Lab on a Chip

Experiments with gradients of soluble bioactive species have significantly advanced with microfluidic developments that enable cell observation and stringent control of environmental conditions. While some methodologies rely on flow to establish gradients, other opt for flow-free conditions, which is particularly beneficial for studying non-adherent and/or shear-sensitive cells. In flow-free devices, bioactive species diffuse either through resistive microchannels in "microchannel-based" devices, a porous membrane in "membrane-based" devices, or a hydrogel in "gel-based" devices. However, despite significant advancements over traditional methods such as "Boyden chambers", these technologies have not widely disseminated in biological laboratories, arguably due to entrenched practices and the intricate skills required for conducting microfluidic assays. Here, we integrated Quake-type pneumatic microvalves in place of microgrooves, membranes, or gels, and developed devices with precise control over residual flow, establishment initial gradient, and long-term stability of gradients. The "Microvalve-based" approach enables the generation of the automatization of delicate microfluidic manipulations, which paves the way for routine applications of controlled and tunable flow-free gradients in academic laboratories and biomedical units.

We demonstrate a new sample-loading strategy ('spot-loading') for Spores-on-a-Chip microfluidic sensors that enables multiplexed experimentation. The previous limitation of one biological sample per device is overcome through controlled sample loading at an intermediate step of the chip fabrication process. As a proof-of-concept, we use dual spore chips to compare the germination behaviours of two spore strains in two distinct microenvironments.

The tumor microenvironment (TME) plays critical roles in cancer development, aggressiveness, and treatment resistance. The TME comprises cellular (stromal) components as well as gradients of physicochemical properties, including hypoxia and acidosis. Understanding of how hypoxia and acidosis gradients impact cancer phenotypes is lacking, in large part due to challenges in precisely mimicking and controlling such gradients in a manner compatible with the growth of cancer- and stromal cells. Here, we design and validate a microfluidic device enabling orthogonal gradients of oxygen and pH. Both gradients are established by diffusion from a nearby source and sink in the observation area in the absence of flow. This produces linear gradients at steady state. Our device enables a wide range of spatiotemporally resolved analyses, from omics to live cell imaging, interrogating the impact of the physicochemical TME on disease development. The design is easily adaptable, making it valuable for a wide range of questions involving physicochemical gradients.

Understanding how metabolic deprivation shapes tumor behavior requires in vitro systems that faithfully reproduce millimeter-scale biochemical gradients. Here, we introduce MilliFlow3D, an open hydrogel-based microfluidic platform that generates controlled metabolic gradients and supports in situ spheroid formation, real-time imaging, and intact retrieval for spatial analyses. Gradients are generated by passive diffusion across a structured agarose microwell array positioned between two perfusion channels. Using a fluorescent tracer and numerical simulations, we show that MilliFlow3D establishes stable linear gradients with local metabolite levels matching those reported in avascular tumor regions. Using HCT116 colorectal cancer spheroids, we demonstrate that a L-glutamine gradient imposes graded effects on growth and proliferation: spheroid expansion decreases from high- to low-glutamine regions, and Ki-67-positive cells progressively shift toward the periphery under glutamine depletion. Thanks to the platforms spatial accessibility, these phenotypic responses could in the future be coupled to molecular readouts, enabling spatial mapping of metabolic pathway activity along the gradient. Altogether, MilliFlow3D provides a robust and versatile platform to investigate how heterogeneous metabolic landscapes sculpt tumor behavior and to identify context-dependent metabolic vulnerabilities with high analytical precision.

Compartmentalized microfluidic chips have demonstrated tremendous potential to create in vitro minimalistic environments for the reproduction of the neural circuitry of the brain. Although the protocol for seeding neural soma in these devices is well known and has been widely used in myriad studies, the accurate control of the number of neurites passing through the microchannels remains challenging. However, the regulation of axonal density among different groups of neurons is still a requirement to assess the inherent structural connectivity between neuronal populations. In this work, we report the effect of microchannel patterning strategies on the modulation of neuronal connectivity by applying dimensional modifications on microchannel-connected microfluidic chambers. Our results show that those strategies can modulate the direction and the number of neuronal projections of passage, therefore regulating the strength of the structural connections between two populations of neurons. With this approach, we provide innovative microfluidic design rules for the engineering of in vitro physiologically relevant neural networks.

Antibiotics are often prescribed before pathogens are identified and susceptibility to the prescribed drug is confirmed; laboratory results may take up to 3 days. Using rapid antibiotic susceptibility testing (AST) this timeline can be compressed. We designed a microfluidic ladder-based system that generates a twofold serial dilution of antibiotics comparable to current national and international standards. This consolidated design, with minimal handling steps, has cut down the time-to-result for AST from 16-20 h to 4-5 h. Our system has a 91.75 % rate of agreement with the commercial AST system for Gram-negative and Gram-positive bacterial isolates from canine urinary tract infections (UTI) tested against seven clinically relevant antibiotics. Overall, the system showed a matching rate of 92.71 % - 94.54 % with Gram-negative pathogens, and 85.00 % - 88.57 % with Gram-positive pathogens with no statistical difference between the pathogens or antibiotics. We also tested bacteria filtered directly from urine samples, potentially reducing the total sample-to-result time from 2-3 days to 4 hours.

Red blood cells (RBCs) must be highly deformable to transit through the microvasculature to deliver oxygen to tissues. The loss of RBC deformability resulting from pathology, natural aging, or storage in blood bags can impede the proper function of these cells. A variety of methods have been developed to measure RBC deformability, but these methods require specialized equipment, long measurement time, and highly skilled personnel. To address this challenge, we investigated whether a machine learning approach could be applied to determine donor RBC deformability using single cell microscope images. We used the microfluidic ratchet device to sort RBCs based on deformability. Sorted cells are then imaged and used to train a deep learning model to classify RBCs based on deformability. This model correctly predicted deformability of individual RBCs with 84 {+/-} 11% accuracy averaged across ten donors. Using this model to score the deformability of RBC samples were accurate to within 4.4 {+/-} 2.5% of the value obtained using the microfluidic ratchet device. While machine learning methods are frequently developed to automate human image analysis, our study is remarkable in showing that deep learning of single cell microscopy images could be used to measure RBC deformability, a property not normally measurable by imaging. Measuring RBC deformability by imaging is also desirable because it can be performed rapidly using a standard microscopy system, potentially enabling RBC deformability studies to be performed as part of routine clinical assessments.

A systematic optimization of throughput and operational stability in Sorting by Interfacial Tension (SIFT) is presented. Reducing droplet size and enabling a broader distribution of droplet trajectories increased the number of droplets processed per sorting element, resulting in about a four fold improvement in throughput from 30 to 125 droplets per second. Throughput was further enhanced through device parallelization, with devices incorporating two and four independent sorting regions demonstrated. These configurations distributed droplets evenly across sorting elements that exhibited comparable pH sorting thresholds, indicating similar flow conditions and drag forces within each region. Among the designs evaluated, the two-element configuration provided the optimal balance of throughput, robustness, and simplicity, achieving maximum throughputs of about 250 droplets per second. Throughput and pH sorting thresholds were preserved throughout two hours of continuous sorting. The improved platform was applied to examine the relationship between cellular glycolysis and iron homeostasis at the single-cell level for Jurkat cells, revealing a subpopulation of highly glycolytic cells with significantly elevated iron uptake, consistent with prior reports linking iron regulation and T cell metabolism. Collectively, these advances expand the scale, stability, and biological applicability of SIFT, enabling large-scale functional studies while facilitating the capture of rare and metabolically distinct cell populations.

Given the emergence of antimicrobial drug resistance, it is critical to understand the heterogeneity of response to an antibiotic within a population of cells. Since the drug can exert a selection pressure that leads to the emergence of resistant phenotypes. To date, neither bulk nor single-cell methods are able to link the heterogeneity of single-cell susceptibility to the population-scale response to antibiotics. Here we present a platform that measures the ability of individual E. coli cells to form small colonies at different ciprofloxacin concentrations, by using anchored microfluidic drops and an image and data analysis pipelines. The microfluidic results are benchmarked against classical microbiology measurements of antibiotic susceptibility, showing an agreement between the pooled microfluidic chip and replated bulk measurements. Further, the experimental likelihood of a single cell to form a colony is used to provide a probabilistic antibiotic susceptibility curve. In addition to the probabilistic viewpoint, the microfluidic format enables the characterization of morphological features over time for a large number of individual cells. This pipeline can be used to compare the response of different bacterial strains to antibiotics with different action mechanisms.

Microfluidic neuro-engineering design rules have been widely explored to create in vitro neural networks with the objective to replicate physiologically relevant structures of the brain. Several neurofluidic strategies have been reported to study the connectivity of neurons, either within a population or between two separated populations, through the control of the directionality of their neuronal projections. Yet, the in vitro regulation of the growth kinetics of those projections remains challenging. Here, we describe a new neurofluidic chip with a triangular design that allows the accurate monitoring of neurite growth kinetics in a neuronal culture. This device permits to measure the maximum achievable length of projecting neurites over time and to report variations in neurite length under several conditions. Our results show that, by applying positive or negative hydrostatic pressure to primary rat hippocampal neurons, neurite growth kinetics can be tuned. This work presents a pioneering approach for the precise characterization of neurite length dynamics within an in vitro minimalistic environment.

In nature and in technical systems, microbial cells are often exposed to rapidly fluctuating environmental conditions. These conditions can vary in quality, e.g., existence of a starvation zone, and quantity, e.g., average residence time in this zone. For strain development and process design, cellular response to such fluctuations needs to be systematically analysed. However, the existing methods for physically emulating rapidly changing environmental conditions are limited in spatio-temporal resolution. Hence, we present a novel microfluidic system for cultivation of single cells and small cell clusters under dynamic environmental conditions (dynamic microfluidic single-cell cultivation (dMSCC)). This system enables to control nutrient availability and composition between two media with second to minute resolution. We validate our technology using the industrially relevant model organism Corynebacterium glutamicum. The organism was exposed to different oscillation frequencies between nutrient excess (feasts) and scarcity (famine). Resulting changes in cellular physiology, such as the colony growth rate and cell morphology were analysed and revealed significant differences with growth rate and cell length between the different conditions. dMSCC also allows to apply defined but randomly changing nutrient conditions, which is important for reproducing more complex conditions from natural habitats and large-scale bioreactors. The presented system lays the foundation for the cultivation of cells under complex changing environmental conditions.

While many cell culture systems are sensitive to the conditions in which cells are introduced into the system, we find that in situ differentiated tube-shaped microfluidic organoids have a particularly high sensitivity. Preliminary experiments using conventional seeding techniques revealed that biases in initial cell number and distribution dramatically impacted organoid shape and behavior downstream. Residual flows during seeding further complicated the process, dispersing cells to undesirable locations within the chip. To address this problem, a a robotic seeding system for controlling the process of inserting cells into microfluidic chips was developed. Environmental control of temperature, CO2, and humidity was implemented by modifying a commercial Arduino-controlled incubator. An eight-channel syringe pump controlled flow to eight cell dispensers, while a vertical leadscrew stage raised and lowered them, and a set of stackable flexure micromanipulators individually controlled the X and Y position of each cell dispenser. The flexure manipulators were 3D printed, driven by low-cost motors and electronics, and required little assembly and no alignment, resulting in a cheap and scalable method of controlling a dense array of micromanipulators. A dual objective microscope on a motorized gantry used an oblique lighting system to observe the seeding process, allowing for real-time interventions or passive observation of automated protocols. The robotic cell seeding system provided a platform for optimizing a sensitive process towards increasing the repeatability and physiological relevance of tube-shaped microfluidic organoids.

Population heterogeneity in bacterial phenotypes, such as antibiotic resistance, is increasingly recognized as a medical concern. Heteroresistance (HR) occurs when a predominantly susceptible bacterial population harbors a rare resistant subpopulation. During antibiotic exposure, these resistant bacteria can be selected and lead to treatment failure. Standard antibiotic susceptibility testing (AST) methods often fail to reliably detect these subpopulations due to their low frequency, highlighting the need for new diagnostic approaches. Here, we present a droplet microfluidics method where bacteria are encapsulated in droplets containing growth medium and antibiotics. The growth of rare resistant cells is detected by observing droplet shrinkage under microscopy. We validated this method for three clinically important antibiotics in Escherichia coli isolates obtained from bloodstream infections and showed that it can detect resistant subpopulations as infrequent as 10-6 using only 200 to 300 droplets. Additionally, we designed a multiplex microfluidic chip to increase the throughput of the assay.

Neuropathic pain remains a significant challenge due to limited understanding of sensory signal transmission mechanisms along the sensory pathway. The sensory pathway involves peripheral nociceptors in the dorsal root ganglia (DRG) that transmit signals from skin to the central nervous system via dorsal horn neurons. Current in vitro models lack the compartmentalization and resolution needed to investigate signal modulation at distinct anatomical sites along this pathway. Here, we developed a three-compartment microfluidic platform combining human induced pluripotent stem cell (iPSC)-derived sensory neurons (hiSNs) with human primary epidermal keratinocytes (HPEKs) and iPSC-derived dorsal horn neurons (hiDHNs) in a spatially organized arrangement. The platform integrates polydimethylsiloxane (PDMS) axon-guiding microstructures with high-density microelectrode arrays (HD-MEAs), enabling single-axon electrophysiological recordings and sub-cellular level stimulation. We established viable co-cultures maintained for up to six weeks and characterized spontaneous activity across all conditions. Keratinocytes increased the number of active sensory neuron axons and their firing rates, demonstrating peripheral modulation of neuronal activity. Systematic frequency-dependent electrical stimulation revealed low-pass filtering properties at sensory neuron somata, with filtering characteristics modulated by co-culture with keratinocytes. This platform enables compartment-specific investigation of signal processing in the human sensory pathway and provides a tool for studying neuropathic pain mechanisms and testing potential therapeutics.

The morphology of bacteria is modified by antibiotic stress while also serving to survive the antibiotic. However associating morphological descriptors with quantitative measurements of cell survival remains elusive. Here we present a workflow to generate morphological signatures for the progeny of individual cells for 168 different antibiotic conditions. The workflow uses stationary microfluidic droplets, to encapsulate and grow bacteria, and confocal microscopy to image the contents of each droplet. A custom image analysis pipeline is developed to interact with the images in order to label of the morphologies within a subset of the images and train a neural network. The network yields a multidimensional morphological signature for 82000 droplets, showing the co-existence of different morphologies even for the progeny of individual cells. The morphological signatures are different for varying antibiotic type and concentration, thus providing a way to distinguish antibiotics by their mode of action. By combining these morphological signatures with the digital detection of survival within droplets, this workflow can serve to understand the emergence of antibiotic resistance or to identify antimicrobial activity of unknown substances.

There is a pressing need for functional testing platforms that use human, live tumor tissue to better predict traditional and immunotherapy responses. Such platforms should also retain as much of the native tumor microenvironment (TME) as possible, as many cancer drug actions rely on TME-dependent mechanisms. Present high-throughput testing platforms that have some of these features, e.g. based on patient-derived tumor organoids, require a growth step that alters the TME. On the other hand, micro-dissected tumor tissue "spheroids" that retain an intact TME have shown promising responses to immunomodulators acting on native immune cells. Here we demonstrate a microfluidic 96-well platform designed for drug treatment of hundreds of similarly-sized, cuboidal micro-tissues ("cuboids") produced from a single tumor sample. Four cuboids per well are automatically arrayed into the platform using hydrodynamic trapping. The microfluidic device, entirely fabricated in thermoplastics, features microvalves that fluidically isolate each well after the cuboid loading step. Since the platform effectively makes the most of scarce tumor tissue, we believe it could ultimately be applied to human biopsies for drug discovery and personalized oncology, altogether bypassing animal testing.

Neurodegenerative diseases represent an increasing health burden, with a worrying lack of models recapitulating the hallmarks of the pathology. Recently, lab-on-a-chip technology has opened new reliable alternatives to conventional in vitro models able to replicate key aspects of human physiology. For instance, microfluidics allows to mimic the extracellular accumulation of misfolded proteins in the finely controlled microenvironment, thanks to the intrinsic high surface-area-to-volume ratio. Automated microfluidic platforms offer advantages in implementing high-throughput, standardized and parallelized assays, suitable for drug screenings and developing new therapeutic approaches in a cost-effective way. However, the major challenges in the broad application of automated lab-on-a-chip in biological research are the lack of production robustness and ease of use of the devices. Here, we present an automated microfluidic platform able to host the rapid conversion of human induced pluripotent stem cells (hiPSCs) into neurons via NGN2 viral programming in a user-friendly manner. The design of the platform, built with multilayer soft-lithography techniques, shows easiness in the fabrication and assembly thanks to the simple geometry and experimental reproducibility at the same time. The all operations are automatically managed from the cell seeding, medium change, doxycycline-mediated neuronal induction, and selection of the genetically engineered cells, to the analysis, including immunofluorescence assay. Our results show a high-throughput, efficient and homogenous conversion of hiPSCs in neurons in 10 days showing the expression of mature marker MAP2, and calcium signaling. The neurons-on-chip model here described represents a fully automated loop system able to address the challenges in the field of neurodegenerative diseases and improve current preclinical models.

Antimicrobial susceptibility testing (AST) remains the cornerstone of effective antimicrobial selection and optimization in patients. Despite recent advances in rapid pathogen identification and resistance marker detection with molecular diagnostics, phenotypic AST methods remain relatively unchanged over the last few decades. Guided by the principles of microfluidics, we describe the application of a multi-liquid-phase microfluidic system, named under-oil open microfluidic systems (UOMS) to achieve a rapid phenotypic AST. UOMS provides a next-generation solution for AST (UOMS-AST) by implementing and recording a pathogen antimicrobial activity in micro-volume testing units under an oil overlay with label-free, single-cell resolution optical access. UOMS-AST can accurately and rapidly determine antimicrobial activity from nominal sample/bacterial cells in a system aligned with clinical laboratory standards. Further, we combine UOMS-AST with cloud lab data analytic techniques for real-time image analysis and report generation to provide a rapid (i.e., <4 h) sample-to-answer turnaround time, shedding light on its utility as a next-generation phenotypic AST platform for clinical application.

How distinct regional identities emerge within a single developing brain remains poorly understood. Current in vitro models address this by fusing independently generated organoids, but this introduces variability in size, maturation state, and connectivity, confounding the study of regionalization itself. Here, we present a microfluidic platform that supports the co-development of different tissue identities within a single, continuous 3D culture domain. The device integrates controlled microfluidic flow with real-time fluorescence imaging, providing stable perfusion and high-resolution tracking of molecular transport without the need for embedded sensors or disruptive sampling. By delivering SAG, a Sonic hedgehog pathway agonist, to one surface of mouse forebrain organoids, we induced spatially segregated ventral (Nkx2.1+) and dorsal (Pax6+) domains within a unified tissue architecture. Controlled morphogen delivery is sufficient to drive region-specific fate specification without organoid fusion, offering a practical, scalable alternative for studying tissue regionalization in vitro.

Tumor models that recapitulate 3D architecture are essential for understanding how cellular organization and microenvironmental interactions govern therapeutic response in human cancers. Here, we developed a microfluidic microphysiological system that enables controlled and scalable culture and drug testing of non-small cell lung cancer spheroids and patient-derived organoids. The platform integrated U-shaped microwells with dual-channel loading to support de novo spheroid formation, efficient trapping of pre-formed spheroids, and loading of intact organoids with reduced size heterogeneity. Tumor spheroids and organoids maintained high viability and structural integrity during long-term on-chip culture, and constrained microscale confinement produced ellipsoidal geometries that deviate from idealized spherical assumptions. Baseline genotype-dependent responses to KRAS G12C and EGFR inhibitors were preserved across agarose and microfluidic formats, establishing a validated reference state. Building on this baseline, fibroblast- and endothelial-derived cues consistently attenuated responses to targeted therapies across conditioned media, mixed co-culture, and spatially organized configurations. Resistance phenotypes converged on a dominant role for paracrine signaling, while increasing architectural complexity primarily enhanced morphological fidelity rather than altering therapeutic response. These findings establish a microphysiological framework that decouples tumor-intrinsic drug sensitivity from microenvironment-mediated modulation, enabling the systematic evaluation of paracrine resistance mechanisms in NSCLC.