Label-Free Immunoassay for Multiplex Detections of Foodborne Bacteria in Chicken Carcass Rinse with Surface Plasmon Resonance Imaging

Abstract

The frequent outbreaks of foodborne pathogens have stimulated the demand of biosensors capable of rapid and multiplex detection of contaminated food. In this study, surface plasmon resonance imaging (SPRi) was used in simultaneous label-free detection of multiple foodborne pathogens, mainly Salmonella spp. and Shiga-toxin producing Escherichia coli (STEC), in commercial chicken carcass rinse. The antibodies were immobilized on the same SPRi sensor chip as a label-free immunoassay. Their immobilization concentrations were optimized to be ranging from 0.25 to 1.0 mg/mL, and independent of pH values. This label-free immunoassay achieved 10⁶ colony-forming unit (CFU)/mL limit of detection for Salmonella, which was further improved to 1.0 CFU/mL with overnight bacteria enrichment. The injected samples with different bacteria, Salmonella Enteritidis, STEC, and Listeria monocytogenes, have been identified by the same biochip. Moreover, the SPRi signals revealed complex interference effects among coexisting bacteria species in heterogeneous bacteria solutions. This SPRi-based immunoassay demonstrates the great potential in high-throughput screening of multiple pathogenic bacteria coexisting in chicken carcass rinse. The reliability of antibody immobilization and cross-reactions of different antibodies on the same biochip are the major challenges of practical application of SPRi.

Introduction

S almonella and Escherichia coli O157 are among the leading causes of foodborne outbreaks in the United States every year. Various techniques have been utilized to improve the detection sensitivity and operation time (Suo et al., 2010), including immunoblotting (Tenover et al., 1994; Magi and Liberatori, 2005), plasmid fingerprinting (Millemann et al., 1995), chromosomal DNA restriction patterns using pulsed-field gel electrophoresis (Liesegang and Tschäpe, 2002), PCR for serotyping (Malorny et al., 2004), and surface-enhanced Raman scattering (SERS) immunoassay (Chen et al., 2015; Wu, 2019). In one SERS study using in situ synthesis of silver nanoparticles, E. coli, Pseudomonas aeruginosa, methicillin-resistant Staphylococcus aureus, and Listeria spp. have been distinguished with limit of detection (LOD) of 10³ colony-forming unit (CFU)/mL (Chen et al., 2015).

The traditional cell culture-based methods take days of incubation and tedious work by well-trained microbiology professionals. PCR is increasingly used as the new standard method, but it requires additional gene extraction step, challenging primer design, and precise temperature control (Ogunremi et al., 2017). Therefore, it is in high demand to develop rapid, easy-to-operate, and reliable methods for the detection and differentiation of bacterial species and their serotypes, especially when they coexist in the same food sample.

Surface plasmon resonance imaging (SPRi) is an emerging optical technique allowing rapid and label-free screening of multiple targets simultaneously (Steiner, 2004). With the help of automated microarrayer, the SPRi sensor chip can hold microarray spots composed of antibodies (Templier et al., 2017), aptamers (Linman et al., 2010), streptavidin-biotin coupling system (Knoll et al., 2000), or small organic molecules (Bocková et al., 2019), for high-throughput assays. SPRi has been used for several multiplex detections, including the Fusarium mycotoxins (Hossain and Maragos, 2018), pancreatic islet hormones (Castiello and Tabrizian, 2018), blood group typing (Szittner et al., 2019), and human serum proteins (Soler et al., 2014). SPRi has been gradually accepted and used in many bioanalytical areas because of its label-free nature and reasonable cost.

Herein we developed an SPRi microarray biosensor for completely label-free detection of Salmonella and Escherichia coli O157 coexisting in chicken carcass rinse samples. The experimental setup is based on a typical Kretschmann prism-coupling configuration as shown in Figure 1. The sample solution contaminated with Salmonella and Escherichia coli O157 is pumped through a flow cell channel, where the substrate is a sensor chip immobilized with different types of antibodies as microarray spots. This sensor chip selectively captures each type of pathogenic bacteria through its corresponding antibody, and in turn show the real-time SPRi signal response of the entire microarray surface, including all antibody spots at the detector end. The purpose of this study is to demonstrate the potential of SPRi microarray in the rapid detection of pathogens coexisting in food matrix samples (Chen and Park, 2016).

FIG. 1.

Experimental setup of surface plasmon resonance imaging for high-throughput assay of pathogenic bacteria. The reaction protocol used for spotting antibody to Au film surface is shown in Supplementary Figure S1. CCD, charge-coupled device; IgG, immunoglobulin G; LED, light-emitting diode.

During a typical flow cell detection shown in Figure 1, it takes 20 min or even faster to show the SPRi responses of 54 different antibody spots, each of which can be potentially used for a certain bacteria type during rapid screening of mixture sample. In addition, it is a completely label-free detection of various bacteria bodies without any prelabeling or additional enhancing to the SPRi signals. When the bacteria number increases in the sample, the complexity generated from cross-reaction and other practical issues are unproportionally increased. So far, this study has shown the largest number of antibodies immobilized on the same SPRi biochip used to detect as many as six different bacteria. Therefore, this SPRi method is especially useful in the real-world bacteria detections, with significant improvement from cell culture-based or PCR-based methods on the detection time and versatility.

Materials and Methods

Materials and bacteria culture

SPRi Biochips™ were purchased from Horiba Scientific (Edison, NJ, USA). Mercaptoundecanoic acid, 1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide hydrochloride, N-hydroxysuccinimide, skim milk powder, and Trizma^® hydrochloride were obtained from Sigma-Aldrich (St. Louis, MO, USA). Buffered peptone water (BPW), phosphate-buffered saline (PBS 10 × ), anti-Salmonella antibody (PA1-85849), and mouse immunoglobulin G (IgG) control were purchased from ThermoFisher Scientific (Waltham, MA, USA). Anti-Escherichia coli O/K (ab31499) was obtained from Abcam (Cambridge, MA, USA). Tryptic soy broth (TSB), tryptic soy agar (TSA), and brilliant green sulfa agar were from BD (Franklin Lakes, NJ, USA). Raw chicken breast was obtained from a local supermarket.

Bacterial strains Salmonella Typhimurium and E. coli were chicken carcass rinsate isolates and have been stored at −80°C long term and 4°C short term. Before each experiment, a loopful of bacterial colonies maintained on TSA slants were inoculated into 10 mL of TSB medium and incubated at 37°C overnight. The bacterial culture was harvested, rinsed once with PBS, and serially diluted in PBS or chicken rinse sample for SPRi injections (Chen and Park, 2018b). Cells were enumerated by plating onto TSA plates.

SPRi sensor chip preparation and sample detection

The protocols used for the functionalization of SPRi Biochip™ (Horiba) with anti-Salmonella, anti-E. coli antibodies, and IgG controls have been described in previous publication (Chen and Park, 2018b). Antibodies and IgG were spotted using XactII™ compact microarray spotter (LabNEXT, West New York, NJ, USA) with humidity >75% (Chen and Park, 2018b). The blocking reagent is skim milk protein from 5% skim milk solution (details in Supplementary Data). Test samples contain Salmonella Typhimurium and Escherichia coli O157:H7 in either PBS or chicken rinse overnight culture (CROC) in BPW (background aerobic count ∼10⁷ CFU/mL).

For the rapid screening of four foodborne bacteria, the bacteria Shiga-toxin producing Escherichia coli (STEC; O26, O45, O103, O111, O121, and O145), Listeria monocytogenes, and Salmonella Enteritidis were incubated at 37°C overnight. Campylobacter jejuni was incubated at 37°C for 48 h under microaerobic conditions. Then 100 μL of L. monocytogenes was reinoculated with 10 mL TSB. Next, 1.5 mL of each bacteria solution was centrifuged at 8000 rpm for 5 min followed by discarding the supernatant. The pellet was resuspended in 1.5 mL of PBS and used in SPRi sample injections.

During SPRi experiments, the flow rate was set to 120 μL/min in and out for the running buffer (PBS) and regeneration solution (50 mM NaOH). A 15 min flow rate pause was used to provide longer time for antibody binding, immediately after the sample moved from injection loop to flow cell (Chen and Park, 2018b). The SPRi signal used as the maximum value was collected at 19.5 min. The SPRi sensorgrams were analyzed using the SPRi Analysis software (Horiba) and Microsoft Excel (Microsoft, Seattle, WA, USA).

The signals from the replicated spots of each antibody were averaged, then subtracted by the ones from the IgG control for the final SPRi reflectivity variation (ΔR) values. The LOD is the concentration of bacteria at which the SPRi signal equals to three times standard deviation of blank measurements. Sensorgrams were plotted with Origin 2019 (OriginLab, Northampton, MA, USA), and the SPRi difference images were cropped and arranged into figures with Microsoft Office Picture Manager (Microsoft).

Results and Discussion

Duplex detections of Salmonella Typhimurium and Escherichia coli O157 in PBS

For the results shown through this article, the samples injected were all viable cells of Salmonella Typhimurium, E. coli, or other types of bacteria immediately from cell culture, since the goal of this study is the multiplex detection of viable bacteria cells for food safety. Those cells were not in their best growth conditions when passing through the flow cell system, but antibodies used in this study detect all cells independent of their physiological state, so the bacteria with changed physiological state can still be detected after injection. Therefore, even if some bacteria can stay in a “dormant” state, which keep their virulence and the ability to resuscitate when in favorable conditions and could infect the food, they will still be captured and detected by this method.

The sample solutions of Salmonella Typhimurium, E. coli, and the mixture of these two bacteria were successively injected, and the results of duplex detections are shown in Figure 2. In this study, the reconstructed curves as red and blue in Figure 2A–C show the averaged SPRi ΔR values for anti-Salmonella spots and anti-E. coli spots, respectively. The inset images in Figure 2A–C are the real-time SPRi of the antibody array on biochip surface (i.e., SPRi difference images) corresponding to the SPRi plots along time. One intriguing discovery is that the antibody spots printed by low concentration (250 μg/mL) of anti-E. coli demonstrate higher ΔR values than the high concentration (500 μg/mL), whereas it is reversed for anti-Salmonella spots.

FIG. 2.

Antibody spotting pattern and corresponding biochip difference images during the detection of Salmonella Typhimurium. (A) Only Salmonella Typhimurium in sample. (B) Only Escherichia coli in sample. (C) Both Salmonella Typhimurium and E. coli in sample. (D) The microarray pattern designed for anti-Salmonella and anti-E. coli with different concentrations, corresponding to each inset real-time surface plasmon resonance image of this biochip.

This difference may be related to the potential interference of surface immobilization to antibody binding process, and how the bacteria interact with their immobilized antibodies (Soler et al., 2019). In addition, the influence of pH values and spotting concentrations to the antibody binding activity are shown in Supplementary Figures S2 and S3 (Chen and Park, 2018b). The regeneration tests of this biochip by 10 mM Glycine-HCl (pH 1.5) and 50 mM NaOH (pH 13) are summarized in Supplementary Figures S4–S6. In brief, the antibody against Salmonella Typhimurium has shown largest SPRi signal changes during the regeneration process, which is consistent with its specificity to the injected sample solution of Salmonella Typhimurium (Fig. 2C).

When samples were dissolved in PBS, the LOD values are 5 × 10⁶ CFU/mL for Salmonella Typhimurium and 8 × 10⁶ CFU/mL for Escherichia coli O157 (Fig. 3), which are consistent with our previous study where serotypes Salmonella Enteritidis, Salmonella Heidelberg, and Salmonella Typhimurium all showed LOD of 10⁶ CFU/mL in PBS and chicken rinse matrix (Chen and Park, 2018b). For Salmonella Typhimurium, the LOD values obtained by label-free SPRi detections are relatively higher compared with our previous result by SPR (∼10⁴ CFU/mL) (Wang et al., 2017). The difference comes from several sources, including surface blocking methods, bacteria capture mechanism, and detection mechanisms of different instruments.

FIG. 3.

Final surface plasmon resonance imaging reflectivity variations (final ΔR) after spiking various concentrations of (A) Salmonella Typhimurium and (B) Escherichia coli O157 into phosphate-buffered saline buffer to obtain their corresponding limit of detection values. CFU, colony-forming unit.

The aim of this SPRi biosensor is to achieve rapid multiplex detections of pathogenic bacteria for presumptive screening from commercial food samples. Therefore, sensitivity is compromised for short operation time and ease of use. Depending on the specific aim of the detection, secondary antibodies and nanoparticles can be used to further amplify the SPRi signals, resulting in improvement of LOD. Another interesting observation is that for antibody spots at both high and low concentrations, the presence of another pathogenic bacteria slightly improved the SPRi signal of the target bacteria (Supplementary Fig. S7C, D). The cause of this signal enhancement may be less biological and more instrumental, since more components in the solution could change its optical properties in the SPRi system, and in turn affect the overall signal changes before and after the antibody binding.

The specificity of antibodies to Salmonella Typhimurium was further scrutinized by rapid screening of six different bacteria, Salmonella Enteritidis, Salmonella Heidelberg, Salmonella Typhimurium, E. coli, Enterococcus faecalis, and Listeria innocua. The biochip images are shown in Figure 4, and the SPRi ΔR signals in Figure 5. The SPRi signals from anti-Salmonella spots proportionally increase with their spotting concentrations, especially for three Salmonella samples, this trend is more significant because the antibody was produced by immunizing rabbits with these serotypes, and it is consistent with previous study (Chen and Park, 2018b).

FIG. 4.

Testing the specificity of anti-Salmonella to six different bacteria, Salmonella Enteritidis, Salmonella Heidelberg, Salmonella Typhimurium, Escherichia coli, Enterococcus faecalis, and Listeria innocua. Anti-Salmonella spotting pattern (upper part) and corresponding real-time biochip difference images (lower part) with the injection of diffident bacteria type.

FIG. 5.

Testing the specificity of anti-Salmonella to six different bacteria, with surface plasmon resonance sensorgrams of six different bacterial samples from anti-Salmonella immobilized at (A) 250 μg/mL, (B) 200 μg/mL, (C) 150 μg/mL, and (D) 100 μg/mL. The sensorgrams were corrected by signal from immunoglobulin G control spots immobilized at equal concentrations.

Compared with the spotting concentration of 1.0 mg/mL in previous study (Chen and Park, 2018b), the spotted antibody as low as 100 μg/mL has shown enough specificity to distinguish Salmonella Typhimurium from other bacteria. Other serotypes of Salmonella bacteria have generated cross-reaction signals, but the 20-min detection time is long enough to distinguish all of the different bacteria samples. The clear relationship between antibody spotting concentration and SPRi signals demonstrates its great potential for quantitative label-free detection of multiple bacteria (cell bodies instead of genes) in complex sample matrix, and so far, six bacteria types in this study are the largest number reported.

Detections of Salmonella Typhimurium and Escherichia coli O157 in chicken carcass rinse

Bacterial strains Salmonella Typhimurium and E. coli were spiked into raw chicken breast from local supermarket, then the chicken carcass rinse solutions were injected as the SPRi samples. The antibody microarray design and image are listed in Supplementary Figure S8. The details of experiments procedure are shown in Supplementary Tables S1–S3 and Supplementary Figure S9. The changes of SPRi ΔR values along increasing concentrations of bacteria in the injected chicken rinsate are shown in Supplementary Figure S10. Note that the chicken rinse samples contain high level of indigenous microflora, of which the total aerobic count is in the magnitude of 10⁷ CFU/mL. Therefore, comparing Figure 3A, B, and Supplementary Figure S10, the antibody microarray (pattern shown in Fig. 4) was only slightly influenced by the high level of interfering species in the sample solutions.

To further improve the label-free sensitivity to the food samples, BPW enrichment was used to increase the bacteria concentration in the sample solution. The experimental process of bacteria BPW enrichment in chicken rinse is included in Supplementary Table S1. A series of initial concentrations (Supplementary Table S4) were inoculated into CROC so that the bacteria concentration in the injected sample could reach the LOD threshold (∼10⁶ CFU mL⁻¹ shown in Fig. 3 and Supplementary Fig. S10). After overnight inoculation, the samples with different initial concentrations were injected, and Figure 6 shows their corresponding SPRi ΔR without any other signal enhancement.

FIG. 6.

CROC experimental results. Final SPRi reflectivity variations (ΔR) of chicken samples artificially inoculated with different concentrations of Salmonella Typhimurium and Escherichia coli O157. Samples were incubated in buffered peptone water at 37°C overnight before SPRi measurements. SPRi, surface plasmon resonance imaging.

The SPRi ΔR values did not show clear proportional relationship with the initial bacteria concentrations in Supplementary Table S4, because the overnight bacteria growth may have strong competition effect and cause large deviation from the initial ratio of those concentrations. Another issue in this test is that the original concentrations of bacteria were all estimated by manual counting methods, and the SPRi calibration curves obtained were based on those estimate concentrations (Supplementary Fig. S10), which may already deviate from the actual cell numbers in the SPRi flow-channel system.

Moreover, these calibration curves have very narrow dynamic ranges and low correlation coefficients. Therefore, those concentration values estimated from calibration curves are less reliable in the CROC experiments, so they were not used to calculate the bacteria concentrations from the SPRi signals in Figure 6. In the CROC samples, the bacteria counting numbers obtained by conventional method were always ∼10⁸ or 10⁹ CFU/mL⁻¹, but SPRi signals had reflected more trivial concentration changes.

Overall, the enrichment based on long-time cell culture greatly increased the sensitivity, but innovative sampling methods have potential to further improve sensitivity and dynamic range of SPRi detection when analyzing bacteria in food matrices, and it will be addressed in our further research (Chen and Park, 2018a).

Rapid multiplex detections of four foodborne bacteria

To further test the reliability of the SPRi antibody microarray, it was used to screen different bacteria types in a solution composed of either Salmonella Enteritidis, STEC O157 (shown as STEC in Fig. 7), L. monocytogenes, or C. jejuni. The antibodies used in this study are listed in Supplementary Table S5. The summary of this rapid screening is shown in Figure 7, according to the SPRi signal response of each antibody to different sample.

FIG. 7.

Rapid multiplex detections of four foodborne bacteria. On x-axis, Listeria monocytogenes is the sample injection of L. monocytogenes, O157 is the sample injection of Escherichia coli O157, and SE is the sample injection of Salmonella Enteritidis. Campylobacter jejuni sample injection did not provide specific surface plasmon resonance imaging response so it is not shown here. In the legend, Salm-1 and Salm-2 refer to the same commercial anti-Salmonella from different batches. STEC refers to the commercial anti-E. coli, L. monocytogenes 1 and 2 refers to the anti-L. monocytogenes from Abcam and Invitrogen, respectively. C. jejuni refers to the commercial anti-C. jejuni, and IgG refers to the IgG control. Details of the antibodies are shown in Supplementary Table S5. STEC, Shiga-toxin producing Escherichia coli.

In general, antibodies against Salmonella provided the strongest signals, with the monoclonal antibody (Salm-1, red, in Fig. 7) providing the strongest ΔR value 3.433%, whereas the polyclonal antibody (Salm-2, purple, in Fig. 7) providing the relatively lower ΔR value 2.324%. This difference indicates that the monoclonal antibody has higher specificity than the polyclonal antibody. The antibodies against Listeria showed relatively weak signals compared with the high background from other antibodies, whereas the antibody against C. jejuni did not give specific SPRi response under current microarray conditions. It is difficult to reduce the nonspecific background signals since the antibodies against their corresponding bacteria have different affinity values, but was functioning simultaneously on the same microarray surface.

Another factor affecting the SPRi signals may relate to certain collective effects in the heterogeneous bacteria communities, which can cause complex or abnormal behaviors of some bacteria, such as L. monocytogenes and C. jejuni in this case (García-Bayona and Comstock, 2018; Troselj et al., 2018). Overall, this rapid screening test has proved that SPRi microarray is fully capable of high-throughput bacteria detections and revealed new issues of screening multiple bacteria coexisting in the same sample, which is always a challenge for real-world bioanalysis, and fundamental research as well.

Conclusions

In this study a label-free and multiplex detection method based on SPRi antibody microarray has been used to analyze bacteria Salmonella Typhimurium and Escherichia coli O157 in PSB and in chicken rinse sample. SPRi sensor chip proved to be capable of detecting these two bacteria both individually and simultaneously. Although the LOD is relatively high (10⁶ CFU/mL⁻¹) compared with theoretical SPRi studies, the biochip functionalization and detections of bacteria in food samples were efficient and repeatable. When detecting bacteria in the chicken rinse, overnight BPW enrichment can significantly increase the bacteria concentrations to enhance signals without additional labeling or signal enhancement process.

Another practical issue is the wide range of antibody spotting concentrations (from 100 μg/mL to 1.0 mg/mL) that all provide certain specificity to distinguish one or two bacteria. However, for multiplex detection of four or six bacteria in our study, the antibody spotting concentrations have to be compromised for their different binding activity so that each antibody can provide enough signal for its target. Other intriguing discoveries include certain SPRi signal improvement of the target pathogen with the presence of a second pathogen, and background interference from unknown sample solutions.

Future developments of the microarray design and SPRi instrumentation will improve the sensitivity and specificity for real-world applications of SPRi label-free detections in food safety and other biosensing areas, and this study is the first step for developing portable SPRi biosensor using new designs and techniques.

Footnotes

Acknowledgment

The authors thank Dr. Nasreen Bano for the maintenance and preparation of bacterial culture for experiments.

Disclaimer

Mention of trade names or commercial products in this article is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture.

Disclosure Statement

No competing financial interests exist.

Funding Information

No funding was received for this article.

Supplementary Material

Supplementary Data

Supplementary Figure S1

Supplementary Figure S2

Supplementary Figure S3

Supplementary Figure S4

Supplementary Figure S5

Supplementary Figure S6

Supplementary Figure S7

Supplementary Figure S8

Supplementary Figure S9

Supplementary Figure S10

Supplementary Table S1

Supplementary Table S2

Supplementary Table S3

Supplementary Table S4

Supplementary Table S5

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Supplementary Material

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