An embedded microfluidic valve for dynamic control of cellular communication.

The communication between different cell populations is an important aspect of many natural phenomena that can be studied with microfluidics. Using microfluidic valves, these complex interactions can be studied with a higher level of control by placing a valve between physically separated populations. However, most current valve designs do not display the properties necessary for this type of system, such as providing variable flow rate when embedded inside a microfluidic device. While some valves have been shown to have such tunable behavior, they have not been used for dynamic, real-time outputs. We present an electric solenoid valve that can be fabricated completely outside of a cleanroom and placed into any microfluidic device to offer control of dynamic fluid flow rates and profiles. After characterizing the behavior of this valve under controlled test conditions, we developed a regression model to determine the required input electrical signal to provide the solenoid the ability to create a desired flow profile. With this model, we demonstrated that the valve could be controlled to replicate a desired, time-varying pattern for the interface position of a co-laminar fluid stream. Our approach can be performed by other investigators with their microfluidic devices to produce predictable, dynamic fluidic behavior. In addition to modulating fluid flows, this work will be impactful for controlling cellular communication between distinct populations or even chemical reactions occurring in microfluidic channels.

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.

Soft magnetic thin film deformation with a bistable electropermanent magnet.

Physically soft magnetic materials (PSMMs) represent an emerging class of materials that can change shape or rheology in response to an external magnetic field. However, until now, no studies have investigated using an electropermanent magnet (EPM) and magnetic repulsion to magnetically deform PSMMs. Such capabilities would enable the ability to deform PSMMs without the need for continuous electrical input and produce PSMM film deformation without an air gap, as would be required with magnetic attraction. To address this, we introduce a PSMM-EPM architecture in which the shape of a soft deformable thin film is controlled by switching between bistable on/off states of the EPM circuit. We characterized the deflection of a PSMM thin film when placed at controlled distances normal to the surface of the EPM and compared its response for cases when the EPM is in the 'on' and 'off' states. This work is the first to demonstrate a magnetically repelled soft deformable thin film that achieves two electronically-controlled modes of deformation through the on and off states of an EPM. This work has the potential to advance the development of new magneto-responsive soft materials and systems.

Nanotopographical Cues Tune the Therapeutic Potential of Extracellular Vesicles for the Treatment of Aged Skeletal Muscle Injuries.

Skeletal muscle regeneration relies on the tightly temporally regulated lineage progression of muscle stem/progenitor cells (MPCs) from activation to proliferation and, finally, differentiation. However, with aging, MPC lineage progression is disrupted and delayed, ultimately causing impaired muscle regeneration. Extracellular vesicles (EVs) have attracted broad attention as next-generation therapeutics for promoting tissue regeneration. As a next step toward clinical translation, strategies to manipulate EV effects on downstream cellular targets are needed. Here, we developed an engineering strategy to tune the therapeutic potential of EVs using nanotopographical cues. We found that EVs released by young MPCs cultured on flat substrates (fEVs) promoted the proliferation of aged MPCs while EVs released by MPCs cultured on nanogratings (nEVs) promoted myogenic differentiation. We then employed a bioengineered 3D muscle aging model to optimize the administration protocol and test the therapeutic potential of fEVs and nEVs in a high-throughput manner. We found that the sequential administration first of fEVs during the phase of MPC proliferative expansion (i.e., 1 day after injury) followed by nEV administration at the stage of MPC differentiation (i.e., 3 days after injury) enhanced aged muscle regeneration to a significantly greater extent than fEVs and nEVs delivered either in isolation or mixed. The beneficial effects of the sequential EV treatment strategy were further validated in vivo, as evidenced by increased myofiber size and improved functional recovery. Collectively, our study demonstrates the ability of topographical cues to tune EV therapeutic potential and highlights the importance of optimizing the EV administration strategy to accelerate aged skeletal muscle regeneration.

Characterization of neural mechanotransduction response in human traumatic brain injury organoid model.

The ability to model physiological systems through 3D neural in-vitro systems may enable new treatments for various diseases while lowering the need for challenging animal and human testing. Creating such an environment, and even more impactful, one that mimics human brain tissue under mechanical stimulation, would be extremely useful to study a range of human-specific biological processes and conditions related to brain trauma. One approach is to use human cerebral organoids (hCOs) in-vitro models. hCOs recreate key cytoarchitectural features of the human brain, distinguishing themselves from more traditional 2D cultures and organ-on-a-chip models, as well as in-vivo animal models. Here, we propose a novel approach to emulate mild and moderate traumatic brain injury (TBI) using hCOs that undergo strain rates indicative of TBI. We subjected the hCOs to mild (2 s[Formula: see text]) and moderate (14 s[Formula: see text]) loading conditions, examined the mechanotransduction response, and investigated downstream genomic effects and regulatory pathways. The revealed pathways of note were cell death and metabolic and biosynthetic pathways implicating genes such as CARD9, ENO1, and FOXP3, respectively. Additionally, we show a steeper ascent in calcium signaling as we imposed higher loading conditions on the organoids. The elucidation of neural response to mechanical stimulation in reliable human cerebral organoid models gives insights into a better understanding of TBI in humans.

Controlling C2C12 Cytotoxicity on Liquid Metal Embedded Elastomer (LMEE).

Liquid metal embedded elastomers (LMEEs) are highly stretchable composites comprising microscopic droplets of eutectic gallium-indium (EGaIn) liquid metal embedded in a soft rubber matrix. They have a unique combination of mechanical, electrical, and thermal properties that make them attractive for potential applications in flexible electronics, thermal management, wearable computing, and soft robotics. However, the use of LMEEs in direct contact with human tissue or organs requires an understanding of their biocompatibility and cell cytotoxicity. In this study, the cytotoxicity of C2C12 cells in contact with LMEE composites composed of EGaIn droplets embedded with a polydimethylsiloxane (PDMS) matrix is investigated. In particular, the influence of EGaIn volume ratio and shear mixing time during synthesis on cell proliferation and viability is examined. The special case of electrically-conductive LMEE composites in which a percolating network of EGaIn droplets is created through "mechanical sintering" is also examined. This study in C2C12 cytotoxicity represents a first step in determining whether LMEE is safe for use in implantable biomedical devices and biohybrid systems.

Freeform 3D Ice Printing (3D-ICE) at the Micro Scale.

Water is one of the most important elements for life on earth. Water's rapid phase-change ability along with its environmental and biological compatibility also makes it a unique structural material for 3D printing of ice structures reproducibly and accurately. This work introduces the freeform 3D ice printing (3D-ICE) process for high-speed and reproducible fabrication of ice structures with micro-scale resolution. Drop-on-demand deposition of water onto a -35 °C platform rapidly transforms water into ice. The dimension and geometry of the structures are critically controlled by droplet ejection frequency modulation and stage motions. The freeform approach obviates layer-by-layer construction and support structures, even for overhang geometries. Complex and overhang geometries, branched hierarchical structures with smooth transitions, circular cross-sections, smooth surfaces, and micro-scale features (as small as 50 µm) are demonstrated. As a sample application, the ice templates are used as sacrificial geometries to produce resin parts with well-defined internal features. This approach could bring exciting opportunities for microfluidics, biomedical devices, soft electronics, and art.

Inducing Vascular Grammars for Anomaly Classification in Brain Angiograms.

As machine learning is used to make strides in medical diagnostics, few methods provide heuristics from which human doctors can learn directly. This work introduces a method for leveraging human observable structures, such as macroscale vascular formations, for producing assessments of medical conditions with relatively few training cases, and uncovering patterns that are potential diagnostic aids. The approach draws on shape grammars, a rule-based technique, pioneered in design and architecture, and accelerated through a recursive subgraph mining algorithm. The distribution of rule instances in the data from which they are induced is then used as an intermediary representation enabling common classification and anomaly detection approaches to identify indicative rules with relatively small data sets. The method is applied to seven-tesla time-of-flight angiography MRI (n = 54) of human brain vasculature. The data were segmented and induced to generate representative grammar rules. Ensembles of rules were isolated to implicate vascular conditions reliably. This application demonstrates the power of automated structured intermediary representations for assessing nuanced biological form relationships, and the strength of shape grammars, in particular for identifying indicative patterns in complex vascular networks.

Microfluidics for understanding model organisms.

New microfluidic systems for whole organism analysis and experimentation are catalyzing biological breakthroughs across many fields, from human health to fundamental biology principles. This perspective discusses recent microfluidic tools to study intact model organisms to demonstrate the tremendous potential for these integrated approaches now and into the future. We describe these microsystems' technical features and highlight the unique advantages for precise manipulation in areas including immobilization, automated alignment, sorting, sensory, mechanical and chemical stimulation, and genetic and thermal perturbation. Our aim is to familiarize technologically focused researchers with microfluidics applications in biology research, while providing biologists an entrée to advanced microengineering techniques for model organisms.

Cell alignment modulated by surface nano-topography - Roles of cell-matrix and cell-cell interactions.

The propensity of cells to align in particular directions is relevant to a number of areas, including tissue engineering and biohybrid robotics. Cell alignment is modulated through various extracellular conditions including surface topographies, mechanical cues from cell-matrix interactions, and cell-cell interactions. Understanding of these conditions provides guidance for desirable cellular structure constructions. In this study, we examine the roles of surface topographies and cell-cell interactions in inducing cell alignment. We employed wavy surface topographies at the nanometer scale as a model extracellular environment for cell culture. The results show that, within a certain range of wavelengths and amplitudes of the surface topographies, cell alignment is dependent on cell confluency. This dependence on both topology and confluency suggests interplay between cell-cell and cell-matrix interactions in inducing cell alignment. Images of sparsely distributed and confluent cells also demonstrated clear differences in the structures of their focal adhesion complexes. To understand this effect, we introduced anti-N-cadherin to cell culture to inhibit cell-cell interactions. The results show that, when anti-N-cadherin was applied, cells on wavy surfaces required greater confluency to achieve the same alignment compared to that in the absence of anti-N-cadherin. The understanding of the cell alignment mechanisms will be important in numerous potential applications such as scaffold design, tissue repair, and development of biohybrid robotic systems. STATEMENT OF SIGNIFICANCE: Cell alignment plays a critical role in numerous biological functions. Advances in tissue engineering utilizes cell alignment to restore, maintain, or even replace different types of biological tissues. The clinical impact that tissue engineering has made is facilitated by advancements in the understanding of interactions between scaffolds, biological factors, and cells. This work further elucidates the role of cell-cell interactions in promoting the organization of biological tissues.

Mechanostimulation of Multicellular Organisms Through a High-Throughput Microfluidic Compression System.

During embryogenesis, coordinated cell movement generates mechanical forces that regulate gene expression and activity. To study this process, tools such as aspiration or coverslip compression have been used to mechanically stimulate whole embryos. These approaches limit experimental design as they are imprecise, require manual handling, and can process only a couple of embryos simultaneously. Microfluidic systems have great potential for automating such experimental tasks while increasing throughput and precision. This article describes a microfluidic system developed to precisely compress whole Drosophila melanogaster (fruit fly) embryos. This system features microchannels with pneumatically actuated deformable sidewalls and enables embryo alignment, immobilization, compression, and post-stimulation collection. By parallelizing these microchannels into seven lanes, steady or dynamic compression patterns can be applied to hundreds of Drosophila embryos simultaneously. Fabricating this system on a glass coverslip facilitates the simultaneous mechanical stimulation and imaging of samples with high-resolution microscopes. Moreover, the utilization of biocompatible materials, like PDMS, and the ability to flow fluid through the system make this device capable of long-term experiments with media-dependent samples. This approach also eliminates the requirement for manual mounting which mechanically stresses samples. Furthermore, the ability to quickly collect samples from the microchannels enables post-stimulation analyses, including -omics assays which require large sample numbers unattainable using traditional mechanical stimulation approaches. The geometry of this system is readily scalable to different biological systems, enabling numerous fields to benefit from the functional features described herein including high sample throughput, mechanical stimulation or immobilization, and automated alignment.

3D Collagen Vascular Tumor-on-a-Chip Mimetics for Dynamic Combinatorial Drug Screening.

Disease models, including in vitro cell culture and animal models, have contributed significantly to developing diagnostics and treatments over the past several decades. The successes of traditional drug screening methods were generally hampered by not adequately mimicking critical in vivo features, such as a 3D microenvironment and dynamic drug diffusion through the extracellular matrix (ECM). To address these issues, we developed a 3D dynamic drug delivery system for cancer drug screening that mimicks drug dissemination through the tumor vasculature and the ECM by creating collagen-embedded microfluidic channels. Using this novel 3D ECM microsystem, we compared viability of tumor pieces with traditionally used 2D methods in response to three different drug combinations. Drug diffusion profiles were evaluated by simulation methods and tested in the 3D ECM microsystem and a 2D 96-well setup. Compared with the 2D control, the 3D ECM microsystem produced reliable data on viability, drug ratios, and combination indeces. This novel approach enables higher throughput and sets the stage for future applications utilizing drug sensitivity predicting algorithms based on dynamic diffusion profiles requiring only minimal patient tissue. Our findings moved drug sensitivity screening closer to clinical implications with a focus on testing combinatorial drug effects, an option often limited by the amount of available patient tissues.

Toward sustainable desalination using food waste: capacitive desalination with bread-derived electrodes.

Each year approximately 1.3 billion tons of food is either wasted or lost. One of the most wasted foods in the world is bread. The ability to reuse wasted food in another area of need, such as water scarcity, would provide a tremendous sustainable outcome. To address water scarcity, many areas of the world are now implementing desalination. One desalination technology that could benefit from food waste reuse is capacitive deionization (CDI). CDI has emerged as a powerful desalination technology that essentially only requires a pair of electrodes and a low-voltage power supply. Developing freestanding carbon electrodes from food waste could lower the overall cost of CDI systems and the environmental and economic impact from food waste. We created freestanding CDI electrodes from bread. The electrodes possessed a hierarchical pore structure that enabled both high salt adsorption capacity and one of the highest reported values for hydraulic permeability to date in a flow-through CDI system. We also developed a sustainable technique for electrode fabrication that does not require the use of common laboratory equipment and could be deployed in decentralized locations and developing countries with low-financial resources.

Hierarchical Machine Learning for High-Fidelity 3D Printed Biopolymers.

A hierarchical machine learning (HML) framework is presented that uses a small dataset to learn and predict the dominant build parameters necessary to print high-fidelity 3D features of alginate hydrogels. We examine the 3D printing of soft hydrogel forms printed with the freeform reversible embedding of suspended hydrogel method based on a CAD file that isolated the single-strand diameter and shape fidelity of printed alginate. Combinations of system variables ranging from print speed, flow rate, ink concentration to nozzle diameter were systematically varied to generate a small dataset of 48 prints. Prints were imaged and scored according to their dimensional similarity to the CAD file, and high print fidelity was defined as prints with less than 10% error from the CAD file. As a part of the HML framework, statistical inference was performed, using the least absolute shrinkage and selection operator to find the dominant variables that drive the error in the final prints. Model fit between the system parameters and print score was elucidated and improved by a parameterized middle layer of variable relationships which showed good performance between the predicted and observed data (R2 = 0.643). Optimization allowed for the prediction of build parameters that gave rise to high-fidelity prints of the measured features. A trade-off was identified when optimizing for the fidelity of different features printed within the same construct, showing the need for complex predictive design tools. A combination of known and discovered relationships was used to generate process maps for the 3D bioprinting designer that show error minimums based on the chosen input variables. Our approach offers a promising pathway toward scaling 3D bioprinting by optimizing print fidelity via learned build parameters that reduce the need for iterative testing.

Toward Vasculature in Skeletal Muscle-on-a-Chip through Thermo-Responsive Sacrificial Templates.

Developing new approaches for vascularizing synthetic tissue systems will have a tremendous impact in diverse areas. One area where this is particularly important is developing new skeletal muscle tissue systems, which could be utilized in physiological model studies and tissue regeneration. To develop vascularized approaches a microfluidic on-chip design for creating channels in polymer systems can be pursued. Current microfluidic tissue engineering methods include soft lithography, rapid prototyping, and cell printing; however, these have limitations such as having their scaffolding being inorganic, less desirable planar vasculature geometry, low fabrication efficiency, and limited resolution. Here we successfully developed a circular microfluidic channel embedded in a 3D extracellular matrix scaffolding with 3D myogenesis. We used a thermo-responsive polymer approach with micromilling-molding and designed a mixture of polyester wax and paraffin wax to fabricate the sacrificial template for microfluidic channel generation in the scaffolding. These findings will impact a number of fields including biomaterials, biomimetic structures, and personalized medicine in the future.

Probing coordinated co-culture cancer related motility through differential micro-compartmentalized elastic substrates.

Cell development and behavior are driven by internal genetic programming, but the external microenvironment is increasingly recognized as a significant factor in cell differentiation, migration, and in the case of cancer, metastatic progression. Yet it remains unclear how the microenvironment influences cell processes, especially when examining cell motility. One factor that affects cell motility is cell mechanics, which is known to be related to substrate stiffness. Examining how cells interact with each other in response to mechanically differential substrates would allow an increased understanding of their coordinated cell motility. In order to probe the effect of substrate stiffness on tumor related cells in greater detail, we created hard-soft-hard (HSH) polydimethylsiloxane (PDMS) substrates with alternating regions of different stiffness (200 and 800 kPa). We then cultured WI-38 fibroblasts and A549 epithelial cells to probe their motile response to the substrates. We found that when the 2 cell types were exposed simultaneously to the same substrate, fibroblasts moved at an increased speed over epithelial cells. Furthermore, the HSH substrate allowed us to physically guide and separate the different cell types based on their relative motile speed. We believe that this method and results will be important in a diversity of areas including mechanical microenvironment, cell motility, and cancer biology.

Written in Blood: Applying Shape Grammars to Retinal Vasculatures.

Purpose: Blood vessel networks within the retina are crucial for maintaining tissue perfusion and therefore good vision. Their complexity and unique patterns often require a steep learning curve for humans to identify trends and changes in the shape and topology of the networks, even though there exists much information important to identifying disease within them. Methods: Through image processing, the vasculature is isolated from other features of the fundus images, forcing the viewer to focus on the complex vascular feature. This article explores an approach using a grammar based on shape to describe retinal vasculature and to generate realistic and increasingly unrealistic artificial vascular networks that are then reviewed by ophthalmologists via digital survey. The ophthalmologists are asked whether these artificial vascular networks appeared realistic or unrealistic. Results: With only three rules (initiate, branch, and curve), the grammar accomplishes these goals. Networks are generated by adding noise to rule parameters present in existing networks. Via the survey of synthetic networks generated with different noise parameters, a correlation between noise in the branch rule and realistic association is revealed. Conclusions: By creating a language to describe retinal vasculature, this article allows for the potential of new insight into such an important but less understood feature of the retina, which in the future may play a role in diagnosing or helping to predict types of ocular disease. Translational Relevance: Applying shape grammar to describe retinal vasculature permits new understanding, which in turn provides the potential for new diagnostic tools.

3D In Vitro Neuron on a Chip for Probing Calcium Mechanostimulation.

The evolution of tissue on a chip systems holds promise for mimicking the response of biological functionality of physiological systems. One important direction for tissue on a chip approaches are neuron-based systems that could mimic neurological responses and lessen the need for in vivo experimentation. For neural research, more attention has been devoted recently to understanding mechanics due to issues in areas such as traumatic brain injury (TBI) and pain, among others. To begin to address these areas, a 3D Nerve Integrated Tissue on a Chip (NITC) approach combined with a Mechanical Excitation Testbed (MET) System is developed to impose external mechanical stimulation toward more realistic physiological environments. PC12 cells differentiated with nerve growth factor, which were cultured in a controlled 3D scaffolds, are used. The cells are labeled in a 3D NITC system with Fluo-4-AM to examine their calcium response under mechanical stimulation synchronized with image capture. Understanding the neural responses to mechanical stimulation beyond 2D systems is very important for neurological studies and future personalized strategies. This work will have implications in a diversity of areas including tissue-on-a-chip systems, biomaterials, and neuromechanics.

Decidual Vasculopathy Identification in Whole Slide Images Using Multiresolution Hierarchical Convolutional Neural Networks.

After a child is born, the examination of the placenta by a pathologist for abnormalities, such as infection or maternal vascular malperfusion, can provide important information about the immediate and long-term health of the infant. Detection of the pathologic placental blood vessel lesion decidual vasculopathy (DV) has been shown to predict adverse pregnancy outcomes, such as preeclampsia, which can lead to mother and neonatal morbidity in subsequent pregnancies. However, because of the high volume of deliveries at large hospitals and limited resources, currently a large proportion of delivered placentas are discarded without inspection. Furthermore, the correct diagnosis of DV often requires the expertise of an experienced perinatal pathologist. We introduce a hierarchical machine learning approach for the automated detection and classification of DV lesions in digitized placenta slides, along with a method of coupling learned image features with patient metadata to predict the presence of DV. Ultimately, the approach will allow many more placentas to be screened in a more standardized manner, providing feedback about which cases would benefit most from more in-depth pathologic inspection. Such computer-assisted examination of human placentas will enable real-time adjustment to infant and maternal care and possible chemoprevention (eg, aspirin therapy) to prevent preeclampsia, a disease that affects 2% to 8% of pregnancies worldwide, in women identified to be at risk with future pregnancies.