Lab on a Chip最新文献

Drug development is a costly and timely process with high risks of failure during clinical trials. Although in vitro tissue models have significantly advanced over the years, thus fostering a transition from animal-derived models towards human-derived models, failure rates still remain high. Current cell-based assays are still not able to provide an accurate prediction of the clinical success or failure of a drug candidate. To overcome the limitations of current methods, a variety of microfluidic systems have been developed as powerful tools that are capable of mimicking (micro)physiological conditions more closely by integrating physiological fluid flow conditions, mechanobiological cues and concentration gradients, to name only a few. One major advantage of these biochip-based tissue cultures, however, is their ability to seamlessly connect different organ models, thereby allowing the study of organ-crosstalk and metabolic byproduct effects. This is especially important when assessing absorption, distribution, metabolism, and excretion (ADME) processes of drug candidates, where an interplay between various organs is a prerequisite. In the current review, a number of in vitro models as well as microfluidic dual- and multi-organ systems are summarized with a focus on absorption (skin, lung, gut) and metabolism (liver). Additionally, the advantage of multi-organ chips in identifying a drug's on and off-target toxicity is discussed. Finally, the potential high-throughput implementation and modular chip design of multi-organ-on-a-chip systems within the pharmaceutical industry is highlighted, outlining the necessity of reducing handling complexity.

Current drug development pipelines are time-consuming and prone to a significant percentage of failure, partially due to the limited availability of advanced human preclinical models able to better replicate the in vivo complexity of our body. To contribute to an advancement in this field, we developed an in vitro multi-organ-on-a-chip system, that we named PEGASO platform, which enables the dynamic culturing of human cell-based models relevant for drug testing. The PEGASO platform is composed of five independent connected units, which are based on a previously developed millifluidic organ-on-a-chip device (MINERVA 2.0), hosting human primary cells and iPSC-derived cells recapitulating key biological features of the gut, immune system, liver, blood–brain-barrier and brain that were fluidically connected and challenged to model the physiological passage of donepezil, a drug prescribed for Alzheimer's disease. The nutrient medium flow rate of the connected units was tuned to obtain suitable oxygenation and shear stress values for the cells cultured in dynamic condition. A computational model was at first developed to simulate donepezil transport within the platform and to assess the drug amount reaching the last organ-on-a-chip. Then, we demonstrated that after 24 hours of donepezil administration, the drug was actually transported though the cell-based models of the platform which in turn were found viable and functional. Donepezil efficacy was confirmed by the decreased acetylcholinesterase activity at the brain model and by the increased expression of a donepezil-relevant multi-drug transporter (P-gp). Overall, the PEGASO platform is an innovative in vitro tool for drug screening and personalized medicine applications which holds the potential to be translated to preclinical research and improve new drug development pipelines.

Non-Newtonian droplets are used across various applications, including pharmaceuticals, food processing, drug delivery and material science. However, predicting droplet formation using such complex fluids is challenging due to the intricate multiphase interactions between fluids with varying viscosities, elastic properties and geometrical constraints. In this study, we introduce a novel hybrid machine-learning architecture that integrates dimensional analysis with machine learning to predict the flow rates required to generate droplets with specified sizes in systems involving non-Newtonian fluids. Unlike previous approaches, our model is designed to accommodate shear-rate-dependent viscosities and a simple estimate of the elastic properties of the fluids. It provides accurate predictions of the dispersed and continuous phases flow rates for given droplet length, height, and viscosity curves, even when the fluid properties deviate from those used during training. Our model demonstrates strong predictive power, achieving R² values of up to 0.82 for unseen data. The significance of our work lies in its ability to generalize across a broad range of non-Newtonian systems having different viscosity curves, offering a powerful tool for optimizing droplet generation. This model represents a significant advancement in the application of machine learning to microfluidics, providing new opportunities for efficient experimental design in complex multiphase systems.

Amino acids are essential for protein synthesis and metabolic processes in support of homeostatic balance and healthy body functions. This study quantitatively investigates eccrine sweat as a significant channel for loss of amino acids during exercise, to improve an understanding of amino acid turnover and to provide feedback to users on the need for supplement intake. The measurement platform consists of a soft, skin-interfaced microfluidic system for real-time analysis of amino acid content in eccrine sweat. This system relies on integrated fluorometric assays and smartphone-based imaging techniques for quantitative analysis, as a simple, cost-effective approach that does not require electrochemical sensors, electronics or batteries. Human subject studies reveal substantial amino acid losses in sweat from working muscle regions during prolonged physical activities, thereby motivating the need for dietary supplementation. The findings suggest potential applications in healthcare, particularly in athletic and clinical settings, where maintaining amino acid balance is critical for ensuring proper homeostasis.

Over the past 15 years, smartphones have had a transformative effect on everyday life. These devices also have the potential to transform molecular analysis over the next 15 years. The cameras of a smartphone, and its many additional onboard features, support optical detection and other aspects of engineering an analytical device. This article reviews the development of smartphones as platforms for portable chemical and biological analysis. It is equal parts conceptual overview, technical tutorial, critical summary of the state of the art, and outlook on how to advance smartphones as a tool for analysis. It further discusses the motivations for adopting smartphones as a portable platform, summarizes their enabling features and relevant optical detection methods, then highlights complementary technologies and materials such as 3D printing, microfluidics, optoelectronics, microelectronics, and nanoparticles. The broad scope of research and key advances from the past 7 years are reviewed as a prelude to a perspective on the challenges and opportunities for translating smartphone-based lab-on-a-chip devices from prototypes to authentic applications in health, food and water safety, environmental monitoring, and beyond. The convergence of smartphones with smart assays and smart apps powered by machine learning and artificial intelligence holds immense promise for realizing a future for molecular analysis that is powerful, versatile, democratized, and no longer just the stuff of science fiction.

Biological nanomaterials have unique magnetic and density characteristics that can be employed to isolate them into subpopulations. Extracellular nanovesicles (EVs) are crucial for cellular communication; however, their isolation poses significant challenges due to their diverse sizes and compositions. We present EV-Lev, a microfluidic magnetic levitation technique for high-throughput, selective isolation of small EVs (<200 nm) from human plasma. EV-Lev overcomes the challenges posed by the subtle buoyancy characteristics of EVs, whose small size and varied densities complicate traditional magnetic levitation techniques. It employs antibody-coated polymer beads of varying densities, integrating immuno-affinity and microfluidics to isolate EVs from sub-milliliter plasma volumes efficiently. It facilitates rapid, simultaneous sorting of EV subpopulations based on surface markers, such as CD9, CD63, and CD81, achieving high yield and purity. Subsequent size and morphology analyses confirmed that the isolated EVs maintain their structural integrity. EV-Lev could help uncover the cargo and function of EV subpopulations associated with multiple diseases including cancer, infectious diseases and help to discover potential biomarkers in small volume samples, while offering a portable, cost-effective, and straightforward assay scheme.

Soft actuators have developed over the last decade for diverse applications including industrial machines and biomedical devices. Integration of chemical sensors with soft actuators would be beneficial in analyzing chemical and environmental conditions, but there have been limited devices to achieve such sensing capabilities. In this work, we developed a thin-film soft actuator integrated with a paper-based chemical sensor, termed a microfluidic paper-based analytical soft actuator (μPAC). μPAC consists of (1) a silicone thin film with a 3D-printed pneumatic chamber and (2) a cellulose paper. This cellulose paper offers dual functions: the strain-limiting layer of a soft actuator and the substrate for the chemical sensor for a paper-based analytical device (μPAD). We characterized the design parameters of the actuators-namely, (1) thickness of silicone thin film, (2) chamber length, and (3) Young's modulus of silicone thin film-to evaluate the actuation performance. These characterizations suggested that the cellulose paper served as a suitable self-straining layer of the actuator, making μPAC a chemical sensor that can actuate simultaneously. Highlighting the unique capability of μPAC, we demonstrated the local detection of pH on the curved target surface. Overall, this research demonstrated the rapid fabrication of actuating chemical sensors with a unique design by combining soft actuators and μPAD, enabling chemical sensing on various surface topologies by dynamically making conformal contact.

Hydrogel fibers are promising biomaterials for a broad range of biomedical applications, including biosensing, drug delivery, and tissue engineering. Different types of microfluidic devices have been developed for hydrogel fiber spinning, however, they often require skillful fabrication procedures with special instruments such as 3D printers and clean-room facilities. On the other hand, microfluidic devices with predetermined and fixed configurations are susceptible to clotting, contamination, and damage, thereby creating a significant barrier for potential users. Herein, we describe a plug-and-play (PnP) microfluidic device for hydrogel fiber spinning. The PnP device was designed to be assembled in a modular manner based on simple mounting of PDMS elastomers on commercial Lego® blocks. Easy disassembly and re-assembly make the device user-friendly, since cleaning or replacing individual modules is convenient. We demonstrated the application of our PnP microfluidic device in alginate (Alg) hydrogel fiber spinning by using a single-module or double-module device. Moreover, thanks to the PnP approach, multi-layered fibers can be produced by using a triple-module device. As proof-of-principle, we fabricated pH-sensitive multi-layered fibers that could be used for monitoring biological environments, showcasing the potential of such a PnP device in advancing biomedical research related to functional fibers.

High-throughput multi-analyte point-of-care detection is often constrained by the limited number of analytes that can be effectively monitored. This study introduces bio-inspired microfluidic designs optimized for multi-analyte detection using 38–42 biosensors. Drawing inspiration from the human spinal cord and leaf vein networks, these perfusion-oriented designs ensure uniform flow velocity and consistent molecular capture while maintaining spatial separation to prevent cross-talk. In silico optimizations achieved velocity profile uniformity with coefficients of variance of 0.89% and 0.86% for the spine- and leaf-inspired designs, respectively. However, simulations revealed that velocity uniformity alone is insufficient for accurate molecular capture prediction without consistent reaction site channel dimensions. The bio-inspired designs demonstrated superior performance, stabilizing—coefficient of variance below 20%—in DNA capture within 10 minutes, compared to 68 minutes for a simple branched design. This work underscores the potential of bio-inspired microfluidics to enable scalable, uniform, and high-performance systems for multi-analyte detection.

A longstanding challenge in microfluidics has been the efficient delivery of fluids from macro-scale pumping systems into microfluidic devices, known as the “world-to-chip” problem. Thus far, the entire industry has accepted the use of imperfect, rigid tubing and connectors as the ecosystem within which to operate, which, while functional, are often cumbersome, labor-intensive, prone to errors, and ill-suited for high-throughput experimentation. In this paper, we introduce TapeTech microfluidics, a flexible and scalable solution designed to address the persistent “world-to-chip” problem in microfluidics, particularly in organ-on-a-chip (OoC) applications. TapeTech offers a streamlined alternative, utilizing adhesive tape and thin-film polymers to create adaptable, integrated multi-channel ribbon connectors that simplify fluidic integration with pumps and reservoirs. Key features of TapeTech include reduced pressure surges, easy priming, rapid setup, easy multiplexing, and broad compatibility with existing devices and components, which are essential for maintaining stable fluid dynamics and protecting sensitive cell cultures. Furthermore, TapeTech is designed to flex around the lids of Petri dishes, enhancing sterility and transportability by enabling easy transfer between incubators, biosafety cabinets (BSCs), and microscopes. The rapid design-to-prototype iteration enabled by TapeTech allows users to quickly develop connectors for a wide range of microfluidic devices. Importantly, we showcase the utility of TapeTech in OoC cultures requiring fluid flow. We also highlight other utilities, such as real-time microscopy and a well-plate medium exchanger. The accessibility of this technology should enable more laboratories to simplify design and setup of microfluidic experiments, and increase technology adoption.