Electrochemical Surface-Enhanced Raman Spectroscopy as a

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Electrochemical Surface-Enhanced Raman Spectroscopy as a Platform for Bacterial Detection and Identification Taylor P. Lynk, Clarissa S. Sit, and Christa L. Brosseau Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b02806 • Publication Date (Web): 15 Oct 2018 Downloaded from http://pubs.acs.org on October 17, 2018

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Analytical Chemistry

Electrochemical Surface-Enhanced Raman Spectroscopy as a Platform for Bacterial Detection and Identification Taylor P. Lynk, Clarissa S. Sit, and Christa L. Brosseau* Department of Chemistry, Saint Mary's University, Halifax, Nova Scotia, B3H 3C3, Canada. E-mail: [email protected]; Fax: +1-902-496-8104; Tel: +1-902-496-8175 Abstract The field of bacterial screening is in need of a rapid, easy to use, sensitive and selective sensing platform for bacterial detection and identification. Current methods of bacterial identification lack time efficiency, resulting in problems for many sectors of society. Surface-enhanced Raman spectroscopy (SERS) has been investigated as a possible candidate for bacterial screening due to its demonstrated ability to detect biological molecules with a high degree of sensitivity. However, the field of bacterial screening using SERS is currently facing limitations such as signal irreproducibility, weak spectra, and difficulty differentiating between strains based on the SERS spectra of bacteria alone. The current study reports on the first ever use of electrochemical surfaceenhanced Raman spectroscopy (EC-SERS) for bacterial screening. The results of this study demonstrate the ability of EC-SERS to greatly improve upon the SERS performance for the detection of Gram-positive and Gram-negative bacteria both in terms of improved peak intensities and spectral richness. EC-SERS shows great promise in its ability to advance SERS-based bacterial screening, and could potentially be used for more efficient species discrimination at the point-of-need (PON).

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Bacteria-inflicted infectious diseases are one of the leading causes of mortality worldwide, resulting in millions of deaths and hospitalizations annually.1 Efficient bacterial detection is critical in many industries, including clinical diagnosis, the food industry, water and environmental control, and military defence.1,2 In the medical sector, rapid identification of bacterial infection is key to timely and effective treatment. Improper or inefficient bacterial identification can result in further spread of infectious disease and delayed treatment. Current methods of bacterial detection can lead to massive delays in sample processing, and can be very costly.3 The food industry is also greatly affected by bacterial contamination of food products which can lead to widespread illness, death, and recalls that result in financial losses for businesses. In addition, environmental monitoring of bacteria, specifically in water, is essential to ensure that the public has access to a safe water source.3,4 Finally, while it accounts for a small amount of the need for bacterial screening, detection of biological warfare agents is of great importance for military applications in order to identify possible bioterrorism agents.3 Globally, there is a need for a cost effective, rapid, sensitive, accurate, portable, and easy to use bacterial detection platform. While many techniques and instruments have been investigated for this application, the field is still lacking a detection platform that encompasses all of the necessary requirements, including matching or improving upon the sensitivity and selectivity of current methods. Current methods for bacterial identification include polymerase chain reaction (PCR), enzyme-linked immunosorbent assay (ELISA), and culture and colony counting.2,5 PCR based methods analyze DNA, and have been shown to be more specific, sensitive, and time efficient than other methods.6 However, PCR is expensive, not able to distinguish between living and dead bacterial cells, can experience high false positive and negative rates, and is highly susceptible to cross-contamination.6 ELISA, as well as other immunological tests are based on specific 2 ACS Paragon Plus Environment

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antigen/antibody binding.6 These tests have low sensitivity with some antibodies having low affinity for their targets in addition to potential interferences from complex matrices.6 Cultures grown on differential agar media followed by colony counting is a time consuming and laborious process, providing final results that can be difficult to interpret. Issues can also arise with bacteria not being readily cultivable due to starvation and stress conditions from being in food and environmental samples.6 On average, these existing methods can take up to 7 or 8 days to yield results, which is certainly unacceptable considering the importance of the applications of bacterial screening.2,5 Due to its high sensitivity, selectivity, time efficiency, and low cost, surface-enhanced Raman spectroscopy (SERS) has been explored as a possible candidate to be used as a platform for bacterial detection and identification. Raman spectroscopy is a spectroscopic technique based on inelastic scattering of monochromatic light as a result of the interaction of incident electromagnetic radiation with molecular vibrations.7 Raman spectroscopy provides valuable molecular fingerprints, but is an inherently weak technique due to the very low proportion of Raman scattered photons.8 SERS was developed in the late 1970’s, and helps to overcome the inherent weakness associated with Raman spectroscopy.9 SERS benefits from the interaction between incident light and a nanostructured noble metal surface, which causes a significant enhancement of the normal Raman signal.9-11 This highly sensitive technique has been shown to be able to detect biological molecules and has therefore recently been considered as a candidate for bacterial detection.11 SERS spectra obtained from bacteria have been shown to be attributed primarily to the metabolites of the purine degradation pathway caused by the rapid onset of the bacterial starvation response; these metabolites include adenine, guanine, AMP, uric acid, xanthine and 3 ACS Paragon Plus Environment


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hypoxanthine.12 Guanosine has also recently been identified as another molecule that contributes to the SERS spectra of bacteria. Efforts by various research groups to detect signal originating from the cell wall / membrane, separate from these metabolite species, have yielded varied results, and as such much controversy remains in the field of bacterial SERS-based sensing. The outermost layer of Gram-positive bacteria consists of a thick layer of peptidoglycan (cell wall), which also contains lipids and proteins, while the outermost layer of Gram-negative bacteria consists of an outer membrane containing lipopolysaccharides.1 Because of the similarity of SERS bacteria spectra in literature, complex data analysis methods are used to differentiate between different strains of bacteria. For example, Patel et al. have reported on a barcoding method based on the second derivative of the spectra coupled with multivariate statistical analysis for bacterial strain identification using SERS.13 It would be beneficial for rapid bacterial detection to move away from these complex data analytics approaches to obtain results at the PON. Electrochemical-surface enhanced Raman spectroscopy (EC-SERS) couples SERS with the application of an electric potential to the nanostructured working electrode in the presence of an electrolyte.14 This technique allows for an analyte to be detected at a desired applied voltage, and can simulate a more biologically relevant electric field environment.16 EC-SERS has been shown to provide more intense spectra as compared to the normal SERS spectra of various molecules by up to a ten-fold signal enhancement on average, and can be done using cost effective and portable instrumentation.16-18 Previous studies by our group and others have shown that through a combination of surface charge manipulation of the SERS substrate and electrochemical desorption of interfering capping agents on the SERS substrate surface, adsorption of the target analyte is promoted.14-18 Due to the sharp distance dependence of the SERS enhancement, this then results in a favourable condition for enhanced SERS signals, compared to non-EC-SERS. In a 4 ACS Paragon Plus Environment

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similar way, it is expected that through manipulation of the surface charge of the SERS substrate, a favourable electrostatic condition can be met such that bacterial cell adhesion to the SERS substrate is supported. In particular, as different bacterial strains will have differing surface charges, having the ability to tune the SERS substrate surface charge will provide some selection in this bacterial screening method. Because of the promise of EC-SERS as a biosensor, the present work will investigate the use of EC-SERS for bacterial detection in an attempt to improve upon normal (non EC-SERS) SERS bacterial detection. In summary, the present study found that the normal SERS spectra of bacteria could be greatly enhanced by using EC-SERS. The spectra collected using EC-SERS could then be used to differentiate between two species of bacteria using a simple spectral database containing the spectra of the previously identified nucleotide breakdown products. To the best of our knowledge, this work represents the first time EC-SERS has been used for bacterial detection and discrimination.

Materials and Methods Reagents and Materials Potassium phosphate dibasic (>98%) and agar powder (lab grade) were purchased from Anachemia Canada (Montréal, QC, CA) and potassium phosphate (>99%) and sodium citrate was purchased from ACP Chemicals (Montréal, QC, CA). Silver nitrate (99.9995%) and citric acid (99+%) were purchased from Alfa Aesar (Ward Hill, MA, USA). Sodium chloride, pyridine, and tryptone (bacteriological) were purchased from VWR International (Solon, OH, USA). Microbiology Fermtech® yeast extract was purchased from EMD Millipore Corporation (Billerica, MA, USA). Ethanol (95%), potassium chloride (≥99%), hypoxanthine (>99%), guanosine (≥98%), guanine (98%), xanthine (≥99%), uric acid (>99%), adenosine 5’ mono5 ACS Paragon Plus Environment


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phosphate disodium salt (AMP) (>99%), NaBH4 ( ≥ 99%), cysteamine (>98%), and adenine (≥99%) were purchased from Sigma Aldrich (St, Louis, MO, USA). A culture of E. coli K-12 was obtained from Bio Rad Laboratories (Hercules, CA, USA) and a culture of B. megaterium was obtained from Boreal Scientific (St. Catherines, ON, CA). All chemicals were used as received without further purification. All solutions were prepared using Millipore water (solution resistivity ≥18.2 MΩ cm). Glassware was cleaned in neat sulfuric acid and thoroughly rinsed with Millipore water prior to use. Screen printed electrodes used to fashion the EC-SERS substrates and the WaveNow Potentiostat/Galvanostat System were purchased from Pine Research Instrumentation (Durham, NC, USA). Microscopic Studies The nanoparticles and electrodes were imaged using a TESCAN MIRA 3 LMU Variable Pressure Schottky Field Emission Scanning Electron Microscope (SEM), with a maximum resolution of 1.2 nm at 30 kV. The silver nanoparticle (AgNP) colloid suspension was prepared for SEM imaging by drop coating 5 μL of the suspension onto a 5 x 5 mm silicon wafer chip (Ted Pella, Inc., Redding, CA, USA). Electrodes containing bacteria were dried using 95% ethanol prior to imaging. SEM image processing was done with the assistance of ImageJ software (NIH, Maryland, USA). Spectroscopic Studies Ultraviolet-visible (UV-vis) spectroscopic studies were completed using a Cary 60 UV-vis spectrometer (Agilent Technologies, Santa Clara, CA, USA). EC-SERS measurements were collected using a DXR Smart Raman spectrometer equipped with 780 nm and 532 nm lasers (Thermo Fisher Scientific, Mississauga, ON, Canada). The spectrometer resolution is 3 cm-1 and 6 ACS Paragon Plus Environment

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it is equipped with an air-cooled CCD detector. The DXR Smart Raman spectrometer is coupled to a Pine Research Instrumentation portable USB Wavenow potentiostat/galvanostat (Durham, NC, USA) to perform EC-SERS measurements using pH 7.4 phosphate buffer as an electrolyte. For EC-SERS, the applied potential ranged from 0.0 V to -1.0 V vs. Ag/AgCl in increments of 0.1 V, all potentials are reported vs. Ag/AgCl. All spectra are corrected for laser power and acquisition time for ease of comparison. Data was analyzed using Origin 9.0 software (OriginLab Corporation, Northampton, MA, USA). Silver Nanoparticle (AgNP) Synthesis 1.0 mL of aqueous silver nitrate solution (0.1 M), 3.4 mL of aqueous sodium citrate (5%), and 0.6 mL of aqueous citric acid (0.17 M) were added into a 250 mL three-neck flat-bottom flask with 95.0 mL of water. 0.2 mL of freshly prepared sodium borohydride solution (0.1 mM) was then added into the above mixture at room temperature under magnetic stirring. The mixture was allowed to stand at room temperature for 1 min and then brought to boil under reflux within 20 min under magnetic stirring. After boiling for 1 h, the dark yellow solution was allowed to cool to room temperature. To concentrate the AgNPs, aliquots of 20 x 1.0 mL of colloidal suspension were added to Eppendorf tubes, which were then centrifuged at 8,000 rpm for 20 minutes (Labnet PRISM microcentrifuge, Edison, NJ, USA). The supernatant was then removed and discarded, and the remaining pellet was placed into one tube and centrifuged again. The volume of the final AgNP paste was determined and made up to 50 μL with water. This was repeated as many times as necessary for each batch of AgNPs. Preparation of EC-SERS Substrates



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Screen printed electrodes were modified with nanoparticles for use in EC-SERS sensing. The screen printed electrodes were used as received, with no pre-treatment or pre-cleaning beforehand. Three layers of the AgNP paste were drop coated onto the working electrode surface of a carbon screen printed electrode in 5 µL aliquots, drying fully between layers. Three layers of the AgNP paste was found to be the optimal amount to ensure uniform surface coverage. The electrodes were then immersed in 0.5 M KCl for 30 minutes, rinsed with ultrapure water and dried prior to application of the probe to remove citrate from the metal surface. Previous studies have indicated that the chloride ion can efficiently displace the citrate capping agent at the AgNP surface, allowing for enhanced SERS signals.17 For EC-SERS characterization of the 7 nucleotide breakdown products, 5 µL of a 5 mM aqueous solution was deposited onto the modified electrode surface and allowed to dry prior to spectroscopic studies. Previous studies by our group using these AgNP substrates have reported average enhancement factors of 106-108, very typical for SERS substrates prepared using citrate-reduced silver colloids. Growth and Preparation of Bacteria A single colony of E. coli K-12 or B. megaterium bacteria was taken from a nutrient agar plate and transferred into a sterile disposable 15 mL plastic tube containing 10 mL of nutrient broth. The tube was then loosely capped and incubated at 37°C, rotating at 120 rpm for approximately 16 hours. The resulting cloudy bacterial solution was then centrifuged for 10 minutes at 4000 rpm once to remove the supernatant, and washed (re-suspended and centrifuged) two additional times with water and once more with phosphate buffer to remove the remaining growth medium. The result of this process was a concentrated pellet of bacterial cells. Bacteria Immersion Method for EC-SERS


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A concentrated bacterial cell pellet obtained using the procedure described above was resuspended in 5 mL of phosphate buffer in a disposable glass vial. A KCl treated silver coated screen printed electrode was then placed in the bacterial suspension and incubated at room temperature so that the working electrode was completely immersed in the bacterial suspension. After 16 hours, the electrode was removed and dried completely in air for several minutes prior to spectroscopic studies.

Results and Discussion AgNP Characterization The AgNPs used in this study were near-monodisperse spheres with an average diameter of 24 nm ± 3 nm. Figure S1 in the supporting information shows an SEM image of the AgNPs and the UV-vis spectrum of the diluted colloid for the AgNPs used in this study. The size of the spheres was assessed based on analysis of the SEM images using ImageJ software. The localized surface plasmon resonance band at approximately 394 nm is indicative of silver nanospheres.19 The presence of only one peak and the low full-width half-max (FWHM) value for the extinction profile (~60 nm) suggests the nanoparticles are non-anisotropic spherical particles that are nearmonodisperse.19 Substrate Preparation Carbon screen printed electrodes (SPEs) were used as the substrate, with the nanoparticle coating drop-cast onto the working electrode (WE). These electrodes cost only several dollars to purchase and are disposable, making them an ideal candidate for cost-effective substrates. All substrates used underwent a chloride displacement treatment by immersing the substrate in 0.5 M KCl for 30 minutes, followed by a 30 second rinse with water. This process was done to remove 9 ACS Paragon Plus Environment


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citrate from the surface of the nanoparticles, as citrate was used as the capping agent in the nanoparticle synthesis. The chloride displacement treatment works by displacing the surface adsorbed citrate ion with Cl-, as it has a strong specific adsorption on Ag.20 Citrate is Raman active: it produces strong peaks at ~935 and ~1404 cm-1 that are assigned to ν(C–COO) and νs(COO) vibrations, respectively, as well as peaks at 810 and 840 cm-1 from ν(CCCC-O) vibrations, and a peak at 1033 cm-1 from ν(C-O) vibrations.20 Additionally, citrate is negatively charged and can cause electrostatic repulsion of analytes. Citrate therefore has the ability to significantly interfere with the adhesion of bacteria to the surface of the electrode, as well as detection of the bacteria. Chloride, however, only produces one strong peak at 240 cm-1, which is due to the ν(Ag-Cl) vibration, and therefore interferes less with the EC-SERS spectrum.20 EC-SERS Characterization of Nucleotide Breakdown Products As

the

SERS

spectra

for

bacteria

have

previously

been shown to be attributed to the nucleotide breakdown products adenine, hypoxanthine, xanthine, guanine, uric acid, AMP and guanosine, it was vital to first establish reference EC-SERS data for all seven molecules in order to determine if EC-SERS spectra obtained for bacteria could be attributed to one or a combination of these molecules. All nucleotide breakdown products studied in this work were prepared as 5.0 mM aqueous solutions prior to analysis. For each molecule studied, 5.0 μL of a 5.0 mM aqueous solution was drop coated onto the working electrode surface of a KCl-treated screen printed electrode and allowed to dry. The pH of some solutions was adjusted to ensure complete dissolution of the solute. The screen printed electrode was then placed into the glass electrochemical cell and used to acquire a normal (non EC-SERS) SERS spectrum, termed the “in air” spectrum. pH 7.4 Phosphate buffer, the supporting electrolyte, was then added to the cell (enough to fully cover the working electrode) after being purged of oxygen 10 ACS Paragon Plus Environment

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using argon gas, and the cell was connected to the potentiostat. Prior to the application of a potential, a spectrum was obtained at open circuit potential (OCP), which is termed the OCP cathodic spectrum. The OCP spectrum represents the SERS spectrum in electrolyte solution, but in the absence of an applied voltage. A spectrum was always then recorded with an applied potential of 0 V vs Ag/AgCl. The applied potential was then progressively stepped in the negative (cathodic) direction in increments of 0.1 V until a final potential of -1.0 V was reached. The anodic and cathodic limits were chosen as 0.0 V and -1.0 V respectively, in order to prevent oxidation of the metal as well as the evolution of H2(g). Both of these issues would lead to electrode damage and an obliteration of the SERS signal. For ease of viewing, Figure 1 shows the SERS spectra for each of the seven nucleotide breakdown products at various negative potentials, as well as the spectrum obtained for a mixture of all seven of the products. The same study as was done for the individual nucleotide breakdown products was done for an equal molar mixture of all seven breakdown products in order to investigate what molecules would dominate the SERS spectra when all were present in a mixture. The optimized negative spectrum acquired at -1.0 V for the mixture of all seven breakdown products was run through a spectral database containing the optimized EC-SERS spectra for all individual breakdown products to determine the relative contributions of the individual products on the spectra of the mixture. For this database, Thermo Scientific™ OMNIC™ Specta Software was used, creating a database of spectral data through a unique combination of spectral identification tools, interpretation algorithms, and scientific documentation. It was found that adenine was the main contributor to the mixture signal, with an 88% match to the spectrum of the mixture. Hypoxanthine, guanine, xanthine, guanosine, AMP, and uric acid matched 75%, 44%, 41%, 38%, 15%, and 9% respectively with the spectrum of the mixture. This indicates that when 11 ACS Paragon Plus Environment


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all breakdown products are combined, the spectral peaks of adenine and hypoxanthine dominate the spectrum. This could suggest that adenine and hypoxanthine could be main contributors to the EC-SERS spectra of bacteria if no cell wall / membrane components are detected.

Figure 1: EC-SERS spectra of all seven nucleotide breakdown products; uric acid, AMP, guanosine, guanine, adenine, hypoxanthine, and xanthine, including a mixture of all seven on a AgNP modified SPE. The spectra included are the optimized spectra from various negative potentials and have been scaled as indicated to be similar in intensity for ease of comparison. All spectra were collected using a 780 nm laser line at a laser power of 80 mW for an acquisition time of 30 seconds.

EC-SERS of E. coli K-12 and B. megaterium


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The E. coli K-12 strain of bacteria was used to optimize EC-SERS measurement conditions for bacterial screening, as E. coli is a prime target pathogenic bacterium and is currently one of the two most commonly detected strains of bacteria. It is important to note that a non-pathogenic strain of E. coli (E. coli K-12) that cannot survive in the human gut was used in this work. Figure 2A shows the cathodic progression of the EC-SERS signal for E. coli K-12 bacteria going from OCP to -1.0 V in increments of 0.1 V and Figure 2B shows the EC-SERS comparison of the spectra. The cathodic progression (Figure 2A) demonstrates the potential dependance of the SERS signal for the bacteria. Clearly, the application of a negative voltage is able to significantly improve the spectral quality compared to the signal at OCP; the signal at -1.0 V shows many more peaks, and the peaks are much more intense (~5X greater). The EC-SERS comparison, Figure 2B, shows a comparison of the “in air” spectrum, which is the electrode before the addition of the supporting electrolyte, which is reflective of the normal SERS spectrum (non-EC-SERS spectrum). This is the spectrum that would be obtained if only normal SERS was employed. The “OCP cathodic” spectrum was acquired after the electrode was connected to the electrochemical cell and the electrolyte was added, but no potential was applied. The “-1.0 V” spectrum is the spectrum that was obtained by applying a voltage of -1.0 V while the SERS spectrum was acquired, which shows more well defined and more intense peaks than the “in air” spectrum, demonstrating the great impact EC-SERS can have and the improvement it provides as compared to normal SERS. The magnitude of signal enhancement achieved using EC-SERS over non EC-SERS was up to 5 times greater.



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Figure 2: (A) EC-SERS signal for E. coli K-12 on the AgNP modified SPE deposited using the immersion method in phosphate buffer at OCP and the cathodic progression from 0.0 V to -1.0 V in 100.0 mV increments and (B) EC-SERS comparison of the spectrum collected in air as well as at OCP and -1.0 V. Data was collected using a 780 nm laser line with a laser power of 80 mW and an acquisition time of 30 seconds.

In order for EC-SERS to be used as a method for bacterial screening, it was important to investigate the reproducibility of the SERS data obtained during different trials. Figure 3 shows a comparison of three trials using EC-SERS for the detection of E. coli K-12. Figure 3 shows the excellent reproducibility of the SERS spectrum obtained using EC-SERS for bacterial detection, however the signal intensities did vary. For comparison purposes, the spectra were multiplied by the factor indicated in the figure. The lack of reproducibility of the signal intensities obtained suggests that further development of this method is required in order for EC-SERS to be a quantitative detection tool for bacteria. This limitation is possibly a result of the non-uniform arrangement of SERS-active “hot-spots” that are generated when the SERS-active substrate is prepared using a colloidal sol, as was done in this case. Even with excellent near-monodisperse nanoparticles, a simple drop-casting of them onto a solid substrate will leave areas which are rich in SERS-active hot-spots and other areas which are hot-spot deficient, leading to substrate-to14 ACS Paragon Plus Environment

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substrate variability. A solution to this problem is to have an EC-SERS substrate with more inherent order in multiple dimensions for the SERS-enhancing objects, and this will be the subject of a subsequent work in this area by our team. In addition, this signal variation may very well be due to the natural variation in the number of colony forming units (CFU) present in the bacterial culture during the incubation time, as this parameter is difficult to control precisely. However, despite this limitation, the reproducibility of the SERS spectrum itself indicates that EC-SERS can reproducibly provide the same spectrum for a strain of bacteria, which is vital for strain differentiation through qualitative analysis, and hence this technique remains a viable tool for qualitative bacterial screening in the present case.



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Figure 3: Optimized negative EC-SERS spectra for E. coli K-12 bacteria using the immersion method (in phosphate buffer) for three separate trials on silver substrates using a 780 nm laser line with a laser power of 80 mW and an acquisition time of 30 seconds.

After optimizing the experimental conditions for the immersion method of bacterial deposition onto the WE with E. coli, it was important to establish if this method could be applied to other strains of bacteria to achieve EC-SERS profiling. To test this, B. megaterium was used, which is a Gram-positive strain of bacteria. The EC-SERS data could also be used as a means of comparison with the EC-SERS data for E. coli to determine if EC-SERS is able to differentiate between a Gram-positive and a Gram-negative strain of bacteria. Figure 4 A shows the cathodic progression of the SERS spectra and 4 B shows the EC-SERS comparison for B. megaterium, which was deposited onto a silver electrode using the immersion method. The cathodic progression of the SERS spectra indicate that the signal for B. megaterium benefits from the application of a negative potential, as was observed for E. coli. The optimized negative EC-SERS spectrum is -1.0 V, with the strongest signal being present at this potential. The EC-SERS comparison shows the improvement of the signal from the normal SERS spectrum (the in air spectrum) to the spectrum at -1.0 V. These results show promise for EC-SERS for bacterial screening, as two strains of bacteria with different cellular makeup behaved similarly in response to an applied voltage. This indicates that this method can be used to screen for multiple strains of bacteria without significant alteration of the method.


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Figure 4: (A) EC-SERS signal for B. megaterium K-12 on the AgNP modified SPE deposited using the immersion method in phosphate buffer at OCP and the cathodic progression from 0.0 V to -1.0 V in 100.0 mV increments and (B) EC-SERS comparison of the spectrum collected in air as well as at OCP and -1.0 V. Data was collected using a 780 nm laser line with a laser power of 80 mW and an acquisition time of 30 seconds. It was important to investigate the reproducibility of the EC-SERS signal produced using this method with B. megaterium. Figure 5 shows a comparison of the optimized negative spectra (at -1.0 V) for three separate experiments done using the immersion method of B. megaterium for EC-SERS screening. As was previously demonstrated with E. coli, the SERS spectrum obtained for B. megaterium was found to be reproducible. The signal intensities were again not reproducible enough for quantitative analysis, highlighting an area of this research to be optimized in the future.



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Figure 5: Optimized negative EC-SERS spectra for B. megaterium bacteria using the immersion method (in phosphate buffer) for three separate trials on silver substrates using a 780 nm laser line with a laser power of 80 mW and an acquisition time of 30 seconds. The great improvement of EC-SERS over normal SERS for the detection of bacteria can likely be attributed to a number of factors. The metal surface becomes less positively charged as the potential is stepped in the negative (cathodic) direction (potential of zero charge for Ag(poly) ≈ -0.95 V vs Ag/AgCl), causing the negatively charged chloride molecules to desorb from the surface at approximately -0.5 V, where the Ag-Cl peak disappears.16,21 Once the chloride is desorbed, this likely allows the biological material to better access the surface of the WE. ECSERS is also known to simulate more biologically relevant electric field conditions, which may encourage the bacteria to adhere to the surface of the electrode more so than it would with regular SERS.20,22


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Figure 6 shows an overlay of the average spectrum of E. coli and of B. megaterium collected using the immersion method in phosphate buffer with the spectra of the nucleotide breakdown products with the spectral intensities scaled for ease of comparison. The similarities between the spectra for both strains of bacteria and those of the breakdown products are visually evident, which indicated that cell wall / membrane components of the bacteria were not detected in this study. Visual analysis, however, is not a reliable means of spectral comparison, as it is only qualitative and prone to human error. Therefore, in order to compare the EC-SERS spectra obtained for bacteria to the spectra of the seven nucleotide breakdown products, a library database was constructed using Omnic® Specta containing the optimized negative EC-SERS spectra of the seven products of nucleotide degradation. By using software to compare the spectra, a quantitative result can be obtained that is not prone to operator error. The bacterial EC-SERS spectra were then run through this database to determine the percent match with each of the spectra in the database (the spectra of the nucleotide breakdown products). The percent match was generated based on the similarity of the spectrum being searched with those in the selected library. Table 1 shows the results of running three optimized negative EC-SERS spectra (-1.0 V spectra) of E. coli and B. megaterium through the database. It was found that the major contributors to the spectrum of E. coli were xanthine and guanine, with 80% and 74% matches, respectively. The results in Table 1 show that the major contributor to the EC-SERS spectrum of B. megaterium was adenine with a 74% match. Xanthine, hypoxanthine, and guanine all contributed approximately equally to the B. megaterium spectrum, with percent match values of ~50%.



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Figure 6: Overlay of average spectrum of E. coli and B. megaterium collected using the immersion method in phosphate buffer with those of the nucleotide breakdown products on AgNP modified SPEs using the 780 nm laser line at a power of 80 mW with an acquisition time of 30 seconds. Spectra are multiplied by noted factors for ease of comparison.

Table 1: Contributions of EC-SERS spectra of nucleotide breakdown products to the EC-SERS spectra of E. coli and B. megaterium collected using the immersion method in phosphate buffer. Nucleotide Breakdown Product Adenine Guanine Xanthine Hypoxanthine AMP Uric Acid Guanosine

% Match with E. coli 35 ± 6 74 ± 6 80 ± 4 41 ± 6 6±1 4±1 49 ± 9

% Match with B. megaterium 74 ± 4 55 ± 7 49 ± 6 55 ± 3 9±3 4±1 45 ± 4


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The above results show that by using a database of the EC-SERS spectra of the seven nucleotide breakdown products that contribute to the SERS spectra of bacteria, the strains E. coli and B. megaterium could be differentiated. Figure S2 shows a visual comparison of the optimized negative EC-SERS spectra taken at -1.0 V for E. coli and B. megaterium. This demonstrates the value of being able to numerically differentiate between the two strains, as they are both visually very similar with the exception of different relative intensities of the two major peaks. The spectra of both strains of bacteria had low spectral matches with uric acid and AMP, indicating that they either contribute very little to the EC-SERS spectrum of bacteria, or their spectral intensities were too weak to be visible in the spectrum that is dominated by the stronger spectral peaks of the other breakdown products. Overall, this database was able to provide a fast and efficient means to differentiate between E. coli and B. megaterium. SEM Analysis of EC-SERS Substrate with Bacteria An SEM image of the surface of the WE of the SPE after EC-SERS analysis (Figure 7) shows the E. coli K-12 bacteria on the AgNP modified surface. The E. coli K-12 bacteria are rodlike, and appear as dark shadows in the SEM image. Based on the SEM images of the WE after EC-SERS measurements were obtained, it is clear that there were not many bacterial cells on the surface. Assuming a 25 μm laser spot diameter at focus, on average there are approximately eight E. coli K-12 bacterial cells in the area for which a single SERS spectrum is collected.



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Figure 7: SEM image showing the surface of the AgNP WE with four E. coli K-12 bacterial cells taken at 5.0 kV. Arrows indicate location of bacterial cells.

An SEM image of the surface of the WE of the SPE after EC-SERS analysis (Figure 8) shows the B. megaterium bacteria on the AgNP coated surface. The B. megaterium bacteria are also rod-like, and appear as dark shadows in the SEM image. Again, assuming a 25 μm laser spot diameter at focus, on average there are approximately six B. megaterium bacterial cells in the area for which a SERS spectrum is collected, which is close to the average amount of E. coli K-12 cells that were within the laser spot. In both cases, the bacterial cells were imaged after the EC-SERS 22 ACS Paragon Plus Environment

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measurements were conducted, and indicate that the cells remain intact despite the electrochemical perturbation that was applied. Even though the bacterial cells are present on the electrode, it is clear from the SERS data obtained that the signal observed is due only to the small molecule metabolites, and is unrelated to the presence of these few cells. More recent findings from our group, which will be reported on in a forthcoming publication, indicate that the same EC-SERS signals are observed if the bacterial suspension is first filtered with a 0.2 m syringe filter prior to electrode incubation. Similar studies by others have also noted this observation that the SERS signal remains after the removal of bacterial cells.12 This observation further supports the conclusion that the SERS signal observed for bacterial systems are heavily dominated by contributions from small molecule metabolites, as was the conclusion of this current work.



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Figure 8. SEM image showing the surface of the AgNP WE with six B. megaterium bacterial cells taken at 5.0 kV. Arrows indicate the location of bacterial cells.

Conclusions This study investigated the detection and differentiation of Gram-negative E. coli K-12 and Gram-positive B. megaterium using EC-SERS, which is the first study to employ EC-SERS for bacterial screening. The results show improved spectra for EC-SERS of bacteria over normal SERS of bacteria, suggesting that EC-SERS can greatly improve upon the normal SERS performance as a potential screening method for bacteria. While EC-SERS of bacteria did not


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provide any spectral peaks from cell wall / membrane components of the Gram-positive or the Gram-negative bacteria, it was able to differentiate between the bacterial strains using a database of the nucleotide breakdown products that contribute to SERS spectra of bacteria in literature. Future studies will include adjusting the sample preparation method to allow for point-of-need screening and detecting bacteria from real-world samples, such as biological fluids and food products. In addition, optimization of this EC-SERS sensor for robust quantitative analysis is underway.

Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: SEM image of AgNPs, extinction spectrum for Ag colloidal sol, SERS comparisons for bacterial strains.

Conflicts of Interest The authors declare no competing financial interest.

Acknowledgments The authors would like to recognize Dr. Xiang Yang for his assistance with FE-SEM and Mrs. Carmen Cranley for providing the bacteria used in the study. C. L. Brosseau and C. Sit acknowledge funding support for this project from the Natural Sciences and Engineering Research Council. Infrastructure support was provided by the Canada Foundation for Innovation and the Nova Scotia Research and Innovation Trust. C.L. Brosseau acknowledges support from the Canada Research Chairs program.



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References (1) Ahmed, A.; Rushworth, J. V.; Hirst, N. A.; Millner, P. A. Biosensors for Whole-Cell Bacterial Detection. Clin. Microbiol. Rev. 2014, 27, 631-646. (2) Lazcka, O.; Del Campo E. J.; Muñoz, F. X. Pathogen detection: A perspective of traditional methods and biosensors. Biosens. Bioelectron. 2007, 22, 1205-1217. (3) Alocilja, E. C.; Radke. S. M. Market analysis of biosensors for food safety. Biosens. Bioelectron. 2003, 18, 841-846. (4) Hinks, J.; Han, E. J. Y.; Wang, V. B.; Seviour, T. W.; Marsili, E.; Loo J. S. C.; Wuertz, S. Napthoquinone glycolysides for bioelectronalaytical enumeration of the faecal indicator Escherichia coli. Microb. Biotechnol. 2016, 9, 749-757. (5) Cho, I. H.; Bhandari, P.; Patel, P.; Irudayaraj, J. Membrane filter-assisted surface-enhanced Raman spectroscopy for the rapid detection of E. coli 0157:H7 in ground beef. Biosens. Bioelectron. 2015, 64, 171-176. (6) Paniel, N.; Baudart, J.; Hayat, A.; Barthelmebs. L. Aptasensor and genosensor methods for detection of microbes in real world samples. Methods 2013, 64, 229-240. (7) Bumbrah, G. S.; Sharma, R. M. Raman spectroscopy – Basic principle, instrumentation and selected applications for the characterization of drugs and abuse. Egypt. J. Forensic Sci. 2016, 6, 209-215. (8) Kudelski, A. Analytical applications of Raman spectroscopy. Talanta 2008, 76, 1-8. (9) Haynes, C. L.; McFarland, A. D.; Van Duyne, R. P. Surface-Enhanced Raman Spectroscopy. Anal. Chem. 2005, 77, 338A-346A. (10) Sharma, B.; Frontiera, R. R.; Henry, A. I.; Ringe, E.; Van Duyne, R. P. SERS: Materials, applications, and the future. Mater. Today 2012, 15, 16-25. 26 ACS Paragon Plus Environment

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(11) Kneipp, K. Surface Enhanced Raman Scattering (SERS) of Nucleic Acids Adsorbed on Colloidal Silver Particles. J. Mol. Struct. 1986, 145, 173-179. (12) Premasiri, W. R.; Lee, J. C.; Sauer-Budge, A.; Théberge, R.; Costello, C. E.; Ziegler, L, D. The biochemical origins of the surface-enhanced Raman spectra of bacteria: a metabolomics profiling by SERS. Anal. Bioanal. Chem. 2016, 408, 4631-4647. (13) Patel, I. S.; Premasiri, W. D.; Moir, D. T.; Ziegler, L. D. Barcoding bacterial cells: a SERS‐based methodology for pathogen identification. J. Raman Spectrosc. 2008, 39, 16601672. (14) Abdelsalam, M. E.; Bartlett, P. N.; Baumber, J. J.; Cintra, S.; Kelf, T. A.; Russell, A. Electrochemical SERS at a structured gold surface. Electrochem. Commun. 2005, 7, 740744. (15) Wain, A. J.; O’Connell, M. A. Advances in surface-enhanced vibrational spectroscopy at electrochemical interfaces. Adv. Phys. 2017, 2, 188-209. (16) Karaballi, R. A.; Nel, A.; Krishnan, S.; Blackburn, J.; Brosseau, C. L. Development of an electrochemical surface-enhanced Raman spectroscopy (EC-SERS) aptasensor for direct detection of DNA hybridization. Phys. Chem. Chem. Phys. 2015, 17, 21356-21363. (17) Greene, B. H. C.; Alhatab, D. S.; Pye, C. C.; Brosseau, C. L. Electrochemical-Surface Enhanced Raman Spectroscopic (EC-SERS) Study of 6-Thiouric Acid: A Metabolite of the Chemotherapy Drug Azathioprine. J. Phys. Chem. C. 2017, 121, 8084-8090. (18) Robinson, A. M.; Harroun, S. G.; Bergman, J.; Brosseau, C. L. Portable Electrochemical Surface-Enhanced Raman Spectroscopy System for Routine Spectroelectrochemical Analysis. Anal. Chem. 2012, 84, 1760-1764.



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(19) Lynk, T. P.; Clarke, O. J. R.; Kesavan, N.; Brosseau, C. L. Development of a sustainable plasmon-enhanced spectroelectrochemical sensor using avocado pit (Persea americana) extract. Sens. Actuators B Chem. 2018, 257, 270-277. (20) Karaballi, R. A.; Merchant, S.; Power, S. R.; Brosseau, C. L. Electrochemical surfaceenhanced Raman spectroscopy (EC-SERS) study of the interaction between protein aggregates and biomimetic membranes. J. Phys. Chem. Chem. Phys. 2018, 20, 4513-4526. (21) Larkin, D.; Guyer, K. L.; Hupp, J. T.; Weaver, M. J. J. Determination of specific adsorption of some simple anions at a polycrystalline silver-aqueous interface using differential capacitance and kinetic probe techniques. Electroanal. Chem. 1982, 138, 401–423. (22) Vezvaie, M.; Brosseau, C. L.; Lipkowski, J. Electrochemical SERS study of a biomimetic membrane supported at a nanocavity patterned Ag electrode. Electrochim. Acta 2013, 110, 120-132.

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