Unraveling Antimicrobial Susceptibility of Bacterial ... - ACS Publications

Unraveling Antimicrobial Susceptibility of Bacterial Networks on Micropillar Architectures Using Intrinsic Phase-Shift Spectroscopy Heidi Leonard,† Sarel Halachmi,‡ Nadav Ben-Dov,† Ofer Nativ,‡ and Ester Segal*,†,§ †

Department of Biotechnology and Food Engineering, ‡Department of Urology, Bnai Zion Medical Center, Faculty of Medicine, and The Russell Berrie Nanotechnology Institute, Technion−Israel Institute of Technology, Haifa 3200003, Israel

§

S Supporting Information *

ABSTRACT: With global antimicrobial resistance becoming increasingly detrimental to society, improving current clinical antimicrobial susceptibility testing (AST) is crucial to allow physicians to initiate appropriate antibiotic treatment as early as possible, reducing not only mortality rates but also the emergence of resistant pathogens. In this work, we tackle the main bottlenecks in clinical AST by designing biofunctionalized silicon micropillar arrays to provide both a preferable solid−liquid interface for bacteria networking and a simultaneous transducing element that monitors the response of bacteria when exposed to chosen antibiotics in real time. We harness the intrinsic ability of the micropillar architectures to relay optical phase-shift reflectometric interference spectroscopic measurements (referred to as PRISM) and employ it as a platform for culture-free, label-free phenotypic AST. The responses of E. coli to various concentrations of five clinically relevant antibiotics are optically tracked by PRISM, allowing for the minimum inhibitory concentration (MIC) values to be determined and compared to both standard broth microdilution testing and clinic-based automated AST system readouts. Capture of bacteria within these microtopologies, followed by incubation of the cells with the appropriate antibiotic solution, yields rapid determinations of antibiotic susceptibility. This platform not only provides accurate MIC determinations in a rapid manner (total assay time of 2−3 h versus 8 h with automated AST systems) but can also be employed as an advantageous method to differentiate bacteriostatic and bactericidal antibiotics. KEYWORDS: antimicrobial susceptibility, real-time antimicrobial susceptibility tests, bacterial resistance, micropillars, diffraction grating spectroscopy

T

health organizations, such as the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO), emphasize the urgency in minimizing the extensive use of antibiotics11 and improving current clinical diagnostics of infections, allowing rapid and accurate determination of the appropriate antibiotic for an infection.12,13 In particular, improving and expediting the methods to determine the type and minimum inhibitory concentration (MIC) of an antimicrobial agent that will be most effective against an infection, namely, antimicrobial susceptibility testing (AST), will ensure that patients are prescribed the correct antibiotic in a timely manner, reducing episodes of resistance, and in turn saving more lives and reducing the burden on healthcare systems.3,14,15

he extensive use of antibiotics, approximated at one million tons since the 1940s, combined with their inappropriate prescription and the lack of newly developed antimicrobial drugs, has resulted in a global antimicrobial resistance crisis.1,2 By the year 2050, antimicrobial resistance (AMR) is predicted to cost the world over $100 trillion cumulatively and claim 10 million lives per year, surpassing cancer to become the leading cause of death worldwide.3,4 This prediction came one step closer to reality in 2015, when Escherichia coli (E. coli) bearing a plasmid-mediated resistance gene to colistin, a last resort antibiotic, was discovered in people and livestock in China,5 followed by its spread to Europe,6,7 Canada,8 and the USA,9 where it was recently identified in 2016. With plasmid-mediated resistance, resistance to last-resort antibiotics can be passed down not only to daughter cells but also to neighboring, unrelated cells via several different mechanisms of horizontal gene transfer, making it exceptionally difficult to control.10 Thus, major © 2017 American Chemical Society

Received: March 30, 2017 Accepted: May 9, 2017 Published: May 9, 2017 6167

DOI: 10.1021/acsnano.7b02217 ACS Nano 2017, 11, 6167−6177

Article

www.acsnano.org

Article

ACS Nano Classic clinical AST is commonly performed manually using laborious techniques that can require at least 18 h,16 such as the disk diffusion method and broth microdilution (BMD) testing, which has attained reference standard status for all other AST methods. Presently, AST is routinely accomplished using either conventional manual methods or growth-dependent automated systems, such as the BD Phoenix (Becton, Dickinson and Company), the Microscan WalkAway (Beckman Coulter, Inc.), or the VITEK 2 (bioMérieux SA): all of which are BMD-based. Critical limitations of these methods include the need for a laboratory setting, the requirement of relatively large numbers of viable microorganisms, complicated preanalytical processing, limited microorganism spectrum, analytical variability, lengthy assay times, and, importantly, the cost.17 Nevertheless, with these sophisticated systems, targeted antibiotic therapy can only be accomplished the following working day, as the systems typically take 7−20 h to attain AST results,18,19 during which time patients are often administered generic antibiotics due to deteriorating health conditions.14 Thus, recent research has focused on developing more sensitive and rapid alternate methodologies for AST, such as time-lapse optical microscopy and imaging,20−22 flow cytometry,23 Raman spectroscopy,24 fluorescence and bioluminescence assays,25−28 resonators,29−32 light-scattering techniques,33,34 plasmon effects,35 colorimetric assays,36 and electrochemical profiling.31 However, some of these methods still suffer from shortcomings, such as the use of expensive equipment, which restricts their potential application to a laboratory setting, costly labeling probes, analysis of unrepresentative sample populations, analysis of secondary indicators of growth, and heavy statistical analysis. Additional strategies employing nonphenotypic, mostly nucleic acid-based AST methods, such as polymerase chain reaction (PCR), have also become of increasing interest.17,37,38 However, these methods cannot detect all resistance markers, are expensive, and, thus, have not been widely adopted. While next-generation, wholegenome sequencing of microorganisms could provide technological opportunities to predict resistance, the comprehensive determination of MICs for all relevant antimicrobials may be confounded by the ever-elevating degree of genetic heterogeneity in bacterial species. This persistent problem is expected to discourage the genomic approach to AST.17 Consequently, in order to expedite AST, while minimizing sample processing and employing simple instrumentation, we have developed a multifunctional, label-free AST platform employing optical phase-shift reflectometric interference spectroscopic measurements (referred to as PRISM) for rapid MIC determinations. With this platform, biofunctionalized Si pillar-type phase gratings provide both the growth substrate for the selected microorganisms and transducing element that optically monitors the response of bacteria when exposed to chosen antibiotics in real time. While cell-based biosensing techniques employing diffraction gratings39−46 and reflectometric interference spectroscopy47−52 on Si transducers are widely known, they typically operate in subwavelength regimes and/or rely on the effective medium model in which the spacing between topological features, d, is much smaller than the wavelength of the incident light, λ.53 Bacteria and cell monitoring with these previous methods become difficult as the microorganisms, typically 1−5 μm in at least one of their dimensions, cannot fit within the interstitial space of the gratings or mesoporous features, which in turn lowers their sensitivity and makes them prone to interference effects.42

Thus, we have employed two-dimensional phase gratings with topological features characterized by d ≫ λ to ensure that bacterial cells can comfortably fit and preferentially colonize within the transducing element for PRISM. In this work, we introduce the concept of incorporating lectin-modified Si micropillars as reflectance phase gratings, particularly for rapid, label-free antibiotic susceptibility evaluation of model bacteria, E. coli. Within the interstitial space of the micropillar gratings, cells can freely move and colonize, while preventing colonization on top of the transducing element (as opposed to within), and thus minimizing losses in optical response. While colonization patterns and preferences of bacteria have been studied within nano- and micropillar arrays,54−57 the optical properties of such structures have never been harvested to track bacterial responses in real time. Thus, PRISM of photonic chips consisting of pillar gratings is employed as a platform antibiogram capable of monitoring bacterial responses to antibiotics in real time. We optically track the response of E. coli to various concentrations of five clinically relevant antibiotics and compare the MIC values determined by PRISM to both standard broth microdilution testing and VITEK 2 readouts. We show that capture of bacteria within the microtopology followed by incubation of the cells with the appropriate antibiotic solution yields rapid determinations of antibiotic susceptibility. In particular, unhindered proliferation of bacteria within the pillar gratings is correlated to an increase in optical path difference and sub-MIC conditions, while bacteriostatic and bactericidal responses correlate to either no net change or a decrease in optical path difference, respectively, as monitored by PRISM. Our results demonstrate that this platform not only provides accurate MIC determinations in a rapid manner (total assay time of 3 h versus 8 h with a state-ofthe-art automated AST system, VITEK 2) but can also be employed as an advantageous method to differentiate bacteriostatic and bactericidal antibiotics.

RESULTS AND DISCUSSION Principals of the Assay: PRISM Platform of Pillar-Type Gratings. The underlying principle of PRISM for monitoring antibiotic susceptibility is based on the simultaneous colonization of bacteria within a lamellar phase grating and phase-shift reflectometric interference measurements of the bacteria within the microtopology collected in real time (Figure 1). The PRISM assays are performed in a series of heated microfluidic injection channels controlled by a motorized stage. Briefly, photonic chips consisting of two-dimensional lamellar gratings, specifically periodic Si micropillar arrays, are each fixed in the center of a heated flow channel. Each injection channel contains a syringe injection port and an outlet port to waste with one Si device positioned in the center, as illustrated in Figure 1A. Each photonic chip is functionalized with wheat germ agglutinin (WGA), which is a lectin specific for N-acetylD-glucosamine. Thus, this biofunctionalization step is aimed to enhance the capture of bacteria on the chip surface during the seeding stage, as WGA binds to N-acetylglucosamine found within the bacterial cell wall.58,59 For optical PRISM assays, the chips are illuminated by a collimated broadband light source normally incident to the pillar gratings, which act as a binary grating operating in the zeroth order of the diffraction pattern. The resulting reflectance spectrum of the chip exhibits interference fringes (see Figure 1B) as the incident light is separated such that part of the beam is reflected by the top of 6168


Article

ACS Nano

saline (PBS), termed the seeding suspension, is injected into a microfluidic channel to introduce bacteria to the pillar gratings and given 15 min to allow bacteria to settle within the microtopology (Figure 1C-ii). Second, during the antibiotic incubation stage (schematically illustrated in Figure 1D), 500 μL of antibiotic solution constituted in cation-adjusted MuellerHinton broth (CAMHB) to a specific concentration is injected into each channel, and bacterial response is optically monitored by PRISM. If the bacteria continue to colonize and proliferate within the pillar grating, the refractive index of the medium increases, yielding an increase in 2nL (Figure 1E), while cell death or bacteriostatic activity within the gratings results in decreasing or unchanged 2nL values (Figure 1F), all of which is captured in real time. Characterization and Sensitivity of Pillar-Type Gratings. Periodic Si micropillar arrays were fabricated using standard lithography techniques and were subsequently mechanically diced into 5 × 5 mm samples to create the photonic chips. Figure 2 presents false-colored high-resolution

Figure 1. Schematic representation of PRISM assay for monitoring bacterial responses. (A) Microliter solutions of bacterial suspensions or antibiotics in growth medium are injected into a heated microfluidic channel (temperature is adjusted to 37 °C) containing a 5 × 5 mm photonic chip of pillar-type gratings. Collimated broadband light simultaneously illuminates the chip surface, while (B) reflectance spectra are collected in real time by a spectrometer and analyzed, allowing bacterial response to be optically monitored in a noninvasive manner. (C) In the first stage of bacterial response experiments, termed the seeding stage, bacteria are seeded on the Si microstructures of the gratings. (C-i) Initially, bacteria are introduced above the grating and are given 15 min to settle into the microtopology (C-ii). (D) During the subsequent antibiotic incubation stage, introduction of an antibiotic solution at a designated concentration, composed in growth medium, to the colonized bacteria leads to either (D-i) increased bacterial proliferation at ineffective subinhibitory concentrations or (D-ii) growth hindrance and cell death at or above the MIC, which, after applying a fast Fourier transform to the collected reflectance spectra, corresponds to PRISM with (E) an increased 2nL value and (F) a decreased 2nL value after antibiotic incubation, respectively.

the Si micropillars and the other part is reflected by the bottom of the Si micropillars.60,61 The height of the pillar and refractive index of the medium within the structures determine the optical path difference (OPD) between the two parts of the incident light beams, which is calculated by frequency analysis.62 The calculated OPD is equivalent to the value 2nL, in which n represents the refractive index of the filling material and L represents the length (or height) of the pillars, and is presented in Figure 1E and F.44−46,60,61 Colonization of bacteria within the grating leads to a change in the average refractive index of the filling medium correlating to measurable changes in the 2nL value. In particular, Δ2nL (%), or the percent change of 2nL over time, is calculated as 2nL − 2nL0 Δ2nL(%) = × 100% 2nL0 (1)

Figure 2. Time to detection is dependent on the initial capture of bacteria. (A) Fluorescence microscope images of E. coli fixed at t = 0 min of the incubation stage for various concentrations of bacterial seeding suspensions. The black scale bar represents 100 μm. (B) False-colored, cross-sectional HR-SEM micrographs (left) reveal that the periodic arrays of pillars are approximately 3.2 μm in height with ample room for E. coli (green) movement. Plan-view HR-SEM images (right) reveal the widths of the pillars to be approximately 1.3 μm with a corner-to-corner distance of each pillar of approximately 1.1 μm, allowing room for E. coli replication. White scale bars represent 1 μm. (C) Bacteria growth profiles obtained by PRISM within the Si pillar gratings are dependent on the bacterial concentration of the seeding suspension.

scanning electron microscopy (HR-SEM) images of a pillar grating after the incubation stage of E. coli for 3 h with growth medium (bacterial cells are false-colored in green for clarity). The lamellar gratings are characterized by square, offset pillars ∼3.2 μm in height and ∼1.3 μm in width, with an edge-to-edge distance of ∼2.7 μm and a corner-to-corner distance of ∼1.1 μm (Figure 2B). These dimensions not only allow infiltration and mobility of both rod-shaped and spherical bacteria within

in which 2nL0 is the value of 2nL at the time when antibiotic was first introduced into the channel (t = 0 of the incubation time). To monitor the responses of bacteria to antibiotics, experiments are carried out in a two-stage process: First, during the seeding stage (schematically illustrated in Figure 1C), 500 μL of bacterial suspension made in phosphate-buffered 6169


Article

ACS Nano

Figure 3. Bactericidal effect of the antibiotic ceftriaxone at 1 μg mL−1 on E. coli (bacteria concentration in the seeding suspension is 107 cells/ mL) within the pillar grating. (A) CLSM images of bacteria within the microtopologies with and without the antibiotic CRO were sampled every 60 min. Bacteria fluoresce in red following propidium iodide (PI) staining, while the oxidized Si micropillars luminesce at 435 nm. The scale bar represents 10 μm. (B) Real-time PRISM curves of pillar gratings, where Δ2nL values were recorded every 3 min over a 3 h time period without the use of fluorescent labels.

stage. Therefore, the final value of Δ2nL is difficult to compare, but serves as an appropriate criteria for judging the ternary antibiotic susceptibility outcomes (i.e., sensitive, intermediate, or resistant). Bacteria Behavior on Pillar-Type Gratings. To confirm that the Δ2nL readings are an accurate representation of bacterial growth within the microtopologies of the phase gratings, time-lapse confocal laser scanning microscopy (CLSM) and real-time PRISM were performed over a 3 h period. In this set of experiments, a photonic chip, inoculated with E. coli during the seeding stage, was exposed to a high concentration (1 μg mL−1) of the cephalosporin-type antibiotic, ceftriaxone (CRO). As a parallel control, a similar chip was exposed to only nutrient medium without antibiotics. Figure 3 summarizes the results of these experiments and reveals that within the first hour of antibiotic incubation the bacterial cells, fluorescing red with propidium iodide (PI) after fixation with glutaraldehyde, tend to position at the bottom of the pillar grating, while slight filamentation of the cells can be observed (Figure 3A-iii). It should be noted that the blue photoluminescence of the oxidized Si grating allows us to spatially correlate the fluorescence of the labeled bacteria within the microtopology, as shown in Figure 3A-ii and -iii. After 2 h of CRO exposure, extreme filamentation of the bacterial cells is observed in CLSM (Figure 3A-vi), although the Δ2nL signal, presented in Figure 3B, remains constant for the CRO-treated sample. It should also be noted that during filamentation the cells tend to propagate on the tops of the pillar microtopologies (also observed by HR-SEM studies; see Figure S3, Supporting Information), thus not contributing to the PRISM signal, while healthy cells remain within the grating. This behavior provides an advantage to PRISM over colorimetry or turbidimetry

the interstitial space of the Si pillars but also provide an anchor for bacteria to attach and proliferate, as shown in Figure 2B. Using the dimensions of the array as measured by HR-SEM, the ideal reflectance spectrum was calculated60,61 and compared to the measured spectrum. Figure S1 presents both spectra and demonstrates the excellent overlap of frequency of the fringes of the measured and calculated data, which deviates only in its intensity. As demonstrated in Figure 2C, E. coli growth on pillar gratings via PRISM can be detected in a relatively short time, even at bacterial concentrations as low as 103 cells mL−1. For seeding suspensions with concentrations ranging from 105 to 108 cells mL−1, the time to detection is faster than typical agar and broth-based assays, which usually take overnight to observe growth, and is still faster than VITEK 2, which requires approximately 7−8 h to complete an assay. With high bacterial concentrations of 108 cells mL−1, the time to detection can be less than 30 min, as apparent by the complete coverage of the diffraction grating with bacteria when beginning the incubation stage (Figure 2A). However, because most clinical assays are run at a lower bacterial concentration, we chose to perform our assays using seeding solutions containing 107 cells mL−1, which leads to moderate coverage of the diffraction grating by bacteria (Figure 2A). It should also be noted that variation of bacterial growth profiles under identical conditions is observed (Figure S2), and with longer assay time, the variation is accentuated. These experiments were performed on microtopologies produced in different batches and using different bacterial seeding solutions. Thus, this variation in bacterial growth profiles may be the result of slight topological differences in each photonic device or differences in the initial number of bacteria adhered to the surface at the time of the incubation 6170


Article

ACS Nano

Figure 4. MIC determinations using PRISM of pillar gratings. (A) In the left column, time-kill curves for various concentrations of antibiotics (GEN, CIP, and CRO) tested against E. coli K-12 (seeding suspension of 107 cells/mL) reveal the MIC for each antibiotic (0.5, 0.05, and 0.05 μg mL−1, respectively). (B) In the right column, the corresponding dose−response curves extracted for every hour of analysis demonstrate that the MIC can be determined within 2−3 h of incubation. All y-axes represent Δ2nL(%).

gratings that provide the basis for more extensive, rapid antibiotic susceptibility testing without the use of elaborate microscopy or labeling. Rapid Antibiotic Susceptibility Evaluation. For studying the feasibility of AST using PRISM, we chose E. coli K-12 as the model microorganism due to the prevalence of pathogenic variants that cause gastroenteritis, urinary tract infections, meningitis, peritonitis, and septicemia in humans and animals, in combination with its rising trend in antibiotic resistance. Five antibiotics were chosen due to their prevalence in AST testing as noted by the Clinical & Laboratory Standards Institute (CLSI) guidelines68 and in cassettes tested in the VITEK 2, in addition to their diverse antimicrobial modes of action. Figure 4A depicts the real-time PRISM curves of E. coli K-12 when exposed to gentamicin (GEN), ciprofloxacin (CIP), and CRO

analyses, which can falsely interpret extreme cell elongation as growth. Figure 3A-viii shows that following incubation with CRO for 3 h profound cell lysis occurs, leaving cell debris and ghosts within the pillar grating, and correlates to a constant Δ2nL signal. Filamentation and lysis in response to cephalosporin exposure is typical, as the antimicrobial mode of action involves the disruption of peptidoglycan synthesis for the cell wall, leading to structurally compromised cells that eventually lyse after enough environmental stress.63−67 Contrarily, the Δ2nL signal for the untreated control cells in nutrient medium is observed to steadily increase around 80−90 min of incubation with only CAMHB, correlating to the proliferation of the bacterial cells within the microtopologies (Figure 3A-iv). Thus, these experiments qualitatively confirm the correlation between the PRISM and bacterial responses within the Si pillar 6171


Article

ACS Nano at concentrations between 0 and 1 μg mL−1. A clear distinction can be made between antibiotic concentrations that have little or no effect on bacterial proliferation and antibiotic concentrations that inhibit bacterial growth similar to the allor-nothing concentration−effect relationship. For example, for CIP concentrations of 0.05 μg mL−1 and above, no change in the Δ2nL values is observed over time, which is indicative of no bacterial growth. On the contrary, when the bacteria are exposed to lower concentrations of CIP, a profound increase in the Δ2nL signal is detected. Thus, in this case, the MIC value for CIP is ≥0.05 μg mL−1 Figure 4B presents a temporal analysis of the bacterial growth data by plotting Δ2nL values extracted every 60 min from the PRISM time−kill curves (Figure 4A) for each antibiotic concentration greater than 0 μg mL−1, providing dose−response plots. In these plots, bacteria growth is plotted versus antibiotic concentration at various time points within the assay not only to reveal the relationship between antibiotic concentration and bacterial growth, but also to elucidate that a distinct MIC value can be determined for each antibiotic between 120 and 180 min of antibiotic incubation. It is interesting to note that for all three studied antibiotics, subinhibitory concentrations resulted in a faster growth rate than bacteria without antibiotic; see Figure 4B. Such behavior is common with some antibiotics, as seen previously with GEN69 and quinolones,70 and is sometimes referred to as hormesis, in which case upside-down U-shaped dose−response curves are observed.71−73 Additionally, GEN has been found to increase the adherence of cells to the uroepithelium at subinhibitory concentrations.74 Thus, lower concentrations of GEN may have actually encouraged the adhesion of cells to the Si micropillar network. CIP, a fluoroquinolone, has been shown to change the hydrophobicity of the bacterial cells, also interfering with their adhesion patterns75 and demonstrating hormetic behavior.76 Many broth-based AST systems do not possess the ability to detect hormetic patterns and adhesion properties without the extensive use of fluorescent probes or PCR, while agar-based tests cannot reveal adhesion patterns, as they do not contain a liquid interface for planktonic (floating) bacteria. Thus, the PRISM platform may provide more information regarding the colonization of bacteria than conventional AST methods. Comparison to Conventional AST Methods. The gold standard BMD testing and the clinically used VITEK 2 were performed on the E. coli K-12 strain to compare MIC values determined by PRISM (Table 1). The MIC values obtained were comparable for all three techniques and matched the SIR (susceptible−intermediate−resistant) profiles of the VITEK 2 readout. Yet, the slight variations in MIC values from PRISM and BMD experiments may be ascribed to a few possible

sources. The most obvious reason for deviation is the result of different concentrations of antibiotics tested. In BMD testing 12 different concentrations were created by 2-fold dilutions, while in PRISM experiments only seven dilutions, spanned across the same orders of magnitude of dilutions, were tested. Importantly, it should be noted that BMD testing analyzes the growth of bacteria in solely a liquid medium, while the microtopologies of PRISM provide a practical solid−liquid interface for bacteria to adhere, proliferate, and even move throughout the chip. This microtopology imitates in vivo infections, such as urinary tract infections, which are not solely caused by planktonic cells and are instead associated with inflammation caused by the colonization of a solid−liquid interface (i.e., urothelium and urine),77 possibly providing a more clinically relevant MIC value. The PRISM platform also can be used to test antibiotics that require higher dosages and/or combinations of antibiotics to observe MICs, the latter of which has been thought to improve selection against resistant microbes.78 Figure 5 demonstrates that 1:19 sulfamethoxazole−trimethoprim (SXT) at 10× the MIC exhibits a bactericidal effect in which Δ2nL decreases over time. In this scenario, cells either disintegrate, lyse, or detach from the diffraction grating as weaknesses in their cell walls and appendages arise to an observable degree. In addition, when the E. coli was grown specifically for ampicillin (AMP) resistance, no MIC was observed for AMP, as shown in Figure 6. The growth of the AMP-resistant cells was significantly slower at reduced concentrations of AMP, suggesting that plasmid maintenance may have slowed the growth of the cells. Although the time-kill curves are shown for incubation assays performed over the span of 5 h, MIC determinations can be made within 2−3 h, as demonstrated by the dose-dependent curves constructed every hour during the assay.

CONCLUSIONS In summary, by exploiting the interface between the Si micropillar topologies and colonized bacteria in suspension, we have designed a robust platform for rapid AST in which the substrate for culturing microorganisms and the transducing elements are the same device. Such a platform does not require fluorescent tags or time-lapse microscopy and, furthermore, allows bacteria to colonize at the solid−liquid interface, similar to a natural environment. While fluorescent labeling and singlecell analysis can provide slightly expedited results, it often relies on expensive equipment and labels. Our platform employing reflectance spectroscopy from a lamellar phase grating and without the use of expensive equipment takes approximately 2−3 h to perform, which is significantly faster than standard BMD or analysis by an automated microbial AST testing system (VITEK 2), and with MIC determinations comparable to both of those standard methods. Furthermore, with this technique we maintain the ability to further multiplex the system to contain more channels by miniaturization of the photonic devices and to lower the signal-to-noise ratio, thus expediting the technique further, something difficult to obtain with expensive standardized equipment. Not only does the platform provide effective determination of MIC values, but it can also provide measurements for detailed, real-time monitoring of antibiotic mechanisms not seen in broth or agar assays for use in a clinical environment in a manner more reflective of a dynamic situation under in vivo conditions.

Table 1. MIC and SIR Determinations for E. coli K-12 Evaluated by PRISM, BMD, and VITEK 2a antibiotic

PRISM

BMD

VITEK 2b

AMP CIP CRO GEN SXT 1:19

>1.00 0.05 0.05 0.50 1.25

>100 0.02 0.03 0.25 1.25

>32 ≤0.25 ≤1 ≤1 ≤20

(R) (S) (S) (S) (S)

a MIC values are expressed in units of μg mL−1. bVITEK 2 readouts include S (susceptible), I (intermediate), and R (resistant) determinations.

6172


Article

ACS Nano

Figure 5. MIC determination for SXT by PRISM of Si micropillar arrays. (A) Time-kill curves for SXT against E. coli K-12 (seeding suspension of 107 cells/mL) tested at higher antibiotic concentrations. (B) The corresponding dose−response curves taken at different time points reveal the bactericidal effect of SXT.

Figure 6. MIC determination using PRISM of pillar gratings with AMP-resistant bacteria. (A) Time-kill curves for various concentrations of AMP against E. coli K-12 (seeding suspension of 107 cells/mL) with AMP resistance. (B) The corresponding dose−response curves taken at different time points emphasize the lack of effect seen by AMP on the AMP-resistant strain of E. coli K-12. concentration of the final working solution and were diluted in CAMHB under sterile conditions. Trimethoprim−sulfamethoxazole was prepared in a 1:19 ratio following Clinical and Laboratory Standards Institute (CLSI) guidelines.68 Preparation of Bacterial Cultures. E. coli K-12 (generously provided by Prof. Sima Yaron, Technion−Israel Institute of Technology) containing a plasmid encoding the expression of GFP and AMP resistance was selected as a model microorganism for all studies. Stock cultures were stored at −70 °C in cryobeads (Microbank, Prolabs Diagnostics, Canada). Overnight cultures were aerobically grown from a single cryobead in 3 mL of CAMHB at 37 °C and then subcultured to ensure the testing of predominantly live bacterial cells in a 1:100 dilution of fresh media for 3 h until reaching mid log phase corresponding to an optical density (OD) at a wavelength of 600 nm (OD600) of ∼0.5. The subculture was diluted in PBS to the desired bacterial concentration to create the seeding suspension. An OD600 of 1.0 corresponded to 8 × 108 cells mL−1. Fabrication of Photonic Chips. Si pillar gratings were fabricated using standard lithography and reactive ion etching techniques at the Micro- Nano- Fabrication and Printing Unit (MNF&PU) (Technion− Israel Institute of Technology). Wafers with developed Si micropillar arrays were then coated with photoresist to protect the microstructures and mechanically diced into 5 × 5 mm samples using an

EXPERIMENTAL SECTION Materials. Cation-adjusted Mueller Hinton II broth, ampicillin, ceftriaxone sodium, ciprofloxacin, gentamicin sulfate, trimethoprim, sulfamethoxazole, N′-(3-triimethoxysilylpropyl)diethylenetriamine, succinic anhydride, 1-ethyl-3-(3-(dimethylamino)propyl)carbodiimide (EDC), N-hydroxysulfosuccinimide (NHS), lectin from Triticum vulgaris (termed wheat germ agglutinin), 2-(N-morpholino)ethanesulfonic acid (MES), MES sodium salt, glutaraldehyde, propidium iodide, and acetonitrile were purchased from SigmaAldrich, Israel. Acetone was supplied by Gadot, Israel. Absolute ethanol, aqueous hydrofluoric acid (48%), all PBS salts, DMSO, H2O2, and diisopropylethylamine were supplied by Merck, Germany. Acetic acid and sulfuric acid were supplied by Bio-Lab Ltd., Israel. Solutions. All aqueous solutions were prepared in Milli-Q water (18.2 MΩ cm). PBS was constituted of 137 mM NaCl, 2.7 mM KCl, 1.8 mM KH2PO4, and 10 mM Na2HPO4. Luria-Butani (LB) agar was prepared from 10 g mL−1 tryptone, 5 g mL−1 yeast extract, 5 g mL−1 NaCl, and 15 g mL−1 agar supplemented with 100 μg mL−1 AMP to ensure a pure culture. PBS, LB, and CAMHB solutions were autoclaved prior to their use. MES buffer was composed of 54 mM MES and 46 mM MES sodium salt. Filtered antibiotic stock solutions were prepared at a concentration of 1 mg mL−1 in Mill-Q water. Intermediate antibiotic solutions were prepared in water at 100× the 6173


Article

ACS Nano

in which 2nL0 is the value of 2nL at the time when antibiotic was first introduced into the channel (t = 0 of the incubation time). Broth Microdilution Testing and VITEK 2 Analysis. Standard broth microdilution testing was performed as outlined in CLSI protocols.68 Two-fold dilutions of antibiotics were prepared in microwell plates and examined for their inhibition ability against a bacteria suspension of 5 × 106 cells mL−1. Optical absorbance measurements for each set of antibiotic dilutions were collected at a wavelength of 600 nm (Varioskan Flash; Thermo Scientific, USA) after incubation at 37 °C overnight (n = 6). For VITEK 2 (bioMérieux, France) analysis, an LB agar plate was inoculated directly by a cryobead. After overnight incubation at 37 °C, colonies from the plate were suspended in saline solution and administered into the VITEK 2 system. Characterization of Devices. HR-SEM of the pillar gratings was performed after fixation of the devices with 2.5% glutaraldehyde in PBS, followed by washing with water and a dilution series of ethanol (50−70−100%) for 2 min. Samples were gently dried under nitrogen and observed by an Ultra Plus high-resolution scanning electron microscope equipped with a Schottky field-emission gun (Carl Zeiss, Germany) operating at an acceleration voltage of 1 kV. Bacteria HRSEM images were false-colored using Adobe Photoshop CS6 software. CLSM was performed on samples stained with 20 μg mL−1 PI after glutaraldehyde fixation using an LSM 700 confocal laser scanning microscope (Carl Zeiss, Germany) connected to an Axio Observer Z1 inverted microscope fitted with an EC Plan Neofluar 40× oil immersion objective. Images were acquired by Zen software (Carl Zeiss, Germany). Si photoluminescence and fluorescence emitted by bacteria stained with PI were observed using 405 and 555 nm laser excitation wavelengths, respectively. Fluorescent microscope images were also obtained by a fluorescence microscope (ZEISS upright) after staining with PI, using the Axio Cam MRc (Zeiss) camera.

automated dicing saw (DAD3350; Disco, Japan). The photonic chips were thoroughly cleaned with acetone to remove photoresist and debris. After rinsing with Milli-Q water, the chips were immersed in piranha solution (3:1 v/v H2SO4−H2O2) for 2 h and washed with water, followed by rinsing in ethanolic HF solution (1:1 v/v) for 30 s. Subsequently, the samples were washed with ethanol and dried under a stream of nitrogen. The resulting photonic chips were thermally oxidized in a tube furnace (Lindberg/Blue M 1200 °C Split-Hinge; Thermo Scientific, USA) at 800 °C for 1 h in ambient air. WGA Immobilization. Oxidized photonic chips were silanized in 2% v/v N′-(3-triimethoxysilylpropyl)diethylenetriamine (50% ethanol, 0.6% acetic acid) for 1 h, followed by rinsing with ethanol and drying under a stream of nitrogen. The chips were subsequently incubated in a solution of succinic anhydride (10 mg mL−1 in acetonitrile, 4% v/v diisopropylethylamine) for 3 h, followed by rinsing with ethanol and drying under a stream of nitrogen. Amine activation was promoted by incubation in a solution containing NHS (10 mg mL−1) and EDC (20 mg mL−1) in MES buffer (supplemented with 10% DMSO) for 1 h, after which the samples were rinsed with ethanol and dried under a stream of nitrogen. In the final step, WGA was conjugated to the oxidized surfaces by storing each device in 30 μL of WGA solution (1 mg mL−1 WGA in 10% DMSO) for up to 1 week at 4 °C until used. Prior to use, substrates were rinsed with PBS. PRISM. A custom-designed, aluminum manifold housed seven injection channels and was controlled by a motorized linear translation stage (Thorlabs, Inc.). Each injection channel contained a syringe injection port, an outlet port to waste, and one Si chip positioned in the center, as outlined in Figure 1. The photonic chip was fixed in a square depression in the center of the channel and held in place by a rubber gasket further sealed by an acrylic spacer and aluminum housing. Prior to all experiments, all parts were sterilized by UV irradiation for 40 min. For each experiment, the photonic chips were placed in the channels, parts and housing were sealed, and the system was briefly rinsed with 70% ethanol, followed by introduction of PBS. The PBS was allowed to incubate in the channels for at least 30 min while the temperature of the manifold, devices, and solution equilibrated. Each optical PRISM experiment was then carried out in two stages. First, during the seeding stage, 500 μL of the bacterial suspension in PBS, termed the seeding suspension, was injected into each channel and allowed 15 min to settle within the Si microstructures. Then, for the incubation stage, 500 μL of antibiotic solution made in CAMHB was injected into each channel. Reflectance signal was continuously monitored throughout the experiments. Data Acquisition and Analysis. A bifurcated fiber optic (Ocean Optics, USA), fitted with a collimating lens and arranged normal to the Si phase grating, both illuminated the chip via an attenuated HL-2000 light source (Ocean Optics, USA) and transmitted reflected light to a FLAME2000 CCD spectrometer (Ocean Optics, USA). The motorized stage moved the heated manifold to the desired position after spectral acquisition. Each spectral acquisition contained an average of 375 scans at a 20 ms integration time and was controlled by LabView (National Instruments, USA). As a result, the reflectance spectrum of each channel was recorded every 207 s. Frequency analysis of the reflectance spectra was performed using IgorPro software (Wavemetrics, USA) in which each collected reflectance spectrum was first plotted as a function of reflectance versus wavelength in the range of 450−900 nm, where the signal-to-noise ratio was the highest. Wavelength was inverted to yield wavenumber (cm−1), and the resulting plots were interpolated with a cubic spline and manipulated by a fast Fourier transform (FFT). The maximum value from the FFT corresponded to the optical path difference equivalent to 2nL, in which n represents the refractive index of the filling material and L represents the height of the pillars.44−46,60,61 Values of 2nL were plotted versus time, in which time 0 was considered the time at which antibiotic was administered into the system. Δ2nL (%), or the percent change of 2nL over time, was calculated as Δ2nL(%) =

ASSOCIATED CONTENT S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acsnano.7b02217. Calculated and measured reflectance spectra, variation within experiments, and SEM of filamentous bacteria (PDF)

AUTHOR INFORMATION Corresponding Author

*E-mail: [email protected]. ORCID

Ester Segal: 0000-0001-9472-754X Notes

The authors declare no competing financial interest.

ACKNOWLEDGMENTS This work was supported by the Institute Merieux and the Israeli Ministry of Science (Grant No. 3-12071). The authors would like to thank O. Ternyak and D. Peselev of the MicroNano- Fabrication and Printing Unit (MNF&PU) at the Technion for their assistance with gratings fabrication. We gratefully acknowledge the advice of Y. Danin-Poleg regarding sample handling and the fruitful discussions with Y. Kashi and Y. Geffen. We also thank Y. Haimov and S. Tsesses for their assistance in designing and building the controllable stage. REFERENCES (1) Andersson, D. I.; Hughes, D. Antibiotic Resistance and Its Cost: Is It Possible to Reverse Resistance? Nat. Rev. Microbiol. 2010, 8, 260− 271.

2nL − 2nL0 × 100% 2nL0 6174


Article

ACS Nano

(20) Fredborg, M.; Rosenvinge, F. S.; Spillum, E.; Kroghsbo, S.; Wang, M.; Sondergaard, T. E. Rapid Antimicrobial Susceptibility Testing of Clinical Isolates by Digital Time-Lapse Microscopy. Eur. J. Clin. Microbiol. Infect. Dis. 2015, 34, 2385−2394. (21) Le Page, S.; Raoult, D.; Rolain, J. M. Real-Time Video Imaging as a New and Rapid Tool for Antibiotic Susceptibility Testing by the Disc Diffusion Method: A Paradigm for Evaluating Resistance to Imipenem and Identifying Extended-Spectrum Beta-Lactamases. Int. J. Antimicrob. Agents 2015, 45, 61−65. (22) Chung, C. Y.; Wang, J. C.; Chuang, H. S. Rapid Bead-Based Antimicrobial Susceptibility Testing by Optical Diffusometry. PLoS One 2016, 11, e0148864. (23) Huang, T. H.; Ning, X. H.; Wang, X. J.; Murthy, N.; Tzeng, Y. L.; Dickson, R. M. Rapid Cytometric Antibiotic Susceptibility Testing Utilizing Adaptive Multidimensional Statistical Metrics. Anal. Chem. 2015, 87, 1941−1949. (24) Liu, C. Y.; Han, Y. Y.; Shih, P. H.; Lian, W. N.; Wang, H. H.; Lin, C. H.; Hsueh, P. R.; Wang, J. K.; Wang, Y. L. Rapid Bacterial Antibiotic Susceptibility Test Based on Simple Surface-Enhanced Raman Spectroscopic Biomarkers. Sci. Rep. 2016, 6, 23375. (25) Dong, T.; Zhao, X. Y. Rapid Identification and Susceptibility Testing of Uropathogenic Microbes via Immunosorbent ATPBioluminescence Assay on a Microfluidic Simulator for Antibiotic Therapy. Anal. Chem. 2015, 87, 2410−2418. (26) Quach, D. T.; Sakoulas, G.; Nizet, V.; Pogliano, J.; Pogliano, K. Bacterial Cytological Profiling (Bcp) as a Rapid and Accurate Antimicrobial Susceptibility Testing Method for Staphylococcus Aureus. EBioMedicine 2016, 4, 95−103. (27) Sun, H.; Liu, Z. Z.; Hu, C.; Ren, K. N. Cell-on-Hydrogel Platform Made of Agar and Alginate for Rapid, Low-Cost, Multidimensional Test of Antimicrobial Susceptibility. Lab Chip 2016, 16, 3130−3138. (28) Zhao, E. G.; Chen, Y. L.; Chen, S. J.; Deng, H. Q.; Gui, C.; Leung, C. W. T.; Hong, Y. N.; Lam, J. W. Y.; Tang, B. Z. A Luminogen with Aggregation-Induced Emission Characteristics for Wash-Free Bacterial Imaging, High-Throughput Antibiotics Screening and Bacterial Susceptibility Evaluation. Adv. Mater. 2015, 27, 4931−4937. (29) Etayash, H.; Khan, M. F.; Kaur, K.; Thundat, T. Microfluidic Cantilever Detects Bacteria and Measures Their Susceptibility to Antibiotics in Small Confined Volumes. Nat. Commun. 2016, 7, 12947. (30) France, D.; Johnson, W.; Cordell, W.; Walls, F. Rapid Antimicrobial Susceptibility Testing through Phase Noise Measurements of Cellular Biophysics. Biophys. J. 2016, 110, 200a. (31) Ma, F.; Rehman, A.; Sims, M.; Zeng, X. Q. Antimicrobial Susceptibility Assays Based on the Quantification of Bacterial Lipopolysaccharides via a Label Free Lectin Biosensor. Anal. Chem. 2015, 87, 4385−4393. (32) Cermak, N.; Olcum, S.; Delgado, F. F.; Wasserman, S. C.; Payer, K. R.; M, A. M.; Knudsen, S. M.; Kimmerling, R. J.; Stevens, M. M.; Kikuchi, Y.; et al. High-Throughput Measurement of Single-Cell Growth Rates Using Serial Microfluidic Mass Sensor Arrays. Nat. Biotechnol. 2016, 34, 1052−1059. (33) Bugrysheva, J. V.; Lascols, C.; Sue, D.; Weigel, L. M. Rapid Antimicrobial Susceptibility Testing of Bacillus Anthracis, Yersinia Pestis, and Burkholderia Pseudomallei by Use of Laser Light Scattering Technology. J. Clin. Microbiol. 2016, 54, 1462−1471. (34) Hayden, R. T.; Clinton, L. K.; Hewitt, C.; Koyamatsu, T.; Sun, Y. L.; Jamison, G.; Perkins, R.; Tang, L.; Pounds, S.; Bankowski, M. J. Rapid Antimicrobial Susceptibility Testing Using Forward Laser Light Scatter Technology. J. Clin. Microbiol. 2016, 54, 2701−2706. (35) Syal, K.; Iriya, R.; Yang, Y. Z.; Yu, H.; Wang, S. P.; Haydel, S. E.; Chen, H. Y.; Tao, N. J. Antimicrobial Susceptibility Test with Plasmonic Imaging and Tracking of Single Bacterial Motions on Nanometer Scale. ACS Nano 2016, 10, 845−852. (36) Ramis, I. B.; Cnockaert, M.; von Groll, A.; Nogueira, C. L.; Leao, S. C.; Andre, E.; Simon, A.; Palomino, J. C.; da Silva, P. E. A.; Vandamme, P.; et al. Antimicrobial Susceptibility of Rapidly Growing Mycobacteria Using the Rapid Colorimetric Method. Eur. J. Clin. Microbiol. Infect. Dis. 2015, 34, 1403−1413.

(2) Ventola, C. L. The Antibiotic Resistance Crisis: Part 1: Causes and Threats. Pharm. Ther. 2015, 40, 277−283. (3) O’Neill, J. Tackling Drug-Resistant Infections Globally: Final Report and Recommendations; The Review on Antimicrobial Resistance: London, May 2016. (4) O’Neill, J. Antimicrobial Resistance: Tackling a Crisis for the Health and Wealth of Nations; Review on Antimicrobial Resistance: London, Dec 2014. (5) Liu, Y. Y.; Wang, Y.; Walsh, T. R.; Yi, L. X.; Zhang, R.; Spencer, J.; Doi, Y.; Tian, G. B.; Dong, B. L.; Huang, X. H.; et al. Emergence of Plasmid-Mediated Colistin Resistance Mechanism Mcr-1 in Animals and Human Beings in China: A Microbiological and Molecular Biological Study. Lancet Infect. Dis. 2016, 16, 161−168. (6) Hasman, H.; Hammerum, A. M.; Hansen, F.; Hendriksen, R. S.; Olesen, B.; Agerso, Y.; Zankari, E.; Leekitcharoenphon, P.; Stegger, M.; Kaas, R. S.; et al. Detection of Mcr-1 Encoding Plasmid-Mediated Colistin-Resistant Escherichia Coli Isolates from Human Bloodstream Infection and Imported Chicken Meat, Denmark 2015. Euro Surveill. 2015, 20, 2−6. (7) Doumith, M.; Godbole, G.; Ashton, P.; Larkin, L.; Dallman, T.; Day, M.; Day, M.; Muller-Pebody, B.; Ellington, M. J.; de Pinna, E.; et al. Detection of the Plasmid-Mediated Mcr-1 Gene Conferring Colistin Resistance in Human and Food Isolates of Salmonella Enterica and Escherichia Coli in England and Wales. J. Antimicrob. Chemother. 2016, 71, 2300−2305. (8) Payne, M.; Croxen, M. A.; Lee, T. D.; Mayson, B.; Champagne, S.; Leung, V.; Bariso, S.; Hoang, L.; Lowe, C. Mcr-1-Positive ColistinResistant Escherichia Coil in Traveler Returning to Canada from China. Emerging Infect. Dis. 2016, 22, 1673−1675. (9) McGann, P.; Snesrud, E.; Maybank, R.; Corey, B.; Ong, A. C.; Clifford, R.; Hinkle, M.; Whitman, T.; Lesho, E.; Schaecher, K. E. Escherichia Coli Harboring Mcr-1 and Bla(Ctx-M) on a Novel Incf Plasmid: First Report of Mcr-1 in the United States. Antimicrob. Agents Chemother. 2016, 60, 4420−4421. (10) von Wintersdorff, C. J. H.; Penders, J.; van Niekerk, J. M.; Mills, N. D.; Majumder, S.; van Alphen, L. B.; Savelkoul, P. H. M.; Wolffs, P. F. G. Dissemination of Antimicrobial Resistance in Microbial Ecosystems through Horizontal Gene Transfer. Front. Microbiol. 2016, 7, 173. (11) Bush, K.; Courvalin, P.; Dantas, G.; Davies, J.; Eisenstein, B.; Huovinen, P.; Jacoby, G. A.; Kishony, R.; Kreiswirth, B. N.; Kutter, E.; et al. Tackling Antibiotic Resistance. Nat. Rev. Microbiol. 2011, 9, 894− 896. (12) World Health Organization. Global Action Plan on Antimicrobial Resistance; 2015. (13) Frieden, T. Antibiotic Resistance Threats in the United States, 2013; Centers for Disease Control and Prevention, U.S. Department of Health and Human Services, 2013. (14) O’Neill, J. Rapid Diagnostics: Stopping Unnecessary Use of Antibiotics; The Review on Antimicrobial Resistance: London, 2015. (15) Roberts, R. R.; Hota, B.; Ahmad, I.; Scott, R. D.; Foster, S. D.; Abbasi, F.; Schabowski, S.; Kampe, L. M.; Ciavarella, G. G.; Supino, M.; et al. Hospital and Societal Costs of Antimicrobial-Resistant Infections in a Chicago Teaching Hospital: Implications for Antibiotic Stewardship. Clin. Infect. Dis. 2009, 49, 1175−1184. (16) Jorgensen, J. H.; Ferraro, M. J. Antimicrobial Susceptibility Testing: A Review of General Principles and Contemporary Practices. Clin. Infect. Dis. 2009, 49, 1749−1755. (17) van Belkum, A.; Dunne, W. M. Next-Generation Antimicrobial Susceptibility Testing. J. Clin. Microbiol. 2013, 51, 2018−2024. (18) Fredborg, M.; Andersen, K. R.; Jorgensen, E.; Droce, A.; Olesen, T.; Jensen, B. B.; Rosenvinge, F. S.; Sondergaard, T. E. Real-Time Optical Antimicrobial Susceptibility Testing. J. Clin. Microbiol. 2013, 51, 2047−2053. (19) Sellenriek, P.; Holmes, J.; Ferrett, R.; Drury, R.; Storch, G. A. Comparison of Microscan Walk-Away, Phoenix and Vitek-Two Microbiology Systems Used in the Identification and Susceptibility Testing of Bacteria. In Abstracts of 105th General Meeting; American Society for Microbiology: Atlanta, GA, June 5−9, 2005, LR900. 6175


Article

ACS Nano (37) Dona, V.; Kasraian, S.; Lupo, A.; Guilarte, Y. N.; Hauser, C.; Furrer, H.; Unemo, M.; Low, N.; Endimiani, A. Multiplex Real-Time PCR Assay with High-Resolution Melting Analysis for Characterization of Antimicrobial Resistance in Neisseria Gonorrhoeae. J. Clin. Microbiol. 2016, 54, 2074−2081. (38) Schoepp, N. G.; Khorosheva, E. M.; Schlappi, T. S.; Curtis, M. S.; Humphries, R. M.; Hindler, J. A.; Ismagilov, R. F. Digital Quantification of DNA Replication and Chromosome Segregation Enables Determination of Antimicrobial Susceptibility after Only 15 minutes of Antibiotic Exposure. Angew. Chem., Int. Ed. 2016, 55, 9556−9560. (39) St John, P. M.; Davis, R.; Cady, N.; Czajka, J.; Batt, C. A.; Craighead, H. G. Diffraction-Based Cell Detection Using a Microcontact Printed Antibody Grating. Anal. Chem. 1998, 70, 1108−1111. (40) Ryckman, J. D.; Liscidini, M.; Sipe, J. E.; Weiss, S. M. Porous Silicon Structures for Low-Cost Diffraction-Based Biosensing. Appl. Phys. Lett. 2010, 96, 171103. (41) Lv, C. W.; Jia, Z. H.; Liu, Y. J.; Mo, J. Q.; Li, P.; Lv, X. Y. AngleResolved Diffraction Grating Biosensor Based on Porous Silicon. J. Appl. Phys. 2016, 119, 094502. (42) Fang, Y.; Ferrie, A. M.; Fontaine, N. H.; Mauro, J.; Balakrishnan, J. Resonant Waveguide Grating Biosensor for Living Cell Sensing. Biophys. J. 2006, 91, 1925−1940. (43) Bai, W.; Spivak, D. A. A Double-Imprinted Diffraction-Grating Sensor Based on a Virus-Responsive Super-Aptamer Hydrogel Derived from an Impure Extract. Angew. Chem., Int. Ed. 2014, 53, 2095−2098. (44) Massad-Ivanir, N.; Mirsky, Y.; Nahor, A.; Edrei, E.; BonannoYoung, L. M.; Ben Dov, N.; Sa’ar, A.; Segal, E. Trap and Track: Designing Self-Reporting Porous Si Photonic Crystals for Rapid Bacteria Detection. Analyst 2014, 139, 3885−3894. (45) Ude, C.; Ben-Dov, N.; Jochums, A.; Li, Z. P.; Segal, E.; Scheper, T.; Beutel, S. Online Analysis of Protein Inclusion Bodies Produced in E-Coli by Monitoring Alterations in Scattered and Reflected Light. Appl. Microbiol. Biotechnol. 2016, 100, 4147−4159. (46) Mirsky, Y.; Nahor, A.; Edrei, E.; Massad-Ivanir, N.; Bonanno, L. M.; Segal, E.; Sa'ar, A. Optical Biosensing of Bacteria and Cells Using Porous Silicon Based, Photonic Lamellar Gratings. Appl. Phys. Lett. 2013, 103, 033702. (47) Tang, Y. Y.; Zhen, L.; Liu, J. Q.; Wu, J. M. Rapid Antibiotic Susceptibility Testing in a Microfluidic pH Sensor. Anal. Chem. 2013, 85, 2787−2794. (48) Mohrle, B.; Kohler, K.; Jaehrling, J.; Brock, R.; Gauglitz, G. Label-Free Characterization of Cell Adhesion Using Reflectometric Interference Spectroscopy (Rifs). Anal. Bioanal. Chem. 2006, 384, 407−413. (49) Massad-Ivanir, N.; Shtenberg, G.; Zeidman, T.; Segal, E. Construction and Characterization of Porous SiO2/Hydrogel Hybrids as Optical Biosensors for Rapid Detection of Bacteria. Adv. Funct. Mater. 2010, 20, 2269−2277. (50) Massad-Ivanir, N.; Shtenberg, G.; Raz, N.; Gazenbeek, C.; Budding, D.; Bos, M. P.; Segal, E. Porous Silicon-Based Biosensors: Towards Real-Time Optical Detection of Target Bacteria in the Food Industry. Sci. Rep. 2016, 6, 38099. (51) Schwartz, M. P.; Derfus, A. M.; Alvarez, S. D.; Bhatia, S. N.; Sailor, M. J. The Smart Petri Dish: A Nanostructured Photonic Crystal for Real-Time Monitoring of Living Cells. Langmuir 2006, 22, 7084− 7090. (52) Alvarez, S. D.; Schwartz, M. P.; Migliori, B.; Rang, C. U.; Chao, L.; Sailor, M. J. Using a Porous Silicon Photonic Crystal for Bacterial Cell-Based Biosensing. Phys. Status Solidi A 2007, 204, 1439−1443. (53) Niklasson, G. A.; Granqvist, C. G.; Hunderi, O. Effective Medium Models for the Optical Properties of Inhomogeneous Materials. Appl. Opt. 1981, 20, 26−30. (54) Ge, X.; Leng, Y.; Lu, X.; Ren, F. Z.; Wang, K. F.; Ding, Y. H.; Yang, M. Bacterial Responses to Periodic Micropillar Array. J. Biomed. Mater. Res., Part A 2015, 103, 384−396. (55) Hochbaum, A. I.; Aizenberg, J. Bacteria Pattern Spontaneously on Periodic Nanostructure Arrays. Nano Lett. 2010, 10, 3717−3721.

(56) Epstein, A. K.; Hochbaum, A. I.; Kim, P.; Aizenberg, J. Control of Bacterial Biofilm Growth on Surfaces by Nanostructural Mechanics and Geometry. Nanotechnology 2011, 22, 494007. (57) Jahed, Z.; Shahsavan, H.; Verma, M. S.; Rogowski, J. L.; Seo, B. B.; Zhao, B.; Tsui, T. Y.; Gu, F. X.; Mofrad, M. R. Bacterial Networks on Hydrophobic Micropillars. ACS Nano 2017, 11, 675−683. (58) Lotan, R.; Sharon, N.; Mirelman, D. Interaction of Wheat-Germ Agglutinin with Bacterial-Cells and Cell-Wall Polymers. Eur. J. Biochem. 1975, 55, 257−262. (59) Monsigny, M.; Roche, A. C.; Sene, C.; Magetdana, R.; Delmotte, F. Sugar-Lectin Interactions - How Does Wheat-Germ-Agglutinin Bind Sialoglycoconjugates. Eur. J. Biochem. 1980, 104, 147−153. (60) Strong, J.; Vanasse, G. A. Lamellar Grating Far-Infrared Interferomer. J. Opt. Soc. Am. 1960, 50, 113−118. (61) Manzardo, O.; Michaely, R.; Schadelin, F.; Noell, W.; Overstolz, T.; De Rooij, N.; Herzig, H. P. Miniature Lamellar Grating Interferometer Based on Silicon Technology. Opt. Lett. 2004, 29, 1437−1439. (62) Vilensky, R.; Bercovici, M.; Segal, E. Oxidized Porous Silicon Nanostructures Enabling Electrokinetic Transport for Enhanced DNA Detection. Adv. Funct. Mater. 2015, 25, 6725−6732. (63) Braga, P. C.; DalSasso, M.; Maci, S. Cefodizime: Effects of SubInhibitory Concentrations on Adhesiveness and Bacterial Morphology of Staphylococcus Aureus and Escherichia Coli: Comparison with Cefotaxime and Ceftriaxone. J. Antimicrob. Chemother. 1997, 39, 79− 84. (64) Fredborg, M.; Rosenvinge, F. S.; Spillum, E.; Kroghsbo, S.; Wang, M.; Sondergaard, T. E. Automated Image Analysis for Quantification of Filamentous Bacteria. BMC Microbiol. 2015, 15, 255. (65) Möller, J.; Emge, P.; Avalos Vizcarra, I.; Kollmannsberger, P.; Vogel, V. Bacterial Filamentation Accelerates Colonization of Adhesive Spots Embedded in Biopassive Surfaces. New J. Phys. 2013, 15, 125016. (66) Eng, R. H. K.; Cherubin, C.; Smith, S. M.; Buccini, F. Inoculum Effect of Beta-Lactam Antibiotics on Enterobacteriaceae. Antimicrob. Agents Chemother. 1985, 28, 601−606. (67) Justice, S. S.; Hunstad, D. A.; Cegelski, L.; Hultgren, S. J. Morphological Plasticity as a Bacterial Survival Strategy. Nat. Rev. Microbiol. 2008, 6, 162−168. (68) CLSI Supplement M100s. In Performance Standards for Antimicrobial Susceptibility Testing, 26th ed.; Clinical and Laboratory Standards Institute: Wayne, PA, 2016. (69) Theophel, K.; Schacht, V. J.; Schluter, M.; Schnell, S.; Stingu, C. S.; Schaumann, R.; Bunge, M. The Importance of Growth Kinetic Analysis in Determining Bacterial Susceptibility against Antibiotics and Silver Nanoparticles. Front. Microbiol. 2014, 5, 544. (70) Yoh, M.; Frimpong, E. K.; Voravuthikunchai, S. P.; Honda, T. Effect of Subinhibitory Concentrations of Antimicrobial Agents (Quinolones and Macrolide) on the Production of Verotoxin by Enterohemorrhagic Escherichia Coli O157:H7. Can. J. Microbiol. 1999, 45, 732−739. (71) Yim, G.; Wang, H. H. M.; Davies, J. The Truth About Antibiotics. Int. J. Med. Microbiol. 2006, 296, 163−170. (72) Calabrese, E. J. Hormesis: A Revolution in Toxicology, Risk Assessment and MedicineRe-Framing the Dose-Response Relationship. EMBO Rep. 2004, 5, S37−S40. (73) Calabrese, E. J. Getting the Dose-Response Wrong: Why Hormesis Became Marginalized and the Threshold Model Accepted. Arch. Toxicol. 2009, 83, 227−247. (74) Zhanel, G. G.; Nicolle, L. E. Effect of Subinhibitory Antimicrobial Concentrations (Sub-Mics) on In-Vitro Bacterial Adherence to Uroepithelial Cells. J. Antimicrob. Chemother. 1992, 29, 617−627. (75) Wojnicz, D.; Jankowski, S. Effects of Subinhibitory Concentrations of Amikacin and Ciprofloxacin on the Hydrophobicity and Adherence to Epithelial Cells of Uropathogenic Escherichia Coli Strains. Int. J. Antimicrob. Agents 2007, 29, 700−704. 6176


Article

ACS Nano (76) Linares, J. F.; Gustafsson, I.; Baquero, F.; Martinez, J. L. Antibiotics as Intermicrobial Signaling Agents Instead of Weapons. Proc. Natl. Acad. Sci. U. S. A. 2006, 103, 19484−19489. (77) Donlan, R. M. Biofilm Formation: A Clinically Relevant Microbiological Process. Clin. Infect. Dis. 2001, 33, 1387−1392. (78) Chait, R.; Craney, A.; Kishony, R. Antibiotic Interactions that Select against Resistance. Nature 2007, 446, 668−671.

6177


Unraveling Antimicrobial Susceptibility of Bacterial ... - ACS Publications

Recommend Documents