Isolating the sources of heterogeneity in nano-engineered particle-cell interactions.
Ontology highlight
ABSTRACT:
SUBMITTER: Johnston ST
PROVIDER: S-EPMC7276543 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Nanoindentation experiments are performed using an atomic force microscope (AFM) to quantify the spatial distribution of mechanical properties of plant cell walls at nanometre length scales. At any specific location on the cell wall, a complex (non-linear) force-indentation response occurs that can be deconvoluted using a unique multiregime analysis (MRA). This allows an unambiguous evaluation of the local transverse elastic modulus of the wall. Nanomechanical measurements on suspension-cultured cells (SCCs), derived from Italian ryegrass (Lolium multiflorum) starchy endosperm, show three characteristic modes of deformation and a spatial distribution of elastic moduli across the surface. 'Soft' and 'hard' domains are found across length scales between 0.1 µm and 3 µm, which is well above a typical pore size of the polysaccharide mesh. The generality and wider applicability of this mechanical heterogeneity is verified through in planta characterization on leaf epidermal cells of Arabidopsis thaliana and L. multiflorum The outcomes of this research provide a basis for uncovering and quantifying the relationships between local wall composition, architecture, cell growth, and/or morphogenesis.
| S-EPMC4861025 | biostudies-literature
Project description:Task-fMRI researchers have great flexibility as to how they analyze their data, with multiple methodological options to choose from at each stage of the analysis workflow. While the development of tools and techniques has broadened our horizons for comprehending the complexities of the human brain, a growing body of research has highlighted the pitfalls of such methodological plurality. In a recent study, we found that the choice of software package used to run the analysis pipeline can have a considerable impact on the final group-level results of a task-fMRI investigation (Bowring et al., 2019, BMN). Here we revisit our work, seeking to identify the stages of the pipeline where the greatest variation between analysis software is induced. We carry out further analyses on the three datasets evaluated in BMN, employing a common processing strategy across parts of the analysis workflow and then utilizing procedures from three software packages (AFNI, FSL, and SPM) across the remaining steps of the pipeline. We use quantitative methods to compare the statistical maps and isolate the main stages of the workflow where the three packages diverge. Across all datasets, we find that variation between the packages' results is largely attributable to a handful of individual analysis stages, and that these sources of variability were heterogeneous across the datasets (e.g., choice of first-level signal model had the most impact for the balloon analog risk task dataset, while first-level noise model and group-level model were more influential for the false belief and antisaccade task datasets, respectively). We also observe areas of the analysis workflow where changing the software package causes minimal differences in the final results, finding that the group-level results were largely unaffected by which software package was used to model the low-frequency fMRI drifts.
| S-EPMC8764489 | biostudies-literature
Project description:The activation of well-defined numbers of integrin molecules in predefined areas by adhesion of tissue cells to biofunctionalized micro-nanopatterned surfaces was used to determine the minimum number of activated integrins necessary to stimulate focal adhesion formation. This was realized by combining micellar and conventional e-beam lithography, which enabled deposition of 6 nm large gold nanoparticles on predefined geometries. Patterns with a lateral spacing of 58 nm and a number of gold nanoparticles, ranging from 6 to 3000 per adhesive patch, were used. For ?(v) ?(3)-integrin activation, gold nanoparticles were coated with c(-RGDfK-)-thiol peptides, and the remaining glass surface was passivated to prevent non-specific protein adsorption and cell adhesion. Results show that focal adhesion formation is dictated by the underlying hierarchical nanopattern. Adhesive patches with side lengths of 3000 nm and separated by 3000 nm, or with side lengths of 1000 nm and separated by 1000 nm, containing approximately 3007 ± 193 or 335 ± 65 adhesive gold nanoparticles, respectively, induced the formation of actin-associated, paxillin-rich focal adhesions, comparable in size and shape to classical focal adhesions. In contrast, adhesive patches with side lengths of 500, 250 or 100 nm, and separated from adjacent adhesive patches by their respective side lengths, containing 83 ± 11, 30 ± 4, or 6 ± 1 adhesive gold nanoparticles, respectively, showed a significant increase in paxillin domain length, caused by bridging the pattern gap through an actin bundle in order to mechanically, synergistically strengthen each single adhesion site. Neither paxillin accumulation nor adhesion formation was induced if less than 6 c(-RGDfK-)-thiol functionalised gold nanoparticles per adhesion site were presented to cells.
| S-EPMC3114879 | biostudies-literature
Project description:Visualizing macromolecular complexes by single-particle electron microscopy (EM) entails stringent biochemical purification, specimen preparation, low-dose imaging, and 3D image reconstruction. Here, we introduce the "monolayer purification" method, which employs nickel-nitrilotriacetic acid (Ni-NTA) functionalized lipids for simultaneously purifying His-tagged complexes directly from cell lysates while producing specimens suitable for single-particle EM. The method was established by using monolayers containing Ni-NTA lipid to specifically adsorb His-tagged transferrin-transferrin receptor (Tf-TfR) complexes from insect and mammalian cell extracts. The specificity and sensitivity of the method could be improved by adding imidazole to the extracts. The monolayer-purified Tf-TfR samples could be vitrified and used to calculate a 3D reconstruction of the complex. Monolayer purification was then used to rapidly isolate ribosomal complexes from bacteria by overexpressing a single His-tagged ribosomal subunit. The resulting monolayer samples allowed calculation of a cryo-EM 3D reconstruction of the Escherichia coli 50S ribosomal subunit.
| S-EPMC2290746 | biostudies-literature
Project description:A key challenge in nano-science is to design ligand-coated nano-particles that can bind selectively to surfaces that display the cognate receptors above a threshold (surface) concentration. Nano-particles that bind monovalently to a target surface do not discriminate sharply between surfaces with high and low receptor coverage. In contrast, "multivalent" nano-particles that can bind to a larger number of ligands simultaneously, display regimes of "super selectivity" where the fraction of bound particles varies sharply with the receptor concentration. We present numerical simulations that show that multivalent nano-particles can be designed such that they approach the "on-off" binding behavior ideal for receptor-concentration selective targeting. We propose a simple analytical model that accounts for the super selective behavior of multivalent nano-particles. The model shows that the super selectivity is due to the fact that the number of distinct ligand-receptor binding arrangements increases in a highly nonlinear way with receptor coverage. Somewhat counterintuitively, our study shows that selectivity can be improved by making the individual ligand-receptor bonds weaker. We propose a simple rule of thumb to predict the conditions under which super selectivity can be achieved. We validate our model predictions against the Monte Carlo simulations.
| S-EPMC3131366 | biostudies-literature
Project description:A fundamental and intrinsic property of any device or natural system is its relaxation time relax, which is the time it takes to return to equilibrium after the sudden change of a control parameter [1]. Reducing ?relax, is frequently necessary, and is often obtained by a complex feedback process. To overcome the limitations of such an approach, alternative methods based on driving have been recently demonstrated [2, 3], for isolated quantum and classical systems [4-9]. Their extension to open systems in contact with a thermostat is a stumbling block for applications. Here, we design a protocol, named Engineered Swift Equilibration (ESE), that shortcuts time-consuming relaxations, and we apply it to a Brownian particle trapped in an optical potential whose properties can be controlled in time. We implement the process experimentally, showing that it allows the system to reach equilibrium times faster than the natural equilibration rate. We also estimate the increase of the dissipated energy needed to get such a time reduction. The method paves the way for applications in micro and nano devices, where the reduction of operation time represents as substantial a challenge as miniaturization [10].
| S-EPMC5011424 | biostudies-literature
Project description:The transduction time between signal initiation and final response provides valuable information on the underlying signaling pathway, including its speed and precision. Furthermore, multi-modality in a transduction-time distribution indicates that the response is regulated by multiple pathways with different transduction speeds. Here, we developed a method called density physics-informed neural networks (Density-PINNs) to infer the transduction-time distribution from measurable final stress response time traces. We applied Density-PINNs to single-cell gene expression data from sixteen promoters regulated by unknown pathways in response to antibiotic stresses. We found that promoters with slower signaling initiation and transduction exhibit larger cell-to-cell heterogeneity in response intensity. However, this heterogeneity was greatly reduced when the response was regulated by slow and fast pathways together. This suggests a strategy for identifying effective signaling pathways for consistent cellular responses to disease treatments. Density-PINNs can also be applied to understand other time delay systems, including infectious diseases.
| S-EPMC10873160 | biostudies-literature
Project description:Here we construct mechanically flexible and optically transparent thin film solid state supercapacitors by assembling nano-engineered carbon electrodes, prepared in porous templates, with morphology of interconnected arrays of complex shapes and porosity. The highly textured graphitic films act as electrode and current collector and integrated with solid polymer electrolyte, function as thin film supercapacitors. The nanostructured electrode morphology and the conformal electrolyte packaging provide enough energy and power density for the devices in addition to excellent mechanical flexibility and optical transparency, making it a unique design in various power delivery applications.
| S-EPMC3481118 | biostudies-literature
Project description:Compartmentalized co-localization of enzymes and their substrates represents an attractive approach for multi-enzymatic synthesis in engineered cells and biocatalysis. Sequestration of enzymes and substrates would greatly increase reaction efficiency while also protecting engineered host cells from potentially toxic reaction intermediates. Several bacteria form protein-based polyhedral microcompartments which sequester functionally related enzymes and regulate their access to substrates and other small metabolites. Such bacterial microcompartments may be engineered into protein-based nano-bioreactors, provided that they can be assembled in a non-native host cell, and that heterologous enzymes and substrates can be targeted into the engineered compartments. Here, we report that recombinant expression of Salmonella enterica ethanolamine utilization (eut) bacterial microcompartment shell proteins in E. coli results in the formation of polyhedral protein shells. Purified recombinant shells are morphologically similar to the native Eut microcompartments purified from S. enterica. Surprisingly, recombinant expression of only one of the shell proteins (EutS) is sufficient and necessary for creating properly delimited compartments. Co-expression with EutS also facilitates the encapsulation of EGFP fused with a putative Eut shell-targeting signal sequence. We also demonstrate the functional localization of a heterologous enzyme (β-galactosidase) targeted to the recombinant shells. Together our results provide proof-of-concept for the engineering of protein nano-compartments for biosynthesis and biocatalysis.
| S-EPMC3299773 | biostudies-literature
Project description:Several approaches have been used in the past to model heterogeneity in bacterial cell populations, with each approach focusing on different source(s) of heterogeneity. However, a holistic approach that integrates all the major sources into a comprehensive framework applicable to cell populations is still lacking. In this work we present the mathematical formulation of a cell population master equation (CPME) that describes cell population dynamics and takes into account the major sources of heterogeneity, namely stochasticity in reaction, DNA-duplication, and division, as well as the random partitioning of species contents into the two daughter cells. The formulation also takes into account cell growth and respects the discrete nature of the molecular contents and cell numbers. We further develop a Monte Carlo algorithm for the simulation of the stochastic processes considered here. To benchmark our new framework, we first use it to quantify the effect of each source of heterogeneity on the intrinsic and the extrinsic phenotypic variability for the well-known two-promoter system used experimentally by Elowitz et al. (2002). We finally apply our framework to a more complicated system and demonstrate how the interplay between noisy gene expression and growth inhibition due to protein accumulation at the single cell level can result in complex behavior at the cell population level. The generality of our framework makes it suitable for studying a vast array of artificial and natural genetic networks. Using our Monte Carlo algorithm, cell population distributions can be predicted for the genetic architecture of interest, thereby quantifying the effect of stochasticity in intracellular reactions or the variability in the rate of physiological processes such as growth and division. Such in silico experiments can give insight into the behavior of cell populations and reveal the major sources contributing to cell population heterogeneity.
| S-EPMC3175140 | biostudies-literature