Lab on a Chip最新文献

Paper-based microfluidic devices offer an ideal platform for biological and environmental detection because they are low-cost, small, disposable, and fill by natural capillary action. In this tutorial review, we discuss the surface modification of paper-based microfluidic devices with functional polymers using the initiated chemical vapor deposition (iCVD) process. The iCVD process is solventless and therefore ideal for coating cellulose paper because there are no surface tension effects or solvent compatibility issues. The process can also be scaled up for roll-to-roll manufacturing. The chemical functionality of the iCVD coating can be tuned by varying the monomer and the structure of the coating can be tuned by varying the processing parameters.

Microfluidics provides unique opportunities for the high throughput selection of motile sperm with improved DNA integrity for assisted reproductive technologies (ARTs). Here, through a parametric study on dimensions and geometrical angles, a butterfly-shaped chip (BSC) is presented to isolate sperm with high progressive motility and intact DNA at a separation rate of 1125 sperm per minute. Using finite element simulations, the flow field and shear rates in the device were optimized to leverage the inherent motility characteristics of sperm for maximum selection throughput. The device incorporates a triple selection mechanism in series, initially activating sperm rheotaxis by rotation against the semen flow, penetrating the counter buffer flow and swimming against the direction of the buffer flow, leaving dead cells and debris behind, and subsequently leveraging boundary-following behavior to direct progressively motile sperm to swim along the walls and reach the device outlet. The device selects over 4.1 million sperm per mL within 20 minutes, with 29.2%, 68.2%, and 57.3% improvement in total motility, DNA integrity, and velocity parameter (VCL), as compared with the conventional swim-up method, respectively. Overall, the performance of the device to separate sperm with approximately 95.9% total motility, 97.8% viability, and 96.6% DNA integrity at high concentrations demonstrates its potential for enhancing the efficiency of conventional treatment methods.

Heterogeneous particles co-focusing to a single stream is a vital prerequisite for cell counting and enumeration, playing an essential role in flow cytometry and single-cell analysis. Microfluidics-based inertial focusing holds great research prospects due to its simplicity of devices, ease of operation, high throughput, and freedom from external fields. Combining microfluidic channels with two or more different geometries has become a powerful tool for high-efficiency particle focusing. Here, we explored hybrid microfluidic channels for heterogeneous particle co-focusing. Four different annular channels with obstacles distributed on the inner wall were constructed and simulated, obtaining constantly variable secondary flows. Then we used four different fluorescent particles with the size of 10 μm, 12 μm 15 μm, and 20 μm as well as their mixture to perform the inertial focusing experiments of multi-sized particles. Theoretical simulation and experimental results demonstrated a focusing efficiency of >99%. Finally, we further utilized human white blood cells to estimate the co-focusing performance of our hybrid microfluidic channel, resulting in a high focusing efficiency of >92% and a high throughput of ≈8000 cell s⁻¹. The hybrid microfluidic channels, capable of high-precision heterogeneous particle co-focusing, could pave a broad avenue for microfluidic flow cytometry and single-cell analysis.

Microfluidic technology widely used in generating monodisperse emulsion droplets often suffers from complexity, scalability, applicability to practical fluids, as well as operation instability due to its susceptibility to flow perturbations, low clearance, and depletion of surfactants. Herein, we present a monolithic 3D-printed step-emulsification device (3D-PSD) for scalable and robust production of high viscosity emulsion droplets up to 208.16 mPa s, which cannot be fully addressed using conventional step-emulsification devices. By utilizing stereo-lithography (SLA), 24 triangular nozzles with a pair of 3D void flow distributors are integrated within the 3D-PSD to ensure uniform flow distribution followed by monodisperse droplet formation. The outlets positioned vertically downward enables gravity-assisted clearing to prevent droplet accumulation and thereby maintain size monodispersity. Deposition of silica nanoparticles (SiNP) within the device was also shown to alter the surface wettability from hydrophobic to hydrophilic, enabling the production of both water-in-oil (W/O) as well as oil-in-water (O/W) emulsion droplets, operated at a maximum production rate of up to 50 mL h⁻¹. The utility of the device is further verified through continuous production of biodegradable polycaprolactone (PCL) microparticles using O/W emulsion as templates. We envision that the 3D-PSD presented in this work marks a significant leap in high-throughput production of high viscosity emulsion droplets as well as the particle analogs.

Artificial intelligence (AI) is revolutionizing medicine by automating tasks like image segmentation and pattern recognition. These AI approaches support seamless integration with existing platforms, enhancing diagnostics, treatment, and patient care. While recent advancements have demonstrated AI superiority in advancing microfluidics for point of care (POC) diagnostics, a gap remains in comparative evaluations of AI algorithms in testing microfluidics. We conducted a comparative evaluation of AI models specifically for the two-class classification problem of identifying the presence or absence of bubbles in microfluidic channels under various imaging conditions. Using a model microfluidic system with a single channel loaded with 3D transparent objects (bubbles), we challenged each of the tested machine learning (ML) (n = 6) and deep learning (DL) (n = 9) models across different background settings. Evaluation revealed that the random forest ML model achieved 95.52% sensitivity, 82.57% specificity, and 97% AUC, outperforming other ML algorithms. Among DL models suitable for mobile integration, DenseNet169 demonstrated superior performance, achieving 92.63% sensitivity, 92.22% specificity, and 92% AUC. Remarkably, DenseNet169 integration into a mobile POC system demonstrated exceptional accuracy (>0.84) in testing microfluidics at under challenging imaging settings. Our study confirms the transformative potential of AI in healthcare, emphasizing its capacity to revolutionize precision medicine through accurate and accessible diagnostics. The integration of AI into healthcare systems holds promise for enhancing patient outcomes and streamlining healthcare delivery.

Isothermal nucleic acid amplification tests (NAATs) are a vital tool for point-of-care (POC) diagnostics. These assays are well-suited for rapid, low-cost POC diagnostics for infectious diseases compared to traditional PCR tests conducted in central laboratories. There has been significant development of POC NAATs using paper-based diagnostic devices because they provide an affordable, user-friendly, and easy to store format; however, the difficulties in integrating separate liquid components, resuspending dried reagents, and achieving a low limit of detection hinder their use in commercial applications. Several studies report low assay efficiencies, poor detection output, and poorer limits of detection in porous membranes compared to traditional tube-based protocols. Recombinase polymerase amplification is a rapid, isothermal NAAT that is highly suited for POC applications, but requires viscous reaction conditions that has poor performance when amplifying in a porous paper membrane. In this work, we show that we can dramatically improve the performance of membrane-based recombinase polymerase amplification (RPA) of HIV-1 DNA and viral RNA by employing a coin cell-based vibration mixing platform. We achieve a limit of detection of 12 copies of DNA per reaction, nearly 50% reduction in time to threshold (from ∼10 minutes to ∼5 minutes), and an overall fluorescence output increase up to 16-fold when compared to unmixed experiments. This active mixing strategy enables reactions where the target and reaction cofactors are isolated from each other prior to the reaction. We also demonstrate amplification using a low-cost vibration motor for both temperature control and mixing, without the requirement of any additional heating components.

Pathologies in adipose (fat) tissue function are linked with human diseases such as diabetes, obesity, metabolic syndrome, and cancer. Dynamic, rapid release of metabolites has been observed in adipocyte cells and tissue, yet higher temporal resolution is needed to adequately study this process. In this work, a microfluidic device with precise and regular valve-automated droplet sampling, termed a microfluidic analog-to-digital converter (μADC), was used to sample secretions from ∼0.75 mm diameter adipose explants from mice, and on-chip salt water electrodes were used to merge sampled droplets with reagent droplets from two different fluorometric coupled enzyme assays. By integrating sampling and assays on-chip, either glycerol or non-esterified fatty acids (NEFA), or both, were quantified optically within merged 12 nanoliter droplets using a fluorescence microscope with as high as 20 second temporal resolution. Limits of detection were 6 μM for glycerol (70 fmol) and 0.9 μM for NEFA (10 fmol). Multiple ex vivo adipose tissue explants were analyzed with this system, all showing clear increases in lipolytic function after switching from feeding to fasting conditions. Enabled by high temporal resolution, lipolytic oscillations of both glycerol and NEFA were observed for the first time in the range of 0.2 to 1.6 min⁻¹. Continuous wavelet transform (CWT) spectrograms and burst analyses (0.1 to 4.0 pmol bursts) revealed complex dynamics, with multiplexed assays (duplex for glycerol and NEFA) from the same explants showing mostly discordant bursts. These data support separate mechanisms of NEFA and glycerol release, although the connection to intracellular metabolic oscillations remains unknown. Overall, this device allowed automated and highly precise temporal sampling of tissue explants at high resolution and programmable downstream merging with multiple assay reagents, revealing unique biological information. Such device features should be applicable to various other tissue or spheroid types and to other assay formats.

Cell sorting holds broad applications in fields such as early cancer diagnosis, cell differentiation studies, drug screening, and single-cell sequencing. However, achieving high-throughput and high-purity in label-free single-cell sorting is challenging. To overcome this issue, we propose a label-free, high-throughput, and high-accuracy impedance-activated cell sorting system based on impedance detection and dual membrane pumps. Leveraging the low-latency characteristics of FPGA, the system facilitates real-time dual-frequency single-cell impedance detection with high-throughput (5 × 10⁴ cells per s) for HeLa, MDA-MB-231, and Jurkat cells. Furthermore, the system accomplishes low-latency (less than 0.3 ms), label-free, high-throughput (1000 particles per s) and high-accuracy (almost 99%) single-particle sorting using FPGA-based high-precision sort-timing prediction. In experiments with Jurkat and MDA-MB-231 cells, the system achieved a throughput of up to 1000 cells per s, maintaining a pre-sorting purity of 28.57% and increasing post-sorting purity to 97.09%. These findings indicate that our system holds significant potential for applications in label-free, high-throughput cell sorting.