Stochastic description of the ligand-receptor interaction of biologically active substances at extremely low doses

A Nanoparticle-Based Affinity Sensor that Identifies and Selects Highly Cytokine-Secreting Cells

We developed a universal method termed OnCELISA to detect cytokine secretion from individual cells by applying a capture technology on the cell membrane. OnCELISA uses fluorescent magnetic nanoparticles as assay reporters that enable detection on a single-cell level in microscopy and flow cytometry and fluorimetry in cell ensembles. This system is flexible and can be modified to detect different cytokines from a broad range of cytokine-secreting cells. Using OnCELISA we have been able to select and sort highly cytokine-secreting cells and identify cytokine-secreting expression profiles of different cell populations in vitro and ex vivo. We show that this system can be used for ultrasensitive monitoring of cytokines in the complex biological environment of atherosclerosis that contains multiple cell types. The ability to identify and select cell populations based on their cytokine expression characteristics is valuable in a host of applications that require the monitoring of disease progression.

Decoration of Reduced Graphene Oxide Nanosheets with Aryldiazonium Salts and Gold Nanoparticles toward a Label-Free Amperometric Immunosensor for Detecting Cytokine Tumor Necrosis Factor-α in Live Cells

In this study, a label-free electrochemical immunosensor was developed for detection of cytokine tumor necrosis factor-alpha (TNF-α). First, AuNPs loaded reduced graphene oxides nanocomposites (RGO-ph-AuNP) were prepared, and then, a mixed layer of 4-carbxyphenyl and 4-aminophenyl phosphorylcholine (PPC) was modified to the surface of AuNPs for the subsequent modification of anti-TNF-α capture antibody (Ab₁) to form the capture surface (Au-RGO-ph-AuNP-ph-PPC(-ph-COOH)) for the analyte TNF-α with the antifouling property. For reporting the presence of analyte, the anti-TNF-α detection antibody (Ab₂) was modified to the graphene oxides which have been modified with the 4-ferrocenylaniline through diazonium chemistry to form Ab₂-GO-ph-Fc. Then, a sandwich assay was formed on gold surfaces for the quantitative detection of TNF-α based on the electrochemical signal of ferrocene. X-ray photoelectron spectra (XPS), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FT-IR), UV-vis, and electrochemistry were used for characterization of the stepwise fabrications on the interface. The prepared electrochemical immunosensor was successfully used for the detection of TNF-α over the range of 0.1-150 pg mL^-1. The lowest detection limit of this immunosensor is 0.1 pg mL^-1 TNF-α in 50 mM phosphate buffer at pH 7.0. The fabricated immunosensor provided high selectivity and stability and can be used to detect TNF-α secreted by live BV-2 cells with comparable accuracy to enzyme-linked immunosorbent assay (ELISA) but with lower limit of detection.

Application of atomic force microscopy for characteristics of single intermolecular interactions

Atomic force microscopy (AFM) can be used to make measurements in vacuum, air, and water. The method is able to gather information about intermolecular interaction forces at the level of single molecules. This review encompasses experimental and theoretical data on the characterization of ligand-receptor interactions by AFM. The advantage of AFM in comparison with other methods developed for the characterization of single molecular interactions is its ability to estimate not only rupture forces, but also thermodynamic and kinetic parameters of the rupture of a complex. The specific features of force spectroscopy applied to ligand-receptor interactions are examined in this review from the stage of the modification of the substrate and the cantilever up to the processing and interpretation of the data. We show the specificities of the statistical analysis of the array of data based on the results of AFM measurements, and we discuss transformation of data into thermodynamic and kinetic parameters (kinetic dissociation constant, Gibbs free energy, enthalpy, and entropy). Particular attention is paid to the study of polyvalent interactions, where the definition of the constants is hampered due to the complex stoichiometry of the reactions.

A danger of low copy numbers for inferring incorrect cooperativity degree

BACKGROUND

A dose-response curve depicts the fraction of bound proteins as a function of unbound ligands. Dose-response curves are used to measure the cooperativity degree of a ligand binding process. Frequently, the Hill function is used to fit the experimental data. The Hill function is parameterized by the value of the dissociation constant and the Hill coefficient, which describes the cooperativity degree. The use of Hill's model and the Hill function has been heavily criticised in this context, predominantly the assumption that all ligands bind at once, which resulted in further refinements of the model. In this work, the validity of the Hill function has been studied from an entirely different point of view. In the limit of low copy numbers the dynamics of the system becomes noisy. The goal was to asses the validity of the Hill function in this limit, and to see in what ways the effects of the fluctuations change the form of the dose-response curves.

RESULTS

Dose-response curves were computed taking into account effects of fluctuations. The effects of fluctuations were described at the lowest order (the second moment of the particle number distribution) by using the previously developed Pair Approach Reaction Noise EStimator (PARNES) method. The stationary state of the system is described by nine equations with nine unknowns. To obtain fluctuation-corrected dose-response curves the equations have been investigated numerically.

CONCLUSIONS

The Hill function cannot describe dose-response curves in a low particle limit. First, dose-response curves are not solely parameterized by the dissociation constant and the Hill coefficient. In general, the shape of a dose-response curve depends on the variables that describe how an experiment (ensemble) is designed. Second, dose-response curves are multi-valued in a rather non-trivial way.

Stochasticity in physiologically based kinetics models: implications for cancer risk assessment

In case of low-dose exposure to a substance, its concentration in cells is likely to be stochastic. Assessing the consequences of this stochasticity in toxicological risk assessment requires the coupling of macroscopic dynamics models describing whole-body kinetics with microscopic tools designed to simulate stochasticity. In this article, we propose an approach to approximate stochastic cell concentration of butadiene in the cells of diverse organs. We adapted the dynamics equations of a physiologically based pharmacokinetic (PBPK) model and used a stochastic simulator for the system of equations that we derived. We then coupled kinetics simulations with a deterministic hockey stick model of carcinogenicity. Stochasticity induced substantial modifications relative to dose-response curve, compared with the deterministic situation. In particular, there was nonlinearity in the response and the stochastic apparent threshold was lower than the deterministic one. The approach that we developed could easily be extended to other biological studies to assess the influence of stochasticity at macroscopic scale for compound dynamics at the cell level.

Photostable single-molecule nanoparticle optical biosensors for real-time sensing of single cytokine molecules and their binding reactions

We synthesized tiny stable silver nanoparticles (2.6 +/- 1.1 nm) and used its small surface area and functional groups to control single molecule detection (SMD) volumes on single nanoparticles. These new approaches allowed us to develop intrinsic single molecule nanoparticle optical biosensors (SMNOBS) for sensing and imaging of single human cytokine molecules, recombinant human tumor necrosis factor-alpha (TNFalpha), and probing its binding reaction with single monoclonal antibody (MAB) molecules in real-time. We found that SMNOBS retained their biological activity over months and showed exceptionally high photostability. Our study illustrated that smaller nanoparticles exhibited higher dependence of optical properties on surface functional groups, making it a much more sensitive biosensor. Localized surface plasmon resonance spectra (LSPRS) of SMNOBS showed a large red shift of peak wavelength of 29 +/- 11 nm, as single TNFalpha molecules bound with single MAB molecules on single nanoparticles. Utilizing its LSPRS, we quantitatively measured its binding reaction in real time at single molecule (SM) level, showing stochastic binding kinetics of SM reactions with binding equilibrium times ranging from 30 to 120 min. SMNOBS exhibited extraordinarily high sensitivity and selectivity, and a notably wide dynamic range of 0-200 ng/mL (0-11.4 nM). Thus, SMNOBS is well suited for the fundamental study of biological functions of single protein molecules and SM interactions of chemical functional groups with the surface of nanoparticles, as well as development of effective disease diagnosis and therapy.

Statistical approach in search for optimal signal in simple olfactory neuronal models

Several models (concentration detectors and a flux detector) for coding of odor intensity in olfactory sensory neurons are investigated. Behavior of the system is described by different stochastic processes of binding the odorant molecules to the receptors and their activation. Characteristics how well the odorant concentration can be estimated from the knowledge of response, the number of activated neurons, are studied. The approach is based on the Fisher information and analogous measures. These measures of optimality are computed and applied to locate the odorant concentration which is most suitable for coding. The results are compared with the classical deterministic approach which judges the optimal odorant concentration via steepness of the input-output function.

Design and synthesis of single-nanoparticle optical biosensors for imaging and characterization of single receptor molecules on single living cells

At the cellular level, a small number of protein molecules (receptors) can induce significant cellular responses, emphasizing the importance of molecular detection of trace amounts of protein on single living cells. In this study, we designed and synthesized silver nanoparticle biosensors (AgMMUA-IgG) by functionalizing 11.6 +/- 3.5-nm Ag nanoparticles with a mixed monolayer of 11-mercaptoundecanoic acid (MUA) and 6-mercapto-1-hexanol (1:3 mole ratio) and covalently conjugating IgG with MUA on the nanoparticle surface. We found that the nanoparticle biosensors preserve their biological activity and photostability and can be utilized to quantitatively detect individual receptor molecules (T-ZZ), map the distribution of receptors (0.21-0.37 molecule/microm(2)), and measure their binding affinity and kinetics at concentrations below their dissociation constant on single living cells in real time over hours. The dynamic range of detection is 0-50 molecules per cell. We also found that the binding rate (2-27 molecules/min) is highly dependent upon the coverage of receptors on living cells and their ligand concentration. The binding association and dissociation rate constants and affinity constant are k1 = (9.0 +/- 2.6) x 10(3) M(-1) s(-1), k(-1) = (3.0 +/- 0.4) x 10(-4) s(-1), and KB = (4.3 +/- 1.1) x 10(7) M(-1), respectively.

Monte Carlo simulations of VEGF binding to cell surface receptors in vitro

The vascular endothelial growth factor (VEGF) family binds multiple endothelial cell surface receptors. Our goal is to build comprehensive models of these interactions for the purpose of simulating angiogenesis. In view of low concentrations of growth factors in vivo and in vitro, stochastic modeling of molecular interactions may be necessary. Here, we compare Monte Carlo simulations of the stochastic binding of VEGF and two of its major receptors on cells in vitro to equivalent deterministic simulations. In the range of typical VEGF concentrations, the stochastic and deterministic models are in agreement. However, we observe significant variability in receptor binding, which may be linked to biological stochastic events, e.g., blood vessel sprout initiation. We study patches of cell surface of varying sizes to investigate spatial integration of the signal by the cell, which impacts directly the variability of binding, and find significant variability up to the single-cell level. Dimerization of VEGF receptors does not significantly alter the variability in ligand binding. A 'sliding window' approach demonstrated no reduction in the variability of binding by temporal integration. The variability is expected to be more prominent in in vivo situations where the number of ligand molecules available for binding is less.