Biosensors and Bioelectronics 66 (2015) 445–453

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Ultrasensitive immunochromatographic assay for the simultaneous detection of five chemicals in drinking water Changrui Xing a, Liqiang Liu a, Shanshan Song a, Min Feng b, Hua Kuang a,n, Chuanlai Xu a a b

State Key Lab of Food Science and Technology, School of Food Science and Technology, Jiangnan University, Wuxi, JiangSu 214122, PR China Huaian Entry-Exit Inspection and Quarantine Bureau, Huaian 223001, PR China

art ic l e i nf o

a b s t r a c t

Article history: Received 19 September 2014 Received in revised form 17 November 2014 Accepted 1 December 2014 Available online 3 December 2014

In this paper, we describe the development of a multicomponent lateral-flow assay based on an antibody–antigen reaction for the rapid and simultaneous detection of trace contaminants in water, including a heavy metal, algal toxin, antibiotic, hormone, and pesticide. The representative analytes chosen for the study were lead (Pb(II), microcystin–leucine–arginine (MC–LR), chloramphenicol (CAP), testosterone (T), and chlorothalonil (CTN). Five different antigens were immobilized separately in five test lines on a nitrocellulose membrane. The monoclonal antibodies specifically recognized the corresponding antigens, and there was no cross-reactivity between the antibodies in the detection assay. Samples or standards containing the five analytes were preincubated with the freeze-dried colloidal-gold-labeled monoclonal antibody conjugates to improve the sensitivity of the assay. The results were obtained within 20 min with a paper-based sensor. The cut-off values for the strip test were 4 ng/mL for Pb(II), 1 ng/mL for MC–LR, 0.1 ng/mL for CAP, 5 ng/mL for T, and 5 ng/mL for CTN. The assay was evaluated using spiked water samples, and the accuracy and reproducibility of the results were good. In summary, this lateralflow device provides an effective and rapid method for the onsite detection of multiple contaminants in water samples, with no treatment or devices required. & 2014 Elsevier B.V. All rights reserved.

Keywords: Gold nanoparticle immunochromatographic strip Simultaneous detection Microcystin–leucine–arginine Chloramphenicol Testosterone

1. Introduction Organic contaminants and inorganic pollutants in surface water pose a risk to water quality and the health of ecosystems throughout the world (Nikolaou et al., 2007; Jones et al., 2002; Subedi et al., 2012; Xu et al., 2009; Golet et al., 2002; Tong et al., 2011). Recent studies of water pollution have showed that the most pervasive contamination is found in China (Liang et al., 2013; Wu et al., 2011). Pharmaceuticals and personal-care products (PPCPs), including prescription drugs, over-the-counter preparations (antibiotics, anti-inflammatory drugs, sedatives, and eikonogen), and cosmetics (heavy metals), are detected in surface water, groundwater, rivers, lakes, soil, and food (Wang et al., 2014; Bu et al., 2013). The most common pharmaceutical compounds in the environment are antibiotics, anti-inflammatory drugs, lipid regulators, cancer therapeutic drugs, steroids, and related hormones (testosterone) (Nikolaou et al., 2007; Richardson and Ternes, 2011; Richardson and Ternes, 2014). These chemicals are widely used and released into the environment and may have toxic effects on living organisms, n

Corresponding author. E-mail address: [email protected] (H. Kuang).

http://dx.doi.org/10.1016/j.bios.2014.12.004 0956-5663/& 2014 Elsevier B.V. All rights reserved.

including humans (Subedi et al., 2012). To assess the risks of these small molecules in drinking water or biological samples, we have developed an efficient, accurate, and simple method to simultaneously detect trace concentrations of small molecules in water samples. In this study, five analytes were used as model targets and were simultaneously detected with a strip sensor: lead (Pb (II)), microcystin–leucine–arginine (MC–LR), chloramphenicol (CAP), testosterone (T), and chlorothalonil (CTN). Lead is a naturally occurring element that is toxic to both humans and animals, although it has some beneficial uses. Lead accumulates in our bodies over time and can reduce kidney function, produce anemia, and slow growth. Lead can cross the placental barrier, reducing fetal growth and causing premature birth (Edwards et al., 2009). To prevent the potential health effects of long-term exposure to lead, the National Primary Drinking Water Regulations of China have set the maximum concentration level to 15 ng/mL. Because lead is a persistent and toxic environmental contaminant, a simple and sensitive method must be developed for its detection in drinking water. MC–LR produced by cyanobacteria is the most toxic naturally occurring toxin (Graham et al., 2010). Water ecosystems are severely contaminated with cyanobacteria in China (Srivastava et al., 2013). The contamination of drinking water or surface water

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presents a significant hazard for human health, causing hepatic necrosis and hemorrhage. To ensure water safety, the recommended limit for MC–LR in drinking water is 1 ng/mL. Chloramphenicol is extensively used in veterinary practice as a broad-spectrum antibiotic against a variety of pathogens. Its systemic use or topical application can cause fatal adverse effects in humans, including aplastic anemia (Page., 1991). The use of CAP in food-producing animals was specifically prohibited in the United States and the European Union (EU) in 1994 (Stolker and Brinkman, 2005). However, the illegal use of CAP causes the pollution of rivers and ultimately the drinking water supplies (Wang et al., 2014). Testosterone is a steroid hormone of the androgen group. Although it can be beneficial to health, the abuse of T is prevalent in today's society. It is administered to animals at poultry farms and fishing grounds to improve their growth rates. However, T disrupts normal physiological functions and interferes with fertility processes. It also migrates through the environment, reducing water quality and damaging environmental and human health (Kolpin et al., 2002). Chlorothalonil (2,4,5,6-tetrachloroisophthalonitrile) is the third most commonly used fungicide in the USA and has been detected in ambient air and groundwater. The national standards of China limit the level of CTN in drinking water to 10 ng/mL (GB 5749– 2006). Chlorothalonil may be a carcinogen in humans because it has been showed to cause kidney cancer in animals. Long exposure causes nose bleeds and skin rashes (Murphy and Haith, 2006).

These analytes probably occur simultaneously in environmental water and pose a hazard to human health. Many methods have been developed to detect these analytes so that their contamination of environmental water can be monitored. Although liquid chromatography–mass spectrometry (LC–MS) and inductively coupled plasma (ICP)-MS can detect many analogs of these compounds or chemically similar analytes, no instrumental method for the simultaneous determination of Pb(II), MC–LR, CAP, T, and CTN have been developed because their properties differ too markedly. Instrumental methods, such LC–MS and ICP-MS, have been used to detect one or two of them (Li et al., 2006; Neffling et al., 2009; Sheridan et al., 2008; Mattern et al., 1991; Singh, 2008). These methods have the advantage of accurate quantification and have been widely used as reference methods. However, they are costly and time-consuming, and expensive instruments and skilled operators are required. The special instruments required for these detection processes make such methods unsuitable for the on-site and simultaneous detection of various contaminants. The wide occurrence of PPCPs requires rapid and cost-effective methods for the simultaneous detection of multiple analytes. As showed in Table 1, many analytical methods have been used to detect multianalytes, including disease biomarkers, small molecules, oligonucleotide targets, biological thiol molecules, proteins, heavy metals, and mycotoxins, in single samples. In Table 1, the advances in multiplex detection by different analytical methods were listed including Raman scattering technology,

Table 1 Advances in in multiplex detections. Analytical methods

Analytes

Raman scattering

Intrinsic cancer biomarkers Small molecules Oligonucleotide targets

Dinish et al. (2014) Cecchini et al. (2013) Cao et al. (2002)

Fluorescent emission

Small molecules Biological thiols molecules Multiprotein interactions Small molecules

Nakano et al. (2013) Yang et al. (2014), Liu et al. (2014a,b,c,d) Galperin et al. (2004) Liu et al. (2007)

Spectrally encoded beads

Proteins

Wilson et al. (2007) Han et al. (2001)

Immunoprecipitation

Colon cancer biomarker candidates

Lin et al. (2013)

Electrochemical detection

Heavy Heavy Heavy Heavy

Lin et al. (2011) Xu et al. (2014a,b) Rattanarat et al. (2014) Cho et al. (2012)

Multiplex ELISA

Graft versus host disease (GVHD) biomarkers Disease biomarkers Human tumor markers Human tumor markers

Frampton et al. (2014) Rissin et al. (2010) de la Rica and Stevens (2012) Gaster et al. (2009)

Microchip platform

Type 1 diabetes (T1D) markers Liver fibrosis markers A panel of molecules Mycotoxin

Zhang et al. (2014a,b) Shi et al. (2007) Das et al. (2012) Wang et al. (2012)

Array test strip

Heavy metals

Liu and Lin (2014)

Immunochromatographic assay

Mycotoxin Mycotoxin Sulfonamides A panel of molecules

Wang et al. (2013a,b) Li et al. (2013) Guo et al. (2010) Our method

metals metals metals metals

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fluorescent emission spectra, electrochemical analysis, multiplex ELISA, immunochromatographic assay, microchip platform, electrochemical impedance spectroscopy, immunoprecipitation and spectrally encoded beads. These multianalyte detections were based on immune reactions, proteins interactions, nucleic acid interactions and so on. These detection technologies all have their own advantages in multiplex detection. Compared with other approaches, ELISAs and antibody-based immunochromatographic assay have many advantages in the application. No expensive instrument needed, the operation was simple, detection time was short and the cost was low. Furthermore, the antibodies and materials for assembling kits and strips were commercially available. ELISAs are an important alternative method for the detection of both organic and inorganic chemical substances (Kuang et al., 2013; Zhou et al., 2011; Uraipong et al., 2013; Liu et al., 2014a,b,c,d; Dong et al., 2014; Mu et al., 2014; Ning et al., 2014), with the advantages of high sensitivity, high specificity, time efficiency, and low cost in detecting small molecules and proteins. Instrument-free methods to detect multiple residues make possible the simultaneous detection of multiple analytes present in the same sample matrix. Antibody-based immunochromatographic strips have been developed in response to the need for rapid screening methods to monitor water and food safety (Kuang et al., 2013; Xing et al., 2014a,b; 2013a,b; Chen et al., 2013). The strip method plays an important role in the rapid detection of pollutants in the field because it is inexpensive, simple, and can be performed on-site. Multicomponent strip sensors based on antibodies (Wang et al., 2013a; Li et al., 2013; Zong et al., 2012; Wang et al., 2012; Zhang et al., 2011) or DNA (Zhang et al., 2014a,b, 2013) have been especially widely applied in point-ofcare diagnostics and environmental monitoring. In this study, a highly sensitive and specific multicomponent strip biosensor was developed based on different antibodies, for the simultaneous and high-throughput detection of Pb(II), MC–LR, CAP, T, and CTN within single water samples.

2. Material and methods 2.1. Reagents and apparatus Microcystin–leucine–arginine was purchased from Express Technology Co. Ltd. Lead (1000 μg/mL in 1% HNO3) was purchased from the National Institute of Metrology P. R. China (Beijing, China). Chlorothalonil, methyltestosterone, testosterone, and chloramphenicol were purchased from Sigma. 1-(4-Isothiocyanobenzyl) ethylenediamine-N,N,N′,N′-tetraacetic acid (ITCBE) was purchased from Dojindo Laboratories (Shanghai, China). Goat anti-mouse immunoglobulin (IgG) antibody was purchased from Jackson ImmunoResearch Laboratories. All buffer solutions were prepared with ultrapure water from a Milli-Q Ultrapure System. The CTN– bovine serum albumin (BSA) conjugate, MC–LR–BSA conjugate, MT–BSA conjugate, Pb(II)–ITCBE–BSA conjugate, and CAP–BSA conjugate were synthesized in our laboratory. The anti-Pb(II) monoclonal antibody (mAb), anti-MC–LR mAb, anti-CAP mAb, anti-T mAb, and anti-CTN mAb were produced in our laboratory (Kuang et al., 2013; Liu et al., 2014a,b,c,d; Xu et al., 2014a,b). Polyvinylchloride sheets, the sample pad (glass fiber membrane, GL-b01), and the absorbance pad (H5079) were purchased from JieYi Biotechnology Co., Ltd. (Shanghai, China). The nitrocellulose (NC) membrane (Unisart CN140) was from Sartorius Stedim Biotech GmbH. The CM4000 Guillotine Cutting Module (Shanghai Kinbio Tech Co., Ltd, China) and the Dispensing Platform (Shanghai Kinbio Tech Co., Ltd, China) were used to manufacture the test strips.

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2.2. Hapten–protein conjugate preparation Pb(II)–ITCBE–BSA, MC–LR–BSA, CAP–BSA, methyltestosterone (MT)–BSA, and CTN–BSA were synthesized with the following methods. CAP succinate (CAP-HS) was conjugated to BSA with a mixed anhydride, based on the method of Marco et al. (1993). Briefly, tri-n-butylamine (0.1 mmol) and then isobutyl chloroformate (0.12 mmol) were added to CAP-HS (0.1 mmol) dissolved in 1 mL of ice-bath-cooled anhydrous dimethyl formamide. The mixture was reacted for 30 min and then added slowly to BSA solution (100 mg in 10 mL of 0.2 M borate–boric, pH 8.7). The reaction was maintained at room temperature for 6 h. The conjugates were dialyzed in phosphate-buffered saline (PBS; 0.01 M solution, pH 7.4). MC–LR–BSA was synthesized with the procedure described by Zeck et al. (2001), as reported in detail in a paper from our laboratory (Liu et al., 2014a,b,c,d), with some modification. BSA was first thiol (SH)-modified by conjugation with 6-acetylthiohexanoic acid using the 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide (EDC)/N-hydroxysuccinimide (NHS) method. The SH-modified protein was then reacted with the β-carbon of the N-methyldehydroalanine residue of microcystin by nucleophilic addition. The pH was adjusted to 12 and the reaction was stirred overnight at room temperature. The conjugates were dialyzed in PBS. Using a conjugation protocol and a heterobifunctional reagent (ITCBE), Pb(II) was conjugated to BSA as follows (Kuang et al., 2013). BSA (10 mg) was added to 0.1 M Hepes buffer solution (pH 9.0) containing 2 mM ITCBE and 2 mM lead ions. The final protein concentration was adjusted to 2 mg/mL. The reaction mixture was lightly stirred overnight at room temperature and the pH was maintained at 9.0. The unreacted ITCBE and Pb(II)–ITCBE complex were then removed from the metal-chelated protein conjugate with three rounds of ultrafiltration–centrifugation at 6200
g for 30 min each. Methyltestosterone was first chemically modified at the C3 cyclohexyl ring with O-carboxymethyl-oxime to yield methyltestosterone-3-O-carboxymethyl-oxime (MT-CMO). Briefly, MT (1 M) was dissolved in anhydrous pyridine, to which carboxymethoxylamine hemihydrochloride (2 M) was then added, with continuous stirring. The reaction was maintained at 37 °C for 6 h. The pyridine was removed under nitrogen, leaving an oil. The mixture was then dissolved in ethyl acetate and washed three times with H2O. The aqueous phase was removed and the ether organic phase was dried with Na2SO4 and filtered. The ethyl acetate was removed under reduced pressure. The crude solid was collected as a white powder. The haptenic analog of MT-CMO was then conjugated to BSA in an EDC/NHS-mediated reaction. Synthesis of the CTN hapten and its conjugation to BSA has been described in detail elsewhere (Liu et al., 2014a,b,c,d). Briefly, CTN was first derived with 6-aminocaproic acid. The product (the hapten CTN–COOH) was coupled to BSA with the EDC/NHS method. 2.3. Preparation of antibody–gold nanoparticle (AuNP) conjugates All the monoclonal antibodies produced in our laboratory were first purified with the caprylic acid–ammonium sulfate method and then with a protein G immunoaffinity column. The indirect competitive ELISAs for detection of five chemicals were developed. The general procedure was performed as follows. Microwell plates with 96 wells were coated (100 mL/well) with hapten–BSA complex in CBS for 2 h at 37 °C. After being washed three times with phosphate buffered saline (PBS, 137 mM NaCl, 3 mM KCl, 10 mM phosphate, pH 7.4) containing 0.05% Tween 20 (PBST), the plates were blocked with 2% BSA in CBS for 2 h at 37 °C. After being

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washed again, the plates were air-dried 20 min at 37 °C. 50 mL purified antibodies were incubated in the presence of 50 mL different concentrations of analytes in PBS containing ITCBE. After 0.5 h at 37 °C, the plates were washed with PBST and the amount of antibody captured by the hapten–BSA complex was bound by the goat anti-mouse IgG-horseradish peroxidase(HRP) conjugate.

After washing to separate the unbound goat anti-mouse IgG-HRP conjugate, 3,3′,5,5′-Tetramethylbenzidine (TMB) substrate was added and oxidized by HRP into the final product. The results were read by the microplate reader at 450 nm after 15 min. Here, the antibody and antigen concentration was optimized and ITCBE was used to chelate the Pb(II) for formation the Pb(II)–ITCBE complex

Fig. 1. Schematic illustration of multi-strip. sensor.

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2.4. Preparation of the test strip

in solution, which could be recognized by the anti-Pb(II) monoclonal antibody. The antibody–AuNP conjugates were prepared with the following steps. Briefly, the particle concentration of the 15-nm Au NPs solution (1 nM) was determined. Then, the pH of 10 mL of AuNP solution was adjusted to 8.2 by adding 40 mL of 0.1 M K2CO3. An aliquot (100 mg) of purified anti-MC–LR mAb, in 400 mL of ultrapure water, was added drop-wise to 10 mL of the pH-adjusted AuNP solution with gentle stirring. After the mixture was stirred for 30 min, 0.5 mL of 10% (w/v) BSA was added drop-wise into the AuNP solution and stirred for 1 h. The conjugate solution was centrifuged twice to remove any unconjugated antibody and BSA. The precipitated anti-MC–LRmAb–AuNP conjugate was resuspended in 1 mL of boric acid buffer solution (0.002 M boric acid, 0.2% BSA, and 0.02% NaN3). The anti-Pb, anti-CAP, anti-T, and anti-CTN mAb conjugates were prepared in the same way under their individually optimized conditions.

The composition of the test strip is showed in Fig. 1A. The NC membrane, absorbent pad, and sample pad were assembled sequentially onto a plastic backing sheet. The sample pad was impregnated with a buffer solution (0.2% BSA, 0.2% Tween 20 in 0.01 M PBS) and air-dried overnight before use. The NC membrane was then coated with the goat anti-mouse IgG antibody and five kinds of antigens, which formed one control line and five test lines, with the rapid test dispenser platform (HM3035) at a jetting rate of 1 μL/cm. The NC membrane was dried at 37 °C for 3 h and stored in a desiccator. The optimal volumetric proportions of the five mAb–AuNP conjugates were added to a centrifuge tube, mixed, and freeze-dried with a vacuum freeze dryer. The strips and the freeze-dried mAb–AuNP conjugate mixtures were stored in a desiccator at room temperature until use. 2.5. Characterization of the multiplex strip The multicomponent biosensor system developed to quantify five analytes simultaneously was based on the competitive/

Table 2 IC50 of the analytes and the chemical structures information. Compound Pb(II) MC-LR

IC50(ELISA) (ng/mL)

Structure 2þ

[Pb

]

1.3 0.27

OH

OH

CAP

0.01

NH

O

Cl

N+

O-

O

Cl

OH

T

0.11

H

H

CTN

O

Cl Cl

N

H

0.36

Cl

Cl

N

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inhibitory interaction between the free analytes in the sample and the hapten–protein conjugates spatially separated on the NC membrane for the mAb–AuNP conjugate mixture. Typically, a 200 μL aliquot of a serially diluted standard solution containing all the five analytes (Pb(II), MC–LR, CAP, T, and CTN) was added to the freeze-dried mAb–AuNP conjugate mixtures and allowed to react for 5 min at room temperature. This preincubation step ensured a complete reaction between the mAbs–AuNP conjugates and the free analytes in the sample. The reaction solution was then added to the strip. The results were judged with the naked eye after 20 min. In this time, the liquid moved up the NC membrane with the aid of the absorbent pad. The color intensity of the test zone

was inversely proportional to the concentration of analyte in the sample. As showed in Fig. 1A, if no analyte was present in the sample solution, the mAb–AuNP conjugates would bind to the five test lines and the control line, forming red bands. If the concentrations of the five analytes were all above certain values, the mAb–AuNP conjugates would bind to the corresponding free analytes. Therefore, the analyte–mAbs–AuNP complexes would not bind to the hapten–protein conjugates or aggregate on the five test lines, so no red bands would form. They passed through the test lines, reacted with the goat-mouse IgG antibody, and gathered on the control line, forming deep red bands. The sensitivity of the multicomponent biosensor system was

Fig. 2. (A) Typical photo image of detection five analytes by strip sensor in PBS. (B) Optical density profiles of the Test lines (TL-1,2,3,4,5) and Control line (CL) recorded by detection different concentrations of five analytes.

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determined with a sample mixture containing the five analytes. Sample solutions containing different concentrations of the five analytes were serially diluted in PBS and used as the standards. The results were evaluated with the naked eye. The specificity of this method was evaluated by testing a single analyte as showed in Fig. 1B. The analytes were specifically recognized by their corresponding antibodies.

for anti-Pb(II), 5 μg/mL for anti-MC-LR, 4 μg/mL for anti-CAP, 6 μg/mL for anti-T, and 8 μg/mL for anti-CTN mAbs. BSA was used as a stabilizer to reduce nonspecific reactions, and 50 μL of 10% BSA was added drop-wise with stirring to 1 mL of each mAb–AuNP conjugate. Under the optimized conditions, the mAb–AuNP solutions were stable after two rounds of centrifugation and resuspension.

2.6. Assay of water samples with the multiplex strip

3.2. Optimization of the strip

Water samples were used to evaluate the analytical performance and the applicability of the multistrip method for the detection of small molecules in the environment. Drinking water samples that were previously showed to contain no analytes were artificially contaminated with the five pollutants and tested.

The performance of the strip is mainly affected by the hapten– protein conjugates and the antibody sensitivity. However, the high sensitivity of the antibody is the most decisive factor. Here, the IC50 values for the five competitors were determined with indirect ELISAs. The results showed in Table 2 indicate that these antibodies were highly sensitivity in strip detection (IC50 values of 1.3 ng/mL for Pb(II), 0.27 ng/mL for MC–LR, 0.01 ng/mL for CAP, 0.11 ng/mL for T, and 0.36 ng/mL for CTN). The hapten–protein conjugates were synthesized with standard bioconjugation techniques, as described above. Specifically, MT–BSA was used as the heterohapten–protein for the highly sensitive detection of T. On the test lines, the optimal concentration of Pb(II)–ITCBE–BSA was 0.5 mg/mL, of MC–LR–BSA was 2 mg/mL, of CAP–BSA was 1 mg/mL, of MT–BSA was 2 mg/mL, and of CTN–BSA was 4 mg/mL. The concentration of goat anti-mouse IgG antibody was 0.5 mg/mL. Under these optimized conditions, the multicomponent strip sensor showed clear test lines and good sensitivity.

3. Results and discussion 3.1. Preparation of antibody–AuNP conjugates The AuNP were prepared in our laboratory as previously described, with some modifications (Chen et al., 2009). Typically, 500 mL of 0.01% HAuCl4 solution was boiled thoroughly with constant stirring and 10 mL of 1% trisodium citrate solution was quickly added. The color of the solution changed to wine red within approximately 1 min, and the reaction solution was boiled for a further 5 min. The solution was then cooled to room temperature and stored at 4 °C before antibody labeling. pH plays a key role in the process of antibody–AuNP conjugation because the solution pH determines the charge and stability of the conjugate. Colloidal gold adopts a negative charge and has an affinity for many proteins. The antibody–AuNP conjugates were formed through electrostatic interactions between the IgGs and the surfaces of the charged particles. After antibody conjugation, the colloidal gold must also be stabilized with BSA or polyethylene glycol. The volume of K2CO3 and the final concentration of the antibody were optimized separately for each of the five antibodies. The amount of 0.1 M K2CO3 used was optimized in the range of 1–8 μL/mL of AuNP solution. The results indicated that 4 μL, 4 μL, 5 μL 3.5 μL, and 4.5 μL of 0.1 M K2CO3 for each mL of AuNP solution was optimal for anti-Pb(II), anti-MC–LR, anti-CAP, anti-T, and anti-CTN mAb, respectively. The concentrations of the mAbs were also optimized to achieve the required visibility and the best sensitivity. The optimal mAb concentrations for the five mAb–AuNP conjugates were 3 μg/mL

3.3. Validation of the multiplex strip This strip device can be used by an untrained person with no instruments. The cut-off values for the strip test are used for the semi-quantitative judgment when the multicomponent strip sensor is used for on-site detection. The cut-off values are defined as the concentrations producing no color on the test lines because these results can be read with the naked eye. At the same time, the negative control strip has clearly visible lines. Different concentrations of the five analytes were mixed in PBS containing 1 mM ITCBE to generate standards to be analyzed with the multistrip. As the anti-Pb(II) monoclonal antibody could only recognize the Pb(II)-ITCBE complex, PBS containing ITCBE was used to chelate the Pb(II) for formation the complex in solution before detection (Kuang et al., 2013). As showed in Fig. 2A, the color intensity of the test lines decreased as the analyte concentrations increased. The cut-off levels

Fig. 3. Typical photo image of detection the single analyte.

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Table 3 Accuracy and reproductivity evaluation of the developed multi-strip sensor in drinking water samples. CTN/T/CAP/MC-LR/Pb(II) Concentration (ng/mL)

0/0/0/0/0 2.5/2.5/0.05/0.5/2 5/5/0.1/1/4 10/10/0.2/2/8 a b c

Visual results of drinking water samples (n¼ 20) Test line-1

Test line-2

Test line-3

Test line-4

Test line-5

a 7b þc þ

 7 þ þ

 7 þ þ

 7 þ þ

 7 þ þ

Negative result. The test line is obviously observed. Weakly positive result. Light test line is observed. Positive result. No test line is observed.

for this method, based on visual inspection, were 4 ng/mL for Pb (II), 1 ng/mL for MC–LR, 0.1 ng/mL for CAP, 5 ng/mL for T, and 5 ng/mL for CTN. The cut-off values for Pb(II) (4 ng/mL), MC–LR (1 ng/mL), and CTN (5 ng/mL) conformed to the maximum residue limits in drinking water set by the Ministry of Health of China. With a cutoff value of 0.1 ppb, CAP meets the minimum required performance level for CAP set by the EU. This method can be used for the fast on-site detection of T in aquaculture, and the multistrip is also a useful tool for the inspection of household water by ordinary people without instruments. The optical density profiles of the test lines (TL-1, -2, -3, -4, and -5) and the control line (CL) were recorded by strip reader and showed in Fig. 2B. The weakly positive results (the test line is clearly lighter than the negative result but has not disappeared) indicate that the multiplex strip works when the concentrations of Pb(II), MC–LR, CAP, T, and CTN are 0.5 ng/mL, 0.25 ng/mL, 0.05 ng/mL, 2.5 ng/mL, and 1.25 ng/mL, respectively. The standard curves of multiplex detection of Pb(II), MC-LR,CAP,T and CTN were showed in Fig. S1

A rapid and simple high-sensitivity strip test for environment pollutants will substantially advance research requiring on-site detection, and allow fast initial screening. In recent years, the need to simultaneously detect multiple analytes has increased with the increases in PPCP abuse and the cumulative effects of industrial pollutants. However, the development of a technology for the simultaneous detection of five or more analytes in a single sample was challenging, as well as interesting. In this paper, we have presented a method of simultaneously detecting five typical environmental pollutants: a heavy metal, an algal toxin, an antibiotic, a hormone, and a pesticide. These five pollutants are the most common contaminants of water and may be present in water at the same time. They are also difficult to identify and quantify with analytical instruments. Here, five different antibodies were produced to detect these pollutants at sub-nanogram concentrations. Colorimetric test strips were constructed based on these five antibodies to detect all these pollutants simultaneously in a single test.

4. Conclusions 3.4. Specificity and water sample analysis To ensure that there is no interference in the simultaneous detection of the five analytes, the specificity of the strip sensor was evaluated by testing each analyte separately to exclude false-positive results. The five antibodies were produced in our laboratory and the cross-reactivity of each antibody was evaluated with an ELISA or the strip sensor. The results, showed in Fig. 3, show that the five antibodies were specific for their respective antigens and that there was no interference between them, even when the concentration of interferent was high (400 ng/mL Pb(II), 100 ng/mL MC–LR, 10 ng/mL CAP, 500 ng/mL T, and 500 ng/mL CTN). As showed in Table 2, the structures of the analytes are very different. Because this determines the specificity of the antigen– antibody reaction, there is no interference by the other analytes in the detection of each analyte. Therefore, this multicomponent strip sensor is efficient and can be applied to the evaluation of multiple analytes. Spiked blank drinking water samples were tested with the strip sensor. As showed in Table 3, the strip sensor performed well when the threshold concentrations (cut-off value) were analyzed. The reproducibility tests of the analytes in the water samples were based on 20 independent experiments. The stability test showed that neither the reaction intensity nor sensitivity was influenced during storage for six months. The stability of antibody–AuNP conjugates in different media including drinking water, tap water and river water were done. In Fig.S2, the results showed that the immobilization of the antibodies on the Au nanoparticles were stable. Therefore, this method can be used for the rapid preliminary screening of actual samples.

This study demonstrates a novel approach to the detection of five different contaminants that may exist simultaneously in drinking water, using an effective and rapid multistrip technology. This device can be used for the fast (within 25 min), semiquantitative detection with the naked eye. In this study, a preincubation step was used to ensure the high sensitivity and stability of the strip sensor. This method allows the highthroughput testing of small samples and should be a powerful tool for monitoring small molecules in the environment.

Acknowledgements This work is financially supported by the Key Programs from MOST (2012AA06A303, 2012BAK17B10, 2012BAD29B04), and Grants from Natural Science Foundation of Jiangsu Province, MOF and MOE (2013KJ31, BE2013613, BE2013611, 201310128, 201310135).

Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.bios.2014.12.004.

References Bu, Q., Wang, B., Huang, J., Deng, S., Yu, G., 2013. J. Hazard. Mater. 262, 189–211.

C. Xing et al. / Biosensors and Bioelectronics 66 (2015) 445–453

Cao, Y.C., Jin, R., Mirkin, C.A., 2002. Science 297, 1536–1540. Cecchini, M.P., Turek, V.A., Paget, J., Kornyshev, A.A., Edel, J.B., 2013. Nat. Mater. 12, 165–171. Chen, W., Bian, A., Agarwal, A., Liu, L., Shen, H., Wang, L., Xu, C., Kotov, N.A., 2009. Nano Lett. 9, 2153–2159. Chen, X.J., Liu, L.Q., Kuang, H., Song, S.S., Xu, C.L., 2013. Anal. Methods 5, 6234–6239. Cho, E.S., Kim, J., Tejerina, B., Hermans, T.M., Jiang, H., Nakanishi, H., Yu, M., Patashinski, A.Z., Glotzer, S.C., Stellacci, F., Grzybowski, B.A., 2012. Nat. Mater. 11, 978–985. Das, J., Cederquist, K.B., Zaragoza, A.A., Lee, P.E., Sargent, E.H., Kelley, S.O., 2012. Nat. Chem. 4, 642–648. de la Rica, R., Stevens, M.M., 2012. Nat. Nanotechnol. 7, 821–824. Dinish, U.S., Balasundaram, G., Chang, Y.-T., Olivo, M., 2014. Sci. Rep. 4. Dong, J.X., Xu, C., Wang, H., Xiao, Z.L., Gee, S.J., Li, Z.F., Wang, F., Wu, W.J., Shen, Y.D., Yang, J.Y., Sun, Y.M., Hammock, B.D., 2014. J. Agric. Food Chem. 62, 8752–8758. Edwards, M., Triantafyllidou, S., Best, D., 2009. Environ. Sci. Technol. 43, 1618–1623. Frampton, J.P., White, J.B., Simon, A.B., Tsuei, M., Paczesny, S., Takayama, S., 2014. Sci. Rep. 4, 4878. Galperin, E., Verkhusha, V.V., Sorkin, A., 2004. Nat. Methods 1, 209–217. Gaster, R.S., Hall, D.A., Nielsen, C.H., Osterfeld, S.J., Yu, H., Mach, K.E., Wilson, R.J., Murmann, B., Liao, J.C., Gambhir, S.S., Wang, S.X., 2009. Nat. Med. 15, 1327–1332. Golet, E.M., Alder, A.C., Giger, W., 2002. Environ. Sci. Technol. 36, 3645–3651. Graham, J.L., Loftin, K.A., Meyer, M.T., Ziegler, A.C., 2010. Environ. Sci. Technol. 44, 7361–7368. Guo, Y., Ngom, B., Le, T., Jin, X., Wang, L., Shi, D., Wang, X., Bi, D., 2010. Anal. Chem. 82, 7550–7555. Han, M.Y., Gao, X.H., Su, J.Z., Nie, S., 2001. Nat. Biotechnol. 19, 631–635. Jones, O.A.H., Voulvoulis, N., Lester, J.N., 2002. Water Res. 36, 5013–5022. Kolpin, D.W., Furlong, E.T., Meyer, M.T., Thurman, E.M., Zaugg, S.D., Barber, L.B., Buxton, H.T., 2002. Environ. Sci. Technol. 36, 1202–1211. Kuang, H., Xing, C., Hao, C., Liu, L., Wang, L., Xu, C., 2013. Sensors, 13; , pp. 4214–4224. Li, L., Hu, B., Xia, L., Jiang, Z., 2006. Talanta 70, 468–473. Li, X., Li, P., Zhang, Q., Li, R., Zhang, W., Zhang, Z., Ding, X., Tang, X., 2013. Biosens. Bioelectron. 49, 426–432. Liang, S., Liu, Z., Crawford-Brown, D., Wang, Y., Xu, M., 2013. Environ. Sci. Technol. 48, 1103–1113. Lin, D., Alborn, W.E., Slebos, R.J., Liebler, D.C., 2013. J. Proteome Res. 12, 5996–6003. Lin, Z., Li, X., Kraatz, H.B., 2011. Anal. Chem. 83, 6896–6901. Liu, J., Lee, J.H., Lu, Y., 2007. Anal. Chem. 79, 4120–4125. Liu, J., Sun, Y.Q., Huo, Y., Zhang, H., Wang, L., Zhang, P., Song, D., Shi, Y., Guo, W., 2014a. J. Am. Chem. Soc. 136, 574–577. Liu, L., Lin, H., 2014. Anal. Chem. 86, 8829–8834. Liu, L., Xing, C., Yan, H., Kuang, H., Xu, C., 2014b. Sensors 14, 14672–14685. Liu, L., Yan, H., Zhang, X., Kuang, H., Xu, C., 2014c. Food Agric. Immunol., 1–10. Liu, L.Q., Kuang, H., Peng, C.F., Wang, L.B., Xu, C.L., 2014d. Anal. Methods 6, 229–234. Marco, M.P., Nasiri, M., Kurth, M.J., Hammock, B.D., 1993. Chem. Res. Toxicol. 6, 284–293. Mattern, G.C., Liu, C.H., Louis, J.B., Rosen, J.D., 1991. J. Agric. Food Chem. 39, 700–704. Mu, H., Lei, H., Wang, B., Xu, Z., Zhang, C., Ling, L., Tian, Y., Hu, J., Sun, Y., 2014. J. Agric. Food Chem. 62, 7804–7812. Murphy, R.R., Haith, D.A., 2006. Environ. Sci. Technol. 41, 1038–1043. Nakano, S., Fukuda, M., Tamura, T., Sakaguchi, R., Nakata, E., Morii, T., 2013. J. Am. Chem. Soc. 135, 3465–3473. Neffling, M.-R., Spoof, L., Meriluoto, J., 2009. Anal. Chim. Acta. 653, 234–241. Nikolaou, A., Meric, S., Fatta, D., 2007. Anal. Bioanal. Chem. 387, 1225–1234.

453

Ning, F., Peng, H., Li, J., Chen, L., Xiong, H., 2014. J. Agric. Food Chem. 62, 7436–7443. Page, S.W., 1991. Aust. Vet. J. 68, 1–2. Rattanarat, P., Dungchai, W., Cate, D., Volckens, J., Chailapakul, O., Henry, C.S., 2014. Anal. Chem. 86, 3555–3562. Richardson, S.D., Ternes, T.A., 2011. Anal. Chem. 83, 4614–4648. Richardson, S.D., Ternes, T.A., 2014. Anal. Chem. 86, 2813–2848. Rissin, D.M., Kan, C.W., Campbell, T.G., Howes, S.C., Fournier, D.R., Song, L., Piech, T., Patel, P.P., Chang, L., Rivnak, A.J., Ferrell, E.P., Randall, J.D., Provuncher, G.K., Walt, D.R., Duffy, D.C., 2010. Nat. Biotechnol. 28, 595–599. Sheridan, R., Policastro, B., Thomas, S., Rice, D., 2008. J. Agric. Food Chem. 56, 3509–3516. Shi, M., Peng, Y., Zhou, J., Liu, B., Huang, Y., Kong, J., 2007. Biosens. Bioelectron. 22, 2841–2847. Singh, R.J., 2008. Steroids 73, 1339–1344. Srivastava, A., Singh, S., Ahn, C.-Y., Oh, H.-M., Asthana, R.K., 2013. Environ. Sci. Technol. 47, 8999–9013. Stolker, A.A.M., Brinkman, U.A.T., 2005. J. Chromatogr. A 1067, 15–53. Subedi, B., Du, B., Chambliss, C.K., Koschorreck, J., Rüdel, H., Quack, M., Brooks, B.W., Usenko, S., 2012. Environ. Sci. Technol. 46, 9047–9054. Tong, C., Zhuo, X., Guo, Y., 2011. J. Agric. Food Chem. 59, 7303–7309. Uraipong, C., Wong, V., Lee, N.A., 2013. Bull. Environ. Contam. Toxicol. 90, 585–590. Wang, Dan, Tao, S.Q., Zhao Wen, Guang, Lu Shu, Fu, Qiu Zhao, Gang, Yu, 2014. Chin. Sci. Bull. 59, 743–751. Wang, Y.-K., Yan, Y.-X., Ji, W.-H., Wang, H.-a, Li, S.-Q., Zou, Q., Sun, J.-H., 2013a. J. Agric. Food Chem. 61, 5031–5036. Wang, Y., Liu, N., Ning, B., Liu, M., Lv, Z., Sun, Z., Peng, Y., Chen, C., Li, J., Gao, Z., 2012. Biosens. Bioelectron. 34, 44–50. Wang, Y., Tang, L.-J., Jiang, J.-H., 2013b. Anal. Chem. 85, 9213–9220. Wilson, R., Spiller, D.G., Prior, I.A., Veltkamp, K.J., Hutchinson, A., 2007. ACS Nano 1, 487–493. Wu, R., Qian, S.S., Hao, F., Cheng, H., Zhu, D., Zhang, J., 2011. Environ. Sci. Technol. 45, 6041–6048. Xing, C., Feng, M., Hao, C., Xu, L., Wang, L., Xu, C., 2013a. Immunol Investig. 42, 221–234. Xing, C., Hao, C., Liu, L., Xu, C., Kuang, H., 2013b. Food Agric. Immunol. 25, 432–442. Xing, C., Liu, L., Zhang, X., Kuang, H., Xu, C., 2014a. Anal. Methods 6, 6247–6253. Xing, C.R., Kuang, H., Hao, C.L., Liu, L.Q., Wang, L.B., Xu, C.L., 2014b. Food Agric. Immunol. 25, 287–300. Xu, N., Xu, L., Ma, W., Kuang, H., Xu, C., 2014a. Food Agric. Immunol., 1–11 http://dx. doi.org/10.1080/09540105.2014.950201. Xu, W.H., Zhang, G., Zou, S.C., Ling, Z.H., Wang, G.L., Yan, W., 2009. Water Environ. Res. 81, 248–254. Xu, X., Duan, G., Li, Y., Liu, G., Wang, J., Zhang, H., Dai, Z., Cai, W., 2014b. ACS Appl. Mater. Interfaces 6, 65–71. Yang, X.-F., Huang, Q., Zhong, Y., Li, Z., Li, H., Lowry, M., Escobedo, J.O., Strongin, R. M., 2014. Chem. Sci. 5, 2177–2183. Zeck, A., Eikenberg, A., Weller, M.G., Niessner, R., 2001. Anal. Chim. Acta 441, 1–13. Zhang, B., Kumar, R.B., Dai, H., Feldman, B.J., 2014a. Nat. Med. 20, 948–953. Zhang, D., Li, P., Zhang, Q., Zhang, W., 2011. Biosens. Bioelectron. 26, 2877–2882. Zhang, H., Hu, X., Fu, X., 2014b. Biosens. Bioelectron. 57, 22–29. Zhang, M., Ge, L., Ge, S.G., Yan, M., Yu, J.H., Huang, J.D., Liu, S., 2013. Biosens. Bioelectron. 41, 544–550. Zhou, Y., Tian, X.L., Li, Y.S., Zhang, Y.Y., Yang, L., Zhang, J.H., Wang, X.R., Lu, S.Y., Ren, H.L., Liu, Z.S., 2011. Biosens. Bioelectron. 30, 310–314. Zong, C., Wu, J., Wang, C., Ju, H., Yan, F., 2012. Anal. Chem. 84, 2410–2415.