Mechanism of DNA origami folding elucidated by mesoscopic simulations

Many experimental and computational efforts have sought to understand DNA origami folding, but the time and length scales of this process pose significant challenges. Here, we present a mesoscopic model that uses a switchable force field to capture the behavior of single- and double-stranded DNA motifs and transitions between them, allowing us to simulate the folding of DNA origami up to several kilobases in size. Brownian dynamics simulations of small structures reveal a hierarchical folding process involving zipping into a partially folded precursor followed by crystallization into the final structure. We elucidate the effects of various design choices on folding order and kinetics. Larger structures are found to exhibit heterogeneous staple incorporation kinetics and frequent trapping in metastable states, as opposed to more accessible structures which exhibit first-order kinetics and virtually defect-free folding. This model opens an avenue to better understand and design DNA nanostructures for improved yield and folding performance.

Flexible, scalable, high channel count stereo-electrode for recording in the human brain

Over the past decade, stereotactically placed electrodes have become the gold standard for deep brain recording and stimulation for a wide variety of neurological and psychiatric diseases. Current electrodes, however, are limited in their spatial resolution and ability to record from small populations of neurons, let alone individual neurons. Here, we report on an innovative, customizable, monolithically integrated human-grade flexible depth electrode capable of recording from up to 128 channels and able to record at a depth of 10 cm in brain tissue. This thin, stylet-guided depth electrode is capable of recording local field potentials and single unit neuronal activity (action potentials), validated across species. This device represents an advance in manufacturing and design approaches which extends the capabilities of a mainstay technology in clinical neurology.

Radial bimetallic structures via wire arc directed energy deposition-based additive manufacturing

Bimetallic wire arc additive manufacturing (AM) has traditionally been limited to depositions characterized by single planar interfaces. This study demonstrates a more complex radial interface concept, with in situ mechanical interlocking and as-built properties suggesting a prestressed compressive effect. A 308 L stainless core is surrounded by a mild steel casing, incrementally maintaining the interface throughout the Z-direction. A small difference in the thermal expansion coefficient between these steels creates residual stresses at their interface. X-ray diffraction analysis confirms phase purity and microstructural characterization reveals columnar grain growth independent of layer transitions. Hardness values are consistent with thermal dissipation characteristics, and the compressive strength of the bimetallic structures shows a 33% to 42% improvement over monolithic controls. Our results demonstrate that biomimetic radial bimetallic variation is feasible with improved mechanical response over monolithic compositions, providing a basis for advanced structural design and implementation using arc-based metal AM.

First lift-off and flight performance of a tailless flapping-wing aerial robot in high-altitude environments

Flapping flight of animals has captured the interest of researchers due to their impressive flight capabilities across diverse environments including mountains, oceans, forests, and urban areas. Despite the significant progress made in understanding flapping flight, high-altitude flight as showcased by many migrating animals remains underexplored. At high-altitudes, air density is low, and it is challenging to produce lift. Here we demonstrate a first lift-off of a flapping wing robot in a low-density environment through wing size and motion scaling. Force measurements showed that the lift remained high at 0.14 N despite a 66% reduction of air density from the sea-level condition. The flapping amplitude increased from 148 to 233 degrees, while the pitch amplitude remained nearly constant at 38.2 degrees. The combined effect is that the flapping-wing robot benefited from the angle of attack that is characteristic of flying animals. Our results suggest that it is not a simple increase in the flapping frequency, but a coordinated increase in the wing size and reduction in flapping frequency enables the flight in lower density condition. The key mechanism is to preserve the passive rotations due to wing deformation, confirmed by a bioinspired scaling relationship. Our results highlight the feasibility of flight under a low-density, high-altitude environment due to leveraging unsteady aerodynamic mechanisms unique to flapping wings. We anticipate our experimental demonstration to be a starting point for more sophisticated flapping wing models and robots for autonomous multi-altitude sensing. Furthermore, it is a preliminary step towards flapping wing flight in the ultra-low density Martian atmosphere.

Chemo-mechanical diffusion waves explain collective dynamics of immune cell podosomes

Immune cells, such as macrophages and dendritic cells, can utilize podosomes, mechanosensitive actin-rich protrusions, to generate forces, migrate, and patrol for foreign antigens. Individual podosomes probe their microenvironment through periodic protrusion and retraction cycles (height oscillations), while oscillations of multiple podosomes in a cluster are coordinated in a wave-like fashion. However, the mechanisms governing both the individual oscillations and the collective wave-like dynamics remain unclear. Here, by integrating actin polymerization, myosin contractility, actin diffusion, and mechanosensitive signaling, we develop a chemo-mechanical model for podosome dynamics in clusters. Our model reveals that podosomes show oscillatory growth when actin polymerization-driven protrusion and signaling-associated myosin contraction occur at similar rates, while the diffusion of actin monomers drives wave-like coordination of podosome oscillations. Our theoretical predictions are validated by different pharmacological treatments and the impact of microenvironment stiffness on chemo-mechanical waves. Our proposed framework can shed light on the role of podosomes in immune cell mechanosensing within the context of wound healing and cancer immunotherapy.

Rapid biomechanical imaging at low irradiation level via dual line-scanning Brillouin microscopy

Brillouin microscopy is a technique for mechanical characterization of biological material without contact at high three-dimensional resolution. Here, we introduce dual line-scanning Brillouin microscopy (dLSBM), which improves acquisition speed and reduces irradiation dose by more than one order of magnitude with selective illumination and single-shot analysis of hundreds of points along the incident beam axis. Using tumor spheroids, we demonstrate the ability to capture the sample response to rapid mechanical perturbations as well as the spatially resolved evolution of the mechanical properties in growing spheroids.

Mechanics of the cellular microenvironment as probed by cells in vivo during zebrafish presomitic mesoderm differentiation

Tissue morphogenesis, homoeostasis and repair require cells to constantly monitor their three-dimensional microenvironment and adapt their behaviours in response to local biochemical and mechanical cues. Yet the mechanical parameters of the cellular microenvironment probed by cells in vivo remain unclear. Here, we report the mechanics of the cellular microenvironment that cells probe in vivo and in situ during zebrafish presomitic mesoderm differentiation. By quantifying both endogenous cell-generated strains and tissue mechanics, we show that individual cells probe the stiffness associated with deformations of the supracellular, foam-like tissue architecture. Stress relaxation leads to a perceived microenvironment stiffness that decreases over time, with cells probing the softest regime. We find that most mechanical parameters, including those probed by cells, vary along the anteroposterior axis as mesodermal progenitors differentiate. These findings expand our understanding of in vivo mechanosensation and might aid the design of advanced scaffolds for tissue engineering applications.

Real-time structure search and structure classification for AlphaFold protein models

Last year saw a breakthrough in protein structure prediction, where the AlphaFold2 method showed a substantial improvement in the modeling accuracy. Following the software release of AlphaFold2, predicted structures by AlphaFold2 for proteins in 21 species were made publicly available via the AlphaFold Database. Here, to facilitate structural analysis and application of AlphaFold2 models, we provide the infrastructure, 3D-AF-Surfer, which allows real-time structure-based search for the AlphaFold2 models. In 3D-AF-Surfer, structures are represented with 3D Zernike descriptors (3DZD), which is a rotationally invariant, mathematical representation of 3D shapes. We developed a neural network that takes 3DZDs of proteins as input and retrieves proteins of the same fold more accurately than direct comparison of 3DZDs. Using 3D-AF-Surfer, we report structure classifications of AlphaFold2 models and discuss the correlation between confidence levels of AlphaFold2 models and intrinsic disordered regions.

A kinematic and EMG dataset of online adjustment of reach-to-grasp movements to visual perturbations

Control of reach-to-grasp movements for deft and robust interactions with objects requires rapid sensorimotor updating that enables online adjustments to changing external goals (e.g., perturbations or instability of objects we interact with). Rarely do we appreciate the remarkable coordination in reach-to-grasp, until control becomes impaired by neurological injuries such as stroke, neurodegenerative diseases, or even aging. Modeling online control of human reach-to-grasp movements is a challenging problem but fundamental to several domains, including behavioral and computational neuroscience, neurorehabilitation, neural prostheses, and robotics. Currently, there are no publicly available datasets that include online adjustment of reach-to-grasp movements to object perturbations. This work aims to advance modeling efforts of reach-to-grasp movements by making publicly available a large kinematic and EMG dataset of online adjustment of reach-to-grasp movements to instantaneous perturbations of object size and distance performed in immersive haptic-free virtual environment (hf-VE). The presented dataset is composed of a large number of perturbation types (10 for both object size and distance) applied at three different latencies after the start of the movement.

Adaptive and multifunctional hydrogel hybrid probes for long-term sensing and modulation of neural activity

To understand the underlying mechanisms of progressive neurophysiological phenomena, neural interfaces should interact bi-directionally with brain circuits over extended periods of time. However, such interfaces remain limited by the foreign body response that stems from the chemo-mechanical mismatch between the probes and the neural tissues. To address this challenge, we developed a multifunctional sensing and actuation platform consisting of multimaterial fibers intimately integrated within a soft hydrogel matrix mimicking the brain tissue. These hybrid devices possess adaptive bending stiffness determined by the hydration states of the hydrogel matrix. This enables their direct insertion into the deep brain regions, while minimizing tissue damage associated with the brain micromotion after implantation. The hydrogel hybrid devices permit electrophysiological, optogenetic, and behavioral studies of neural circuits with minimal foreign body responses and tracking of stable isolated single neuron potentials in freely moving mice over 6 months following implantation.

dCas9 regulator to neutralize competition in CRISPRi circuits

CRISPRi-mediated gene regulation allows simultaneous control of many genes. However, highly specific sgRNA-promoter binding is, alone, insufficient to achieve independent transcriptional regulation of multiple targets. Indeed, due to competition for dCas9, the repression ability of one sgRNA changes significantly when another sgRNA becomes expressed. To solve this problem and decouple sgRNA-mediated regulatory paths, we create a dCas9 concentration regulator that implements negative feedback on dCas9 level. This allows any sgRNA to maintain an approximately constant dose-response curve, independent of other sgRNAs. We demonstrate the regulator performance on both single-stage and layered CRISPRi-based genetic circuits, zeroing competition effects of up to 15-fold changes in circuit I/O response encountered without the dCas9 regulator. The dCas9 regulator decouples sgRNA-mediated regulatory paths, enabling concurrent and independent regulation of multiple genes. This allows predictable composition of CRISPRi-based genetic modules, which is essential in the design of larger scale synthetic genetic circuits.

All-printed stretchable corneal sensor on soft contact lenses for noninvasive and painless ocular electrodiagnosis

Electroretinogram examinations serve as routine clinical procedures in ophthalmology for the diagnosis and management of many ocular diseases. However, the rigid form factor of current corneal sensors produces a mismatch with the soft, curvilinear, and exceptionally sensitive human cornea, which typically requires the use of topical anesthesia and a speculum for pain management and safety. Here we report a design of an all-printed stretchable corneal sensor built on commercially-available disposable soft contact lenses that can intimately and non-invasively interface with the corneal surface of human eyes. The corneal sensor is integrated with soft contact lenses via an electrochemical anchoring mechanism in a seamless manner that ensures its mechanical and chemical reliability. Thus, the resulting device enables the high-fidelity recording of full-field electroretinogram signals in human eyes without the need of topical anesthesia or a speculum. The device, superior to clinical standards in terms of signal quality and comfortability, is expected to address unmet clinical needs in the field of ocular electrodiagnosis.

Asynchronous release sites align with NMDA receptors in mouse hippocampal synapses

Neurotransmitter is released synchronously and asynchronously following an action potential. Our recent study indicates that the release sites of these two phases are segregated within an active zone, with asynchronous release sites enriched near the center in mouse hippocampal synapses. Here we demonstrate that synchronous and asynchronous release sites are aligned with AMPA receptor and NMDA receptor clusters, respectively. Computational simulations indicate that this spatial and temporal arrangement of release can lead to maximal membrane depolarization through AMPA receptors, alleviating the pore-blocking magnesium leading to greater activation of NMDA receptors. Together, these results suggest that release sites are likely organized to activate NMDA receptors efficiently.

Opto-thermoelectric microswimmers

Inspired by the "run-and-tumble" behaviours of Escherichia coli (E. coli) cells, we develop opto-thermoelectric microswimmers. The microswimmers are based on dielectric-Au Janus particles driven by a self-sustained electrical field that arises from the asymmetric optothermal response of the particles. Upon illumination by a defocused laser beam, the Janus particles exhibit an optically generated temperature gradient along the particle surfaces, leading to an opto-thermoelectrical field that propels the particles. We further discover that the swimming direction is determined by the particle orientation. To enable navigation of the swimmers, we propose a new optomechanical approach to drive the in-plane rotation of Janus particles under a temperature-gradient-induced electrical field using a focused laser beam. Timing the rotation laser beam allows us to position the particles at any desired orientation and thus to actively control the swimming direction with high efficiency. By incorporating dark-field optical imaging and a feedback control algorithm, we achieve automated propelling and navigation of the microswimmers. Our opto-thermoelectric microswimmers could find applications in the study of opto-thermoelectrical coupling in dynamic colloidal systems, active matter, biomedical sensing, and targeted drug delivery.

Aspiration-assisted bioprinting of the osteochondral interface

Osteochondral defects contain damage to both the articular cartilage and underlying subchon- dral bone, which remains a significant challenge in orthopedic surgery. Layered structure of bone, cartilage and the bone-cartilage interface must be taken into account in the case of biofabrication of the osteochondral (OC) interface. In this study, a dual layered OC interface was bioprinted using a newly developed aspiration-assisted bioprinting (AAB) technique, which has been the first time that scaffold-free bioprinting was applied to OC interface engineering. Tissue spheroids, made of human adipose-derived stem cells (ADSCs), were differentiated in three dimensions (3D) into chondrogenic and osteogenic spheroids, which were confirmed by immunostaining and histology qualitatively, and biochemistry assays and gene expression, quantitatively. Remarkably, the OC interface was bioprinted by accurate positioning of a layer of osteogenic spheroids onto a sacrificial alginate support followed by another layer of chondrogenic spheroids overlaid by the same support. Spheroids in individual zones fused and the maintenance of phenotypes in both zones confirmed the successful biofabrication of the histomorphologically-relevant OC interface. The biofabrication of OC tissue model without the use of polymeric scaffolds unveils great potential not only in regenerative medicine but also in drug testing and disease modeling for osteoarthritis.

A Deep Learning Framework for Design and Analysis of Surgical Bioprosthetic Heart Valves

Bioprosthetic heart valves (BHVs) are commonly used as heart valve replacements but they are prone to fatigue failure; estimating their remaining life directly from medical images is difficult. Analyzing the valve performance can provide better guidance for personalized valve design. However, such analyses are often computationally intensive. In this work, we introduce the concept of deep learning (DL) based finite element analysis (DLFEA) to learn the deformation biomechanics of bioprosthetic aortic valves directly from simulations. The proposed DL framework can eliminate the time-consuming biomechanics simulations, while predicting valve deformations with the same fidelity. We present statistical results that demonstrate the high performance of the DLFEA framework and the applicability of the framework to predict bioprosthetic aortic valve deformations. With further development, such a tool can provide fast decision support for designing surgical bioprosthetic aortic valves. Ultimately, this framework could be extended to other BHVs and improve patient care.

Machine learning metrology of cell confinement in melt electrowritten three-dimensional biomaterial substrates

Tuning cell shape by altering the biophysical properties of biomaterial substrates on which cells operate would provide a potential shape-driven pathway to control cell phenotype. However, there is an unexplored dimensional scale window of three-dimensional (3D) substrates with precisely tunable porous microarchitectures and geometrical feature sizes at the cell's operating length scales (10-100 μm). This paper demonstrates the fabrication of such high-fidelity fibrous substrates using a melt electrowriting (MEW) technique. This advanced manufacturing approach is biologically qualified with a metrology framework that models and classifies cell confinement states under various substrate dimensionalities and architectures. Using fibroblasts as a model cell system, the mechanosensing response of adherent cells is investigated as a function of variable substrate dimensionality (2D vs. 3D) and porous microarchitecture (randomly oriented, "non-woven" vs. precision-stacked, "woven"). Single-cell confinement states are modeled using confocal fluorescence microscopy in conjunction with an automated single-cell bioimage data analysis workflow that extracts quantitative metrics of the whole cell and sub-cellular focal adhesion protein features measured. The extracted multidimensional dataset is employed to train a machine learning algorithm to classify cell shape phenotypes. The results show that cells assume distinct confinement states that are enforced by the prescribed substrate dimensionalities and porous microarchitectures with the woven MEW substrates promoting the highest cell shape homogeneity compared to non-woven fibrous substrates. The technology platform established here constitutes a significant step towards the development of integrated additive manufacturing-metrology platforms for a wide range of applications including fundamental mechanobiology studies and 3D bioprinting of tissue constructs to yield specific biological designs qualified at the single-cell level.