding partners to six sites of receptor autophosphorylation in IGF1R. The models were parameterized using available protein copy number and site-specific affinity measurements, and restructured to allow for network generation. We find that recruitment is influenced by the protein abundance profile of a cell, with different patterns of recruitment in different cell lines. Furthermore, in a given cell line, we find that pairs of IGF1R binding partners may be recruited in a correlated or anti-correlated fashion. We demonstrate that the simulations of the model have greater predictive power than protein copy number and/or binding affinity data, and that even a simple analytical model cannot reproduce the predicted recruitment ranking obtained via simulations. These findings represent testable predictions and indicate that the outputs of IGF1R signaling depend on cell line-specific properties in addition to the properties that are intrinsic to the biomolecules involved.

A Critical Comparison of Rejection-Based Algorithms for Simulation of Large Biochemical Reaction Networks

The rejection-based simulation technique has been applying to improve the computational efficiency of the stochastic simulation algorithm (SSA) in simulating large reaction networks, which are required for a thorough understanding of biological systems. We compare two recently proposed simulation methods, namely the composition-rejection algorithm (SSA-CR) and the rejection-based SSA (RSSA), aiming for this purpose. We discuss the right interpretation of the rejection-based technique used in these algorithms in order to make an informed choice when dealing with different aspects of biochemical networks. We provide the theoretical analysis as well as the detailed runtime comparison of these algorithms on concrete biological models. We highlight important factors that are omitted in previous analysis of these algorithms. The numerical comparison shows that for reaction networks where the search cost is expensive then SSA-CR is more efficient, and for reaction networks where the update cost is dominant, often the case in practice, then RSSA should be the choice.

Timescale Separation of Positive and Negative Signaling Creates History-Dependent Responses to IgE Receptor Stimulation

The high-affinity receptor for IgE expressed on the surface of mast cells and basophils interacts with antigens, via bound IgE antibody, and triggers secretion of inflammatory mediators that contribute to allergic reactions. To understand how past inputs (memory) influence future inflammatory responses in mast cells, a microfluidic device was used to precisely control exposure of cells to alternating stimulatory and non-stimulatory inputs. We determined that the response to subsequent stimulation depends on the interval of signaling quiescence. For shorter intervals of signaling quiescence, the second response is blunted relative to the first response, whereas longer intervals of quiescence induce an enhanced second response. Through an iterative process of computational modeling and experimental tests, we found that these memory-like phenomena arise from a confluence of rapid, short-lived positive signals driven by the protein tyrosine kinase Syk; slow, long-lived negative signals driven by the lipid phosphatase Ship1; and slower degradation of Ship1 co-factors. This work advances our understanding of mast cell signaling and represents a generalizable approach for investigating the dynamics of signaling systems.

Slow update stochastic simulation algorithms for modeling complex biochemical networks

The stochastic simulation algorithm (SSA) based modeling is a well recognized approach to predict the stochastic behavior of biological networks. The stochastic simulation of large complex biochemical networks is a challenge as it takes a large amount of time for simulation due to high update cost. In order to reduce the propensity update cost, we proposed two algorithms: slow update exact stochastic simulation algorithm (SUESSA) and slow update exact sorting stochastic simulation algorithm (SUESSSA). We applied cache-based linear search (CBLS) in these two algorithms for improving the search operation for finding reactions to be executed. Data structure used for incorporating CBLS is very simple and the cost of maintaining this during propensity update operation is very low. Hence, time taken during propensity updates, for simulating strongly coupled networks, is very fast; which leads to reduction of total simulation time. SUESSA and SUESSSA are not only restricted to elementary reactions, they support higher order reactions too. We used linear chain model and colloidal aggregation model to perform a comparative analysis of the performances of our methods with the existing algorithms. We also compared the performances of our methods with the existing ones, for large biochemical networks including B cell receptor and FcϵRI signaling networks.

Stochastic simulation of biochemical reactions with partial-propensity and rejection-based approaches

We present in this paper a new exact algorithm for improving performance of exact stochastic simulation algorithm. The algorithm is developed on concepts of the partial-propensity and the rejection-based approaches. It factorizes the propensity bounds of reactions and groups factors by common reactant species for selecting next reaction firings. Our algorithm provides favorable computational advantages for simulating of biochemical reaction networks by reducing the cost for selecting the next reaction firing to scale with the number of chemical species and avoiding expensive propensity updates during the simulation. We present the details of our new algorithm and benchmark it on concrete biological models to demonstrate its applicability and efficiency.

IL-33 fine tunes mast cell degranulation and chemokine production at the single-cell level

BACKGROUND

Mast cells are versatile key components of allergy and inflammation known to respond to both innate and adaptive immunologic stimuli. However, the response of individual mast cells to cumulative stimuli remains poorly understood.

OBJECTIVES

We sought to dissect mast cell responses at the single-cell level and their potentiation by IL-33.

METHODS

We monitored mast cell degranulation in real time by exploiting the capacity of fluorochrome-labeled avidin to stain degranulating cells. During the degranulation process, the granule matrix is externalized and immediately bound by fluorochrome-labeled avidin present in the culture medium. The degranulation process is monitored by using either time-lapse microscopy or fluorescence-activated cell sorting analysis.

RESULTS

Single-cell analysis revealed a strong heterogeneity of individual mast cell degranulation responses. We observed that the number of degranulating mast cells was graded according to the FcεRI stimulation strength, whereas the magnitude of individual mast cell degranulation remained unchanged, suggesting an all-or-none response of mast cells after FcεRI triggering. IL-33 pretreatment increased not only the number of degranulating and chemokine-producing mast cells but also the magnitude of individual mast cell degranulation and chemokine production.

CONCLUSION

We illustrate the effect of IL-33 on mast cell biology at the single-cell level by showing that IL-33 potentiates IgE-mediated mast cell responses by both increasing the number of responding cells and enhancing the responses of individual mast cells.

On the rejection-based algorithm for simulation and analysis of large-scale reaction networks

Stochastic simulation for in silico studies of large biochemical networks requires a great amount of computational time. We recently proposed a new exact simulation algorithm, called the rejection-based stochastic simulation algorithm (RSSA) [Thanh et al., J. Chem. Phys. 141(13), 134116 (2014)], to improve simulation performance by postponing and collapsing as much as possible the propensity updates. In this paper, we analyze the performance of this algorithm in detail, and improve it for simulating large-scale biochemical reaction networks. We also present a new algorithm, called simultaneous RSSA (SRSSA), which generates many independent trajectories simultaneously for the analysis of the biochemical behavior. SRSSA improves simulation performance by utilizing a single data structure across simulations to select reaction firings and forming trajectories. The memory requirement for building and storing the data structure is thus independent of the number of trajectories. The updating of the data structure when needed is performed collectively in a single operation across the simulations. The trajectories generated by SRSSA are exact and independent of each other by exploiting the rejection-based mechanism. We test our new improvement on real biological systems with a wide range of reaction networks to demonstrate its applicability and efficiency.

A novel eremophilane lactone inhibits FcεRI-dependent release of pro-inflammatory mediators: structure-dependent bioactivity

BACKGROUND

Allergic inflammation is primarily mediated by immune effector cells such as mast cells and basophils that release proinflammatory cytokines. Both mast cells and basophils are activated via their high affinity IgE receptor (FcεRI) which initiates the release of proinflammatory mediators such as histamine and tumor necrosis factor (TNF). Considerable effort has been focused on finding an effective basophil stabilizer that inhibits the activation of FcεRI-activated mediator release. Recently, eremophilane lactones, a novel family of sesquiterpene compound originally isolated from Petasites japonicas (Sieb. et Zucc.), have been described, and it has been postulated that they may have anti-inflammatory activity, particularly in allergic disease.

OBJECTIVE

Our objective was to determine the effect of two eremophilane lactones derived from 6β-angeloyloxy-3β,8-dihydroxyeremophil-7(11)-en-12,8β-olide (F-1) on immunoglobulin E (IgE)-dependent release of pro-inflammatory mediators by a basophil cell line, RBL-2H3, a model system for FcεRI-mediated activation of pro-inflammatory mediator release.

METHODS

The parent compound (F-1) was chemically modified to produce F-1a [6β-angeloyloxy-3β-benzoyloxy-8-hydroxyeremophil-7(11)-en-12,8β-olide] and F-1b [6β-angeloyloxy-3β,8-diacetoxyeremophil-7(11)-en-12,8β-olide]. RBL-2H3 cells were sensitized with DNP-specific IgE and then activated with DNP-BSA. The effect of these compounds on IgE-dependent basophil degranulation was assessed by measuring the release of β-hexosaminidase (b-hex). In addition, TNF release was measured via ELISA.

RESULTS

The phenylacetyl reaction modified C-8 and produced F-1a whereas acetylation of F-1 produced F-1b. F-1a was not cytotoxic to RBL-2H3 cells even at 50 μM, but F-1b was slightly cytotoxic at 50 μM, reducing viability of the cells by approximately 15 %. Neither F-1a nor F-1b inhibited FcεRI-dependent activation of RBL-2H3 cells when the cells were pretreated for only 30 min with the compounds. However, 24 h pretreatment with F-1a inhibited antigen-dependent degranulation by as much as 60 % and TNF production by as much as 90 %. F-1b had no effect on RBL-2H3 activation via FcεRI.

CONCLUSIONS

These results indicate that F-1a inhibits degranulation of RBL-2H3 cells activated via the high affinity IgE receptor, FcεRI, and that this effect is dependent upon hydroxylation of the third carbon.

Enhancement of performance in porous bead-based microchip sensors: Effects of chip geometry on bio-agent capture

Measuring low concentrations of clinically-important biomarkers using porous bead-based lab-on-a-chip (LOC) platforms is critical for the successful implementation of point-of-care (POC) devices. One way to meet this objective is to optimize the geometry of the bead holder, referred to here as a micro-container. In this work, two geometric micro-containers were explored, the inverted pyramid frustum (PF) and the inverted clipped pyramid frustum (CPF). Finite element models of this bead array assay system were developed to optimize the micro-container and bead geometries for increased pressure, to increase analyte capture in porous bead-based fluorescence immunoassays. Custom micro-milled micro-container structures containing an inverted CPF geometry resulted in a 28% reduction in flow-through regions from traditional anisotropically-etched pyramidal geometry derived from Si-111 termination layers. This novel "reduced flow-through" design resulted in a 33% increase in analyte penetration into the bead and twofold increase in fluorescence signal intensity as demonstrated with C-Reactive Protein (CRP) antigen, an important biomarker of inflammation. A consequent twofold decrease in the limit of detection (LOD) and the limit of quantification (LOQ) of a proof-of-concept assay for the free isoform of Prostate-Specific Antigen (free PSA), an important biomarker for prostate cancer detection, is also presented. Furthermore, a 53% decrease in the bead diameter is shown to result in a 160% increase in pressure and 2.5-fold increase in signal, as estimated by COMSOL models and confirmed experimentally by epi-fluorescence microscopy. Such optimizations of the bead micro-container and bead geometries have the potential to significantly reduce the LODs and reagent costs for spatially programmed bead-based assay systems of this type.

An Interaction Library for the FcεRI Signaling Network

Antigen receptors play a central role in adaptive immune responses. Although the molecular networks associated with these receptors have been extensively studied, we currently lack a systems-level understanding of how combinations of non-covalent interactions and post-translational modifications are regulated during signaling to impact cellular decision-making. To fill this knowledge gap, it will be necessary to formalize and piece together information about individual molecular mechanisms to form large-scale computational models of signaling networks. To this end, we have developed an interaction library for signaling by the high-affinity IgE receptor, FcεRI. The library consists of executable rules for protein–protein and protein–lipid interactions. This library extends earlier models for FcεRI signaling and introduces new interactions that have not previously been considered in a model. Thus, this interaction library is a toolkit with which existing models can be expanded and from which new models can be built. As an example, we present models of branching pathways from the adaptor protein Lat, which influence production of the phospholipid PIP₃ at the plasma membrane and the soluble second messenger IP₃. We find that inclusion of a positive feedback loop gives rise to a bistable switch, which may ensure robust responses to stimulation above a threshold level. In addition, the library is visualized to facilitate understanding of network circuitry and identification of network motifs.

CD1d expressed in mast cell surface enhances IgE production in B cells by up-regulating CD40L expression and mediator release in allergic asthma in mice

Mast cells play important roles via FcεRI-mediated activation in allergic asthma. A nonpolymorphic MHC I-like molecule CD1d, which is mainly expressed in APCs, presents glycolipid Ag to iTCR on iNKT cells and modulates allergic responses. This study aimed to investigate the role of CD1d on IgE production and mast cell activation related to allergic asthma. Bone marrow-derived mast cells (BMMCs) from C57BL/6 Wild type (WT) or KO (CD1d(-/-)) mice were activated with Ag/Ab (refer to WT-act-BMMCs and KO-act-BMMCs, respectively) or α-Galactosylceramide (WT-αGal-BMMCs, KO-αGal-BMMCs) in the presence of iNKT cells. WT, KO or BMMC-transferred KO mice were sensitized and/or challenged by OVA or α-Gal to induce asthma. KO-act-BMMCs reduced intracellular Ca(2+) levels, expression of signaling molecules (Ras, Rac1/2, PLA2, COX-2, NF-κB/AP-1), mediator release (histamines, leukotrienes and cytokines/chemokines), and total IgE levels versus the corresponding WT-BMMCs. KO mice reduced total and OVA-specific serum IgE levels, number of mast cells, recruiting molecules (CCR2/CCL2, VCAM-1, PECAM-1), expression of tryptase, c-kit, CD40L and cytokine mRNA, co-localization of c-kit and CD1d or iNKT cells in BAL cells or lung tissues, and PCA responses, compared with the corresponding WT mice. BMMC-transferred KO-both mice showed the restoration of all allergic responses versus KO-both mice (Ag/Ab reaction plus α-Gal). KO-αGal-BMMCs or KO-αGal mice did not show any responses. Our data suggest that CD1d-expressed mast cells may function as APC cells for iNKT cells and exacerbate airway inflammation and remodeling through up-regulating IgE production via B cell Ig class switching and mediator release in mast cells of OVA-challenged mice.

MiRNA detection at single-cell resolution using microfluidic LNA flow-FISH

Flow cytometry in combination with fluorescent in situ hybridization (flow-FISH) is a powerful technique that can be utilized to rapidly detect nucleic acids at single-cell resolution without the need for homogenization or nucleic acid extraction. Here, we describe a microfluidic-based method which enables the detection of microRNAs or miRNAs in single intact cells by flow-FISH using locked nucleic acid (LNA)-containing probes. Our method can be applied to all RNA species including mRNA and small noncoding RNA and is suitable for multiplexing with protein immunostaining in the same cell. For demonstration of our method, this chapter details the detection of miR155 and CD69 protein in PMA and ionomycin-stimulated Jurkat cells. We also include instructions on how to set up a microfluidic chip sample preparation station to prepare cells for imaging and analysis on a commercial flow cytometer or a custom-built micro-flow cytometer.

Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems

Rule-based modeling was developed to address the limitations of traditional approaches for modeling chemical kinetics in cell signaling systems. These systems consist of multiple interacting biomolecules (e.g., proteins), which themselves consist of multiple parts (e.g., domains, linear motifs, and sites of phosphorylation). Consequently, biomolecules that mediate information processing generally have the potential to interact in multiple ways, with the number of possible complexes and posttranslational modification states tending to grow exponentially with the number of binary interactions considered. As a result, only large reaction networks capture all possible consequences of the molecular interactions that occur in a cell signaling system, which is problematic because traditional modeling approaches for chemical kinetics (e.g., ordinary differential equations) require explicit network specification. This problem is circumvented through representation of interactions in terms of local rules. With this approach, network specification is implicit and model specification is concise. Concise representation results in a coarse graining of chemical kinetics, which is introduced because all reactions implied by a rule inherit the rate law associated with that rule. Coarse graining can be appropriate if interactions are modular, and the coarseness of a model can be adjusted as needed. Rules can be specified using specialized model-specification languages, and recently developed tools designed for specification of rule-based models allow one to leverage powerful software engineering capabilities. A rule-based model comprises a set of rules, which can be processed by general-purpose simulation and analysis tools to achieve different objectives (e.g., to perform either a deterministic or stochastic simulation).

Microfluidic platforms for single-cell protein analysis

Elucidation of the heterogeneity of cells is a challenging task due to the lack of efficient analytical tools to make measurements with single-cell resolution. Microfluidics has emerged as a powerful platform for single-cell analysis with the ability to manipulate small volume and integrate multiple sample preparation steps into one device. In this review, we discuss the differentiating advantages of microfluidic platforms that have been demonstrated for single-cell protein analysis.