A microfluidic cover converts a standard 96-well plate into a mass-transport-controlled immunoassay system

Abstract

96-well microtiter plates, widely used in immunoassays, face challenges such as prolonged assay time and limited sensitivity due to the lack of analyte transport control. Orbital shakers, commonly employed to facilitate mass transport, offer limited improvements and can introduce assay inconsistencies. While microfluidic devices offer performance enhancements, their complexity and incompatibility with existing platforms limit their wide adoption. This study introduces a novel microfluidic 96-well cover designed to convert a standard 96-well plate to a mass-transport-controlled surface bioreactor. The cover employs microfluidic methods to enhance the diffusion flux of analytes toward the receptors immobilized on the well bottom. Both simulation and experimental results demonstrated that the cover significantly enhances the capture rate of analyte molecules, resulting in increased signal strength for various detection methods and a lower detection limit. The cover serves as an effective add-on to standard 96-well plates, offering enhanced assay performance without requiring modifications to existing infrastructure or reagents. This innovation holds promise for improving the efficiency and reliability of microtiter plate based immunoassays.

I. INTRODUCTION

Immunoassays are essential in clinical diagnostics, environmental monitoring, and drug discovery, traditionally relied on 96-well plates for their execution. Such 96-well microplate based immunoassays offer straightforward procedures, multiplexing capability, high selectivity, and sufficient sensitivity for many applications.¹ However, over the years, researchers have also recognized the limitations of traditional immunoassays, such as prolonged assay times, manual pipetting errors, and non-optimized sensitivity for many demanding applications such as ultralow concentration protein detection and single cell analysis.² In response to these challenges, many researchers have put efforts into developing advanced antibodies by using different host species and in vitro production to obtain polyclonal, monoclonal, and recombinant antibodies specifically for the application-needed affinity, specificity, and consistency.^3–5 In addition, the popularization and expansion of automation solutions have significantly reduced human operation and possible errors.^6,7 Furthermore, innovations in chemiluminescent, fluorescent, and colorimetric reagents, coupled with advanced optical detectors, have principally enhanced the detection limits of immunoassays.^8–10 While these advancements have improved assay performance targeting antibody–antigen interactions and detection methods, the slow diffusion-driven mass transport in the 96-well plate incubation, which limits the flux of analytes toward the capture molecules coated on the well bottom surface and the rate of productive collision, has not been well studied or controlled.¹¹

To increase the molecular transport in bioassays, advection is frequently utilized. The most widely used method for advection generation is the orbital shakers because of their ease of use, wide availability, and good compatibility with the standard microtiter plates (e.g., 96 wells).¹² However, both computational and experimental results suggest that the shaker offers limited help in improving molecular transport in the surface bioassay due to the generated transport being mostly limited to the liquid bulk and far from the solid phase. Also, shaking can induce variations in assay results, which leads to low consistency.¹³

Microfluidics, a technology characterized by the precise manipulation of fluids at micrometer-length scales, has shown considerable promise for improving immunoassay and other ligand-binding assays' performance.^14–19 In microfluidics, the analyte molecules are constrained in sub-millimeter scale fluidic layers above the solid phase and experience gains in the collision rate by several orders of magnitude compared to no flow condition.²⁰ However, the requirements for specialized instrumentation, and the incompatibility with the current automated liquid handling and read-out platforms, have collectively hindered the widespread adoption of microfluidics in practical immunoassays.^21,22 Therefore, despite the advantageous metrics provided by microfluidic immunoassay devices and the considerable efforts invested in their development, microtiter plates continue to be the most widely used platform for immunoassays for their simplicity, high throughput, and mature ecosystem.²³

Aiming to achieve the reproducibility and high-efficiency mass transport offered by microfluidics while also capitalizing on the favorable ecosystem and cost-effectiveness of 96-well plates, this study introduces a proof-of-concept approach that leverages a unique 96-well plate cover to bring the advantages of microfluidic mass-transport control to the standard microtiter plate format. This design not only integrates seamlessly with a standard 96-well plate but also optimizes the diffusion flux of analyte molecules using a microfluidics method (Fig. 1). By focusing on enhancing the mass transport in the assay, this cover promises further reductions in assay times, increased sensitivity, and the potential for automation and portability. Furthermore, the design permits the utilization of multiple covers within a single 96-well plate, facilitating parallel operations without modifications to the original plate structure.

FIG. 1.

The design and working principle of the microfluidic cover for the mass-transport-controlled 96-well immunoassay system. (a) Setup of the mass-transport-controlled immunoassay system. (b) Exploded view of the system. (c) Assembled view of the system. (d) Flow path of the sample from the reservoir to the reaction well. (e) Flow path of the sample from the reservoir to the reaction and waste collection well. (f) Bottom view of the reaction well after assembly. (g) Schematic of the mass transport in static incubation. (h) Schematic of the mass transport in flow incubation.

II. DESIGN AND WORKING PRINCIPLES OF THE MASS-TRANSPORT-CONTROLLED 96-WELL IMMUNOASSAY SYSTEM

The constructed immunoassay system embodies several key components: (i) a standard 96-well microplate; (ii) an array of precision-engineered thermoplastic cover, each featuring two 22-gauge metallic tubes; (iii) an elastomeric gasket strategically positioned between the thermoplastic cover and the microplate well, ensuring a hermetically sealed milieu for fluidic operations [Figs. 1(a)–1(c)].

In the experimental setup, four wells of a standard 96-well plate were designated for each test: one served as a sample reservoir, another as a functionalized reaction surface for analyte capture, and two for waste collection. Streptavidin was utilized as a surrogate for the capture molecule (receptor, binding site) typically employed in conventional immunoassays, while the AZDye 488-biotin complex functioned as the analyte. Pneumatic pressure generated by a syringe pump drove the sample from the reservoir well to the reaction well. The sample was introduced into the depths of the reaction well through the plastic cover. Within the micro-chamber formed between the cover and the reaction well bottom, biotin molecules bind to the coated streptavidin. Following this interaction, the sample was directed through two outlets into a waste collection well [Figs. 1(d) and 1(e)]. The bottom view of the reaction well is shown in Fig. 1(f).

The AZDye 488 dye is a green-fluorescent dye that has an identical chemical structure to Alexa Fluor 488 by ThermoFisher. The dye is used as the reporter for fluorescence readout to measure the density and distribution profile of the captured complex molecule.

It should be noted that a key distinction exists between this study and conventional fluorescence sandwich immunoassays (FIAs). In traditional assays, the fluorescent dye is conjugated to the detection antibody, which binds to a different epitope of the analyte compared to the capture antibody, thereby forming a sandwich complex. In contrast, this study directly conjugates the fluorescent dye with the analyte (biotin), aiming to map the distribution of the captured analyte. In subsequent steps, detection antibodies are generally supplied in excess to saturate all captured analytes. Therefore, the analyte capture step is often the performance-limiting step.

A. Reaction, diffusion, and advection—Interplays between analyte transport and surface reaction

At the reaction surface, the association and dissociation activities are based on the equations below:

$$\begin{array}{r}\mathrm{Rate}\mathrm{of}\mathrm{association}=k_{on}\times c_{ag}\times b_m\times f_{free}\end{array},$$ (1)

$$\begin{array}{r}\mathrm{Rate}\mathrm{of}\mathrm{dissociation}=k_{off}\times c_{s_{ag}}\times b_m\times f_{coverage}\end{array},$$ (2)

where $k_{on}$ is the analyte–receptor association rate constant, $k_{off}$ is the dissociation rate constant, b_m is the density of the binding site, and $f_{free}$ and $f_{coverage}$ are the ratios of binding sites that are not occupied or occupied by an analyte molecule, respectively. $c_{ag}$ is the analyte concentration at the reaction surface and $c_{s_{ag}}$ is the density of the captured analyte.

In a pure diffusion-driven (static) incubation assay, the receptors capture the analytes near the solid phase and a thin depletion layer with concentration gradient $\mathrm{\nabla }c$ is formed. The analyte molecules diffuse down concentration gradients with flux $j_D=-D\mathrm{\nabla }c$, where D is the diffusion coefficient of the molecule in the solvent. The receptors capture analyte molecules faster than the mass transport can provide because of the slow diffusion. Consequently, the thickness of the depletion layer grows with time by Einstein's diffusion equation $\delta =\sqrt{2Dt}$, thereby diminishing the concentration gradient at the solid phase. This, in turn, decelerates the diffusion flux of the analyte molecules or, equivalently, the rate at which the analyte molecules reach the reaction surface and have a chance to bind to the receptors [Fig. 1(g)].

The Damköhler number, a dimensionless number, describes the ratio of the binding rate to the mass-transport rate,²⁴

$$\begin{array}{c}Da={\displaystyle \frac{k_{\mathrm{on}}{b}_{\mathrm{m}}\delta }{D}}\end{array}.$$ (3)

If $Da\gg 1$ with the thick depletion layer and low diffusion rate, the assay is limited by mass transport, and the receptors capture analyte molecules faster than the mass transport can provide, as mentioned above. Consequently, the thickness of the depletion layer will continue to increase with time, thereby decelerating the diffusion further.

The goal of this study is to promote the mass transport by introducing advection, therefore lowering the depletion layer, and reduce $Da$ using the cover [Fig. 1(h)]. Figure 2(a) shows the geometry of the insert pillar for the reaction well, which will define the reagent flow path and reaction chamber above the well.

FIG. 2.

Mechanism of the mass-transport control enabled by the microfluidic cover. (a) Enlarged view of the contact region of the cover near the well bottom. (b) Meshed flow geometry with 500 um chamber height used in the finite element simulation of the flow incubation. Comparison of the simulated analyte concentration profile of (c) 50 ul static incubation and (d) 5 ul/min flow incubation at 500 s, with 10 ng/ml of AZDye 488-biotin.

The presence of a flow ensures the continuous introduction of a new sample over the substrate. A laminar Poiseuille flow with flow rate Q could result in a shear rate,

$$\begin{array}{c}\dot{\gamma }={\displaystyle \frac{6\mathrm{Q}}{H^{2}W}}\end{array}$$ (4)

and flow velocity profile,

$$\begin{array}{c}u={\displaystyle \frac{\dot{\gamma }}{H}z(H-z)}\end{array},$$ (5)

where W represents the perimeter of the circle with radius L within the well and H is the height of the reaction chamber [Fig. 2(d)]. The flow velocity maximum in the chamber center $(z=H/2)$ and with a value of zero at the walls ( $z=0$ and $z=H$). Under a constant flow rate Q, W increases as L extends away from the center, resulting in a concomitant reduction in the shear rate.

The Peclet number, a dimensionless parameter that relates transport by diffusion to that by advection, serves as a valuable indicator for the mass-transport condition,²⁴

$$\begin{array}{c}Pe={\displaystyle \frac{{\tau }_{\mathrm{d}}}{{\tau }_{\mathrm{a}}}={\displaystyle \frac{2Q}{WD}}}\end{array}.$$ (6)

If the time required for analyte molecules to diffuse from the chamber top to the bottom, ${\tau }_{\mathrm{d}}$, is less than the time needed for advection, ${\tau }_{\mathrm{a}}$, to carry them across the same distance, in which case $Pe\ll 1$, advection might be too slow to feed above the reaction surface and yield a growing depletion layer. To lower the depletion layer, the advection should be fast enough to result in $Pe\gg 1$.

In such scenarios, the thickness of the depletion layer δ can be calculated as the distance from the reaction surface where a molecule exhibits an equal probability of reaching the subreaction surface via diffusion as it does of exiting the reaction surface through advection,

$$\begin{array}{c}\delta ={\left({\displaystyle \frac{2DL}{\dot{\gamma }}}\right)}^{\frac{1}{3}}\end{array}.$$ (7)

Higher shear rates yield a thinner depletion layer, enhancement of the concentration gradient, and diffusion flux on the reaction surface, thereby shifting the Damköhler number toward reaction-limited conditions [Fig. 1(h)]. To obtain such higher shear rates, either an increase in the flow rate Q or a reduction in the chamber height H is required. While a higher flow rate will result in an increased sample volume, modifying the chamber height can be readily accomplished by altering the geometry of the cover [Figs. 2(a) and 2(b)].

Figures 2(c) and 2(d) show a simulated comparison of the analyte molecule concentration profile between the static incubation and flow incubation at 500 s after sample introduction. Panel (c) shows that the diffusion-driven mass transport in static incubation generated a depletion layer close to 1 mm, which limited the reaction rate. By introducing advection, the thickness of the depletion layer of the static incubation is several times thicker than the one in flow incubation [Fig. 2(d)]. The details of the simulations are illustrated in Sec. III.

III. MATERIALS AND METHODS

A. Fabrication of the cover

Figure 3(a) shows the fabrication procedures of the cover. The body of the cover, fabricated from clear PMMA stock (McMaster-Carr, Cat#8560K369), was designed in SolidWorks and machined using a CNC milling machine (MDA Precision, V8-TC8). Initially, features on the flat side of the cover were milled and drilled. Subsequently, the stock was contoured to the target dimensions of 8.9 × 8.9 mm². Upon flipping the stock with the flat side oriented downward, the features on the pillar side are milled to complete the plastic body of the cover. For sample extraction from the reservoir, an 11 mm long 22-gauge metal tubing was inserted into the corresponding vertical channels and aligned to the height of the spacer feature using a caliper. The metal tube was then advanced by an additional 100 μm to establish a 100 μm gap between the tube's end and the well bottom, facilitating smooth liquid flow. An interface between the cover and the syringe pump was established by inserting another metal tubing into the corresponding vertical channel. This metal tubing was aligned to the top of the pillar, and its opposite end will be connected to the syringe using silicon tubing. To ensure the stability of the metal tubes, a drop of superglue was applied to the plastic surfaces where the metal tubes were exposed.

FIG. 3.

Fabrication procedures of the cover and gasket. (a) Fabrication procedures of the cover. (b) Fabrication procedures of the gasket.

B. Fabrication of the gasket

Figure 3(b) shows the fabrication procedures of the gasket by a casting method. A two-part mold was designed in SolidWorks and fabricated using the CNC machine. In the elastomer casting process, two two-part molds were first assembled to create the desired shape of the elastomer gasket. The upper part provides the exact shape as the wall of the well, and the pillars on the lower part are 0.05 mm thinner than the pillars on the cover. Upon assembly, the minor structural variations induce gasket deformation, thereby ensuring both secure fixation and air tightness.

Once the mold was securely fastened together, the elastomer polymer liquid—a mixture of polydimethylsiloxane (PDMS) (Momentive, RTV-615) and Dragon Skin 10 (Smooth-On) with a 1:1 volume ratio, was prepared. This mixture was then poured into the assembled mold, filling the cavity to form the shape of the gasket. After pouring, the mold was left in a vacuum chamber at −1 bar for an hour to eliminate air bubbles and then put in an 80 °C oven overnight to cure. Once fully cured, the mold was disassembled to reveal the finished elastomer gasket.

C. Custom-made streptavidin-coated plate

To create the coating solution, lyophilized streptavidin (Acro Biosystems, Cat# STN-N5116) was reconstituted with distilled water and diluted with coating buffer (Candor, Cat# 120125). 100 ul of the coating solution was dispensed into the reaction wells on the clear bottom high-binding plates (Greiner, Cat# 655097), giving 1 ug streptavidin per well. The plates were sealed and placed in an incubator at +25 °C for 18 h. After washing three times with 0.05% phosphate-buffered saline with Tween 20 (PBST), the sample reservoir wells and reaction wells were blocked with 300 ul of the blocking solution (Candor, Cat# 110 050) for 1 h to minimize nonspecific binding. Then, the wells were washed with 0.05% PBST three times.

D. Biotinylated fluorophore as sample analyte

The lyophilized AZDye 488 Biotin (Click Chemistry Tools, Cat#1395) was reconstituted and diluted to final mass concentrations with phosphate-buffered saline (PBS). The molar concentration can be calculated using the molecular weight of the AZDye 488 Biotin complex molecule, which is 935 g/mol.

E. Fluorescence readout

An Olympus IX71 microscope that equipped an electron-multiplying CCD (EMCCD) camera (Andor, iXon Ultra 897) was employed to determine both the total amount and spatial distribution profile of the analyte captured post-incubation. 4× and 10× objectives are used for a larger field of view (FOV) and higher resolution of the fluorescence intensity, respectively. The EMCCD camera was set with 2.4× pre-gain and 25× EM gain.

The FOVs of the camera under 4× and 10× objectives are 2.12 × 2.12 and 0.82 × 0.82 mm², characterized using a microscope calibration slide (MUHWA, MH-SM05).

F. Streptavidin-coated plate binding capacity and binding site density b_m characterization

First, a series of 50 ul of AZDye 488 Biotin solutions with different concentrations were introduced into the coated and blocked wells and incubated for a duration of 10 min. Subsequent to three washes with PBST, fluorescence intensities were quantified using a 10× objective to obtain the standard curve and dynamic range [Fig. 4(a)]. A solution at a concentration of 500 ng/ml can saturate all binding sites within a 10-min timeframe, whereas solutions at 0–400 μg/ml show a linear response. To obtain the binding capacity, 50 μl of a 1 μg/ml solution, which contains a 53.476 pmol AZDye 488 Biotin complex, was dispensed to saturate all binding sites. Subsequently, the biotin solution was recovered post-reaction and diluted for the re-assessment of concentration. The concentration of the recovered biotin solution was determined from dilutions falling within the dynamic range. The difference between the concentration of the recovered solution and the initial 1 μg/ml was calculated to quantify the captured biotin, which, in turn, serves as an indicator of the binding capacity. A threefold dilution of the solution yielded a measured concentration of 320.78 ng/ml, while a fourfold dilution resulted in a concentration of 240.60 ng/ml. Correspondingly, the calculated differences relative to the original solution were found to be 5.359 and 5.355 pmol, respectively. Hence, an average of 5.357 pmol binding sites per well, which is equivalent to a density of 1.71 × 10⁻⁷ mol/m², was calculated [Fig. 4(b)].

FIG. 4.

Streptavidin-coated plate binding capacity characterization. (a) Fluorescence intensity of a series of 50 ul of AZDye 488 biotin solution ranged from 100 to 1000 ng/ml incubated for 10 min. (b) Fluorescence intensity (left axis) and calculated binding site coverage (right axis) for the standard curve and dilution/re-assessment tests. Fluorescence intensities were measured under the 10× objective.

G. Binding site coverage ratio calculation

For a more intuitive understanding of analyte capture efficiency, the fluorescence intensity was converted into a binding site coverage ratio. With saturated and control group surfaces exhibiting fluorescence intensities of 3970 and 1430 at the 4× objective and 22 000 and 1070 at a 10× objective, respectively, the corresponding binding site coverage ratios were determined to range from 1 to 0 [Fig. 4(b)].

H. Flow incubation test

A series of flow incubation tests were conducted using different inserts with chamber heights of 200, 300, 400, and 500 um. The samples consisted of AZDye 488-Biotin solutions with concentrations of 10, 20, 40, 60, and 80 ng/ml. For the execution of the tests, the gasket was initially inserted into the streptavidin-coated plate. Following this, 50 ul of sample solution was introduced into the sample reservoir well via pipetting. The channel on the cover was then sealed at the top with tape (Adhesives Research, ARCare 90445Q) before the subsequent positioning of the cover into the gasket. Finally, the air inlet metal tube was connected to a syringe pump through a silicone tube. The flow rate of the syringe pump was set to 5 ul/min to deliver the sample solution in 10 min. The cover and gasket were removed from the plate once the flow was done, and the plate was washed three times with PBST. Then, the result was evaluated under the microscope with the 4× objective.

I. Finite element simulation setup and parameters tuning

To acquire an intuitive understanding of the diverse mechanisms governing diffusion, advection, association, and dissociation under both static and flow incubation conditions, a series of simulations were executed utilizing COMSOL Multiphysics software. The Laminar Flow, Transport of Diluted Species, and Surface Reaction modules were employed to emulate real-world scenarios.

The “Laminar Flow” module calculates the motion for the incompressible Newtonian based on the Navier–Stokes equations,

$$\begin{array}{c}\rho {\displaystyle \frac{\mathrm{\partial }\mathbf{u}}{\mathrm{\partial }t}+\rho (\mathbf{u}\cdot \mathrm{\nabla })\mathbf{u}=\mathrm{\nabla }\cdot [-p\mathbf{I}+\mathbf{K}]+\mathbf{F}}\end{array},$$ (8)

$$\begin{array}{r}\mathbf{K}=\mu (\mathrm{\nabla }\mathbf{u}+(\mathrm{\nabla }\mathbf{u}){}^{\mathrm{T}})\end{array},$$ (9)

$$\begin{array}{r}\rho \mathrm{\nabla }\cdot \mathbf{u}=0\end{array},$$ (10)

where $\rho$ is the density of the fluid, $\mathbf{u}$ is the velocity vector, p is the pressure, $\mathbf{I}$ is the identity tensor, $\mathbf{K}$ is the viscous stress tensor, and $\mathbf{F}$ is the volume force vector. For incompressible flow, $\rho$ is constant or nearly constant, Eq. (10) is the continuity condition.

The “Transport of Diluted Species” module calculates the transport and distribution profile of the analyte molecules through diffusion and advection by solving the mass conservation equation,

$$\begin{array}{r}{\displaystyle \frac{\mathrm{\partial }{c}_{ag}}{\mathrm{\partial }t}+\mathrm{\nabla }\cdot {\mathbf{J}}_D+\mathbf{u}\cdot \mathrm{\nabla }{c}_{ag}=R_{ag}}\end{array},$$ (11)

$$\begin{array}{r}{\mathbf{J}}_D=-D\mathrm{\nabla }{c}_{ag}\end{array},$$ (12)

where $c_{ag}$ is the concentration of the analyte molecules, D denotes the diffusion coefficient, $R_{ag}$ is the reaction rate, $\mathbf{u}$ is the mass averaged velocity vector, and ${\mathbf{J}}_D$ is the diffusive flux vector.

For the flow incubation, one “Inflow” node, where both the sample concentration $c_{ag}$ and inlet boundary were defined, and one “Outflow” node, where the outlet boundary was defined, were added into the “Transport of Diluted Species” module. In the “Transport Properties” node, the velocity vector, which describes the advection, was selected to retrieve from the “Laminar Flow” module. In the Laminar Flow module, the flow rate Q was set to 5 ul/min to match the flow incubation experiments. The walls are set to be “No slip,” which means the flow velocity is 0 at the walls, to match Eqs. (4) and (5).

For static incubation, the “Laminar Flow” module was not used, nor were the “Inflow” and “Outflow” nodes within the “Transport of Diluted Species” module. Instead, sample concentrations were specified in the “Initial Value” node of the “Transport of Diluted Species” module.

The reaction rate, constituting the sum of the association and dissociation activities as defined by Eqs. (1) and (2), was manually input into a “Reaction” node within the “Surface Reaction” module. Concurrently, the binding site density b_m was entered into the “Surface Properties” node. Subsequently, this reaction rate was coupled to the “Transport of Diluted Species” module via a “flux” node to update free analyte molecules in the sample.

The geometry for static incubation was configured as a right cylinder to represent 50 ul of liquid in a well. The geometries for flow incubations are set to match the reaction chamber, and the inlet and outlet ports are set to 2 mm above the reaction surface. Physics-controlled meshes with “Fine” element size are used for the simulation, which yields ∼13 000 domain elements for the static incubation simulation and ∼500 000 domain elements for the flow incubations.

To perform the simulation, the remaining undetermined factors required include (1) the diffusion coefficient of the AZDye 488 Biotin complex in the solvent; (2) the association rate constant k_on; and (3) the dissociation rate constant k_off of the streptavidin and biotin. From the literature, the association rate K_on is about 10⁸ M⁻¹ s⁻¹, and the dissociation rate K_off is about 10⁻⁷ s⁻¹, yielding an equilibrium dissociation constant K_d = 10⁻¹⁴ to 10⁻¹⁵ M.^25–27 The diffusion coefficient can be estimated to be at 10¹⁰ m²/s magnitude.²⁸ To let the simulation match the experiments accurately, the above parameters should be determined.

A series of parameter sweep studies were conducted in the simulation to find the best match of the three parameters. We have discerned that either a tenfold amplification or diminution of Kon and Koff does not yield observable variations in the outcomes in the simulation of static incubation. This is because K_on directly affects the concentration of analyte molecules near the reaction surface, thereby generating a negative feedback mechanism to balance the association rate. Moreover, the K_off between streptavidin and biotin is so low that it substantially exceeds the assay time required to induce a single dissociation event. Therefore, K_on and K_off are set to 10⁸ M⁻¹ s⁻¹ and 10⁻⁷ s⁻¹, respectively. With the parameter sweep for the diffusion coefficient, 1.95 × 10¹⁰ m²/s yields the simulation results that match the experimental static incubation described in Fig. 4. The verification of correlation between the simulation and experiment results of flow incubation is described in Sec. IV.

IV. RESULTS

In the field of immunoassay detection techniques, colorimetric, chemiluminescence, and fluorescence methods are predominantly employed. While colorimetric and chemiluminescence are enzyme-based techniques and, therefore, depend on the total quantity of captured analytes, fluorescence detection has the potential to capitalize on the highest local concentrations across the entire effective reaction surface. Consequently, it is essential to evaluate and compare the binding site coverage ratios within the entire effective reaction surface and the center area to those under static incubation conditions. However, due to the limitations imposed by the FOV of the microscope/camera setup, measurements can only be obtained for the central 2.12 × 2.12 mm² square within the π × 2.5 × 2.5 mm² circular area. Therefore, the binding site coverage ratio for the entire effective reaction surface must be derived from the simulation results.

As shown in Fig. 5(a), to ascertain that the parameter-tuned simulation results remain accurate under flow incubation conditions, two key metrics were compared to the experimental results: the binding site coverage ratio profile extending radially from the center to the outer boundary of the FOV, and the total binding site coverage ratio within the FOV.

FIG. 5.

Validation of simulation in flow incubation. (a) Schematic diagram of the reaction surface, FOV, and the line for radial line binding site coverage ratio profile analysis. The comparison of simulated and experimental radial line binding site coverage ratio profile analysis for chamber height of (b) 200, (c) 300, (d) 400, and (e) 500 um. (f) Legends apply to panels (b)–(e).

Finally, the binding site coverage ratios of the entire effective reaction surface and at the center (3 × 3 pixels) of the reaction surface, which could be used as a measuring target, were evaluated and compared with the static incubation conditions.

A. Radial binding site coverage ratio profile analysis

The results of the radial line binding site coverage ratio profile analysis are depicted in Figs. 5(b)–5(f). The congruence between solid lines (experimental data) and dashed lines (simulation results) demonstrates that the simulation offers a robust correlation with the flow incubation experiments in the horizontal radial directions. It can be observed that as the distance from the sample inlet increases, both W and L values enlarge, leading to a decrease in the shear rate, an increase in the thickness of the depletion layer $\delta$, and a consequent attenuation in diffusion flux. Consequently, the binding site coverage ratio also diminishes, which is in alignment with Eqs. (3), (4), and (7).

B. Average binding site coverage ratio within FOV

Figure 6 presents a comparison of the binding site coverage ratios between simulated results (colored solid lines) and experimental data (colored dots with error bars). It can be observed that as the chamber height decreases, the binding site coverage ratio increases for a given analyte concentration, thereby indicating enhanced sensitivity. Importantly, a high degree of concordance was observed between simulations and experiments.

FIG. 6.

The comparison of the simulated and experimental results within FOV of the optical microscope readout. Reduced chamber height results in elevated binding site coverage for a given analyte concentration. A high degree of concordance was observed between simulation and experimental data.

C. Average binding site coverage ratio within the whole reaction surface

Owing to the concordance between the simulation and experimental results delineated in Secs. IV A and IV B, it is concluded that the simulated average binding site coverage within the whole reaction surface can match the experimental results, which cannot be measured.

Figure 7 presents a comparison of the average binding site coverage across the entire reaction surface for both flow and static incubation conditions. The data reveal an increase in captured analyte molecules, ranging from 1.35-fold (at 80 ng/ml with a 500 μm chamber height) to 2.4-fold (at 20 ng/ml with a 200 μm chamber height). This increase can be interpreted as signal amplification, pertinent for both colorimetric and chemiluminescence detection methods. The amplification becomes more pronounced at lower concentrations and reduced chamber heights. Furthermore, the concentration at 10 ng/ml becomes detectable when utilizing flow incubation.

FIG. 7.

Simulation result of the average binding site coverage within the whole reaction surface compared to the experimental static incubation results. The comparison indicates 1.35–2.4 times captured analyte molecules, which can be interpreted as signal amplification for colorimetric and chemiluminescence detection. 10 ng/ml becomes detectable in flow incubation.

D. Center binding site coverage ratio

Figure 8 presents a comparison of the binding site coverage ratios at the central 3 × 3 pixels. The data reveal a substantial increase in captured analyte molecules, ranging from 6-fold (at 60 ng/ml with a 500 μm chamber height) to 12-fold (at 20 ng/ml with a 200 μm chamber height). This increase can be interpreted as signal amplification, particularly relevant for fluorescence detection methods. The amplification becomes more pronounced at lower concentrations and reduced chamber heights. Furthermore, concentrations as low as 10 ng/ml become detectable when employing flow incubation. However, it is observed that the dynamic range of the flow incubation narrows due to the saturation of binding sites at lower concentrations compared to static incubation.

FIG. 8.

Center binding site coverage ratio comparison of flow incubation and static incubation. The comparison indicates 6–12 times of captured analyte molecules, which can be interpreted as signal amplification for fluorescence detection. 10 ng/ml becomes detectable in flow incubation.

V. DISCUSSION AND CONCLUSIONS

The microfluidic cover introduced herein serves as an add-on to a standard 96-well microtiter plate, enhancing the performance of immunoassays without necessitating modifications or alterations to existing infrastructure or reagents. The primary objective of this study was to improve the flux of analytes toward the capture molecules through the utilization of microfluidic methods, thereby circumventing the inconsistencies introduced by the widely employed orbital shaker. The preliminary experimental results demonstrate the enhanced capture rates of analyte molecules, resulting in up to 12-fold augmented signals for colorimetric, fluorescence, and chemiluminescence signals, as well as a lower limit of detection.

A series of simulations were employed to emulate real-world experimental scenarios, and they exhibited excellent concordance with the experimental results. This simulation approach can assist in the design of similar immunoassay devices.

Several improvements to this system may be considered for future study. The existing version of the cover is principally designed as a feasibility study and requires one syringe per assay. Additionally, a minimum of three wells are required for each assay, which increases the cost. In previous work, a handheld pneumatic liquid handling platform for microfluidic immunoassays on a cartridge was reported, featuring parallel and programmable pressure output.²⁹ The incorporation of a similar clamshell structure and manifold-based pressure delivery system could be adopted for the cover and 96-well format to move the sample reservoir and waste collection chamber to the cover, therefore reducing the waste of wells and prompt parallelization. Miniature piezoelectric pump corporate with on-chip check valves could benefit reagent separation and promise sequential reagent delivery for genuine immunoassays.³⁰ Additionally, owing to current fabrication techniques, the gasket structure limits the effective area that can benefit from advection enhancement on the reaction surface, subsequently leading to diminished analyte capture potential. Future investigations are planned to address this limitation either by refining current fabrication techniques or by exploring alternative methods. The ultimate aim is to obviate the need for a gasket, allowing the cover to friction-fit into the wells and expanding the effective area to encompass the entire well bottom, thereby contributing to further advancements in both analyte capture and signal amplification.

While numerous types of microfluidics immunoassay devices have been developed, there are still practical barriers to clinical application due to the need for specific equipment and complicated fluidic networks. Before overcoming the existing barriers and achieving a level of standardization comparable to that of microtiter plates, which would enable the widespread adoption of microfluidic immunoassays, the cover is expected to find helpful in various clinical settings. These include point-of-care environments, emergency room testing, and pre-surgical examinations, where attributes such as speed, sensitivity, and accuracy are critically important.

AUTHOR DECLARATIONS

Conflict of Interest

The authors have no conflicts to disclose.

Author Contributions

Sheng Wang: Conceptualization (equal); Data curation (lead); Formal analysis (lead); Investigation (equal); Methodology (equal); Validation (lead); Writing – original draft (lead). You Zhou: Investigation (equal); Methodology (equal). Zhenyu Li: Conceptualization (equal); Funding acquisition (lead); Investigation (equal); Methodology (equal); Project administration (lead); Supervision (lead); Writing – review & editing (equal).

DATA AVAILABILITY

The data that support the findings of this study are available from the corresponding author upon reasonable request.