Biomicrofluidics最新文献

Thanks to their softness, biocompatibility, porosity, and ready availability, hydrogels are commonly used in microfluidic assays and organ-on-chip devices as a matrix for cells. They not only provide a supporting scaffold for the differentiating cells and the developing organoids, but also serve as the medium for transmitting oxygen, nutrients, various chemical factors, and mechanical stimuli to the cells. From a bioengineering viewpoint, the transmission of forces from fluid perfusion to the cells through the hydrogel is critical to the proper function and development of the cell colony. In this paper, we develop a poroelastic model to represent the fluid flow through a hydrogel containing a biological cell modeled as a hyperelastic inclusion. In geometries representing shear and normal flows that occur frequently in microfluidic experiments, we use finite-element simulations to examine how the perfusion engenders interstitial flow in the gel and displaces and deforms the embedded cell. The results show that pressure is the most important stress component in moving and deforming the cell, and the model predicts the velocity in the gel and stress transmitted to the cell that is comparable to in vitro and in vivo data. This work provides a computational tool to design the geometry and flow conditions to achieve optimal flow and stress fields inside the hydrogels and around the cell.

Despite the widespread application of microfluidic chips in research fields, such as cell biology, molecular biology, chemistry, and life sciences, the process of designing new chips for specific applications remains complex and time-consuming, often relying on experts. To accelerate the development of high-performance and high-throughput microfluidic chips, this paper proposes an automated Deterministic Lateral Displacement (DLD) chip design algorithm based on reinforcement learning. The design algorithm proposed in this paper treats the throughput and sorting efficiency of DLD chips as key optimization objectives, achieving multi-objective optimization. The algorithm integrates existing research results from our team, enabling rapid evaluation and scoring of DLD chip design parameters. Using this comprehensive performance evaluation system and deep Q-network technology, our algorithm can balance optimal separation efficiency and high throughput in the automated design process of DLD chips. Additionally, the quick execution capability of this algorithm effectively guides engineers in developing high-performance and high-throughput chips during the design phase.

To address the growing need for accurate lung models, particularly in light of respiratory diseases, lung cancer, and the COVID-19 pandemic, lung-on-a-chip technology is emerging as a powerful alternative. Lung-on-a-chip devices utilize microfluidics to create three-dimensional models that closely mimic key physiological features of the human lung, such as the air-liquid interface, mechanical forces associated with respiration, and fluid dynamics. This review provides a comprehensive overview of the fundamental components of lung-on-a-chip systems, the diverse fabrication methods used to construct these complex models, and a summary of their wide range of applications in disease modeling and aerosol deposition studies. Despite existing challenges, lung-on-a-chip models hold immense potential for advancing personalized medicine, drug development, and disease prevention, offering a transformative approach to respiratory health research.

The gut-brain axis (GBA) connects the gastrointestinal tract and the central nervous system (CNS) via the peripheral nervous system and humoral (e.g., circulatory and lymphatic system) routes. The GBA comprises a sophisticated interaction between various mammalian cells, gut microbiota, and systemic factors. This interaction shapes homeostatic and pathophysiological processes and plays an important role in the etiology of many disorders including neuropsychiatric conditions. However, studying the underlying processes of GBA in vivo, where numerous confounding factors exist, is challenging. Furthermore, conventional in vitro models fall short of capturing the GBA anatomy and physiology. Microfluidic platforms with integrated sensors and actuators are uniquely positioned to enhance in vitro models by representing the anatomical layout of cells and allowing to monitor and modulate the biological processes with high spatiotemporal resolution. Here, we first briefly describe microfluidic technologies and their utility in modeling the CNS, vagus nerve, gut epithelial barrier, blood-brain barrier, and their interactions. We then discuss the challenges and opportunities for each model, including the use of induced pluripotent stem cells and incorporation of sensors and actuator modalities to enhance the capabilities of these models. We conclude by envisioning research directions that can help in making the microfluidics-based GBA models better-suited to provide mechanistic insight into pathophysiological processes and screening therapeutics.

The micromechanical measurement field has struggled to establish repeatable techniques because the deforming stresses can be difficult to model. A recent numerical study [Lu et al., J. Fluid Mech. 962, A26 (2023)] showed that viscoelastic capsules flowing through a cross-slot can achieve a quasi-steady strain near the extensional flow stagnation point that is equal to the equilibrium static strain, thereby implying that the capsule's elastic behavior can be captured in continuous device operation. However, no experimental microfluidic cross-slot studies have reported quasi-steady strains for suspended cells or particles to our knowledge. Here, we demonstrate experimentally the conditions necessary for the cross-slot microfluidic device to replicate a uniaxial creep test at the microscale and at relatively high throughput. By using large dimension cross-slots relative to the microparticle diameter, our cross-slot implementation creates an extensional flow region that is large enough for agarose hydrogel microparticles to achieve a strain plateau while dwelling near the stagnation point. This strain plateau will be key for accurately and precisely measuring viscoelastic properties of small microscale biological objects. We propose an analytical mechanical model to extract linear viscoelastic mechanical properties from observed particle strain histories. Particle image velocimetry measurements of the unperturbed velocity field is used to estimate where in the device particles experienced extensional flow and where the mechanical model might be applied to extract mechanical property measurements. Finally, we provide recommendations for applying the cross-slot microscale creep experiment to other biomaterials and criteria to identify particles that likely achieved a quasi-steady strain state.

Droplet microfluidics has emerged as a versatile and powerful tool for various analytical applications, including single-cell studies, synthetic biology, directed evolution, and diagnostics. Initially, access to droplet microfluidics was predominantly limited to specialized technology labs. However, the landscape is shifting with the increasing availability of commercialized droplet manipulation technologies, thereby expanding its use to non-specialized labs. Although these commercial solutions offer robust platforms, their adaptability is often constrained compared to in-house developed devices. Consequently, both within the industry and academia, significant efforts are being made to further enhance the robustness and automation of droplet-based platforms, not only to facilitate technology transfer to non-expert laboratories but also to reduce experimental failures. This Perspective article provides an overview of recent advancements aimed at increasing the robustness and accessibility of systems enabling complex droplet manipulations. The discussion encompasses diverse aspects such as droplet generation, reagent addition, splitting, washing, incubation, sorting, and dispensing. Moreover, alternative techniques like double emulsions and hydrogel capsules, minimizing or eliminating the need for microfluidic operations by the end user, are explored. These developments are foreseen to facilitate the integration of intricate droplet manipulations by non-expert users in their workflows, thereby fostering broader and faster adoption across scientific domains.

Atmospheric ice-nucleating particles (INPs) make up a vanishingly small proportion of atmospheric aerosol but are key to triggering the freezing of supercooled liquid water droplets, altering the lifetime and radiative properties of clouds and having a substantial impact on weather and climate. However, INPs are notoriously difficult to model due to a lack of information on their global sources, sinks, concentrations, and activity, necessitating the development of new instrumentation for quantifying and characterizing INPs in a rapid and automated manner. Microfluidic technology has been increasingly adopted by ice nucleation research groups in recent years as a means of performing droplet freezing analysis of INPs, enabling the measurement of hundreds or thousands of droplets per experiment at temperatures down to the homogeneous freezing of water. The potential for microfluidics extends far beyond this, with an entire toolbox of bioanalytical separation and detection techniques developed over 30 years for medical applications. Such methods could easily be adapted to biological and biogenic INP analysis to revolutionize the field, for example, in the identification and quantification of ice-nucleating bacteria and fungi. Combined with miniaturized sampling techniques, we can envisage the development and deployment of microfluidic sample-to-answer platforms for automated, user-friendly sampling and analysis of biological INPs in the field that would enable a greater understanding of their global and seasonal activity. Here, we review the various components that such a platform would incorporate to highlight the feasibility, and the challenges, of such an endeavor, from sampling and droplet freezing assays to separations and bioanalysis.

Circulating tumor cells are central to metastasis, a particularly malign spread of cancer beyond its original location. While rare, there is growing evidence that the clusters of circulating tumor cells are significantly more harmful than individual cells. Microfluidic platforms constitute the core of circulating tumor cell cluster research, allowing cluster detection, analysis, and treatment. In this work, we propose a new mathematical model of circulating tumor cell clusters and apply it to simulate the dynamics of the aggregates inside a microfluidic channel with the external flow of a fluid. We leverage our previous model of the interactions of circulating tumor cells with varying clustering affinities and introduce explicit bonds between the cells that makeup a cluster. We show that the bonds have a visible impact on the cluster dynamics and that they enable the reproduction of known cluster flow and deformation patterns. Furthermore, we demonstrate that the dynamics of these aggregates are sensitive to bond properties, as well as initialization and flow conditions. We believe that our modeling framework represents a valuable mesoscopic formulation with an impact beyond circulating tumor cell clusters, as cell aggregates are common in both nature and applications.

Gap junction connectivity is crucial to intercellular communication and plays a key role in many critical processes in developmental biology. However, direct analysis of gap junction connectivity in populations of developing cells has proven difficult due to the limitations of patch clamp and dye diffusion based technologies. We re-examine a microfluidic technique based on the principle of laminar flow, which aims to electrically measure gap junction connectivity. In the device, the trilaminar flow of a saline sheathed sucrose solution establishes distinct regions of electrical conductivity in the extracellular fluid spanning an NRK-49F cell monolayer. In theory, the sucrose gap created by laminar flow provides sufficient electrical isolation to detect electrical current flows through the gap junctional network. A novel calibration approach is introduced to account for stream width variation in the device, and elastomeric valves are integrated to improve the performance of gap junction blocker assays. Ultimately, however, this approach is shown to be ineffective in detecting changes in gap junction impedance due to the gap junction blocker, 2-APB. A number of challenges associated with the technique are identified and analyzed in depth and important improvements are described for future iterations.

In the field of microfluidics, high-pressure microfluidics technology, which utilizes high driving pressure for microfluidic analysis, is an evolving technology. This technology combines microfluidics and pressurization, where the flow of fluid is controlled by means of high-pressure-driven devices greater than 10 MPa. This paper first reviews the existing high-pressure microfluidics systems and describes their components and applications. Then, it summarizes several materials used in the microfabrication of high-pressure microfluidics chips, reviewing their properties, processing methods, and bonding methods. In addition, advanced laser processing techniques for the microfabrication of high-pressure microfluidics chips are described. Last, the paper examines the analytical detection methods employed in high-pressure microfluidics systems, encompassing optical and electrochemical detection methods. The review of analytical detection methods shows the different functions and application scenarios of high-pressure microfluidics systems. In summary, this study provides an efficient and advanced microfluidics system, which can be widely used in chemical engineering, food industry, and environmental engineering under high pressure conditions.