This is a pre-print of an article published in Analytical and Bioanalytical Chemistry. The final authenticated version is available online at: https://dx.doi.org/10.1007/s00216-020-02616-6 Analytical & Bioanalytical Chemistry A compact plasmonic biosensor device for the rapid and efficient monitoring of gluten free diet directly in human urine Journal: Analytical and Bioanalytical Chemistry Manuscript ID Draft Fo Type of Paper: Research Paper Date Submitted by the n/a rP Author: Complete List of Authors: Pelaez, E. Cristina; Institut Català de Nanociència i Nanotecnologia ICN2, CSIC and CIBER-BBN, Nanobiosensors and Bioanalytical applications ee Estevez, M.-Carmen; Institut Català de Nanociència i Nanotecnologia ICN2, CSIC and CIBER-BBN, Nanobiosensors and Bioanalytical applications Dominguez, Remedios ; Biomedal S.L rR Sousa, Carolina; University of Seville Faculty of Pharmacy, Department of Microbiology and Parasitology Cebolla, Angel; Biomedal S.L Lechuga, Laura; Institut Català de Nanociència i Nanotecnologia ICN2, ev CSIC and CIBER-BBN, Nanobiosensors and Bioanalytical applications POC device, Gluten Immunogenic Peptides, 33-mer gliadin peptide, Keywords: iew celiac disease, urine, gluten-free diet Analytical & Bioanalytical Chemistry Page 2 of 31 1 2 3 4 A compact plasmonic biosensor device for the rapid and efficient 5 monitoring of gluten free diet directly in human urine 6 7 8 E. Cristina Peláeza, M.-Carmen Esteveza*, Remedios Domínguezb, Carolina Sousac, Angel Cebollab 9 and Laura M. Lechugaa 10 11 12 a Nanobiosensors and Bioanalytical Applications Group, Catalan Institute of Nanoscience and 13 Nanotechnology (ICN2), CSIC, CIBER-BBN and BIST, Campus UAB Bellaterra, 08193 Barcelona, 14 Spain. 15 b Biomedal S.L., Polígono Industrial Parque Plata, c/ Calzada Romana 40, 41900 Camas, Seville, 16 Spain. 17 c Department of Microbiology and Parasitology, Faculty of Pharmacy, University of Seville, 18 19 c/Profesor García González, S/N, 41012 Seville, Spain 20 21 *Corresponding author: M.-Carmen Estevez: mcarmen.estevez@icn2.cat Fo 22 23 24 Abstract rP 25 26 Celiac disease (CD) is a chronic autoimmune disorder induced in genetically susceptible individuals 27 28 by the ingestion of gluten from wheat, rye, barley or certain varieties of oats. A careful diet follow-up ee 29 is necessary to avoid health complications associated with long-term gluten intake by the celiac 30 31 patients. Small peptides (GIP, gluten immunogenic peptides) derived from the gluten digestion, which rR 32 are excreted in the urine and feces, have emerged as promising biomarkers to monitor gluten intake. 33 34 We have implemented a simple and sensitive label-free point-of-care (POC) device based on Surface 35 ev 36 Plasmon Resonance for the direct detection of these biomarkers in urine. The assay employs specific 37 monoclonal antibodies and has been optimized for the detection of the 33-mer -gliadin, known as 38 iew 39 the main immunogenic peptide of wheat gluten, and for the detection of GIP. Direct detection in 40 undiluted urine has been accomplished by using biosensing chips containing a robust and stable 41 42 biorecognition layer, obtained after carefully optimizing the biofunctionalization protocol. Excellent 43 44 Limits of Detection have been reached (1.6 – 4.0 ng mL-1), which ensures the detection of gluten 45 peptides even when the gluten intake is around the maximum tolerable amount in the digestive tract 46 47 (< 50 mg) for celiac individuals. No sample pretreatment, extraction, or dilution is required, and the 48 analysis takes less than 15 min. The assay has been validated with real samples. These features pave 49 50 the way towards an end-user easy–to-handle biosensor device for the rapid monitoring of gluten free 51 52 diet (GFD) and the follow-up of the health status in celiac patients. 53 54 55 Keywords: POC device; Gluten Immunogenic Peptides; 33-mer gliadin peptide; Celiac disease; urine; 56 Gluten-free diet 57 58 59 60 1 Page 3 of 31 Analytical & Bioanalytical Chemistry 1 2 3 4 5 6 Introduction 7 Celiac disease (CD) is an autoimmune enteropathy that leads to the destruction of the villi lining the 8 9 small intestine. It is triggered by the ingestion of gluten-containing cereals in genetically predisposed 10 11 individuals [1]. CD causes severe symptoms including chronic diarrhea, fatigue, abdominal 12 distension, weight loss and also anaemia, osteoporosis and neurological symptoms due to 13 14 malabsorption of nutrients [2,3]. The prevalence is approximately 1 % worldwide but varies greatly 15 in different geographical areas [4,5]. It is estimated that the number of undiagnosed cases is even 16 17 higher [6,7]. Moreover, an increasing incidence of CD is observed, which can be partially attributed to 18 the improvement in diagnostic techniques and disease awareness [8]. 19 20 Gluten refers to a heterologous group of proteins composed of prolamin and glutelin fractions. The 21 Fo 22 prolamin fraction known as gliadin contains the main toxic components [3]. Once consumed, gluten is 23 24 partially digested into gliadin peptides (10 to 50 amino acids), which are resistant to degradation by rP 25 intestinal proteases of the epithelial barrier of the intestinal mucosa [9]. These peptides are 26 27 deaminated by enzyme tissue transglutaminase (tTG) converting them into more immunogenic 28 ee molecules that activate the adaptive immune system [1]. This response involves antigen-presenting 29 30 cells (APC) that express the haplotypes HLA-DQ2 and DQ8, the main genetic predisposition factor 31 that triggers an immune response against digested gluten [10,11]. The APC are highly reactive to rR 32 33 gliadin-specific T cells, which produce a cascade of inflammatory mediators, resulting in a damage to 34 35 the intestinal mucosa manifested as villous atrophy, characteristic of CD [1]. Currently there is no ev 36 cure for CD and the only therapeutic treatment is a strict adherence to a gluten-free diet (GFD) in 37 38 daily life [12,13]. One of the main problems nowadays related to maintaining a GFD is the presence iew 39 of hidden gluten in food [14]. Approximately, more than half of commercially available food contains 40 41 gluten from wheat, barley, rye or oats, including those products where it is used as thickener or binder 42 43 [15]. According to the regulation Codex Alimentarius (CODEX STAN118-1979, revised in 2008) 44 [16], the total level of gluten in gluten-free food does not exceed 20 mg Kg-1. Therefore, constant 45 46 vigilance is always required as the adhesion to a strict diet with zero gluten intakes is almost 47 impossible given its ubiquity, cross-contamination or the inadequate food labelling regulations 48 49 [12,17,18]. As a result, persistent symptoms and enteropathy are common among celiac patients who 50 51 are trying to follow a GFD. Specifically, research shows that 25-40 % of adults with CD have 52 persistent enteropathy after two years on a GFD. Children are thought to recover more quickly, and 53 54 data suggest that 5-19 % of celiac children on a GFD may have persistent enteropathy despite 55 treatment with a GFD for at least one year. Moreover, the amount of gluten that celiac patients can 56 57 tolerate has not yet been established due to the variability between individuals [18]. However, the 58 effects of low gluten consumption in celiac patients have suggested that daily consumption of up to 50 59 60 mg can cause significant damage in the small intestine [19]. Hence, a daily intake of gluten less than 2 Analytical & Bioanalytical Chemistry Page 4 of 31 1 2 3 10 mg is recommended to be safe and unlikely to cause significant abnormalities or to trigger 4 5 symptoms [6]. 6 7 Currently, several companies have commercialized detection assays such as Quanta Flash® Celiac 8 (Werfen Company, Spain), Alegria® (Orgentec Diagnostika, Germany), EliA™ Celikey (tTG) / 9 10 EliA™ Gliadin (Thermo Scientific Phadia, USA) and Anti-Gliadin GAF-3X ELISA / Anti-tTG 11 12 ELISA (Euroimmun, Germany) to diagnose celiac disease, and iVYLISA GIP™ (Biomedal, Spain) 13 to monitor the adherence to the GFD. They are commonly based on solid-phase immunoassays (i.e. 14 15 ELISA, chemiluminescence and immunofluorescence) performed with bench-top, high throughput 16 instruments. They are sensitive and specific tools, although are also considerably expensive. Their 17 18 handling requires specialized skills and commonly these analysis are done in central laboratories and 19 20 research centers. However, their location in primary healthcare settings is more limited and definitely 21 less accessible to the patient himself [18,20–22]. Fo 22 23 The development of portable, compact and easy-to-handle tools entails significant advantages for its 24 self-use by patients, as it allows direct detection or personalized monitoring of therapies usually with rP 25 26 a small amount of a biological sample. Such is the case of the qualitative tests based on lateral flow 27 28 immunochromatography (LFT) Sintomax® (Augurix SA, Switzerland), Xeliac® Test (Eurospital, ee 29 30 Italy), and BiocardTM celiac test (AniBiotech®, Vantaa, Finland) [22–25]. These companies have 31 developed qualitative, direct, rapid and easy tests that support the monitoring or the diagnosis of the rR 32 33 disease by the end-user. However, the limitation of these tests is mainly related to the lack of 34 quantification of the analyte concentration to know the evolution of the immune response to 35 ev 36 treatment. In other instances (i.e. GlutenDetect®, Biomedal, Spain), the sensitivity might not be 37 enough to detect small intakes of gluten, and the use of additional cocktails for dilution and 38 iew 39 pretreatment of samples complicate the steps done by the user and restrict its continuous use at home. 40 41 We have designed a compact, portable and easy-to-use device based on Surface Plasmon Resonance 42 43 (SPR) biosensing that overcomes the limitations mentioned above, to specifically and quantitatively 44 monitor gluten intake levels from a simple urine analysis. The device is based on immunoassays 45 46 similar to the ones used in the iVYCHECK GIP Urine™ test (Biomedal, Spain), where gluten 47 immunogenic peptides (GIP), originated after gliadin degradation, are detected in urine and feces 48 49 samples using highly specific monoclonal antibodies (mAb). These antibodies (G12 and A1) have a 50 51 high affinity for GIP, especially for the -gliadin 33-mer peptide (MW 3.9 kDa), a small-sized and 52 digestion-resistant peptide, which is the most immunodominant toxic peptide to the celiac patients 53 54 [18,22]. A customized biofunctionalization strategy has been precisely established, successfully 55 providing the direct, quantitative analysis of whole urine. No previous extraction, pretreatment or 56 57 even dilution step is needed. The assays are implemented in a compact biosensor prototype, showing 58 59 excellent levels of detection with both specific antibodies and wide dynamic ranges that allow 60 detecting minimum concentrations of GIP for a strict GFD or low gluten intake. 3 Page 5 of 31 Analytical & Bioanalytical Chemistry 1 2 3 4 5 Materials and Methods 6 7 Urine sample collection 8 Two different types of urine samples were collected according to the donor’s gluten-containing diet. 9 10 Group 1 samples are obtained from celiac disease patients and healthy volunteers on strict gluten-free 11 diet (GFD) and were used during the optimization and assessment of immunoassays. Group 2 samples 12 13 are collected from healthy individuals following an unrestricted normal gluten-containing diet. The 14 15 2-gliadin 33-mer peptide concentrations were analysed with the iVYCHECK GIP Urine™ lateral 16 flow device and quantified with the iVYCHECK GIP Reader™ from Biomedal S.L. (Seville, Spain) 17 18 [17]. All volunteers were provided with sterile falcons (50 mL). Urine samples were collected and 19 20 stored at -20 ºC. Written consent was obtained from all volunteers that provided samples. 21 Fo 22 POC device and detection assay format 23 24 The plasmonic biosensor device involves a miniaturized platform (20 x 20 cm2) (see Fig.1) which rP 25 26 incorporates the optical setup, the biosensor chip and the flow cell to bring samples over the sensing 27 area. The sensor chips consist of a gold thin layer (1 nm Ti / 49 nm Au) evaporated on glass 28 ee 29 substrates. Additional external components, which can be further integrated such as input light source 30 and spectrometer, are also incorporated. For a detailed description of the biosensor device and 31 rR 32 working principle, please refer to the Supplementary Information (SI). Briefly, the device tracks in 33 34 real time the shift of the resonance peak (ΔλSPR) of the gold sensor chip which arises when refractive 35 ev index changes occur its surface, which are originated after binding or desorbing events occurs (see Fig 36 37 S1B in SI). Thus, the sensing is direct and label-free. The selected detection assay is based on an 38 iew indirect competitive immunoassay, where a similar compound of the target analyte (i.e. competitor) is 39 40 immobilized on the sensor surface and competes with the analyte for the binding of a fixed 41 concentration of the specific antibody (see Fig. 2 for a representative scheme of the assay). In this 42 43 way, the concentration of analyte is inversely proportional to the signal observed, being maximum at 44 45 zero analyte concentration. The competitor in this case was the prolamin working group (PWG) 46 gliadin, which contains the epitopes the antibody is specific for. A covalent attachment of the PWG 47 48 gliadin was selected. 49 50 51 Sensor chip biofunctionalization and assay development 52 53 Details on the chemical and immunochemical reagents and buffers are summarized in the SI. The 54 formation of a self-assembled monolayer (SAM) onto the sensor chip surface was carried out using 55 56 HS-C11-(EG)6-O-CH2-COOH (1 mM) after overnight incubation at room temperature. Then, sensor 57 chips were rinsed with ethanol and dried with a N2 stream. Biofunctionalization procedure was 58 59 performed in-situ in order to optimize the immobilization conditions. Carboxylic groups of thiols 60 4 Analytical & Bioanalytical Chemistry Page 6 of 31 1 2 3 were activated with a solution of 3(3-dimethylaminopropyl)carbodiimide hydrochloride (EDC), N- 4 5 hydroxysulfosuccinimide (NHS) (EDC/NHS, 0.2 M /0.05 M) in MES buffer in a flow rate of 20 µL 6 min-1. A solution of PWG gliadin (50 µg mL-1) in acetate buffer pH 5.0 was injected after activation at 7 8 a flow rate of 10 µL min-1. Then, the remaining unreacted carboxylic groups were deactivated with a 9 10 solution of 1 M ethanolamine pH 8.5 at a flow rate of 30 µL min-1 for 2 min (see Fig. 2). An ex-situ 11 immobilization was also used to generate the sensor chips for routine-based analysis following the 12 13 optimized conditions. In this case, for the activation step, 200 µL of the EDC/NHS solution was 14 dropped over the SAM-functionalized sensor chips (20 min), followed by the overnight incubation at 15 16 4 ºC with PWG gliadin (50 µg mL-1) and the ethanolamine deactivation step (2 min). In between each 17 step, sensor chips were rinsed with Milli-Q water and dried with a stream of N2. Chips were mounted 18 19 on the optical device and kept in a continuous flow of PBST buffer at 25 µL min-1 before 20 21 measurements. Fo 22 23 A fixed concentration of each monoclonal antibody was chosen after performing non-competitive 24 assays (i.e. in the absence of analyte) over PWG gliadin-biofunctionalized sensor chips: G12 mAb (2 rP 25 26 µg mL-1) and A1 mAb (5 µg mL-1). The antibody concentration was selected so that (i) it enabled the 27 analysis under non-saturation conditions in PBST buffer and (2) the signal was high enough to allow 28 ee 29 monitoring a broad range of analyte concentrations (data not shown). A stock solution of target - 30 31 gliadin 33-mer peptide (1 mg mL-1 in PBS) was used to prepare the standards to obtain the calibration rR 32 curve (from 0.1 ng mL-1 to 4.0 µg mL-1 in PBST buffer) using both antibodies. The incubation time 33 34 between the antibody and the analyte was set to 15 min at room temperature. Sample containing the 35 ev 36 antibody-analyte mixture was then flowed through the sensor chip at a flow rate of 25 µL min-1. Free 37 antibodies not bound to the 33-mer peptide in solution can then interact with the immobilized PWG 38 iew 39 gliadin. Total dissociation of the antibody-PWG gliadin interaction was efficiently accomplished by 40 injecting a 5 mM NaOH solution during 120 s. Calibration curves for the 33-mer peptide detection 41 42 were obtained. The average (ΔλSPR) and standard deviation (SD) of triplicate analysis were graphed 43 and the data were adjusted to a dose-response inhibition equation, as described elsewhere [26,27]. The 44 45 analytical parameters as Limit of Detection (LOD), the half inhibitory concentration (IC50), and the 46 47 dynamic range (the interval between IC80 – IC20) were calculated. Intra-assay (two calibration curves 48 obtained with the same biofunctionalized sensor chip) and inter-assay (three calibration curves 49 50 obtained with three different biofunctionalized sensor chips) variability was assessed. 51 52 53 Analysis of urine samples 54 55 The effect of urine on the assay performance (i.e. nonspecific adsorption) was removed by adding a 56 blocking layer consisting of BSA 10 mg mL-1 diluted in PBST. This solution was injected over the 57 58 sensor chip for 2 min right before the injection of each urine sample. The conditions to achieve full 59 removal of nonspecific adsorptions from urine are summarized in the SI. Control experiments related 60 5 Page 7 of 31 Analytical & Bioanalytical Chemistry 1 2 3 to the effect of urine were done with urine collected from Group 1 donors (i.e. adhered to a GFD and 4 5 therefore, with no 33-mer peptide present). The effect of urine in the assay was evaluated with 6 calibration curves generated using -gliadin 33-mer peptide standards prepared in urine from Group 7 8 1 donors. Additional calibration curves were obtained for GIP instead of only 33-mer peptide by using 9 10 real urine sample containing digested GIP. The GIP concentration of this urine sample was previously 11 quantified with the lateral flow test iVYCHECK GIP Urine™ and the iVYCHECK GIP Reader™. The 12 13 sample was subjected to a preconcentration step using centrifugal filters Amicon Ultra 10 kDa to 14 15 obtain a stock GIP solution of known concentration. Then, a set of serial dilutions at different 16 concentrations of GIP were prepared with Group 1 urine as solvent and analyzed with each 17 18 monoclonal antibody. Finally, 21 patient samples from Group 2 donors were directly analyzed with 19 the biosensor and the concentration of GIP calculated from the calibration curves. The results were 20 21 compared with those values determined with the commercial detection assay (iVYCHECK GIP Fo 22 Urine™, Biomedal) and expressed as % Recovery: 23 24 [𝐺𝐼𝑃𝑠 ]𝑆𝑃𝑅 1 Recovery, % = [𝐺𝐼𝑃𝑠] × 100 rP 25 𝑖𝑉𝑌𝐶𝐻𝐸𝐶𝐾 𝐺𝐼𝑃 𝑈𝑟𝑖𝑛𝑒 ™ 26 27 Those urine samples whose concentration fell below the LOD were preconcentrated before analysis 28 using the centrifugal filters Amicon Ultra 10 kDa. Samples whose concentration was above the ee 29 30 dynamic range required additional dilution using Group 1 urine. All samples were analyzed in 31 triplicate. rR 32 33 34 35 Results and discussion ev 36 37 Bringing diagnostics close to the primary care or to the patient itself requires simple, yet sensitive, 38 fast and direct analysis of a readily extracted sample. Urine sample is ideal however its direct iew 39 40 detection is not always possible depending on the detection assay. Manipulation of the sample to 41 obtain a reliable signal commonly involves sample pre-treatment or dilution at best, eventually 42 43 affecting the sensitivity required and moving away from the concept of point-of-care. Our detection 44 assay is based on an indirect competitive assay performed on a label-free optical device and we have 45 46 newly customized the sensing chip biofunctionalization to facilitate the direct addition in urine and to 47 48 extend the half-life of the biosensing chips. 49 50 Immunoassay development 51 Two monoclonal antibodies (G12 and A1) with a high affinity for 33-mer peptide have been 52 53 considered for the assay development. They show reactivity to different prolamin epitopes from 54 55 wheat, barley, rye and some oat varieties. The G12 mAb recognizes QPQ-(L/Q)-P-(Y/F) and A1 mAb 56 reacts against the epitopes Q-(Q/L)-P(Y/F)-PQP. Both antibodies are currently used in research, food 57 58 analysis and clinical monitoring of CD and implemented in commercial assays such as ELISA and 59 LFT for the detection of GIP excreted in human feces and urine [22,28–30]. For the indirect 60 6 Analytical & Bioanalytical Chemistry Page 8 of 31 1 2 3 competitive assay, PWG gliadin (MW 33 to 45 kDa), which contains the epitopes the antibody is 4 5 specific for, is immobilized on the sensor chip surface at a fixed concentration and competes with the 6 free 33-mer peptide (or the family of GIP in a real sample) for the binding of the monoclonal 7 8 antibodies (G12 or A1) (see Fig. 2). To facilitate the attachment of PWG gliadin, a self-assembled 9 10 monolayer (SAM) with reactive groups (-COOH group) was added to the gold sensor chip. 11 12 Contrary to the use of conventional aliphatic thiolated compounds (i.e mercaptoundecanoic acid or 13 mercaptohexadecanoic acid), in this case we have selected thiol groups containing ethylene glycol 14 15 units in their structure in order to prevent non-specific adsorptions from the urine matrix [31,32]. A 16 reactive compound (HS-C11-(EG)6-O-CH2-COOH) and an inert one (HS-C11-(EG)4-OH) used as 17 18 lateral spacer to spread the PWG gliadin on the sensor chip surface were initially selected. Parameters 19 20 such as SAM composition at different COOH:OH molar ratios (3:7, 1:1, 7:3 and 1:0) and suitable 21 buffer to attach the PWG gliadin (50 µg mL-1) were tested (see Fig. S2A in SI). Those conditions Fo 22 23 resulting in higher PWG gliadin immobilization signal (ΔλSPR 12.7 ± 1.5 nm) were selected (SAM 24 ratio COOH:OH 1:0 -no spacers- and acetate buffer pH 5.0 for PWG gliadin immobilization). rP 25 26 According to the results obtained, acid pH (between pH 4.0 and 5.5) can improve considerably the 27 immobilization signal due to the electrostatic interactions between the SAM layer and the PWG 28 ee 29 gliadin (i.e. it increases the local concentration of PWG gliadin on the surface, resulting in a more 30 31 efficient coupling yield). Fig. S2B in SI shows a real-time sensorgram of an in-situ immobilization for rR 32 the selected conditions. Then, the in-situ immobilization was adapted to an ex-situ strategy, with the 33 34 aim of generating enough sensor chips to perform routine analysis and to study their stability over 35 ev time. The same signal for the antibody detection was virtually obtained over gliadin coated sensor 36 37 chips functionalized via either the in-situ or the ex-situ protocol (i.e. ΔλSPR 1.2 ± 0.1 for G12 mAb and 38 iew 39 1.0 ± 0.1 for A1 mAb, see Fig. S2C in SI), confirming that both procedures were equally effective and 40 that the biofunctionalization is stable and reproducible. The optimal concentration of antibodies was 41 42 determined by non-competitive assays. Moreover, the total dissociation of the PWG gliadin-antibody 43 interaction was guaranteed using NaOH 5 mM without damaging gliadin integrity, allowing the 44 45 repeated use of the sensor chip. The recognition layer was viable for at least 20 cycles of interactions 46 with G12 mAb and 32 cycles with A1 mAb (see Fig. S3A and B in SI, respectively). Under these 47 48 conditions, calibration curves using both antibodies were obtained by measuring different 33-mer 49 50 peptide concentrations. 51 52 Fig. S4 in SI shows two representative calibration curves using both mAbs and different 33-mer 53 peptide concentrations in PBST buffer. Table S1 in SI, shows the analytical parameters of the curves. 54 55 The LOD using G12 mAb is around one order of magnitude lower than A1 mAb (i.e ~2 ng mL-1 and 56 ~20 ng mL-1, respectively). Also, the sensitivity reflected in the IC50 values is more than one order of 57 58 magnitude better for G12 mAb assay. Nevertheless, the dynamic range for the assay with A1 mAb is 59 60 wider compared with the one obtained with G12 mAb, as a direct consequence of its lower slope (i.e. 7 Page 9 of 31 Analytical & Bioanalytical Chemistry 1 2 3 ~-1.4 for G12 mAb assay and ~-1 for A1 mAb assay), which extends the measurement range. This 4 5 eventually can be an advantage as a higher number of real samples may fall within this range and, 6 7 therefore, this increases the assay capabilities. The intra- and inter-assay evaluation confirms the high 8 reproducibility and stability of the immunoassays in the same sensor chip or in different sensor chips, 9 10 especially in the case of the G12 assay. Fig. S5A and B in SI, shows representative sensorgrams 11 obtained for different 33-mer peptide concentrations for both G12 and A1 assays which exemplifies 12 13 the low level of noise of the signals for each measurement. 14 15 Effect of the urine in the assay performance 16 17 Urine, as any other human fluids, can affect the immunoassays, as it is commonly reported [26,33], 18 either by hindering the interaction between the receptor and the target or by the non-specific 19 20 adsorption of some of its components on the sensor surface. This eventually results in worst assay 21 performance, being necessary the implementation of sample pretreatment or dilution steps. To Fo 22 23 minimize or avoid any of these undesired effects, we have employed thiols containing ethylene glycol 24 units in their structure (EG-thiols) [31]. The generation of this ethylene glycol layer did not however rP 25 26 completely remove non-desired adsorptions when analyzing pure urine (see red sensorgram in the Fig. 27 28 3A, λSPR ~ 0.5 nm) or even after diluting urine 50 % (blue sensorgram in Fig. 3A, λSPR ~ 0.25 nm). ee 29 Nevertheless, introducing an extra blocking step consisting of BSA (10 mg mL-1 in PBST) for 2 min 30 31 before each urine sample analysis, resulted in the formation of an extra layer (as evidenced by the rR 32 33 resultant positive signal of ΔλSPR around 0.12 ± 0.02 nm, see Fig. S6.A) with antifouling properties 34 which completely suppressed any kind of adsorptions derived from urine (see Fig. 3A, green and 35 ev 36 orange sensorgrams for 50 % and urine 100 %, respectively, where ΔλSPR~0). The experimental 37 conditions of this extra step were precisely optimized to achieve maximum efficiency (see SI for 38 iew 39 detailed description and Fig. S6 therein). Moreover, under these conditions, the addition of BSA did 40 not seem to affect the interaction of the antibody with the PWG gliadin layer, as signals for both G12 41 42 and A1 antibodies remained the same as can be checked in Fig 3B. The regeneration and reusability 43 44 of such sensor surfaces were similarly assessed, and a slightly higher deterioration was observed 45 compared with PBST measurements (see SI and Fig. S6 therein). Nevertheless, it was still possible to 46 47 have active sensor surfaces for around 15-17 cycles, for both G12 and A1 antibodies. 48 Complete calibration curves prepared directly in urine 100% indeed demonstrate that the developed 49 50 strategy do not affect at any level the assay performance compared with 50% urine or in PBST (see 51 52 Fig. 3C and D, for calibration curves with G12 and A1 antibodies, respectively, and Table 1 for a 53 summary of the assays analytical features). As can be observed, the differences are minimal and the 54 55 curves look analogous. The LOD and IC50 are very similar for both 100 and 50 % urine, which 56 confirm the excellent effect that the extra blocking with BSA causes regarding the removal of any 57 58 interference. A slight variation in the LOD and in the slope of the assay is observed, compared with 59 PBST buffer (i.e. it decreases as the amount of urine increases) which translates in broader working 60 8 Analytical & Bioanalytical Chemistry Page 10 of 31 1 2 3 range when whole urine is analyzed although these differences are minimal, thus not hindering the 4 5 attempt of directly analyzing real samples. Real-time sensorgrams for different 33-mer peptide 6 concentrations incubated with both G12 and A1 antibodies are shown in Fig. S7A and B in SI. 7 8 9 10 Selectivity of G12 and A1 mAb for GIP in real urine samples 11 Previous studies have demonstrated the capability of G12 and A1 mAbs of recognizing other toxic 12 13 immunogenic peptides generated from gluten (GIP) present in wheat, barley, rye and oats. The 14 affinity however varies significantly depending on the epitope. From those studies, A1 showed a 15 16 broader specificity that G12 [18,34]. After consumption of gluten from different origin, several GIP 17 18 will be present in the patient’s urine and their quantification might therefore be affected due to the 19 recognition pattern of the antibody employed. Therefore, in order to mimic a real sample analysis, 20 21 calibration curves with both antibodies were done using a mixture of GIP obtained from a real urine Fo 22 sample instead of single 33-mer peptide, applying the optimal conditions described above. Fig. 4 23 24 shows the calibration curves obtained in urine 100 % using GIP with G12 mAb (purple) and A1 mAb rP 25 26 (orange), which are compared with those obtained with 33-mer peptide for G12 mAb (blue) and A1 27 mAb (green). Calibration curves of 33-mer peptide and digested GIP obtained with G12 mAb (blue 28 ee 29 and purple lines) were very similar in terms of analytical parameters (see data summarized in Table 30 1). These results highlight the high specificity of the G12 mAb to directly measure the 33-mer peptide 31 rR 32 in urine in a set of gliadin-derived peptides without affecting the resulting average sensitivity. On the 33 contrary, for the case of A1 antibody assays (green and orange calibration curves), although the LOD 34 35 were similar, the IC50 was twice smaller when using GIP mixture, which reflects that the recognition ev 36 37 pattern of this antibody indeed includes other peptides, maybe with even higher affinity than for 33- 38 mer peptide [35]. Also, using GIP as target analyte resulted in a more restricted dynamic range, but iew 39 40 still broader than the one obtained with G12 mAb. These characteristics may eventually benefit the 41 real sample analysis, by avoiding previous dilution in highly concentrated samples, for example. 42 43 Nevertheless, in a more real scenario where the target analyte is a mixture of peptides, the assay using 44 45 G12 mAb offers a better sensitivity for GIP than using A1 mAb. Besides, the coefficients of 46 variability (CV) of key parameters of 33-mer and GIP immunoassays with G12 mAb, which are 47 48 significantly improved (see Table S2 in SI) compared with the one obtained in PBST (shown in Table 49 S1 in SI), proved the excellent reproducibility and great robustness of our plasmonic biosensor. 50 51 52 Moreover, for the case of G12 mAb based assay, our biosensor device offers a better performance 53 54 than the semi-quantitative lateral flow test (iVYCHECK GIP Urine™) which relies on the use of A1 55 56 and G12 antibodies. This test is capable of detecting GIP in urine in the range of 6.48-100 ng mL-1 57 although it requires a sample extraction or dilution pretreatment in order to isolate the peptides and/or 58 59 to remove interferences [22,36]. Conversely, our biosensor can directly analyze the urine collected 60 9 Page 11 of 31 Analytical & Bioanalytical Chemistry 1 2 3 from the individuals and no additional treatment (besides adding the antibody to the sample) is 4 5 needed, with an even lower LOD, set at 1.6 ng mL-1. 6 7 8 Table 1. Analytical parameters of all the competitive immunoassays in buffer and urine 9 LOD (IC90) IC50 Working range 10 mAb Target Sample (ng mL-1) (ng mL-1) Slope (IC80/IC20) (ng mL-1) 11 Mean ± SD Mean ± SD 12 33-mer PBST 3.6 ± 0.3 14.4 ± 1.6 6.0 ± 0.5 / 34.5 ± 5.0 -1.566 13 33-mer Urine-PBST 1:1 3.8 ± 0.9 18.3 ± 3.2 6.6 ± 1.5 / 50.6 ± 6.0 -1.290 14 G12 33-mer Urine 100 % 1.6 ± 0.1 15.2 ± 2.2 3.6 ± 0.2 / 56.2 ± 13.3 -0.869 15 16 GIP Urine 100 % 1.7 ± 0.1 11.6 ± 0.6 3.4 ± 0.1 / 35.4 ± 3.0 -1.114 17 33-mer PBST 12.7 ± 0.5 75.5 ± 4.7 23.5 ± 0.7 / 256.7 ± 26 -1.095 18 33-mer Urine-PBST 1:1 8.9 ± 1.6 127.1 ± 8.4 23.7 ± 1.9 / 610.5 ± 47 -0.797 A1 19 33-mer Urine 100 % 4.7 ± 1.3 111.8 ± 12.1 14.7 ± 3.2 / 702.3 ± 110 -0.623 20 GIP Urine 100 % 4.0 ± 1.1 41.2 ± 4.7 9.1 ± 1.2 / 172.1 ± 43.3 -0.864 21 (Mean value ± SD for three replicates of inter-assays obtained in 3 different biofunctionalized chip). Fo 22 23 24 Sensor chip storage and stability rP 25 26 The stability over time of the sensor chips were studied following a systematic optimization of several 27 sensitive parameters (data not disclosed due to confidentiality). Under the optimal conditions of 28 ee 29 preparation, biofunctionalization and storage, we observed that the biofunctionalized sensor chips 30 were stable for up to 6 months without affecting their integrity and functionality, as can be seen in 31 rR 32 Fig. S8 in the SI, where urine samples spiked with 33-mer peptide (at the corresponding IC50 33 34 concentration) were analyzed after different span times. As can be observed very similar signals were 35 ev obtained for the two assays using either G12 or A1 antibodies (CV of 3.58 % and 11.30 % 36 37 respectively), which is indicative of the excellent robustness and stability of our protocol for at least 38 iew the evaluated period of time. 39 40 41 42 Validation with real urine samples 43 In order to evaluate the accuracy and reliability of the biosensor device, 21 urine samples collected 44 45 from Group 2 donors (healthy volunteers who had previously ingested a certain amount of gluten) 46 were analyzed with our plasmonic biosensor and the data compared with the results obtained with the 47 48 iVYCHECK GIP Urine™ test [22]. The signals were interpolated in both G12 and A1 mAb 49 50 calibration curves of digested GIP. Each sample was measured three times obtaining the average, 51 standard deviation and the recovery. Results are summarized in Table 2. The accuracy offered by our 52 53 approach is excellent, with recovery values of 96 - 101 % and 91 – 111 % for both G12 and A1 54 antibody assays, respectively. The excellent correlation of our biosensor device with the iVYCHECK 55 56 GIP Urine™ test can be seen also in the Fig. 5 with an almost perfect correlation (i.e. slope close to 1 57 58 in both cases: 0.96 for G12-based assay and 1.03 for A1 based assay). As can be seen in the 59 correlation plot, for higher concentrations (>80 ng mL-1) a slight drift appears, with a tendency to 60 10 Analytical & Bioanalytical Chemistry Page 12 of 31 1 2 3 increase the slope (i.e. overestimate) for the A1-based assay and with a trend to decrease the slope 4 5 (i.e. underestimate) for the G12-based assay. These trends, however, are not that significant for the 6 range of concentration evaluated. Moreover, the level of precision and reproducibility is also evident 7 8 from the SD values observed for each of the samples. Overall, the performed validation corroborates 9 10 the excellent features achieved with our biosensor approach, in terms of reproducibility, accuracy and 11 precision for the quantification of GIP. 12 13 14 Table 2. Real samples validation of the plasmonic device 15 iVYCHECK Plasmonic device 16 17 Sample GIP Urine™ G12-based assay Recovery, A1-based assay Recovery, 18 [GIP] (ng mL-1) [GIP] (ng mL-1) (%) [GIP] (ng mL-1) (%) 19 20 P1 19.10 19.2 ± 1.1 100.5 ± 5.5 21.2 ± 1.0 111.1 ± 5.2 21 P2 12.30 12.5 ± 0.6 101.4 ± 4.6 11.5 ± 0.6 93.7 ± 4.7 Fo 22 P3 24.10 23.5 ± 1.1 97.7 ± 4.7 26.7 ± 0.5 110.7 ± 2.1 23 24 P4 27.97 28.4 ± 1.2 101.7 ± 4.3 25.5 ± 4.6 91.2 ± 16.4 P5 12.80 12.6 ± 0.2 98.7 ± 1.4 12.3 ± 0.4 96.3 ± 3.2 rP 25 26 P6 9.27 9.3 ± 0.1 100.5 ± 1.2 8.9 ± 1.2 95.8 ± 2.3 27 P7* 130.6 126 ± 5.0 96.5 ± 3.8 136.2 ± 1.1 104.2 ± 0.8 28 ee 29 P8 35.8 34.5 ± 1.2 96.4 ± 3.5 35.2 ± 2.3 98.3 ± 6.3 30 P9 25.9 26.2 ± 0.4 101.3 ± 1.6 26.3 ± 0.9 101.5 ± 3.3 31 P10 24.5 23.6 ± 1.6 96.3 ± 6.7 23.8 ±0.1 97.2 ± 0.6 rR 32 P11 14.2 14.0 ± 0.5 98.8 ± 3.5 14.3 ± 1.0 100.4 ± 7.1 33 34 P12 17.2 17.2 ± 0.1 100.2 ± 0.5 16.2 ± 1.4 94.2 ± 8.2 35 P13* 7.4 7.2 ± 0.2 97.6 ± 2.3 8.0 ± 1.2 108.1 ± 16.7 ev 36 P14 28.0 28.4 ± 1.4 101.4 ± 4.9 28.0 ± 0.6 100.1 ± 2.2 37 P15 21.9 21.7 ± 0.5 99.0 ± 2.3 21.6 ± 0.8 98.7 ± 3.8 38 iew 39 P16* 94.5 91.8 ± 3.4 97.1 ± 3.6 93.0 ± 1.2 98.4 ± 1.3 40 P17 17.7 17.4 ± 0.3 98.1 ± 1.5 17.2 ± 0.1 97.3 ± 0.8 41 P18 32.5 32.9 ± 0.6 101.1 ± 1.8 34.4 ± 2.1 105.9 ± 6.6 42 P19 13.3 13.2 ± 0.1 99.4 ± 1.1 13.7 ± 0.7 103.2 ± 5.3 43 44 P20 15.6 15.3 ± 0.4 98.1 ± 2.3 15.7 ± 0.7 100.3 ± 4.4 45 P21 34.1 34.0 ± 0.8 99.8 ± 2.3 35.7 ± 2.1 104.8 ± 6.2 46 aEach value represents the mean ± SD of triplicate measurements. 47 *Out of dynamic range (Sample P13 was preconcentrated and measured only with the A1 mAb assay. P7 and P16 were 48 diluted five and four times respectively and measured with the G12 mAb assay. 49 50 Evaluation of the biosensor device for low gluten intake diet follow-up 51 52 We have performed a quantitative test to monitor the consumption of gluten in diet. The G12 based 53 54 assay was selected because its lower limit of quantification (LOQ = IC80 of 3.4 ng mL-1) compared 55 with the A1 based assay (9.1 ng mL-1). Urine samples were collected from two healthy volunteers 56 57 from Group 2, who had an uncontrolled gluten diet (UGD). Later, they started a supervised gluten- 58 free diet for at least 36 hours. Between days 1 (24 h) and 2 (48 h), urine samples were collected to 59 60 corroborate that the volunteers had adhered to a GFD for a sufficient time. In the next days, volunteers 11 Page 13 of 31 Analytical & Bioanalytical Chemistry 1 2 3 ingested a certain amount of gluten every day (50, 150 and 250 mg at night at the same time each 4 5 day). Urine samples were collected in the morning each day. The G12 antibody was directly added to 6 the urine samples and incubated for 15 min before analyzing them with the biosensor as described 7 8 above. Fig. 6 shows the evolution of GIP concentration in urine during the controlled gluten diet. As 9 10 expected, maximum levels of GIP were observed during regular diet (first day) which significantly 11 decreased after the 36 h-period of non-gluten consumption (which fell around the LOQ of the assay 12 13 (around 4 ng mL-1). Then the gradual increase in the gluten intake nicely correlates with an increase in 14 the concentration of peptides excreted in urine. This representative study exemplifies the great 15 16 potential our biosensor device has as a convenient tool for diet monitoring or for disease therapy 17 follow-up. 18 19 20 21 Conclusions Fo 22 23 We have developed a plasmonic device that allows rapid, highly reproducible, accurate and non- 24 invasive gluten intake control using a small amount of patient's urine (200 µL). We demonstrate that rP 25 26 with the explicitly designed biofunctionalization protocol, the plasmonic biosensor directly quantifies 27 28 GIP generated after digestion of gluten and excreted in the urine. No extraction or any other type of ee 29 sample pretreatment or purification is needed, which greatly simplifies the analysis and speeds up its 30 31 implementation as an autonomous POC device that could be easily used by celiac patients to monitor rR 32 33 their own adherence to GFD. Excellent levels of sensitivity and specificity are reached using two 34 different monoclonal antibodies (G12 and A1) that show a different recognition pattern of GIP. In 35 ev 36 both cases the LODs achieved (around 1.7 and 4.0 ng mL-1 respectively), guarantee the detection of 37 minimal quantities of GIP even after low gluten intake. This device shows exceptional reproducibility, 38 iew 39 reliability and accuracy, demonstrated in the correlation analysis with a significant number of real 40 41 samples, reaching recoveries close to 100 %, which corroborates the feasibility and robustness of the 42 biosensing method. So far, the biosensor chips show a promising storage stability of 6 months 43 44 although further studies will cover a broader time span to ensure a storage capability in compliance 45 with commercialized tests standards. Moreover, further integration of the biosensor chip in a 46 47 disposable microfluidics cartridge will enable their integration and potential manufacturing. This 48 biosensing strategy implemented in the plasmonic biosensor is highly attractive to monitor the 49 50 treatment of celiac disease and to assess any potential infringement of a gluten-free diet, as it has been 51 52 illustrated with the tracking of the GIP levels in two individuals subjected to a controlled gluten diet. 53 Overall, the developed biosensor device holds great potential as a convenient sensing tool for the 54 55 label-free non-invasive celiac disease follow-up, for promoting self-monitoring of celiac patients in 56 the daily life and it reduces the need for additional invasive investigations on follow-up. 57 58 59 60 Acknowledgments 12 Analytical & Bioanalytical Chemistry Page 14 of 31 1 2 3 We acknowledge the financial support from URINETEST Project (Retos Colaboración URINETEST 4 5 RTC-2016-5452-1). 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Fo 22 [32] Schilp S, Rosenhahn A, Pettitt ME, Bowen J, Callow ME, Callow J, Grunze M. 23 24 Physicochemical properties of (ethylene glycol)-containing self-assembled monolayers rP 25 relevant for protein and algal cell resistance. Langmuir. 2009;25:10077–82. 26 27 [33] Vega B, Calle A, Sánchez A, Lechuga LM, Ortiz AM, Armelles G, Rodríguez-Frade JM, 28 ee 29 Mellado M. Real-time detection of the chemokine CXCL12 in urine samples by surface 30 plasmon resonance. Talanta. 2013;109:209–15. 31 rR 32 [34] Comino I, Real A, Gil-Humanes J, Pistón F, de Lorenzo L, Moreno ML, López-Casado MA, 33 Lorite P, Cebolla A, Torres MI, Barro F, Sousa C. Significant differences in coeliac 34 35 immunotoxicity of barley varieties. Mol. Nutr. Food Res. 2012;56:1697–1707. ev 36 [35] Cebolla A, Moreno ML, Coto L, Sousa C. Gluten immunogenic peptides as standard for the 37 38 evaluation of potential harmful prolamin content in food and human specimen, Nutrients. iew 39 40 2018;10:1–16. 41 [36] Sousa C, Comino I, Real A, Vivas S, Cebolla A. Determination of levels of immunogenic 42 43 gluten peptides in human samples (2013). WO/2012/089868, Filed December 28, 2011, 44 Published July, 5, 2012. 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 15 Page 17 of 31 Analytical & Bioanalytical Chemistry 1 2 3 4 5 Figure Legends 6 7 Figure 1. Photograph of the SPR biosensor platform showing the main units 8 9 10 Figure 2. Schematic representation of the biofunctionalization strategy and the 33-mer peptide 11 detection assay 12 13 14 Figure 3. (A) Blocking effect of BSA (10 mg mL-1) to minimize non-specific adsorptions of urine. 15 (B) Comparison of antibody signals in PBST, urine-PBST 1:1, and undiluted urine. [G12 mAb] = 2 16 µg mL-1 and [A1 mAb] = 5 µg mL-1 over surfaces blocked with BSA (10 mg mL-1)(C) Calibration 17 curve for 33-mer peptide in PBST, urine-PBST 1:1 and urine 100 % using [G12 mAb] = 2 µg mL-1 18 and (D) using [A1 mAb] = 5 µg mL-1. Each point represents the mean ± SD of three replicates 19 20 21 Figure 4. Calibration curves in urine 100 % obtained using: 33-mer peptide and G12 mAb (blue); 33- Fo 22 mer peptide and A1 mAb (green); GIP extracted from urine and G12 mAb (purple); GIP and A1 mAb 23 (orange). Each point represents the mean ± SD of three replicates obtained with three different sensor 24 chips rP 25 26 27 Figure 5. Correlation plot of GIP concentration of urine samples between iVYCHECK GIP Urine™ 28 test and POC biosensor device. Slopes for assays using G12 mAb and A1 mAb are 0.96 and 1.03 ee 29 respectively. (Each spot represents the mean ± SD of triplicate measurements) 30 31 rR 32 Figure 6. Levels of GIP in urine collected from two individuals determined with the G12 mAb 33 biosensor during a controlled low gluten intake. The monitoring was done during a regular diet, 34 followed by 2 days of GFD, and by a controlled gluten intake of 50, 150 and 250 mg in the following 35 ev days. Each spot represents the mean ± SD of triplicate measurements. (Sample corresponding to a 36 37 gluten intake of 250 mg for Patient 2 was not collected) 38 iew 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 16 Analytical & Bioanalytical Chemistry Page 18 of 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Fo 20 21 22 23 rP 24 25 26 ee 27 28 29 Photograph of the SPR biosensor platform showing the main units rR 30 31 99x70mm (300 x 300 DPI) 32 ev 33 34 35 36 iew 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 19 of 31 Analytical & Bioanalytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Fo 20 21 22 23 rP 24 25 26 ee 27 28 Schematic representation of the biofunctionalization strategy and the 33-mer peptide detection assay 29 rR 30 160x104mm (300 x 300 DPI) 31 32 ev 33 34 35 36 iew 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 & Bioanalytical Chemistry Page 20 of 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Fo 20 21 22 23 rP 24 25 26 ee 27 28 29 rR 30 31 32 (A) Blocking effect of BSA (10 mg mL-1) to minimize non-specific adsorptions of urine. (B) Comparison of ev 33 antibody signals in PBST, urine-PBST 1:1, and undiluted urine. [G12 mAb] = 2 µg mL-1 and [A1 mAb] = 5 34 µg mL-1 over surfaces blocked with BSA (10 mg mL-1)(C) Calibration curve for 33-mer peptide in PBST, 35 urine-PBST 1:1 and urine 100 % using [G12 mAb] = 2 µg mL-1 and (D) using [A1 mAb] = 5 µg mL-1. Each 36 point represents the mean ± SD of three replicates iew 37 149x119mm (300 x 300 DPI) 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 21 of 31 Analytical & Bioanalytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Fo 20 21 22 23 rP 24 25 26 ee 27 28 29 rR 30 31 32 Calibration curves in urine 100 % obtained using: 33-mer peptide and G12 mAb (blue); 33-mer peptide and ev 33 A1 mAb (green); GIP extracted from urine and G12 mAb (purple); GIP and A1 mAb (orange). Each point 34 represents the mean ± SD of three replicates obtained with three different sensor chips 35 36 99x79mm (300 x 300 DPI) iew 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 & Bioanalytical Chemistry Page 22 of 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Fo 20 21 22 23 rP 24 25 26 ee 27 28 29 rR 30 31 32 Correlation plot of GIP concentration of urine samples between iVYCHECK GIP Urine™ test and POC ev 33 biosensor device. Slopes for assays using G12 mAb and A1 mAb are 0.96 and 1.03 respectively. (Each spot 34 represents the mean ± SD of triplicate measurements) 35 36 99x79mm (300 x 300 DPI) iew 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 23 of 31 Analytical & Bioanalytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Fo 20 21 22 23 rP 24 25 26 ee 27 28 29 rR 30 31 32 Levels of GIP in urine collected from two individuals determined with the G12 mAb biosensor during a ev 33 controlled low gluten intake. The monitoring was done during a regular diet, followed by 2 days of GFD, and 34 by a controlled gluten intake of 50, 150 and 250 mg in the following days. Each spot represents the mean ± 35 SD of triplicate measurements. (Sample corresponding to a gluten intake of 250 mg for Patient 2 was not 36 collected) iew 37 99x79mm (300 x 300 DPI) 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Analytical & Bioanalytical Chemistry Page 24 of 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Supplementary Information 15 16 17 18 19 20 A compact plasmonic biosensor device for the rapid and efficient 21 monitoring of gluten free diet directly in human urine Fo 22 23 24 rP 25 E. Cristina Peláeza, M.-Carmen Esteveza*, Remedios Domínguezb, Carolina Sousac, Angel Cebollab 26 27 and Laura M. Lechugaa 28 ee 29 a Nanobiosensors and Bioanalytical Applications Group, Catalan Institute of Nanoscience and 30 Nanotechnology (ICN2), CSIC, CIBER-BBN and BIST, Campus UAB Bellaterra, 08193 Barcelona, 31 Spain. rR 32 b 33 Biomedal S.L., Polígono Industrial Parque Plata, c/ Calzada Romana 40, 41900 Camas, Seville, 34 Spain. 35 ev c Department of Microbiology and Parasitology, Faculty of Pharmacy, University of Seville, 36 c/Profesor García González, S/N, 41012 Seville, Spain 37 38 iew 39 *Corresponding author: M.-Carmen Estevez: mcarmen.estevez@icn2.cat 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 25 of 31 Analytical & Bioanalytical Chemistry 1 2 3 4 1. Chemicals and immunoreagents 5 6 Acetone, ethanol absolute and isopropanol were purchased from Panreac-Applichem (Barcelona, 7 Spain). Alkanethiols: HS-C11-(EG)6-O-CH2-COOH (MW: 526.73 g mol-1) and HS-C11-(EG)4-OH 8 (MW: 380.48 g mol-1) were acquired from Prochimia (Poland). Bovine serum albumin (BSA), 1- 9 ethyl-3(3-dimethylaminopropyl)carbodiimide hydrochloride (EDC), N-hydroxysulfosuccinimide 10 11 (NHS), ethanolamine hydrochloride, NaOH, centrifugal filters Amicon Ultra 10 kDa (0.5 mL Ultracel 12 membrane of regenerated cellulose 10000 MWCO Millipore) and all reagents for buffers preparation 13 were purchased from Sigma-Aldrich (Steinhem, Germany). Buffers employed were the following: 14 MES (2-(-N-morpholino)ethanesulfonic acid 100 mM and 500 mM NaCl, pH 5.5), acetate buffer (10 15 mM pH from 4.0 to 5.5), PBS (1 mM phosphate, 137 mM NaCl, 2.7 mM KCl, pH 7.4), PBST (PBS 16 17 with 0.5 % Tween 20), and PBST-BSA (PBST with BSA 10 mg mL-1). Monoclonal antibodies G12 18 and A1, prolamin working group (PWG) gliadin and -gliadin 33-mer peptide (33-mer) were 19 provided by Biomedal S.L. (Seville, Spain). 20 21 Fo 22 2. SPR biosensor device and operation principle 23 For the SPR set-up, optical components are attached to a triangular platform with a fix optimal 24 incidence angle of 70 º. Gold sensing chip is clamped between the fluid cell (4 µL) and a trapezoidal rP 25 prism. A collimated halogen light (HL-2000, Ocean Optics, USA) hits the sensing area, set in 26 transverse-magnetic (TM) polarization mode. The reflected light is collected by a fiber-coupled to a 27 28 CCD spectrometer (Ocean Optics, Jazz, Module, USA). A syringe pump connected to the cell ee 29 continuously delivers fluid over the sensor chip. Samples are added through a loop (200 µL) 30 connected to a 6-port injection valve. A homemade readout software (National Instruments, Labview, 31 USA) is used for real-time monitoring of the shift of the plasmonic resonance peak at λSPR (675 nm) rR 32 via polynomial fit (see Fig. S1A in SI). Reflectivity spectra are acquired every 3 ms and 300 spectra 33 34 are averaged to generate the resonant spectrum. Chemical interactions over the sensing area cause a 35 RI increase, which results in a redshift of the position of the resonance peak (ΔλSPR), whereas the ev 36 desorption events result in blueshift. These displacements can be monitored and related to the 37 concentration of the analyte to be evaluated (see Fig S1B in SI). Prior to surface functionalization, 38 iew sensor chips were cleaned by successive heating and sonication cycles for 1 min with acetone and 39 40 isopropanol. Then, they were dried with N2 stream and placed in a UV-O3 cleaner (BioForce 41 Nanoscience, USA) for 30 min. Finally, chips were rinsed with ethanol and dried with N2 stream. 42 43 3. BSA blocking step. Optimization and regeneration conditions of the sensor chip 44 45 In the case of evaluating urine, it was necessary to perform a previous blocking step with BSA in 46 PBST to eliminate non-specific adsorptions from the urine matrix. The injection time of BSA in 47 PBST was evaluated at a flow rate of 25 μL min-1 during 0, 30, 60, 120, 180, 240 and 300 s. Fig. S6A 48 in SI, shows signals of urine-PBST 1:1 and urine 100 % for each injection time. In all cases, signal 49 obtained after adding BSA is very similar (red line). Applying blocking buffer during 60 s, non- 50 51 specific adsorptions for urine-PBST 1:1 are removed completely. In case of urine 100 %, it was 52 necessary an injection time of 120 s. These injection times were therefore selected depending on the 53 media employed to evaluate the calibration curve (urine 50 % or 100 %). The blocking capability of 54 this approach was studied by successive BSA-urine 100 % regeneration cycles (i.e. with neither 55 antibody nor analyte) over the coated-PWG gliadin sensor chip. Fig. S6B in SI shows signals of urine 56 57 100 %. In each injection, signal increases due to the bulk effects from components of urine and decays 58 to the baseline signal (i.e. no net adsorption on the sensor surface). This is observed for at least 38 59 cycles without detecting significant damage or non-efficient blocking of the sensor surface. The same 60 experiment was performed but adding the antibody to the urine 100 %. Fig. S6C and D in SI show the Analytical & Bioanalytical Chemistry Page 26 of 31 1 2 3 signals obtained from antibodies G12 (2 µg mL-1) and A1 (5 µg mL-1) in urine 100 %, respectively. 4 Signals are high reproducible and stable until the cycle 17 for G12 and cycle 19 for A1, respectively, 5 6 when they start decreasing. This might be related to a loss of effectiveness of the blocking step or the 7 degradation of the immobilized layer due to the successive addition of proteins, complex media and 8 basic conditions. 9 10 11 12 13 14 15 16 17 18 19 20 21 Fo 22 23 24 Figure S1. (A) Spectra showing wavelength displacement of the resonance peak (Δλ SPR) to refractive rP 25 index changes (n2>n1). (B) Real-time monitoring of wavelength displacements (Δλ (nm) vs. time (s)). 26 27 28 ee 29 30 31 rR 32 33 34 35 ev 36 37 38 iew 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 27 of 31 Analytical & Bioanalytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Fo 22 23 24 rP 25 26 27 28 ee 29 30 31 rR 32 Figure S2. (A) Optimization of PWG gliadin (50 µg mL-1) immobilization with EG-COOH:EG-OH 33 thiols at different ratios (3:7, 1:1, 7:3, 1:0) and at different pH conditions (from 4.0 to 7.4) (B) Real- 34 time sensorgram showing in-situ immobilization steps in a SAM ratio 1:0 (carboxylic groups 35 ev activation, PWG gliadin immobilization (50 µg mL-1) in acetate buffer pH 5.0 and blocking of 36 37 remaining activated groups. (C) Comparison of the antibodies signals of G12 (2 µg mL-1) and A1 (5 38 µg mL-1) for in-situ and ex-situ immobilization process, respectively. iew 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 Figure S3. Sensorgrams showing the ΔλSPR for throughout successive measurement (in PBST) and 58 regeneration cycles (in NaOH 5 mM) of (A) G12 mAb (2 µg mL-1); (B) A1 mAb (5 µg mL-1). 59 60 Analytical & Bioanalytical Chemistry Page 28 of 31 1 2 3 4 5 100 6   N o r m a liz e d s ig n a l 7 8 80 9 10 60 11 12 40 13 14 20 G 12 m A b 15 A 1 m A b 16 0 17 10 -2 10 -1 10 0 10 1 10 2 10 3 10 4 18 -1 19 G lia d in 3 3 m e r P e p tid e (n g m L ) 20 21 Figure S4. Calibration curves of intra-assays of −gliadin 33-mer peptide detection by indirect Fo 22 competitive immunoassay in PBST using the antibodies G12 mAb (red curve) and A1 mAb (blue 23 curve). Each data represents the mean ± SD of two replicates. (Intra-assays are two complete assays 24 with the same biofunctionalized chip). rP 25 26 27 28 ee 29 30 31 rR 32 33 34 35 ev 36 37 38 iew 39 40 41 42 43 44 Figure S5. Real-time sensorgram of different −gliadin 33-mer peptide concentrations in PBST 45 buffer using: (A) G12 mAb and (B) A1 mAb. 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 29 of 31 Analytical & Bioanalytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 Fo 22 23 24 rP 25 26 27 28 ee 29 30 31 rR 32 Figure S6. (A) Effect of the blocking time with BSA (10 mg mL-1 in PBST) on the removal of non- 33 specific adsorptions from urine 100 % and urine-PBST 1:1. Corresponding signal resultant from the 34 adsorption of the BSA blocking layer is shown for each time of injection (B) Δλ signals obtained 35 ev 36 throughout successive cycles of blocking (BSA 10 mg mL-1), urine (100 %) and regeneration (NaOH 37 5 mM). Neither antibody nor target analytes are present in the solutions. (C) Δλ signals obtained 38 throughout successive cycles of blocking (BSA 10 mg mL-1), urine (100 %) containing G12 mAb (2 iew 39 µg mL-1)) and regeneration (NaOH 5 mM); (D) Δλ signals obtained throughout successive cycles of 40 41 blocking (BSA 10 mg mL-1), urine (100 % containing A1 mAb (5 µg mL-1)) and regeneration (NaOH 42 5 mM). 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 Figure S7. Real-time sensorgrams for different −gliadin 33-mer peptide concentrations prepared in 59 60 urine 100 % using: (A) G12 mAb and (B) A1 mAb. Analytical & Bioanalytical Chemistry Page 30 of 31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Figure S8. Stability of chips over time. Real time sensorgrams of urine samples spiked with 33-mer 20 peptide obtained in sensor chips stored different periods of time using (A) [G12] = 2 µg mL-1 and [33- 21 mer] = 15.6 ng mL-1 ; (B) [A1]= 5 µg mL-1 and [33-mer] = 125 ng mL-1. A representative sensorgram Fo 22 for the maximum signal (i.e. only antibody, zero analyte concentration) in urine 100 % is shown for 23 24 each antibody (purple line). rP 25 26 27 28 ee 29 30 31 rR 32 33 34 35 ev 36 37 38 iew 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 31 of 31 Analytical & Bioanalytical Chemistry 1 2 3 Table S1. Intra-assay and inter-assay variability for the G12 and A1-based indirect competitive 4 immunoassays for the detection of 33-mer peptide in PBST 5 6 7 Intra-assay a Inter-assay b Antibodies Parameter 8 Mean ± SD CV (%) Mean ± SD CV (%) 9 Δλmax (nm) 1.19 ± 0.08 6.72 1.17 ± 0.07 5.98 10 LOD (IC90) (ng mL-1) 2.13 ± 0.10 4.69 2.43 ± 0.81 33.3 11 IC50 (ng mL-1) 12.6 ± 2.98 23.7 13.4 ± 3.21 24.0 12 G12 mAb LOQ (IC80) (ng mL-1) 4.42 ± 0.81 18.3 4.55 ± 1.08 23.7 13 IC20 (ng mL-1) 33.3 ± 5.88 17.7 37.0 ± 8.10 21.9 14 Slope -1.43 ± 0.04 2.80 -1.25 ± 0.16 12.8 15 Δλmax (nm) 0.94 ± 0.07 7.45 0.98 ± 0.12 12.2 16 17 LOD (IC90) (ng mL-1) 21.8 ± 12.4 57.0 34.2 ± 17.3 50.7 18 IC50 (ng mL-1) 219.7 ± 70.4 32.1 219.7 ± 34.9 15.9 A1 mAb 19 LOQ (IC80) (ng mL-1) 53.4 ± 15.2 28.5 55.8 ± 22.9 41.1 20 IC20 (ng mL-1) 777.3 ± 231 29.7 727.3 ± 122 16.8 21 Slope -1.01 ± 0.07 6.93 -1.04 ± 0.37 35.6 Fo 22 a 2 complete curves obtained with the same biofunctionalized chip 23 b 3 complete curves obtained with 3 different biofunctionalized chips 24 rP 25 26 27 28 Table S2. Inter-assaya variability for the G12 and A1-based indirect competitive immunoassays in ee 29 100 % urine for the detection of 33-mer peptide and GIP 30 31 rR G12 mAb A1 mAb 32 Target Sample Parameter 33 Mean ± SD CV (%) Mean ± SD CV (%) 34 Δλmax (nm) 1.26 ± 0.06 4.76 0.85 ± 0.01 1.18 35 ev LOD (ng mL-1) 1.65 ± 0.03 1.82 4.70 ± 1.36 28.9 36 Urine IC50 (ng mL-1) 15.2 ± 2.21 14.5 111.8 ± 12.1 10.8 37 33-mer 100 % LOQ (ng mL-1) 3.62 ± 0.22 6.08 14.7 ± 3.20 21.8 38 iew 39 IC20 (ng mL-1) 56.2 ± 13.3 23.7 702.3 ± 110 15.7 40 Slope -0.87 ± 0.07 8.05 -0.62 ± 0.07 11.3 41 Δλmax (nm) 1.18 ± 0.01 0.85 0.93 ± 0.04 4.30 42 43 LOD (ng mL-1) 1.67 ± 0.09 5.39 4.02 ± 1.15 28.6 44 Urine IC50 (ng mL-1) 11.5 ± 0.61 5.30 41.2 ± 4.74 11.5 GIP 45 100 % LOQ (ng mL-1) 3.44 ± 0.03 0.87 9.15 ± 1.23 13.4 46 IC20 (ng mL-1) 35.4 ± 3.00 8.47 172 ± 43.3 25.2 47 48 Slope -1.11 ± 0.06 5.40 -0.86 ± 0.17 19.8 a 49 3 complete curves obtained with 3 different biofunctionalized chips 50 51 52 53 54 55 56 57 58 59 60