Spatial proteomics in three-dimensional intact specimens.

Spatial molecular profiling of complex tissues is essential to investigate cellular function in physiological and pathological states. However, methods for molecular analysis of large biological specimens imaged in 3D are lacking. Here, we present DISCO-MS, a technology that combines whole-organ/whole-organism clearing and imaging, deep-learning-based image analysis, robotic tissue extraction, and ultra-high-sensitivity mass spectrometry. DISCO-MS yielded proteome data indistinguishable from uncleared samples in both rodent and human tissues. We used DISCO-MS to investigate microglia activation along axonal tracts after brain injury and characterized early- and late-stage individual amyloid-beta plaques in a mouse model of Alzheimer's disease. DISCO-bot robotic sample extraction enabled us to study the regional heterogeneity of immune cells in intact mouse bodies and aortic plaques in a complete human heart. DISCO-MS enables unbiased proteome analysis of preclinical and clinical tissues after unbiased imaging of entire specimens in 3D, identifying diagnostic and therapeutic opportunities for complex diseases. VIDEO ABSTRACT.

High-Throughput Screening to Advance In Vitro Toxicology: Accomplishments, Challenges, and Future Directions.

Traditionally, chemical toxicity is determined by in vivo animal studies, which are low throughput, expensive, and sometimes fail to predict compound toxicity in humans. Due to the increasing number of chemicals in use and the high rate of drug candidate failure due to toxicity, it is imperative to develop in vitro, high-throughput screening methods to determine toxicity. The Tox21 program, a unique research consortium of federal public health agencies, was established to address and identify toxicity concerns in a high-throughput, concentration-responsive manner using a battery of in vitro assays. In this article, we review the advancements in high-throughput robotic screening methodology and informatics processes to enable the generation of toxicological data, and their impact on the field; further, we discuss the future of assessing environmental toxicity utilizing efficient and scalable methods that better represent the corresponding biological and toxicodynamic processes in humans.

Versatile, modular, and customizable magnetic solid-droplet systems.

Magnetic miniature robotic systems have attracted broad research interest because of their precise maneuverability in confined spaces and adaptability to diverse environments, holding significant promise for applications in both industrial infrastructures and biomedical fields. However, the predominant construction methodology involves the preprogramming of magnetic components into the system's structure. While this approach allows for intricate shape transformations, it exhibits limited flexibility in terms of reconfiguration and presents challenges when adapting to diverse materials, combining, and decoupling multiple functionalities. Here, we propose a construction strategy that facilitates the on-demand assembly of magnetic components, integrating ferrofluid droplets with the system's structural body. This approach enables the creation of complex solid-droplet robotic systems across a spectrum of length scales, ranging from 0.8 mm to 1.5 cm. It offers a diverse selection of materials and structural configurations, akin to assembling components like building blocks, thus allowing for the seamless integration of various functionalities. Moreover, it incorporates decoupling mechanisms to enable selective control over multiple functions, leveraging the fluidity, fission/fusion, and magneto-responsiveness properties inherent in the ferrofluid. Various solid-droplet systems have validated the feasibility of this strategy. This study advances the complexity and functionality achievable in small-scale magnetic robots, augmenting their potential for future biomedical and other applications.

Bioinspired multiscale adaptive suction on complex dry surfaces enhanced by regulated water secretion.

Suction is a highly evolved biological adhesion strategy for soft-body organisms to achieve strong grasping on various objects. Biological suckers can adaptively attach to dry complex surfaces such as rocks and shells, which are extremely challenging for current artificial suction cups. Although the adaptive suction of biological suckers is believed to be the result of their soft body's mechanical deformation, some studies imply that in-sucker mucus secretion may be another critical factor in helping attach to complex surfaces, thanks to its high viscosity. Inspired by the combined action of biological suckers' soft bodies and mucus secretion, we propose a multiscale suction mechanism which successfully achieves strong adaptive suction on dry complex surfaces which are both highly curved and rough, such as a stone. The proposed multiscale suction mechanism is an organic combination of mechanical conformation and regulated water seal. Multilayer soft materials first generate a rough mechanical conformation to the substrate, reducing leaking apertures to micrometres (~10 µm). The remaining micron-sized apertures are then sealed by regulated water secretion from an artificial fluidic system based on the physical model, thereby the suction cup achieves long suction longevity on complex surfaces but minimal overflow. We discuss its physical principles and demonstrate its practical application as a robotic gripper on a wide range of complex dry surfaces. We believe the presented multiscale adaptive suction mechanism is a powerful unique adaptive suction strategy which may be instrumental in the development of versatile soft adhesion.

Single-step precision programming of decoupled multiresponsive soft millirobots.

Stimuli-responsive soft robots offer new capabilities for the fields of medical and rehabilitation robotics, artificial intelligence, and soft electronics. Precisely programming the shape morphing and decoupling the multiresponsiveness of such robots is crucial to enable them with ample degrees of freedom and multifunctionality, while ensuring high fabrication accuracy. However, current designs featuring coupled multiresponsiveness or intricate assembly processes face limitations in executing complex transformations and suffer from a lack of precision. Therefore, we propose a one-stepped strategy to program multistep shape-morphing soft millirobots (MSSMs) in response to decoupled environmental stimuli. Our approach involves employing a multilayered elastomer and laser scanning technology to selectively process the structure of MSSMs, achieving a minimum machining precision of 30 µm. The resulting MSSMs are capable of imitating the shape morphing of plants and hand gestures and resemble kirigami, pop-up, and bistable structures. The decoupled multistimuli responsiveness of the MSSMs allows them to conduct shape morphing during locomotion, perform logic circuit control, and remotely repair circuits in response to humidity, temperature, and magnetic field. This strategy presents a paradigm for the effective design and fabrication of untethered soft miniature robots with physical intelligence, advancing the decoupled multiresponsive materials through modular tailoring of robotic body structures and properties to suit specific applications.

Efficient automatic design of robots.

Robots are notoriously difficult to design because of complex interdependencies between their physical structure, sensory and motor layouts, and behavior. Despite this, almost every detail of every robot built to date has been manually determined by a human designer after several months or years of iterative ideation, prototyping, and testing. Inspired by evolutionary design in nature, the automated design of robots using evolutionary algorithms has been attempted for two decades, but it too remains inefficient: days of supercomputing are required to design robots in simulation that, when manufactured, exhibit desired behavior. Here we show de novo optimization of a robot's structure to exhibit a desired behavior, within seconds on a single consumer-grade computer, and the manufactured robot's retention of that behavior. Unlike other gradient-based robot design methods, this algorithm does not presuppose any particular anatomical form; starting instead from a randomly-generated apodous body plan, it consistently discovers legged locomotion, the most efficient known form of terrestrial movement. If combined with automated fabrication and scaled up to more challenging tasks, this advance promises near-instantaneous design, manufacture, and deployment of unique and useful machines for medical, environmental, vehicular, and space-based tasks.

Soft robotics informs how an early echinoderm moved.

The transition from sessile suspension to active mobile detritus feeding in early echinoderms (c.a. 500 Mya) required sophisticated locomotion strategies. However, understanding locomotion adopted by extinct animals in the absence of trace fossils and modern analogues is extremely challenging. Here, we develop a biomimetic soft robot testbed with accompanying computational simulation to understand fundamental principles of locomotion in one of the most enigmatic mobile groups of early stalked echinoderms-pleurocystitids. We show that these Paleozoic echinoderms were likely able to move over the sea bottom by means of a muscular stem that pushed the animal forward (anteriorly). We also demonstrate that wide, sweeping gaits could have been the most effective for these echinoderms and that increasing stem length might have significantly increased velocity with minimal additional energy cost. The overall approach followed here, which we call "Paleobionics," is a nascent but rapidly developing research agenda in which robots are designed based on extinct organisms to generate insights in engineering and evolution.

Robotic data acquisition with deep learning enables cell image-based prediction of transcriptomic phenotypes.

Single-cell whole-transcriptome analysis is the gold standard approach to identifying molecularly defined cell phenotypes. However, this approach cannot be used for dynamics measurements such as live-cell imaging. Here, we developed a multifunctional robot, the automated live imaging and cell picking system (ALPS) and used it to perform single-cell RNA sequencing for microscopically observed cells with multiple imaging modes. Using robotically obtained data that linked cell images and the whole transcriptome, we successfully predicted transcriptome-defined cell phenotypes in a noninvasive manner using cell image-based deep learning. This noninvasive approach opens a window to determine the live-cell whole transcriptome in real time. Moreover, this work, which is based on a data-driven approach, is a proof of concept for determining the transcriptome-defined phenotypes (i.e., not relying on specific genes) of any cell from cell images using a model trained on linked datasets.

Biofabrication of Living Actuators.

The impact of tissue engineering has extended beyond a traditional focus in medicine to the rapidly growing realm of biohybrid robotics. Leveraging living actuators as functional components in machines has been a central focus of this field, generating a range of compelling demonstrations of robots capable of muscle-powered swimming, walking, pumping, gripping, and even computation. In this review, we highlight key advances in fabricating tissue-scale cardiac and skeletal muscle actuators for a range of functional applications. We discuss areas for future growth including scalable manufacturing, integrated feedback control, and predictive modeling and also propose methods for ensuring inclusive and bioethics-focused pedagogy in this emerging discipline. We hope this review motivates the next generation of biomedical engineers to advance rational design and practical use of living machines for applications ranging from telesurgery to manufacturing to on- and off-world exploration.

Will microfluidics enable functionally integrated biohybrid robots?

The next robotics frontier will be led by biohybrids. Capable biohybrid robots require microfluidics to sustain, improve, and scale the architectural complexity of their core ingredient: biological tissues. Advances in microfluidics have already revolutionized disease modeling and drug development, and are positioned to impact regenerative medicine but have yet to apply to biohybrids. Fusing microfluidics with living materials will improve tissue perfusion and maturation, and enable precise patterning of sensing, processing, and control elements. This perspective suggests future developments in advanced biohybrids.

Coordinating tiny limbs and long bodies: Geometric mechanics of lizard terrestrial swimming.

Although typically possessing four limbs and short bodies, lizards have evolved diverse morphologies, including elongate trunks with tiny limbs. Such forms are hypothesized to aid locomotion in cluttered/fossorial environments but propulsion mechanisms (e.g., the use of body and/or limbs to interact with substrates) and potential body/limb coordination remain unstudied. Here, we use biological experiments, a geometric theory of locomotion, and robophysical models to investigate body-limb coordination in diverse lizards. Locomotor field studies in short-limbed, elongate lizards (Brachymeles and Lerista) and laboratory studies of fully limbed lizards (Uma scoparia and Sceloporus olivaceus) and a snake (Chionactis occipitalis) reveal that body-wave dynamics can be described by a combination of standing and traveling waves; the ratio of the amplitudes of these components is inversely related to the degree of limb reduction and body elongation. The geometric theory (which replaces laborious calculation with diagrams) helps explain our observations, predicting that the advantage of traveling-wave body undulations (compared with a standing wave) emerges when the dominant thrust-generation mechanism arises from the body rather than the limbs and reveals that such soil-dwelling lizards propel via "terrestrial swimming" like sand-swimming lizards and snakes. We test our hypothesis by inducing the use of traveling waves in stereotyped lizards via modulating the ground-penetration resistance. Study of a limbed/undulatory robophysical model demonstrates that a traveling wave is beneficial when propulsion is generated by body-environment interaction. Our models could be valuable in understanding functional constraints on the evolutionary processes of elongation and limb reduction as well as advancing robot designs.

Synthetic growth by self-lubricated photopolymerization and extrusion inspired by plants and fungi.

Many natural organisms, such as fungal hyphae and plant roots, grow at their tips, enabling the generation of complex bodies composed of natural materials as well as dexterous movement and exploration. Tip growth presents an exemplary process by which materials synthesis and actuation are coupled, providing a blueprint for how growth could be realized in a synthetic system. Herein, we identify three underlying principles essential to tip-based growth of biological organisms: a fluid pressure driving force, localized polymerization for generating structure, and fluid-mediated transport of constituent materials. In this work, these evolved features inspire a synthetic materials growth process called extrusion by self-lubricated interface photopolymerization (E-SLIP), which can continuously fabricate solid profiled polymer parts with tunable mechanical properties from liquid precursors. To demonstrate the utility of E-SLIP, we create a tip-growing soft robot, outline its fundamental governing principles, and highlight its capabilities for growth at speeds up to 12 cm/min and lengths up to 1.5 m. This growing soft robot is capable of executing a range of tasks, including exploration, burrowing, and traversing tortuous paths, which highlight the potential for synthetic growth as a platform for on-demand manufacturing of infrastructure, exploration, and sensing in a variety of environments.

Linking global top-down views to first-person views in the brain.

Humans and other animals have a remarkable capacity to translate their position from one spatial frame of reference to another. The ability to seamlessly move between top-down and first-person views is important for navigation, memory formation, and other cognitive tasks. Evidence suggests that the medial temporal lobe and other cortical regions contribute to this function. To understand how a neural system might carry out these computations, we used variational autoencoders (VAEs) to reconstruct the first-person view from the top-down view of a robot simulation, and vice versa. Many latent variables in the VAEs had similar responses to those seen in neuron recordings, including location-specific activity, head direction tuning, and encoding of distance to local objects. Place-specific responses were prominent when reconstructing a first-person view from a top-down view, but head direction-specific responses were prominent when reconstructing a top-down view from a first-person view. In both cases, the model could recover from perturbations without retraining, but rather through remapping. These results could advance our understanding of how brain regions support viewpoint linkages and transformations.

Programmable soft valves for digital and analog control.

In soft devices, complex actuation sequences and precise force control typically require hard electronic valves and microcontrollers. Existing designs for entirely soft pneumatic control systems are capable of either digital or analog operation, but not both, and are limited by speed of actuation, range of pressure, time required for fabrication, or loss of power through pull-down resistors. Using the nonlinear mechanics intrinsic to structures composed of soft materials-in this case, by leveraging membrane inversion and tube kinking-two modular soft components are developed: a piston actuator and a bistable pneumatic switch. These two components combine to create valves capable of analog pressure regulation, simplified digital logic, controlled oscillation, nonvolatile memory storage, linear actuation, and interfacing with human users in both digital and analog formats. Three demonstrations showcase the capabilities of systems constructed from these valves: 1) a wearable glove capable of analog control of a soft artificial robotic hand based on input from a human user's fingers, 2) a human-controlled cushion matrix designed for use in medical care, and 3) an untethered robot which travels a distance dynamically programmed at the time of operation to retrieve an object. This work illustrates pathways for complementary digital and analog control of soft robots using a unified valve design.

Magnetohydrodynamic levitation for high-performance flexible pumps.

We use magnetohydrodynamic levitation as a means to create a soft, elastomeric, solenoid-driven pump (ESP). We present a theoretical framework and fabrication of a pump designed to address the unique challenges of soft robotics, maintaining pumping performance under deformation. Using a permanent magnet as a piston and ferrofluid as a liquid seal, we model and construct a deformable displacement pump. The magnet is driven back and forth along the length of a flexible core tube by a series of solenoids made of thin conductive wire. The magnet piston is kept concentric within the tube by Maxwell stresses within the ferrofluid and magnetohydrodynamic levitation, as viscous lift pressure is created due to its forward velocity. The centering of the magnet reduces shear stresses during pumping and improves efficiency. We provide a predictive model and capture the transient nonlinear dynamics of the magnet during operation, leading to a parametric performance curve characterizing the ESP, enabling goal-driven design. In our experimental validation, we report a shut-off pressure of 2 to 8 kPa and run-out flow rate of 50 to 320 mL⋅min-1, while subject to deformation of its own length scale, drawing a total of 0.17 W. This performance leads to the highest reported duty point (i.e., pressure and flow rate provided under load) for a pump that operates under deformation of its own length scale. We then integrate the pump into an elastomeric chassis and squeeze it through a tortuous pathway while providing continuous fluid pressure and flow rate; the vehicle then emerges at the other end and propels itself swimming.

Changes in Resting State Functional Connectivity Associated with Dynamic Adaptation of Wrist Movements.

Dynamic adaptation is an error-driven process of adjusting planned motor actions to changes in task dynamics (Shadmehr, 2017). Adapted motor plans are consolidated into memories that contribute to better performance on re-exposure. Consolidation begins within 15 min following training (Criscimagna-Hemminger and Shadmehr, 2008), and can be measured via changes in resting state functional connectivity (rsFC). For dynamic adaptation, rsFC has not been quantified on this timescale, nor has its relationship to adaptative behavior been established. We used a functional magnetic resonance imaging (fMRI)-compatible robot, the MR-SoftWrist (Erwin et al., 2017), to quantify rsFC specific to dynamic adaptation of wrist movements and subsequent memory formation in a mixed-sex cohort of human participants. We acquired fMRI during a motor execution and a dynamic adaptation task to localize brain networks of interest, and quantified rsFC within these networks in three 10-min windows occurring immediately before and after each task. The next day, we assessed behavioral retention. We used a mixed model of rsFC measured in each time window to identify changes in rsFC with task performance, and linear regression to identify the relationship between rsFC and behavior. Following the dynamic adaptation task, rsFC increased within the cortico-cerebellar network and decreased interhemispherically within the cortical sensorimotor network. Increases within the cortico-cerebellar network were specific to dynamic adaptation, as they were associated with behavioral measures of adaptation and retention, indicating that this network has a functional role in consolidation. Instead, decreases in rsFC within the cortical sensorimotor network were associated with motor control processes independent from adaptation and retention.SIGNIFICANCE STATEMENT Motor memory consolidation processes have been studied via functional magnetic resonance imaging (fMRI) by analyzing changes in resting state functional connectivity (rsFC) occurring more than 30 min after adaptation. However, it is unknown whether consolidation processes are detectable immediately (<15 min) following dynamic adaptation. We used an fMRI-compatible wrist robot to localize brain regions involved in dynamic adaptation in the cortico-thalamic-cerebellar (CTC) and cortical sensorimotor networks and quantified changes in rsFC within each network immediately after adaptation. Different patterns of change in rsFC were observed compared with studies conducted at longer latencies. Increases in rsFC in the cortico-cerebellar network were specific to adaptation and retention, while interhemispheric decreases in the cortical sensorimotor network were associated with alternate motor control processes but not with memory formation.

Single-center retrospective assessment of robotic-assisted simultaneous pancreas-kidney transplants: Exploring clinical utility.

The diabetic population is witnessing a rise in obesity rates, creating specific hurdles for individuals seeking pancreas transplantation because they are frequently disqualified due to their elevated body weight. Introducing a robotic-assisted approach to transplantation has been proven to yield improved outcomes, particularly in patients with obesity. A retrospective analysis was conducted between January 2015 and September 2023. The study included a total of 140 patients, with 16 receiving robotic-assisted simultaneous pancreas-kidney transplantation (RSPK) and 124 undergoing open approach simultaneous pancreas-kidney transplantation (OSPK) during the study period. The median age was 45 (36.8-52.7) and 44.5 years (36.8-51.8) (RSPK vs OSPK, P = .487). There were no significant differences in demographics except body mass index (RSPK vs OSPK, 34.9 vs 28.1, P < .001) and a higher percentage of patients with high cardiac risk in the RSPK group. The robotic approach has a lengthier overall operative time and warm ischemia time. Surgical and nonsurgical complications at 30-days and 1-year grafts and patient survival (93.8% vs 96.8%, RSPK vs OSPK, P = .521) were similar. Our findings suggest that employing robotic assistance in simultaneous pancreas-kidney transplantation is safe. Wider adoption and utilization of this technique could potentially improve transplant accessibility for individuals with obesity and diabetes.

Resting state functional connectivity associated with impaired proprioception post-stroke.

Deficits in proprioception, the knowledge of limb position and movement in the absence of vision, occur in ~50% of all strokes; however, our lack of knowledge of the neurological mechanisms of these deficits diminishes the effectiveness of rehabilitation and prolongs recovery. We performed resting-state functional magnetic resonance imaging (fMRI) on stroke patients to determine functional brain networks that exhibited changes in connectivity in association with proprioception deficits determined by a Kinarm robotic exoskeleton assessment. Thirty stroke participants were assessed for proprioceptive impairments using a Kinarm robot and underwent resting-state fMRI at 1 month post-stroke. Age-matched healthy control (n = 30) fMRI data were also examined and compared to stroke data in terms of the functional connectivity of brain regions associated with proprioception. Stroke patients exhibited reduced connectivity of the supplementary motor area and the supramarginal gyrus, relative to controls. Functional connectivity of these regions plus primary somatosensory cortex and parietal opercular area was significantly associated with proprioceptive function. The parietal lobe of the lesioned hemisphere is a significant node for proprioception after stroke. Assessment of functional connectivity of this region after stroke may assist with prognostication of recovery. This study also provides potential targets for therapeutic neurostimulation to aid in stroke recovery.

A Graphene-Mica-Based Photo-Thermal Actuator for Small-Scale Soft Robots.

Small-scale soft robots demonstrate intricate life-like behavior and allow navigation through arduous terrains and confined spaces. However, the primary challenges in soft robotics are 1) creating actuators capable of quick, reversible 22D-to-3D shape morphing with adjustable stiffness, 2) improving actuation force and robustness for wider applications, and 3) designing holistic systems for untethered manipulation and flexible multimodality in practical scenarios. Here, mechanically compliant paper-like robots are presented with multiple functionalities. The robots are based on photothermally activated polymer bimorph actuators that incorporate graphene for the photo-thermal conversion of energy and muscovite mica, with its high Young's modulus, providing the required stiffness. Conversion of light into heat leads to thermal expansion and bending of the stress-mismatched structures. The actuators are designed on the basis of a modified Timoshenko model, and numerical simulations are employed to evaluate their actuation performance. The membranes can be utilized for light-driven programmable shape-morphing. Localized control allows the implementation of active hinges at arbitrary positions within the membrane. Integrated into small-scale soft robots in mass production, the membrane facilitates locomotion, rolling, and flipping of the robots. Further, grasping and kicking mechanisms are demonstrated, highlighting the potential of such actuators for future applications.

Recent Progress on Flexible Self-Powered Tactile Sensing Platforms for Health Monitoring and Robotics.

Over the past decades, tactile sensing technology has made significant advances in the fields of health monitoring and robotics. Compared to conventional sensors, self-powered tactile sensors do not require an external power source to drive, which makes the entire system more flexible and lightweight. Therefore, they are excellent candidates for mimicking the tactile perception functions for wearable health monitoring and ideal electronic skin (e-skin) for intelligent robots. Herein, the working principles, materials, and device fabrication strategies of various self-powered tactile sensing platforms are introduced first. Then their applications in health monitoring and robotics are presented. Finally, the future prospects of self-powered tactile sensing systems are discussed.