Characterization of Cell Boundary and Confocal Effects Improves Quantitative FRAP Analysis

Fluorescence recovery after photobleaching (FRAP) is an important tool used by cell biologists to study the diffusion and binding kinetics of vesicles, proteins, and other molecules in the cytoplasm, nucleus, or cell membrane. Although many FRAP models have been developed over the past decades, the influence of the complex boundaries of 3D cellular geometries on the recovery curves, in conjunction with regions of interest and optical effects (imaging, photobleaching, photoswitching, and scanning), has not been well studied. Here, we developed a 3D computational model of the FRAP process that incorporates particle diffusion, cell boundary effects, and the optical properties of the scanning confocal microscope, and validated this model using the tip-growing cells of Physcomitrella patens. We then show how these cell boundary and optical effects confound the interpretation of FRAP recovery curves, including the number of dynamic states of a given fluorophore, in a wide range of cellular geometries—both in two and three dimensions—namely nuclei, filopodia, and lamellipodia of mammalian cells, and in cell types such as the budding yeast, Saccharomyces pombe, and tip-growing plant cells. We explored the performance of existing analytical and algorithmic FRAP models in these various cellular geometries, and determined that the VCell VirtualFRAP tool provides the best accuracy to measure diffusion coefficients. Our computational model is not limited only to these cells types, but can easily be extended to other cellular geometries via the graphical Java-based application we also provide. This particle-based simulation—called the Digital Confocal Microscopy Suite or DCMS—can also perform fluorescence dynamics assays, such as number and brightness, fluorescence correlation spectroscopy, and raster image correlation spectroscopy, and could help shape the way these techniques are interpreted.     

Coherent Timescales and Mechanical Structure of Multicellular Aggregates

Multicellular aggregates are an excellent model system to explore the role of tissue biomechanics in specifying multicellular reorganization during embryonic developments and malignant invasion. Tissue-like spheroids, when subjected to a compressive force, are known to exhibit liquid-like behaviors at long timescales (hours), largely because of cell rearrangements that serve to effectively dissipate the applied stress. At short timescales (seconds to minutes), before cell rearrangement, the mechanical behavior is strikingly different. The current work uses shape relaxation to investigate the structural characteristics of aggregates and discovers two coherent timescales: one on the order of seconds, the other tens of seconds. These timescales are universal, conserved across a variety of tested species, and persist despite great differences in other properties such as tissue surface tension and adhesion. A precise mathematical theory is used to correlate the timescales with mechanical properties and reveals that aggregates have a relatively strong envelope and an unusually “soft” interior (weak bulk elastic modulus). This characteristic is peculiar, considering that both layers consist of identical units (cells), but is consistent with the fact that this structure can engender both structural integrity and the flexibility required for remodeling. In addition, tissue surface tension, elastic modulus, and viscosity are proportional to each other. Considering that these tissue-level properties intrinsically derive from cellular-level properties, the proportionalities imply precise coregulation of the latter and in particular of the tension on the cell-medium and cell-cell interfaces.     

Extensive Heterogeneity and Intrinsic Variation in Spatial Genome Organization

1. Download high-res image (220KB)
2. Download full-size image

     

Packing Density of the Amyloid Precursor Protein in the Cell Membrane

Plasma membrane proteins organize into structures named compartments, microdomains, rafts, phases, crowds, or clusters. These structures are often smaller than 100 nm in diameter. Despite their importance in many cellular functions, little is known about their inner organization. For instance, how densely are molecules packed? Being aware of the protein compaction may contribute to our general understanding of why such structures exist and how they execute their functions. In this study, we have investigated plasma membrane crowds formed by the amyloid precursor protein (APP), a protein well known for its involvement in Alzheimer’s disease. By combining biochemical experiments with conventional and super-resolution stimulated emission depletion microscopy, we quantitatively determined the protein packing density within APP crowds. We found that crowds occurring with reasonable frequency contain between 20 and 30 molecules occupying a spherical area with a diameter between 65 and 85 nm. Additionally, we found the vast majority of plasmalemmal APP residing in these crowds. The model suggests a high molecular density of protein material within plasmalemmal APP crowds. This should affect the protein’s biochemical accessibility and processing by nonpathological α-secretases. As clustering of APP is a prerequisite for endocytic entry into the pathological processing pathway, elucidation of the packing density also provides a deeper understanding of this part of APP’s life cycle.     

Determination of Dynamical Heterogeneity from Dynamic Neutron Scattering of Proteins

Motional displacements of hydrogen (H) in proteins can be measured using incoherent neutron-scattering methods. These displacements can also be calculated numerically using data from molecular dynamics simulations. An enormous amount of data on the average mean-square motional displacement (MSD) of H as a function of protein temperature, hydration, and other conditions has been collected. H resides in a wide spectrum of sites in a protein. Some H are tightly bound to molecular chains, and the H motion is dictated by that of the chain. Other H are quite independent. As a result, there is a distribution of motions and MSDs of H within a protein that is denoted dynamical heterogeneity. The goal of this paper is to incorporate a distribution of MSDs into models of the H incoherent intermediate scattering function, $I\left(Q,t\right)$, that is calculated and observed. The aim is to contribute information on the distribution as well as on the average MSD from comparison of the models with simulations and experiment. For example, we find that simulations of $I\left(Q,t\right)$ in lysozyme are well reproduced if the distribution of MSDs is bimodal with two broad peaks rather than a single broad peak.     

Role of the Disulfide Bond in Prion Protein Amyloid Formation: A Thermodynamic and Kinetic Analysis

Prion diseases are associated with the structural conversion of prion protein (PrP) to a β-sheet-rich aggregate, PrP^Sc. Previous studies have indicated that a reduction of the disulfide bond linking C179 and C214 of PrP yields an amyloidlike β-rich aggregate in vitro. To gain mechanistic insights into the reduction-induced aggregation, here I characterized how disulfide bond reduction modulates the protein folding/misfolding landscape of PrP, by examining 1) the equilibrium stabilities of the native (N) and aggregated states relative to the unfolded (U) state, 2) the transition barrier separating the U and aggregated states, and 3) the final structure of amyloidlike misfolded aggregates. Kinetic and thermodynamic experiments revealed that disulfide bond reduction decreases the equilibrium stabilities of both the N and aggregated states by ～3 kcal/mol, without changing either the amyloidlike aggregate structure, at least at the secondary structural level, or the transition barrier of aggregation. Therefore, disulfide bond reduction modulates the protein folding/misfolding landscape by entropically stabilizing disordered states, including the U and transition state of aggregation. This also indicates that the equilibrium stability of the N state, but not the transition barrier of aggregation, is the dominant factor determining the reduction-induced aggregation of PrP.     

Simulating Genetic Circuits in Bacterial Populations with Growth Heterogeneity

We computationally study genetic circuits in bacterial populations with heterogeneities in the growth rate. To that end, we present a stochastic simulation method for gene circuits in populations of cells and propose an efficient implementation that we call the “Next Family Method”. Within this approach, we implement different population setups, specifically Chemostat-type growth and growth in an ideal Mother Machine and show that the population structure and its statistics are different for the different setups whenever there is growth heterogeneity. Such dependence on the population setup is demonstrated, in the case of bistable systems with different growth rates in the stable states, to have distinctive signatures on quantities including the distributions of protein concentration and growth rates, and hysteresis curves. Applying this method to a bistable antibiotic resistance circuit, we find that as a result of the different statistics in different population setups, the estimated minimal inhibitory concentration of the antibiotic becomes dependent on the population setup in which it is measured.     

Size Matters: Ryanodine Receptor Cluster Size Heterogeneity Potentiates Calcium Waves

Ryanodine receptors (RyRs) mediate calcium (Ca)-induced Ca release and intracellular Ca homeostasis. In a cardiac myocyte, RyRs group into clusters of variable size from a few to several hundred RyRs, creating a spatially nonuniform intracellular distribution. It is unclear how heterogeneity of RyR cluster size alters spontaneous sarcoplasmic reticulum (SR) Ca releases (Ca sparks) and arrhythmogenic Ca waves. Here, we tested the impact of heterogeneous RyR cluster size on the initiation of Ca waves. Experimentally, we measured RyR cluster sizes at Ca spark sites in rat ventricular myocytes and further tested functional impacts using a physiologically detailed computational model with spatial and stochastic intracellular Ca dynamics. We found that the spark frequency and amplitude increase nonlinearly with the size of RyR clusters. Larger RyR clusters have lower SR Ca release threshold for local Ca spark initiation and exhibit steeper SR Ca release versus SR Ca load relationship. However, larger RyR clusters tend to lower SR Ca load because of the higher Ca leak rate. Conversely, smaller clusters have a higher threshold and a lower leak, which tends to increase SR Ca load. At the myocyte level, homogeneously large or small RyR clusters limit Ca waves (because of low load for large clusters but low excitability for small clusters). Mixtures of large and small RyR clusters potentiates Ca waves because the enhanced SR Ca load driven by smaller clusters enables Ca wave initiation and propagation from larger RyR clusters. Our study suggests that a spatially heterogeneous distribution of RyR cluster size under pathological conditions may potentiate Ca waves and thus afterdepolarizations and triggered arrhythmias.     

Multivalent Binding of a Ligand-Coated Particle: Role of Shape,Size, and Ligand Heterogeneity

We utilize a multiscale modeling framework to study the effect of shape, size, and ligand composition on the efficacy of binding of a ligand-coated particle to a substrate functionalized with the target receptors. First, we show how molecular dynamics along with steered molecular dynamics calculations can be used to accurately parameterize the molecular-binding free energy and the effective spring constant for a receptor-ligand pair. We demonstrate this for two ligands that bind to the ${\alpha }_5{\beta }_1$-domain of integrin. Next, we show how these effective potentials can be used to build computational models at the meso- and continuum-scales. These models incorporate the molecular nature of the receptor-ligand interactions and yet provide an inexpensive route to study the multivalent interaction of receptors and ligands through the construction of Bell potentials customized to the molecular identities. We quantify the binding efficacy of the ligand-coated-particle in terms of its multivalency, binding free-energy landscape, and the losses in the configurational entropies. We show that 1) the binding avidity for particle sizes less than 350 nm is set by the competition between the enthalpic and entropic contributions, whereas that for sizes above 350 nm is dominated by the enthalpy of binding; 2) anisotropic particles display higher levels of multivalent binding compared to those of spherical particles; and 3) variations in ligand composition can alter binding avidity without altering the average multivalency. The methods and results presented here have wide applications in the rational design of functionalized carriers and also in understanding cell adhesion.     

Quantitative Analysis of the Correlation between Cell Size and Cellular Uptake of Particles

The size of a cell is central to many functions, including cellular communication and exchange of materials with the environment. This modeling and experimental study focused on understanding how the size of a cell determines its ability to uptake nanometer-scale extracellular materials from the environment. Several mechanisms in the cell plasma membrane mediate cellular uptake of nutrients, biomolecules, and particles. These mechanisms involve recognition and internalization of the extracellular molecules via endocytic components, such as clathrin-coated pits, vacuoles, and micropinocytic vesicles. Because the demand for an external resource could be different for cells of different sizes, the collective actions of these various endocytic routes should also vary based on the cell size. Here, using a reaction-diffusion model, we analyze single-cell data to interrogate the one/one mapping between the size of the MDA-MB 231 breast cancer cells and their ability to uptake nanoparticles. Our analysis indicates that under both reaction- and diffusion-controlled regimes, cellular uptake follows a linear relationship with the cell radius. Furthermore, this linear dependency is insensitive to particle size variation within 20–200 nm range. This result is counterintuitive because the general perception is that cellular uptake is proportional to the cell volume (mass) or surface area and hence follow a cubic or square relationship with the cell radius. A further analysis using our model reveals a potential mechanism underlying this linear relationship.     

Structural Characterization of N-WASP Domain V Using MD Simulations with NMR and SAXS Data

Because of their large conformational heterogeneity, structural characterization of intrinsically disordered proteins (IDPs) is very challenging using classical experimental methods alone. In this study, we use NMR and small-angle x-ray scattering (SAXS) data with multiple molecular dynamics (MD) simulations to describe the conformational ensemble of the fully disordered verprolin homology domain of the neural Aldrich syndrome protein involved in the regulation of actin polymerization. First, we studied several back-calculation software of SAXS scattering intensity and optimized the adjustable parameters to accurately calculate the SAXS intensity from an atomic structure. We also identified the most appropriate force fields for MD simulations of this IDP. Then, we analyzed four conformational ensembles of neural Aldrich syndrome protein verprolin homology domain, two generated with the program flexible-meccano with or without NMR-derived information as input and two others generated by MD simulations with two different force fields. These four conformational ensembles were compared to available NMR and SAXS data for validation. We found that MD simulations with the AMBER-03w force field and the TIP4P/2005s water model are able to correctly describe the conformational ensemble of this 67-residue IDP at both local and global level.     

ReSynPlex: Respiratory Syndrome Linked Pathogens Multiplex Detection and Characterization

Background

Pneumonia is a common disease that is a leading cause of mortality worldwide and frequently requires hospital admission. A large number of infectious agents may be involved and as a result of the potential severity of the infection, a clear and quick diagnostic is mandatory in order to take the appropriate decision for the health of the patient. However, a real gap exists between the multiplicity of information required by the practitioner and the diagnostic tools available.

Structure and Polymorphism of Amyloid and Amyloid-Like Aggregates

Biochemistry (Moscow) - Amyloids are protein aggregates with the cross-β structure. The interest in amyloids is explained, on the one hand, by their role in the development of socially...     

On the Stability of the Soluble Amyloid Aggregates

Many amyloid proteins form metastable soluble aggregates (or protofibrils, or protein nanoparticles, with characteristic sizes from ∼10 to a few hundred nm). These can coexist with protein monomers and amyloid precipitates. These soluble aggregates are key determinants of the toxicity of these proteins. It is therefore imperative to understand the physical basis underlying their stability. Simple nucleation theory, typically applied to explain the kinetics of amyloid precipitation, fails to predict such intermediate stable states. We examine stable nanoparticles formed by the Alzheimer's amyloid-β peptide (40 and 42 residues), and by the protein barstar. These molecules have different hydrophobicities, and therefore have different short-range attractive interactions between the molecules. We also vary the pH and the ionic strength of the solution to tune the long-range electrostatic repulsion between them. In all the cases, we find that increased long-range repulsion results in smaller stable nanoparticles, whereas increased hydrophobicity produces the opposite result. Our results agree with a charged-colloid type of model for these particles, which asserts that growth-arrested colloid particles can result from a competition between short-range attraction and long-range repulsion. The nanoparticle size varies superlinearly with the ionic strength, possibly indicating a transition from an isotropic to a linear mode of growth. Our results provide a framework for understanding the stability and growth of toxic amyloid nanoparticles, and provide cues for designing effective destabilizing agents.     

Quantitative Characterization of Cellular Membrane-Receptor Heterogeneity through Statistical and Computational Modeling

Cell population heterogeneity can affect cellular response and is a major factor in drug resistance. However, there are few techniques available to represent and explore how heterogeneity is linked to population response. Recent high-throughput genomic, proteomic, and cellomic approaches offer opportunities for profiling heterogeneity on several scales. We have recently examined heterogeneity in vascular endothelial growth factor receptor (VEGFR) membrane localization in endothelial cells. We and others processed the heterogeneous data through ensemble averaging and integrated the data into computational models of anti-angiogenic drug effects in breast cancer. Here we show that additional modeling insight can be gained when cellular heterogeneity is considered. We present comprehensive statistical and computational methods for analyzing cellomic data sets and integrating them into deterministic models. We present a novel method for optimizing the fit of statistical distributions to heterogeneous data sets to preserve important data and exclude outliers. We compare methods of representing heterogeneous data and show methodology can affect model predictions up to 3.9-fold. We find that VEGF levels, a target for tuning angiogenesis, are more sensitive to VEGFR1 cell surface levels than VEGFR2; updating VEGFR1 levels in the tumor model gave a 64% change in free VEGF levels in the blood compartment, whereas updating VEGFR2 levels gave a 17% change. Furthermore, we find that subpopulations of tumor cells and tumor endothelial cells (tEC) expressing high levels of VEGFR (>35,000 VEGFR/cell) negate anti-VEGF treatments. We show that lowering the VEGFR membrane insertion rate for these subpopulations recovers the anti-angiogenic effect of anti-VEGF treatment, revealing new treatment targets for specific tumor cell subpopulations. This novel method of characterizing heterogeneous distributions shows for the first time how different representations of the same data set lead to different predictions of drug efficacy.