Development of an Enhanced-Throughput Radial Cell Migration Device.

It is often desirable to evaluate the ability of cells to move in an unrestricted manner in multiple directions without chemical gradients. By combining the standard radial migration assay with injection-molded gaskets and a rigid fixture, we have developed a highly reliable and sensitive method for observing and measuring radial cell migration. This method is adapted for use on high-throughput automated imaging systems. The use of injection-molded gaskets enables low-cost replacement of cell-wetted components. Moreover, the design enables secondary placement of attractants and co-cultures. This device and its enhanced throughput permit the use of therapeutic screening to evaluate phenotypic responses, for example, cancer cell migration response due to drugs or chemical signals. This approach is orthogonal to other 2D cell migration applications, such as scratch wound assays, although here we offer a noninvasive, enhanced-throughput device, which currently is not commercially available but is easily constructed. The proposed device is a systematic, reliable, rapid application to monitor phenotypic responses to chemotherapeutic screens, genetic alterations (e.g., RNAi and CRISPR), supplemental regimens, and other approaches, offering a reliable methodology to survey unbiased and noninvasive cell migration.

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography.

Brain metastases are the most lethal cancer lesions; 10-30% of all cancers metastasize to the brain, with a median survival of only ~5-20 months, depending on the cancer type. To reduce the brain metastatic tumor burden, gaps in basic and translational knowledge need to be addressed. Major challenges include a paucity of reproducible preclinical models and associated tools. Three-dimensional models of brain metastasis can yield the relevant molecular and phenotypic data used to address these needs when combined with dedicated analysis tools. Moreover, compared to murine models, organ-on-a-chip models of patient tumor cells traversing the blood brain barrier into the brain microenvironment generate results rapidly and are more interpretable with quantitative methods, thus amenable to high throughput testing. Here we describe and demonstrate the use of a novel 3D microfluidic blood brain niche (µmBBN) platform where multiple elements of the niche can be cultured for an extended period (several days), fluorescently imaged by confocal microscopy, and the images reconstructed using an innovative confocal tomography technique; all aimed to understand the development of micro-metastasis and changes to the tumor micro-environment (TME) in a repeatable and quantitative manner. We demonstrate how to fabricate, seed, image, and analyze the cancer cells and TME cellular and humoral components, using this platform. Moreover, we show how artificial intelligence (AI) is used to identify the intrinsic phenotypic differences of cancer cells that are capable of transit through a model µmBBN and to assign them an objective index of brain metastatic potential. The data sets generated by this method can be used to answer basic and translational questions about metastasis, the efficacy of therapeutic strategies, and the role of the TME in both.

IL-4/IL-13 Stimulated Macrophages Enhance Breast Cancer Invasion Via Rho-GTPase Regulation of Synergistic VEGF/CCL-18 Signaling.

Tumor associated macrophages (TAMs) are increasingly recognized as major contributors to the metastatic progression of breast cancer and enriched levels of TAMs often correlate with poor prognosis. Despite our current advances it remains unclear which subset of M2-like macrophages have the highest capacity to enhance the metastatic program and which mechanisms regulate this process. Effective targeting of macrophages that aid cancer progression requires knowledge of the specific mechanisms underlying their pro-metastatic actions, as to avoid the anticipated toxicities from generalized targeting of macrophages. To this end, we set out to understand the relationship between the regulation of tumor secretions by Rho-GTPases, which were previously demonstrated to affect them, macrophage differentiation, and the converse influence of macrophages on cancer cell phenotype. Our data show that IL-4/IL-13 in vitro differentiated M2a macrophages significantly increase migratory and invasive potential of breast cancer cells at a greater rate than M2b or M2c macrophages. Our previous work demonstrated that the Rho-GTPases are potent regulators of macrophage-induced migratory responses; therefore, we examined M2a-mediated responses in RhoA or RhoC knockout breast cancer cell models. We find that both RhoA and RhoC regulate migration and invasion in MDA-MB-231 and SUM-149 cells following stimulation with M2a conditioned media. Secretome analysis of M2a conditioned media reveals high levels of vascular endothelial growth factor (VEGF) and chemokine (C-C motif) ligand 18 (CCL-18). Results from our functional assays reveal that M2a TAMs synergistically utilize VEGF and CCL-18 to promote migratory and invasive responses. Lastly, we show that pretreatment with ROCK inhibitors Y-276332 or GSK42986A attenuated VEGF/CCL-18 and M2a-induced migration and invasion. These results support Rho-GTPase signaling regulates downstream responses induced by TAMs, offering a novel approach for the prevention of breast cancer metastasis by anti-RhoA/C therapies.

Stigmatic Microscopy Enables Low-Cost, 3D, Microscale Particle Imaging Velocimetry in Rehydrating Aqueous Two-Phase Systems.

This paper describes the construction of a novel stigmatic microscope and image analysis algorithm to simultaneously analyze convective mixing both inside and outside of rehydrating µL-scale aqueous two-phase system (ATPS) droplets. Stigmatic microscopy is inexpensive and advantageous because it modifies the point-spread function of fluorescent particles to enable measurement of their 3D positions from single 2D images, without needing to take slices. In one application of the technique, the convection patterns captured clarify how different ATPS formulations succeed or fail to exclude cells for patterning. Particle flow traces reveal speed and directionality of circulation, indicating temporary eddies at the outer edge of the rehydrating droplet. In another application, the speed of circulation during rehydration was analyzed for different ATPS formulations and the results used to develop a new fast ELISA procedure. While this paper focuses on ATPS rehydration, the microscope and algorithm should be applicable to a broad range of microfluidic flows where microscale 3D convection is important.

A temporary indwelling intravascular aphaeretic system for in vivo enrichment of circulating tumor cells.

Circulating tumor cells (CTCs) have become an established biomarker for prognosis in patients with various carcinomas. However, current ex vivo CTC isolation technologies rely on small blood volumes from a single venipuncture limiting the number of captured CTCs. This produces statistical variability and inaccurate reflection of tumor cell heterogeneity. Here, we describe an in vivo indwelling intravascular aphaeretic CTC isolation system to continuously collect CTCs directly from a peripheral vein. The system returns the remaining blood products after CTC enrichment, permitting interrogation of larger blood volumes than classic phlebotomy specimens over a prolonged period of time. The system is validated in canine models showing capability to screen 1-2% of the entire blood over 2 h. Our result shows substantial increase in CTC capture, compared with serial blood draws. This technology could potentially be used to analyze large number of CTCs to facilitate translation of analytical information into future clinical decisions.

A platform for artificial intelligence based identification of the extravasation potential of cancer cells into the brain metastatic niche.

Brain metastases are the most lethal complication of advanced cancer; therefore, it is critical to identify when a tumor has the potential to metastasize to the brain. There are currently no interventions that shed light on the potential of primary tumors to metastasize to the brain. We constructed and tested a platform to quantitatively profile the dynamic phenotypes of cancer cells from aggressive triple negative breast cancer cell lines and patient derived xenografts (PDXs), generated from a primary tumor and brain metastases from tumors of diverse organs of origin. Combining an advanced live cell imaging algorithm and artificial intelligence, we profile cancer cell extravasation within a microfluidic blood-brain niche (µBBN) chip, to detect the minute differences between cells with brain metastatic potential and those without with a PPV of 0.91 in the context of this study. The results show remarkably sharp and reproducible distinction between cells that do and those which do not metastasize inside of the device.

Oxygen-promoted catalyst sintering influences number density, alignment, and wall number of vertically aligned carbon nanotubes.

A lack of synthetic control and reproducibility during vertically aligned carbon nanotube (CNT) synthesis has stifled many promising applications of organic nanomaterials. Oxygen-containing species are particularly precarious in that they have both beneficial and deleterious effects and are notoriously difficult to control. Here, we demonstrated diatomic oxygen's ability, independent of water, to tune oxide-supported catalyst thin film dewetting and influence nanoscale (diameter and wall number) and macro-scale (alignment and density) properties for as-grown vertically aligned CNTs. In particular, single- or few-walled CNT forests were achieved at very low oxygen loading, with single-to-multi-walled CNT diameters ranging from 4.8 ± 1.3 nm to 6.4 ± 1.1 nm over 0-800 ppm O2, and an expected variation in alignment, where both were related to the annealed catalyst morphology. Morphological differences were not the result of subsurface diffusion, but instead occurred via Ostwald ripening under several hundred ppm O2, and this effect was mitigated by high H2 concentrations and not due to water vapor (as confirmed in O2-free water addition experiments), supporting the importance of O2 specifically. Further characterization of the interface between the Fe catalyst and Al2O3 support revealed that either oxygen-deficit metal oxide or oxygen-adsorption on metals could be functional mechanisms for the observed catalyst nanoparticle evolution. Taken as a whole, our results suggest that the impacts of O2 and H2 on the catalyst evolution have been underappreciated and underleveraged in CNT synthesis, and these could present a route toward facile manipulation of CNT forest morphology through control of the reactive gaseous atmosphere alone.

High-Fidelity Replica Molding of Glassy Liquid Crystalline Polymer Microstructures.

Liquid crystalline polymers have recently been engineered to exhibit complex macroscopic shape adaptivity, including optically- and thermally driven bending, self-sustaining oscillation, torsional motion, and three-dimensional folding. Miniaturization of these novel materials is of great interest for both fundamental study of processing conditions and for the development of shape-changing microdevices. Here, we present a scalable method for high-fidelity replica molding of glassy liquid crystalline polymer networks (LCNs), by vacuum-assisted replica molding, along with magnetic field-induced control of the molecular alignment. We find that an oxygen-free environment is essential to establish high-fidelity molding with low surface roughness. Identical arrays of homeotropic and polydomain LCN microstructures are fabricated to assess the influence of molecular alignment on the elastic modulus (E = 1.48 GPa compared to E = 0.54 GPa), and side-view imaging is used to quantify the reversible thermal actuation of individual LCN micropillars by high-resolution tracking of edge motion. The methods and results from this study will be synergistic with future advances in liquid crystalline polymer chemistry, and could enable the scalable manufacturing of stimuli-responsive surfaces for applications including microfluidics, tunable optics, and surfaces with switchable wetting and adhesion.

On-Demand Isolation and Manipulation of C. elegans by In Vitro Maskless Photopatterning.

Caenorhabditis elegans (C. elegans) is a model organism for understanding aging and studying animal behavior. Microfluidic assay techniques have brought widespread advances in C. elegans research; however, traditional microfluidic assays such as those based on soft lithography require time-consuming design and fabrication cycles and offer limited flexibility in changing the geometric environment during experimentation. We present a technique for maskless photopatterning of a biocompatible hydrogel on an NGM (Agar) substrate, enabling dynamic manipulation of the C. elegans culture environment in vitro. Maskless photopatterning is performed using a projector-based microscope system largely built from off-the-shelf components. We demonstrate the capabilities of this technique by building micropillar arrays during C. elegans observation, by fabricating free-floating mechanisms that can be actuated by C. elegans motion, by using freehand drawing to isolate individual C. elegans in real time, and by patterning arrays of mazes for isolation and fitness testing of C. elegans populations. In vitro photopatterning enables rapid and flexible design of experiment geometry as well as real-time interaction between the researcher and the assay such as by sequential isolation of individual organisms. Future adoption of image analysis and machine learning techniques could be used to acquire large datasets and automatically adapt the assay geometry.

Conformal Robotic Stereolithography.

Additive manufacturing by layerwise photopolymerization, commonly called stereolithography (SLA), is attractive due to its high resolution and diversity of materials chemistry. However, traditional SLA methods are restricted to planar substrates and planar layers that are perpendicular to a single-axis build direction. Here, we present a robotic system that is capable of maskless layerwise photopolymerization on curved surfaces, enabling production of large-area conformal patterns and the construction of conformal freeform objects. The system comprises an industrial six-axis robot and a custom-built maskless projector end effector. Use of the system involves creating a mesh representation of the freeform substrate, generation of a triangulated toolpath with curved layers that represents the target object to be printed, precision mounting of the substrate in the robot workspace, and robotic photopatterning of the target object by coordinated motion of the robot and substrate. We demonstrate printing of conformal photopatterns on spheres of various sizes, and construction of miniature three-dimensional objects on spheres without requiring support features. Improvement of the motion accuracy and development of freeform toolpaths would enable construction of polymer objects that surpass the size and support structure constraints imparted by traditional SLA systems.

Robofurnace: a semi-automated laboratory chemical vapor deposition system for high-throughput nanomaterial synthesis and process discovery.

Laboratory research and development on new materials, such as nanostructured thin films, often utilizes manual equipment such as tube furnaces due to its relatively low cost and ease of setup. However, these systems can be prone to inconsistent outcomes due to variations in standard operating procedures and limitations in performance such as heating and cooling rates restrict the parameter space that can be explored. Perhaps more importantly, maximization of research throughput and the successful and efficient translation of materials processing knowledge to production-scale systems, relies on the attainment of consistent outcomes. In response to this need, we present a semi-automated lab-scale chemical vapor deposition (CVD) furnace system, called "Robofurnace." Robofurnace is an automated CVD system built around a standard tube furnace, which automates sample insertion and removal and uses motion of the furnace to achieve rapid heating and cooling. The system has a 10-sample magazine and motorized transfer arm, which isolates the samples from the lab atmosphere and enables highly repeatable placement of the sample within the tube. The system is designed to enable continuous operation of the CVD reactor, with asynchronous loading∕unloading of samples. To demonstrate its performance, Robofurnace is used to develop a rapid CVD recipe for carbon nanotube (CNT) forest growth, achieving a 10-fold improvement in CNT forest mass density compared to a benchmark recipe using a manual tube furnace. In the long run, multiple systems like Robofurnace may be linked to share data among laboratories by methods such as Twitter. Our hope is Robofurnace and like automation will enable machine learning to optimize and discover relationships in complex material synthesis processes.

Statistical analysis of variation in laboratory growth of carbon nanotube forests and recommendations for improved consistency.

While many promising applications have been demonstrated for vertically aligned carbon nanotube (CNT) forests, lack of consistency in results (e.g., CNT quality, height, and density) continues to hinder knowledge transfer and commercialization. For example, it is well known that CNT growth can be influenced by small concentrations of water vapor, carbon deposits on the reactor wall, and experiment-to-experiment variations in pressure within the reaction chamber. However, even when these parameters are controlled during synthesis, we found that variations in ambient lab conditions can overwhelm attempts to perform parametric optimization studies. We established a standard growth procedure, including the chemical vapor deposition (CVD) recipe, while we varied other variables related to the furnace configuration and experimental procedure. Statistical analysis of 280 samples showed that ambient humidity, barometric pressure, and sample position in the CVD furnace contribute significantly to experiment-to-experiment variation. We investigated how these factors lead to CNT growth variation and recommend practices to improve process repeatability. Initial results using this approach reduced run-to-run variation in CNT forest height and density by more than 50%.

Engineering of micro- and nanostructured surfaces with anisotropic geometries and properties.

Widespread approaches to fabricate surfaces with robust micro- and nanostructured topographies have been stimulated by opportunities to enhance interface performance by combining physical and chemical effects. In particular, arrays of asymmetric surface features, such as arrays of grooves, inclined pillars, and helical protrusions, have been shown to impart unique anisotropy in properties including wetting, adhesion, thermal and/or electrical conductivity, optical activity, and capability to direct cell growth. These properties are of wide interest for applications including energy conversion, microelectronics, chemical and biological sensing, and bioengineering. However, fabrication of asymmetric surface features often pushes the limits of traditional etching and deposition techniques, making it challenging to produce the desired surfaces in a scalable and cost-effective manner. We review and classify approaches to fabricate arrays of asymmetric 2D and 3D surface features, in polymers, metals, and ceramics. Analytical and empirical relationships among geometries, materials, and surface properties are discussed, especially in the context of the applications mentioned above. Further, opportunities for new fabrication methods that combine lithography with principles of self-assembly are identified, aiming to establish design principles for fabrication of arbitrary 3D surface textures over large areas.