Multiscale engineered human skeletal muscles with perfusable vasculature and microvascular network recapitulating the fluid compartments.

Creating a vasculature in engineered human skeletal muscle tissues (ehSMTs) enables us to create thick tissues, increase cell survival in implantation, provide models of blood-organ barriers for drug testing, and enhance muscle differentiation through paracrine signaling. Here, contractile ehSMTs with a central perfusable vascular channel and microvascular networks growing from this central vasculature into the surrounding skeletal muscle tissue were newly demonstrated. Because coculturing muscle cells and endothelial cells requires incompatible media, we recapitulated thein vivoextracellular fluid compartments between blood plasma and interstitial fluid by creating anin vitroperfusable vasculature running through skeletal muscle tissue with a physiologic cell density. By using this model, we constructed large vascularized ehSMTs and showed the potential to be utilized for drug testing platforms. Also, we found that coculturing with two separate media from an early stage of muscle differentiation led to increased contractile force, thicker myotubes, and improved muscle differentiation.

Tri-culture of spatially organizing human skeletal muscle cells, endothelial cells, and fibroblasts enhances contractile force and vascular perfusion of skeletal muscle tissues.

Constructing engineered human skeletal muscle tissues that resemble the function and microstructure of human skeletal muscles is key to utilizing them in a variety of applications such as drug development, disease modeling, regenerative medicine, and engineering biological machines. However, current in vitro skeletal muscle tissues are far inferior to native muscles in terms of contractile function and lack essential cues for muscle functions, particularly heterotypic cell-cell interactions between myoblasts, endothelial cells, and fibroblasts. Here, we develop an engineered muscle tissue with a coaxial three-layered tubular structure composed of an inner endothelial cell layer, an endomysium-like layer with fibroblasts in the middle, and an outer skeletal muscle cell layer, similar to the architecture of native skeletal muscles. Engineered skeletal muscle tissues with three spatially organized cell types produced thicker myotubes and lowered Young's modulus through extracellular matrix remodeling, resulting in 43% stronger contractile force. Furthermore, we demonstrated that fibroblasts localized in the endomysium layer induced angiogenic sprouting of endothelial cells into the muscle layer more effectively than fibroblasts homogeneously distributed in the muscle layer. This layered tri-culture system enables a structured spatial configuration of the three main cell types of skeletal muscle and promotes desired paracrine signaling, resulting in improved angiogenesis and increased contractile force. This research offers new insights to efficiently obtain new human skeletal muscle models, transplantable tissues, and actuators for biological machines.

A computational modeling of invadopodia protrusion into an extracellular matrix fiber network.

Invadopodia are dynamic actin-rich membrane protrusions that have been implicated in cancer cell invasion and metastasis. In addition, invasiveness of cancer cells is strongly correlated with invadopodia formation, which are observed during extravasation and colonization of metastatic cancer cells at secondary sites. However, quantitative understanding of the interaction of invadopodia with extracellular matrix (ECM) is lacking, and how invadopodia protrusion speed is associated with the frequency of protrusion-retraction cycles remains unknown. Here, we present a computational framework for the characterization of invadopodia protrusions which allows two way interactions between intracellular branched actin network and ECM fibers network. We have applied this approach to predicting the invasiveness of cancer cells by computationally knocking out actin-crosslinking molecules, such as α-actinin, filamin and fascin. The resulting simulations reveal distinct invadopodia dynamics with cycles of protrusion and retraction. Specifically, we found that (1) increasing accumulation of MT1-MMP at tips of invadopodia as the duration of protrusive phase is increased, and (2) the movement of nucleus toward the leading edge of the cell becomes unstable as duration of the retractile phase (or myosin turnover time) is longer than 1 min.

TeachBot: Towards teaching robotics fundamentals for human-robot collaboration at work.

The shortage of skilled workers who can use robots is a crucial issue hampering the growth of manufacturing industries. We present a new type of workforce training system, TeachBot, in which a robotic instructor delivers a series of interactive lectures using graphics and physical demonstration of its arm movements. Furthermore, the TeachBot allows learners to physically interact with the robot. This new human-computer interface, integrating oral and graphical instructions with motion demonstration and physical touch, enables to create engaging training materials. Effective learning takes place when the learner simultaneously interacts with an embodiment of new knowledge. We apply this "Learning by Touching" methodology to teach basic concepts, e.g. how a shaft encoder and feedback control work. In a pilot randomized control test with a small number of human subjects, we find suggestive evidence that Learning by Touching enhances learning effectiveness in this robotic context for adult learners. Students whose learning experience included touching the robot as opposed to watching it delivers the lessons showed gains in their ability to integrate knowledge about robotics. The "touching" group showed statistically significant gains in self-efficacy, which is an important antecedent to further learning and successful use of new technologies, as well as gains in knowledge about robotic concepts that trend toward significance.

Inherent Haptic Feedback From Supernumerary Robotic Limbs.

Supernumerary Robotics Limbs, or SuperLimbs for short, are wearable extra limbs for augmenting the wearer. SuperLimbs are attached directly to a human and, thereby, transmit a force from the environment to the human body. This inherent haptic feedback allows the human to perceive the interaction between the robot and the environment, monitor its actions, and effectively control the robot. This article addresses basic properties and the usefulness of the inherent haptic feedback from SuperLimbs in two exemplary cases. First, we show that the inherent haptic feedback allows the wearer to close the loop and manually regulate the force output of the SuperLimb. Second, we show that the inherent haptic feedback is sufficient for the wearer to supervise the autonomous actions of the SuperLimb. This ability is a critical requirement for safely and effectively performing multiple tasks simultaneously with the natural limbs and SuperLimbs. Together, these findings suggest the importance of designing SuperLimbs to take advantage of the inherent haptic feedback.

Multi-cell ECM compaction is predictable via superposition of nonlinear cell dynamics linearized in augmented state space.

Cells interacting through an extracellular matrix (ECM) exhibit emergent behaviors resulting from collective intercellular interaction. In wound healing and tissue development, characteristic compaction of ECM gel is induced by multiple cells that generate tensions in the ECM fibers and coordinate their actions with other cells. Computational prediction of collective cell-ECM interaction based on first principles is highly complex especially as the number of cells increase. Here, we introduce a computationally-efficient method for predicting nonlinear behaviors of multiple cells interacting mechanically through a 3-D ECM fiber network. The key enabling technique is superposition of single cell computational models to predict multicellular behaviors. While cell-ECM interactions are highly nonlinear, they can be linearized accurately with a unique method, termed Dual-Faceted Linearization. This method recasts the original nonlinear dynamics in an augmented space where the system behaves more linearly. The independent state variables are augmented by combining auxiliary variables that inform nonlinear elements involved in the system. This computational method involves a) expressing the original nonlinear state equations with two sets of linear dynamic equations b) reducing the order of the augmented linear system via principal component analysis and c) superposing individual single cell-ECM dynamics to predict collective behaviors of multiple cells. The method is computationally efficient compared to original nonlinear dynamic simulation and accurate compared to traditional Taylor expansion linearization. Furthermore, we reproduce reported experimental results of multi-cell induced ECM compaction.

Extracellular matrix remodelling induced by alternating electrical and mechanical stimulations increases the contraction of engineered skeletal muscle tissues.

Engineered skeletal muscles are inferior to natural muscles in terms of contractile force, hampering their potential use in practical applications. One major limitation is that the extracellular matrix (ECM) not only impedes the contraction but also ineffectively transmits the forces generated by myotubes to the load. In the present study, ECM remodelling improves contractile force in a short time, and a coordinated, combined electrical and mechanical stimulation induces the desired ECM remodelling. Notably, the application of single and combined stimulations to the engineered muscles remodels the structure of their ECM networks, which determines the mechanical properties of the ECM. Myotubes in the tissues are connected in parallel and in series to the ECM. The stiffness of the parallel ECM must be low not to impede contraction, while the stiffness of the serial ECM must be high to transmit the forces to the load. Both the experimental results and the mechanistic model suggest that the combined stimulation through coordination reorients the ECM fibres in such a way that the parallel ECM stiffness is reduced, while the serial ECM stiffness is increased. In particular, 3 and 20 minutes of alternating electrical and mechanical stimulations increase the force by 18% and 31%, respectively.

Multicell migration tracking within angiogenic networks by deep learning-based segmentation and augmented Bayesian filtering.

Cell migration is a key feature for living organisms. Image analysis tools are useful in studying cell migration in three-dimensional (3-D) in vitro environments. We consider angiogenic vessels formed in 3-D microfluidic devices (MFDs) and develop an image analysis system to extract cell behaviors from experimental phase-contrast microscopy image sequences. The proposed system initializes tracks with the end-point confocal nuclei coordinates. We apply convolutional neural networks to detect cell candidates and combine backward Kalman filtering with multiple hypothesis tracking to link the cell candidates at each time step. These hypotheses incorporate prior knowledge on vessel formation and cell proliferation rates. The association accuracy reaches 86.4% for the proposed algorithm, indicating that the proposed system is able to associate cells more accurately than existing approaches. Cell culture experiments in 3-D MFDs have shown considerable promise for improving biology research. The proposed system is expected to be a useful quantitative tool for potential microscopy problems of MFDs.

Computational modeling of three-dimensional ECM-rigidity sensing to guide directed cell migration.

Filopodia have a key role in sensing both chemical and mechanical cues in surrounding extracellular matrix (ECM). However, quantitative understanding is still missing in the filopodial mechanosensing of local ECM stiffness, resulting from dynamic interactions between filopodia and the surrounding 3D ECM fibers. Here we present a method for characterizing the stiffness of ECM that is sensed by filopodia based on the theory of elasticity and discrete ECM fiber. We have applied this method to a filopodial mechanosensing model for predicting directed cell migration toward stiffer ECM. This model provides us with a distribution of force and displacement as well as their time rate of changes near the tip of a filopodium when it is bound to the surrounding ECM fibers. Aggregating these effects in each local region of 3D ECM, we express the local ECM stiffness sensed by the cell and explain polarity in the cellular durotaxis mechanism.

Automated tracking and quantification of angiogenic vessel formation in 3D microfluidic devices.

Angiogenesis, the growth of new blood vessels from pre-existing vessels, is a critical step in cancer invasion. Better understanding of the angiogenic mechanisms is required to develop effective antiangiogenic therapies for cancer treatment. We culture angiogenic vessels in 3D microfluidic devices under different Sphingosin-1-phosphate (S1P) conditions and develop an automated vessel formation tracking system (AVFTS) to track the angiogenic vessel formation and extract quantitative vessel information from the experimental time-lapse phase contrast images. The proposed AVFTS first preprocesses the experimental images, then applies a distance transform and an augmented fast marching method in skeletonization, and finally implements the Hungarian method in branch tracking. When applying the AVFTS to our experimental data, we achieve 97.3% precision and 93.9% recall by comparing with the ground truth obtained from manual tracking by visual inspection. This system enables biologists to quantitatively compare the influence of different growth factors. Specifically, we conclude that the positive S1P gradient increases cell migration and vessel elongation, leading to a higher probability for branching to occur. The AVFTS is also applicable to distinguish tip and stalk cells by considering the relative cell locations in a branch. Moreover, we generate a novel type of cell lineage plot, which not only provides cell migration and proliferation histories but also demonstrates cell phenotypic changes and branch information.

A process engineering approach to increase organoid yield.

Temporal manipulation of the in vitro environment and growth factors can direct differentiation of human pluripotent stem cells into organoids - aggregates with multiple tissue-specific cell types and three-dimensional structure mimicking native organs. A mechanistic understanding of early organoid formation is essential for improving the robustness of these methods, which is necessary prior to use in drug development and regenerative medicine. We investigated intestinal organoid emergence, focusing on measurable parameters of hindgut spheroids, the intermediate step between definitive endoderm and mature organoids. We found that 13% of spheroids were pre-organoids that matured into intestinal organoids. Spheroids varied by several structural parameters: cell number, diameter and morphology. Hypothesizing that diameter and the morphological feature of an inner mass were key parameters for spheroid maturation, we sorted spheroids using an automated micropipette aspiration and release system and monitored the cultures for organoid formation. We discovered that populations of spheroids with a diameter greater than 75 µm and an inner mass are enriched 1.5- and 3.8-fold for pre-organoids, respectively, thus providing rational guidelines towards establishing a robust protocol for high quality intestinal organoids.

Three-Dimensional Characterization of Mechanical Interactions between Endothelial Cells and Extracellular Matrix during Angiogenic Sprouting.

We studied the three-dimensional cell-extracellular matrix interactions of endothelial cells that form multicellular structures called sprouts. We analyzed the data collected in-situ from angiogenic sprouting experiments and identified the differentiated interaction behavior exhibited by the tip and stalk cells. Moreover, our analysis of the tip cell lamellipodia revealed the diversity in their interaction behavior under certain conditions (e.g., when the heading of a sprout is switched approximately between the long-axis direction of two different lamellipodia). This study marks the first time that new characteristics of such interactions have been identified with shape changes in the sprouts and the associated rearrangements of collagen fibers. Clear illustrations of such changes are depicted in three-dimensional views.

Detecting cell division of Pseudomonas aeruginosa bacteria from bright-field microscopy images with hidden conditional random fields.

An approach to automatically detect bacteria division with temporal models is presented. To understand how bacteria migrate and proliferate to form complex multicellular behaviours such as biofilms, it is desirable to track individual bacteria and detect cell division events. Unlike eukaryotic cells, prokaryotic cells such as bacteria lack distinctive features, causing bacteria division difficult to detect in a single image frame. Furthermore, bacteria may detach, migrate close to other bacteria and may orientate themselves at an angle to the horizontal plane. Our system trains a hidden conditional random field (HCRF) model from tracked and aligned bacteria division sequences. The HCRF model classifies a set of image frames as division or otherwise. The performance of our HCRF model is compared with a Hidden Markov Model (HMM). The results show that a HCRF classifier outperforms a HMM classifier. From 2D bright field microscopy data, it is a challenge to separate individual bacteria and associate observations to tracks. Automatic detection of sequences with bacteria division will improve tracking accuracy.

Cell Invasion Dynamics into a Three Dimensional Extracellular Matrix Fibre Network.

The dynamics of filopodia interacting with the surrounding extracellular matrix (ECM) play a key role in various cell-ECM interactions, but their mechanisms of interaction with the ECM in 3D environment remain poorly understood. Based on first principles, here we construct an individual-based, force-based computational model integrating four modules of 1) filopodia penetration dynamics; 2) intracellular mechanics of cellular and nuclear membranes, contractile actin stress fibers, and focal adhesion dynamics; 3) structural mechanics of ECM fiber networks; and 4) reaction-diffusion mass transfers of seven biochemical concentrations in related with chemotaxis, proteolysis, haptotaxis, and degradation in ECM to predict dynamic behaviors of filopodia that penetrate into a 3D ECM fiber network. The tip of each filopodium crawls along ECM fibers, tugs the surrounding fibers, and contracts or retracts depending on the strength of the binding and the ECM stiffness and pore size. This filopodium-ECM interaction is modeled as a stochastic process based on binding kinetics between integrins along the filopodial shaft and the ligands on the surrounding ECM fibers. This filopodia stochastic model is integrated into migratory dynamics of a whole cell in order to predict the cell invasion into 3D ECM in response to chemotaxis, haptotaxis, and durotaxis cues. Predicted average filopodia speed and that of the cell membrane advance agreed with experiments of 3D HUVEC migration at r(2) > 0.95 for diverse ECMs with different pore sizes and stiffness.

Fabrication and characterization of optogenetic, multi-strip cardiac muscles.

Cardiac tissue engineering aims to recreate functional tissue constructs similar to the structure and function of the native myocardium. To date, in vitro tissue constructs lack the architectural complexity of a vascular network and the precise motor unit control of muscle fibers. Here, we present a method to construct engineered multi-strip cardiac muscle that simulates the bundle-like architecture of the native myocardium. Densely packed primary myocytes and cardiac fibroblasts were co-cultured with optogenetic, non-excitable cells. The resulting 3D syncytium triggered contraction upon localized blue light illumination to selectively activate and pace the multi-strip cardiac muscles, similar to the activity of pacemaker cells. Acting on a single load, we demonstrated graded force production through light-modulated multi-strip recruitment. These results demonstrate an in vitro platform of optogenetic, multi-strip cardiac muscles that can be used in a wide variety of applications, such as drug discovery, tissue engineering, and bio-hybrid robotic systems.

In vivo label-free quantification of liver microcirculation using dual-modality microscopy.

Microcirculation lesion is a common symptom of chronic liver diseases in the form of vasculature deformation and circulation alteration. In acute to chronic liver diseases such as biliary atresia, microcirculation lesion can have an early onset. Detection of microcirculation lesion is meaningful for studying the progression of liver disease. We have combined wide-field fluorescence microscopy and a laser speckle contrast technique to characterize hepatic microcirculation in vivo without labeling in a bile-duct ligation rat fibrosis model of biliary atresia. Through quantitative image analysis of four microcirculation parameters, we observed significant microcirculation lesion in the early to middle stages of fibrosis. This bimodal imaging method is useful to assess hepatic microcirculation lesion for the study of liver diseases.

Three-dimensionally printed biological machines powered by skeletal muscle.

Combining biological components, such as cells and tissues, with soft robotics can enable the fabrication of biological machines with the ability to sense, process signals, and produce force. An intuitive demonstration of a biological machine is one that can produce motion in response to controllable external signaling. Whereas cardiac cell-driven biological actuators have been demonstrated, the requirements of these machines to respond to stimuli and exhibit controlled movement merit the use of skeletal muscle, the primary generator of actuation in animals, as a contractile power source. Here, we report the development of 3D printed hydrogel "bio-bots" with an asymmetric physical design and powered by the actuation of an engineered mammalian skeletal muscle strip to result in net locomotion of the bio-bot. Geometric design and material properties of the hydrogel bio-bots were optimized using stereolithographic 3D printing, and the effect of collagen I and fibrin extracellular matrix proteins and insulin-like growth factor 1 on the force production of engineered skeletal muscle was characterized. Electrical stimulation triggered contraction of cells in the muscle strip and net locomotion of the bio-bot with a maximum velocity of ∼ 156 µm s(-1), which is over 1.5 body lengths per min. Modeling and simulation were used to understand both the effect of different design parameters on the bio-bot and the mechanism of motion. This demonstration advances the goal of realizing forward-engineered integrated cellular machines and systems, which can have a myriad array of applications in drug screening, programmable tissue engineering, drug delivery, and biomimetic machine design.

Dll4-containing exosomes induce capillary sprout retraction in a 3D microenvironment.

Delta-like 4 (Dll4), a membrane-bound Notch ligand, plays a fundamental role in vascular development and angiogenesis. Dll4 is highly expressed in capillary endothelial tip cells and is involved in suppressing neighboring stalk cells to become tip cells during angiogenesis. Dll4-Notch signaling is mediated either by direct cell-cell contact or by Dll4-containing exosomes from a distance. However, whether Dll4-containing exosomes influence tip cells of existing capillaries is unknown. Using a 3D microfluidic device and time-lapse confocal microscopy, we show here for the first time that Dll4-containing exosomes causes tip cells to lose their filopodia and trigger capillary sprout retraction in collagen matrix. We demonstrate that Dll4 exosomes can freely travel through 3D collagen matrix and transfer Dll4 protein to distant tip cells. Upon reaching endothelial sprout, it causes filopodia and tip cell retraction. Continuous application of Dll4 exosomes from a distance lead to significant reduction of sprout formation. This effect correlates with Notch signaling activation upon Dll4-containing exosome interaction with recipient endothelial cells. Furthermore, we show that Dll4-containing exosomes increase endothelial cell motility while suppressing their proliferation. These data revealed novel functions of Dll4 in angiogenesis through exosomes.

Utilization and control of bioactuators across multiple length scales.

In this review, we summarize the recent developments in the emerging field of bioactuators across a multitude of length scales. First, we discuss the use and control of biomolecules as nanoscale actuators. Molecular motors, such as DNA, kinesin, myosin, and F1-ATPase, have been shown to exert forces in the range between 1 pN to 45 pN. Second, we discuss the use and control of single and small clusters of cells to power microscale devices. Microorganisms, such as flagellated bacteria, protozoa, and algae, can naturally swim at speeds between 20 µm s(-1) to 2 mm s(-1) and produce thrust forces between 0.3 pN to 200 pN. Individual and clustered mammalian cells, such as cardiac and skeletal cells, can produce even higher contractile forces between 80 nN to 3.5 µN. Finally, we discuss the use and control of 2D- and 3D-assembled muscle tissues and muscle tissue explants as bioactuators to power devices. Depending on the size, composition, and organization of these hierarchical tissue constructs, contractile forces have been demonstrated to produce between 25 µN to 1.18 mN.

A Bayesian filtering approach to incorporate 2D/3D time-lapse confocal images for tracking angiogenic sprouting cells interacting with the gel matrix.

We present a new approach to incorporating information from heterogeneous images of migrating cells in 3D gel. We study 3D angiogenic sprouting, where cells burrow into the gel matrix, communicate with other cells and create vascular networks. We combine time-lapse fluorescent images of stained cell nuclei and transmitted light images of the background gel to track cell trajectories. The nuclei images are sampled less frequently due to photo toxicity. Hence, 3D cell tracking can be performed more reliably when 2D sprout profiles, extracted from gel matrix images, are effectively incorporated. We employ a Bayesian filtering approach to optimally combine the two heterogeneous images with different sampling rates. We construct stochastic models to predict cell locations and sprout profiles and condition the likelihood of nuclei location by the sprout profile. The conditional distribution is non-Gaussian and the cell dynamics is non-linear. To jointly update cell and sprout estimates, we use a Rao-Blackwell particle filter. Simulation and experimental results show accurate tracking of multiple cells along with sprout formation, demonstrating synergistic effects of incorporating the two types of images.