Receptor clustering affects signal transduction at the membrane level in the reaction-limited regime

Quartz Crystal Microbalance Method to Measure Nanoparticle-Receptor Interactions and Evaluate Nanoparticle Design Efficiency

Conjugation of biomolecules on the surface of nanoparticles (NPs) to achieve active targeting is widely investigated within the scientific community. However, while a basic framework of the physicochemical processes underpinning bionanoparticle recognition is now emerging, the precise evaluation of the interactions between engineered NPs and biological targets remains underdeveloped. Here, we show how the adaptation of a method currently used to evaluate molecular ligand-receptor interactions by quartz crystal microbalance (QCM) can be used to obtain concrete insights into interactions between different NP architectures and assemblies of receptors. Using a model bionanoparticle grafted with oriented apolipoprotein E (ApoE) fragments, we examine key aspects of bionanoparticle engineering for effective interactions with target receptors. We show that the QCM technique can be used to rapidly measure construct-receptor interactions across biologically relevant exchange times. We contrast random adsorption of the ligand at the surface of the NPs, resulting in no measurable interaction with target receptors, to grafted oriented constructs, which are strongly recognized even at lower graft densities. The effects of other basic parameters impacting the interaction such as ligand graft density, receptor immobilization density, and linker length were also efficiently evaluated with this technique. Dramatic changes in interaction outcomes with subtle alterations in these parameters highlight the general importance of measuring the interactions between engineered NPs and target receptors ex situ early on in the construct development process for the rational design of bionanoparticles.

The Impact of Membrane Protein Diffusion on GPCR Signaling

Spatiotemporal signal shaping in G protein-coupled receptor (GPCR) signaling is now a well-established and accepted notion to explain how signaling specificity can be achieved by a superfamily sharing only a handful of downstream second messengers. Dozens of Gs-coupled GPCR signals ultimately converge on the production of cAMP, a ubiquitous second messenger. This idea is almost always framed in terms of local concentrations, the differences in which are maintained by means of spatial separation. However, given the dynamic nature of the reaction-diffusion processes at hand, the dynamics, in particular the local diffusional properties of the receptors and their cognate G proteins, are also important. By combining some first principle considerations, simulated data, and experimental data of the receptors diffusing on the membranes of living cells, we offer a short perspective on the modulatory role of local membrane diffusion in regulating GPCR-mediated cell signaling. Our analysis points to a diffusion-limited regime where the effective production rate of activated G protein scales linearly with the receptor–G protein complex’s relative diffusion rate and to an interesting role played by the membrane geometry in modulating the efficiency of coupling.

3D mesh processing using GAMer 2 to enable reaction-diffusion simulations in realistic cellular geometries

Recent advances in electron microscopy have enabled the imaging of single cells in 3D at nanometer length scale resolutions. An uncharted frontier for in silico biology is the ability to simulate cellular processes using these observed geometries. Enabling such simulations requires watertight meshing of electron micrograph images into 3D volume meshes, which can then form the basis of computer simulations of such processes using numerical techniques such as the finite element method. In this paper, we describe the use of our recently rewritten mesh processing software, GAMer 2, to bridge the gap between poorly conditioned meshes generated from segmented micrographs and boundary marked tetrahedral meshes which are compatible with simulation. We demonstrate the application of a workflow using GAMer 2 to a series of electron micrographs of neuronal dendrite morphology explored at three different length scales and show that the resulting meshes are suitable for finite element simulations. This work is an important step towards making physical simulations of biological processes in realistic geometries routine. Innovations in algorithms to reconstruct and simulate cellular length scale phenomena based on emerging structural data will enable realistic physical models and advance discovery at the interface of geometry and cellular processes. We posit that a new frontier at the intersection of computational technologies and single cell biology is now open.

Characterizing Large-Scale Receptor Clustering on the Single Cell Level: A Comparative Plasmon Coupling and Fluorescence Superresolution Microscopy Study

Spatial clustering of cell membrane receptors has been indicated to play a regulatory role in signal initiation, and the distribution of receptors on the cell surface may represent a potential biomarker. To realize its potential for diagnostic purposes, scalable assays capable of mapping spatial receptor heterogeneity with high throughput are needed. In this work, we use gold nanoparticle (NP) labels with an average diameter of 72.17 ± 2.16 nm as bright markers for large-scale epidermal growth factor receptor (EGFR) clustering in hyperspectral plasmon coupling microscopy and compare the obtained clustering maps with those obtained through fluorescence superresolution microscopy (direct stochastic optical reconstruction microscopy, dSTORM). Our dSTORM experiments reveal average EGFR cluster sizes of 172 ± 99 and 150 ± 90 nm for MDA-MB-468 and HeLa, respectively. The cluster sizes decrease after EGFR activation. Hyperspectral imaging of the NP labels shows that differences in the EGFR cluster sizes are accompanied by differences in the average separations between electromagnetically coupled NPs. Because of the distance dependence of plasmon coupling, changes in the average interparticle separation result in significant spectral shifts. For the experimental conditions investigated in this work, hyperspectral plasmon coupling microscopy of NP labels identified the same trends in large-scale EGFR clustering as dSTORM, but the NP imaging approach provided the information in a fraction of the time. Both dSTORM and hyperspectral plasmon coupling microscopy confirm the cortical actin network as one structural component that determines the average size of EGFR clusters.

Supported lipid bilayer platforms to probe cell mechanobiology

Mammalian and bacterial cells sense and exert mechanical forces through the process of mechanotransduction, which interconverts biochemical and physical signals. This is especially important in contact-dependent signaling, where ligand-receptor binding occurs at cell-cell or cell-ECM junctions. By virtue of occurring within these specialized junctions, receptors engaged in contact-dependent signaling undergo oligomerization and coupling with the cytoskeleton as part of their signaling mechanisms. While our ability to measure and map biochemical signaling within cell junctions has advanced over the past decades, physical cues remain difficult to map in space and time. Recently, supported lipid bilayer (SLB) technologies have emerged as a flexible platform to mimic and perturb cell-cell and cell-ECM junctions, allowing one to study membrane receptor mechanotransduction. Changing the lipid composition and underlying substrate tunes bilayer fluidity, and lipid and ligand micro- and nano-patterning spatially control positioning and clustering of receptors. Patterning metal gridlines within SLBs confines lipid mobility and introduces mechanical resistance. Here we review fundamental SLB mechanics and how SLBs can be engineered as tunable cell substrates for mechanotransduction studies. Finally, we highlight the impact of this work in understanding the biophysical mechanisms of cell adhesion. This article is part of a Special Issue entitled: Interactions between membrane receptors in cellular membranes edited by Kalina Hristova.

Dynamical Organization of Syntaxin-1A at the Presynaptic Active Zone

Synaptic vesicle fusion is mediated by SNARE proteins forming in between synaptic vesicle (v-SNARE) and plasma membrane (t-SNARE), one of which is Syntaxin-1A. Although exocytosis mainly occurs at active zones, Syntaxin-1A appears to cover the entire neuronal membrane. By using STED super-resolution light microscopy and image analysis of Drosophila neuro-muscular junctions, we show that Syntaxin-1A clusters are more abundant and have an increased size at active zones. A computational particle-based model of syntaxin cluster formation and dynamics is developed. The model is parametrized to reproduce Syntaxin cluster-size distributions found by STED analysis, and successfully reproduces existing FRAP results. The model shows that the neuronal membrane is adjusted in a way to strike a balance between having most syntaxins stored in large clusters, while still keeping a mobile fraction of syntaxins free or in small clusters that can efficiently search the membrane or be traded between clusters. This balance is subtle and can be shifted toward almost no clustering and almost complete clustering by modifying the syntaxin interaction energy on the order of only 1 kBT. This capability appears to be exploited at active zones. The larger active-zone syntaxin clusters are more stable and provide regions of high docking and fusion capability, whereas the smaller clusters outside may serve as flexible reserve pool or sites of spontaneous ectopic release.

Space-induced bifurcation in repression-based transcriptional circuits

BACKGROUND

Albeit the molecular mechanisms of gene expression are well documented, our understanding of their dynamics is much less advanced. Recent experimental evidence has revealed that gene expression might be accurately organized in space, with several molecular actors localized to specific positions in the cell. However, the influence of this spatial localization on the dynamics of gene expression is unclear. This issue is also central in synthetic biology, where one usually considers the spatial localization in the cell of the genes of the inserted synthetic construct as irrelevant for its temporal dynamics.

RESULTS

Here, we assessed the influence of the spatial distribution of the genes on the dynamics of 3-gene transcriptional ring networks regulated by repression, i.e. repressilator circuits, using individual-based modelling to simulate their dynamics in two and three space dimensions. Our simulations suggest that variations of spatial parameters - namely the degree of demixing of the positions of the gene or the spatial range of the mRNA and proteins (i.e. the typical distance they travel before degradation) - have dramatic effects by switching the dynamical regime from spontaneous oscillations to a stationary state where each species fluctuates around a constant value. By analogy with the bifurcations arising from the variation of kinetic parameters, we referred to those transitions as space-induced bifurcations.

CONCLUSIONS

Taken together, our results strongly support the idea that the spatial organization of the molecular actors of transcriptional networks is crucial for the dynamics of gene expression and suggest that the spatial localization of the synthetic genes in the cell could be used as an additional toggle to control the dynamics of the inserted construct in synthetic biology experiments.

Simulation tools for particle-based reaction-diffusion dynamics in continuous space

Particle-based reaction-diffusion algorithms facilitate the modeling of the diffusional motion of individual molecules and the reactions between them in cellular environments. A physically realistic model, depending on the system at hand and the questions asked, would require different levels of modeling detail such as particle diffusion, geometrical confinement, particle volume exclusion or particle-particle interaction potentials. Higher levels of detail usually correspond to increased number of parameters and higher computational cost. Certain systems however, require these investments to be modeled adequately. Here we present a review on the current field of particle-based reaction-diffusion software packages operating on continuous space. Four nested levels of modeling detail are identified that capture incrementing amount of detail. Their applicability to different biological questions is discussed, arching from straight diffusion simulations to sophisticated and expensive models that bridge towards coarse grained molecular dynamics.

Ligand-induced homotypic and heterotypic clustering of apolipoprotein E receptor 2

ApoE Receptor 2 (ApoER2) and the very low density lipoprotein receptor (VLDLR) are type I transmembrane proteins belonging to the LDLR family of receptors. They are neuronal proteins found in synaptic compartments that play an important role in neuronal migration during development. ApoER2 and VLDLR bind to extracellular glycoproteins, such as Reelin and F-spondin, which leads to phosphorylation of adaptor proteins and subsequent activation of downstream signaling pathways. It is thought that ApoER2 and VLDLR undergo clustering upon binding to their ligands, but no direct evidence of clustering has been shown. Here we show strong clustering of ApoER2 induced by the dimeric ligands Fc-RAP, F-spondin, and Reelin but relatively weak clustering with the ligand apoE in the absence of lipoproteins. This clustering involves numerous proteins besides ApoER2, including amyloid precursor protein and the synaptic adaptor protein PSD-95. Interestingly, we did not observe strong clustering of ApoER2 with VLDLR. Clustering was modulated by both extracellular and intracellular domains of ApoER2. Together, our data demonstrate that several multivalent ligands for ApoER2 induce clustering in transfected cells and primary neurons and that these complexes included other synaptic molecules, such as APP and PSD-95.