Detection of extremely low concentration waterborne pathogen using a multiplexing self-referencing SERS microfluidic biosensor
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It is challenging to achieve ultrasensitive and selective detection of waterborne pathogens at extremely low levels (i.e., single cell/mL) using conventional methods. Even with molecular methods such as ELISA or PCR, multi-enrichment steps are needed which are labor and cost intensive. In this study, we incorporated nano-dielectrophoretic microfluidic device with Surface enhanced Raman scattering (SERS) technique to build a novel portable biosensor for easy detection and characterization of Escherichia coli O157:H7 at high sensitivity level (single cell/mL).
A multiplexing dual recognition SERS scheme was developed to achieve one-step target detection without the need to separate target-bound probes from unbound ones. With three different SERS-tagged molecular probes targeting different epitopes of the same pathogen being deployed simultaneously, detection of pathogen targets was achieved at single cell level with sub-species specificity that has not been reported before in single-step pathogen detection.
The self-referencing protocol implements with a Nano-dielectrophoretic microfluidic device potentially can become an easy-to-use, field-deployable spectroscopic sensor for onsite detection of pathogenic microorganisms.
Pathogen detection and identification is of the utmost importance for medicine, food safety, public health and security, and water and environmental quality control . The World Health Organization (WHO) identified that contaminated water serves as a mechanism to transmit communicable diseases such as diarrhea, cholera, dysentery, typhoid and guinea worm infection. Except for poor water, sanitation and hygiene services (WASH) conditions in communities and institutional settings, slow detection strategies have also been exacerbating the spread of those infectious diseases. Timing is extremely important in pathogen detection and the delay or inaccurate diagnosis of the pathogenic infection is always the primary cause of mortality or serious illness. Traditional and standard pathogen detection methods rely on off-line laboratory procedures (consist of multiple cultural enrichment steps, isolation of bacterial colonies, identification) and may take up to 8 days to yield an answer . This slow process clearly can’t provide a sufficient protection from exposure to water borne pathogens within public drinking water. Outside traditional culturing, many methods have been developed to promote the detection efficacy, such as polymerase chain reaction (PCR), enzyme-linked immunosorbent assay (ELISA), and surface plasmon resonance (SPR) sensors [3, 4, 5, 6]. These techniques provide high selectivity and reliability; however, they usually require intensive sample preparation and special equipment and trained users . Furthermore, in reality, the competitor organisms in water samples can cross-react with detection systems, rendering false-positive results, or can grow to levels that will mask target organisms. Hence, there is a compelling need for the development of easy-to-use biosensors that could give highly sensitive and reliable detection results, and even allow on-site field monitoring .
Surface-enhanced Raman scattering (SERS), as a label-free/non-destructive optical technique, has been widely used in pathogen discrimination [9, 10, 11, 12]. The distinct “fingerprinting” Raman spectra of microorganisms can be enhanced at rough noble metal nanostructures’ surfaces, which is essentially important in pathogen detection since discrimination of different bacterial species and strains is difficult. Recently, various nanostructures with different surface features have been employed to amplify the enhancement of SERS signals in bacterial identifications at cellular and molecular levels. However, it is still a challenge to obtain repeatable and reproducible SERS spectroscopic results at complicated experimental conditions. The degree of metallic nanoparticles aggregation, the different size of metal colloids, and the inhomogeneous distributions of nanoparticles on cells all affect the SERS signal reproducibility. To overcome those limitations, specific antibodies and Raman tags molecules are introduced into nanostructures to probe the target biomolecules and produce a high-specific and reproducible SERS signals [13, 14, 15]. However, the simultaneous presence of nanoparticles, SERS reporters, and biological samples generates highly overlapping and complex spectra which make it difficult to identify the target bacteria. Therefore, it is necessary to integrate statistical analysis techniques into bacterial SERS discrimination for data mining [14, 16, 17, 18, 19, 20].
Chemical and biological materials
Hexadecyltrimethylammoniumbromide (CTAB, ≥99%); Gold(III) chloride trihydrate (HAuCl4.3H2O, 99.9 + %); Sodium borohydride (NaBH4, ≥99%); Silver nitrate (AgNO3, ≥99%); L-Ascorbic acid (AA, ≥99.0%); 4-Aminothiophenol (4-ATP, 97%); 3-Amino-1,2,4-triazole-5-thiol (ATT, 95%); Phosphate-buffered saline (PBS), 10× concentrate. Ethylene glycol (EG, 99%), sodium sulfide (Na2S, 99%); Polyvinylpyrrolidone (PVP, 99%); 3-Mercaptopropionic acid (≥99%). All reagents are purchased from Sigma-Aldrich. E.coli O157: H7 (No. 43888) and E.coli K12 (29425) frozen-dried strains were purchased from ATCC (Manassas, VA, USA). Anti-E.coli antibodies were purchased from Abcam (Cambridge, MA, USA). 18.2 MΩ.cm E-pure water is used for all regents’ preparation.
Bacterial sample preparation for Raman spectroscopic analysis
Different bacterial strains were cultured in petri dishes of 60 mm × 15 mm that have a layer of agar-based Luria Broth medium. After 18 h 37°C incubation, the desired bacteria colonies were inoculated in liquid Luria Broth medium for liquid culture. After 18 h incubation at 37°C, bacterial solution was transferred to 15 mL centrifuge tubes and concentrated under 3000 RPM speed for 3 min. After removing the supernatant, the dense pellets of bacteria were obtained for subsequent Raman identification tests or series dilution.
Functionalization Gold nanorods (GNRs) with 4-ATP and ATT and antibodies
GNRs were synthesized following the standard protocol in literatures . 3 mL of 10 mM 4-ATP and ATT (pH=2) was added into 24 mL GNR-CTAB with LSPR OD (optical density) =6. The mixture solution was kept in disposable scintillation vials at 60°C oil bath with 180 rpm stirring speed for around 19 h. Then, functionalized GNRs solution was washed twice by centrifugation (6000×g for 10 min) with 20mM CTAB and pH=4 pure water. Finally suspend the products in 0.25 mL water. For antibody conjugation, 0.75 μg anti-E.coli O157:H7 mouse monoclonal antibodies (P3C6, ab75244) were incubated with 500 μL GNR-4ATP (OD=11.2); 0.75 μg another anti-E.coli O157:H7 mouse monoclonal antibodies (3011, ab20976) were incubated with 500 μL GNR-ATT.
Functionalization of Cage with 3-MPA and antibodies
Gold cages were synthesized following the standard protocol in literatures . The 3-MPA-gold nanocages were prepared by ligand-exchange reaction between 3-MPA and PVP stabilized gold nanocages. The cages solution were diluted to 100 mL with OD=1.0. Ligand-exchange reaction was performed at room temperature by mixing the prepared cage solution with a 100 μL of aqueous solution of 20 mM 3-MPA under shaking treatment. The mixed solution was treated overnight under the room temperature. After centrifuging, the supernatant were removed. The pellet was washed with pure water for 1 time. For antibody conjugation, 0.75 μg anti-E.coli O157:H7 rabbit polyclonal antibodies (HRP ab68450 from Abcam) were incubated with 500 μL Cage-3MPA.
DXR Raman microscope (Thermo Scientific, Waltham, MA, USA) was used for Raman spectra acquisition with 780 nm excitation at 10 mW, 10× objective, and 25 μm slit. The laser exposure time was 5 s and spectral resolution was 2.4–4.4 cm−1. Different batches of nanoprobes were used for each mixed sample to test the reproducibility of the SERS measurement. Several droplets of sample solutions were placed on gold-coated microscope slide, and multiple SERS spectra were obtained from different positions on each droplet. The OMNIC™ suite (Thermo Scientific, Waltham, MA, USA) was used for data processing. The focusing point in the colloidal state liquid sample is randomly selected for all collection in order to obtain a big and random database to fulfill the requirement in the following statistical analysis.
Nano-DEP microfluidic device operation
By using the microfluidic device, cell enrichment could be achieved in a continuous sample preparation step. Two different E.coli strains were mixed: E.coli O157:H7 (pathogenic) and E.coli K12 (non-pathogenic) and used to test the efficiency of the microfluidic device. The mixture was used so that the specificity of the self-referencing mechanism in the following SERS measurement can be investigated. Two E. coli strains were diluted to extremely low concentrations and mixed together uniformly. 1 mL of the mixed cell suspension at 100 CFU/mL was passed through the microfluidic device. At a flow rate of 1μL per min, it took about 17 h for the 1 mL sample to be processed. The concentrated samples then were collected.
The spectra were firstly baseline-corrected, smoothed and area normalized. An iterative polynomial background removal algorithm was implemented to remove background fluorescence from the Raman spectral data .
Principal components analysis (PCA) is a common statistical technique that is used to reduce the number of dimensions of data with a minimum loss of information . The goal of PCA is to determine the data patterns and underlying factors that cause the similarities and differences of the original data without any prior knowledge. Those factors are orthogonal basis and called principal components (PCs). For each PC score, the influence (weight) of the original spectral data is found in its corresponding loading profile. In this study, PCA was performed using MatLab (Mathworks, Inc., Natick, MA).
Result and discussion
Multiple bioconjugated gold nanoparticles (AuNPs) as SERS nanoprobes for bacterial identification at 10 CFU/mL
In our self-referencing scheme, the SERS signatures of the target bacteria were observed superimposed with the SERS signals of the Raman tags. The assessment through the dual signals (superimposed target and tag Raman signatures) supported a specific recognition of the targets in a single step with no washing/separation needed. However, the complex nature of the SERS imparts the implementation of the self-referencing scheme with a lot of variations. Dual signals could only be observed when the conjugated bacterial cell wall components fall into the hotspot regions of the nanoprobes. Hot spots are highly localized regions of intense local field enhancement believed to be caused by local surface plasmon resonances. In practice, the variation of nanoprobe conjugation location and density on the surface of the bacterial cells was very high. Therefore, the enhanced-type spectra were not expected in every single measurement. Among all the spectra collected, most were non-enhanced Raman spectra (data not shown) in which the significantly enhanced Raman fingerprint signatures were not identifiable from both Raman tag and bacteria; and 20% of the acquired spectra were considered as SERS spectra. Among the SERS spectra, two types could be identified: one was non-binding (probe alone) type in which only the featured peaks from three Raman tag molecules could be found, indicating that the probes were not bound to bacterial cells; the other was binding (dual signal) type in which both Raman tags’ peaks and bacterial peaks were significantly enhanced. The multiplex scheme we employed to detect multiple epitopes further added to the complication of the analysis. As shown in Fig. 3, even the non-binding spectra do not always show identical characteristics: in some measurements the three probe signals were not all detectable at the same intensity level. The randomly occurrence of hot spots is to blame for this inconsistency.
Band assignment of E.coli O157:H7 featured peaks shown in multiple self-referencing SERS measurement
Amide 1 vibration
Phenylalanine in protein
Adenine ring stretching
γ(NH2) adenine, polyadenine, Phenylalanine in protein
Phenylalanine in protein
Phenylalanine in protein
adenine, glycosidic ring mode, DNA
S-S bond stretching
Multivariate statistical analysis for rapid discrimination and classification of target bacteria
Result of the probe & dual spectra validation with a SVM model (the first 58 PCs are used only)
Enrichment of samples by using Nano-DEP microfluidic device
Our results showed the LOD could be improved to 101 CFU/mL by using the multiple epitopes self-referencing recognition strategy. Even though this SERS-based scheme could already provide such ultrasensitive and rapid detection results, the sensitivity needs further improvement for it to be a frontline solution to pathogen detection. It has been reported that the infectious dose of E.coli O157:H7 bacteria is only 10 cells per gram of food and 0.2 CFU/mL in environmental sample, which underlines the desirability for extremely sensitive and specific pathogen detection .
This novel multiplex self-referencing SERS pathogen detection scheme offered high sensitivity (101 CFU/mL) and strain level discrimination by measuring the superimposed SERS signatures with multiple characteristic peaks. Furthermore, the superimposed spectra could be obtained directly with no washing being performed. Compared to the ELISA kits, this platform successfully isolated and identified bacteria in water samples without the need for repeated wash steps and secondary antibody reporting, hence significantly reducing the operation processes, detection time and the cost. In addition, this platform integrated with an excellent separation and concentration apparatus of Nano-DEP microfluidic device further improves the limit of detection (LOD) to 100 CFU/mL. The integration of microfluidic devices with SERS detection yielded simple and miniaturized instrumentation that was suitable for the detection and characterization of small volume of chemical and biological analytes with high sensitivity and specificity. Multivariate statistical analysis techniques (PCA and SVM) firmly confirms the positive identification of targets in the presence of overwhelming non-target interference, with a detection accuracy above 95%. It has the potential to become a powerful, highly sensitive biosensor for onsite detection of pathogens at extremely low levels.
Research reported in this publication was supported by Iowa State University Foundation and Iowa VA medical center.
Availability of data and material
Data available in a public (institutional, general or subject specific) repository that issues datasets with DOIs (non-mandated deposition).
CW carried out the nanoparticles/probes fabrication, characterization, collected SERS spectra data, performed the statistical data analysis, and drafted the manuscript. FM fabricated and tested the Nano-DEP microfluidic device. Dr. CY and Dr. JL participated in its design and helped to revise the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
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