Culture-Free Detection of Bacterial Pathogens on Plasmonic

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Culture-free Detection of Bacterial Pathogens on Plasmonic Nanopillar Arrays Using Rapid Raman Mapping Juhui Ko, Sung-Gyu Park, Sangyeop Lee, Xiaokun Wang, ChaeWon Mun, Sunho Kim, Dong-Ho Kim, and Jaebum Choo ACS Appl. Mater. Interfaces, Just Accepted Manuscript • DOI: 10.1021/acsami.7b15085 • Publication Date (Web): 06 Feb 2018 Downloaded from http://pubs.acs.org on February 7, 2018

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ACS Applied Materials & Interfaces

Culture-free Detection of Bacterial Pathogens on Plasmonic Nanopillar Arrays Using Rapid Raman Mapping

Juhui Ko,† Sung-Gyu Park,‡ Sangyeop Lee,† Xiaokun Wang,† Chaewon Mun,‡ Sunho Kim,‡ Dong-Ho Kim,‡,* and Jaebum Choo†,*

†

Department of Bionano Technology, Hanyang University, Ansan 15588, South Korea

‡

Advanced Functional Thin Films Department, Korea Institute of Materials Science (KIMS),

Changwon 51508, South Korea

J.K. and S.-G,P. contributed equally. *Correspondence should be addressed to J.C. (e-mail: [email protected]).

ACS Paragon Plus Environment

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ABSTRACT We utilized a fast Raman spectral mapping technique for fast detection of bacterial pathogens. Three-dimensional (3D) plasmonic nanopillar arrays were fabricated using the nanolithographyfree process consisting of maskless Ar plasma treatment of a polyethylene terephthalate (PET) substrate and subsequent metal deposition. Bacterial pathogens were immobilized on the positively charged poly(L-lysine) (PLL)-coated 3D plasmonic substrate through electrostatic interactions. Then the bacterial surfaces were selectively labeled with antibody-conjugated surface-enhanced Raman scattering (SERS) nanotags, and Raman mapping images were collected and statistically analyzed for quantitative analysis of bacteria. Salmonella typhimurium was selected as a model pathogen bacterium to confirm the efficacy of our SERS imaging technique. Minimum number of Raman mapping points with statistical reliability was determined to reduce assay time. It was possible to get a statistically reliable standard calibration curve for 529 pixels (laser spot with 60 µm interval), which required a total mapping time of 45 min to get a standard calibration curve for five different concentrations of bacteria in the 0∼106 CFU/mL range. No amplification step was necessary for quantification because low-abundance target bacteria could be measured using the Raman spectral mapping technique. Therefore, this approach allows accurate quantification of bacterial pathogens without any culturing or enrichment process.

KEYWORDS: surface-enhanced Raman scattering (SERS); culture-free detection; nanopillar pattern; bacterial pathogen; SERS nanotag


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1. INTRODUCTION Facile detection of bacterial pathogens is crucial for diagnosis of infectious diseases and foodborne illnesses. Bacteria colony counting is considered the gold standard in clinical diagnostics, but this requires a long culture time (up to several days) because of the preenrichment and selective differential plating steps.1,2 Real-time polymerase chain reaction (RTPCR) is also extensively used as a highly sensitive microbiological identification technique, but it also requires tedious sample pre-treatment steps including DNA extraction and signal amplification by a sequential thermo-cycling process.3,4 In particular, any erroneous amplification of contaminants or unrelated gene sequences leads to false-positive signals and incorrect identification in RT-PCR. Therefore, there is demand for a new sensing platform that allows rapid, sensitive and reliable detection of bacterial pathogens.5,6 Nanotechnology-based biosensors for fast detection of biological analytes have been investigated extensively in recent years.7,8 Among them, surface-enhanced Raman scattering (SERS)-based biosensors have attracted much attention because of their highly sensitive detection capability. Raman scattering is an extremely inefficient process with low scattering cross sections that are approximately 14 orders of magnitude smaller than the absorption cross sections of fluorescent dye molecules. To achieve a high sensitivity, the scattering intensity must be greatly increased. SERS has been shown promise in overcoming the low-sensitivity problems inherent in conventional Raman spectroscopy. Raman enhancement can take place either by increasing the electric field experienced by the molecule (electromagnetic enhancement) or by changing the molecular polarizability of the adsorbate (chemical enhancement). Using the SERS


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technique, it is known that the detection sensitivity is enhanced up to 10–14 orders of magnitude over conventional Raman spectroscopy. As described above, bacteria colony counting or RT-PCR methods need pre-enrichment1,2 or amplification processes3-6 because a very low concentration of target analytes is difficult to detect during the initial stages. However, in case of SERS detection, pre-enrichment process might not be necessary because a very small concentration of target analyte can be directly detected due to its high sensitivity.9-12 Various types of Raman reporter-labeled SERS nanotags have been used as detection probes in SERS-based biosensors.13-16 When reporter molecules are adsorbed onto the surface of gold or silver nanoparticles, their Raman signals are greatly enhanced at SERS-active sites, known as “hot junctions”, as a result of electromagnetic and chemical enhancement effects.17-19 SERS nanotags, functionalized with receptors (antibodies or aptamers), can be selectively accumulated on the surfaces of the bacteria, and a high density of electromagnetic “hot junctions” can be formed, which can potentially overcome the low detection sensitivity problems inherent in conventional methods for bacterial pathogens. There have been many studies about the application of SERS biosensor which is capable of isolating and detecting bacterial pathogens. SERS-based bacterial assays have been investigated in two different ways; one is the direct detection of specific bacterial species using their intrinsic SERS signals20,21 and the other is the indirect detection of bacteria using antibody-conjugated SERS nanotags.22-24 The indirect assay is more complicated than the direct assay but it is much more sensitive due to a larger Raman cross section of SERS nanotags. An important issue that remains to be resolved in SERS-based biosensor is the poor reproducibility of the SERS platforms. Two-dimensional (2D) or three-dimensional (3D) plasmonic substrates have been used for SERS-based bioassays. However, poor SERS signal


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uniformity has impeded reliable quantitative analysis of target molecules, due to the random and heterogeneous distribution of hot junctions over the plasmonic substrates.25-27 This non-uniform substrate makes it difficult to determine an accurate SERS intensity value by averaging several randomly selected point readings on the substrate. To resolve this problem, a fast Raman mapping technique was recently developed.28-30 This method has been successfully utilized to trace the distribution of specific biomarkers on cell surfaces as well as to quantify the amount of specific biomarker proteins in clinical fluids. Here, SERS images were obtained through continuous mapping of the characteristic Raman peak of SERS nanotags. Then, all SERS signals in each pixel were averaged to achieve a quantitative analysis on the mapped area. Therefore, this approach leads to an average ensemble effect of heterogeneous SERS intensities obtained from the randomly-distributed hot junctions.31-33 In this study, we report a SERS imaging-based biosensor for the quantitative evaluation of culture-free bacterial pathogens anchoring SERS nanotags onto bacteria-captured 3D Ag@Au core-shell nanopillars. High areal density (45/µm2) of plasmonic nanopillar array leads to a good signal uniformity and reproducibility. Using this highly uniform SERS detection platform, we were able to realize reliable SERS imaging-based detection of bacterial pathogens without any culturing or enrichment processes. The 3D Ag@Au core-shell nanopillar arrays was fabricated by a simple two-step sequence using Ar plasma etching of polyethylene terephthalate (PET) substrates and subsequent metal deposition onto polymer nanopillars by thermal evaporation. Positively charged poly(L-lysine) (PLL) was coated on the 3D Ag@Au substrate, and bacterial cells were captured on the surface through electrostatic interactions. Antibody-conjugated SERS nanotags were then added to selectively label the bacteria surfaces, and finally fast SERS mapping images were collected for quantitative analysis of bacteria. Salmonella typhimurium


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was selected as a model pathogen bacterium to confirm the efficacy of our experimental approach. S. typhimurium is a pathogenic gram-negative bacteria that causes food poisoning and intestinal infectious diseases.34, 35 To validate the diagnostic feasibility of our proposed SERSbased imaging technique on a large area SERS substrate, we developed several analytical models using statistical treatments of all the mapping data. First, Raman signals of SERS nanotags bound only to bacteria were extracted and averaged from all Raman mapping points to exclude data points for empty spaces on the 3D SERS substrate. Second, the minimum number of mapping points with statistical reliability was estimated to reduce the overall detection time of the assay. Herein, we propose a new sensing method for rapid and reliable quantification of culture-free bacterial pathogens.

2. EXPERIMENTAL SECTION 2.1. Materials Gold (III) chloride trihydrate (HAuCl4·3H2O), sodium citrate dehydrate (99%), 1-ethyl-3-(3dimethylaminopropyl carbodiimide (EDC), N-hydroxysuccinimide (NHS), bovine serum albumin (BSA), dihydrolipoic acid (DHLA), ethanolamine, and poly(L-lysine) solution were purchased from Sigma-Aldrich (St. Louis, MO, USA). Malachite green isothiocyanate (MGITC) was purchased from Invitrogen (Eugene, OR, USA). Phosphate buffered saline (PBS, pH 7.4) was purchased from Thermo Fisher Scientific Corporation (Carlsbad, CA, USA). Antibodies for Salmonella were purchased from Abcam (Cambridge, UK). Deionized water was purified using a Milli-Q water purification system (Millipore Corporation, Billerica, MA, USA).


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2.2. Preparation of microorganism samples Salmonella typhimurium was supplied by the Korea Centers for Disease Control and Prevention. Before the microbiological experiment, all samples and glassware were sterilized by autoclaving at 121°C for 30 min. Cultures of Salmonella strains were grown on nutrient agar overnight, transferred into an Erlenmeyer flask containing Luria-Bertani nutrient broth at an initial optical density (OD600) of 0.1 at 600 nm, and allowed to grow at 37°C under 1,000 rpm rotation. Cultures were centrifuged and washed twice with PBS to yield final bacterial solutions. Then the bacteria were stored in a refrigerator at 4°C for further use.

2.3. Preparation of SERS nanotags AuNPs were synthesized using the citrate-reduction method reported by Frens.36 In brief, 100 mL of 0.01% gold chloride trihydrate solution was heated to its boiling point, and then 1.0 mL of 1% trisodium citrate dehydrate solution was added under vigorous stirring. Within a few seconds, the solution changed from faintly blue to brilliant red, indicating the formation of AuNPs. After the mixture was boiled for 20 min, it was removed from the heat and stirred for 1 h. To prepare SERS-active nanoprobes, 0.5 µL of 0.1 mM MGITC was added to 1.0 mL of 0.1 nM AuNPs, and the mixture was reacted for 1 h under stirring. A 2.5 µL aliquot of 0.1 mM DHLA was first added into 1 mL of AuNP solution. After incubation for 1 h, 2.5 µL of 0.1 mM EDC and NHS were added and allowed to react with the activated –COOH terminal groups of the DHLA molecules for 15 min. Finally, 1.0 µL of 1 mg/mL anti-Salmonella antibody (ab35156) was added to the NHS-activated AuNPs and reacted for 1 h. Unreacted NHS groups on the surfaces of the AuNPs were deactivated by adding 2.5 µL of 0.1 mM ethanolamine for 20


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min. Nonspecific binding chemicals were removed by centrifuging, and the final SERS nanotags were washed with PBS buffer solution three times.

2.4. Maskless fabrication of 3D plasmonic nanopillar arrays A PET polymer substrate with a thickness of 125 µm was purchased from Panac Inc. and was used without modification. PET plasma treatment was performed using a custom-built 13.56 MHz RF ion etching instrument (SNTEK, Co. Ltd). The inlet Ar flow rate and the working pressure were fixed at 5 standard cubic centimeters per minute (sccm) and 80 mTorr during plasma treatment (3 min). The plasma power was 100 W. A shadow mask pattern comprising a circular hole with a diameter of 2 mm and a period of 6 mm was designed. A shadow mask with a 7×7 (detection points) array was then firmly attached to the PET film after Ar plasma treatment. 200 nm-thick Ag and 12 nm-thick Au core-shell nanostructures were directly grown on the PET nanopillars using a thermal evaporation system (SNTEK, Co. Ltd) with a deposition rate of 1.8 Å/s and 2.2 Å/s, respectively. Base pressure of the chamber was 5×10-6 Torr.

2.5. Immunoassays SERS substrates (2×2 mm2) were immersed in 0.01% poly(L-lysine) (PLL) aqueous solution for 24 h, and then air-dried for 12 h. Positively charged PLL-coated SERS substrates allow for highly efficient loading of bacterial cells on their surfaces through electrostatic interactions. Six different concentrations of Salmonella typhimurium bacteria ranging from 106 to 0 CFU mL-1 were dropped onto each well and incubated for 30 min with low-speed shaking on an agitator at room temperature. The substrate was washed with PBS buffer, rinsed to remove unbound bacteria, and then blocked with PBS containing 1% human serum albumin for 30 min at room


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temperature. Finally, 100 µL of 0.01 nM anti- Salmonella antibody-conjugated SERS nanotags were dropped onto each well and reactions were allowed to proceed for 30 min to form immunocomplexes. Prior to SERS detection, the substrate was rinsed once again and dried. Surface morphologies were characterized by Field emission scanning electron microscopy (FESEM; JEOL JSM-6700F).

2.6. SERS measurement and data analysis Raman measurements were performed using a Renishaw inVia Raman microscope system (Renishaw, UK). A Renishaw He–Ne laser, operating at a wavelength (λ) of 632.8 nm with a laser power of 0.3 mW was used as the excitation source. A holographic notch filter located in the collection path was used to remove the Rayleigh line from the collected Raman scattering. Raman scattering data were collected using a charge-coupled device (CCD) camera. All spectra were calibrated to the 520 cm-1 silicon line. An additional CCD camera was fitted to an optical microscope to obtain optical images, and a ×20 objective lens was used to focus the laser on the SERS substrate. The exposure time was 1 s and the detection spectral range was 750-1800 cm-1.

3. RESULTS AND DISCUSSION 3.1. Fabrication and characterizations of plasmonic nanopillars Fabrication of the 3D Ag@Au core-shell nanopillars was based on maskless plasma treatment of a smooth polymer surface.37,38 In this study, we treated PET surfaces with maskless Ar plasma to generate a high areal density of polymer nanopillars (Figure 1a). Ag@Au core-shell nanopillars were then formed by thermally evaporating a 200 nm-thick Ag layer and a 12 nm-


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thick Au layer sequentially onto the polymer nanopillar layers. These Ag@Au core-shell nanopillars prevent photocatalytic degradation of biomolecules on Ag nanostructures.39 Additionally, these Ag@Au nanostructures

improve air stability compared to Ag

nanostructures.40-42 Our nanofabrication strategy has clear advantages over other nanolithography processes in that it is a cost-effective high-throughput method of producing wafer-scale plasmonic substrates. Figure 1b shows a patterned large-area (4×4 cm2) plasmonic substrate with a pattern of 49 circles (7×7 wells, each well enclosed with Ag@Au core-shell nanopillars). These plasmonic nanopillar-patterned arrays can be also used for SERS analysis of multiple samples on a single substrate. It is also noteworthy that our nanopillar array is useful for direct transfer of analyte molecules because it is highly flexible, as shown in Figure 1b. Figures 1c and 1d show scanning electron microscopy (SEM) images (different scales) of Ag@Au core-shell nanopillar structures after 3 min of Ar plasma treatment of the PET substrate and subsequent evaporation of the 200 nm Ag layer with a 12 nm Au layer shell. Average diameter and areal density of Ag@Au core-shell nanopillars were estimated to be 75 nm and 45/µm2 (or 5.6×108/one detection point), respectively. Plasmonic nanopillars with a high aspect ratio (AR) of 3 were obtained after 3 min of Ar plasma treatment (Figure 1d). The aspect ratio and areal density could be controlled by tuning operating parameters. To investigate the effects of 3D plasmonic nanopillar structures on SERS signal enhancement, 0.01 mM malachite green isothiocyanate (MGITC, Raman reporter molecule) was labeled on 2D Au smooth and 3D Ag@Au core-shell substrates. Figures 2a and 2b show schematic illustrations (left), top-view SEM images (middle), and Raman mapping images (right) for the 2D and 3D gold substrates, respectively. Raman signals were measured using a point mapping method with a 20× objective lens, and the laser spot size was 1 µm. An x-y translational stage was scanned in


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1 µm × 1 µm steps over a 50 µm (x-axis) and 50 µm (y-axis) range by the computer control, and a total of 2500 Raman spectra were obtained for the 2D and 3D substrates. Here, the average Raman peak intensity of MGITC at 1616 cm-1 for the 3D SERS substrate was approximately two orders of magnitude stronger than that for the 2D substrate. This demonstrated that a stronger SERS enhancement effect was obtained through enhancement of localized surface plasmon effects among dense 3D core-shell nanopillars. A good SERS substrate exhibits not only high enhancement capability but also good signal uniformity. In Figures 2c and 2d, the Raman spectra and intensity distributions of 0.01 mM MGITC, recorded from 50 randomly selected spots for 2D (left) and 3D (right) SERS substrates, are displayed. Relative standard deviations (RSDs) of the Raman peak intensity of MGITC at 1616 cm-1 were 10.99% (2D substrate) and 4.26% (3D substrate), respectively. This indicates that signal uniformity for the 3D substrate was much better than that for the 2D substrate. The high areal density (45/µm2) of 3D Ag@Au core-shell nanopillars significantly improved uniformity by reducing spot-to-spot fluctuations. Based on our experimental data, we concluded that our 3D Ag@Au core-shell substrate demonstrated a good signal uniformity as well as a high SERS enhancement capability. In addition, the enhancement factor (EF) and limit of detection (LOD) of MGITC were determined by the previously reported methods.43,44 Details for their determination were described in supporting information (Figures S1 and S2). Their EF and LOD were determined to be 2.30 × 106 and 6.65 ×10−10 M, respectively for the 3D nanopillar substrate. The final goal of this research was the development of an SERS-based imaging technique for bacteria detection that does not require any culturing or enrichment processes. Accordingly, we utilized SERS nanotags to further enhance Raman signals by inducing electromagnetic coupling effects between AuNPs and 3D Ag@Au core-shell nanopillar structures. To investigate the


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enhancement effect of AuNPs combined with 3D Ag@Au core-shell substrate, 40 nm AuNPs were attached to 3D nanopillar structures using aminothiophenol (ATP). First, ATP molecules were immobilized on the pillar surfaces through Au-S covalent bonds, and then AuNPs were bound to the terminal –NH2 groups of ATP through electrostatic interactions between the positively charged amine groups and negatively charged AuNPs. ATP can be used as a Raman reporter molecule because it has considerable SERS signal intensity. Schematic illustrations (left), corresponding top-view SEM images (middle), and SERS-mapping images (right) for 3D and AuNP-bound 3D substrates are shown in Figures 3a and 3b, respectively. As shown in the SEM image in Figure 3b, nanoparticles were efficiently attached to the surfaces of the 3D nanopillar structures by ATP. Their corresponding Raman mapping images in Figure 3a and 3b demonstrate that AuNPs greatly enhanced the ATP Raman signals. Raman mapping images were achieved using a point-mapping method in 100 nm×100 nm steps over a 10 µm (x-axis) and 10 µm (y-axis) range (total 10,000 pixels). Here, the SERS mapping image color was displayed by a color-decoding method using the variation in Raman peak intensity of ATP at 1520 cm-1. The brighter the image pixel, the higher the concentration of AuNPs attached to the 3D substrate. To achieve more reproducible data, the Raman signals for 10,000 pixel points were averaged to evaluate their enhancement effects. Figure 3c compares the Raman spectra of ATP for the 3D substrate (red line) with that of the AuNP-bound 3D substrate (blue line). As shown in this figure, the SERS signals were enhanced approximately three times by AuNPs attachment because additional hot junctions emerged from the nanogaps between AuNPs and 3D nanopillars. It was previously reported that SERS signals are greatly enhanced by electromagnetic coupling of localized surface plasmon of gold nanoparticles with surface plasmon polariton of the metal surface underneath.45


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3.2. Raman mapping for bacterial pathogens To apply the SERS imaging-based detection technique for quantitative evaluation of bacterial pathogens, target bacteria need to be captured on the surface of the SERS substrate. A convenient method to immobilize bacterial pathogens on a 3D SERS substrate is to exploit electrostatic interactions between the bacterial wall and the surface of the SERS substrate. For this purpose, the 3D substrate was coated with 0.01% PLL aqueous solution. Negatively charged bacteria were electrostatically fixed on the positively charged PLL-coated SERS substrate. Then antibody-conjugated SERS nanotags were selectively immobilized on the surface wall of Salmonella typhimurium bacteria. A schematic illustration of our SERS-based imaging sensor for the selective detection of bacteria is shown in Figure 4a. Salmonella typhimurium bacteria were fixed on the PLL-coated 3D SERS substrate, and then antibody-conjugated SERS nanotags were bound on the surface membrane of the bacteria. A top-view SEM image of SERS nanotaglabeled bacteria captured on the PLL-coated 3D SERS substrate is provided in Figure 4b. Insert SEM image on the upper left side demonstrates that antibody-conjugated SERS nanotags successfully combined with the bacteria. Because there were multiple antibodies on the surfaces of AuNPs, some AuNPs contacted nanopillars at the edges of the bacteria. Then, hot junctions emerged from the nanogaps between AuNPs and the Ag@Au core-shell nanopillar surfaces. As previously reported, Raman signals of SERS nanotags on 3D nanostructures are much stronger than those based on a 2D flat surface. Figure 4c demonstrates Salmonella typhimurium antibodyconjugated SERS nanotags used in this study. The size, shape, antibody conjugation conditions, and Raman signal properties of SERS nanotags were characterized using transmission electron microscopy (TEM), dynamic light scattering (DLS), Raman spectroscopy, and UV/Vis


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absorption measurements (Figure S3). The average Raman peak intensity of MGITC for the 3D SERS substrate is at least three orders of magnitude stronger than that for the 2D substrate. This indicates that the electromagnetic field enhancement resulting from the excitation of localized surface plasmon resonance at hot spots strongly depends on the nanoscale structure of SERS substrate. Although SERS nanotags are not close to the surface in the case of pathogenic bacteria, SERS nanoparticles bound onto the edges of bacteria are expected to induce a strong electromagnetic coupling effect. Therefore, the enhancement effect for 3D substrate is much stronger than that for 2D substrate form the SERS mapping images as shown in Figure S4. Figure 5a shows SERS mapping images measured with a peak intensity at 1615 cm-1 for different concentrations of Salmonella typhimurium. Mapping images of 140×140 pixels (1 pixel = 15 µm × 15 µm) were collected for various concentrations in the range of 0 ~ 106 CFU/mL. Here, the SERS mapping area for each concentration of bacteria was 2100 µm × 2100 µm, and the exposure time and accumulation numbers were 0.5 s and 1, respectively. To cover this area, 19,600 Raman spectra were collected for each concentration of bacteria, and it took approximately 4 hr to obtain SERS mapping images for each concentration. The total number of pixels in one detection point was estimated to be 12,194. It is important to note that the Raman mapping intensities in the detection area were not uniform because Salmonella typhimurium bacteria were randomly loaded on the nanopillar substrate, as shown in Figure 4b. Inside each detection point, 0.1 nM SERS nanotags immobilized on the surfaces of the bacteria appeared as red dots but other areas appeared dark, as shown in Figure 5a. With an increase in the bacteria concentration from 0 to 106 CFU/mL, more SERS nanotags attached to the bacteria, leading to an increase in the number of red dots.


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3.3. SERS imaging-based platform for culture-free detection of bacterial pathogens To assess the diagnostic feasibility of our proposed SERS-based imaging technique, SERS signals obtained from the SERS nanotags, which were only bound to bacteria, should be extracted and averaged from the SERS mapping images. A plot of relative Raman signal intensities at 1615 cm-1 for all 12,194 pixel points for various concentrations of bacteria is shown in Figure 5b. Only Raman intensity distributions above the threshold line (red), contributed by SERS nanotags attached to bacteria, were considered in the statistical analyses. Raman intensity distributions below the threshold line were not considered because they were measured from empty areas without any bacteria. Here, the threshold value was defined as the detection limit based on the optical density from the instrument, which we calculated from the average signal intensity and standard deviation (SD) for the negative control (blank sample). In other words, it was determined from the average Raman signal intensity for 0 CFU/mL (average intensity+3×SD).46,47 When Raman point mappings were performed for the internal square area of the circle, it took approximately 2 hr to collect 8,281 pixel points (laser spot with 15 µm interval) for each concentration of bacteria. To decrease assay time, the total number of Raman mapping points was decreased sequentially by increasing the interval of the laser spot. Assay times and SDs for 105 CFU/mL of bacteria as the total number of Raman mapping points was decreased are compared in Table 1. When the interval of the laser spot increased from 15 µm to 240 µm, the number of detection points decreased from 8,281 to 36 pixels, and the corresponding assay time decreased from 2 hours to 36 sec. Relative Raman signal intensity distributions at 1615 cm-1 for different numbers of Raman mapping points are displayed in Figure S5. The number of mapping points over the threshold line for various concentrations of bacteria are displayed in Figure 6a. Corresponding


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standard curves as a function of bacteria concentration for different numbers of total mapping points are plotted in Figure 6b. SDs from three measurements are indicated by error bars. The results indicate that the standard calibration curve fit very well even when the number of mapping points was reduced to 529 pixels (60 µm interval). For this number of mapping points, it took 45 min (9 min for each concentration) to obtain a full calibration curve. However, the SD for each concentration increased greatly when the number of mapping points was reduced from 529 pixels to 144 pixels (120 µm interval), suggesting that using this number of pixels, accurate quantitative analysis of bacteria is not possible (Figure 6b). The SD increased from 1.65 (529 pixels) to 2.57 (144 pixels) as shown in Table 1. Based on our statistical analysis, we concluded that the minimum number of mapping points for reliable analysis of bacteria was 529 pixels, with a mapping time of 9 min required for each concentration of bacteria.

4. CONCLUSIONS We developed a SERS-imaging based assay platform that uses a fast mapping technique for culture-free detection of Salmonella typhimurium bacteria. For this purpose, we fabricated a 3D Ag@Au core-shell nanopillar substrate to achieve better structural uniformity and controllability for SERS detection. Using this homogeneous SERS substrate, we realized reliable SERS imaging of bacterial pathogens. Our Raman imaging method based on a fast mapping technique, combined with a 3D SERS substrate and SERS nanotags, allows a reliable quantification of bacterial pathogens. Since a very low concentration of target bacteria can be directly quantified using an SERS-based imaging sensor, this technique does not require pre-enrichment or amplification processes, unlike conventional detection methods. To validate the proposed SERS-


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based imaging method, we performed statistical analyses of all of the mapping spectra. It was possible to get a statistically reliable standard calibration curve for 529 pixels (laser spot with 60 µm interval), which required a total mapping time of 45 min to get a standard calibration curve for five different concentrations of bacteria in the 0∼106 CFU/mL range. In the case of RT-PCR, also known as quantitative PCR (qPCR), it took approximately 2 hr to obtain assay results because of DNA extraction and a long amplification time by repetitive thermos-cycling steps. The SERS-based fast mapping technique using a highly uniform 3D SERS substrate also decreases assay cost since it only requires minimal handling steps of bacterial pathogens. Furthermore, recently developed portable Raman systems with a low price also contribute to the reduction of assay cost. This new assay technique opens up new avenues for quantification of bacterial pathogens without any culturing or enrichment process. This approach also has great potential to be modified for use for the estimation of other microorganisms.

ASSOCIATED CONTENT Supporting Information TEM, DLS, Raman, and UV/Vis spectral data for the characterization of SERS nanotags; distributions of relative Raman signal intensities at 1615 cm-1 for different laser spot intervals from 15 µm (8,281 pixels) to 240 µm (36 pixels).

AUTHOR INFORMATION Corresponding Authors *E-mail: [email protected] *E-mail: [email protected]


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Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Funding National Research Foundation of Korea (R11-2008-0061852 and K20904000004-12A050000410). Ministry of Agriculture, Food and Rural Affairs (MAFRA-316080-04). Korean Institute of Materials Science (PNK 5060) Agency for Chemical & Biological Detection Research Center (CBDRC) Notes The authors declare no competing financial interest.

ACKNOWLEDGEMENTS The National Research Foundation of Korea supported this work through grant numbers R112008-0061852 and K20904000004-12A0500-00410. This work was supported from the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through the Advanced Production Technology Development Program funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA-316080-04). This work was also supported by the Fundamental Research Program (PNK 5060) of the Korean Institute of Materials Science (KIMS) and the Agency for Defense Development through Chemical & Biological Detection Research Center (CBDRC).

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Figure 1. (a) Schematic illustration of the maskless fabrication of the 3D plasmonic nanopillar array. (b) 7×7 SERS arrays over a large area (16 cm2). SEM images of (c) top view and (d) tilted view of the Ag@Au core-shell nanopillar array. Inset in (d) illustrates Ag@Au core-shell nanopillars

with

an

aspect


ratio

of

3.

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Figure 2. Schematic illustrations (left), SEM (middle), and Raman mapping (right) images of Raman reporter MGITC-labeled (a) 2D Au and (b) 3D Ag@Au substrates. Raman mapping images of MGITC were measured at the 1616 cm-1 peak over a 0.05×0.05 mm2 range. The scale bars display the color decoding for different Raman intensities. (c) SERS spectra of MGITC measured for 50 spots chosen randomly from 2D (left) and 3D (right) substrates. (d) Raman intensity distributions of the peaks at 1616 cm-1 for 2D (left) and 3D (right) substrates.


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Figure 3. Schematic animation (left), SEM (middle), and Raman mapping (right) images of 4-ATP on (a) 3D and (b) AuNP-bound 3D substrates. The scale bars display the color decoding for different Raman intensities. (c) Comparison of average Raman spectra of ATP for the 3D substrate (red line) with that for the AuNP-bound 3D substrate (blue line).


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Figure 4. (a) Schematic illustration of the SERS-based imaging sensor for the selective detection of bacteria on a 3D substrate. Antibody-conjugated SERS nanotags were bound on the surface membrane of bacteria. (b) SEM images of SERS nanotag-labeled bacteria captured on the PLL-coated 3D SERS substrate. Insert SEM image in the upper left side demonstrates that antibodyconjugated SERS nanotags combined successfully with bacteria. (c) Schematic illustration of Salmonella typhimurium antibodyconjugated

SERS


nanotags.

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Figure 5. (a) Raman mapping images of Salmonella typhimurium in the 0 ~ 106 CFU/mL range. Inside each round circle, SERS nanotags immobilized on the surface of bacteria appeared as red dots but other areas appeared dark. (b) Distributions of relative Raman signal intensities at 1615 cm-1 of 12,194 mapping points for different concentrations of bacteria. Only Raman intensity distributions above the threshold line (red) were considered in the statistical analysis.

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Figure 6. (a) Mapping points over the threshold line (%) in the 0 ~ 106 CFU/mL range. (b) Corresponding standard calibration curves as a function of bacteria concentration for different numbers of total mapping points. Error bars indicate standard deviations of three measurements. 31 ACS Paragon Plus Environment

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Table 1. Statistical analysis of mapping points over the threshold line (105 CFU/mL) for reliable quantification of bacterial pathogens

Total Number of Mapping Points 8281

2116

961

529

144

36

Detection Interval of Laser Spot (µm)

15

30

45

60

120

240

Number of Points over Threshold Line

3,770

949

432

241

67

17

Points over Threshold line (%)

45.52

44.86

44.92

45.50

46.28

46.56

Standard Deviation

-

0.951

1.18

1.65

2.57

8.28

RSD (%)

-

2.12

2.63

3.63

5.55

17.79

Analysis Time (min.)

120

36

16

9

2.4

0.6


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TOC


Culture-Free Detection of Bacterial Pathogens on Plasmonic

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