A Cu-T1 Sensor for Versatile Analysis

Subscriber access provided by READING UNIV

Article 1

A Cu-T sensor for versatile analysis Mingling Dong, Wenshu Zheng, Yiping Chen, Bei Ran, Zhiyong Qian, and Xingyu Jiang Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.7b04971 • Publication Date (Web): 18 Jan 2018 Downloaded from http://pubs.acs.org on January 20, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Analytical Chemistry is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 8 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

A Cu-T1 Sensor for Versatile Analysis Mingling Dong †,‡, Wenshu Zheng ‡, Yiping Chen *,‡, Bei Ran †,‡, Zhiyong Qian *,†, and Xingyu Jiang *,‡,§ †

State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center. Chengdu, 610041, P. R. China ‡ Beijing Engineering Research Center for BioNanotechnology and CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, CAS Center for Excellence in Nanoscience, National Center for NanoScience and Technology, No. 11 Zhongguancun Beiyitiao, Beijing 100190, P. R. China § The University of Chinese Academy of Sciences, 19 A Yuquan Road, Shijingshan District, Beijing, 100049, P. R. China Corresponding Authors E. mail: [email protected](YP Chen), phone number: (86)10 82545631; E. mail: [email protected] (ZY Qian), phone number: (86)28-85501986; E. mail: [email protected] (XY Jiang), phone number: (86)10 8254 5558. ABSTRACT: Conventional magnetic sensors usually employ Fe-based magnetic materials as signal probes. In this work, we find that Cu(II) is also a useful longitudinal relaxation time (T1) signal-based magnetic probe. We adopt bathocuproinedisulfonic acid disodium salt hydrate (BCS) to chelate Cu(I) and form a stable Cu(I)-BCS complex in aqueous solution, and find the significant difference in T1 value of water protons between Cu(II) aqueous solution and Cu(I)-BCS complex aqueous solution. Redox reaction can convert Cu(II) to Cu(I) followed by the complexation of BCS, which results in apparent change of T1 that can serve as magnetic signal readout, which is the basis of this Cu-T1 sensor. Many redox reactions between Cu(II) and Cu(I) allow this Cu-T1 sensor to not only realize “one-step mode” assay such as ascorbic acid, protein and alkaline phosphatase, but also enable “multi-step mode” immunoassay, such as biomacromolecules and small molecules. This Cu-T1 sensor employs Cu ion as signal readout, providing an alternative tool for biochemical analysis.

would result in instability and affect the accuracy;11 (3) The sensitivity is relatively low when it is employed for detection of trace targets. To enhance sensitivity, we have developed a MRS sensor combined with magnetic separation for one-step, rapid, and sensitive detection of pathogens6 and miRNA12 with straightforward operations. However, the other two disadvantages still limit the application of MRS sensors. To solve the above problems of conventional MRS sensor, paramagnetic ions might pave a new way. Paramagnetic ions with unpaired electrons can greatly alter longitudinal relaxation of water protons in paramagnetic solutions on account of exchange interaction between electron spin and proton spin of water molecules in magnetic field.13,14 Cu2+ (i.e. Cu(II)) is a typical paramagnetic ion, and it has strong electron spin relaxation, so T1 value of water protons in Cu(II) aqueous solution is shorter than that of water protons in aqueous solution.15 Previous works suggest that the alternation of valence state of paramagnetic ions can result in the significant change in the T1 signal, which can be used as signal enhancement strategy in magnetic resonance imaging.14,16 In our group, we also have developed a gold nanoparticles-mediated visual

Magnetic biosensors based on Fe-based magnetic materials attract increasing attention in the fields of clinical diagnosis, food safety, environmental monitoring and laboratory research, and show great advantages.1-4 For example, magnetic biosensors only need simple sample pre-treatment and can easily avoid matrix interference of samples because complex samples have negligible magnetic signal, which can greatly improve detection efficiency and reduce cost.5 Among these magnetic biosensors, the magnetic relaxation switching (MRS) sensor is classical, homogeneous and washing-free magnetic biosensor.6,7 MRS sensor relies on target-induced aggregation (or disaggregation) of magnetic nanoparticles (MNPs) to detect a wide range of biomolecules. In this strategy, MNPs need to conjugate some recognition elements, such as antibody and aptamer, to specifically bind to the surface of target, which will result in the change of state of MNPs.8 At present, the applications of MRS sensor in the fields of biochemical analysis still have three disadvantages: (1) The bio-conjugation process of MNPs and antibody not only affects stability of MNPs and activity of antibody but also wastes expensive antibody;9,10 (2) Nonspecific adsorption and individual aggregation of MNPs

1 ACS Paragon Plus Environment

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 8

EXPERIMENTAL SECTION Comparison of T1 value between Cu(II) and Cu(I)-BCS complex. After we prepared the solutions with different concentrations of CuCl2 from 0.078 mM to 10 mM, T1 values of different solutions were measured by NMR. Meanwhile, CuCl powders were dissolved by BCS solutions in a 1:2 ratio to form Cu(I)-BCS complex and T1 values of different solutions with Cu-BCS complex were measured by NMR. The change of T1 (ΔT1) referred to the change of T1 between the paramagnetic solutions and aqueous solutions. Detection of AA by Cu-T1 sensor. Different concentrations of AA were mixed with CuCl2 and BCS to incubate at 37 o C for 5 min and T1 values of the mixed solutions were measured by NMR. Detection of protein by Cu-T1 sensor. Take bovine serum albumin (BSA) for example, different concentrations of BSA were mixed with NaOH, CuCl2 and BCS at a volume o ratio of 2:1:1:2 at a temperature of 37 C for 5 min and we analyzed the resulting mixture by NMR to obtain T1 values. Milk samples were purchased from a local supermarket in Beijing and the process for detection of protein in milk samples referred to the steps above. Detection of ALP by Cu-T1 sensor. Different concentrations of ALP were incubated with a certain amount of AAP at o 37 C for 30 min. CuCl2 and BCS were mixed with above soluo tion at a volume ratio of 1:1:1 to react at 37 C for 5 min. T1 values of different resulting mixture were obtained by NMR. Detection of model protein by Cu-T1 sensor. The rabbit anti-human IgG was regarded as model protein. Rabbit antihuman IgG was added to react with human IgG coated on o 96-well plates in advance at 37 C for 1 h and we washed the plates using PBST buffer. Alkaline phosphatase-goat antio rabbit IgG was added into the plates for incubation at 37 C for 1 h followed by the washing using TBST buffer. 100 uL of o AAP was added to incubate at 37 C for 30 min. The solution was mixed with CuCl2 and BCS at a volume ratio of 1:1:1 for o incubation at 37 C for 5 min. T1 values of resulting mixture were measured via NMR. Detection of SAs by Cu-T1 sensor. BSA-SAs, as coating o antigen, were added to incubate on 96-well plates at 4 C for 12 h. We washed the plates using PBST buffer and added o blocking buffer (3% BSA) to incubate at 37 C for 2 h followed by discarding the solutions. Different concentrations of SMR were mixed with SAs-Ab to react for 20 min in advance. o Above reaction mixture was added into plates at 37 C for 1 h. After washing three times by PBST buffer, we added alkaline phosphatase-goat anti-mouse IgG into each well and put the o whole plates at 37 C for 1 h. We removed the solutions and washed the plates using TBST buffer. 100 µL of AAP was addo ed for a reaction at 37 C for 30 min. Reaction mixture was added to react with CuCl2 and BCS at a volume ratio of 1:1:1 o for incubation at 37 C for 5 min. T1 values of resulting mixture were measured via NMR. Detection of other SAs by CuT1 sensor referred to the steps above. We used inversion-recovery pulse sequences for T1 measurements with the following parameters: the NMR frequency, o 62.16 MHz; pulse separation, 10 ms; 90 pulse width, 32 µs; o 180 pulse width, 64 µs; repetition time, 10 s; number of scans, 1.

Scheme 1. The mechanism of Cu-T1 sensor based on redox reactions and complexation for versatile sensing. T1 value of water protons between Cu(II) and Cu(I)-BCS complex aqueous solution is different. Some targets can directly or indirectly convert Cu(II) to Cu(I) in redox reactions. Thus, the change of T1 values can serve as signal readout to reflect the concentrations of targets in this sensor.

sensor for quantification of protein based on the redox reaction between Cu(II) and Cu(I).17 Considering that Cu(II) can be easily converted to Cu(I) by redox reaction with reducing agents, T1 values may serve as magnetic signal readout in response to degree of redox reaction. In this work, we employ bathocuproinedisulfonic acid disodium salt hydrate (BCS) to chelate Cu(I) and form the stable Cu(I)-BCS complex in aqueous solution because Cu(I) cannot exist in aqueous solution without BCS. We find that the significant difference in T1 value of water protons between Cu(II) aqueous solution and Cu(I)-BCS complex aqueous solution. The T1 value will increase upon the complexation of Cu(I) with BCS because Cu(I) loses some of its coordinated water molecules.18 Based on this phenomenon, we develop a versatile Cu-T1 sensor integrated the T1 difference between Cu(II) and Cu(I)-BCS and many types of redox reactions that can convert Cu(II) to Cu(I). In this sensor, the change of T1 (ΔT1) can reflect the amount of target in samples, which is the basis of quantitative analysis of Cu-T1 sensor (Scheme 1). Compared with conventional MRS sensor, Cu-T1 sensor can avoid complicated operations such as the bio-conjugation process of MNPs and antibody because the paramagnetic ions can act as signal readout. Besides, the Cu-T1 sensor allows detection of multiple targets since many types of redox reactions can convert Cu(II) to Cu(I) that can result in the ΔT1 signal. Thus, we provide a MNPs-free, stable, convenient and multifunctional Cu-T1 sensor, which makes it possible for both “one-step mode” assay and “multi-step mode” immunoassay.


Page 3 of 8 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry We repeated all the experiments at least three times to ensure the accuracy of the measurement.

mediated redox reaction. The Cu 2p3/2 XPS are different before and after use of AA. Before adding AA, the binding energy at 935 eV is assigned to Cu(II), accompanied by the characteristic Cu(II) satellite peaks (938-945 eV) (Figure 1E).27 In contrast, after the redox reaction between AA and Cu(II) , the lower binding energy at 932 eV suggests the presence of Cu(I) or Cu(0) species (Figure 1E).27,28 The Cu 2p3/2 XPS is too similar to differentiate between Cu(I) and Cu(0), so their difference depends only on the Cu LMM Auger spectra.27,29 In the Cu LMM Auger spectra, binding energy at 915 eV indicates that Cu(I) is the main species (Figure 1F).29 The XPS characterization demonstrates the redox reaction can convert Cu(II) to Cu(I). In addition, we also employ cyclic voltammetry (CV) to demonstrate the redox process of Cu(II) by AA (Figure S2).

RESULTS AND DISCUSSION Mechanism of Cu-T1 sensor. In this Cu-T1 sensor, we introduce BCS, a chelating agent, to chelate with Cu(I) forming stable complexes19-21 because Cu(I) cannot stably exist in aqueous solution except in concentrated hydrochloric acid and strong ammonium hydroxide.22-24 We test T1 of water protons in Cu(II)/Cu(I)-BCS aqueous solutions. There is a positive correlation between the concentration of Cu(II) ions and T1 value (Figure 1A). Paramagnetic ions have water molecules around in aqueous solution to form a hydration layer.25,26 Solomon-Bloembergen theory has suggested that electron spin relaxation plays an important role in the relaxation of water protons in paramagnetic solutions.13,14,18 Cu(II) is a typical type of paramagnetic ions with strong electron spin, and it can decrease T1 values of water proton and has strong longitudinal relaxation of water proton (Figure 1A). In contrast, Cu(I)-BCS complex aqueous solution (BCS/Cu(I)=2:1 ratio20) has weak longitudinal relaxation of water proton at the same concentration (Figure 1A). The reason is that the complexation of Cu(I) with ligands displaces a number of coordinated water molecules and generally reduces longitudinal relaxation.18 The results show that T1 is much lower in Cu(II) aqueous solutions than that of Cu(I)-BCS complex aqueous solutions at the same concentrations of copper element, which also means there is a significant difference in T1 values between Cu(II) and Cu(I)-BCS (Figure 1A). In addition, BCS has no effect on the T1 value of water proton in Cu(II) aqueous solutions (Figure S1), which further suggests that BCS is a specific chelating agent for Cu(I). To investigate the feasibility of Cu-T1 sensor, we prepare the mixed solutions with Cu(II) and Cu(I)-BCS at different molar ratio, and the total concentration of Cu element is constant. We measure the T1 value of each mixed solution and the change of T1 (ΔT1) refers to the change of T1 of water protons between the mixed Cu(II)/Cu(I)-BCS aqueous solution and pure water solutions. The ΔT1 increases when the proportion of Cu(II) decreases in mixed solutions (Figure 1B). Thus, the ΔT1 is related to the concentration of Cu(II), which confirms the feasibility of CuT1 sensor. We use ascorbic acid (AA) as reducing agent to investigate the role of BCS in Cu-T1 sensor. The ΔT1 value significantly increases with the increased concentration of AA in the presence of BCS, in contrast, the increase of ΔT1 value is not obvious without BCS, and the optimized concentration of BCS is 10 mM (Figure 1C). Thus, the BCS plays a role in Cu-T1 sensor due to its chelation with Cu(I). We also find that the ΔT1 value is related to the concentration of Cu(II) under the same concentration of BCS, and the optimized concentration of Cu(II) is 2.5 mM (Figure 1 D). We employ the X-ray photoelectron spectroscopy (XPS) to confirm the change of valence state of Cu in the AA-

Figure 1. Mechanism of Cu-T1 sensor. (A) The difference of T1 values of water protons between Cu(II) and Cu(I)-BCS complex. (B) The ΔT1 values of mixed solutions consisting of different proportions of Cu(II) and Cu(I)-BCS complex, and the total concentration of Cu element is constant. (C) Optimization of concentration of BCS. (D) Optimization of concentration of Cu(II). (E) Cu 2p XPS and (F) Cu LMM Auger spectra of mixture in Cu-T1 sensor.

Detection of ascorbic acid (AA) by Cu-T1 sensor. We use this Cu-T1 sensor to detect ascorbic acid (AA), which is an important component in fruits and vegetables.30 In addition, the shortage of AA in human beings may cause severe disease.31 Thus, the detection of AA is crucial in botany and life science. We detect AA by Cu-T1 sensor in above optimized conditions (Figure 2A). The limit of detection (LOD) is calculated using formula: LOD=3S/M,



Figure 3. The Cu-T1 sensor for detection of protein. (A) The scheme of Cu-T1 sensor for detecting protein. (B) Comparison of Bradford assay, BCA assay, and Cu-T1 sensor for detection of protein. (C) The linear range of Bradford, BCA assay, and Cu-T1 sensor for detecting protein. Figure 2. The Cu-T1 sensor for detection of AA. (A) The standard curve of Cu-T1 sensor for detecting AA. (B) The linear range of Cu-T1 sensor for detecting AA. (C) Comparison of national standard, Cu-T1 sensor and commercial test strips for detection of AA in orange samples.

the relaxation time of water proton according to SBM theory. We have tested the ΔT1 value in mixed solutions with Cu(II) and protein, and the ΔT1 value is much less than that in Cu-T1 sensor (Figure S4). So proteins can still convert Cu(II) to Cu(I) followed by the complexation of BCS that causes an apparent change of T1 signal even though Cu(II) is chelated with proteins at first. Taking bovine serum albumin (BSA) as model protein, we investigate the response of T1 signal to [BSA] in different alkaline conditions using sodium hydroxide (NaOH) (Figure S5A). Excess alkaline might bring about the inactivation of proteins, whereas insufficient alkaline cannot completely reduce Cu(II). In this experiment, the optimal concentration of NaOH is 40 mM. We investigate how the pH affects the T1 value of water protons. The T1 value of water protons in Cu(II) aqueous solutions differs at different pH conditions (Figure S6). We also need to optimize the concentration of Cu(II) for detection of different analytes. When detecting a particular analyte, we maintain the proper concentration of Cu(II) under the same pH. We also optimize the concentrations of Cu(II) and BCS (Figure S5B and C). We investigate the sensitivity of this Cu-T1 sensor under the optimized conditions. The LOD of this Cu-T1 sensor for detecting BSA is 2.86 µg/mL and the linear range is from 10-400 µg/mL (Figure 3C). There are some conventional methods for protein assay, such as the Bradford assay and the bicinchoninic acid (BCA) assay. For comparison, these two methods can also detect the [BSA], the LOD of Bradford assay and BCA assay for detection of BSA is 4.16 µg/mL and 11.5 µg/mL respectively (Figure 3B). In addition, the linear ranges of Bradford assay and BCA assay are from 20-200 μg/mL and 100-400 μg/mL, which suggests that the linear range of Cu-T1 sensor is broader and LOD is lower than both the Bradford and BCA assay (Figure 3C). We try to analyze the milk samples purchased from a local supermarket by the above three methods. For skim milk samples, results from BCA assay and our method match well the labeled

where S represents the value of the standard deviation of blank samples, and M represents the slope of standard curve within the low concentration range.32 The LOD for AA can reach 60.9 µM using Cu-T1 sensor and the linear range falls in the range between 625 µM and 1 mM (Figure 2B). For commercial test strips, the LOD for AA is 156.25 µM by naked eyes (Figure S3), which proves that Cu-T1 sensor has the better sensitivity for detection of AA. More importantly, we can directly detect AA in real samples without sample pre-treatment, such as orange samples. We can directly squeeze the orange into the mixture solutions of Cu(II) and BCS without any pre-treatment for obtaining T1 value, and we can calculate the concentrations of AA referring to standard curve. Although Cu(II) exists in the orange samples, the content of Cu(II) is only about 0.1 µg/g, which is far less than the reaction concentration of Cu(II) and can be negligible. In addition, the content of glutathione (GSH) in orange samples is minimal and can be ignored. Thus, Cu-T1 sensor can avoid the interference of Cu(II) and GSH present in the orange samples. However, the color of orange samples indeed affects the readout of the commercial test strips (Figure 2C). The sample 2 and sample 4 have no significant difference in color using commercial test strips; however, the concentrations of AA in these two samples have obvious difference employing Cu-T1 sensor and the national standard (2, 6-dichlorindophenol titration). Thus, the CuT1 sensor is more suitable for detecting AA in complex samples. Detection of protein by Cu-T1 sensor. Given that proteins have the reducing ability to convert Cu(II) into Cu(I) in alkaline solutions,17,33 we apply Cu-T1 sensor to determinate the [protein] (Figure 3A). Proteins might also coordinate to Cu(II) to form complex that has an effect on


Page 4 of 8

Page 5 of 8 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry AA by dephosphorylation,35,38 the T1 signal generated in redox reaction can reflect the amount of AA that accordingly corresponds to enzymatic activity of ALP. We optimize the key parameters to increase the analytical performance of the Cu-T1 sensor, including the pH value of Tris-HCl buffer and the concentration of AAP. 5 mM of AAP and Tris-HCl buffer (pH=9) are the optimized conditions in this sensor (Figure S7A and B). The LOD of this sensor for detection of ALP is 0.42 U/L and linear range is from 29.3 U/L to 3750 U/L using optimized reagent dosage (Figure 4A). Since ALP is a labeling enzyme in immunoassay, we can realize “multi-step mode” immunoassay based on Cu-T1 sensor (Figure 4B). The linear range is from 0.625 µg/mL to 40 µg/mL and LOD is 312 ng/mL for detection of rabbit anti-human IgG (Figure 4C). We compare this Cu-T1 sensor for immunoassay with conventional ELISA utilizing p-nitrophenylphosphate (pNPP) as substrate. The LOD of pNPP-based method for detection of rabbit anti-human IgG is 366 ng/mL and linear range is from 0.625 µg/mL to 10 µg/mL, which proves that the sensitivity and linear range of Cu-T1 sensor for immunoassay are better than that of conventional ELISA method (Figure 4D).

data on the packages; however, the results from Bradford assay differ greatly because Bradford assay is more susceptible to interference by complicated components in milk (Table 1). BCA assay and our method are more tolerant to componential interference except for lipid. When it comes to fresh milk samples, results are different from the labeled data due to the lipid in milk.34 We can detect the protein in fresh milk samples by both BCA assay and CuT1 sensor after centrifuged to remove lipid and the measured values conform to the real quantity of protein in milk (Table 1). The content of GSH in milk samples is much less than that of protein, which has little influence on detecting protein in milk. Although the measured values approximately agree with the protein content labeled on the packages, there is a slight discrepancy because many additives, as interfering substance, exist in the milk samples. Table 1. Comparison of the three methods in the determination of protein in skim milk and fresh milk samples. 1 to 5 represent the skim milk samples and 6 to 10 represent the fresh milk samples.

Skim milk (g/100 mL)

Fresh milk (g/100 mL)

Sam -ples

Bradford

BCA

Cu-T1 sensor

Claimed on the package

1

3.6±0.49

3.3±0.48

3.3±0.38

3.0

2

3.2±0.98

3.3±0.51

2.9±0.69

3.0

3

3.6±0.32

3.5±0.38

3.2±0.91

3.4

4

4.2±0.87

3.7±0.37

3.4±0.59

3.6

5

4.6±0.49

3.1±0.53

3.4±0.70

3.0

6

6.2±1.13

3.4±0.34

3.4±0.39

3.6

7

4.0±0.55

3.6±0.59

2.8±0.28

3.2

8

4.7±0.36

3.8±0.62

3.8±0.90

3.4

9

2.0±0.60

2.7±0.10

2.5±0.40

3.0

10

4.9±0.88

3.1±0.49

3.5±0.20

3.2

Figure 4. The Cu-T1 sensor for detection of model protein. (A) The standard curve of Cu-T1 sensor for detecting ALP and ALP activity ranges from 0-5000 U/L. (B) The scheme of CuT1 sensor for detection of model protein in “multi-step mode” immunoassay. (C) The standard curve of pNPP method and Cu-T1 sensor for detection of rabbit anti-human IgG. (D) The linear range of Cu-T1 sensor and pNPP method for detection of rabbit anti-human IgG.

Detection of sulfonamides (SAs) in milk samples by Cu-T1 sensor. To investigate the practical application of this Cu-T1 sensor, we use Cu-T1 sensor to detect sulfonamides (SAs) in milk samples. SAs are a kind of widely used antimicrobial drugs in animal husbandry for the prophylaxis and treatment of infectious diseases for decades.39,40 The abuse of SAs may result in residues of SAs in foods of animal origin that are major concerns to consumers and supervisory organs because of the posed serious health hazards.38 Thus, it is of importance to detect SAs using sensitive and selective methods. Taking sul-

Detection of model protein by Cu-T1 sensor. Besides the "one-step mode" assay, Cu-T1 sensor can also realize “multi-step mode” immunoassay based on alkaline phosphatase (ALP), a labeling enzyme in immunoassay.35-37 Since ALP can convert ascorbic acid phosphate (AAP) to



famerazine (SMR) for example, Cu-T1 sensor can detect SMR in this ALP-mediated immunoassay (Figure 5A). We optimize the conditions of Cu-T1 sensor (Figure S8) and perform the quantitative detection of SMR under the optimized conditions (Figure 5B and C). The LOD is 4.00 µg/L and linear range is from 78.12 µg/L to 5000 µg/L in this Cu-T1 sensor. For conventional pNPP-ELISA method, the LOD is 5.23 µg/L and linear range is from 78.12 µg/L to 2500 µg/L (Figure 5C). We test the specificity of this Cu-T1 sensor by assaying many antibiotics. ΔT1 from SAs (sulfamerazine (SMR), sulfadimethoxine (SDM), sulfadiazine (SDZ), sulfamethazine (SMZ)) measurement is much higher than those from other analogues (tetracycline (TET) and chloramphenicol (CAP) (Figure S9), suggesting that this strategy has good specificity for detection of SAs because capture antibody has broad recognition for different SAs.

of Cu-T1 sensor in complex samples, we detect SAs in spiked bovine serum samples. Cu-T1 sensor enables recoveries ranging from 93.7% to 116.1% with coefficient of variations of 4.2% to 9.1% in bovine serum (Table S1), which suggests that this sensor has good performance for detection of SAs in complicated samples. Although LC-MS/MS is a gold standard method for detection of antibiotic residue in food samples because of its high sensitivity and accuracy, it needs complex sample pre-treatment and the cost is high. In contrast, Cu-T1 sensor is a straightforward and rapid method that only needs simple sample pretreatment, and has great potential in detection of trace antibiotic residue in complex samples.

CONCLUSION In conclusion, we present a Cu-T1 sensor combined redox reaction between Cu(II) and Cu(I) with complexation simultaneously, offering a versatile analysis for a wide range of target. This Cu-T1 sensor can not only simplify the analysis process in “one-step mode” assay, but also is MNPs-free that can avoid disadvantages in conventional MRS sensors, which provides a new strategy to improve analytical performance of magnetic sensor. In further work, we will focus on how to integrate this sensor with microfluidic technology to realize automatic and highthroughput analysis.

ASSOCIATED CONTENT Supporting Information Materials and equipment and additional analytical data. Figure S1-S9 Table S1

Figure 5. The analytical performance of Cu-T1 sensor for detection of SAs in milk samples. (A) The scheme of Cu-T1 sensor for detection of SAs. (B) The standard curve of Cu-T1 sensor and pNPP method for detection of SMR. (C) The linear range of Cu-T1 sensor and pNPP method for detection of SMR. (D) Comparison of Cu-T1 sensor and conventional ELISA for detection of SAs in milk samples.

AUTHOR INFORMATION Corresponding Authors [email protected] (YP Chen) [email protected] (ZY Qian) [email protected] (XY Jiang)

Notes

We detect the concentrations of SAs in 12 real milk samples using the Cu-T1 sensor to further evaluate the application of Cu-T1 sensor. The SAs levels in the real milk samples are pre-determined by liquid chromatographytandem mass spectrometry (LC-MS/MS), which is a gold method for detection of trace antibiotic in food samples.41 The permitted maximum level of SAs in milk sample is 100 µg/L, if the concentration of SAs is above 100 µg/L, it is defined as positive or unqualified samples. The concentrations of SAs in sample 4 and sample 12 are detected to be above 100 µg/L by Cu-T1 sensor and LC-MS/MS, which are positive samples. In addition, other samples are negative samples. However, only sample 4 is detected to be SAs-positive sample by conventional ELISA (Figure 5D), suggesting that the accuracy of Cu-T1 sensor for detection of SAs in complicated sample is better than that of conventional ELISA because Cu-T1 sensor has better antijamming capability. To further investigate the application

The authors declare no competing financial interests.

ACKNOWLEDGMENTS We thank the National Science Foundation of China (81671784, 21505027, 81361140345, 21535001, 81730051， 21761142006), the Ministry of Science and Technology of China (2013YQ190467), Chinese Academy of Sciences (XDA09030305, 121D11KYSB20170026), the National Natural Science Fund for Distinguished Young Scholars (NSFC31525009) and Sichuan Innovative Research Team Program for Young Scientists (2016TD0004) for financial support.

REFERENCES (1) Chen, Y.; Xianyu, Y.; Sun, J.; Niu, Y.; Wang, Y.; Jiang, X. Nanoscale 2016, 8, 1100-1107.


Page 6 of 8

Page 7 of 8 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry (2) Gao, J.; Gu, H.; Xu, B. Acc. Chem. Res. 2009, 42, 1097-1107. (3) Gao, Z.; Ma, T.; Zhao, E.; Docter, D.; Yang, W.; Stauber, R. H.; Gao, M. Small 2016, 12, 556-576. (4) Wang, Z.; Wu, D.; Wu, G.; Yang, N.; Wu, A. J. Hazard. Mater. 2013, 244, 621-627. (5) Liu, H.; Dong, H.; Chen, Z.; Lin, L.; Chen, H.; Li, S.; Deng, Y. J. Biomed. Nanotechnol. 2017, 13, 1333-1343. (6) Chen, Y.; Xianyu, Y.; Wang, Y.; Zhang, X.; Cha, R.; Sun, J.; Jiang, X. ACS Nano 2015, 9, 3184-3191. (7) Ma, W.; Yin, H.; Xu, L.; Wang, L.; Kuang, H.; Xu, C. Chem. Commun. 2013, 49, 5369-5371. (8) Alcantara, D.; Lopez, S.; García-Martin, M. L.; Pozo, D. Nanomed. Nanotechnol. Biol. Med. 2016, 12, 1253-1262. (9) Avvakumova, S.; Colombo, M.; Tortora, P.; Prosperi, D. Trends Biotechnol. 2014, 32, 11-20. (10) Lassenberger, A.; Scheberl, A.; Stadlbauer, A.; Stiglbauer, A.; Helbich, T.; Reimhult, E. ACS Appl. Mater. Interfaces 2017, 9, 33433353. (11) Min, C.; Shao, H.; Liong, M.; Yoon, T.-J.; Weissleder, R.; Lee, H. ACS Nano 2012, 6, 6821-6828. (12) Lu, W.; Chen, Y.; Liu, Z.; Tang, W.; Feng, Q.; Sun, J.; Jiang, X. ACS Nano 2016, 10, 6685-6692. (13) Bloembergen, N.; Morgan, L. J. Chem. Phys. 1961, 34, 842-850. (14) Gore, J.; Kang, Y. Phys. Med. Biol. 1984, 29, 1189. (15) Bloembergen, N. J. Chem. Phys. 1957, 27, 572-573. (16) Bryar, T. R.; Knight, R. J. Geophys. Res. Lett. 2002, 29, 2197-2200. (17) Zhu, K.; Zhang, Y.; He, S.; Chen, W.; Shen, J.; Wang, Z.; Jiang, X. Anal. Chem. 2012, 84, 4267-4270. (18) Lauffer, R. B. Chem. Rev. 1987, 87, 901-927. (19) Cobine, P. A.; Pierrel, F.; Bestwick, M. L.; Winge, D. R. J. Biol. Chem. 2006, 281, 36552-36559. (20) Campos, C.; Guzmán, R.; López-Fernández, E.; Casado, Á. Anal. Biochem. 2009, 392, 37-44. (21) Suzuki, T.; Ota, Y.; Kasuya, Y.; Mutsuga, M.; Kawamura, Y.;

Tsumoto, H.; Nakagawa, H.; Finn, M.; Miyata, N. Angew. Chem. Int. Ed. 2010, 49, 6817-6820. (22) Stevenson, J.; Barwinska-Sendra, A.; Tarrant, E.; Waldron, K. Formatex 2013, 1, 468-479. (23) Biswas, S.; Ghosh, A. Polyhedron 2012, 39, 31-37. (24) Li, Y.; Trush, M. A. Arch. Biochem. Biophys. 1993, 300, 346-355. (25) McConnell, H. M. J. Chem. Phys. 1956, 25, 709-711. (26) Koenig, S. H.; Brown, R. D. Magn. Reson. Med. 1984, 1, 478-495. (27) Liu, P.; Hensen, E. J. M. J. Am. Chem. Soc. 2013, 135, 14032-14035. (28) Tran Ngoc, H.; Rousse, G.; Zanna, S.; Lucas, I. T.; Xu, X.; Menguy, N.; Mougel, V.; Fontecave, M. Angew. Chem. Int. Ed. 2017, 56, 4792-4796. (29) Gao, D.; Zegkinoglou, I.; Divins, N. J.; Scholten, F.; Sinev, I.; Grosse, P.; Cuenya, B. R. ACS Nano 2017, 11, 4825-4831. (30) Wimalasiri, P.; Wills, R. B. H. J. Chromatogr. 1983, 256, 368-371. (31) Levine, M. N. Engl. J. Med. 1986, 314, 892-902. (32) Ran, B.; Xianyu, Y.; Dong, M.; Chen, Y.; Qian, Z.; Jiang, X. Anal. Chem. 2017, 89, 6113-6119. (33) Zhang, Y.; Guo, Y.; Xianyu, Y.; Chen, W.; Zhao, Y.; Jiang, X. Adv. Mater. 2013, 25, 3802-3819. (34) Kessler, R. J.; Fanestil, D. D. Anal. Biochem. 1986, 159, 138-142. (35) Xianyu, Y.; Wang, Z.; Jiang, X. ACS Nano 2014, 8, 12741-12747. (36) Xuan, Z.; Li, M.; Rong, P.; Wang, W.; Li, Y.; Liu, D. Nanoscale 2016, 8, 17271-17277. (37) Zhang, Z.; Chen, Z.; Wang, S.; Cheng, F.; Chen, L. ACS Appl. Mater. Interfaces 2015, 7, 27639-27645. (38) Yang, L.; Gao, M. X.; Zhan, L.; Gong, M.; Zhen, S. J.; Huang, C. Z. Nanoscale 2017, 9, 2640-2645. (39) Dmitrienko, S. G.; Kochuk, E. V.; Apyari, V. V.; Tolmacheva, V. V.; Zolotov, Y. A. Anal. Chim. Acta 2014, 850, 6-25. (40) Franek, M.; Diblikova, I.; Cernoch, I.; Vass, M.; Hruska, K. Anal. Chem. 2006, 78, 1559-1567. (41) Meng, J.; Wu, R. Food. Ind. 2008, 3, 60-70.



For TOC use only

8 Environment ACS Paragon Plus

Page 8 of 8

A Cu-T1 Sensor for Versatile Analysis

Recommend Documents