Tuneable hydrogel patterns in pillarless microfluidic devices.

Organ-on-chip (OOC) technology has recently emerged as a powerful tool to mimic physiological or pathophysiological conditions through cell culture in microfluidic devices. One of its main goals is bypassing animal testing and encouraging more personalized medicine. The recent incorporation of hydrogels as 3D scaffolds into microfluidic devices has changed biomedical research since they provide a biomimetic extracellular matrix to recreate tissue architectures. However, this technology presents some drawbacks such as the necessity for physical structures as pillars to confine these hydrogels, as well as the difficulty in reaching different shapes and patterns to create convoluted gradients or more realistic biological structures. In addition, pillars can also interfere with the fluid flow, altering the local shear forces and, therefore, modifying the mechanical environment in the OOC model. In this work, we present a methodology based on a plasma surface treatment that allows building cell culture chambers with abutment-free patterns capable of producing precise shear stress distributions. Therefore, pillarless devices with arbitrary geometries are needed to obtain more versatile, reliable, and biomimetic experimental models. Through computational simulation studies, these shear stress changes are demonstrated in different designed and fabricated geometries. To prove the versatility of this new technique, a blood-brain barrier model has been recreated, achieving an uninterrupted endothelial barrier that emulates part of the neurovascular network of the brain. Finally, we developed a new technology that could avoid the limitations mentioned above, allowing the development of biomimetic OOC models with complex and adaptable geometries, with cell-to-cell contact if required, and where fluid flow and shear stress conditions could be controlled.

Experimentally-guided in silico design of engineered heart tissues to improve cardiac electrical function after myocardial infarction.

Engineered heart tissues (EHTs) built from human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) showed promising results for cardiac function restoration following myocardial infarction. Nevertheless, human iPSC-CMs have longer action potential and lower cell-to-cell coupling than adult-like CMs. These immature electrophysiological properties favor arrhythmias due to the generation of electrophysiological gradients when hiPSC-CMs are injected in the cardiac tissue. Culturing hiPSC-CMs on three-dimensional (3D) scaffolds can promote their maturation and influence their alignment. However, it is still uncertain how on-scaffold culturing influences the overall electrophysiology of the in vitro and implanted EHTs, as it requires expensive and time consuming experimentation. Here, we computationally investigated the impact of the scaffold design on the EHT electrical depolarization and repolarization before and after engraftment on infarcted tissue. We first acquired and processed electrical recordings from in vitro EHTs, which we used to calibrate the modeling and simulation of in silico EHTs to replicate experimental outcomes. Next, we built in silico EHT models for a range of scaffold pore sizes, shapes (square, rectangular, auxetic, hexagonal) and thicknesses. In this setup, we found that scaffolds made of small (0.2 mm2), elongated (30° half-angle) hexagons led to faster EHT activation and better mimicked the cardiac anisotropy. The scaffold thickness had a marginal role on the not engrafted EHT electrophysiology. Moreover, EHT engraftment on infarcted tissue showed that the EHT conductivity should be at least 5% of that in healthy tissue for bidirectional EHT-myocardium electrical propagation. For conductivities above such threshold, the scaffold made of small elongated hexagons led to the lowest activation time (AT) in the coupled EHT-myocardium. If the EHT conductivity was further increased and the hiPSC-CMs were uniformly oriented parallel to the epicardial cells, the total AT and the repolarization time gradient decreased substantially, thus minimizing the likelihood for arrhythmias after EHT transplantation.

A Mathematical Modelling Study of Chemotactic Dynamics in Cell Cultures: The Impact of Spatio-temporal Heterogeneity.

As motivated by studies of cellular motility driven by spatiotemporal chemotactic gradients in microdevices, we develop a framework for constructing approximate analytical solutions for the location, speed and cellular densities for cell chemotaxis waves in heterogeneous fields of chemoattractant from the underlying partial differential equation models. In particular, such chemotactic waves are not in general translationally invariant travelling waves, but possess a spatial variation that evolves in time, and may even oscillate back and forth in time, according to the details of the chemotactic gradients. The analytical framework exploits the observation that unbiased cellular diffusive flux is typically small compared to chemotactic fluxes and is first developed and validated for a range of exemplar scenarios. The framework is subsequently applied to more complex models considering the chemoattractant dynamics under more general settings, potentially including those of relevance for representing pathophysiology scenarios in microdevice studies. In particular, even though solutions cannot be constructed in all cases, a wide variety of scenarios can be considered analytically, firstly providing global insight into the important mechanisms and features of cell motility in complex spatiotemporal fields of chemoattractant. Such analytical solutions also provide a means of rapid evaluation of model predictions, with the prospect of application in computationally demanding investigations relating theoretical models and experimental observation, such as Bayesian parameter estimation.

Modelling cell adaptation using internal variables: Accounting for cell plasticity in continuum mathematical biology.

Cellular adaptation is the ability of cells to change in response to different stimuli and environmental conditions. It occurs via phenotypic plasticity, that is, changes in gene expression derived from changes in the physiological environment. This phenomenon is important in many biological processes, in particular in cancer evolution and its treatment. Therefore, it is crucial to understand the mechanisms behind it. Specifically, the emergence of the cancer stem cell phenotype, showing enhanced proliferation and invasion rates, is an essential process in tumour progression. We present a mathematical framework to simulate phenotypic heterogeneity in different cell populations as a result of their interaction with chemical species in their microenvironment, through a continuum model using the well-known concept of internal variables to model cell phenotype. The resulting model, derived from conservation laws, incorporates the relationship between the phenotype and the history of the stimuli to which cells have been subjected, together with the inheritance of that phenotype. To illustrate the model capabilities, it is particularised for glioblastoma adaptation to hypoxia. A parametric analysis is carried out to investigate the impact of each model parameter regulating cellular adaptation, showing that it permits reproducing different trends reported in the scientific literature. The framework can be easily adapted to any particular problem of cell plasticity, with the main limitation of having enough cells to allow working with continuum variables. With appropriate calibration and validation, it could be useful for exploring the underlying processes of cellular adaptation, as well as for proposing favourable/unfavourable conditions or treatments.

The Role of Mechanical Properties and Structure of Type I Collagen Hydrogels on Colorectal Cancer Cell Migration.

Mechanical interactions between cells and their microenvironment play an important role in determining cell fate, which is particularly relevant in metastasis, a process where cells invade tissue matrices with different mechanical properties. In vitro, type I collagen hydrogels have been commonly used for modeling the microenvironment due to its ubiquity in the human body. In this work, the combined influence of the stiffness of these hydrogels and their ultrastructure on the migration patterns of HCT-116 and HT-29 spheroids are analyzed. For this, six different types of pure type I collagen hydrogels by changing the collagen concentration and the gelation temperature are prepared. The stiffness of each sample is measured and its ultrastructure is characterized. Cell migration studies are then performed by seeding the spheroids in three different spatial conditions. It is shown that changes in the aforementioned parameters lead to differences in the mechanical stiffness of the matrices as well as the ultrastructure. These differences, in turn, lead to distinct cell migration patterns of HCT-116 and HT-29 spheroids in either of the spatial conditions tested. Based on these results, it is concluded that the stiffness and the ultrastructural organization of the matrix can actively modulate cell migration behavior in colorectal cancer spheroids.

A mechanobiological model for tumor spheroid evolution with application to glioblastoma: A continuum multiphysics approach.

BACKGROUND: Spheroids are in vitro quasi-spherical structures of cell aggregates, eventually cultured within a hydrogel matrix, that are used, among other applications, as a technological platform to investigate tumor formation and evolution. Several interesting features can be replicated using this methodology, such as cell communication mechanisms, the effect of gradients of nutrients, or the creation of realistic 3D biological structures. The main objective of this work is to link the spheroid evolution with the mechanical activity of cells, coupled with nutrient consumption and the subsequent cell dynamics. METHOD: We propose a continuum mechanobiological model which accounts for the most relevant phenomena that take place in tumor spheroid evolution under in vitro suspension, namely, nutrient diffusion in the spheroid, kinetics of cellular growth and death, and mechanical interactions among the cells. The model is qualitatively validated, after calibration of the model parameters, versus in vitro experiments of spheroids of different glioblastoma cell lines. RESULTS: Our model is able to explain in a novel way quite different setups, such as spheroid growth (up to six times the initial configuration for U-87 MG cell line) or shrinking (almost half of the initial configuration for U-251 MG cell line); as the result of the mechanical interplay of cells driven by cellular evolution. CONCLUSIONS: Glioblastoma tumor spheroid evolution is driven by mechanical interactions of the cell aggregate and the dynamical evolution of the cell population. All this information can be used to further investigate mechanistic effects in the evolution of tumors and their role in cancer disease.

A comparison between the effect of systemic and coated drug delivery in osteoporotic bone after dental implantation.

The increased life expectancy has boomed the demand of dental implants in the elderly. As a consequence, considering the effect of poorer bone quality, due to aging or associated diseases such as osteoporosis, on the success of dental restoration is becoming increasingly important. Bisphosphonates are one of the most used drugs to overcome the effect of osteoporosis as they increase bone density. Bisphosphonates modify the physiological bone remodeling process by adhering to the bone surface, reducing the activity of osteoclasts. This study aims at comparing the effect on bone remodeling of two drug delivery methods of Bisphosphonates: local delivery by coating the implant surface and systemic delivery. A chemo-mechano-biological bone remodeling model validated in a previous paper was used here. The two drug delivery schemes were modeled by means of a finite element approach. In the systemic drug delivery case, the amount of drug that reaches the bone compartment was calculated using a pharmacokinetic model while in the local drug delivery system, the dose was calculated using Fickean diffusion. In particular, the effect of Zoledronate is studied here. The two drug delivery approaches are compared between them and with a control case with no drug. The results show that the use of Bisphosphonates increases the mechanical strength of bone, thus improving the implant fixation along time. Systemic drug delivery affects the entire skeleton, while local drug delivery only affects the area around the dental implant, which reduces the side effects of Bisphosphonates, such as increasing the mineral content, which may promote bone brittleness and microdamage far from the implant. These results support the conclusion that dental implants coated with Bisphosphonates can be a good solution for osteoporotic or low bone density patients without the long-term side effects of systemic drug delivery.

Fabrication of human myocardium using multidimensional modelling of engineered tissues.

Biofabrication of human tissues has seen a meteoric growth triggered by recent technical advancements such as human induced pluripotent stem cells (hiPSCs) and additive manufacturing. However, generation of cardiac tissue is still hampered by lack of adequate mechanical properties and crucially by the often unpredictable post-fabrication evolution of biological components. In this study we employ melt electrowriting (MEW) and hiPSC-derived cardiac cells to generate fibre-reinforced human cardiac minitissues. These are thoroughly characterized in order to build computational models and simulations able to predict their post-fabrication evolution. Our results show that MEW-based human minitissues display advanced maturation 28 post-generation, with a significant increase in the expression of cardiac genes such as MYL2, GJA5, SCN5A and the MYH7/MYH6 and MYL2/MYL7 ratios. Human iPSC-cardiomyocytes are significantly more aligned within the MEW-based 3D tissues, as compared to conventional 2D controls, and also display greater expression of C×43. These are also correlated with a more mature functionality in the form of faster conduction velocity. We used these data to develop simulations capable of accurately reproducing the experimental performance. In-depth gauging of the structural disposition (cellular alignment) and intercellular connectivity (C×43) allowed us to develop an improved computational model able to predict the relationship between cardiac cell alignment and functional performance. This study lays down the path for advancing in the development ofin silicotools to predict cardiac biofabricated tissue evolution after generation, and maps the route towards more accurate and biomimetic tissue manufacture.

Understanding glioblastoma invasion using physically-guided neural networks with internal variables.

Microfluidic capacities for both recreating and monitoring cell cultures have opened the door to the use of Data Science and Machine Learning tools for understanding and simulating tumor evolution under controlled conditions. In this work, we show how these techniques could be applied to study Glioblastoma, the deadliest and most frequent primary brain tumor. In particular, we study Glioblastoma invasion using the recent concept of Physically-Guided Neural Networks with Internal Variables (PGNNIV), able to combine data obtained from microfluidic devices and some physical knowledge governing the tumor evolution. The physics is introduced in the network structure by means of a nonlinear advection-diffusion-reaction partial differential equation that models the Glioblastoma evolution. On the other hand, multilayer perceptrons combined with a nodal deconvolution technique are used for learning the go or grow metabolic behavior which characterises the Glioblastoma invasion. The PGNNIV is here trained using synthetic data obtained from in silico tests created under different oxygenation conditions, using a previously validated model. The unravelling capacity of PGNNIV enables discovering complex metabolic processes in a non-parametric way, thus giving explanatory capacity to the networks, and, as a consequence, surpassing the predictive power of any parametric approach and for any kind of stimulus. Besides, the possibility of working, for a particular tumor, with different boundary and initial conditions, permits the use of PGNNIV for defining virtual therapies and for drug design, thus making the first steps towards in silico personalised medicine.

Image-based Cardiac Electrophysiology Simulation through the Meshfree Mixed Collocation Method.

State-of-the-art solvers for in silico cardiac electro-physiology employ the Finite Element Method to solve complex anatomical models. While this is a robust and accurate tech-nique, it requires a high-quality mesh to prevent its accuracy from being severely deteriorated. The generation of a good quality mesh for realistic anatomical models can be very time-consuming, making the translation to the clinics challenging, especially if we try to use patient-specific geometries.Aiming to tackle this challenge, we propose an image-based model generation approach based on the meshfree Mixed Col-location Method. The flexibility provided by this method during model generation allows building meshfree models directly from the image data in an automatic procedure. Furthermore, this approach allows interpreting the simulation results directly in the voxel coordinates system of the image.We simulate electrical propagation in a porcine biventricular model with the proposed method and we compare the results with those obtained using the Finite Element Method. We conclude that the proposed method can generate results that are in good agreement with the Finite Element Method solution, alleviating the requirement of a mesh and user-input during modeling with only minimum efficiency overhead.

Evidence-Based Selection on the Appropriate FIT Cut-Off Point in CRC Screening Programs in the COVID Pandemic.

Background: The COVID pandemic has forced the closure of many colorectal cancer (CRC) screening programs. Resuming these programs is a priority, but fewer colonoscopies may be available. We developed an evidence-based tool for decision-making in CRC screening programs, based on a fecal hemoglobin immunological test (FIT), to optimize the strategy for screening a population for CRC. Methods: We retrospectively analyzed data collected at a regional CRC screening program between February/2014 and November/2018. We investigated two different scenarios: not modifying vs. modifying the FIT cut-off value. We estimated program outcomes in the two scenarios by evaluating the numbers of cancers and adenomas missed or not diagnosed in due time (delayed). Results: The current FIT cut-off (20-µg hemoglobin/g feces) led to 6,606 colonoscopies per 100,000 people invited annually. Without modifying this FIT cut-off value, when the optimal number of individuals invited for colonoscopies was reduced by 10-40%, a high number of CRCs and high-risk adenomas (34-135 and 73-288/100.000-people invited, respectively) will be undetected every year. When the FIT cut-off value was increased to where the colonoscopy demand matched the colonoscopy availability, the number of missed lesions per year was remarkably reduced (9-36 and 29-145/100.000 people, respectively). Moreover, the unmodified FIT scenario outcome was improved by prioritizing the selection process based on sex (males) and age, rather than randomly reducing the number invited. Conclusions: Assuming a mismatch between the availability and demand for annual colonoscopies, increasing the FIT cut-off point was more effective than randomly reducing the number of people invited. Using specific risk factors to prioritize access to colonoscopies should be also considered.

Predicting cell behaviour parameters from glioblastoma on a chip images. A deep learning approach.

The broad possibilities offered by microfluidic devices in relation to massive data monitoring and acquisition open the door to the use of deep learning technologies in a very promising field: cell culture monitoring. In this work, we develop a methodology for parameter identification in cell culture from fluorescence images using Convolutional Neural Networks (CNN). We apply this methodology to the in vitro study of glioblastoma (GBM), the most common, aggressive and lethal primary brain tumour. In particular, the aim is to predict the three parameters defining the go or grow GBM behaviour, which is determinant for the tumour prognosis and response to treatment. The data used to train the network are obtained from a mathematical model, previously validated with in vitro experimental results. The resulting CNN provides remarkably accurate predictions (Pearson's ρ > 0.99 for all the parameters). Besides, it proves to be sound, to filter noise and to generalise. After training and validation with synthetic data, we predict the parameters corresponding to a real image of a microfluidic experiment. The obtained results show good performance of the CNN. The proposed technique may set the first steps towards patient-specific tools, able to predict in real-time the tumour evolution for each particular patient, thanks to a combined in vitro-in silico approach.

On the effect of antiresorptive drugs on the bone remodeling of the mandible after dental implantation: a mathematical model.

Bone remodeling identifies the process of permanent bone change with new bone formation and old bone resorption. Understanding this process is essential in many applications, such as optimizing the treatment of diseases like osteoporosis, maintaining bone density in long-term periods of disuse, or assessing the long-term evolution of the bone surrounding prostheses after implantation. A particular case of study is the bone remodeling process after dental implantation. Despite the overall success of this type of implants, the increasing life expectancy in developed countries has boosted the demand for dental implants in patients with osteoporosis. Although several studies demonstrate a high success rate of dental implants in osteoporotic patients, it is also known that the healing time and the failure rate increase, necessitating the adoption of pharmacological measures to improve bone quality in those patients. However, the general efficacy of these antiresorptive drugs for osteoporotic patients is still controversial, requiring more experimental and clinical studies. In this work, we investigate the effect of different doses of several drugs, used nowadays in osteoporotic patients, on the evolution of bone density after dental implantation. With this aim, we use a pharmacokinetic-pharmacodynamic (PK/PD) mathematical model that includes the effect of antiresorptive drugs on the RANK/RANK-L/OPG pathway, as well as the mechano-chemical coupling with external mechanical loads. This mechano-PK/PD model is then used to analyze the evolution of bone in normal and osteoporotic mandibles after dental implantation with different drug dosages. We show that using antiresorptive agents such as bisphosphonates or denosumab increases bone density and the associated mechanical properties, but at the same time, it also increases bone brittleness. We conclude that, despite the many limitations of these very complex models, the one presented here is capable of predicting qualitatively the evolution of some of the main biological and chemical variables associated with the process of bone remodeling in patients receiving drugs for osteoporosis, so it could be used to optimize dental implant design and coating for osteoporotic patients, as well as the drug dosage protocol for patient-specific treatments.

Force Spectroscopy Imaging and Constriction Assays Reveal the Effects of Graphene Oxide on the Mechanical Properties of Alginate Microcapsules.

Microencapsulation of cells in hydrogel-based porous matrices is an approach that has demonstrated great success in regenerative cell therapy. These microcapsules work by concealing the exogenous cells and materials in a robust biomaterial that prevents their recognition by the immune system. A vast number of formulations and additives are continuously being tested to optimize cell viability and mechanical properties of the hydrogel. Determining the effects of new microcapsule additives is a lengthy process that usually requires extensive in vitro and in vivo testing. In this paper, we developed a workflow using nanoindentation (i.e., indentation with a nanoprobe in an atomic force microscope) and a custom-built microfluidic constriction device to characterize the effect of graphene oxide (GO) on three microcapsule formulations. With our workflow, we determined that GO modifies the microcapsule stiffness and surface properties in a formulation-dependent manner. Our results also suggest, for the first time, that GO alters the conformation of the microcapsule hydrogel and its interaction with subsequent coatings. Overall, our workflow can infer the effects of new additives on microcapsule surfaces. Thus, our workflow can contribute to diminishing the time required for the validation of new microcapsule formulations and accelerate their clinical translation.

Mathematical formulation and parametric analysis of in vitro cell models in microfluidic devices: application to different stages of glioblastoma evolution.

In silico models and computer simulation are invaluable tools to better understand complex biological processes such as cancer evolution. However, the complexity of the biological environment, with many cell mechanisms in response to changing physical and chemical external stimuli, makes the associated mathematical models highly non-linear and multiparametric. One of the main problems of these models is the determination of the parameters' values, which are usually fitted for specific conditions, making the conclusions drawn difficult to generalise. We analyse here an important biological problem: the evolution of hypoxia-driven migratory structures in Glioblastoma Multiforme (GBM), the most aggressive and lethal primary brain tumour. We establish a mathematical model considering the interaction of the tumour cells with oxygen concentration in what is called the go or grow paradigm. We reproduce in this work three different experiments, showing the main GBM structures (pseudopalisade and necrotic core formation), only changing the initial and boundary conditions. We prove that it is possible to obtain versatile mathematical tools which, together with a sound parametric analysis, allow to explain complex biological phenomena. We show the utility of this hybrid "biomimetic in vitro-in silico" platform to help to elucidate the mechanisms involved in cancer processes, to better understand the role of the different phenomena, to test new scientific hypotheses and to design new data-driven experiments.

A mechano-chemo-biological model for bone remodeling with a new mechano-chemo-transduction approach.

Bone remodeling is a fundamental biological process that develops in bone tissue along its whole lifetime. It refers to a continuous bone transformation with new bone formation and old bone resorption that changes the internal microstructure and composition of the tissue. The main objectives of bone remodeling are: repair of the internal microcracks; adaptation of the macroscopic stiffness and strength to the actual changing mechanical demands; and control of the calcium homeostasis. Understanding this process and predicting its evolution is critical to reduce the effects of long-term disuse as happens during periods of reduced mobility. It is also important in the design of bone implants to avoid long-term stress shielding. Many mathematical models have been proposed from the earliest purely phenomenological to the latest that include biological knowledge. However, there still exists a lack of connection between the mechanical driving force and the biochemical and cell processes it triggers. Here, and following previous works that model independently the mechanobiological and biochemical processes in bone remodeling, we present a more complete model, useful for both cortical and trabecular bone, that uses a new mechanotransduction approach based on the effect of strains onto the bonding-unbonding rate of RANK/RANKL/OPG receptor-ligand reactions. We compare the results of this model with previous ones, showing a good agreement in similar conditions. We also apply it to realistic situations such as a femoral bone after implantation of a hip prosthesis, getting similar results to the clinical ones in the final bone density distribution. Finally, we extend this approach to the anisotropic case, getting not only the mean density, but also the directional homogenization of the microstructure. This biochemical approach permits, not only to predict the bone evolution under changes in the mechanical loads, but also, to consider anabolic and catabolic drugs to control bone density, such as those used in osteoporosis.

Author Correction: Enabling cell recovery from 3D cell culture microfluidic devices for tumour microenvironment biomarker profiling.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Finite element comparison of the effect of absorbers' design in the surrounding bone of dental implants.

Despite the high success rate achieved in current dental implantation, there are still important problems to solve like incomplete early osteointegration, bone damage, and long-term implant loosening. Highly compliant stress absorbers are a possible solution to these problems. Although several works examined the stress-strain distribution in bone without and with absorbers to show their favorable results, none of them analyzed their impact on long-term remodeling. Here, we analyze this effect by comparing the evolution of stress and bone mass density without and with different designs of absorbers with those of the healthy tooth. Several finite element models with ABAQUS, corresponding to each design considered, were built to obtain the mechanical conditions in bone and implant. Then a mechanobiological bone remodeling model that considers damage accumulation and its repair during the remodeling process was used to compute the bone density redistribution. This approach allows assessing both the short-term density loss and the relative micromovement between bone and implant. We analyze the stress distributions in both bone and implant as well as the relative micromovement of the implant. We also present the evolution of damage and bone volume fraction. These results show that the addition of absorbers can reduce the stress in the bone around the implant. The obtained results also show that using stress absorbers reduces damage in bone, while increasing the number of absorbers does not necessarily improves damage reduction. We conclude that using implants with a correct design of absorbers prevents damage and stress shielding, reducing implant loosening.

Force spectroscopy-based simultaneous topographical and mechanical characterization to study polymer-to-polymer interactions in coated alginate microspheres.

Cell-laden hydrogel microspheres have shown encouraging outcomes in the fields of drug delivery, tissue engineering or regenerative medicine. Beyond the classical single coating with polycations, many other different coating designs have been reported with the aim of improving mechanical properties and in vivo performance of the microspheres. Among the most common strategies are the inclusion of additional polycation coatings and the covalent bonding of the semi-permeable membranes with biocompatible crosslinkers such as genipin. However, it remains challenging to characterize the effects of the interactions between the polycations and the hydrogel microspheres over time in vitro. Here we use a force spectroscopy-based simultaneous topographical and mechanical characterization to study polymer-to-polymer interactions in alginate microspheres with different coating designs, maintaining the hydrogels in liquid. In addition to classical topography parameters, we explored, for the first time, the evolution of peak/valley features along the z axis via thresholding analysis and the cross-correlation between topography and stiffness profiles with resolution down to tens of nanometers. Thus, we demonstrated the importance of genipin crosslinking to avoid membrane detachment in alginate microspheres with double polycation coatings. Overall, this methodology could improve hydrogel design rationale and expedite in vitro characterization, therefore facilitating clinical translation of hydrogel-based technologies.

Enabling cell recovery from 3D cell culture microfluidic devices for tumour microenvironment biomarker profiling.

The tumour microenvironment (TME) has recently drawn much attention due to its profound impact on tumour development, drug resistance and patient outcome. There is an increasing interest in new therapies that target the TME. Nonetheless, most established in vitro models fail to include essential cues of the TME. Microfluidics can be used to reproduce the TME in vitro and hence provide valuable insight on tumour evolution and drug sensitivity. However, microfluidics remains far from well-established mainstream molecular and cell biology methods. Therefore, we have developed a quick and straightforward collagenase-based enzymatic method to recover cells embedded in a 3D hydrogel in a microfluidic device with no impact on cell viability. We demonstrate the validity of this method on two different cell lines in a TME microfluidic model. Cells were successfully retrieved with high viability, and we characterised the different cell death mechanisms via AMNIS image cytometry in our model.