A Microfluidic Chip-Based MRS Immunosensor for Biomarker Detection via Enzyme-Mediated Nanoparticle Assembly

Conventional immunoassay methods have their common defects, such as tedious processing steps and inadequate sensitivity, in detecting whole blood. To overcome the above problems, we report a microfluidic chip–based magnetic relaxation switching (MRS) immunosensor via enzyme-mediated nanoparticles to simplify operation and amplify the signal in detecting whole blood samples. In the silver mirror reaction with catalase (CAT) as the catalyst, H₂O₂ can effectively control the production of Ag NPs. The amount of Ag NPs formed further affects the degree of aggregation of magnetic nanoparticles (MNP_S), which gives rise to the changes of transverse relaxation time (T₂). Both sample addition and reagent reaction are carried out in the microfluidic chip, thereby saving time and reagent consumption. We also successfully apply the sensor to detect alpha-fetoprotein (AFP) in real samples with a satisfied limit of detection (LOD = 0.56 ng/ml), which is superior to the conventional ELISA.

Introduction

Immunoassays have always been irreplaceable methods in disease diagnosis, environmental monitoring, and medical treatment evaluation (Kingsmore, 2006; Rusling et al., 2010; Barbosa and Reis, 2017). Many methods, including gold immunochromatography assay (GICA) (Wang et al., 2018; Yang et al., 2021), chemiluminescent immunoassay (CLIA) (Iranifam, 2014; Fan et al., 2021), and enzyme-linked immunoassay (ELISA) (Raleigh et al., 1992; Zhang et al., 2019), were designed for tumor marker detection. The above conventional methods have their superiority and have been applied to clinical practice. However, they also have their intrinsic shortcomings. GICA is always accompanied by the problems such as low sensitivity, false positives, and inaccurate quantification due to simple operation and imprecise testing equipment (Giljohann et al., 2010; Ji et al., 2019). CLIA is a highly sensitive detection method, but it uses chemiluminescent substances as markers, which leads to defects such as high background values, poor stability, and low reproducibility (Zhang et al., 2018; Xiao and Xu, 2020). As a typical commercialized product, ELISA has the advantage of high stability and good repeatability, while elaborate preprocessing and washing steps lead to the strict requirement for professional operators (Zhao et al., 2019; Sheng et al., 2021). Therefore, there is an urgent need for a low background, high-throughput, and easy-to-operate system to achieve rapid, sensitive, and accurate analysis of targets.

Recently, people have devoted much energy to the exploitation of novel biosensors for detecting biomarkers (Jia et al., 2017; Cui et al., 2020; Huang et al., 2020). Magnetic relaxation switching (MRS) sensors based on nuclear magnetic resonance (NMR) are becoming widespread focus (Choi et al., 2017; Tian et al., 2017; Chen et al., 2018b; Dong et al., 2018) in biochemical analysis for their low background, high sensitivity, rapidness, and simplicity. MRS assays have been employed as a powerful tool to identify and quantify a wide range of targets, including proteins (Liang et al., 2011), microorganisms (Zhao et al., 2019), nucleic acids (Liong et al., 2013), small molecules (Atanasijevic et al., 2006), and other biomarkers. This method changes the T₂ value of neighboring water protons of magnetic nanoparticles along with their state switching between dispersion and aggregation. And the changes of ΔT₂ correlate with the concentration of the target in the sample. Thus, ΔT₂ can be used as analytical signal in MRS immunosensor. Usually, negligible magnetic substance existed in biological samples. Compared with electrochemical (Wang D. et al., 2015; Fan et al., 2020; Song et al., 2020) or optical (Walling et al., 2009; Zhou et al., 2015; Yin et al., 2017) sensors, it has the advantages of lower background value. Thus, it can be used to detect opaque samples, such as muddy water (Jia et al., 2017; Porion and Delville, 2020), milk (Chen et al., 2013; Wang S. et al., 2015), and whole blood (Neely et al., 2013; Mylonakis et al., 2015) directly. However, the MRS sensor suffers from its relatively low sensitivity and not-so-simple preprocessing steps when it comes to the analysis of trace substance in complex samples (Yin et al., 2016). According to a report, one kind of magnetic/silver nanoassemblies (Ag-MNPs) mediated by an enzyme cascade reaction can greatly enhance the state change of MNPs (from dispersed state to aggregated state). Therefore, it can improve the sensitivity of the conventional MRS sensor dramatically (Chen et al., 2018a).

Microfluidic chips have a good prospect in immunoassay because they can be integrated with different elements such as pumps, valves, and electronics (Chao et al., 2016; Wang and Fu, 2018; Wolf et al., 2018). Reagent storage, protein adsorption, sample separation, and microfluid flow characteristics and direction control in microfluidic chips have become the off-the-shelf technique. Thus, microfluidic chips have the potential to replace tedious pretreatment procedures (Rothbauer et al., 2018).

To realize low cost, high sensitivity, and rapid detection for the tumor biomarkers such as alpha-fetoprotein (AFP), herein, we developed an amplified MRS immunosensor via enzyme-mediated cascade reaction with a microfluidic chip. First, we labeled the capture antibody (Ab₁) on magnetic beads (MBs) to generate MB-Ab₁. We labeled the enzyme catalase (CAT) and detection antibody (Ab₂) on polystyrene (PS) microspheres at the same time to prepare CAT-PS-Ab₂ for forming an enzymatic amplification system (Figure 1A). Then the two conjugations (MBs-Ab₁ and CAT-PS-Ab₂) and analyte are injected into the snake-shaped channel of the microfluidic chip and are mixed via fluid inertia. The targets (T) can specifically bind antibodies modified on MBs and PS to form a sandwich structure (MBs-Ab₁-T-Ab₂-PS-CAT). An NdFeB magnet is placed at the bottom of the storage chamber to enrich the MBs. The pressure valve on the right controls the direction of the fluid, and realizes the functions of washing and reagent-adding (Figure 1C). CAT possesses a high catalytic effect on the substrate H₂O₂ to generate water and oxygen, which can adjust the aggregated degree of Ag-MNPs through the decomposition of H₂O₂ (Figure 1B). When 30 nm carboxyl-modified magnetic nanoparticles (MNPs₃₀-COOH), Ag⁺, and H₂O₂ are mixed without CAT, Ag⁺ is reduced by sufficient H₂O₂ to silver nanoparticles (Ag NPs) immediately, and fewer Ag⁺ was adsorbed or diffused on the surface of magnetic nanoparticles, which hindered the formation of Ag-MNPs₃₀. While there is CAT existing, the concentration of H₂O₂ is controlled at a relative lower level, and the formation of Ag NPs is retarded. Abundant Ag⁺ on the surface of magnetic particles can facilitate the aggregation of Ag-MNPs₃₀ (Figure 1D). Finally, the NMR signal of Ag-MNPs₃₀ was recorded.

FIGURE 1

FIGURE 1. (A) Generation of MBs labeled capture antibody (MBs-Ab1) and PS microparticle labeled both CAT and detection antibody (CAT-PS-Ab2). (B) Role of CAT in the formation of Ag-MNPs. (C) A microfluidic chip–based MRS immunosensor for biomarker detection. (D) CAT labeled on the surface of PS can catalyze H₂O₂ and mediate the assembly aggregation of Ag-MNPs, which results in a lower NMR signal.

Experimental Section

Materials and Apparatus

Alpha-fetoprotein (AFP, 1 mg/ml), anti-AFP capture antibody (Ab₁, 5 mg/ml), anti-AFP detection antibody (Ab₂, 5 mg/ml), carcinoembryonic antigen (CEA, 1 mg/ml), and human IgG (IgG, 10 mg/ml) were obtained from Hotgen Biotech Inc. (Beijing, China). Catalase (CAT) is from bovine liver, 1-ethyl-3-[3-dimethylaminopropyl] carbodiimide hydrochloride (EDC), N-hydroxysulfosuccinimide (sulfo-NHS), and bovine serum albumin (BSA) were purchased from Sigma-Aldrich (Shanghai, China). 30 nm carboxylic acid–modified magnetic nanoparticles (5 mg/ml) and 30 nm amino-modified magnetic nanoparticles (5 mg/ml) were purchased from Ocean NanoTech, LLC (United States), 250 nm carboxylic acid–modified magnetic beads (MBs, 10 mg/ml) were purchased from micromod Partikeltechnologie GmbH (Germany), and carboxyl-functionalized polystyrene microspheres (PS, 100 mg/ml, d = 1 µm) were purchased from the Bangs Laboratories, Inc. (United States). Hydrogen peroxide (H₂O₂) was obtained from Beijing Chemical Works (China). Potassium hydroxide (KOH), AgNO₃, and NH₃·H₂O were obtained from Beijing Chemical Reagents Co., Ltd (China). Polydimethylsiloxane (PDMS) and curing agent (Sylgard 184) were purchased from Dow Corning Inc. (MI, United States). Tablets of phosphate buffer saline (PBS, 0.01 M, pH 7.4) and Tween-20 were purchased from Amresco LLC (United States). Milli-Q water was generated throughout a Millipore water system (United States).

The PT-10s plasma cleaner for PDMS bonding was produced by Shenzhen Sanhoptt Co. Ltd (China). 1.5T (60 MHz) nuclear magnetic resonance (NMR) spectrometer system for the measurement of transverse relaxation time was obtained from Shanghai Huantong Science and Education Equipment Co., Ltd (China). JEM-2100F (200 kV) field emission transmission electron microscope (TEM) for characterization of nanoparticle morphologies was obtained from Japan Electron Optics Laboratory Ltd (Japan). 2,300–001M EnSpire multimode plate reader was obtained from PerkinElmer Inc (United States). Zetasizer Nano ZS (Malvern Panalytical Ltd, United Kingdom) dynamic light scattering (DLS) was used for particle size and zeta potential measurements. The SLA 3D printer for manufacture of molds was provided by Yangzhou SHINING 3D Co. Ltd (China).

Design of the Snake-Shaped Microfluidic Chip

The components and cast molds of snake-shaped microfluidic chip (SSMC) are designed using Unigraphics NX 10.0 3D modeling software (Siemens PLM Software, Berlin, Germany). Specifically, the components of the SSMC consist of three parts: PDMS channel layer, PDMS substrate layer, and NdFeB magnet, and three pressure valve stages (Figure 2A and Figure S1). The PDMS channel layer (35 mm* 55 mm* 3.5 mm) has a total of four injection ports. The three injection ports on the top converge into the snake-shaped channel in 45° interval arrangement. We have optimized the design of the snake-shaped channel to mix the sample sufficiently. To be specific, three narrow-broad repeated units were used. The diameter of the spacious and thin channels is 0.6 mm and 0.12 mm, respectively. To make the sample achieve efficient mixing, and uneven flow velocity and pressure in the channel (Figure 2B), there is a valve hole (2.8 mm* 2.8 mm) for installing pressure valve on the lower side. It is connected to the waste chamber (200 μl), the bottom injection port, the reaction chamber (280 μl), and the reservoir (50 μl). The PDMS substrate layer (35 mm* 55 mm* 3 mm) is dug into a circular groove to place the magnet. There is also a valve hole corresponding to the PDMS channel layer below it. The pressure valve (3 mm* 3 mm) and the valve hole are assembled by an interference fit. It is designed with three holes (0.6 mm* 0.6 mm) in height to control the flow direction in the channel. An NdFeB magnet is fixed in the groove of the substrate layer.

FIGURE 2

FIGURE 2. (A) Schematic diagram of SSMC. (B) Optimized size of snake-shaped mixing channel. (C) The molds of microfluidic chips. (D) A picture of real SSMC.

Fabrication and Assembly of the SSMC

First, we designed the three-dimensional model of molds and pressure valve by the 3D modeling software. Then we used the SLA 3D printer to automatically print the molds and pressure valve with the photosensitive resin (Figure 2C). After printing, the mold is cleaned in an ultrasonic cleaning machine containing alcohol to remove unmolded resin on the surface. A 9.5:1 (w/w) mixture of PDMS matrix and curing agent was vacuumized in the vacuum drying oven. The dual PDMS layer molds including uniform and bubble-free mixture were placed in a thermostatic oven at 75°C for 1.5 h, and then the dual PDMS layer in the mold was removed with tweezers. The next step is to assemble the SSMC chip as follows: 1) disposing the dual PDMS layers in the plasma cleaner for 60 s (220 w working power and 2.0 l/min gas flow rate), 2) slowly squeezing out the bubbles of the chip by hand and make them stick together, and 3) fixing the pressure valve on the dual PDMS layers to ensure the first hole aligned with the chip channel (Figure 2D).

Preparation of Ag(NH₃)₂OH Solution

To prepare the Ag(NH₃)₂OH solution (Tollen’s reagent), 200 µl of NH₃·H₂O (15 M) was dropwise added into 6 ml of AgNO₃ (0.1 M), while stirring mixture solution until the brown precipitate dissolved. Then 3 ml of KOH solution (0.8 M) was added, and the brown precipitate reformed. To dissolve the precipitate, 200 µl of NH₃·H₂O was added again. Finally, deionized water (Milli-Q water) was added to a final volume of 25 ml, and was stored in the dark at 4°C.

Preparation of MB-Ab₁ Conjugate

First, 5 mg of suspended MBs was transferred into 1 ml of the MES buffer (80 nM, pH 6.0). After that, 80 µl of EDC (10 mg/ml) and 40 µl of NHS (10 mg/ml) were added, and the mixture was activated for 0.5 h at room temperature (RT). The activated MBs were washed three times using 1 ml of PBS buffer (pH 7.4, 0.01 M) with a magnetic separator, and then were dispersed in 0.5 ml of PBS solution. 0.5 mg of Ab1 was added into the above solution and stirred with 300 rpm for 1.5 h at RT. After that, 0.5 ml of 3% BSA solution was added to block the surface of MBs for 0.5 h. The MB-Ab1 conjugate was magnetically separated from the free Ab1, and the conjugate was washed three times using 1 ml of PBST buffer (PBS buffer with 0.5% Tween-20). The MB-Ab₁ conjugate was resuspended in 0.5 ml of PBS with 0.1% BSA and stored at 4°C for further use.

Preparation of CAT-PS-Ab₂ Conjugate

First, 10 mg of suspended PS microspheres was transferred into 0.9 ml of deionized water in a clean ultrafiltration tube (100 kDa filter), and then was centrifuged at 8,500 rpm for 10 min. The collected PS microspheres were resuspended in 0.5 ml of deionized water, and 120 µl of EDC (10 mg/ml) and 60 µl of NHS (10 mg/ml) were added to it. The reaction mixture was stirred with 300 rpm for 30 min at RT, and was diluted using 1 ml of PBS buffer (pH 7.4, 0.01 M). After that, different quantities of Ab₂ and CAT were added into the microparticle suspension. The above mixture solution was stirred mildly for 2 h at RT. Then 200 µl of 5% BSA solution was subsequently added and wobbled gently for 30 min. The obtained mixture was centrifuged for 10 min at 6,000 rpm, and was resuspended in 1 ml of PBS solution. The previously mentioned centrifuged and resuspended steps were repeated three times. The CAT-PS-Ab₂ conjugate was resuspended in 1 ml of PBS (pH 7.4, 0.1% BSA) and stored at 4°C for further use.

Procedures of Detecting AFP and Samples

First, we add 160 μl Tollen’s reagent (2.4 mM) and 20 μl MNPs₃₀-COOH (0.5 μg/ml) into the reaction chamber of SSMC. Then the detecting procedures are carried out as follows: 1) we add 20 µl of MB-Ab1 (0.1 mg/ml), 100 µl of different concentrations of AFP (1,280 ng/ml, 640 ng/ml, 320 ng/ml, 160 ng/ml, 80 ng/ml, 40 ng/ml, 20 ng/ml, 10 ng/ml, 5 ng/ml or 2.5 ng/ml, and 0.625 ng/ml or 0 ng/ml), and 20 µl of CAT-PS-Ab₂ (0.05 mg/ml) in the injection ports separately. The magnet absorbs the MB-Ab1-T-Ab2-PS-CAT conjugates mixed by snake-shaped channel, and uncombined MB-Ab₁ are discarded in the reservoir. 2) After 15 min, we wash the conjugates remaining in the channel with PBS (0.01 M, pH 7.4), and uncombined AFP and CAT-PS-Ab₂ are washed into the waste chamber. 3) Press the pressure valve to ensure the second hole aligned with the chip channel. We add 20 µl H₂O₂ (250 µM) from bottom injection port to the reservoir. The conjugates react with H₂O₂ for 5 min. 4) Open the reaction chamber through the third hole aligned with channel. The suspension in the reservoir is flowed into the reaction chamber (Figure 3A). 5) After 5 min, we use the NMR analyzer to measure the ΔT₂ value of 20 µl extracted mixture from reservoir (Supplementary Figure S2).

FIGURE 3

FIGURE 3. (A) The process of detecting AFP and the direction of the pressure valve at each step. (B) Different NMR signals in the dispersed state and the aggregated state.

The parameters of the inversion recovery pulse sequences for ΔT₂ measurements are as follows: NMR frequency, 62.16 MHz; pulse separation, 10 m; 90° pulse width, 32 μs; 180° pulse width, 64 μs; number of scans, one; and repetition time, 10 s. To obtain the limit of detection (LOD), we employ the following formula: LOD = 3S/M (where S is the value of the standard deviation of blank samples and M is the slope of standard curve within the low-concentration range).

When MNPs are in a dispersed state, they show a higher NMR signal. Once Ag-MNPs30 probe was formed, it shows a lower NMR signal due to the aggregated state of MNPs (Figure 3B). To control the flow direction of the reagent in the channel at each step, the pressure valve is switched as shown in Figure 3A.

Results and Discussion

Surface Charge of MNPs/Ag-MNPs₃₀

The difference in surface charge of MNPs₃₀ significantly affects the formation of Ag NPs. We tested the Zeta potentials of MNPs₃₀-COOH, Ag-MNPs₃₀-COOH, MNPs₃₀-NH₂, and Ag-MNPs₃₀-NH₂ (Figure 4A). Results indicate that only MNPs₃₀-NH₂ have a positively charged surface (ζ = 26 mV), and MNPs₃₀-COOH show a negatively charged surface (ζ = −13.5 mV). Whether positively charged or negatively charged MNPs can combine with negatively charged Ag NPs (ζ = −27.2 mV), charges in the surface of MNPs will become more negative. It means that Ag NPs in quantity gathered on the surface of magnetic nanoparticles.

FIGURE 4

FIGURE 4. (A) Zeta potentials of Ag NPs, MNPs₃₀-COOH, Ag-MNPs₃₀-COOH, MNPs₃₀-NH2, and Ag-MNPs₃₀-NH2. (B) DLS analysis of Ag NPs, MNPs₃₀-COOH, Ag-MNPs₃₀-COOH, MNPs₃₀-NH2, and Ag-MNPs₃₀-NH2.

DLS results show that the average sizes of both Ag-MNPs₃₀-NH₂ (105 nm) and Ag-MNPs₃₀-COOH (135 nm) are larger than those of magnetic particles itself, that is, MNPs₃₀ (30 nm) (Figure 4B), which indicates that both MNPs₃₀-COOH and MNPs₃₀-NH₂ can be aggregated with Ag NPs. It is consistent with the result of zeta potential results.

Formation and Characterization of Ag-MNPs₃₀

We selected one positively charged surface (MNPs₃₀-NH₂) and one negatively charged surface (MNPs₃₀-COOH) of magnetic nanoparticles to prepare the magnetic/silver nanoassemblies to select the appropriate magnetic nanoparticles to carry out the next experiment. We utilize TEM to observe the morphology and structure of Ag-MNPs₃₀-COOH and Ag-MNPs₃₀-NH₂. When the concentrations of MNPs₃₀-COOH and MNPs₃₀-NH₂ are at 5 μg/ml, the shapes and diameters of the two particles are almost the same (Figures 5A,B). Once Tollen’s reagent and H₂O₂ are pouring into the magnetic nanoparticle solution, the results showed an obvious distinction. In MNPs₃₀-COOH solution, a well-arranged flower-like structure attributed to Ag-MNPs₃₀-COOH was founded (Figure 5C). However, a blended and irregular structure of Ag-MNPs₃₀-NH₂ was observed when MNPs₃₀-NH₂ was used as the substrate in the same condition. Most of MNPs₃₀-NH₂ are still dispersed in the solution and do not absorb on the surface of Ag NPs (Figure 5D).

FIGURE 5

FIGURE 5. Characterization of MNPs₃₀-COOH, MNPs₃₀-NH₂, Ag NPs, Ag-MNPs₃₀-COOH, and Ag-MNPs₃₀-NH₂ by transmission electron microscope (TEM). (A) The shape of MNPs₃₀-COOH. (B) The shape of MNP₃₀-NH₂. (C) The appearance of Ag-MNPs₃₀-COOH. (D) The appearance of Ag-MNPs₃₀-NH₂.

The reasons why MNPs with negative charged surface can form the regular shape are as follows (Wang et al., 2009; Gilroy et al., 2016): First, Ag⁺ is adsorbed on the negatively charged surface of MNPs₃₀, and a local concentration difference of positive charge was formed. Then, the enriched Ag⁺ is reduced by H₂O₂ to Ag NPs and finally forms the uniformed Ag-MNPs probe.

For comparison, we also photographed the morphology of pure Ag NPs without MNPs₃₀-COOH (Supplementary Figure S3). It showed that the shape of Ag NPs was not uniform. The surface atoms on the small size of Ag NPs have higher energy than the interior material. According to the principle of minimum energy, Ag NPs would spontaneously tend to aggregate to form irregular accumulation. The above results suggest that pure Ag NPs and Ag-MNPs₃₀-NH₂ cannot form a regular shape as required in our experiment, while MNPs₃₀-COOH with negatively charged surface can get uniform size of Ag-MNPs₃₀ for further use.

In addition, we employ UV-Vis spectrum to testify the principle of Ag NPs-MNPs₃₀ assembly probe formation. Individual MNPs₃₀-COOH or MNPs₃₀-NH₂ has no obvious characteristic absorption from 300 to 700 nm (Supplementary Figure S4). After injection of H₂O₂, the color of the Ag⁺ solution turns black, and a strong absorption peak appears at 450 nm, indicating that H₂O₂ can promote the formation of Ag NPs. The color of the solution gradually turns brown, and the absorption peak has a hypochromatic shift to 400 nm when MNPs₃₀-COOH was used as the substrate, while the color only becomes weak, and absorption decreases with it when MNPs₃₀-NH₂ was used. These comparisons explain that negatively charged MNPs₃₀-COOH interacts with Ag NPs to promote the formation of probe better than MNPs₃₀-NH₂. This is consistent with the above results.

Response Performance of Ag-MNPs₃₀ to H₂O₂

As one of the substrates in the redox system of Ag⁺ and H₂O₂, the amount of H₂O₂ consumed determined the amount of Ag NPs or Ag-MNPs₃₀ generated. The Ag NPs can be used as optical signal and Ag-MNPs₃₀ can be used as magnetic signal readout respectively. Hence, we investigate the response of Ag NPs or Ag-MNPs₃₀ toward H₂O₂ by ultraviolet absorption spectrum and MRS.

First, we dilute MNPs₃₀-COOH from 0 μg/ml to 50 μg/ml (Supplementary Figure S5). With the increase of concentration of MNPs, the ΔT₂ value decreases as a ladder. That is a good proof that the concentration of MNPs₃₀ has immediate effect on ΔT₂. Next, we study the sensitivity and detection range of Ag-MNPs₃₀ response to H₂O₂. When ΔT₂ is employed for the readout, the detection range of 1.6 μM–10 mM was observed (Supplementary Figure S6A), and a linear relationship is 1.6 μM–1 mM ($y=26.5x+32.5, R^2=0.99, \mathrm{LOD}=0.53 \mathrm{\mu M}$) (Supplementary Figure S6C). If there was an optical signal–based readout, the detection range for H₂O₂ would be 40 μM–10 mM (Supplementary Figure S6B), and a linear range 40 μM–5 mM was observed ($y=0.62x-0.21, R^2=0.94,\text{ LOD}=5.2 \text{μM}$) (Supplementary Figure S6D). Therefore, compared with ultraviolet absorption detection, the MRS sensor has enhanced about 10 times sensitivity in the H₂O₂ catalytic reaction.

Optimization of Experiment Condition of the MRS Sensor

The above result indicates that the ΔT₂ generated by the MRS sensor is easily affected by the concentration of H₂O₂. The optimal ΔT₂ value appeared at 1 mM H₂O₂. We need to further optimize the concentrations of Ag⁺ and MNPs₃₀, reagent addition sequence, reaction time, and coupling ratio of CAT and Ab₂ on the surface of the PS particles.

We added 0.05 μg/ml, 0.5 μg/ml, and 5 μg/ml MNPs₃₀ to varying H₂O₂ concentrations. The gradient of ΔT₂ at 0.5 μg/ml is the largest. By contrast, the changes in ΔT₂ are the smallest when MNPs₃₀ were added in 5 μg/ml (Supplementary Figure S7A).

Different orders of reagent addition would also affect the extent of reaction. We compared the results of two adding sequences: 1) MNPs₃₀, Tollen’s reagent, and H₂O₂, and 2) Tollen’s reagent, H₂O₂, and MNPs₃₀ (Supplementary Figure S7B). Obviously, the former is superior to the latter. It can be explained that H₂O₂ will reduce Tollen’s reagent first into Ag NPs and cannot form Ag-MNPs₃₀ in the latter sequences. In order to simplify the chip design and operation, we first add H₂O₂, and then add the MNPs₃₀ and Tollen’s reagent mixture. Under the above optimized conditions, we add different concentrations of Tollen’s reagent, and the maximum ΔT₂ value is observed at 2.4 mM of Ag⁺ (Supplementary Figure S7C). And after 90 s, the reaction reached a plateau (Supplementary Figure S7D). Hence, the optimal concentration of Tollen’s reagent and reaction time are 2.4 mM of Ag⁺ and 90 s separately.

In addition to the abovementioned optimization of the MRS sensor, the coupling ratio of CAT and Ab₂ on the surface of the PS particles and the concentration of H₂O₂ should also be considered in the process of detecting AFP.

The coupling ratio is a critical factor for the recognition reaction and the effect of signal amplification. When the molar ratio of CAT to Ab is 10:1, ΔT₂ reaches the largest value (Supplementary Figure S8A); thus, we select 10:1 as the optimized molar ratio of CAT/Ab₂ for the next steps.

We next explore the effect of H₂O₂ concentration on the detection performance, since CAT could degrade H₂O₂ to affect the formation of Ag-MNPs₃₀ and result in the change of ΔT₂. Although the ΔT₂ value shows best performance at 500 µM of H₂O₂, a higher concentration leads to a narrower detection range for detecting AFP. In consideration of both sensitivity and detection range, we choose 250 µM H₂O₂ for further experiments (Supplementary Figure S8B).

Sensitivity and Selectivity

Under the above optimized conditions, we compare the analytical performance of the MRS sensor and conventional pNPP (para-nitrophenylphosphate)-based ELISA for the detection of AFP. AFP is a tumor marker for the diagnosis of primary liver cancer (Jalanko et al., 1978; Shen et al., 2020). A high AFP generally means the occurrence of liver cancer. The content of AFP in normal human serum is less than 20 ng/ml. In the AFP detection based on MRS sensor, we employ the MB_S with Ab₁ and PS microparticles coupled with Ab₂ and CAT as immunological carriers to accomplish mixture, enrichment, and separation. In the redox system with H₂O₂ as the reducing agent, the concentration of AFP depends on the ΔT₂ signal caused by the degree of Ag-MNPs₃₀ aggregation. The ΔT₂ increases as the concentration of AFP changes from 0 to 1,280 ng/ml, and there is a linear relationship from 2.5 to 160 ng/ml. The linear equation is $y = 0.646x+1.019 \left(R^2=0.97\right)$, and the LOD of MRS sensor for detecting AFP is 0.56 ng/ml (Figures 6A,C). In comparison, the linear range of the pNPP-based ELISA for the detection of AFP is 20–320 ng/ml. The linear equation is $y =0.00471x+0.01217 \left(R^2=0.99\right)$, and the LOD is 7 ng/ml (Figures 6B,D). Thus, the sensitivity of MRS sensor for AFP detection has been improved about 12.5 folds compared with the conventional pNPP-based ELISA.

FIGURE 6

FIGURE 6. The sensitivity of MRS sensor and pNPP-based ELISA for detecting AFP. (A) The detection range of MRS sensor for detecting AFP. (B) The detection range of pNPP-based ELISA for detecting AFP. (C) The linear range of MRS sensor for detecting AFP. (D) The linear range of pNPP-based ELISA for detecting AFP.

We also investigated the selectivity of the MRS sensor for detecting AFP (160 ng/ml), by using CEA (100 ng/ml) and human IgG (5 mg/ml) as the interferent (Supplementary Figure S9). We chose CEA as the interferent because CEA is also one of the liver cancer markers in human serum. We chose human IgG as the interferent because human serum contains a large amount of IgG (7–16.6 mg/ml), which may interfere the detection of AFP. The relative errors of ΔT₂ between AFP with and without interfering agents were 1.6% (CEA as the interferent) and 5.4% (human IgG as the interferent). Moreover, ΔT₂ of CEA, IgG are far lower than AFP. It suggests that the selectivity of the MRS sensor provides a prerequisite for the practical application for detections of AFP in complex samples.

Real Sample Analysis

To confirm the application of our approach, we compare MRS sensor with pNPP-based ELISA in detecting AFP with 20 whole blood samples and 20 serum samples. The MRS sensor based on magnetic signals can directly detect AFP in both whole blood samples and serum samples (Figure 7A). The OD intensity of pNPP-based ELISA is determined by spectrophotometric colorimetry, which is applicable to serum samples instead of the red whole blood samples in clinical diagnosis (Supplementary Figure 7B). In 20 serum samples, samples from 11 to 20 are all detected to be AFP positive by the MRS sensor, and other samples are detected to be AFP negative. The difference is that samples 14 and 16 appear false negative (Supplementary Figure S10). The quantitative results of the MRS sensor for detecting AFP in serum samples agree well with those of pNPP-based ELISA with a correlation coefficient of 0.97 (Supplementary Figure S11).

FIGURE 7

FIGURE 7. (A) Correlation between the detection of AFP in whole blood samples and serum samples by MRS sensor. (B) Correlation between the detection of AFP in whole blood samples and serum samples by the pNPP-based ELISA.

To show the benefit of our method, we have compared the analytical performance of the MRS sensor with that of the MRS sensor in other reported works (Fan et al., 2014; Chen et al., 2016) for AFP detection in terms of detection time, LOD, performance of operations, consumption of reagents, dependence of devices, and sample types (Table 1). As we can see, the MRS sensor we developed shows a relative lower LOD, requires minimal sample, and can detect target in whole blood samples directly, while ELISA and CLIA can only be used in serum. Although GICA has good sensitivity as the MRS sensor, it requires higher consumption of reagents.

TABLE 1

TABLE 1. The characteristics of the MRS sensor are compared with those of the MRS sensor in other methods.

Conclusion

In conclusion, we employed a microfluidic chip–based MRS sensor via enzyme-triggered nanoparticle assembly to replace elaborate manual operation and enhance the detecting sensitivity. Compared with conventional pNPP-based ELISA, the MRS sensor we developed is a competitive sensor for detecting whole blood samples because of its low background signal and high sensitivity. This work also has the potential of high throughput detection, and we will focus on optimizing the microfluidic chip, automatic detection equipment, and immune reaction system to achieve the application of multiple marker detection in the clinical diagnosis.

Data Availability Statement

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

Ethics Statement

The studies involving human participants were reviewed and approved by the Ethics Committee of Yangzhou University Medical College. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author Contributions

BY: supervision, experiments, and funding. CQ: writing original draft and graphs. SW: proof reading and methodology. TZ: investigation and funding. XW: editing and validation. All authors contributed to the article and approved the submitted version.

Funding

This work was financially supported by the National Natural Science Foundation of China (Grant No. 52075138), the Natural Science Foundation of Jiangsu (Grant No. BK20190872), and the China Scholarship Council (Grant No. 201808140212).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fchem.2021.688442/full#supplementary-material

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