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Regulating Bacterial Behavior within Hydrogels of Tunable Viscoelasticity
Engineered living materials (ELMs) are a new class of materials in which living organism incorporated into diffusive matrices uptake a fundamental role in material's composition and function. Understanding how the spatial confinement in 3D can regulate the behavior of the embedded cells is crucial to design and predict ELM's function, minimize their environmental impact and facilitate their translation into applied materials. This study investigates the growth and metabolic activity of bacteria within an associative hydrogel network (Pluronic-based) with mechanical properties that can be tuned by introducing a variable degree of acrylate crosslinks. Individual bacteria distributed in the hydrogel matrix at low density form functional colonies whose size is controlled by the extent of permanent crosslinks. With increasing stiffness and elastic response to deformation of the matrix, a decrease in colony volumes and an increase in their sphericity are observed. Protein production follows a different pattern with higher production yields occurring in networks with intermediate permanent crosslinking degrees. These results demonstrate that matrix design can be used to control and regulate the composition and function of ELMs containing microorganisms. Interestingly, design parameters for matrices to regulate bacteria behavior show similarities to those elucidated for 3D culture of mammalian cells.
Evolutionary design of explainable algorithms for biomedical image segmentation
An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. However, these frameworks require large human-annotated datasets for training and the resulting “black box” models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming-based computational strategy that generates fully transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets. This Few-Shot Learning method confers tremendous flexibility, speed, and functionality to this approach. We then deploy Kartezio to solve a series of semantic and instance segmentation problems, and demonstrate its utility across diverse images ranging from multiplexed tissue histopathology images to high resolution microscopy images. While the flexibility, robustness and practical utility of Kartezio make this fully explicable evolutionary designer a potential game-changer in the field of biomedical image processing, Kartezio remains complementary and potentially auxiliary to mainstream Deep Learning approaches.
Computational design and optimization of electro-physiological sensors
Electro-physiological sensing devices are becoming increasingly common in diverse applications. However, designing such sensors in compact form factors and for high-quality signal acquisition is a challenging task even for experts, is typically done using heuristics, and requires extensive training. Our work proposes a computational approach for designing multi-modal electro-physiological sensors. By employing an optimization-based approach alongside an integrated predictive model for multiple modalities, compact sensors can be created which offer an optimal trade-off between high signal quality and small device size. The task is assisted by a graphical tool that allows to easily specify design preferences and to visually analyze the generated designs in real-time, enabling designer-in-the-loop optimization. Experimental results show high quantitative agreement between the prediction of the optimizer and experimentally collected physiological data. They demonstrate that generated designs can achieve an optimal balance between the size of the sensor and its signal acquisition capability, outperforming expert generated solutions.
Is there more than one stickiness criterion?
Adhesion between an elastic body and a smooth, rigid substrate can lead to large tensile stresses between them. However, most macroscopic objects are microscopically rough, which strongly suppresses adhesion. A fierce debate has unfolded recently as to whether local or global parameters determine the crossover between small and large adhesion. Here, we report simulations revealing that the dependence of the pull-off force Fn on the surface energy γ does not only have two regimes of high and low adhesion but up to four regimes. They are related to contacts, which at the moment of rupture consist of (i) the last individual Hertzian-shaped contact, in which is linear in γ, (ii) a last meso-scale, individual patches with super-linear scaling, (iii) many isolated contact patches with extremely strong scaling, and (iv) a dominating largest contact patch, for which the pull-off stress is no longer negligible compared to the maximum, microscopic pull-off stress. Regime (iii) can be seen as a transition domain. It is located near the point where the surface energy is half the elastic energy per unit area in conformal contact. A criterion for the transition between regimes (i) and (ii) appears difficult to grasp. [Figure not available: see fulltext.].
Scanning electron microscopy preparation of the cellular actin cortex: A quantitative comparison between critical point drying and hexamethyldisilazane drying
The cellular cortex is an approximately 200-nm-thick actin network that lies just beneath the cell membrane. It is responsible for the mechanical properties of cells, and as such, it is involved in many cellular processes, including cell migration and cellular interactions with the environment. To develop a clear view of this dense structure, high-resolution imaging is essential. As one such technique, electron microscopy, involves complex sample preparation procedures. The final drying of these samples has significant influence on potential artifacts, like cell shrinkage and the formation of artifactual holes in the actin cortex. In this study, we compared the three most used final sample drying procedures: critical-point drying (CPD), CPD with lens tissue (CPD-LT), and hexamethyldisilazane drying. We show that both hexamethyldisilazane and CPD-LT lead to fewer artifactual mesh holes within the actin cortex than CPD. Moreover, CPD-LT leads to significant reduction in cell height compared to hexamethyldisilazane and CPD. We conclude that the final drying procedure should be chosen according to the reduction in cell height, and so CPD-LT, or according to the spatial separation of the single layers of the actin cortex, and so hexamethyldisilazane.
Deformation characteristics of solid-state benzene as a step towards understanding planetary geology
Small organic molecules, like ethane and benzene, are ubiquitous in the atmosphere and surface of Saturn’s largest moon Titan, forming plains, dunes, canyons, and other surface features. Understanding Titan’s dynamic geology and designing future landing missions requires sufficient knowledge of the mechanical characteristics of these solid-state organic minerals, which is currently lacking. To understand the deformation and mechanical properties of a representative solid organic material at space-relevant temperatures, we freeze liquid micro-droplets of benzene to form ~10 μm-tall single-crystalline pyramids and uniaxially compress them in situ. These micromechanical experiments reveal contact pressures decaying from ~2 to ~0.5 GPa after ~1 μm-reduction in pyramid height. The deformation occurs via a series of stochastic (~5-30 nm) displacement bursts, corresponding to densification and stiffening of the compressed material during cyclic loading to progressively higher loads. Molecular dynamics simulations reveal predominantly plastic deformation and densified region formation by the re-orientation and interplanar shear of benzene rings, providing a two-step stiffening mechanism. This work demonstrates the feasibility of in-situ cryogenic nanomechanical characterization of solid organics as a pathway to gain insights into the geophysics of planetary bodies.
T cell stiffness is enhanced upon formation of immunological synapse
T cells are activated by target cells via an intimate contact, termed immunological synapse (IS). Cellular mechanical properties, especially stiffness, are essential to regulate cell functions. However, T cell stiffness at a subcellular level at the IS still remains largely elusive. In this work, we established an atomic force microscopy (AFM)-based elasticity mapping method on whole T cells to obtain an overview of the stiffness with a resolution of ~60 nm. Using primary human CD4+ T cells, we show that when T cells form IS with stimulating antibody-coated surfaces, the lamellipodia are stiffer than the cell body. Upon IS formation, T cell stiffness is enhanced both at the lamellipodia and on the cell body. Chelation of intracellular Ca2+ abolishes IS-induced stiffening at the lamellipodia but has no influence on cell-body-stiffening, suggesting different regulatory mechanisms of IS-induced stiffening at the lamellipodia and the cell body.
In vitro assembly of plasmid DNA for direct cloning in Lactiplantibacillus plantarum WCSF1
Lactobacilli are gram-positive bacteria that are growing in importance for the healthcare industry and genetically engineering them as living therapeutics is highly sought after. However, progress in this field is hindered since most strains are difficult to genetically manipulate, partly due to their complex and thick cell walls limiting our capability to transform them with exogenous DNA. To overcome this, large amounts of DNA (>1 μg) are normally required to successfully transform these bacteria. An intermediate host, like E. coli, is often used to amplify recombinant DNA to such amounts although this approach poses unwanted drawbacks such as an increase in plasmid size, different methylation patterns and the limitation of introducing only genes compatible with the intermediate host. In this work, we have developed a direct cloning method based on in-vitro assembly and PCR amplification to yield recombinant DNA in significant quantities for successful transformation in L. plantarum WCFS1. The advantage of this method is demonstrated in terms of shorter experimental duration and the possibility to introduce a gene incompatible with E. coli into L. plantarum WCFS1.
Reversibly growing crosslinked polymers with programmable sizes and properties
Growth constitutes a powerful method to post-modulate materials’ structures and functions without compromising their mechanical performance for sustainable use, but the process is irreversible. To address this issue, we here report a growing-degrowing strategy that enables thermosetting materials to either absorb or release components for continuously changing their sizes, shapes, compositions, and a set of properties simultaneously. The strategy is based on the monomer-polymer equilibrium of networks in which supplying or removing small polymerizable components would drive the networks toward expansion or contraction. Using acid-catalyzed equilibration of siloxane as an example, we demonstrate that the size and mechanical properties of the resulting silicone materials can be significantly or finely tuned in both directions of growth and decomposition. The equilibration can be turned off to yield stable products or reactivated again. During the degrowing-growing circle, material structures are selectively varied either uniformly or heterogeneously, by the availability of fillers. Our strategy endows the materials with many appealing capabilities including environment adaptivity, self-healing, and switchability of surface morphologies, shapes, and optical properties. Since monomer-polymer equilibration exists in many polymers, we envision the expansion of the presented strategy to various systems for many applications.
Graphene Acid for Lithium‐Ion Batteries—Carboxylation Boosts Storage Capacity in Graphene
Environmentally sustainable, low-cost, flexible, and lightweight energy storage technologies require advancement in materials design in order to obtain more efficient organic metal-ion batteries. Synthetically tailored organic molecules, which react reversibly with lithium, may address the need for cost-effective and eco-friendly anodes used for organic/lithium battery technologies. Among them, carboxylic group-bearing molecules act as high-energy content anodes. Although organic molecules offer rich chemistry, allowing a high content of carboxyl groups to be installed on aromatic rings, they suffer from low conductivity and leakage to the electrolytes, which restricts their actual capacity, the charging/discharging rate, and eventually their application potential. Here, a densely carboxylated but conducting graphene derivative (graphene acid (GA)) is designed to circumvent these critical limitations, enabling effective operation without compromising the mechanical or chemical stability of the electrode. Experiments including operando Raman measurements and theoretical calculations reveal the excellent charge transport, redox activity, and lithium intercalation properties of the GA anode at the single-layer level, outperforming all reported organic anodes, including commercial monolayer graphene and graphene nanoplatelets. The practical capacity and rate capability of 800 mAh g−1 at 0.05 A g−1 and 174 mAh g−1 at 2.0 A g−1 demonstrate the true potential of GA anodes in advanced lithium-ion batteries.