Engineering three-dimensional cellular systems with controlled size, matrix composition and cellular organization is central to disease modelling, drug evaluation and regenerative medicine. In this context, cell-laden hydrogel microspheres have emerged as a platform for constructing microscale living units. Their structure facilitates local cell-matrix interactions and cell-cell communication, while the hydrogel phase provides a tunable microenvironment for cell culture and function. Droplet microfluidics offers a powerful route to produce these microspheres with defined size, composition, architecture and cellular input, allowing fabrication parameters to be linked directly with biological outcomes. In this review, we focus on how droplet microfluidics, hydrogel matrix design and cellular organization converge to transform cell encapsulation into the construction of functional 3D cellular units. We outline the principles of microsphere fabrication, including droplet generation, gelation and post-generation recovery, and discuss how hydrogel matrices regulate processability, bioactivity and reproducibility. We then examine how cell loading, spatial architecture and organoid-centered organization determine microsphere function. Finally, we discuss applications in bioanalysis, drug testing, therapeutic delivery and tissue assembly, and highlight key challenges in cytocompatible processing, recovery, reproducibility, standardization and functional validation. This review aims to provide design principles for transforming cell encapsulation into interpretable and deployable microscale living systems.

Imaging flow cytometry (IFC) combines microscopic techniques and flow cytometry, enabling high-throughput, population-scale single-cell analysis with rich spatial detail and high molecular specificity. To date, most established IFC platforms are fundamentally built around 2D imaging, which is inherently limited in its ability to capture the complex 3D morphology, intracellular organization, and spatially distributed molecular features of single cells under flow. Driven by the growing demand for volumetric cellular information, IFC has progressively evolved from 2D imaging toward 3D imaging flow cytometry through a variety of optical and computational strategies. Existing 3D IFC approaches typically fall into scanning-based and non-scanning modalities, depending on whether volumetric information is assembled through sequential or parallel acquisition, with each paradigm offering complementary strengths in 3D imaging performance and cytometric throughput. Here, we systematically compare the underlying principles and performance characteristics of the most representative scanning-based and non-scanning 3D IFC technologies and discuss current challenges and future opportunities to advance volumetric cytometric analysis in biomedical and translational research.

Label-free tracking of adherent cell migration could enable important insights into biological processes such as tissue repair, inflammatory response, or cancer progression. Nevertheless, visualizing unlabeled animal cells using optical microscopy remains challenging due to low contrast as well as frequent changes in cell shape and number. A promising alternative uses electrical capacitance measurements, which are sensitive to cell adhesion to electrode surfaces. However, prior examples often utilized electrodes with areas larger than single cells, resulting in averaged readouts over multiple cells. Here, we demonstrate label-free, live-cell tracking using a capacitance sensor array with more than 1 million pixels on a 10 micron pitch across an area larger than 1 square centimeter. We show that single cell morphology can be clearly segmented and used to reconstruct migration and proliferation dynamics using optical flow. We further track the spreading of multicellular spheroids, revealing fast-moving peripheral regions led by a collective leader cell "front". Finally, we demonstrate label-free imaging of millimeter-scale honeycomb-shaped tissues without the multi-image stitching often required for conventional microscopy. We utilize mutual capacitance measurements with electrically-programmable electrode spacing to map topographical features of these engineered tissues. Overall, CMOS capacitance imaging arrays enable label-free imaging spanning from single cells to large tissues, in a portable and scalable format for settings where optical microscopy may be difficult to access.

Precise control of fluid transport is critical for paper-based microfluidic analytical devices (μPADs), especially in point-of-care testing (POCT) applications involving multistep reactions and timed reagent delivery. Here, we propose a simple yet versatile strategy to regulate capillary flow on μPADs by overlaying a film above a paper strip as a sliding rail for liquid samples. By systematically investigating the surface wettability of the sliding rail and its distance (0.1-2.4 mm) from the NC paper strip, we demonstrate that flow velocity on μPADs can be efficiently modulated through geometric control and surface modification. The influence of the width of sliding rails is also investigated. For hydrophilic sliding rails, increasing the width of film will accelerate flow velocity, whereas for hydrophobic sliding rails, narrower film widths yield higher flow velocity. Furthermore, we use a three-branch μPAD to show that both sequential and synchronized fluid arrival can be achieved by fine-tuning gap distance and paper strip width. In addition, a proof-of-concept delay valve implemented by introducing a continuous hydrophilic sliding rail above the segmented NC paper strips is successfully validated. A 1 mm interval located at various positions of μPADs will lead to different time delays. Beyond flow control, the sliding rail also enables localized droplet delivery and transport using a Janus hydrophobic-hydrophilic film with defined apertures. This platform supports sequential and parallel reagent delivery, allowing chemical reactions on-chip. The feasibility of this approach is validated through precipitation reactions, colorimetric reactions, and an enzymatic colorimetric glucose assay, which exhibits a strong correlation (R[2] = 0.9980) between gray intensity and glucose concentration under controlled laboratory conditions, suggesting its potential for quantitative applications in high-concentration samples. Overall, this work introduces a highly adaptable and fabrication-friendly μPAD platform with rapid flow tuning and multifunctional reaction capability. The mechanical support and alignment structure (3D-printed frame and spacers) are reusable across experiments, while the paper strips and surface-modified films are single-use consumables to ensure reproducible flow performance. This offers a powerful solution for efficient POCT applications and paper-based microfluidic systems.

Paper-based analytical devices (PADs) are attractive for point-of-need water analysis because of their low cost and operational simplicity. However, slow fluid transport often limits their analytical throughput. Here, we report a layer-by-layer PAD incorporating controlled interfacial gaps to enhance capillary-driven transport for the determination of dissolved inorganic carbon (DIC) in water. The device employs distance-based colorimetric detection using an acid-base system with bromocresol green (BCG) and was benchmarked against a laminated configuration fabricated from the same Whatman grade 4 paper. The layer-by-layer architecture reduced the colorimetric response time from 18 to 2.5 min while achieving limits of detection and quantification of 0.30 and 0.92 mg L[-1], respectively. Analysis of bottled mineral water samples showed good agreement with a reference potentiometric titration method, with relative errors below 7%. These results demonstrate that controlled interfacial engineering can be effectively integrated into distance-based PADs to accelerate analysis without modifying the sensing chemistry, providing a simple and low-cost approach for rapid on-site DIC determination.

We report a smartphone-based microfluidic platform for automated nitrite determination using a paper colourimetric assay. The system combines a V-shaped microfluidic paper-based analytical device (μPAD), a wireless electronic module for capacitive flow monitoring and thermal actuation, and a custom Android application that coordinates the measurement sequence and processes the captured image. Fluid arrival at the sensing zone is identified from changes in capacitance, which trigger the heating step required to collapse a NIPAM-based hydrogel reservoir and release sulfanilic acid from the hydrogel reservoir. Images are acquired under ambient lighting conditions and analysed by an image-processing algorithm that includes circle detection, saturation-value (SV)-based pixel selection, and colour correction using in-frame black and white references. Calibration with standard nitrite solutions showed that the platform achieved a limit of detection (LOD) of 7.63 × 10[-6] M (0.35 mg L[-1] as NO2[-]) after colour correction, improving by about 17% with respect to the uncorrected response. The platform also showed good repeatability (CV < 6% at 10[-4] M) and consistent behaviour when tested with two different smartphones and several lighting conditions. Analysis of tap water and marine samples gave results in agreement with the reference method at the 95% confidence level. The platform combines automated fluid handling, thermally triggered reagent release, and adaptive image-based quantification in a portable format for nitrite analysis outside the laboratory.

Invasive placental cells, extravillous trophoblasts (EVT), remodel maternal vasculature to meet metabolic needs of the growing fetus. Dysregulation of spiral artery remodeling (SAR) is a characteristic of preeclampsia (PE), a severe pregnancy-related condition. We developed a modular new approach methodology (NAM) of human endometrial-specific vascular networks in a polystyrene (PS) microfluidic chip to recapitulate the intricate endometrial vascular microenvironment to investigate SAR. We leveraged the self-assembly of human endometrial microvascular endothelial cells (HEMEC) and normal human lung fibroblasts (NHLF) to successfully develop patent endometrial vascular networks after 3 days of culture. Exogenous proangiogenic factors significantly improved vascular density while maintaining intrinsic endometrial vascular properties. Treatments of menstrual cycle phase-specific hormones differentially affected endometrial vascular density, diameter, permeability, and endogenous secretions of proangiogenic proteins. We evaluated integration potential of EVT cell lines HTR8/SVneo and JEG-3. We measured decreased barrier function and observed increased presence of apoptotic HEMECs after 3 days of tri-culture with JEG-3 cells, whereas HTR8/SVneo-induced apoptosis remained statistically unchanged across days 1 and 3 of integration. Moreover, we confirm HTR8/SVneo and JEG-3 cells differentially interact with HEMEC vasculature and the surrounding perivascular niche. These studies demonstrate the utility of our model to study endometrial vascularization and EVT remodeling.

mRNA-lipid nanoparticles (mRNA-LNPs) have revolutionized nucleic acid therapeutics, yet their clinical success relies heavily on precise manufacturing processes. This review comprehensively explores the critical intersection between microfluidic channel design, lipid self-assembly biophysics, and the subsequent biological performance of mRNA-LNPs. First, we analyze the fundamental fluid mechanics driving representative microfluidic mixers, elucidating how different channel architectures dictate solvent exchange rates and nanoparticle assembly kinetics. We then systematically examine how these specific mixing regimes modulate essential physicochemical characteristics, including lipid distribution, internal nanostructure, particle morphology, size distribution, and overall colloidal stability. Crucially, we bridge the gap between manufacturing and pharmacology by evaluating how mixing-induced physicochemical differences directly translate into distinct biological outcomes, such as cellular uptake mechanisms, endosomal escape efficiency, and systemic pharmacokinetics. By integrating microfluidic engineering principles with biological evaluation, this review provides a mechanistic framework for understanding how continuous-flow assembly processes program the functional behavior of mRNA-LNP systems. Ultimately, we highlight emerging design principles essential for developing the next generation of robust, highly efficient nucleic acid delivery platforms.

Co-culturing cells with mismatched densities, where one cell type adheres to surfaces while the other floats, represents a fundamental challenge in cell biology. This is particularly evident in studying macrophage-adipocyte interactions, where macrophages must engage and clear the apoptotic adipocytes, a process critical to understanding chronic inflammation in obesity and metabolic disease. The density disparity between macrophages, which sink and adhere to culture surfaces, and adipocytes, which float due to their lipid content, has prevented conventional co-culture approaches from achieving sustained cell-cell contact. To address this challenge, we developed a microfluidic system that confines adipocyte-derived lipid droplets in close proximity to macrophages. This platform features recessed micro-traps within the upper surface of a microfluidic chamber that trap buoyant objects while allowing media exchange and delivery of reagents for live-cell and immunofluorescence imaging. Time lapse imaging revealed that the dynamic process of macrophages-dead corpse interactions, showing that individual macrophages cannot engulf entire corpses but instead mechanically deform them. Furthermore, the platform reproduces the Crown-Like Structure (CLS), which is a unique configuration of multiple macrophages enveloping a single dead adipocyte. Long-term culture revealed that CLS effectively clear lipids compared to partial macrophage engagement, providing insights that were previously unattainable with standard histological approaches. Beyond the macrophage-lipid interaction, this platform has potential for studying interactions between adherent cells and buoyant targets, such as microplastics, opening new avenues for research where density mismatch poses a major barrier.

Microfluidic devices offer precise control over polymeric nanoparticle (NP) synthesis. However, rational design of process optimization to obtain a target NP size remains challenging because the relationship between operating parameters, mixing dynamics, and particle formation is nonlinear. Here, we integrate mechanistic analysis, statistical design of experiments (DoE), and machine learning (ML) to develop predictive and inverse design frameworks for the synthesis of poly(lactic-co-glycolic acid) (PLGA) NPs. The effects of flow rate ratio (FRR), PLGA concentration, and microchannel width were systematically investigated using hydrodynamic flow focusing (HFF) microfluidic devices. Mixing time analysis demonstrated that NP size is primarily governed by solvent-to-antisolvent mixing, with equivalent mixing times producing comparable particle sizes despite different combinations of FRR and channel width. A Box-Behnken DoE yielded a highly predictive quadratic model (R[2] = 0.9979), identifying microchannel width as the dominant design variable, followed by PLGA concentration and FRR. Eight machine learning algorithms were subsequently developed and evaluated with gradient boosting achieving the highest predictive accuracy (test R[2] = 0.9900, RMSE = 2.65 nm). Finally, inverse design experiments were performed for target NP sizes of 80, 120, and 160 nm using DoE and a selected subset of ML models. Process conditions predicted by the developed models were implemented experimentally. The gradient boosting model produced NP sizes in closest agreement with the preassigned targets, outperforming DoE and other ML models. This combined mechanistic and data-driven framework enables reliable prediction and inverse design of PLGA NP synthesis and provides a practical strategy for intelligent and on-demand microfluidic-based synthesis of polymeric nanomedicines.

Sensitive and low-cost protein biomarker detection is critical for disease diagnosis. Advanced microfluidic systems can generate miniature reaction compartments for a high-sensitivity assay. However, these platforms often require external instruments, skilled operators, and complex setups. Here, we develop a lab-on-a-capillary (LabCap) platform that integrates photopatterned hydrogel rings within a glass capillary using a reconfigurable stop-flow lithography system. During sample loading and unloading steps, nanoliter-scale aqueous droplets (torodrops) are spontaneously formed around the hydrogel rings, creating isolated reaction compartments without the need for external instruments or an immiscible oil phase. The LabCap platform enables detection of clinically relevant biomarkers, including C-reactive protein (CRP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP). By adjusting the incubation protocol, the detection performance can be tuned to meet different analytical requirements. A 16 min periodic medium exchange protocol yielded calculated limits of detection of 1.3 ng mL[-1] for CRP and 1.8 ng mL[-1] for NT-proBNP, whereas static incubation yielded 0.5 ng mL[-1] for both biomarkers after 1 h and 0.14 ng mL[-1] for CRP at a matched 16 min incubation. In addition, LabCap offers practical advantages, including a low consumable material cost for fabricating one device (<€1), low reagent consumption (<100 μL per assay step), and minimal wash-buffer usage (1 mL). These results demonstrate that LabCap is a simple, cost-effective, and versatile platform for compartmentalized biomarker detection.

Protists rely on motility to explore their environment, enabling them to access resources such as nutrients and light, while avoiding unfavorable conditions and predators. To advance the current understanding of how motility and morphological adaptations modulate spatial navigation and interactions with confining physical environments, this work analysed the motility of E. gracilis and E. acus, which share a similar overall body plan but differ in flagellum length and pellicle configuration. Cell motility was quantified under progressively increasing confinement in microfluidic devices comprising quasi-open spaces (0D), single-wall interactions (1D), and two-wall confinement (2D), including corners and channels with varying geometries and complexities. The motility of Euglenids was characterized using single-cell swimming trajectories, wall-scattering dynamics, trapping time, switching of propulsion mode, swimming velocity, and quantification of successful navigation through structured environments. Five distinct wall-scattering behaviors were observed in low-confinement conditions. Both species mainly escaped geometrical confinements through collision-mediated scattering. E. gracilis exhibited larger average deflection angles and greater variability in its trajectory angles. In 2D corner confinement, E. gracilis presented extended trapping times and larger deflection angles, consistent with an increased interaction with walls. Under strong lateral confinement (channel widths approximately 1-2× the cell diameter), both species shifted from flagellar swimming to friction-assisted back-and-forth motion accompanied by pellicle-driven deformation (metaboly). These transitions in motility behavior were geometry-dependent and species-specific. E. acus exhibited higher metaboly incidence in highly tortuous channels, whereas E. gracilis relied more on flagellar propulsion and back-and-forth motility. Upon exiting channels with quasi straight channels (lower tortuosity), E. gracilis adopted curvilinear trajectories, while E. acus presented straighter trajectories. Our results suggest that E. gracilis did not show significant conditioning to confining environments, while E. acus presented a 'directional memory'-like movement, with trajectories in open spaces strongly influenced by the geometry of the preceding channel. These observations are consistent with the hypothesis that morphological traits, such as flagellum length and pellicle organization, modulate the observed differences in behavioral motility. These findings also provide a framework for understanding how morphological features may influence navigation in structured microenvironments and inform the design of microfluidic systems for applications such as motility-based cell sorting, photo-microbioreactors, and biocomputation.

Precise microflow control underpins microfluidic technologies in precision medicine, yet conventional approaches rely on anisotropic materials or external fields, limiting fabrication simplicity and scalability. Unlike transformation-based or data-driven approaches, we demonstrate that flow anisotropy can be programmed through the layered organization of locally homogeneous, isotropic media via a series-parallel equivalent fluidic network (SPEFN) realized as single-medium metamaterials (SMMs). SPEFN provides an analytically invertible mapping from network topology to the macroscopic effective-viscosity tensor, from which a connectivity-controlled anisotropy law and an exact non-disturbance condition emerge as direct analytical consequences. We experimentally realize three monolithic SMM devices: a spiral-layered director, an annular cloak, and a fan-shaped concentrator. Particle-tracking velocimetry confirms the predicted responses across all devices, including ≈30° streamline reorientation and a concentrator velocity ratio of 1.37 versus a predicted value of 1.38. The cloak exhibits higher apparent suppression than predicted; tracer retention or adhesion visible in Movie S1 may partly explain this difference. The system further shows robust performance under strongly non-uniform inflow and sub-percent fabrication tolerances.

Label-free brightfield microscopy is widely used to monitor organ-on-chip (OOC) cultures, but manual quality assessment can be subjective, labor-intensive, and difficult to standardize across biological conditions. Using a public dataset of 3072 microscopy images from six cell lines, we systematically evaluated four questions relevant to automated OOC quality assessment: (1) how model architecture and image resolution affect performance, (2) whether predictive information is localized or distributed across the image, (3) whether biological metadata provides complementary information beyond the image, and (4) how well learned quality representations transfer across cell lines. We found that (1) both architecture and input resolution influenced performance, with increasing resolution from 224 × 224 to 768 × 768 improving ConvNeXt-Tiny accuracy from 0.852 to 0.910 and improving performance for five of six evaluated convolutional neural networks (CNNs); (2) multiple instance learning and saliency analyses indicated that quality relevant information was distributed across multiple image regions rather than dominated by isolated local defects; (3) biological metadata provided only modest additional benefit, with the best metadata-conditioned model achieving an accuracy of 0.915 compared with 0.910 for image-only classification; and (4) transfer to previously unseen cell lines varied substantially (ROC-AUC 0.783-0.999), while finetuning models with target-cell-line data substantially recovered performance, increasing pooled accuracy from 0.791 to 0.890. Domain-adversarial training did not consistently improve transfer. Rather than introducing a new deep learning architecture, the novelty of this work lies in systematically evaluating these OOC-specific design and generalization factors within a common framework. These findings highlight image resolution, biological-domain validation, and target-domain adaptation as important considerations for robust automated OOC quality assessment.

Label-free particle analysis across micro- and nanoscale particle systems requires multiple specialized instruments, making it difficult to compare measurements across scales within a single workflow. Here we show the use of NanoSpacer/MicroSpacer, a glass-based confinement platform, for particle detection, relative particle counting, and diffusion-based sizing at the single particle level. The platform confines liquid samples between spacer-defined glass surfaces, restricting the vertical observation volume while allowing particles to move freely within the imaging plane. This improves particle visibility under darkfield illumination and supports more reliable particle tracking for diffusion-based sizing in solutions. Diffusion coefficients are obtained from mean-squared displacement (MSD) analysis using open-source software and converted to hydrodynamic size via the Stokes-Einstein relationship. The platform was evaluated across four model systems spanning micro- to nanoscale: (i) binary GFP/mKate bacterial mixtures were quantified at the single-cell level, with GFP ratios of 59%, 18%, and 89% in agreement with correlative flow cytometry (65%, 23%, 86%) and colony-forming unit counts (54%, 17%, 86%); (ii) bacteriophages Polaria and Mystique were detected by darkfield and apparent size estimates returned modal hydrodynamic diameters of approximately 250 nm and 190 nm, consistent with electron microscopy; (iii) putative nanobubbles were produced by chemical and mechanical methods, with modal diameters of approximately 170 nm and 600 nm; (iv) polystyrene nanoplastic particles (100, 200, and 1000 nm) were imaged in freshwater and seawater, where salt-induced aggregation produced clusters spanning orders of magnitude in size. Together, these results establish NanoSpacer/MicroSpacer as a cost-efficient, microscopy-compatible workflow for particle metrology across micro- and nanoscale systems relevant to marine science and microbiology.