Diagnosing the RGS11 Lung Cancer Biomarker: The Integration of

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Diagnosing the RGS11 lung cancer biomarker: The integration of competitive immunoassay and isothermal nucleic acid exponential amplification reaction Ying-Feng Chang, Yi-Qi Huang, Kun-Ming Wu, Amily Fang-Ju Jou, Neng-Yao Shih, and Ja-An Annie Ho Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b04374 • Publication Date (Web): 31 Jan 2019 Downloaded from http://pubs.acs.org on February 5, 2019

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Analytical Chemistry

Diagnosing the RGS11 lung cancer biomarker: The integration of competitive immunoassay and isothermal nucleic acid exponential amplification reaction Ying-Feng Chang1,¶ , Yi-Qi Huang1,¶ , Kun-Ming Wu2,3,4¶ , Amily Fang-Ju Jou1,, Neng-Yao Shih5,*, Ja-an Annie Ho1,* 1 BioAnalytical

Chemistry and Nanobiomedicine Laboratory, Department of Biochemical Science and

Technology, National Taiwan University, Taipei 10617, Taiwan 2Chest

Division, Department of Internal Medicine, Mackay Memorial Hospital, New Taipei 25160, Taiwan

3Department

of Nursing, Mackay Junior College of Medicine, Nursing, and Management, Taipei 25245,

Taiwan 4Department 5National

of Medicine, Mackay Medical College, New Taipei 25245, Taiwan

Institute of Cancer Research, National Health Research Institutes, Tainan 70456, Taiwan

Manuscript submitted to Analytical Chemistry

¶

Authors contribute equally to this research

Jou

is currently an assistant professor at Department of Chemistry, Chung-Yuan Christian University, Chung Li, Taoyuan, Taiwan *Corresponding

authors. Ph. 886-2-33664438; Fax. 886-2-33662271; E-mail: [email protected] (Ho); Ph. 886-6-7000123 ext 65108; Fax. 886-6-2083427; E-mail: [email protected] (Shih)

Author Contributions J.-a.A.H.’s group was responsible for method development and the performance of all the experiments. K.M.W. was responsible for IRB application and collection of the pleural effusion samples. N.Y.S. provided GST-tag RGS11, His-tag RGS11, and IRB-approved clinical samples. All authors discussed the results and implications and commented on the manuscript at all stages. 1

ACS Paragon Plus Environment

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

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Abstract Lung cancer is the primary cause of cancer-associated mortality worldwide, which makes the identification of reliable lung cancer biomarkers a pressing need for early diagnosis and prognosis. RGS11, which is a regulator of G-protein signaling and also a lung cancer biomarker, plays an important role in cancerrelated metastasis. However, trace levels of RGS11 (in the range of pg/mL) in serum samples make it difficult to quantify using currently available Enzyme-Linked ImmunoSorbent Assay (ELISA) kits and, therefore, this hinders progress in the discovery of new approaches for treating lung cancer. The aim of this study is to develop a rapid, sensitive, and reliable platform for the detection of RGS11 lung cancer biomarker based on a suspension immunoassay coupled with an isothermal exponential amplification strategy. Our study was initiated by the functionalization of magnetic beads with anti-RGS11 antibodies (MB-Ab) by EDC/NHS activation. MB-Ab served as a sensing probe for the competitive immuno-recognitions between known concentrations of His-tag RGS11 and unknown concentrations of target RGS11 in serum. The reporter antiHis antibodies, which were modified with primers that induced an isothermal exponential amplification reaction, were subsequently introduced to the reaction mixture that resulted in the formation of immunosandwich complexes. The exponentially amplified DNA duplex that was intercalated with SYBR green was designated as a signal reporter for the assessment of RGS11 in an inversely proportional relationship. The sensing platform was excellent for the determination of RGS11 with an exceptional detection limit of 148 fg/mL and a linear dynamic range of 0.1  10 pg/mL using a minimal sample volume (20 μL) and with a reaction time of 1.5 h. In addition, we challenged the sensing platform with RGS11-spiked samples (in 2x diluted serum), and an acceptable recovery rate (>90%) was observed. Finally, 24 clinical samples acquired 2


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from patients with advanced lung cancer (C), inflammation (I), and heart failure (H) were analyzed by this newly-developed sensing platform and a commercial ELISA kit for validation. This sensing platform has potential in biomedical applications for clinically diagnosing liquid biopsy samples for patients with lung cancer. Moreover, the universal design of our proposed system is easily adapted to detect any other protein if a His-tag recombinant protein is available.

Keywords: Isothermal nucleic acid amplification, exponential amplification reaction, RGS11, immuno-EXPAR, cancer marker

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1. Introduction Lung cancer is regarded as the most common malignancy, and it is also the most common cause of cancer deaths worldwide. The five-year survival rate of 17.7 % for lung cancer is much lower than many other types of cancer (such as colon cancer or breast cancer), which is due to the difficulty of diagnosing the disease until it is in an advanced, often non-curable stage.1,2 Although low-dose computerized tomography (CT) screening is the most frequently used diagnostic approach for high-risk individuals, it exhibits a high falsepositive rate, and the probability of not detecting lung cancer on CT is around 15-50%, which may lead to a decision-making error.2,3 Another possible diagnostic strategy involves assaying biomarkers that are present in liquid biopsy samples, which can become a cost-effective tool for early diagnosis and prognosis.2,4 However, there are currently no specific biomarkers available to detect lung cancer effectively or to monitor its recurrence.5,6 Multi-biomarker detection methods that were developed recently for cancer diagnosis may lead to the improvement in sensitivity. Sensitivity of an assay refers to the ability of a method to identify those with the disease accurately (true positive rate), but these previously described methods for lung cancer diagnosis have typically exhibited poor specificity because their ability to identify those without the disease accurately (true negative rate) decreased.2,7 Therefore, greater effort has been devoted to discover potential proteins that are associated with lung cancer as a precise diagnostic marker. Recently, our team discovered the role of the regulator of G protein signaling 11 (RGS11), which is a member of the R7 subfamily of RGS proteins that promotes cell migration of cancer cells. Moreover, the overexpression of RGS11 was found to be highly associated with primary lung adenocarcinoma in tissue 4


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samples from 91 patients.8 Although we have verified that RGS11 could be a potential diagnostic and/or prognostic biomarker for lung adenocarcinoma based on the clinicopathological features of patients with lung cancer, monitoring of the RGS11 level in blood samples or other biofluidic samples using proteomics approaches is an important and difficult challenge. To date, most available proteomics technologies have been limited in their capability to detect low-abundance biomarkers against the background of serum proteins with high abundance. Thus, many meaningful disease biomarkers may be overlooked.9 The development of more powerful strategies to detect disease-associated proteins than exist currently is urgently needed. In the early 90s, Prof. Cantor and co-workers reported an immuno-polymerase chain reaction (I-PCR), a unique protein detection approach.10 Since then, various nucleic acid amplification techniques (NAATs) have been integrated with immunoassays to circumvent the detection sensitivity issue. I-PCR is different from the exponential signal amplification offered by NAATs or the conventional protein assays, such as ELISA, that convert one substrate molecule into one signaling product molecule (linear amplification). Among all the NAATs, PCR is still the most popular for amplifying and detecting low-abundance nucleic acids, and it is also used most commonly to integrate with immunoassays.10-23 PCR not only exhibits an exponential amplification feature and a high-throughput capability, but it also offers the feasibility of doing multiplex detection.24 The drawbacks associated with PCR-based technology, however, include its high cost, the need of highly skilled personnel, and the requirement of a high number of thermal cycle steps, all of which impede the likelihood of its becoming the mainstream analytical procedure.23,25 In recent years, isothermal NAATs have attracted tremendous attention due to their simplicity, cost 5


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effectiveness, and robustness for fault tolerance from interfering agents that are presented in real sample matrices23,24 Exponential amplification reaction (EXPAR), one of the most efficient isothermal NAATs, was first devised by the Galas’ group for amplification of short oligonucleotides.26 EXPAR combines strand extension and single-strand nicking that leads to rapid amplification of specific short oligonucleotides within a few minutes under isothermal conditions.27 Compared with other isothermal NAATs, EXPAR provides a significant high amplification efficiency of about 106 - 109 fold.28 Based on its amplification kinetics, EXPAR is an excellent candidate for detection of peptides or proteins at trace levels. Zhang and co-workers designed the EXPARbased protein assays for platelet-derived growth factor (PDGF-BB) and transcription factor NF-κB p50.28-30 Those detection approaches are tailor-made for specific proteins, but they lack a universal design for generic detection of proteins. In this study, we developed a sensitive, rapid, and specific EXPAR-based immunoassay, which integrated EXPAR with competitive immunoassay for detecting trace levels of RGS11 lung cancer biomarker in clinical samples that include serum and pleural effusion. We believe that the proposed scheme exhibits high potential to become an effective tool for liquid biopsies, which would increase precision in detecting cancer, and move one step forward towards a new era of personalized medicine.

2. Materials and methods 2.1 Materials The GST-tag RGS11 and His-tag RGS11 were kindly provided by Dr. Neng-Yao Shih from the National Institute of Cancer Research, National Health Research Institutes (NHRI), Tainan, Taiwan. The clinical samples 6


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of pleural effusion were provided by Dr. Neng-Yao Shih (NHRI) and Chi-Mei Medical Center, Tainan, Taiwan (IRB NHRI EC 0930303 and IRB100-05-003) and Dr. Kun-Ming Wu from Chest Division, Department of Internal Medicine, Mackay Memorial Hospital (IRB 18MMHIS097e). The thiolated primer (sequence: SHAAAAAATCAACTGAGGTGGAGGTCAGGCAGT)

and

template

(sequence:

ACTGCCTGACCTCCACCTCA/GCACTGCCTGACCTCCACCTCAGTTGA. The underlined, bolded sequence can be recognized by a restriction endonuclease, Nb.BbvCI) for the use in EXPAR were purchased from Integrated DNA Technologies (Coralville, IA, USA). DNA polymerase (Klenow Fragment), nicking enzyme (Nb.BbvCI), and 10X CutSmart buffer were obtained from New England Biolabs (Ipswich, MA, USA). The commercial human RGS11 ELISA kit was bought from MyBioSource (San Diego, CA, USA). The carboxylate group-modified magnetic beads (12 nm) were purchased from Weistron (Hsinchu, Taiwan). Anti-RGS11 antibody and HRP labeled secondary antibody were acquired from Abcam (Cambridge, UK). Anti-His-tag antibody (Ab) and HRP conjugate substrate kit were obtained from Antibodies-online (Atlanta, GA, USA) and Bio-Rad (Hercules, California, USA), respectively. 2.2 Conjugation of EXPAR primer to anti-His-tag antibody A chemical cross-linking strategy was designed and carried out to conjugate the thiolated primer (EXPAR primer) with Ab (anti-His-tag antibody) covalently. The thiolated primer (modified with a thiol group at 5’ end) was prepared in conjugation buffer (50 mM HEPES, which contained 2 mM EDTA, pH 7.0) before it was ready to couple with antibody (Ab). Concurrently, 28.5 μL of Ab in 50 mM HEPES (pH 7.5) was allowed to react with 1.5 μL of sulfo-SMCC in DMSO (7.77 mg/mL) for 1 h at room temperature. After removal of unreacted sulfo-SMCC by centrifugal filter (50 kDa), 30 μL of thiolated primer (0.230 μM) in conjugation 7


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buffer was mixed with the maleimide-derivatized Ab. The reaction mixture was incubated further for 2 h at room temperature, and let react at 4 oC for another 18 h. Finally, the conjugated Ab coupled with thiolated primer (Ab-P) was subjected to purification by gel filtration.

2.3 Dot Blot and Surface Plasmon Resonance (SPR) Analyses to confirm binding ability between Ab-P and His-tag RGS11 The binding ability between Ab-P and His-tag RGS11 was confirmed by Dot Blot Analysis using the HRPlabeled secondary antibody that specifically recognized Ab-P but not the His-tag RGS11. Briefly, 1.5 μL of Histag RGS11 (0.25 mg/mL) was dot-spotted on a nitrocellulose membrane, air dried, and then placed in a vacuum oven for 1 h. After soaking in blocking buffer (0.02M Tris-HCl, 0.15M NaCl, 0.5% PVP, 0.03% casein and 0.1% NaN3, pH 7.0), the membrane was washed with PBS and vacuum dried again. It was followed by an addition of 10 μL Ab-P (0.024 mg/mL) onto the membrane, and allowed to incubate for 18 h at 4 oC. After washing off the unbound Ab-P, 20 μL HRP-labeled secondary antibody (0.01 mg/mL) was introduced and let react for 1 h at room temperature. Finally, the HRP conjugate substrate kit was used for color development, and the densitometric analysis of dots using ImageJ (National Institute of Health, USA) enabled the quantitation of an immunocomplex [(His-tag RGS11)-(Ab-P)-(HRP-label 2nd Ab)]. Moreover, Biacore T200 (GE Healthcare, Chicago, IL, USA) was also used to analyze this binding event. Concisely, the His-tag RGS11 was immobilized on the CM5 chip surface to obtain a signal up to 2000 RU. Different concentrations of analytes (Ab-P and unmodified Ab) in PBS were introduced into the Biacore system and allowed to react with the Histag RGS11-modified sensing chip at a flow rate of 30 μL/min. The association rate constant (ka), dissociation rate constant (kd), and equilibrium dissociation constant (KD) for binary interactions that occurred between

8


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the immobilized His-tag RGS11 and the analytes were estimated using BIAcore evaluation software. 2.4 Conjugation of anti-RGS11 antibody to carboxylated magnetic beads The anti-RGS11 antibody was immobilized covalently with the carboxylate groups on the surface of magnetic

bead

using

the

EDC

(1-Ethyl-3-(3-dimethylaminopropyl)-carbodiimide)/NHS

(N-

hydroxysulfosuccinimide) strategy. In short, the activation of carboxylate groups on the magnetic beads was performed by reacting with 1 M EDC and 0.25 M sulfo-NHS for 30 min, followed by washing with the immobilization buffer [25 mM 2-(N-morpholino)ethanesulfonic acid (MES) buffer, pH 5.0] three times to wash off the unmobilized molecules. The coupling reaction was initiated by mixing the anti-RGS11 antibody (7.78 μg) with the activated magnetic bead (190 μg) in the immobilization buffer, and it was subsequently incubated for 2 h at room temperature and another 18 h at 4 oC. Finally, the anti-RGS11 antibody-magnetic bead complexes (Ab-MBs) were isolated from the mixture and re-suspended in PBS (including 0.1% lysine, pH 7.2). 2.5 Assay of performance A 15 μL of Ab-MB solution, 10 μL of 5 pM His-tag RGS11 solution, and 20 μL of sample solution that contained RGS (either clinical samples or spiked GST-tag RGS11 in buffer solution) were mixed together to initiate the process of competitive immunoassay. This reaction proceeded for 30 min, followed by an addition of 5 μL of 10 nM Ab-P, and then it was allowed to react for another 30 min. After washing, the Ab-MB was re-suspended in 1X Cutsmart buffer. Subsequently, the ice-cold solution A (500 nM EXPAR template and 500 M dNTP in 1X Cutsmart buffer) and the solution B (0.25 U/μL Klenow fragment and 1 U/μL Nb.BbvCI in 1X Cutsmart buffer) were mixed with the Ab-MB solution to initiate the EXPAR, which was terminated by the 9



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introduction of 60 mM EDTA into the reaction mixture after 12.5 mins. Finally, the SYBR Green solution was added to the terminated-EXPAR mixture, and the fluorescence intensity was measured by a Molecular Devices FlexStation (Sunnyvale, CA, USA) 3 multi-mode fluorescence microplate reader.

3. Results and Discussion 3.1 Detection scheme of the EXPAR-based immunoassay The EXPAR-based immunoassay consisted of two parts, a competitive immunorecognition process and EXPAR amplification (Fig. 1). The reaction materials, which included Ab-MB (anti-RGS11 antibodyfunctionalized magnetic bead complex), His-tag RGS11, and the target RGS11, were mixed together to initiate the competitive immunorecognition. The target and His-tag RGS11 compete for a limited number of binding sites on the magnetic bead. Subsequently, the mixture was added to the solution that contained Ab-P (antiHis-tag antibody coupled with thiolated primer) to form a sandwich complex {Ab-MB/His-tag RGS11/Ab-P}. Finally, the sandwich complexes were isolated and then re-suspended in EXPAR reaction buffer. The number of anti-RGS11 antibody molecules that conjugated on the surface of each carboxylated magnetic bead was estimated to be no more than one, based on quantitation analysis of proteins. EXPAR plays a vital role in the second part of the detection scheme. The EXPAR template is a DNA strand that contains two copies of the primer binding region that are separated by X. Region X is the recognition site for the nicking enzyme (Nb.BbvCI) after the formation of a dsDNA. After mixing the sandwich complexes with the EXPAR template, DNA polymerase, nicking enzyme, and dNTPs, the primer sequence of Ab-P hybridized with the primer binding region on the EXPAR template strand to stimulate the initiation of 10


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EXPAR to generate a new DNA trigger and, subsequently, interacted with the EXPAR template strand for elongation. As expected, hybridization, elongation, nicking, and strand displacement were performed repetitively, which led to an exponential amplification that resulted in generation of a large amount of DNA duplex. Finally, SYBR Green and the duplex DNA-specific dye were added to the terminated EXPAR mixture for signal production. 3.2 Binding affinity between Ab-P and His-tag RGS11 The conjugation of thiolated EXPAR primer to Ab (anti-His-tag antibody) resulted in the formation of AbP. To evaluate the binding ability of Ab-P with His-tag RGS11, it was purified by gel filtration. The unreacted thiolated EXPAR primers can also be fraction-collected during gel filtration for further quantification. After interacting with SYBR Gold fluorescence dye, the collected, unreacted EXPAR primers were measured using a fluorescence spectrophotometer to calculate the number of EXPAR primers on a single Ab molecule. We estimated that each Ab molecule was linked with no more than two (ca. 1.64) EXPAR primers. Next we examined if the recognition affinity of Ab-P remained the same toward His-tag RGS-11 after being modified with the thiolated EXPAR primer. SPR analysis was conducted to investigate the specific interaction between His-tag RGS-11 and Ab-P. The equilibrium dissociation constants of 54 nM and 31 nM were calculated by Biacore T200 for Ab and Ab-P, respectively, and all other kinetic parameters were determined and shown in Fig. 2a. A similar phenomenon was also obtained by Dot Blot Analysis to confirm the specific recognition of HRPlabeled secondary antibody toward Ab-P and Ab. The relative intensity of dots for RGS11Ab and RGS11AbP groups was comparable (Fig. 2b). This indicated that the binding affinity between His-tag RGS11 11



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and Ab-P were similar to the binding affinity between His-tag RGS11 and the unmodified Ab. 3.3 Optimization of EXPAR condition After confirming that the Ab-P still retained its affinity toward His-tag RGS11, we then verified that the primer on the Ab-P could perform EXPAR. The optimization for implementing Ab-P-based EXPAR was therefore carried out. It has been reported extensively that EXPAR demonstrated a superior amplification efficiency due to the synergistic catalytic effect of DNA polymerase and nicking endonucleases.27,28 However, nonspecific amplification products were inevitably produced,31,32 so that the noisy background was the major problem that limited sensitivity and specificity of EXPAR-based quantitative protein analysis.28 The gel electrophoresis images for various Ab-P-based EXPAR with different reaction times (5 min, 10 min, 12.5 min, and 15 min) and different Ab-P concentrations (0, 100 fM, 1 pM, 10 pM, 100 pM, and 1 nM) are provided in Fig. 3). During the first 5 min, EXPAR produced no DNA duplex products in any group that contained Ab-P. But with longer reaction time, more DNA duplex products were formed. When the reaction time reached 10 min, the high Ab-P concentration groups (10 pM, 100 pM, 1 nM) started to produce DNA duplex products. When the reaction time reached 12.5 min, the group that contained the lowest Ab-P concentration (100 fM) produced a significant amount of the DNA duplex products. It was also anticipated that no DNA duplex product would be found in the blank group. However, when reaction time was raised to 15 min, the templates of all groups turned into DNA duplex products, which even included the groups without Ab-P. We suspected that this was due to the nonspecific amplification of DNA polymerase. Hence, 12.5 min was selected as the optimum reaction time for Ab-P-based EXPAR. 3.4 Detection of target proteins 12


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An EXPAR-based immunoassay was herein developed to measure the selected model protein, RGS11, a novel lung cancer marker8. Different dilutions of the target protein in PBS solution (0 fg/mL, 100 fg/mL, 1 pg/mL, 10 pg/mL, 100 pg/mL, and 1 ng/mL) were prepared and allowed to proceed with competitive immunosensing procedures with Ab-MB and His-tag RGS11. Subsequently, Ab-P was introduced to the reaction mixture, and the antibody unit of Ab-P was used to recognize the His-tag; the nucleic acid probe of Ab-P functioned as a primer for the EXPAR amplification process. After 12.5 min of reaction, the fluorescence intensity was measured, which was inversely proportional to the concentration of the target. The doseresponse curve between the Relative Fluorescence Unit (RFU) and the concentration of the target protein over a range of 0 – 1 ng/mL (Fig. 4a); the error bar indicated 1 standard deviation from the mean for at least five repetitions. Based on the definition of International Union of Pure and Applied Chemistry (IUPAC), we defined the operational detection limit (LOD) after subtracting 3 standard deviations of the control that contained no RGS11 from its average at 99.7% confidence interval. Limit of quantification (LOQ), however, is calculated after subtracting 10 standard deviations of the control that contained no RGS11 from its average. The LOD of the proposed EXPAR-based immunoassay for the model protein, RGS11, was estimated to be 37.8 zeptomoles, which was equivalent to 148 fg/mL of the model protein presented in 20 μL of the sample solution. In addition, the LOQ was calculated to be 968 fg/mL, and the linear range was as much as about three orders of magnitude (0.1 –100 pg/mL). After comparing our newly-developed EXPAR-based immunoassay with a recently reported NAAT-based immunoassay (summarized in Table 1a),33-35 we found that the best reported LOD was 6.67 aM, which was designed to detect human prolactin, and it was performed using a rolling circle amplification (RCA) strategy. However, its total assay time was >7 h.35 Another 13



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technique that used MNAzyme for signal amplification only required 1 μL of sample solution and had a total assay time of 12 hrs) to be clinically practicable.34 Moreover, the spike-and-recovery assessment, one of the important methods for validating and assessing the accuracy of analytical techniques, was conducted to evaluate the matrix effect and competence of the EXPAR-based immunoassay. The known amount (1 pg/mL) of GST-tag RGS11 was spiked into an undiluted serum sample obtained from a healthy donor. After that, the EXPAR-based immunoassay was performed, and its fluorescence was subsequently measured. The assay response was then compared to a standard curve prepared using known concentrations of the RGS11 in the standard diluent (Fig 4a). The recovery rates of undiluted serum A and serum B were 102.2% ± 15.5% and 84.0% ± 14.7%, respectively (Fig. 4b). Because the recovery rate of undiluted serum B was lower than 85%, we believed that components in its sample matrix caused the difference and that further adjustments must be made to minimize the discrepancy. The strategy we used for correcting discrepancy between the standard diluent (buffer) and the real sample matrix was to dilute the serum sample in standard diluent. The GST-tag RGS11 was spiked into 1:1 diluted serum at a concentration of 1 pg/mL, and the recovery rates of half-diluted serum A and serum B were calculated as 97.0% ± 12.2% and 94.2% ± 14.0%, respectively. As evidenced by the better spike-andrecovery results, dilution was a good method to correct for poor recovery. 3.5 Comparison of the EXPAR-based immunoassay assay with the commercial ELISA assay in clinical samples 14


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To validate the performance of our newly-developed assay, a commercial ELISA kit was used in parallel to analyze 14 serum samples [seven patients with late-stage lung cancers (denoted by C) and seven patients with lung inflammatory diseases (denoted by I)] and 10 pleural effusion samples [five patients with late-stage lung cancers (C), three patients with heart failure (denoted by H), and two patients with lung inflammatory diseases (I)] for comparison. The concentration of RGS11 in each sample was determined by interpolating into their corresponding calibration curves. Only one of the serum samples acquired from lung cancer patients and two out of seven from patients with lung inflammatory diseases exhibited detectable RGS11 using the traditional ELISA assay (Fig. 5a). On the other hand, in 13 out of 14 samples, except for SC2, we detected RGS11 using our newly-developed EXPAR-based immunoassay, which suggested its sensitivity was relatively higher than that of the traditional ELISA methodology. The relationship of six detectable serum levels of RGS11 (SC7, SI1, SI3 – SI6) between the EXPAR-based immunoassay and ELISA displayed a high correlation, R2= 0.9796 (Fig 5b). Clinical samples of pleural effusion represent the impairment of pleural fluid drainage that results from excess vascular plasma production and/or decreased lymphatic absorption; its etiologies are extensive and range from cardiopulmonary disorders or systemic inflammatory conditions to malignancy. To investigate if the newly-developed immunoassay could be extended further to examine different physiological sample matrices with complexity (rather than serum), we collected five malignant and five non-cancer samples that were associated with pleural effusions from individuals with either inflammatory pneumonia or heart failure (Fig. 5c). Though the presence of aggregated particles of RGS11 in pleural effusions was reported previously,36 there was a strong correlation (R2= 0.9898) between these two methods (Fig. 5d). To summarize, the EXPAR15



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based immunoassay overall showed a higher sensitivity to detect a low level of RGS11 in sera when compared to that of the traditional ELISA. Both methodologies demonstrated a remarkably strong correlation in those detectable samples, which indicated that data obtained from this newly-developed assay were consistent with those from ELISA, but with higher sensitivity. To dissect the possible advantages of EXPAR-based immunoassay to detect RGS11, our sensing strategy had a higher detection rate than the traditional ELISA. This was likely due to the difference in interference caused by the sample matrix effect of interactions between RGS11 and other components that were present in sera or pleural effusions. Matrix components affected the binding of antibodies to RGS11 or they altered the signal-to-noise ratio, which resulted in erroneous readings. Hence, dilution factor is a critical determinant for detection and accurate evaluation of the concentration for the target of interest. Prior to analyses with EXPAR-based immunoassay and traditional ELISA, our samples were first diluted by 5,000 fold and 5 fold, respectively. Different dilution factors were applied because the LOD and linear range for those two methods were not within the same order of magnitude. This suggested that with the traditional ELISA, the recognition capability of the antibody to capture the RGS11 target may have been disrupted by a high sample matrix effect (low dilution factor). Thus, the high detection sensitivity by the EXPAR-based immunoassay suggested that the interferences or unknown inhibitory factors that were induced by the sample matrix effect may be reduced significantly. Therefore, we believe that the level of RGS11 that was determined by the EXPAR-based immunoassay represented a true concentration of RGS11 in sera or pleural effusions. The comparison of the detection characteristics of the proposed EXPAR-based immunoassay and a commercial ELISA kit is summarized in Table 1b. 16


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The clinical indications of the level of RGS11 in sera or plural effusions is not fully understood yet. Regulators of G protein signaling (RGS) proteins, which is an evolutionarily conserved group of intracellular proteins expressed in a variety of cell types that include immune cells, exert their influence through regulation of the activity of the GTPase-activating protein (GAP) on heterotrimeric G proteins and interactions with intracellular signaling complexes. They have also been reported to function in signaling gradients necessary for the directional migration of leukocytes to inflamed tissues. Aberrant expression of some RGS proteins has been linked to dysregulated immunity and/or neoplasia in humans; in contrast, physiological functions of most RGS proteins in leukocytes are fundamentally unknown.37,38 RGS11 belongs to the R7 subfamily of RGS. Recently, it was shown to be overexpressed in lung adenocarcinoma and to have a novel function of promoting cell migration by activation of the c-Raf/ERK/FAK-mediated pathway to upregulate Rac1 activity8. However, its role in immune responses is completely obscure. In the present study, we demonstrated that the RGS11 levels in sera or pleural effusions of patients with inflammatory diseases (pneumonia or mycobacterium tuberculosis) were remarkably high compared with those in patients with late-stage lung cancer and heart failure (Fig. 5). Hence, it is plausible to hypothesize that elevation of RGS11 level is an inflammatory reaction that is similar to the presence of CA 125 in physiological fluids of patients with pulmonary tuberculosis,39 heart failure,40 or the existence of serum prostate specific antigen (PSA) with overall inflammation, or intraepithelial acute/chronic inflammation.41 However, this elevated inflammatory phenomenon is totally suppressed by host cell components (e.g., tumor-associated macrophages, regulatory T cells, or myeloid-derived suppressor cells) and immune-suppression cytokines (TGF-, IL-10, and etc.) in patients with advanced stages of lung cancer (Fig. 5a, 5c).42 Thus, the hypothesis that elevated serum (or 17



Page 18 of 31

pleural effusion) RGS11 and inflammatory diseases are associated is clearly in need of more clinical studies.

4. Conclusion We developed a novel amplification strategy for the immunodetection of RGS11, which is a regulator of G-protein signaling and also a lung cancer biomarker. Our proposed scheme integrated magnetic bead extraction, EXPAR, and a suspension immunoassay into a new format for an immunoanalytical platform that showed better detection efficiency. The sensing platform was excellent for the determination of RGS11 with an exceptional detection limit of 148 fg/mL (equivalent to 37.8 zeptomoles of RGS 11) and a linear dynamic range of 0.1  100 pg/mL using a minimal sample volume (20 L) and with a reaction time of 1.5 h. Our new sensing platform features a satisfactory LOD, reasonable assay time, and high-throughput ability than most of the other previously reported NAAT-based immunoassays. In addition, our sensing platform can be feasibly extended to the detection of other proteins, as long as they can be engineered to be His-tag recombinant proteins. Our design offers a universal amplification platform, and it holds great promise for using it in conjunction with conventional microplate reader that could lead to the enhancement of analytical sensitivity and simplicity of operation for detection of disease-associated proteins.

Conflicts of interest There are no conflicts of interest to declare. Acknowledgments This work was supported by grants obtained from the Ministry of Science and Technology (MOST) of Taiwan as follows: 101-2113-M-002-003-MY3 (JAH), 102-2628-M-002-004-MY4 (JAH), 106-2113-M-002-014-MY3 (JAH), 100-2321-B-400-018 (NYS), 101-2321-B-400-004 (NYS and JAH), and NSC 102-2321-B-400-004 (NYS 18


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and JAH). We are also grateful to Dr. Sheng-Wei Lin of Technology Commons, College of Life Science, National Taiwan University for help with Biacore T200.

19



SUPPORTING INFORMATION

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Table of Contents I.) Figure S1. Effect of His-tag RGS11 concentration on the signal production………………………………………….S-3 II.) Purification of Ab-P (EXPAR primer conjugated with anti-His-tag antibody) by gel filtration chromatography.………………………………………………………………………………………………………………………………S-4 III.) Determination of EXPAR number on an antibody molecule……....................…….……………………..………..S-5

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References 1. 2. 3. 4. 5.

Bilaceroglu, S.; Curr. Opin. Pulm. Med., 2017, 23, 247-253. Broodman, I.; Lindemans, J.; van Sten, J.; Bischoff, R.; Luider, T. J. Proteome Res., 2017, 16, 3-13. Pinsky, P. F. Lung Cancer Manag., 2014, 3, 491-498. Borrebaeck, C. A. K. Nat. Rev. Cancer, 2017, 17, 199-204. Zamay, T. N.; Zamay, G. S.; Kolovskaya, O. S.; Zukov, R. A.; Petrova, M. M.; Gargaun, A.; Berezovski, M. V.; Kichkailo, A.S. Cancers, 2017, 9, 155. 6. Ahn, J.-M. and Cho, J.-Y. J. Mol. Biomark. Diagn., 2013, S4, 001. 7. Li, X.; Asmitananda, T.; Gao, L.; Gai, D.; Song, Z.; Zhang, Y.; Ren, H.; Yang, T.; Chen, T.; Chen, M. Neoplasma, 2012, 59, 500-507. 8. Yang, S. H.; Li, C. F.; Chu, P. Y.; Ko, H. H.; Chen, L. T.; Chen, W. W.; Han, C. H.; Lung, J. H.; Shih, N. Y. Oncotarget, 2016, 7, 31122-31136. 9. Huang, Z.; Yan, G. Q.; Gao, M. X.; Zhang, X. M. Anal. Chem., 2016, 88, 2440-2445. 10. Sano, T.; Smith, C. L.; Cantor, C. R. Science, 1992, 258, 120-122. 11. Nam, J. M.; Thaxton, C. S.; Mirkin, C. A. Science, 2003, 301, 1884-1886. 12. Niemeyer, C. M.; Adler, M.; Wacker, R. Trends Biotechnol., 2005, 23, 208-216. 13. Agasti, S. S.; Liong, M.; Peterson, V. M.; Lee, H.; Weissleder, R. J. Am. Chem. Soc., 2012, 134, 18499-18502. 14. Kazane, S. A.; Sok, D.; Cho, E. H.; Uson, M. L.; Kuhn, P.; Schultz, P. G.; Smider, V. V. Proc. Natl. Acad. Sci. U. S. A., 2012, 109, 3731-3736. 15. Lei, J.; Li, P.; Zhang, Q.; Wang, Y.; Zhang, Z.; Ding, X.; Zang, W. Anal. Chem., 2014, 86, 10841-10846. 16. Liu, X.; Xu, Y.; Xiong, Y.-H.; Tu, Z.; Li, Y.-P.; He, Z.-Y.; Qiu, Y.-L.; Fu, J.-H.; Gee, S. J.; Hammock, B. D. Anal. Chem., 2014, 86, 7471-7477. 17. Mehta, P. K.; Raj, A.; Singh, N. P.; Khuller, G. K. J. Med. Microbiol., 2014, 63, 627-641. 18. Meng, X. Y.; Li, Y. S.; Zhou, Y.; Zhang, Y. Y.; Qiao, B.; Sun, Y.; Yang, L.; Hu, P.; Lu, S. Y.; Ren, H. L.; Zhang, J. H.; Wang, X.R.; Liu, Z. S. Biosens. Bioelectron., 2015, 70, 42-47. 19. Spengler, C.; Niemeyer, M. Analyst, 2015, 140, 6175-6194. 20. Chang, L.; Li, J.; Wang, L. Anal. Chim. Acta, 2016, 910, 12-24. 21. Meng, X. Y.; Li, Y. S.; Zhou, Y.; Sun, Y.; Qiao, B.; Si, C. C.; Hu, P.; Lu, S. Y.; Ren, H. L.; Liu, Z. S.; Qiu, H. J.; Liu, J. Q. Biosens. Bioelectron., 2016, 78, 194-199. 22. van Buggenum, J. A.; Gerlach, J. P.; Eising, S.; Schoonen, L.; van Eijl, R. A.; Tanis, S. E.; Hogeweg, M. N.; Hubner, C.; van Hest, J. C.; Bonger, K. M.; Mulder, K. W. Sci. Rep., 2016, 6, 22675. 23. Zhao, Y. X.; Chen, F.; Li, Q.; Wang, L. H.; Fan, C. H. Chem. Rev., 2015, 115, 12491-12545. 24. Deng, H. M.; Gao, Z. G. Anal. Chim. Acta, 2015, 853, 30-45. 25. Su, L.-C.; Chang, C.-M.; Tseng, Y.-L.; Chang, Y.-F.; Li, Y.-C.; Chang, Y.-S.; Chou, C. Anal. Chem., 2012, 84, 39143920. 26. van Ness, J.; van Ness, L. K.; Galas, D. J. Proc. Natl. Acad. Sci. U. S. A., 2003, 100, 4504-4509. 27. Na, J.; Shin, G. W.; Son, H. G.; Lee, S.-J. V.; Jung, G. Y. Sci. Rep., 2017, 7, 11396. 28. Zhang, Z. Z.; Zhang, C. Y. Anal. Chem., 2012, 84, 1623-1629. 29. Zhang, Y.; Hu, J.; Zhang, C.-Y. Anal. Chem., 2012, 84, 9544-9549. 21



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30. Ma, F.; Yang, Y.; Zhang, C. Y. Anal. Chem., 2014, 86, 6006-6011. 31. Wang, H. H.; Wang, H.; Liu, C. H.; Duan, X. R.; Li, Z. P. Chem. Sci., 2016, 7, 4945-4950. 32. Wang, J. P.; Zou, B. J.; Rui, J. Z.; Song, Q. X.; Kajiyama, T.; Kambara, H.; Zhou, G. H. Microchim. Acta, 2015, 182, 1095-1101. 33. Ren, K. W.; Wu, J.; Ju, H. X.; Yan, F. Anal. Chem., 2015, 87, 1694-1700. 34. Tong, Q.-H.; Tao, T.; Xie, L.-Q.; Lu, H.-J. Biosens. Bioelectron., 2016, 80, 385-391. 35. Chen, H.; Wu, S.; Dong, F.; Cheng, W.; Li, Q.; Ding, S.; Luo, R. Sens. Actuator B-Chem., 2015, 221, 328-333. 36. Stathopoulos, G. T.; Kalomenidis, I. Am. J. Respir. Crit. Care Med., 2012, 186, 487-492. 37. Druey, K. M. Adv.Immunol., 2017, 136, 315-351. 38. Wang, D. Immunopharmacol. Immunotoxicol., 2018, 40,187-192. 39. Mikacic, M.; Vasilj, I.; Vasilj, M.; Bevanda, D.; Simovic, M.; Galic, K. Psychiatr. Danub., 2017, 29, 123-126. 40. Falcao, F.; de Oliveira, F. R. A.; da Silva, M. C. F. C.; Sobral, D. C. Biomark. Med., 2018, 12, 373-381. 41. Umbehr, M. H.; Gurel, B.; Murtola, T. J.; Sutcliffe, S.; Peskoe, S. B.; Tangen, C. M.; Goodman, P. J.; Thompson, I. M.; Lippman, S. M.; Lucia, M. S.; Parnes, H. L.; Drake, C. G.; Nelson, W. G.; De Marzo, A. M.; Platz, E. A. Prostate Cancer Prostatic Dis. 2015, 18, 264-269. 42. Weber, R.; Fleming, V.; Hu, X. Y.; Nagibin, V.; Groth, C.; Altevogt, P.; Utikal, J.; Umansky, V. Front. Immunol., 2018, 9, 1310.

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Figure legends Fig. 1. Schematic diagram of the proposed EXPAR-based immunoassay. Fig. 2. (a) The association rate constant (ka), dissociation rate constant (kd), and equilibrium dissociation constant (KD) for binary interactions occurring between His-tag RGS11 and the analytes were calculated using BIAcore evaluation software. (b) The recognizing abilities of the Ab (unmodified anti-His-tag antibody) and Ab-P (anti-His-tag antibody conjugated with thiolated primer) to the His-tag-RGS11. Fig. 3. The gel electrophoresis results of performing Ab-P-based EXPAR with different reaction times (5 minutes, 10 minutes, 12.5 minutes, and 15 minutes) and with different Ab-P concentrations (0, 100 fM, 1 pM, 10 pM, 100 pM, and 1 nM). Fig. 4. Detection of target protein. (a) The dose-response relationship between the fluorescence intensity and the concentration of the target protein over a range of 0 – 1 ng/mL. The error bars indicate 1 standard deviation for at least five repetitions. (b) Spike-and-recovery assessment. The recovery rates of undiluted serum A and serum B were 102.2% ± 15.5% and 84.0% ± 14.7%, respectively. The recovery rates of halfdiluted serum A and serum B were 97.0% ± 12.2% and 94.2% ± 14.0% respectively. Fig. 5. Real sample analysis and its validation with traditional ELISA [Samples from patients with advanced cancer (C), inflammation (I) and heart failure (H)]. (a) The amount of RGS11 in the serum samples, which was measured utilizing the EXPAR-based immunoassay and a commercial ELISA kit. (b) The correlation between the serum RGS11 levels measured by both assays. (c) The amount of RGS11 in pleural effusion samples, which were measured utilizing the EXPAR-based immunoassay and a commercial ELISA kit. (d) The correlation between RGS11 levels in pleural effusions measured by both assays.

23


Analytical Chemistry 11 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 2 30 3 31 32 4 33 5 34 35 6 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 24 of 31

Figure 1.

Competitive immunoassay 30 min

His-tag RGS11 Polymerase (Klenow fragment)

Formation of sandwich complex 30 min

RGS11

EXPAR 12.5 min

Ab-MB (anti-RGS11 ab-magnetic bead complex)

SYBR Green

Nicking enzyme (Nb.BbvCI)

Separation

Template

Ab-P (primer labeled anti-His ab)

Fig. 1. Schematic diagram of the proposed EXPAR-based immunoassay.

24


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Figure 2.

Fig. 2. (a) The association rate constant (ka), dissociation rate constant (kd), and equilibrium dissociation constant (KD) for binary interactions occurring between His-tag RGS11 and the analytes were calculated using BIAcore evaluation software. (b) The recognizing abilities of the Ab (unmodified anti-His-tag antibody) and Ab-P (anti-His-tag antibody conjugated with thiolated primer) to the His-tag-RGS11.

25


Analytical Chemistry 11 2 3 42 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 3 38 4 39 5 40 41 6 42 7 43 44 8 45 9 46 47 10 48 11 49 50 12 51 13 52 53 14 54 15 55 56 16 57 58 59 60

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Figure 3.

300 200 150 100 75

300 200 150 100 75

EXPAR product

50

50

35 25 20

EXPAR product

35

template

25

1nM 1nM100pM 100pM 10pM 10pM 1pM 1pM100fM 100fM 0 0

20

T+P T+P

15

Ab-P concentrations 10

template

1nM 1nM100pM 100pM 10pM 10pM1pM 1pM100fM 100fM 0 0 T+P T+P

15

Ab-P concentrations

10

oC,55min 25 25oC, min 300 200 150 100 75

oC,10 25 25oC, 10min min

50

300 200 150 100 75

35

50

25 20

EXPAR product

EXPAR product

35

template

25

1nM 1nM 100pM 100pM10pM 10pM1pM 1pM100fM 100fM 0 0

20

template

1nM 1nM 100pM 100pM10pM 10pM1pM 1pM100fM 100fM 0 0

15

Ab-P concentrations

T+P T+P

15

Ab-P concentrations

T+P T+P

10

oC,12.5 25 25oC, 12.5min min

10

oC,15 25 25oC, 15min min

Fig. 3. The gel electrophoresis results of performing Ab-P-based EXPAR with different reaction times (5 minutes, 10 minutes, 12.5 minutes, and 15 minutes) and with different Ab-P concentrations (0, 100 fM, 1 pM, 10 pM, 100 pM, and 1 nM).

26


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Figure 4.

(b) Fluorescence intensity (RFU)

850 800 750

850

y  -72.2  log10 x  10.05

800

R 2  0.9812

750 700 650 600 550

700

10 -1

10 0

10 1

10 2

Concentration of GST-RGS11 (pg/mL)

650 600

y  581.1 

816 - 581.1

150

Recovery rate (%)

(a) Fluorescence intensity (RFU)

11 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 2 19 20 3 21 4 22 23 5 24 6 25 26 7 27 8 28 29 9 30 10 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


1  10-0.81  log 2.2 - x  2 R  0.9999

550 0

10 -1

10 0

10 1

100

50

0 10 2

10 3

Serum A Serum B

non-diluted

2X diluted

Concentration of GST-RGS11 (pg/mL)

Fig. 4. Detection of target protein. (a) The dose-response relationship between the fluorescence intensity and the concentration of the target protein over a range of 0 – 1 ng/mL. The error bars indicate 1 standard deviation for at least five repetitions. (b) Spike-and-recovery assessment. The recovery rates of undiluted serum A and serum B were 102.2% ± 15.5% and 84.0% ± 14.7%, respectively. The recovery rates of halfdiluted serum A and serum B were 97.0% ± 12.2% and 94.2% ± 14.0% respectively.

27



Figure 5.

(a)

(b) 120 90

150

ELISA kit Our platform C: Lung cancer I: Inflammation

Our platform (ng/mL)

RGS11 (ng/mL)

150

60 30 0

y = 0.9705x-3.1730 R 2 = 0.9796

120 90 60 30 0

SC1 SC2 SC3 SC4 SC5 SC6 SC7 SI1 SI2 SI3 SI4 SI5 SI6 SI7

0

30

60

90

120

150

80

100

ELISA kit (ng/mL)

(c)

(d) 90 60

100

ELISA kit Our platform C: Lung cancer I: Inflammation H: Heart failure

Our platform (ng/mL)

120

RGS11 (ng/mL)

11 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 2 36 3 37 38 4 39 5 40 41 6 42 7 43 44 8 45 9 46 47 10 48 11 49 50 51 52 53 54 55 56 57 58 59 60

Page 28 of 31

30

PC1 PC2 PC3 PC4 PC5 PI1 PI2 PH1 PH2 PH3

R 2 = 0.9898

80 60 40 20 0

0

y = 0.9015x+0.0848

0

20

40

60

ELISA kit (ng/mL)

Fig. 5. Real sample analysis and its validation with traditional ELISA [Serum samples from patients with latestage cancer (SC), inflammation (SI), plural effusion samples from patients with late-stage cancer (PC), inflammation (PI), and heart failure (PH)]. (a) The amount of RGS11 in the serum samples, which was measured utilizing the EXPAR-based immunoassay and a commercial ELISA kit. (b) The correlation between the serum RGS11 levels measured by both assays. (c) The amount of RGS11 in pleural effusion samples, which were measured utilizing the EXPAR-based immunoassay and a commercial ELISA kit. (d) The correlation between RGS11 levels in pleural effusions measured by both assays.

28


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Table 1. (a) The comparison of the proposed EXPAR-based immunoassay and the recent NAAT-based immunoassay. (b) The comparison of the detection characteristics of the proposed EXPAR-based immunoassay and a commercial ELISA kit.

(a) Journal / Year

Anal. Chem. / 2015

Sens. Actuator BChem. / 2015

Biosens. Bioelectron. / 2016

This study

Target

CEA

Human prolactin

GFP

RGS11

Method of NAAT

MNAzyme

RCA

RCA

EXPAR

Linear range

0.002 - 500 ng/mL

10 fg/mL 10 ng/mL

pg/mL sub ng/mL

0.1 - 10 pg/mL

LOD

8.33 fM (1.5 pg/mL)

6.67 aM (160 ag/mL)

Sub-fM (few pg/mL)

2.8 fM (148 fg/mL)

Sample volume

1 μL

0 μL

50 μL

20 μL

Assay time

< 1 hr

7 hr

> 12 hr

1.25 hr

High-throughput ability

Feeble

Excellent

Excellent

Excellent

NAAT: nucleic acid amplification technique

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Analytical Chemistry 11 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 2 22 23 3 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

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(b) Commercial ELISA kit

Our platform

Sample volume

50 μL

20 μL

Assay time

Incubation: 1 hr + 15 min

Incubation: 1 hr + 15 min

Sensitivity

0.0155 unit: OD450 / (ng/mL)

72.2 unit: ΔRFU / log (pg/mL)

Linear range

6.25 ng/mL ~ 100 ng/mL

100 fg/mL ~ 10 pg/mL

Limit of detection (LOD)

1.44 ng/mL

148 fg/mL

30


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for TOC only

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Diagnosing the RGS11 Lung Cancer Biomarker: The Integration of

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