Competitive Immunoassays for the Detection of Small Molecules

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Competitive Immunoassays for the Detection of Small Molecules Using Single Molecule Arrays Xu Wang, Limor Cohen, Jun Wang, and David R. Walt J. Am. Chem. Soc., Just Accepted Manuscript • DOI: 10.1021/jacs.8b11185 • Publication Date (Web): 29 Nov 2018 Downloaded from http://pubs.acs.org on November 30, 2018

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Journal of the American Chemical Society

Competitive Immunoassays for the Detection of Small Molecules Using Single Molecule Arrays Xu Wang1,2, Limor Cohen1,2,3, Jun Wang4 and David R. Walt1,2* 1. Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA. 2. Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA. 3. Department of Chemical Biology, Harvard University, Boston, MA 02115, USA. 4. School of Physical and Mathematical Sciences, Nanjing Tech University, Nanjing, Jiangsu 211816, China. Abstract: Small molecule detection is important for many applications including clinical diagnostics, drug discovery, and measurements of environmental samples and agricultural products. Current techniques for small molecule detection suffer from various limitations including low analytical sensitivity and complex sample processing. Furthermore, due to their small size, small molecules are difficult to detect using an antibody pair in a traditional sandwich assay format. To overcome these limitations, we developed an ultra-sensitive competitive immunoassay for small molecule detection using Single Molecule Arrays (Simoa). We show that the competitive Simoa assay is approximately 50-fold more sensitive than the conventional ELISA. We performed theoretical calculations to determine the factors that influence the sensitivity of competitive Simoa assays and used them to achieve maximal sensitivity. We also demonstrate detection of small molecules in complex biological samples. We show that the competitive Simoa assay is a simple, fast, and highly sensitive approach for ultra-sensitive detection of small molecules. 1. Introduction The detection of small molecules, such as peptides, drugs, biogenic amines, small-molecule hormones, antibiotics, organic pollutants, food additives, pesticide and veterinary drug residues, and mycotoxins, is important for many applications including physiological function research, clinical diagnostics, drug discovery, and environmental and food analysis.1-5 The most commonly used approaches for small molecule detection include spectroscopic and chromatographic methods and immunoassays.6 Although spectroscopic and chromatographic approaches are widely used for small molecule detection, sample cleanup such as solid phase extraction or immunoaffinity purification is usually required. Sample pretreatment methods are often tedious, expensive and time-consuming, which limits the utility of these methods.7 Furthermore, the sensitivities of these methods are often not adequate to make the requisite measurements. Immunoassays are attractive alternatives to spectroscopic and chromatographic techniques due to their simplicity, specificity, and ability to obtain highly quantitative measurements.8-11 As a result, many commercial enzyme-linked immunosorbent assay (ELISA) kits have been developed for detecting various analytes such as hormones, mycotoxins, antibiotics, and pesticide and veterinary drug residues.12-14 However, in many cases, the sensitivity of conventional ELISAs is not sufficient to detect low levels of analytes. Thus, various approaches for small molecule detection have been developed to improve the sensitivity, 1

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such as polymerase chain reaction (PCR)-based methods including immuno-PCR and proximity ligation assays.15-18 However, PCR-based assays are often complex, expensive, and laborintensive.19 Other approaches such as open-sandwich immunoassays have also been developed for detecting small molecules.20-22 The assay exploits reassociation of the antibody variable region fragments VH-VL by a bridging antigen, in which the target small molecule is detected in a noncompetitive format. However, the preparation of antibody VH and VL fragments is usually time-consuming and complicated.23 To overcome limitations in analytical sensitivity, we developed an ultra-sensitive approach known as digital ELISA using Single Molecule Arrays (Simoa).24-25 In Simoa, target protein molecules are first captured on antibody-modified paramagnetic beads. A large excess of beads are used relative to the number of target protein molecules to ensure that there is either zero or one target protein molecule bound per bead following Poisson statistics. A second biotinylated detection antibody binds to the captured target protein molecule, and the immune complex is then labeled with streptavidin-β-galactosidase (SβG). The enzyme-labeled beads are resuspended in a fluorogenic substrate solution, resorufin-β-D-galactopyranoside (RGP), and loaded onto an array of microwells (50 fL) in which each well is able to hold only one bead. The wells are sealed with oil and the fluorescent product generated by the enzymatic reaction is confined within the microwells, ensuring high local fluorescence intensity that can be easily detected by a charge coupled device (CCD) camera.26-27 The Simoa technique has also been previously utilized for ultra-sensitive detection of DNA, and microRNAs, in which DNA probes instead of antibodies were used to capture the target molecules.28-30 While Simoa has been used for detecting large molecules, such as proteins and nucleic acids, it is challenging to convert these methods to the detection of small molecules since they are too small to bind to the two different binding agents required for a sandwich-type assay. In this paper, we describe a novel ultra-sensitive competitive Simoa assay for small molecule detection. We selected two important small molecules, cortisol and prostaglandin E2 (PGE2), as model targets. We demonstrate that the analytical sensitivity of these Simoa assays is significantly higher than that of the conventional ELISA. We also performed theoretical calculations to understand the parameters that affect the sensitivity of the competitive Simoa assay. Based on these theoretical calculations, we optimized the competitive Simoa assays to achieve maximal sensitivity. To demonstrate the utility of the competitive Simoa assays, we detected cortisol and PGE2 in biological samples. To the best of our knowledge, this is the first example of a small molecule immunoassay using single molecule detection. The competitive Simoa assay is a simple, fast, and highly sensitive approach for ultra-sensitive detection of small molecules. 2. Experimental Section 2.1 Materials Cortisol solution, hydrocortisone 3-(O-carboxymethyl)oxime (cortisol-3-CMO), β-galactosidase (G5635), anti-mouse IgG (Fc specific) antibody (M4280), 2-(N-morpholino)ethanesulfonic acid (MES), 3,3',5,5'-Tetramethylbenzidine (TMB), streptavidin-horseradish peroxidase (HRP) Amicon Ultra 0.5 mL centrifugal filters, Tween 20, and bovine serum albumin (A7030) were purchased from Sigma Aldrich (St. Louis, MO). 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide hydrochloride (EDC), N-hydroxysulfosuccinimide (sulfo-NHS), NHS-PEG4-Biotin, and anticortisol monoclonal antibody (clone: F4P1A3) were purchased from Thermo Fisher (Waltham, MA). Cortisol-BSA conjugate was purchased from Fitzgerald Industries International (Acton, 2


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MA). Prostaglandin E2 (PGE2) was purchased from Enzo Life Sciences (Farmingdale, NY). Anti-PGE2 monoclonal antibody (414013) was purchased from Cayman Chemical (Ann Arbor, MI). Anti-cortisol monoclonal antibody (clone: XM210) was purchased from GeneTex (Irvine, CA). Detection antibodies were biotinylated using NHS-PEG4-Biotin according to a previously reported method.31 The Simoa HD-1 analyzer and Homebrew assay kits were purchased from Quanterix Corporation (Lexington, MA).32 Homebrew kits include carboxyl-functionalized paramagnetic beads, 159 nM streptavidin-β-galactosidase (SβG) concentrate, 100 μM resorufin β-D-galactopyranoside (RGP), and diluents (Bead Diluent, Sample Diluent, and SβG Diluent). 2.2 Preparation of analyte-β-galactosidase conjugates Cortisol-3-CMO and PGE2 were each dissolved in methanol to a final concentration of 1 mg/mL. EDC and sulfo-NHS were reconstituted in MES buffer (50 mM, pH 6.2) to a final concentration of 10 mg/mL. 25 μL of each of the EDC and sulfo-NHS were added to 50 μL of analyte solution. The mixture was vortexed and incubated at room temperature for 1 h. Cortisol-3-CMO-NHS ester or PGE2-NHS ester was added to 100 μL of 2 mg/mL β-galactosidase in 1× phosphate buffered saline (PBS) with a final molar ratio of 50:1. The solution was mixed and incubated at room temperature for 30 min. Excess NHS esters were removed by using an Amicon filter with a cutoff value of 100 kDa. The concentrations of analyte-β-galactosidase conjugates (E1% [280 nm] = 20.93) were measured on a NanoDrop 2000 Spectrophotometer (Thermo Fisher). Analyte-βgalactosidase conjugates were stored in SβG Diluent at 4 oC. 2.3 Preparation of capture beads Carboxylated 2.7 μm paramagnetic beads (~4×108) were washed three times with 200 μL of bead wash buffer (0.1% Tween 20 in 1×PBS, pH 7.4) and twice with 200 μL of MES buffer (50 mM MES, pH 6.2). 1 mg/mL EDC in MES buffer was freshly prepared and 200 μL was added to the beads and mixed well. The beads were activated on a shaker for 30 min. After activation, the beads were washed once with 200 μL of MES buffer. 200 μL of 10 mg/mL BSA or BSA-cortisol conjugate in MES buffer were added to the activated beads. The beads were incubated at room temperature with shaking for 2 h, and then washed three times with bead wash buffer. The BSAcortisol conjugate coated beads were stored in 200 μL of bead storage buffer (50 mM Tris-HCl with 1% BSA, 1% Triton 100 and 0.15% ProClin 300, pH 7.8) at 4 °C for further use. The BSA coated beads were resuspended in 200 μL of 1×PBS, and then 5 μL of 2.8 mM PGE2NHS ester was added. The beads were vortexed, incubated with shaking for 30 min, and then washed five times with bead wash buffer to remove unreacted PGE2-NHS ester. The BSA-PGE2 coated beads were resuspended in 200 μL of bead storage buffer and stored at 4 oC. In addition, anti-mouse IgG capture beads were prepared according to a previously published method.33 The beads were counted using a Beckman-Coulter multisizer. 2.4 Preparation of reagents and Simoa assay setup A two-step assay configuration was chosen when analyte-BSA-coated beads were used. Specifically, analyte-BSA-coated capture beads were diluted in Bead Diluent (Quanterix) to a concentration of 5,000 beads/μL. Biotinylated detector antibodies were diluted in Detector Diluent (Quanterix) to the desired concentrations (5 pM for anti-cortisol antibodies, and 5 nM for anti-PGE2 antibodies). SβG concentrate was diluted to 200 pM in SβG Diluent (Quanterix). Cortisol and PGE2 standards were serially diluted to desired concentrations in Sample Diluent. The reagents including beads, detector, and SβG were placed in plastic bottles (Quanterix). The samples were loaded onto a 96-well plate (Quanterix). All reagents (capture beads, detector antibodies, SβG, enzyme substrate RGP, Wash Buffer 1, Wash Buffer 2, and Simoa Sealing Oil) 3



were purchased from Quanterix and loaded onto the Simoa HD-1 Analyzer (Quanterix) based on the manufacturer's instructions. 100 μL of bead solution was pipetted into a reaction cuvette. The beads were pelleted with a magnet and the supernatant was removed. Then 100 μL of sample and 25 μL of detector antibody were added and incubated for 30 min. The beads were then pelleted again and the supernatant was removed. Following a series of washes, 100 μL of SβG was added and incubated for 5 min. The beads were washed, resuspended in RGP solution, and loaded onto the array. The array was then sealed with oil and imaged. Images of the arrays were analyzed and AEB (average enzyme per bead) values were calculated by the software in the HD-1 Analyzer. A one-step assay configuration was chosen when analyte-β-galactosidase was used as the competitor. The concentration of anti-mouse beads was 5,000 beads/μL. Unlabeled detector antibodies were diluted in Detector Diluent to the desired concentration (6 pM for anti-cortisol antibodies, and 6 nM for anti-PGE2 antibodies). Analyte-β-galactosidase concentrate was diluted to 1.2 nM in SβG Diluent. 100 μL of bead solution was pipetted into a reaction cuvette. The beads were pelleted with a magnet and the supernatant was removed. Then 100 μL of sample, 25 μL of detector antibody, and 25 μL of analyte-β-galactosidase were added and incubated for 30 min. All the other steps were the same as that of the two-step assay. 2.5 ELISA for cortisol The high-binding 96-well microtiter plate was coated with BSA-cortisol conjugate in PBS (1 μg/mL, 100 μL/well) overnight at 4 °C. After washing, the plate was blocked with 1 % BSA in PBS (200 μL/well) for 1 h at room temperature and subsequently washed. Then biotinylated anticortisol (10 nM, 10 μL/well) in PBS and serial concentrations of cortisol standard (0, 0.001, 0.01, 0.1, 1, 10, 100 and 1,000 ng/mL,100 μL/well) prepared in Sample Diluent were added to the wells. The immunoreaction was allowed to proceed for 1 h. After washing, the plate was incubated with Streptavidin-HRP (2 nM, 100 μL/well) for 30 min. After another washing, TMB substrate (100 μL/well) was added and the plate was incubated for 15 min. Then 50 μL of 1 mol/L hydrochloric acid was added to each well to stop the enzymatic reaction. Finally, the optical density (OD) was recorded at 450 nm on a Tecan Infinite M200 Plate Reader. 2.6 Immunoassay theory A competitive immunoassay is based on the competition between an analyte (Ag) and a labeled analyte (Ag*) for a limited number of antibody (Ab) binding sites. Typically, the concentrations of Ab and Ag* are kept fixed for various concentrations of the analyte of interest, Ag. The immunoassay is governed by two equilibria: [Ab][Ag ∗ ] Kd∗ = (1) [AbAg ∗ ] [Ab][Ag] Kd = (2) [AbAg] where Kd is the dissociation constant for the reaction of Ab with Ag, [Ab] and [Ag] are the binding site concentration of free antibody and the concentration of antigen, respectively, [AbAg] is the concentration of the immune complex, and the superscript * refers to the labeled antigen. The native and labeled antigens may have different affinity constants for the antibody. Here we assume that the two affinity constants are equal (Kd = Kd*), so that one can derive the following equation: [Ag] [Ag ∗ ] = (3) [AbAg ∗ ] [AbAg] 4


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In addition, according to the law of conservation of mass, [Ag*] = [Ag0*] - [AbAg*] (4) [Ag] = [Ag0] - [AbAg] (5) [Ab0 ] = [Ab] + [AbAg] + [AbAg ∗ ] (6) where [Ag0*], [Ag0] and [Ab0] are the total concentration of competitor, analyte, and detection antibody, respectively. Substituting eq 3 for [Ag*] and [Ag] in eqs 4 and 5 yields [Ag 0 ∗ ] [Ag 0 ] = (7) ∗ [AbAg ] [AbAg] Substituting eq 6 for [AbAg] in eq 7 yields [AbAg ∗ ][Ag 0 ] [Ab] = [Ab0 ] − [AbAg ∗ ] − (8) [Ag 0 ∗ ] According to eqs 1 and 4, Kd∗ [AbAg ∗ ] Kd[AbAg ∗ ] [Ab] = = (9) [Ag ∗ ] [Ag 0 ∗ ] − [AbAg ∗ ] and the two eqs 8 and 9 can be combined as [AbAg ∗ ][Ag 0 ] Kd[AbAg ∗ ] ∗ [Ab0 ] − [AbAg ] − = (10) [Ag 0 ∗ ] [Ag 0 ∗ ] − [AbAg ∗ ] Solving eq 10 for [Ag0] yields [Ab0 ] − [AbAg ∗ ] Kd [Ag 0 ] = [Ag 0 ∗ ] 7 − 8 (11) ∗ ∗ [AbAg ] [Ag 0 ] − [AbAg ∗ ] When the concentration of analyte is zero (i.e. [Ag0] = 0), we assume [AbAg*] = S, yielding eq 12. [Ab0 ] − S Kd = (12) S [Ag 0 ∗ ] − S The response, S, can be easily experimentally determined by measuring the signal in the absence of analyte. Based on the definition of IC50, which is the concentration of analyte that causes a 50% inhibition of the maximum response (i.e. 0.5S), the following relationship can be derived: [Ab0 ] − 0.5S Kd 2[Ab0 ] Kd IC50 = [Ag 0 ∗ ] 7 − 8 = [Ag 0 ∗ ] 7 −1− 8 ∗ ∗ 0.5S a [Ag 0 ] − 0.5S [Ag 0 ] − 0.5S 2Kd Kd = [Ag 0 ∗ ] 7 +1− 8 ∗ ∗ [Ag 0 ] − S [Ag 0 ] − 0.5S [Ag 0 ∗ ] [Ag 0 ∗ ] ∗ = [Ag 0 ] + Kd 7 8 (13) [Ag 0 ∗ ] − S [Ag 0 ∗ ] − 0.5S ≥ [Ag 0 ∗ ] + Kd (14) 2.7 Saliva and serum samples Pooled normal human saliva samples were purchased from Innovative Research, Inc (Novi, MI). Saliva supernatant was separated by centrifugation at 13,000g for 20 min and analyzed immediately or stored at −80 °C. Saliva samples were diluted 20, 50 and 100-fold in Quanterix sample buffer, and then different concentrations of cortisol were spiked into the samples for quantitative analysis. Human serum samples from healthy adults were purchased from BioIVT. All subjects provided informed consent and studies were approved by the respective Institutional Review Boards. Serum samples were aliquoted and stored at −80 °C. Samples did not undergo more than one freeze-thaw cycle. Serum samples were diluted 12-fold in Quanterix sample 5



buffer, and then various concentrations of PGE2 were spiked into the samples for quantitative analysis. 2.8 Data analysis Biological samples along with calibration curves were measured using the Simoa HD-1 Analyzer. Standard curves were obtained by plotting the signal responses (AEB and OD) against the logarithm of analyte concentrations using Origin software (Origin 9.5). The 4-parameter logistic equation y = A2 + (A1-A2)/[1 + (x/x0)p] was used for curve fitting in the whole concentration range, where A1 is the maximum signal without analyte, A2 is the minimum signal at infinite concentration, p is the curve slope at the inflection point, and x0 is the IC50 (analyte concentration causing a 50% inhibition of the maximum response). The lower the IC50, the higher the sensitivity. All measurements were performed in triplicate. 3. Results and discussion 3.1 Competitive Simoa assays We developed ultra-sensitive competitive Simoa assays for small molecule detection using two different assay formats (Fig. 1). The difference between the two assay formats is the nature of the competitor. In assay format 1, analyte-modified magnetic beads (MBs) were used as the competitor. Specifically, biofunctionalized MBs and free target molecules in the sample compete for binding to biotin-labeled antibodies. After incubation and magnetic separation, the supernatant containing unbound antibodies was removed. The MBs were then labeled with an enzyme, SβG, via biotin-streptavidin interaction and detected by fluorescent readout on the Simoa platform. The signal was measured in units of average enzyme per bead (AEB), as previously described.25 Because of competition, an increase in the number of target molecules leads to a decrease in the number of biotinylated antibody molecules that bind to the MBs, resulting in lower signal. Thus, the signal (AEB) is inversely proportional to the analyte concentration. In assay format 2, analyte-labeled β-galactosidase was utilized as the competitor. Analytelabeled enzyme was prepared through a reaction between activated carboxyl groups on the analyte and lysine amino groups on β-galactosidase (Fig. S2). As shown in Fig. 1B, labeled enzymes compete with free target molecules in binding to the detection antibodies. The Fc region of the detection antibody was then specifically captured by anti-mouse IgG antibodymodified MBs. Because of competition, an increase in the number of target molecules leads to a decrease in the number of enzyme molecules that bind to the MBs, resulting in lower signal. In this assay format, instead of conjugating the detection antibodies directly to the MBs, we conjugated anti-mouse IgG antibodies, which capture the Fc region of the detection antibodies, to the MBs. Using this format, the detection antibody concentration can be easily controlled and a lower detection antibody concentration can be used. If the detection antibody was directly conjugated to the MBs, the concentration would be more difficult to control and be substantially higher, which may compromise the assay sensitivity.

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Fig. 1 Schematic illustration of competitive Simoa assays using analyte-modified MBs (A) and analyte-labeled β-galactosidase as the competitor (B).

Cortisol was selected as a model analyte since it is an important small molecule that is widely used as a biomarker in clinical diagnostics, particularly for the assessment of adrenal, pituitary and hypothalamic function.34-35 Cortisol may be undetectable in some biological samples when measured by conventional methods such as high-performance liquid chromatography (HPLC) and chemiluminescent immunoassay.36 Thus, ultra-sensitive methods are highly desirable. For the detection of cortisol, either cortisol-modified MBs or cortisol-labeled β-galactosidase was used as the competitor, and monoclonal anti-cortisol antibody F4P1A3 was used as the detection probe. Fig. 2 shows the calibration curves for the detection of cortisol using the two different assay formats. The shape of the response curves for the two assay formats was similar. With increasing cortisol concentration, the signal (AEB) decreased proportionally because of competition. The response curve was slightly right-shifted when cortisol-labeled enzyme was used as the competitor. The half maximal inhibitory concentration (IC50), a parameter that is used to evaluate the sensitivity of a competitive immunoassay, was 3.20 ng/mL for assay format 1 and 4.07 ng/mL for assay format 2, respectively. Cortisol concentrations between 0.01 and 1000 ng/mL can be detected using Simoa. This broad dynamic range, which spans five orders of magnitude, is highly advantageous for cortisol detection in biological samples.

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To evaluate the ability of the competitive Simoa assays to detect other small molecules, a competitive assay for PGE2 was also developed. PGE2 is an important prostaglandin and its activity influences inflammation, fertility and parturition, gastric mucosal integrity, and immune modulation.37-38 PGE2 is present at ultra-low levels in many samples such as urine and plasma.39 Additionally, PGE2 is known to have a relatively short half-life in vivo.40 Thus, it is essential to develop fast and sensitive methods for quantifying PGE2. Either PGE2-modified beads or PGE2labeled β-galactosidase was used as the competitor. Fig. S3 shows the response curves for the detection of PGE2 using the two different Simoa assay formats. The signal decreased with increasing PGE2 concentration. IC50s were 0.202 ng/mL for assay format 1 and 0.153 ng/mL for assay format 2. The competitive Simoa assays are relatively fast, with a total assay time of ~1 h. These results indicate that the competitive Simoa assay is a general method that could be used to detect other small molecules such as peptides, biogenic amines, antibiotics, mycotoxins, veterinary drugs, pesticides, and organic pollutants. 3.2 Comparison with conventional ELISA Competitive ELISAs have been widely used for detection of various small molecules. We compared the analytical performance of competitive Simoa assays with conventional ELISA for the detection of cortisol. Specifically, BSA-cortisol conjugates were immobilized on a microtiter plate for the ELISA. Cortisol molecules in the sample and immobilized BSA-cortisol conjugates competitively bound to biotinylated detection antibodies. After washing, streptavidin-labeled HRP was added along with a chromogenic substrate for signal generation. Calibration curves for the ELISA and Simoa (assay format 1) assays are shown in Fig. 2. The shape of the response curve for the ELISA was similar to that of the Simoa assay. With increasing cortisol concentration, the optical density (OD) for ELISA decreased proportionally. As expected, the response curve for the ELISA was right-shifted to higher concentrations compared with the Simoa assay. The IC50 for the conventional ELISA was 170.23 ng/mL, which was approximately 50-fold higher than the IC50 of the Simoa assay (3.20 ng/mL). This result demonstrates that a competitive Simoa assay is much more sensitive than a conventional ELISA.

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3.3 Evaluation of antibody affinity on immunoassay performance Antibody affinity can have a major impact on immunoassay performance. To test the effect of Kd on Simoa performance, we selected two monoclonal anti-cortisol antibodies with dissociation constants of 0.59 nM (XM210) and 1.0 nM (F4P1A3). Fig. 4 shows the response curves of the Simoa assays. When using the Simoa assay (format 1), the IC50 values were 0.42 ng/mL for the higher affinity antibody and 3.20 ng/mL for the lower affinity antibody. In addition, IC50s were 1.06 ng/mL for XM210 and 4.07 ng/mL for F4P1A3 when cortisol-enzyme was utilized as the competitor (Fig. 4B). When using a conventional ELISA, the IC50 values were 18.60 ng/mL for the higher affinity antibody and 170.23 ng/mL for the lower affinity antibody (Table 1). These results demonstrate that the sensitivity of competitive immunoassays is highly dependent on the affinity of the detection antibody. Additionally, Simoa consistently improves the analytical sensitivity over ELISA, even when lower affinity antibodies are used.

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Fig. 4 Response curves of competitive Simoa assays for the detection of cortisol using two different antibodies. (A) assay format 1 and (B) assay format 2.

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Table 1: Analytical performance of different assays for cortisol. Antibody

Kd (nM) 0.59 1.0

XM210 F4P1A3

ELISA IC50 (ng/mL) 18.60 170.23

Simoa assay format 1 IC50 (ng/mL) 0.42 3.20

Simoa assay format 2 IC50 (ng/mL) 1.06 4.07

3.4 Evaluation of competitor concentration on immunoassay performance In a competitive immunoassay, an analyte and a labeled antigen compete for a limited number of antibody binding sites. Therefore, the competitor concentration may affect the performance of the immunoassay. As shown in Fig. 5A, different concentrations of cortisol-modified MBs, which are used as the competitor, can affect Simoa assay performance. With increasing cortisol concentrations, as expected, the signal decreased proportionally due to competition. However, when the cortisol concentration remained fixed and the number of MBs was varied, we observed that the signal increased as the number of MBs decreased, corresponding to a lower competitor concentration. This result is due to more antibody molecules binding to each bead, resulting in higher AEB values. Additionally, the IC50 of the assays increased with increasing competitor concentration. As shown in Fig. 5B, a linear dependence exists between IC50 and competitor concentration. The regression equation could be fitted to y = 0.031 × C[beads] + 0.2899 (ng/mL, R2 = 0.983, n = 3). We also tested the effect of competitor concentration when cortisol-labeled β-galactosidase was used as the competitor (assay format 2). As shown in Fig. 5C, similar inverted S-curves were obtained for cortisol detection. A higher AEB value was achieved when a higher concentration of competitor was used. The IC50 of the assays increased linearly with increasing competitor concentrations (Fig. 5D, y = 0.00186 × C[cortisol-enzyme] + 0.4380, R2 = 0.992, n = 3). These results demonstrate that the sensitivity of a Simoa competitive immunoassay is also highly dependent on the competitor concentration, as with conventional competition immunoassays. 2.0

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Fig. 5 Response curves of competitive Simoa assays for cortisol detection using different competitor concentrations: (A) assay format 1 and (C) assay format 2. (B) and (D) The relationships between IC50 and competitor concentration.

3.5 Theoretical considerations We performed theoretical calculations to investigate the factors that influence the sensitivity of a competitive assay. The theoretical calculations were derived from simple equations based on bimolecular interactions described in the Experimental section. As shown in eq 13, IC50 is determined by a multi-variable equation that depends on [Ag0*], S, and Kd. Eq 13 can be easily simplified into eq 14, which indicates that the IC50 of a competitive assay is always higher than the dissociation constant. When the competitor concentration is far below the Kd, the IC50 approaches its theoretical limit (Kd). Thus, the IC50 value of a competitive assay has a theoretical limit that cannot be further reduced for a given detection antibody. Our experimental observations are in accordance with these theoretical calculations. Particularly, the IC50 for the detection of cortisol was always higher than 0.2 ng/mL, no matter how we optimized the conditions. [Ag 0 ∗ ] [Ag 0 ∗ ] IC50 = [Ag 0 ] + Kd 7 8 ≥ [Ag 0 ∗ ] + Kd (14) [Ag 0 ∗ ] − S [Ag 0 ∗ ] − 0.5S ∗

In the Simoa assay, as low as 1 pM of detection antibody was used (i.e. [Ab0] = 1 pM). The concentration was much lower than that of the labeled antigen [Ag0*]. Because [Ag0*] >> [Ab0] > S, eq 13 can be simplified into the following equation: IC50 ≈ [Ag 0 ∗ ] + Kd (15) Eq 15 explains why the sensitivity of a competitive assay is highly dependent on the affinity of detection antibody (Fig. 4), and why a linear relationship exists between the IC50 and the competitor concentration (Fig. 5). The experimental data are consistent with theoretical considerations. In addition, according to the theoretical prediction, the sensitivity of a competitive assay can be improved by: (1) reducing the competitor concentration; (2) using a detection antibody with high affinity; or (3) reducing the detection antibody concentration. Using the competitive Simoa technique, it is possible to use very low detection antibody and competitor concentrations. Thus, a competitive Simoa assay can achieve higher sensitivities than the conventional ELISA. 3.6 Analysis of biological samples To evaluate the performance of the competitive Simoa assay in complex biological matrices, cortisol standards were spiked into saliva samples and then analyzed. The concentrations were calculated from the calibration curve. Recovery rates were calculated by subtracting the measured spiked concentration from the unspiked concentration of cortisol and then dividing by the spiked concentration. As shown in Table S1, when the saliva samples were diluted 20-fold, recovery rates ranged from 163.8% to 262.9%, which may be due to matrix effects. Since saliva is a complex biological matrix, the presence of interfering substances, such as mucins, can interfere with the binding of the antibody to cortisol, leading to a high recovery rate.41 Because of the high sensitivity of Simoa assays, the samples can be further diluted to reduce the matrix effects. Recovery rates between 101.9-131.3% and 80.5-110.0% were obtained when the samples were diluted 50 and 100-fold, respectively. These rates are in the analytically acceptable range. In addition, we also measured serum samples spiked with PGE2. As shown in Table S2, recovery rates in the analytically acceptable range (88.0%-127.4%) were obtained. These results 11



demonstrate that the competitive Simoa assay could be used for small molecule detection in complex samples. Conclusions We developed an ultra-sensitive competitive Simoa assay for small molecule detection. We show that the sensitivity of the competitive Simoa assay is approximately 50 times higher than that of the conventional ELISA. Such enhanced analytical sensitivity enables measurements of small molecules at concentrations previously unachievable and opens a window into biological phenomena that were previously unachievable. Furthermore, we developed a working theoretical model to predict which factors influence the sensitivity of competitive Simoa assays. The sensitivity of a competitive immunoassay is primarily dependent on the concentration of competitor and detection antibody, as well as the binding affinity of the detection antibody. In addition, the IC50 of a competitive immunoassay is always higher than the dissociation constant (Kd). We show that our experimental results are consistent with theoretical calculations. We also demonstrate detection of cortisol in saliva and PGE2 in human serum samples. Thus, the competitive Simoa assay is a simple, fast, and highly sensitive approach for ultra-sensitive detection of small molecules and can be utilized for the detection of other small molecules in a diverse range of areas, such as environmental monitoring, food safety, and medical diagnostics. Supporting Information Experimental details and detection results. This material is available free of charge via the Internet at http://pubs.acs.org. AUTHOR INFORMATION *Corresponding Author E-mail: [email protected]. Tel: +1-8573071112 Notes The authors declare the following competing financial interest: David R. Walt is a board member and equity holder of Quanterix Corporation. All other authors declare no competing financial interest. REFERENCES 1. Li, Y.-F.; Sun, Y.-M.; Beier, R. C.; Lei, H.-T.; Gee, S.; Hammock, B. D.; Wang, H.; Wang, Z.; Sun, X.; Shen, Y.-D., Immunochemical Techniques for Multianalyte Analysis of Chemical Residues in Food and the Environment: A Review. Trends Analyt. Chem. 2017, 88, 25-40. 2. Shankaran, D. R.; Gobi, K. V.; Miura, N., Recent Advancements in Surface Plasmon Resonance Immunosensors for Detection of Small Molecules of Biomedical, Food and Environmental Interest. Sens. Actuators B Chem. 2007, 121 (1), 158-177. 3. Hoelder, S.; Clarke, P. A.; Workman, P., Discovery of Small Molecule Cancer Drugs: Successes, Challenges and Opportunities. Mol. Oncol. 2012, 6 (2), 155-176. 4. Rossetti, M.; Ippodrino, R.; Marini, B.; Palleschi, G.; Porchetta, A., Antibody-Mediated Small Molecule Detection Using Programmable DNA-Switches. Anal. Chem. 2018, 90 (13), 8196-8201. 5. Kaiser, L.; Weisser, J.; Kohl, M.; Deigner, H.-P., Small Molecule Detection with Aptamer Based Lateral Flow Assays: Applying Aptamer-C-Reactive Protein Cross-Recognition for Ampicillin Detection. Sci. Rep. 2018, 8 (1), 5628.

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Competitive Immunoassays for the Detection of Small Molecules

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