Collection of continuous rotation MicroED Data from Ion Beam Milled Crystals of Any Size

Microcrystal electron diffraction (MicroED) allows for macromolecular structure solution from nanocrystals. To create crystals of suitable size for MicroED data collection, sample preparation typically involves sonication or pipetting a slurry of crystals from a crystallization drop. The resultant crystal fragments are fragile and the quality of the data that can be obtained from them is sensitive to subsequent sample preparation for cryoelectron microscopy as interactions in the water-air interface can damage crystals during blotting. Here, we demonstrate the use of a focused ion beam to generate lamellae of macromolecular protein crystals for continuous rotation MicroED that are of ideal thickness, easy to locate, and require no blotting optimization. In this manner, crystals of nearly any size may be scooped and milled to desired dimensions prior to data collection, thus streamlining the methodology for sample preparation for MicroED

Qualitative Analyses of Polishing and Precoating FIB Milled Crystals for MicroED

Microcrystal electron diffraction (MicroED) leverages the strong interaction between matter and electrons to determine protein structures from vanishingly small crystals. This strong interaction limits the thickness of crystals that can be investigated by MicroED, mainly due to absorption. Recent studies have demonstrated that focused ion-beam (FIB) milling can thin crystals into ideal-sized lamellae; however, it is not clear how to best apply FIB milling for MicroED. Here, the effects of polishing the lamellae, whereby the last few nanometers are milled away using a low-current gallium beam, are explored in both the platinum-precoated and uncoated samples. Our results suggest that precoating samples with a thin layer of platinum followed by polishing the crystal surfaces prior to data collection consistently led to superior results as indicated by higher signal-to-noise ratio, higher resolution, and better refinement statistics. This study lays the foundation for routine and reproducible methodology for sample preparation in MicroED

Qualitative Analyses of Polishing and Precoating FIB Milled Crystals for MicroED

Microcrystal electron diffraction (MicroED) leverages the strong interaction between matter and electrons to determine protein structures from vanishingly small crystals. This strong interaction limits the thickness of crystals that can be investigated by MicroED, mainly due to absorption. Recent studies have demonstrated that focused ion-beam (FIB) milling can thin crystals into ideal-sized lamellae; however, it is not clear how to best apply FIB milling for MicroED. Here, the effects of polishing the lamellae, whereby the last few nanometers are milled away using a low-current gallium beam, are explored in both the platinum-precoated and uncoated samples. Our results suggest that precoating samples with a thin layer of platinum followed by polishing the crystal surfaces prior to data collection consistently led to superior results as indicated by higher signal-to-noise ratio, higher resolution, and better refinement statistics. This study lays the foundation for routine and reproducible methodology for sample preparation in MicroED

Collection of continuous rotation MicroED Data from Ion Beam Milled Crystals of Any Size

Microcrystal electron diffraction (MicroED) allows for macromolecular structure solution from nanocrystals. To create crystals of suitable size for MicroED data collection, sample preparation typically involves sonication or pipetting a slurry of crystals from a crystallization drop. The resultant crystal fragments are fragile and the quality of the data that can be obtained from them is sensitive to subsequent sample preparation for cryoelectron microscopy as interactions in the water-air interface can damage crystals during blotting. Here, we demonstrate the use of a focused ion beam to generate lamellae of macromolecular protein crystals for continuous rotation MicroED that are of ideal thickness, easy to locate, and require no blotting optimization. In this manner, crystals of nearly any size may be scooped and milled to desired dimensions prior to data collection, thus streamlining the methodology for sample preparation for MicroED

Meredys, a multi-compartment reaction-diffusion simulator using multistate realistic molecular complexes

<p>Abstract</p> <p>Background</p> <p>Most cellular signal transduction mechanisms depend on a few molecular partners whose roles depend on their position and movement in relation to the input signal. This movement can follow various rules and take place in different compartments. Additionally, the molecules can form transient complexes. Complexation and signal transduction depend on the specific states partners and complexes adopt. Several spatial simulator have been developed to date, but none are able to model reaction-diffusion of realistic multi-state transient complexes.</p> <p>Results</p> <p><it>Meredys </it>allows for the simulation of multi-component, multi-feature state molecular species in two and three dimensions. Several compartments can be defined with different diffusion and boundary properties. The software employs a Brownian dynamics engine to simulate reaction-diffusion systems at the reactive particle level, based on compartment properties, complex structure, and hydro-dynamic radii. Zeroth-, first-, and second order reactions are supported. The molecular complexes have realistic geometries. Reactive species can contain user-defined feature states which can modify reaction rates and outcome. Models are defined in a versatile NeuroML input file. The simulation volume can be split in subvolumes to speed up run-time.</p> <p>Conclusions</p> <p><it>Meredys </it>provides a powerful and versatile way to run accurate simulations of molecular and sub-cellular systems, that complement existing multi-agent simulation systems. <it>Meredys </it>is a Free Software and the source code is available at <url>http://meredys.sourceforge.net/</url>.</p

A new multicompartmental reaction-diffusion modeling method links transient membrane attachment of E. coli MinE to E-ring formation

Many important cellular processes are regulated by reaction-diffusion (RD) of molecules that takes place both in the cytoplasm and on the membrane. To model and analyze such multicompartmental processes, we developed a lattice-based Monte Carlo method, Spatiocyte that supports RD in volume and surface compartments at single molecule resolution. Stochasticity in RD and the excluded volume effect brought by intracellular molecular crowding, both of which can significantly affect RD and thus, cellular processes, are also supported. We verified the method by comparing simulation results of diffusion, irreversible and reversible reactions with the predicted analytical and best available numerical solutions. Moreover, to directly compare the localization patterns of molecules in fluorescence microscopy images with simulation, we devised a visualization method that mimics the microphotography process by showing the trajectory of simulated molecules averaged according to the camera exposure time. In the rod-shaped bacterium _Escherichia coli_, the division site is suppressed at the cell poles by periodic pole-to-pole oscillations of the Min proteins (MinC, MinD and MinE) arising from carefully orchestrated RD in both cytoplasm and membrane compartments. Using Spatiocyte we could model and reproduce the _in vivo_ MinDE localization dynamics by accounting for the established properties of MinE. Our results suggest that the MinE ring, which is essential in preventing polar septation, is largely composed of MinE that is transiently attached to the membrane independently after recruited by MinD. Overall, Spatiocyte allows simulation and visualization of complex spatial and reaction-diffusion mediated cellular processes in volumes and surfaces. As we showed, it can potentially provide mechanistic insights otherwise difficult to obtain experimentally

Probability distributed time delays: integrating spatial effects into temporal models

Background: In order to provide insights into the complex biochemical processes inside a cell, modelling approaches must find a balance between achieving an adequate representation of the physical phenomena and keeping the associated computational cost within reasonable limits. This issue is particularly stressed when spatial inhomogeneities have a significant effect on system's behaviour. In such cases, a spatially-resolved stochastic method can better portray the biological reality, but the corresponding computer simulations can in turn be prohibitively expensive.Results: We present a method that incorporates spatial information by means of tailored, probability distributed time-delays. These distributions can be directly obtained by single in silico or a suitable set of in vitro experiments and are subsequently fed into a delay stochastic simulation algorithm (DSSA), achieving a good compromise between computational costs and a much more accurate representation of spatial processes such as molecular diffusion and translocation between cell compartments. Additionally, we present a novel alternative approach based on delay differential equations (DDE) that can be used in scenarios of high molecular concentrations and low noise propagation.Conclusions: Our proposed methodologies accurately capture and incorporate certain spatial processes into temporal stochastic and deterministic simulations, increasing their accuracy at low computational costs. This is of particular importance given that time spans of cellular processes are generally larger (possibly by several orders of magnitude) than those achievable by current spatially-resolved stochastic simulators. Hence, our methodology allows users to explore cellular scenarios under the effects of diffusion and stochasticity in time spans that were, until now, simply unfeasible. Our methodologies are supported by theoretical considerations on the different modelling regimes, i.e. spatial vs. delay-temporal, as indicated by the corresponding Master Equations and presented elsewhere

Multi-Scale Stochastic Simulation of Diffusion-Coupled Agents and Its Application to Cell Culture Simulation

Many biological systems consist of multiple cells that interact by secretion and binding of diffusing molecules, thus coordinating responses across cells. Techniques for simulating systems coupling extracellular and intracellular processes are very limited. Here we present an efficient method to stochastically simulate diffusion processes, which at the same time allows synchronization between internal and external cellular conditions through a modification of Gillespie's chemical reaction algorithm. Individual cells are simulated as independent agents, and each cell accurately reacts to changes in its local environment affected by diffusing molecules. Such a simulation provides time-scale separation between the intra-cellular and extra-cellular processes. We use our methodology to study how human monocyte-derived dendritic cells alert neighboring cells about viral infection using diffusing interferon molecules. A subpopulation of the infected cells reacts early to the infection and secretes interferon into the extra-cellular medium, which helps activate other cells. Findings predicted by our simulation and confirmed by experimental results suggest that the early activation is largely independent of the fraction of infected cells and is thus both sensitive and robust. The concordance with the experimental results supports the value of our method for overcoming the challenges of accurately simulating multiscale biological signaling systems