Microfluidic co-culture platform to quantify chemotaxis of primary stem cells

A multiplex implantable microdevice assay identifies synergistic combinations of cancer immunotherapies and conventional drugs

Systematically identifying synergistic combinations of targeted agents and immunotherapies for cancer treatments remains difficult. In this study, we integrated high-throughput and high-content techniques-an implantable microdevice to administer multiple drugs into different sites in tumors at nanodoses and multiplexed imaging of tumor microenvironmental states-to investigate the tumor cell and immunological response signatures to different treatment regimens. Using a mouse model of breast cancer, we identified effective combinations from among numerous agents within days. In vivo studies in three immunocompetent mammary carcinoma models demonstrated that the predicted combinations synergistically increased therapeutic efficacy. We identified at least five promising treatment strategies, of which the panobinostat, venetoclax and anti-CD40 triple therapy was the most effective in inducing complete tumor remission across models. Successful drug combinations increased spatial association of cancer stem cells with dendritic cells during immunogenic cell death, suggesting this as an important mechanism of action in long-term breast cancer control.

Multiplexed microfluidic chip for cell co-culture

Paracrine signaling is challenging to study in vitro, as conventional culture tools dilute soluble factors and offer little to no spatiotemporal control over signaling. Microfluidic chips offer potential to address both of these issues. However, few solutions offer both control over onset and duration of cell-cell communication, and high throughput. We have developed a microfluidic chip designed to culture cells in adjacent chambers, separated by valves to selectively allow or prevent exchange of paracrine signals. The chip features 16 fluidic inputs and 128 individually-addressable chambers arranged in 32 sets of 4 chambers. Media can be continuously perfused or delivered by diffusion, which we model under different culture conditions to ensure normal cell viability. Immunocytochemistry assays can be performed in the chip, which we modeled and fine-tuned to reduce total assay time to 1 h. Finally, we validate the use of the chip for co-culture studies by showing that HEK293Ta cells respond to signals secreted by RAW 264.7 immune cells in adjacent chambers, only when the valve between the chambers is opened.

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.

In this work, we apply Physically-Guided Neural Networks with Internal Variables (PGNNIV) to the understanding of the Glioblastoma evolution process. We explain the metabolic changes between the proliferative and migrative activity of Glioblastoma cell cultures by using the go or grow activation functions as a pair of internal variables, whose dependence on the oxygen level is unravelled by some building blocks of the whole PGNNIV. Due to its model-free nature, our method is able to identify different classical mechanistic approaches and to outperform cell culture evolution predictions, as we demonstrate in the paper. Unlike Biologically-Informed Neural Networks we can assimilate data obtained from different boundary conditions and under different external stimuli to simulate the tumor progression under arbitrary conditions. We demonstrate this ability by comparing the predictions with different boundary conditions, resulting in different oxygenation conditions. This flexibility enables the use of our proposed method for personalised medical purposes, as the cell culture metabolic information, for a particular tumor, is encapsulated in a sub-network and may be used for arbitrary in silico tests.

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.

Systematic Investigation of Insulin Fibrillation on a Chip

A microfluidic protein aggregation device (microPAD) that allows the user to perform a series of protein incubations with various concentrations of two reagents is demonstrated. The microfluidic device consists of 64 incubation chambers to perform individual incubations of the protein at 64 specific conditions. Parallel processes of metering reagents, stepwise concentration gradient generation, and mixing are achieved simultaneously by pneumatic valves. Fibrillation of bovine insulin was selected to test the device. The effect of insulin and sodium chloride (NaCl) concentration on the formation of fibrillar structures was studied by observing the growth rate of partially folded protein, using the fluorescent marker Thioflavin-T. Moreover, dual gradients of different NaCl and hydrochloric acid (HCl) concentrations were formed, to investigate their interactive roles in the formation of insulin fibrils and spherulites. The chip-system provides a bird’s eye view on protein aggregation, including an overview of the factors that affect the process and their interactions. This microfluidic platform is potentially useful for rapid analysis of the fibrillation of proteins associated with many misfolding-based diseases, such as quantitative and qualitative studies on amyloid growth.

Electrophoretic cytometry of adherent cells

Cell-matrix and cell-cell interactions influence intracellular signalling and play an important role in physiologic and pathologic processes. Detachment of cells from the surrounding microenvironment alters intracellular signalling. Here, we demonstrate and characterise an integrated microfluidic device to culture single and clustered cells in tuneable microenvironments and then directly analyse the lysate of each cell in situ, thereby eliminating the need to detach cells prior to analysis. First, we utilise microcontact printing to pattern cells in confined geometries. We then utilise a microscale isoelectric focusing (IEF) module to separate, detect, and analyse lamin A/C from substrate-adhered cells seeded and cultured at varying (500, 2000, and 9000 cells per cm2) densities. We report separation performance (minimum resolvable pI difference of 0.11) that is on par with capillary IEF and independent of cell density. Moreover, we map lamin A/C and β-tubulin protein expression to morphometric information (cell area, circumference, eccentricity, form factor, and cell area factor) of single cells and observe poor correlation with each of these parameters. By eliminating the need for cell detachment from substrates, we enhance detection of cell receptor proteins (CD44 and β-integrin) and dynamic phosphorylation events (pMLCS19) that are rendered undetectable or disrupted by enzymatic treatments. Finally, we optimise protein solubilisation and separation performance by tuning lysis and electrofocusing (EF) durations. We observe enhanced separation performance (decreased peak width) with longer EF durations by 25.1% and improved protein solubilisation with longer lysis durations. Overall, the combination of morphometric analyses of substrate-adhered cells, with minimised handling, will yield important insights into our understanding of adhesion-mediated signalling processes.

Quantifying 3D chemotaxis in microfluidic-based chips with step gradients of collagen hydrogel concentrations

Cell migration is an essential process involved in crucial stages of tissue formation, regeneration or immune function as well as in pathological processes including tumor development or metastasis. During the last few years, the effect of gradients of soluble molecules on cell migration has been widely studied, and complex systems have been used to analyze cell behavior under simultaneous mechano-chemical stimuli. Most of these chemotactic assays have, however, focused on specific substrates in 2D. The aim of the present work is to develop a novel microfluidic-based chip that allows the long-term chemoattractant effect of growth factors (GFs) on 3D cell migration to be studied, while also providing the possibility to analyze the influence of the interface generated between different adjacent hydrogels. Namely, 1.5, 2, 2.5 and 4 mg ml^-1 concentrations of collagen type I were alternatively combined with 5, 10 or 50 ng ml^-1 concentrations of PDGF and VEGF (as a negative control). To achieve this goal, we have designed a new microfluidic device including three adjacent chambers to introduce hydrogels that allow the generation of a collagen concentration step gradient. This versatile and simple platform was tested by using dermal human fibroblasts embedded in 3D collagen matrices. Images taken over a week were processed to quantify the number of cells in each zone. We found, in terms of cell distribution, that the presence of PDGF, especially in small concentrations, was a strong chemoattractant for dermal human fibroblasts across the gels regardless of their collagen concentration and step gradient direction, whereas the effects of VEGF or collagen step gradient concentrations alone were negligible.

Interplay of chemical and thermal gradient on bacterial migration in a diffusive microfluidic device

Living systems are constantly under different combinations of competing gradients of chemical, thermal, pH, and mechanical stresses allied. The present work is about competing chemical and thermal gradients imposed on E. coli in a diffusive stagnant microfluidic environment. The bacterial cells were exposed to opposing and aligned gradients of an attractant (1 mM sorbitol) or a repellant (1 mM NiSO4) and temperature. The effects of the repellant/attractant and temperature on migration behavior, migration rate, and initiation time for migration have been reported. It has been observed that under competing gradients of an attractant and temperature, the nutrient gradient (gradient generated by cells itself) initiates directed migration, which, in turn, is influenced by temperature through the metabolic rate. Exposure to competing gradients of an inhibitor and temperature leads to the imposed chemical gradient governing the directed cell migration. The cells under opposing gradients of the repellant and temperature have experienced the longest decision time (∼60 min). The conclusion is that in a competing chemical and thermal gradient environment in the range of experimental conditions used in the present work, the migration of E. coli is always initiated and governed by chemical gradients (either generated by the cells in situ or imposed upon externally), but the migration rate and percentage of migration of cells are influenced by temperature, shedding insights into the importance of such gradients in deciding collective dynamics of such cells in physiological conditions.