pHusion - a robust and versatile toolset for automated detection and analysis of exocytosis.

Exocytosis is a fundamental process used by eukaryotes to regulate the composition of the plasma membrane and facilitate cell-cell communication. To investigate exocytosis in neuronal morphogenesis, previously we developed computational tools with a graphical user interface to enable the automatic detection and analysis of exocytic events from fluorescence timelapse images. Although these tools were useful, we found the code was brittle and not easily adapted to different experimental conditions. Here, we developed and validated a robust and versatile toolkit, named pHusion, for the analysis of exocytosis, written in ImageTank, a graphical programming language that combines image visualization and numerical methods. We tested pHusion using a variety of imaging modalities and pH-sensitive fluorophores, diverse cell types and various exocytic markers, to generate a flexible and intuitive package. Using this system, we show that VAMP3-mediated exocytosis occurs 30-times more frequently in melanoma cells compared with primary oligodendrocytes, that VAMP2-mediated fusion events in mature rat hippocampal neurons are longer lasting than those in immature murine cortical neurons, and that exocytic events are clustered in space yet random in time in developing cortical neurons.

4D intravital imaging identifies platelets as the predominant cellular procoagulant surface in a mouse hemostasis model.

Interplay between platelets, coagulation factors, endothelial cells (ECs) and fibrinolytic factors is necessary for effective hemostatic plug formation. This study describes a four-dimensional (4D) imaging platform to visualize and quantify hemostatic plug components in mice with high spatiotemporal resolution. Fibrin accumulation following laser-induced vascular injury was observed at the platelet plug-EC interface, controlled by the antagonistic balance between fibrin generation and breakdown. We observed less fibrin accumulation in mice expressing low levels of tissue factor (TFlow) or F12-/- mice compared to controls, whereas increased fibrin accumulation, including on the vasculature adjacent to the platelet plug, was observed in plasminogen-deficient mice or wild-type mice treated with tranexamic acid (TXA). Phosphatidylserine (PS), a membrane lipid critical for the assembly of coagulation factors, was first detected at the platelet plug-EC interface, followed by exposure across the endothelium. Impaired PS exposure resulted in a significant reduction in fibrin accumulation in cyclophilin D-/- mice. Adoptive transfer studies demonstrated a key role for PS exposure on platelets, and to a lesser degree on ECs, in fibrin accumulation during hemostatic plug formation. Together, these studies suggest that (1) platelets are the functionally dominant procoagulant cellular surface, and (2) plasmin is critical for limiting fibrin accumulation at the site of a forming hemostatic plug.

Polymer Modeling Reveals Interplay between Physical Properties of Chromosomal DNA and the Size and Distribution of Condensin-Based Chromatin Loops.

Transient DNA loops occur throughout the genome due to thermal fluctuations of DNA and the function of SMC complex proteins such as condensin and cohesin. Transient crosslinking within and between chromosomes and loop extrusion by SMCs have profound effects on high-order chromatin organization and exhibit specificity in cell type, cell cycle stage, and cellular environment. SMC complexes anchor one end to DNA with the other extending some distance and retracting to form a loop. How cells regulate loop sizes and how loops distribute along chromatin are emerging questions. To understand loop size regulation, we employed bead-spring polymer chain models of chromatin and the activity of an SMC complex on chromatin. Our study shows that (1) the stiffness of the chromatin polymer chain, (2) the tensile stiffness of chromatin crosslinking complexes such as condensin, and (3) the strength of the internal or external tethering of chromatin chains cooperatively dictate the loop size distribution and compaction volume of induced chromatin domains. When strong DNA tethers are invoked, loop size distributions are tuned by condensin stiffness. When DNA tethers are released, loop size distributions are tuned by chromatin stiffness. In this three-way interaction, the presence and strength of tethering unexpectedly dictates chromatin conformation within a topological domain.

4D intravital imaging studies identify platelets as the predominant cellular procoagulant surface in a mouse model of hemostasis.

Interplay between platelets, coagulation/fibrinolytic factors, and endothelial cells (ECs) is necessary for effective hemostatic plug formation. This study describes a novel four-dimensional (4D) imaging platform to visualize and quantify hemostatic plug components with high spatiotemporal resolution. Fibrin accumulation following laser-induced endothelial ablation was observed at the EC-platelet plug interface, controlled by the antagonistic balance between fibrin generation and breakdown. Phosphatidylserine (PS) was first detected in close physical proximity to the fibrin ring, followed by exposure across the endothelium. Impaired PS exposure in cyclophilinD -/- mice resulted in a significant reduction in fibrin accumulation. Adoptive transfer and inhibitor studies demonstrated a key role for platelets, but not ECs, in fibrin generation during hemostatic plug formation. Inhibition of fibrinolysis with tranexamic acid (TXA) led to increased fibrin accumulation in WT mice, but not in cyclophilinD -/- mice or WT mice treated with antiplatelet drugs. These studies implicate platelets as the functionally dominant procoagulant surface during hemostatic plug formation. In addition, they suggest that impaired fibrin formation due to reduced platelet procoagulant activity is not reversed by TXA treatment.

pHusion: A robust and versatile toolset for automated detection and analysis of exocytosis.

Exocytosis is a fundamental process used by eukaryotic cells to regulate the composition of the plasma membrane and facilitate cell-cell communication. To investigate the role exocytosis plays in neuronal morphogenesis, previously we developed computational tools with a graphical user interface (GUI) to enable the automatic detection and analysis of exocytic events (ADAE GUI) from fluorescence timelapse images. Though these tools have proven useful, we found that the code was brittle and not easily adapted to different experimental conditions. Here, we have developed and validated a robust and versatile toolkit, named pHusion, for the analysis of exocytosis written in ImageTank, a graphical programming language that combines image visualization and numerical methods. We tested this method using a variety of imaging modalities and pH-sensitive fluorophores, diverse cell types, and various exocytic markers to generate a flexible and intuitive package. Using pHusion, we show that VAMP3-mediated exocytosis occurs 30-times more frequently in melanoma cells compared with primary oligodendrocytes, that VAMP2-mediated fusion events in mature rat hippocampal neurons are longer lasting than those in immature murine cortical neurons, and that exocytic events are clustered in space yet random in time in developing cortical neurons. Summary Statement: Exocytosis is an essential process by which cells change shape, alter membrane composition, and communicate with other cells. Though all eukaryotic cells carry out exocytosis, the regulation of vesicle fusion, the cargo of vesicles, and the role exocytosis plays in cell fate differ greatly across cell types. Here, we developed a flexible and robust set of tools to enable automatic identification and analysis of exocytic events across a wide range of cell types, vesicle types, and imaging conditions.

Simulating Dynamic Chromosome Compaction: Methods for Bridging In Silico to In Vivo.

The application of polymer models to chromosome structure and dynamics is a powerful approach for dissecting functional properties of the chromosome. The models are based on well-established bead-spring models of polymers and are distinct from molecular dynamics studies used in structural biology. In this work, we outline a polymer dynamics model that simulates budding yeast chromatin fibers in a viscous environment inside the nucleus using DataTank as a user interface for the C++ simulation. We highlight features for creating the nucleolus, a dynamic region of chromatin with protein-mediated, transient chromosomal cross-links, providing a predictive, stochastic polymer-physics model for versatile analyses of chromosome spatiotemporal organization. DataTank provides real-time visualization and data analytics methods during simulation. The simulation pipeline provides insights into the entangled chromosome milieu in the nucleus and creates simulated chromosome data, both structural and dynamic, that can be directly compared to experimental observations of live cells in interphase and mitosis.

Statistical mechanics of chromosomes: in vivo and in silico approaches reveal high-level organization and structure arise exclusively through mechanical feedback between loop extruders and chromatin substrate properties.

The revolution in understanding higher order chromosome dynamics and organization derives from treating the chromosome as a chain polymer and adapting appropriate polymer-based physical principles. Using basic principles, such as entropic fluctuations and timescales of relaxation of Rouse polymer chains, one can recapitulate the dominant features of chromatin motion observed in vivo. An emerging challenge is to relate the mechanical properties of chromatin to more nuanced organizational principles such as ubiquitous DNA loops. Toward this goal, we introduce a real-time numerical simulation model of a long chain polymer in the presence of histones and condensin, encoding physical principles of chromosome dynamics with coupled histone and condensin sources of transient loop generation. An exact experimental correlate of the model was obtained through analysis of a model-matching fluorescently labeled circular chromosome in live yeast cells. We show that experimentally observed chromosome compaction and variance in compaction are reproduced only with tandem interactions between histone and condensin, not from either individually. The hierarchical loop structures that emerge upon incorporation of histone and condensin activities significantly impact the dynamic and structural properties of chromatin. Moreover, simulations reveal that tandem condensin-histone activity is responsible for higher order chromosomal structures, including recently observed Z-loops.

Phosphoregulation provides specificity to biomolecular condensates in the cell cycle and cell polarity.

Biomolecular condensation is a way of organizing cytosol in which proteins and nucleic acids coassemble into compartments. In the multinucleate filamentous fungus Ashbya gossypii, the RNA-binding protein Whi3 regulates the cell cycle and cell polarity through forming macromolecular structures that behave like condensates. Whi3 has distinct spatial localizations and mRNA targets, making it a powerful model for how, when, and where specific identities are established for condensates. We identified residues on Whi3 that are differentially phosphorylated under specific conditions and generated mutants that ablate this regulation. This yielded separation of function alleles that were functional for either cell polarity or nuclear cycling but not both. This study shows that phosphorylation of individual residues on molecules in biomolecular condensates can provide specificity that gives rise to distinct functional identities in the same cell.

Software for lattice light-sheet imaging of FRET biosensors, illustrated with a new Rap1 biosensor.

Lattice light-sheet microscopy (LLSM) is valuable for its combination of reduced photobleaching and outstanding spatiotemporal resolution in 3D. Using LLSM to image biosensors in living cells could provide unprecedented visualization of rapid, localized changes in protein conformation or posttranslational modification. However, computational manipulations required for biosensor imaging with LLSM are challenging for many software packages. The calculations require processing large amounts of data even for simple changes such as reorientation of cell renderings or testing the effects of user-selectable settings, and lattice imaging poses unique challenges in thresholding and ratio imaging. We describe here a new software package, named ImageTank, that is specifically designed for practical imaging of biosensors using LLSM. To demonstrate its capabilities, we use a new biosensor to study the rapid 3D dynamics of the small GTPase Rap1 in vesicles and cell protrusions.

Transient crosslinking kinetics optimize gene cluster interactions.

Our understanding of how chromosomes structurally organize and dynamically interact has been revolutionized through the lens of long-chain polymer physics. Major protein contributors to chromosome structure and dynamics are condensin and cohesin that stochastically generate loops within and between chains, and entrap proximal strands of sister chromatids. In this paper, we explore the ability of transient, protein-mediated, gene-gene crosslinks to induce clusters of genes, thereby dynamic architecture, within the highly repeated ribosomal DNA that comprises the nucleolus of budding yeast. We implement three approaches: live cell microscopy; computational modeling of the full genome during G1 in budding yeast, exploring four decades of timescales for transient crosslinks between 5kbp domains (genes) in the nucleolus on Chromosome XII; and, temporal network models with automated community (cluster) detection algorithms applied to the full range of 4D modeling datasets. The data analysis tools detect and track gene clusters, their size, number, persistence time, and their plasticity (deformation). Of biological significance, our analysis reveals an optimal mean crosslink lifetime that promotes pairwise and cluster gene interactions through "flexible" clustering. In this state, large gene clusters self-assemble yet frequently interact (merge and separate), marked by gene exchanges between clusters, which in turn maximizes global gene interactions in the nucleolus. This regime stands between two limiting cases each with far less global gene interactions: with shorter crosslink lifetimes, "rigid" clustering emerges with clusters that interact infrequently; with longer crosslink lifetimes, there is a dissolution of clusters. These observations are compared with imaging experiments on a normal yeast strain and two condensin-modified mutant cell strains. We apply the same image analysis pipeline to the experimental and simulated datasets, providing support for the modeling predictions.

Enrichment of dynamic chromosomal crosslinks drive phase separation of the nucleolus.

Regions of highly repetitive DNA, such as those found in the nucleolus, show a self-organization that is marked by spatial segregation and frequent self-interaction. The mechanisms that underlie the sequestration of these sub-domains are largely unknown. Using a stochastic, bead-spring representation of chromatin in budding yeast, we find enrichment of protein-mediated, dynamic chromosomal cross-links recapitulates the segregation, morphology and self-interaction of the nucleolus. Rates and enrichment of dynamic crosslinking have profound consequences on domain morphology. Our model demonstrates the nucleolus is phase separated from other chromatin in the nucleus and predicts that multiple rDNA loci will form a single nucleolus independent of their location within the genome. Fluorescent labeling of budding yeast nucleoli with CDC14-GFP revealed that a split rDNA locus indeed forms a single nucleolus. We propose that nuclear sub-domains, such as the nucleolus, result from phase separations within the nucleus, which are driven by the enrichment of protein-mediated, dynamic chromosomal crosslinks.

Entropy gives rise to topologically associating domains.

We investigate chromosome organization within the nucleus using polymer models whose formulation is closely guided by experiments in live yeast cells. We employ bead-spring chromosome models together with loop formation within the chains and the presence of nuclear bodies to quantify the extent to which these mechanisms shape the topological landscape in the interphase nucleus. By investigating the genome as a dynamical system, we show that domains of high chromosomal interactions can arise solely from the polymeric nature of the chromosome arms due to entropic interactions and nuclear confinement. In this view, the role of bio-chemical related processes is to modulate and extend the duration of the interacting domains.

SIMULATING BIOCHEMICAL SIGNALING NETWORKS IN COMPLEX MOVING GEOMETRIES.

Signaling networks regulate cellular responses to environmental stimuli through cascades of protein interactions. External signals can trigger cells to polarize and move in a specific direction. During migration, spatially localized activity of proteins is maintained. To investigate the effects of morphological changes on intracellular signaling, we developed a numerical scheme consisting of a cut cell finite volume spatial discretization coupled with level set methods to simulate the resulting advection-reaction-diffusion system. We then apply the method to several biochemical reaction networks in changing geometries. We found that a Turing instability can develop exclusively by cell deformations that maintain constant area. For a Turing system with a geometry-dependent single or double peak solution, simulations in a dynamically changing geometry suggest that a single peak solution is the only stable one, independent of the oscillation frequency. The method is also applied to a model of a signaling network in a migrating fibroblast.

A Cut Cell Method for Simulating Spatial Models of Biochemical Reaction Networks in Arbitrary Geometries.

Cells use signaling networks consisting of multiple interacting proteins to respond to changes in their environment. In many situations, such as chemotaxis, spatial and temporal information must be transmitted through the network. Recent computational studies have emphasized the importance of cellular geometry in signal transduction, but have been limited in their ability to accurately represent complex cell morphologies. We present a finite volume method that addresses this problem. Our method uses Cartesian cut cells and is second order in space and time. We use our method to simulate several models of signaling systems in realistic cell morphologies obtained from live cell images and examine the effects of geometry on signal transduction.

Texturing fluids.

We present a novel technique for synthesizing textures over dynamically changing fluid surfaces. We use both image textures as well as bump maps as example inputs. Image textures can enhance the rendering of the fluid by either imparting realistic appearance to it or by stylizing it, whereas bump maps enable the generation of complex micro-structures on the surface of the fluid that may be very difficult to synthesize using simulation. To generate temporally coherent textures over a fluid sequence, we transport texture information, i.e. color and local orientation, between free surfaces of the fluid from one time step to the next. This is accomplished by extending the texture information from the first fluid surface to the 3D fluid domain, advecting this information within the fluid domain along the fluid velocity field for one time step, and interpolating it back onto the second surface -- this operation, in part, uses a novel vector advection technique for transporting orientation vectors. We then refine the transported texture by performing texture synthesis over the second surface using our "surface texture optimization" algorithm, which keeps the synthesized texture visually similar to the input texture and temporally coherent with the transported one. We demonstrate our novel algorithm for texture synthesis on dynamically evolving fluid surfaces in several challenging scenarios.

Gene regulatory networks: a coarse-grained, equation-free approach to multiscale computation.

We present computer-assisted methods for analyzing stochastic models of gene regulatory networks. The main idea that underlies this equation-free analysis is the design and execution of appropriately initialized short bursts of stochastic simulations; the results of these are processed to estimate coarse-grained quantities of interest, such as mesoscopic transport coefficients. In particular, using a simple model of a genetic toggle switch, we illustrate the computation of an effective free energy Phi and of a state-dependent effective diffusion coefficient D that characterize an unavailable effective Fokker-Planck equation. Additionally we illustrate the linking of equation-free techniques with continuation methods for performing a form of stochastic "bifurcation analysis"; estimation of mean switching times in the case of a bistable switch is also implemented in this equation-free context. The accuracy of our methods is tested by direct comparison with long-time stochastic simulations. This type of equation-free analysis appears to be a promising approach to computing features of the long-time, coarse-grained behavior of certain classes of complex stochastic models of gene regulatory networks, circumventing the need for long Monte Carlo simulations.

A bottom-up approach to gene regulation.

The ability to construct synthetic gene networks enables experimental investigations of deliberately simplified systems that can be compared to qualitative and quantitative models. If simple, well-characterized modules can be coupled together into more complex networks with behaviour that can be predicted from that of the individual components, we may begin to build an understanding of cellular regulatory processes from the 'bottom up'. Here we have engineered a promoter to allow simultaneous repression and activation of gene expression in Escherichia coli. We studied its behaviour in synthetic gene networks under increasingly complex conditions: unregulated, repressed, activated, and simultaneously repressed and activated. We develop a stochastic model that quantitatively captures the means and distributions of the expression from the engineered promoter of this modular system, and show that the model can be extended and used to accurately predict the in vivo behaviour of the network when it is expanded to include positive feedback. The model also reveals the counterintuitive prediction that noise in protein expression levels can increase upon arrest of cell growth and division, which we confirm experimentally. This work shows that the properties of regulatory subsystems can be used to predict the behaviour of larger, more complex regulatory networks, and that this bottom-up approach can provide insights into gene regulation.

Numerical computation of diffusion on a surface.

We present a numerical method for computing diffusive transport on a surface derived from image data. Our underlying discretization method uses a Cartesian grid embedded boundary method for computing the volume transport in a region consisting of all points a small distance from the surface. We obtain a representation of this region from image data by using a front propagation computation based on level set methods for solving the Hamilton-Jacobi and eikonal equations. We demonstrate that the method is second-order accurate in space and time and is capable of computing solutions on complex surface geometries obtained from image data of cells.

Biochemical Network Stochastic Simulator (BioNetS): software for stochastic modeling of biochemical networks.

BACKGROUND: Intrinsic fluctuations due to the stochastic nature of biochemical reactions can have large effects on the response of biochemical networks. This is particularly true for pathways that involve transcriptional regulation, where generally there are two copies of each gene and the number of messenger RNA (mRNA) molecules can be small. Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks. RESULTS: We have developed the software package Biochemical Network Stochastic Simulator (BioNetS) for efficiently and accurately simulating stochastic models of biochemical networks. BioNetS has a graphical user interface that allows models to be entered in a straightforward manner, and allows the user to specify the type of random variable (discrete or continuous) for each chemical species in the network. The discrete variables are simulated using an efficient implementation of the Gillespie algorithm. For the continuous random variables, BioNetS constructs and numerically solves the appropriate chemical Langevin equations. The software package has been developed to scale efficiently with network size, thereby allowing large systems to be studied. BioNetS runs as a BioSpice agent and can be downloaded from http://www.biospice.org. BioNetS also can be run as a stand alone package. All the required files are accessible from http://x.amath.unc.edu/BioNetS. CONCLUSIONS: We have developed BioNetS to be a reliable tool for studying the stochastic dynamics of large biochemical networks. Important features of BioNetS are its ability to handle hybrid models that consist of both continuous and discrete random variables and its ability to model cell growth and division. We have verified the accuracy and efficiency of the numerical methods by considering several test systems.