Estimating Bacterial Concentrations in Fibrous Substrates Through a

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Estimating Bacterial Concentration in Fibrous Substrates Through a Combination of Scanning Electron Microscopy and ImageJ Tanmay Bera, Joshua Zhihua Xu, Pierre Alusta, Andrew Fong, Sean W. Linder, and Stephen Torosian Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b04862 • Publication Date (Web): 05 Mar 2019 Downloaded from http://pubs.acs.org on March 12, 2019

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Analytical Chemistry

Estimating Bacterial Concentration in Fibrous Substrates Through a Combination of Scanning Electron Microscopy and ImageJ

Tanmay Bera1,2*, Joshua Xu2, Pierre Alusta3, Andrew Fong1, Sean W. Linder4, Stephen D. Torosian5* 1- Arkansas Laboratory-Nanotechnology Core Facility (ARKL-NanoCore), Office of Regulatory Sciences, Office of Regulatory Affairs (ORS, ORA), U.S. FDA, Jefferson, AR 72079; 2- Division of Bioinformatics and Biostatistics, National Center for Toxicological Research (NCTR), U.S. FDA, Jefferson, AR 72079; 3- Division of Systems Biology, NCTR, U.S. FDA, Jefferson, AR 72079; 4- ORS, ORA, U.S. FDA, Jefferson, AR 72079; 5- Winchester Engineering and Analytical Center (WEAC), ORS, ORA, U.S. FDA, Winchester, MA 01890.

Abstract:

Conventional signal based micro-analytical techniques for estimating bacterial concentrations are often susceptible to false signals. A visual quantification, therefore, may compliment such techniques by providing additional information and support better management decisions in the event of outbreaks. Herein, we explore a method that combines Electron Microscopy (EM) and image analysis technique and allows both visualization and quantification of pathogenic bacteria adherent even to complex non-uniform substrates. Both the estimation and imaging parameters were optimized to reduce the estimation error (E%) close to ± 5%. The method was validated against conventional microbiological techniques such as optical density, flow cytometry and quantitative real-time PCR (qPCR). It could easily be tailored to estimate different species of pathogens such as E. coli O157, L. innocua, S. aureus, E. faecalis and B. anthracis on samples similar to real-time contamination scenarios. The present method is sensitive enough to detect ~100 bacterial CFU/mL but has the potential to estimate even lower concentrations with increased imaging and computational time. Overall, this imaging-based method may greatly complement any signal-based pathogen detection techniques, especially in negating false signals and therefore, may significantly contribute to the field of analytical microbiology and bio-chemistry.

* Authors of correspondence: Stephen Torosian, email: [email protected], Tanmay Bera, email: [email protected] Disclaimer: The views expressed in this work are those of the authors only and do not necessarily express the views/policies of the U.S.F.D.A. The mention of tradenames or specific manufacturers products are for clarification and should not be considered as an endorsement. 1

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Introduction: Pathogen concentration is often linked to their growth and spreading behavior. Thus, a proper detection and estimation of pathogens is critical in managing a contamination scenario and therefore, have significant implications in health and environmental safety. Some simple methods of estimation often used by microbiologists are colony counting or optical density measurements. For more specific quantification, more complex instrumentations such as qPCR, microarray, flow cytometry and even mass spectroscopy have been used1-5. More recent approaches include several types of biosensors where a bacterial concentration is transduced into electrical/optical or other signals resulting in an extremely efficient and repeatable estimation of various bacterial species68. These analytical techniques, both traditional and modern, are mostly signal based and does not allow visualization of the contaminant, which sometimes may cause misinterpretation9-10. Unlike, signal-based techniques, visual analytical methods allow simultaneous visualization and quantification of (biological) information, that allows the human mind to perceive and process the information more easily11-14. Additionally, visible techniques, when complemented with traditional approaches, may significantly help in minimizing false signals15. Recent advents in both visualizing tools and computational capabilities that allow automated and efficient means of quantifying visual information, motivated us to explore the possibility of quantifying microbes in a visual manner16-19. Electron microscopy (EM) is an extremely powerful tool that has been used to acquire images of pathogens, including viruses20. In the past, the use of EM in bacterial identification and quantification had been impaired due to expensive instrumentation and tedious sample preparation steps both of which required highly trained operators. However, modern EMs have become far more simple and user-friendly. Features, such as High Resolution (HR) and Field Emission (FE) enable the operator to image objects at spatial resolution (0.1-10 nm) that is simply beyond the capability of any other kind of microscope21-22. This allows the visualization of bacteria (or any other micro- or nano- sized features) with great morphological/structural clarity which may allow distinct identification and minimum confusion. The steps for sample preparation have also been significantly improved through automation, which allows efficient, consistent and convenient sample preparation. Additionally, it does not require prior sample information and expensive conjugating molecules (primary & secondary anti-bodies) to preferentially highlight the region of interest, as often practiced in fluorescence-based microscopy techniques. They have also become less expensive (with some models less expensive than mid-range confocal microscopes) and are more widely available to both academic and industrial settings, including government and industrial quality control/regulatory laboratories23-25. The present literature, however, suggests that EMs are still being predominantly used for visualization of pathogens, and less so for quantification26. The few attempts that have been made in this area are usually restricted to flat 2D substrates, mostly using transmission electron microscopy (TEM)27-28. Contrary to TEM, Scanning Electron Microscopy (SEM) offers a higher depth of field and larger sample space that allows uneven and much bigger samples to be imaged effortlessly. We believe, it would be more suitable for both imaging and estimating bacterial concentrations and are better suited for real-world samples29. Similarly, ImageJ, is an open source image analysis platform that is primarily used to analyze nano/microstructures on flat or carefully constructed substrates30. Unlike more computationally rigorous image analysis tools (which needs significant computing/programing skills), ImageJ is much simple to use and yet can be used to 2


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analyze a versatile range of visual information. Thus, we set out to explore if images acquired through SEM, could be processed using ImageJ to quantify and estimate pathogen concentrations even for substrates or environments that are non-uniform, three dimensional and complex. Herein, we report a simple and semi-automated method for estimating pathogen concentrations using EM as the imaging tool, and ImageJ as the analysis platform. The method aims to explore the possibility of pathogen estimation in a visual manner. We tested it on complex fibrous substrates due to prevalence of pathogens in such matrices and the difficulty in visualizing them satisfactorily. Substrates such as air and water filters, textiles, food products, medical bandages, protective masks, washing or cleaning scrubbers and wiping tissues are often mediums on or through which pathogens grow and propagate either naturally or potentially in the events of bioterrorism. Therefore, a method that can estimate different bacteria species in their natural medium may allow real-time visualization and estimation of pathogen concentration and may provide other insights such as their anchoring behavior, cellular mechanisms and reproduction rates. This, in turn, may help to better understand microbial interactions with their environment and the molecular mechanisms associated with their niche behavior. The combination of visual and quantitative analysis may add a newer prospective to bacteria-substrate interactions, which could be greatly beneficial towards research on biofilm formation or pathogen biosensor development. EM imaging also allows high spatial resolution along with elemental mapping capability, which may offer further information, especially to the research of microbiology and micro-analysis in the long run. More importantly, estimating pathogen concentration through image analysis opens up the possibility of automated pathogen detection/estimation through more advanced techniques such as machine learning and artificial intelligence. Such prospects therefore bestow this method with great potentials in improving food, health and environmental safety. Experimental methods: Bacterial culture, Sample preparation and SEM Imaging: Cultures of Escherichia coli O157 (ATCC 43894), Enterococcus faecalis (ATCC 19433), Staphylococcus aureus (ATCC 25923) and Bacillus anthracis (avirulent Sterne strain) were grown following the standard protocols. They were subsequently purified through centrifugation and resuspended in saline [phosphate buffer saline (PBS)]. Commercially available polymers (olefins: polypropylene, nylon) fibers, both as spun bound and woven mats, were used as the model fibrous substrates. The substrates were modified or functionalized for either through specific (antibody) or nonspecific binding (using poly lysine coating), after which they were exposed to bacterial suspensions and incubated at 4 0C for 2 to 12 hrs to ensure complete adhesion. They were then treated with 4% glutaraldehyde solution in PBS for ~5 min to inactivate the pathogen and subsequently rinsed with and preserved in PBS solutions. The ‘fixed’ samples were first sequentially dehydrated using ethanol solutions and then dried using a critical point dryer (Autosamdri®-815, from Tousimis Research Corporation) using liquid carbon dioxide, which is known to preserve the actual morphology of bacteria during EM. They were then sputter coated and imaged using FE-SEM (Zeiss Merlin). About 20-25 images were captured for each condition, without capturing multiple images from same region to ensure uniform representation of the whole sample. Image Processing: Grayscale SEM images (as .tiff format) were analyzed with ImageJ with the sequential steps, namely, Set Scale, Threshold, conversion to Binary and Watershed, and finally Analyze Particle (as illustrated schematically in the supplementary information Supplementary 3



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Figure S1). This yielded the outline of bacteria, along with their numbers and sizes. The Circularity and Size parameters, as needed for the estimation were optimized by comparing them to manually counted numbers and estimation errors such as average Estimation Error (E%), Cumulative Estimation Error (En%) (using the formula provided in the supplementary information) were plotted against the number of images (n) analyzed. It is important to note that this method is susceptible to image quality, region of interest, the pathogen and substrate of interest, and can be tuned accordingly. The same steps were repeated for various samples and also for different bacteria to obtain their total volume and most importantly, their size distribution. Studies on validation against conventional techniques, estimating the limit of detection and simulating contamination scenarios: The concentrations of the bacteria were estimated using conventional estimation techniques such as optical density (OD), flow cytometry and real time Polymerase Chain Reaction (qPCR). The bacterial concentrations in suspensions were first estimated using OD and Flow Cytometry (RAPID-B model 9013). However, OD or flow cytometry cannot be used to measure the concentration of adherent bacteria, for which qPCR was used. The substrates after exposure, were divided into two identical pieces with one used for SEM analysis and the other for qPCR. Bacterial estimates obtained from all the different studies were then compared to validate our estimation technique. However, for determining the limit of detection only, flow cytometry and qPCR were conducted as OD measurements are not accurate at lower bacterial concentrations. To mimic pathogen contamination scenarios, various bacterial species were exposed to different fibrous substrates to recreate commonly encountered contaminations. In order to showcase the robustness and diversity of this method, we used common bandages to mimic medical contamination, cotton swabs and tissue papers as samples from environmental and bioterrorism contamination, and ground lean ground beef as the substrate for food contamination. All the samples were processed and imaged as explained earlier. Results and discussion:

Coarse fibrous substrates: Figure 1 illustrates the method for estimating adherent bacterial cells on various fibrous substrates. Substrates with an average fiber diameter of ~250 µm are often used as water filters, nets, and cleaning scrubbers. Figure 1a shows one of several SEM images for this condition taken at 2,000× magnification. The fiber at this magnification appears flat with several E. coli cells dispersed on it at various orientations. The corresponding output image (Supplementary Figure S2a-ii) that yielded the outline, number and size of the intended feature ('signal'- green circles) along with some unwanted features ('noise'- red circles) and a 'miss hit'- (blue circles). Such noise and misshits were observed in many images leading to over- or under-estimations. Multiple iterations using various circularity and size ranges from the previous iteration improved data consistency, but the gain in accuracy does not quite justify the time and effort, hence were not repeated after the second iteration. On the contrary, using a larger number of images (or with larger number of adherent bacteria) did help converge E% and achieve a more precise measurement, as shown in Figure 1b. It is important to note that this method does not claim to yield an absolute value, but provides a practical solution which can be tuned to be accurate to the same order of magnitude. Medium fibrous substrates: Substrates with average fiber diameters of ~40 µm and ~20 µm find applications in water/air filters, medical sutures and bandages. For the sake of consistency and comparison, images were 4


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captured at 2,000× magnification for all the substrates. But for these substrates, the image frames were found to be much bigger than the width of individual fibers (Figures 1c & e). Hence, the image captured bacteria both on and away from the focal plane, along with the edges of the fiber that separates them. Our initial estimation using the whole image frame with the method described previously, showed extremely high errors and noises, mostly from the edges and from the features that are away from the focal plane. Thus, we introduced an additional step of selecting the part of the image where bacteria are only at the focal plane (Supplementary Figure S2 b & c). These images were processed subsequently to obtain bacterial counts (Figures 1d & f). This extra step significantly reduced the error and yielded E = -2.35% and E = -0.53% for substrates with ~40 µm and ~20 µm diameter fibers, respectively. However, in case of ~20 µm fibers, where the individual E reached close to 10%, the parameters En% and ń (minimum no. of images required for estimation) became relevant as they provided the logical number of images needed for this method to be statistically consistent. We also found that imaging along fibers (fiber edges horizontal to the frame) and selecting an area properly allowed one to analyze maximum area of interest which also made the process consistent and reduced error. Fine fibrous substrates: Figure 2a represents an image where the diameter of the fiber is ~0.5–1.0 µm, which is approximately of the same size range as that of bacteria themselves. This presented us with an extremely challenging condition for the estimation due to: i. more depth of field (fibers at different focal planes make it difficult to estimate bacteria lying away from the focal plane) ii. similarity in dimensions (i.e. the segment of fibers that is on the focal plane, when processed would appear like a bacterial cell and be miscounted during an estimation) (Figures 2b, 2c). However, we molded these very limitations to our advantage by cancelling out the over-estimation (due to the dimensional similarity) with the underestimation (due to larger depth of field), by analyzing a large set of images (n = 25). In this case however, the initial optimization process revealed that one set of parameters was inadequate, as the features that were on the focal plane and those that were away from the focal plane could not be separated simply by selecting a region. Hence, two sets of parameters were optimized, one with slightly constrained parameters to estimate the bacteria that were on the focal plane, and another with more relaxed parameters to yield the total number of bacteria (both on and away from the focal plane). Though the individual errors were significantly higher (Abs E > 15%), analyzing a larger number of images (n ≥ 25) allowed the En to fall below ± 5%. As evident from the above discussion, the accuracy of this method depends on intrinsic factors such as fiber dimension of the substrate and the bacteria (Supplementary Table S1). It can be noted that Abs E% are much greater when the dimension of fibers (or the substrate) approaches that of bacteria, (or feature of interest). In such conditions, analyzing larger number of images is required to converge E% and to make the estimation statistically consistent (Supplementary Figure S3). It was found that ń is closely associated with the substrate dimension and increased monotonically with Abs E%. However, it remained mostly unchanged to the order in which the images were processed as both the total and cumulative errors (E% and En%) eventually stabilized with larger n (Supplementary Figure S4). It was further noted that E% remained relatively independent of N (Number of estimated bacteria) but was highly influenced by sample defects or anomalies (Supplementary Figure S5). We observed close to zero %E and Abs E% when images of bare substrates (i.e. no bacterial adhesion) were analyzed, as control image-set, using this method. The 5



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occasional error originated from sample irregularities or anomalies as shown in Supplementary Figure S6. Similar to most methods of image analysis, this method too, yields better estimation when acquired images are free from irregularities and undergoes careful initial optimization that avoids significant under and over estimation. This is particularly important when the substrate dimension approaches that of bacteria, when the applicability of this method begins to stretch to its limits. Effects of Imaging Parameters Other than the substrate, imaging parameters such as the magnification and working distance may also play a role in the estimation process, as a better image simply provides better information. We observed that, when the magnification was too low (i.e. 500× or 1,000× for E. coli adherent to 20 µm dia. fibers) it was challenging to resolve bacterial cells properly. When the magnification was too high (e.g.,10,000× or 20,000×), bacterial cells failed to hold their shape/size during the processing and allowed only much smaller region to be analyzed. All of these factors made the estimation deviate significantly away from the actual value. Thus, a magnification that is good enough to resolve bacteria properly in a relatively wider frame, is best suited for this method (Supplementary Figure S7b). It is noteworthy that the magnification values are not universal and need to be adjusted based on the pathogen size. Similarly, an intermediate working distance (WD - the distance between the sample and the detector) also works best for this method (Supplementary Figure S7d). As shorter WD increased the depth-of-field (i.e. resulting edges of fibers to appear further away from the bacteria) and the longer WD reduces the sharpness or clarity of the images, both of which are contributing to errors during the estimation. Validation against Conventional Techniques & Present Limit of Estimation To evaluate the validity and the detection limit of this method, it was compared to analytical techniques conventionally used in microbiology. Flow cytometry and OD measurement revealed that the E. coli stock suspension had an initial concentration of about ~ 7.8×107 CFU/mL which was serially diluted to achieve concentrations ranging from ~7×105 to 7×102 CFU/mL (supplementary Figure S8). The EM images captured also reflect this varied concentration of adherent bacteria on fibrous substrates (Figure 3). Such images were used to estimate the number of adherent bacteria, and we observed a good correlation between all three modes of estimation namely, flow cytometry, the SEM method and qPCR throughout this range (~7×107 to ~7×102 CFU/mL). We did find that qPCR estimations were slightly lower than the SEM estimation numbers, which were lower than the flow cytometry values. For example, for the suspension measuring 7.0×105 CFU/mL using Flow Cytometry, when exposed to substrates, yielded counts as 6.7×105 and ~6.0×105CFU/mL using the SEM and qPCR methods respectively. This slightly lower count could be due to imperfect adhesion (i.e. not all bacteria in the suspension will adhere to the substrate, some may not bind or may be washed away during sample preparation) and/or due to the loss of genetic material during the lysis of adherent bacteria or other reasons pertaining to sample processing steps. At higher concentrations of bacteria (~105 to ~107 CFU/mL), the estimation method was relatively easy and less erroneous, as there were large number of adherent bacteria visible in each image. But it became increasingly challenging to spot and image bacterial cells, when their density 2 approached ~7×10 CFU/mL. Gathering larger number of images did allow us to estimate the bacterial concentration, however with much greater error. At ~7×101 CFU/mL or lower 6


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concentrations, it was challenging to image and make an estimation within reasonable time frame. 2 This makes our limit of detection to be ~10 CFU/mL at present, with the available resources at hand. We believe there could be a scope of improvement, if better instrumentation and more resources were committed to it. Extension to other bacteria species Figure 4 shows the extension of this method towards other species of bacteria that exhibit different shapes and sizes. Figure 4a shows the errors of estimation for L. innocua, another rod-shaped pathogen that is closely associated with foodborne diseases, especially deleterious for people with a compromised immune system. Figures 4b and 4c show the E% for two spherical shaped bacteria, namely E. faecalis and S. aureus, which are commonly associated with environmental and medical contamination, and have also raised great concern due to their acquired drug resistance. The SEM images for all three types of bacteria were collected, processed and analyzed in the same way as described earlier for E. coli O157. For each species, the size and circularity parameters were optimized first as they were different from those of E. coli, due to morphological differences amongst various bacterial species (as tabulated in Supplementary Table S2). Compared to the rodshaped E. coli or L. innocua, E. faecalis has a button-like diplococcus or short chain morphology, which allows individual cells to appear distinct in processed images. This makes their estimation relatively easier and reduces the errors to less than ± 1%, even when only 6-8 images were analyzed. On the contrary, S. aureus, also a spherical (i.e. coccus) bacterium, tends to grow in clusters. With significant proportion of the cells failing to retain their spherical shapes in the processed images, probably accounts for the higher magnitude in E% even with larger number of images analyzed (n ~15-20 images). This may further suggest that the method may be better suited for bacterial species that i. does not grow in clusters and ii. have a low enough concentration (that also prevents them from clustering), as clustered growth prevented proper visualization of the bacterial cells and contributes to higher estimation error. For all four strains of bacteria, processed output images yielded cellular sizes which, when collectively analyzed, can reveal not just the morphology of bacteria, but also their orientation relative to their surface of adhesion (Supplementary Figure S9). It is also critical to note that the size and circularity parameters for any species (or even sub-species) of bacteria may not be absolute and may require initial optimization for each condition. This is because – unlike particles – bacteria (or other pathogens) are alive and dynamic in nature, and their true morphology depends not just on their species (genetics), but also on culture conditions (epigenetics)31-32. Thus, the initial optimization process to roughly estimate the size and circularity parameters, is a good starting point. Additionally, it also provides the tailorability needed to estimate a variety of pathogens of different size and shapes. Simulation to contamination scenarios: This estimation method was then tested for samples simulating various contamination scenarios to evaluate their actual performance in real-time (Figure 5). To simulate a medical contamination, common medical bandages were soaked in ~103 CFU S. aureus suspension. A representative image of such a contamination is shown in Figure 5a, which E% of 5.43%. Figure 5b represents tap water spiked with ~103 CFU E. faecalis, absorbed onto a cotton swab, to mimic the environmental contamination or forensic samples, where contamination is checked in bodily fluids. To mimic a bioterrorism event, a similar concentration of B. anthracis suspension was 7



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sprinkled onto common tissue paper samples (Figure 5c). The distinct long rod-shaped bacterium cells could easily be estimated using this method. In the final example, we spiked ground beef with similar concentration of E. faecalis, as it had been observed in many events of food contamination, especially in grocery store meats. The fine fibrous nature of the ground beef matrix makes both visualization and estimation extremely challenging (Figure 5d). Moreover, the texture and topology of ground beef created significant variations in the depth of field with bacterial cells often stuck in crevasses, making both imaging and estimation quite challenging. Even in this case, careful imaging and proper implementation of this method helped us lower the E% to -3.65%. All 4 contamination scenarios, along with 4 different bacteria and their corresponding E%, have been tabulated in Supplementary Table S3. It can be observed that it was possible to reduce E% close to 5%, for all 4 conditions described. It is also important to note that the estimation parameters (i.e. circularity and size) may need to be suitably modified according to the bacterial species and their medium in order to reduce E%. Conclusions:

In summary, we described a simple strategy for estimating bacterial concentrations using SEM as the imaging technique and ImageJ as the analysis platform. This EM-based estimation technique uses an initial optimization step to determine estimation (size and circularity) parameters depending on the morphology of the bacteria. These parameters were then used to analyze a large pool of SEM images to quantify the overall bacterial concentrations with less than ± 5% errors. We observed that the accuracy of this estimation method was related to the curvature and unevenness of the substrate. The estimation error was higher when the unevenness or depth of the field increased, or when the dimension of the fibrous substrate approached that of bacteria. In such a scenario, the estimation parameters were suitably adjusted, and more images were analyzed to ensure E% was reduced below ± 5%. This EM-based visual estimation technique was compared to results obtained from conventional analytical tools such as flow cytometry, OD and qPCR, and was found to be congruent. This method, at its present state, cannot detect bacteria under 100 CFU/mL in a statistically consistent manner, using reasonable amount of imaging and analysis time. It was demonstrated to be a versatile method that can estimate various bacterial species such as E. coli, L. innocua, S. aureus and E. faecalis, with an error of ± 5%. However, the method is most effective for conditions, such as distinct and dispersed growth of bacterial cells due to the nature and concentration of bacteria, that allows proper visualization of bacterial cells. Finally, we demonstrated that this method can be used to successfully detect and estimate bacterial contaminations in medical, environmental and food samples, or in the event of a typical bioterrorism attack. We believe the merit of this method lies in its simplicity and in its ability to reliably provide visual evidence of a contamination. Thus, this visual method, used in combination with conventional signal-based methods, may therefore reduce the possibility of false positives. Further studies are underway to automate the whole process into a simple plugin that would enable easy pathogen estimation. Hence, our method not just opens up the prospect for SEMs to be used in analytical microbiology and bio-chemistry but may also have implications in health and environmental safety in the long run.

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Figures and Tables:

Table of Contents (TOC) graphic:

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Figure 1: Estimating the numbers of E. coli cells on fibrous substrates of various diameters. a & b: ~250 µm; c & d: ~40 µm; and e & f: ~20 µm. The top row (a, c & e) shows the representative SEM image for E. coli captured at 2,000× magnification. The yellow dashed line indicates the selected area used for the estimation for fibers with diameters 20 & 40 µm. The bottom row (b, d & f) shows the estimations errors for each condition. The histogram (in red) represents the E% (Average Estimation Error) and the blue dotted line represents the En% (Cumulative Estimation Error for the nth image analyzed). It can be noted that the ń (minimum no. of images required for estimation) gradually increases with the deceasing dimension of the substrates. The finer dimension of the substrates also showed higher N (number of adherent bacterial cells), due to better binding ability of the substrate.

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Figure 2: Estimation method when fiber diameter is ~0.5-1 µm. a: obtained SEM image showing bacteria adhering to fibers; the corresponding output images with parameters optimized for estimating the number of bacteria b: only on the focal plane and c: both on and away from the focal plane; the corresponding estimation errors, d: only on the focal plane and e: their total estimate (both on and away from the focal plane). In this, 25 images (nmax=25) were used to calculate the average error, due to a higher magnitude of individual errors resulting in higher values (~17 and 20) of ń in both cases.

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Figure 3: The validation and detection limit of this method. a: Representative SEM images of bacteria of different concentrations, scale bars: 5 µm; b: Estimated bacterial concentration through flow cytometry, SEM imaging and qPCR measurement; and c: the correlation between flow cytometry values to those from SEM and qPCR estimation, which seemed in good agreement with each other. At present state, our method cannot estimate bacteria below a concentration of ~7×10 CFU/mL, setting this as the present limit of detection.

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Figure 4: The extension of this method to estimate different species of adherent bacterial cells. a-c: average errors of estimation for a: L. innocua, b: E. faecalis and c: S. aureus. The SEM images show differences in morphology for d: E. coli O157, e: L. innocua, f: E. faecalis and g: S. aureus. The scale bars within micrographs are 2 µm, and those within insets are 0.5 µm. Finally, h-k, estimated size distributions of the 4 different bacteria, also highlights their structural differences, with Ã = mean area and w = half width and full maxima.

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Figure 5: Possible application of this method in various fields: a: S. aureus infection on a bandage (medical); b: E. faecalis on cotton swabs (environmental); c: B. anthracis on tissue paper (simulated bioterrorism); d: E. faecalis on ground beef (food). Scale bars are 5 µm.

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Acknowledgements: The authors would like to thank Dr. Pierre Grondin, of Avintiv Polymers for providing us with some of the polymeric substrates. T.B. is grateful to Arkansas Laboratories and Oak Ridge Institute for Science and Education (ORISE) for his ORISE fellowship. The authors are also thankful to Dr. Paul Howard of ORA, FDA; and Drs. Marli Azevedo and Zhen He of NCTR, FDA for their comments and suggestions during the internal review of the manuscript. The authors also acknowledge the Chief Scientist CORES grant (OCS-15-G-479), which supported the study. Supporting Information Available: The Supporting Information provides more details on the experimental section and can be referred to for the regents and/or other specifics on each step of the study. References:

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