Luminescent Nanosensors for Ratiometric Monitoring ofThree-Dimensional Oxygen Gradients in Laboratory andClinical Pseudomonas aeruginosa Biofilms

Megan P. Jewell,a Anne A. Galyean,a* J. Kirk Harris,b Edith T. Zemanick,b,c Kevin J. Casha

aChemical and Biological Engineering Department, Colorado School of Mines, Golden, Colorado, USAbDepartment of Pediatrics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USAcChildren’s Hospital Colorado, Aurora, Colorado, USA

ABSTRACT Bacterial biofilms can form persistent infections on wounds and im-planted medical devices and are associated with many chronic diseases, such as cys-tic fibrosis. These infections are medically difficult to treat, as biofilms are more resis-tant to antibiotic attack than their planktonic counterparts. An understanding of thespatial and temporal variation in the metabolism of biofilms is a critical componenttoward improved biofilm treatments. To this end, we developed oxygen-sensitive lu-minescent nanosensors to measure three-dimensional (3D) oxygen gradients, an ap-plication of which is demonstrated here with Pseudomonas aeruginosa biofilms. Themethod was applied here and improves on traditional one-dimensional (1D) meth-ods of measuring oxygen profiles by investigating the spatial and temporal variationof oxygen concentration when biofilms are challenged with antibiotic attack. We ob-served an increased oxygenation of biofilms that was consistent with cell death fromcomparisons with antibiotic kill curves for PAO1. Due to the spatial and temporalnature of our approach, we also identified spatial and temporal inhomogeneities inthe biofilm metabolism that are consistent with previous observations. Clinical strains ofP. aeruginosa subjected to similar interrogation showed variations in resistance tocolistin and tobramycin, which are two antibiotics commonly used to treat P. aerugi-nosa infections in cystic fibrosis patients.

IMPORTANCE Biofilm infections are more difficult to treat than planktonic infectionsfor a variety of reasons, such as decreased antibiotic penetration. Their complex struc-ture makes biofilms challenging to study without disruption. To address this limita-tion, we developed and demonstrated oxygen-sensitive luminescent nanosensorsthat can be incorporated into biofilms for studying oxygen penetration, distribution,and antibiotic efficacy— demonstrated here with our sensors monitoring antibioticimpacts on metabolism in biofilms formed from clinical isolates. The significance ofour research is in demonstrating not only a nondisruptive method for imaging andmeasuring oxygen in biofilms but also that this nanoparticle-based sensing platformcan be modified to measure many different ions and small molecule analytes.

KEYWORDS biofilms, biosensors, cystic fibrosis, nanosensors, oxygen sensing

While the bulk of bacterial understanding has come from planktonic bacteria,where bacteria grow independently in solution, natural biofilm growth is “a

major mode of microbial life” (1). The biofilm microenvironment contains three-dimensional (3D) chemical gradients, and this chemical heterogeneity is associatedwith complex physiological heterogeneity with the potential to influence variouspathogenic and antimicrobial-resistant behaviors (2, 3).

Pseudomonas aeruginosa is the most common infectious pathogen in the lungs of

Citation Jewell MP, Galyean AA, Kirk Harris J,Zemanick ET, Cash KJ. 2019. Luminescentnanosensors for ratiometric monitoring ofthree-dimensional oxygen gradients inlaboratory and clinical Pseudomonasaeruginosa biofilms. Appl Environ Microbiol85:e01116-19. https://doi.org/10.1128/AEM.01116-19.

Editor Andrew J. McBain, University ofManchester

Copyright © 2019 American Society forMicrobiology. All Rights Reserved.

Address correspondence to Kevin J. Cash,kcash@mines.edu.

* Present address: Anne A. Galyean, Intertox,Inc., Seattle, Washington, USA.

Received 17 May 2019Accepted 9 August 2019

Accepted manuscript posted online 16August 2019Published

METHODS

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patients with cystic fibrosis (4). Chronic infection typically results in biofilm formation,which advances lung damage and often leads to respiratory failure (5). Unfortunately,treatment of P. aeruginosa infections can be difficult due to natural antibiotic resistance(6). There is frequently a lack of correlation between planktonic antibiotic susceptibilitytests and biofilm-based infections (7). Thus, the ability to observe the internal biochem-ical microenvironment as antibiotics alter the biofilms would provide useful informa-tion for researchers.

Spatial heterogeneity exists in other biological systems as well. Other biofilms, suchas those found in pipes (8) and the natural environment (9), all exhibit spatial hetero-geneity where the ability to measure metabolites, such as oxygen, would be beneficial.Sulfate-reducing bioreactors, a passive wastewater treatment approach, show spatialheterogeneity in metabolites and community distribution (10). Models of the humantrabecular meshwork (11) and intestinal crypts (12) display similar levels of heteroge-neity that would also benefit from a way to investigate and understand complex 3Denvironments.

While chemical sensors are an advancement in the investigation of biofilms, theyhave been limited to measuring pH gradients (13, 14), oxygen (13, 15–17), or specificmetal ions (18, 19). Microelectrodes have also been used to measure spatial distribu-tions of gradients and concentrations in biofilms for a wide range of analytes (3, 17,20–24). However, the spatial resolution is limited by the physical tip size, and theinsertion of the microelectrode directly into the biofilm intrinsically disturbs the biofilmphysical structure (25). Furthermore, this approach becomes difficult for frequentsampling and prohibitive for measurements spanning a heterogeneous sample. Themajority of microelectrodes are only capable of producing one-dimensional (1D) mea-surements in the z-dimension. Kenney et al. (26), Acosta et al. (27), and others (28–30)have detected and mapped gradients of oxygen and pH in two-dimensional (2D) and3D cell culture scaffolds. In order to capture temporal dynamics in addition to 2D spatialinformation, planar oxygen optodes are also used, but the information they provide islimited to the optode-biofilm interface (31–33). However, 2D approaches cannot cap-ture depth-wise changes in samples, and other 3D options require particles that arelarge and potentially invasive. Oxygen is an important analyte of interest because it canbe used as a measure of metabolic activity (34). Acosta et al. demonstrated the use ofsilica microparticles for measuring oxygen in bacterial biofilms (35). Many attempts toimage or measure other analytes in biofilms have resulted in two-dimensional maps,limiting spatial understanding (15, 31, 32, 36).

Nanosensors are a technology which can overcome the limitations of currentmeasuring methods by enabling continuous spatiotemporal monitoring of analyteconcentrations in growing biofilms. These nanosensors are a tunable family of sensorsthat are designed for uninterrupted monitoring of in vitro or in vivo physiologicalparameters. The nanosensors are highly plasticized hydrophobic polymer nanoparticleswhich respond to changes in analyte concentration by altering their optical properties(37). These nanosensors (�200-nm diameter) are useful in locations like biofilms whereother techniques, like microelectrodes, fail due to size. Like microelectrodes, nanosen-sors have been developed for a variety of analytes, including pH, ions like K� and Li�,and small molecules and metabolites, like oxygen and histamine. These polymernanosensors can be fabricated with a variety of fluorescent emitters, such as quantumdots (38), carbon dots (39), or organic dyes (40). In addition, sensor response properties,including dynamic range and response midpoint, can be controlled through sensorformulation (41).

In this study, we report the development of our oxygen-sensitive luminescentnanosensor (O2NS) technology for monitoring oxygen spatiotemporal gradientsand demonstrate its utility by monitoring the metabolism of both laboratory andclinically derived P. aeruginosa biofilms and the biofilm response to antibioticattack.

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RESULTSMethod development: sensor design and characterization. Figure 1a illus-

trates the sensor mechanism, where at low O2 concentrations, both the platinumporphyrin [platinum (II) meso-tetra(pentafluorophenyl)porphine (PtTFPP)] and the{4-[4-(dihexadecylamino)styryl]-N-methylpyridinium iodide} (DiA) reference dye lu-minesce. At higher O2 concentrations, oxygen collides with the PtTFPP molecule andquenches the luminescence via nonradiative decay. The fluorescence intensity of theplatinum porphyrin PtTFPP (650 nm) decreases as the sample is exposed to increasingoxygen up to 21% in a linear manner (Fig. 1b). The peak of the DiA reference dye(585 nm) remains constant with respect to oxygen concentrations. Physical propertiesof these nanosensors, as determined by dynamic light scattering (DLS) and phaseanalysis light scattering (PALS), were an effective diameter of 163.4 � 1.5 nm, apolydispersity of 0.15 � 0.02, a zeta potential of �11.5 � 1.34 mV, and a mobility of�0.90 � 0.10 �m-s�1/V-cm�1. Values are listed as the arithmetic means and standarddeviations.

A Stern-Volmer constant, KSV, that allows for back calculation of oxygen concentra-tion was determined. KSV for the O2NS was found to be instrument-specific, and thus,the O2NS was calibrated for each instrument used. The intensities of the PtTFPP signalat 650 nm and the intensities of the reference dye (DiA) signal at 585 nm were dividedto form a ratiometric signal. The ratiometric signal at 0 mg/liter dissolved oxygen (DO)was then divided by the ratiometric signal at each concentration to create a linearcalibration curve. The linear Stern-Volmer relationship between If

0/If and oxygen con-centration was used to fit the calibration data (Fig. 1c), where If

0 is the ratiometricfluorescence intensity in the absence of oxygen and If is the ratiometric fluorescenceintensity at the given concentration of oxygen. Calibration was also performed on theconfocal scanning laser microscope used to obtain results from all microscopy exper-iments. Measurements were taken under ambient conditions (21% O2 conditions/6.65 mg/liter DO) and 0 mg/liter DO to form a two-point calibration curve (Fig. 1d).

FIG 1 (a) Schematic of nanosensor components coloaded into the nanoparticle matrix and luminescence quenching mechanism in response to increasingoxygen concentrations. (b) Fluorescence spectra for ratiometric oxygen nanosensors. Excitation occurs at 450 nm and produces the spectra shown underdifferent oxygen concentrations. As oxygen concentrations increase, the platinum porphyrin peak at 650 nm decreases and the reference peak at 585 nm isinsensitive to changes. This ratiometric sensor response allows us to calculate oxygen concentrations without complications resulting from variable nanosensorconcentration. Normalized ratiometric calibration curves for oxygen nanosensors demonstrate effective sensing of oxygen concentration changes using botha spectrometer (c) and a confocal microscope (d) (used in remaining figures).

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As an initial test to determine if sensors function in the biofilm matrix, dead biofilmswere imaged before and after deoxygenation with glucose and glucose oxidase. As canbe seen in Fig. 2, the ratiometric signal increases as the oxygen in the biofilm decreases.These results indicate that the sensors function similarly in the complex matrix of thebiofilm in comparison with how they function in solution and in simpler matrices, suchas alginate (see Fig. S1 in the supplemental material).

Example application: measuring antibiotic-induced metabolic changes. Ratio-metric signal data can be used to render 3D images of biofilms so that their structurecan be visualized along with information about oxygen concentration within thebiofilm (Fig. 3). The 2D projections of the raw 3D images (before ratiometric division)can be seen in Fig. S2 in the supplemental material. In addition, quantitative informa-tion on the oxygen concentration at an arbitrary location within the biofilm can also bedetermined via Matlab processing and graphed in a manner similar to data obtainedfrom oxygen microelectrodes. This can be seen in Fig. 4. At four locations within thebiofilm, oxygen concentration values were extricated from the Matlab array andgraphed versus depth. There is large variation in oxygen concentration between locations,such as location 3 (a large central area with high DO concentrations) and location 4 (lowDO concentrations throughout).

The location in the biofilm we chose for temporal analysis (location 4 in Fig. 4) hasan oxygen concentration of 1 mg/liter before the addition of colistin, with concentra-tions at or approaching 6 mg/liter at the edges of the biofilm, which can be seen in Fig.5. This finding is consistent with a diffusion limitation of oxygen into the center of thebiofilm as the bacteria consume it. As colistin penetrates the biofilm, oxygen concen-tration also increases, indicating cell death. The center of the biofilm takes the longestamount of time to approach the surrounding oxygen concentration, which can be seenin Movie S1 in the supplemental material.

In clinical strain 1 (CS1), there is an overall higher concentration of oxygen than inPAO1, indicating lower metabolic activity or improved oxygen transport (Fig. 6). Afterthe addition of colistin sulfate, the majority of the biofilm reaches an internal oxygenconcentration approaching ambient conditions (indicating no metabolic activity) after

FIG 2 Biofilms killed with colistin before (panels a and c) and after (panels b and d) the addition ofglucose and glucose oxidase. (a and b) Average ratiometric intensity z-projections of data used tocalibrate O2NS in the confocal microscope. (c and d) The 3D images show that oxygen concentration isuniform through the biofilm. This finding demonstrates that the nanosensors respond to oxygenconcentrations as expected even in the complex biofilm environment.

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30 min. However, there is a small region that maintains an oxygen concentration below1 mg/liter (see Fig. S3 and S4 in the supplemental material), indicating that there iscontinued aerobic respiration in this region. This is potentially a spatially inhomoge-neous area occupied by cells with resistance to colistin.

CS2 shows similar oxygen concentrations near the beginning of the experiment butmaintains concentrations of �5 mg/liter for the majority of the observations. Theoxygen concentration does not appear to increase until the 90-min mark, which couldindicate a delayed reaction to or resistance to colistin.

CS3 shows large differences in structure compared with that of PAO1 (Movie S2 inthe supplemental material) and the other two clinical strains (Movies S3 and S4 in thesupplemental material) tested. In addition, there is a large increase in metabolic activity(indicated by a decrease in oxygen concentration) a few minutes after the addition ofthe colistin sulfate. After this initial change, oxygen concentration is variable over90 min but is never above 4 mg/liter DO, indicating sustained metabolic activity withinthe biofilm. For PAO1 and CS1 samples, plating after experiments showed minimal orno growth (see Fig. S5 in the supplemental material), which supports the assumptionof cell death when observing increased oxygen concentration within the biofilms. Inthe case of CS2 and CS3, both of which demonstrate sustained oxygen consumptionafter antibiotic addition, plating after experiments shows growth similar to platingbefore experiments. This supports the hypothesis that low oxygen concentration (highoxygen consumption) is indicative of active cell metabolism.

In addition to the ability to observe differences in clinical samples, our approach alsoenables the measurement and observation of biofilm responses to different antibiotics.The effect of two different antibiotics (colistin and tobramycin) on oxygen concentra-tion in PAO1 biofilms can be seen in Fig. 7.

DISCUSSIONMethod development: sensor design and characterization. Our nanosensor-

based approach is a method to spatiotemporally monitor analyte heterogeneity andgradients in 3D systems, such as biofilms. Our approach utilizes nanosensors which aresignificantly smaller than the bacteria rather than larger, potentially minimizing impactson biofilm growth while still maintaining the ability to monitor extracellular analyteconcentrations. This is possible as the size of the nanosensors is small enough to notmeasurably impact growth, but the approximately 160-nm diameter of the nanosen-

FIG 3 The 2D orthogonal (a) and 3D (b) views of a live P. aeruginosa PAO1 biofilm created using ratiometric intensity data. Raw confocalimages of the PtTFPP (O2 sensitive) and DiA (reference) signals were processed and divided in ImageJ to produce a stack of images of theratiometric intensity that recapitulates oxygen concentration throughout the biofilm.

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sors ensures that the particles are entrapped in the extracellular polysaccharides (EPSs)of the biofilm and do not leak into the surrounding medium substantially. Along withthe comparison of LIVE/DEAD viability staining of biofilms with and without oxygennanosensors (see Fig. S6 in the supplemental material), this suggests that the additionof nanosensors to growth medium does not significantly impact biofilm structure orbacterial activity.

Fabrication is straightforward, which enables quick application for monitoring ana-lytes in a variety of settings. Because these sensors can be calibrated in a variety ofmatrices and equipment, they are adaptable to many different applications, includingbiofilms of other species, cell culture scaffolds, hydrogels, and other systems where 3Dmonitoring is valuable. In addition, the polymeric nanoparticle platform described hereis adaptable to a wide variety of analytes that might be of interest in biological systems,including oxygen, glucose, ions, and small molecules. The O2NS we developed re-sponds linearly to oxygen concentration, which is consistent with previously reportedobservations (42). Lee and Okura note that the oxygen-sensitive metalloporphyrin usedhere (PtTFPP) demonstrates excellent linearity in the range of interest, but the responsedoes become nonlinear at oxygen concentrations greater than 21 mol% in the gasphase (42). The DiA exhibits minimal change at different oxygen concentrations,although photobleaching begins to occur near the end of the 90-min observationperiods used in the above-described experiments (Fig. S2).

FIG 4 Location versus oxygen concentration plot (right) of PAO1 image (left). Four locations across the biofilm were chosen for graphing, and error barsrepresent standard error of n � 9 pixels surrounding the chosen location. The four locations were chosen arbitrarily and show large spatial variation. Location3, whose representative orthogonal views are shown, has a large gap in the center that has DO concentrations close to those in the surrounding medium. Thisfinding highlights the massive spatial differences in biofilm oxygen concentrations.

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In our current implementation, the biofilm is grown with nanosensors in themedium. This makes the technique directly applicable to in vitro interrogation ofbacterial isolates as we showed here, which were grown in such a manner as to mimicthe microaggregate structure (43, 44) of biofilm infections found in the lungs of cysticfibrosis (CF) patients. However, due to the negligible diffusion of the nanosensors intothe biofilm, this approach is not currently suitable for ex vivo or in situ biofilm samples.In addition, imaging was performed with confocal microscopy. These points limitnanosensors as a tool for scientific research rather than a rapid-use clinical tool. However,

FIG 5 Time lapse of oxygen concentration within PAO1 biofilm after the addition of 512 �g/ml colistin. Oxygenconcentration within the biofilm reaches ambient values by 60 minutes. Error bars represent a standard error ofn � 9 pixels surrounding the chosen location.

FIG 6 Local oxygen concentrations over time within biofilms of P. aeruginosa strain PAO1 and threeclinical strains after the addition of 512 �g/ml colistin. Error bars represent a standard error of n � 9pixels surrounding the chosen location.

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with a consideration to such research applications, the fabrication and use of thesenanosensors build upon equipment already present in many laboratories and corefacilities. This technique is not necessarily faster than culture-based techniques forantibiotic screening applications since biofilm growth (up to 72 h for mature biofilms)is required. However, the use of nanosensors is well suited for research applications (i.e.,measuring metabolism of subpopulations and biofilm homogeneity) that cannot beaddressed with current tools.

Example application: measuring antibiotic-induced metabolic changes. Theability to differentiate between the three clinical strains demonstrates the value of thisapproach. Using these oxygen nanosensors, it is possible to obtain information aboutthe entire biofilm and not just a single point or a few points within it. Biofilms formedby PAO1 have a microaggregate structure (biofilms �100 �m in size) similar to what isseen in the lungs of cystic fibrosis patients (43–45). In these biofilms, we see spatialvariations (Fig. 4) in dissolved oxygen concentration and secondary structures that arereminiscent of liquid and nutrient transport channels like those observed by Wilking etal. (46) and others (47, 48). It is also possible to see parts of the biofilm that are eithernot surface attached or loosely attached to the surface, which would be lost with thephysical disruption of monitoring with the standard microelectrode-based measure-ments. In the case of the first clinical strain, we show that this technique can elucidateareas of different metabolism (Fig. S4). If one were to interrogate this biofilm with amicroelectrode, only measuring a small area (approximately 50 �m by 50 �m) wouldobscure the heterogeneity of the biofilm as a whole. This localization of persistentoxygen consumption could indicate the presence of antibiotic-resistant cells or per-sister cells within the biofilms. In the cases of clinical strains 2 and 3, this approach isable to distinguish responses to antibiotic attack as opposed to only demonstratingresistance. We note different rates of DO concentration variation between the two, withthese variations occurring on measurable time scales (around 15 minutes for CS3 and40 minutes for CS2). CS3 also had remarkably different architecture, producing a largerstructure than those seen in PAO1 and the other clinical strains tested. This highlightsour ability to individually profile these strains with improved measurements overtraditional approaches. The ability to obtain information on spatial homogeneity ofbiofilm metabolism could be elevated with the addition of sensors for other analytes.Two-dimensional maps can provide x-y spatial information on biofilms but are unableto provide z axis information. Because biofilms have a complex, three-dimensionalstructure with channels for the movement of nutrients, water, and oxygen, the com-plete understanding of dynamics within the system requires three-dimensional infor-mation. Our approach allows us to rapidly gather all spatial information, and we areable to do so without disturbing the structure. Temporal information is limited only bythe imaging equipment’s ability to capture data. Similar to any confocal microscopyexperiment, we can increase time resolution at the expense of spatial resolution or withmore advanced imaging hardware.

FIG 7 Comparison of the effects of different antibiotics (4 mg/ml tobramycin and 512 �g/ml colistin) onoxygen concentration within PAO1 biofilms. Error bars represent a standard error of n � 9 pixels surroundingthe chosen location.

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Both antibiotics used in this study were chosen for their relevance to clinical therapyfor CF patients. Tobramycin belongs to the aminoglycoside class of antibiotics andshows the greatest antipseudomonal activity of the aminoglycosides (49). Because ofthis, it is a common choice for combination therapy in CF patients (50). Colistin is apolymyxin antibiotic that is often used to treat P. aeruginosa airway infections in CFpatients. Polymyxins are popular, as they usually retain activity against multidrug-resistant strains of P. aeruginosa (51). Kill curves in P. aeruginosa PAO1 biofilms as wellas clinical strains have been demonstrated by Hengzhuang et al. using the Calgarybiofilm device method (52), and our sensors were able to recapitulate these data witha finer time resolution. The results obtained from O2NS measurements in Fig. 7 areconsistent with the kill curves obtained by Hengzhuang et al. (52) using the modifiedCalgary biofilm device method, although our results also provide information on thestructure of the biofilms and spatial distribution of high- and low-oxygen concentrationregions within the biofilm. Calculations (53) and experimental data (54) from micro-electrodes show that oxygen should penetrate P. aeruginosa biofilms attached tosurfaces to a depth of 60 to 70 �m, but this assumes diffusion into a uniform biofilm.We note in Fig. 4 that there is considerable spatial variation within the biofilm.Therefore, we chose a point near the center of the larger aggregate for temporalanalysis, which presumably has the largest diffusion barrier.

Conclusions. Here, we used polymeric oxygen-sensitive nanosensors to monitor theresponse of lab and clinical Pseudomonas aeruginosa biofilms to antibiotic attack withthree-dimensional spatial data collection and 5-min temporal resolution. The nanosen-sors’ small size, rapid response, and ratiometric signal allow for detailed interrogationof clinical samples to obtain antibiotic susceptibility information as well as data fromwhich pharmacodynamic parameters can be determined. Additionally, these oxygennanosensors enable the elucidation of biofilm heterogeneities that microelectrodes andother techniques cannot capture, such as microaggregate size and shape, localizationof oxygen consumption, and even biofilm features which are not surface attached. Weare able to measure differences in the response of three clinical samples to antimicro-bial attack and overall metabolism via changes in oxygen concentration. This approachfor biofilm monitoring is straightforward to implement for in vitro spatial and temporalmonitoring of biofilm metabolism, and future work will focus on the addition of thenanosensors to preexisting biofilms. The sensor platform’s adaptability and ease ofmanufacture enable the monitoring of multiple or different analytes within the biofilmstructure in future work as well.

MATERIALS AND METHODSHigh-molecular-weight poly(vinyl chloride) (PVC), bis(2-ethylhexyl) sebacate (BEHS), tetrahydrofuran

(THF), dichloromethane (DCM), and phosphate-buffered saline (PBS) were purchased from Sigma-Aldrich(St. Louis, MO, USA). Platinum (II) meso-tetra(pentafluorophenyl)porphine (PtTFPP) was purchased fromFrontier Scientific (Logan, UT, USA). 1,2-Dipalmitoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy(polyethylene glycol)-750] ammonium salt in chloroform (PEG-lipid) was purchased from Avanti PolarLipids (Alabaster, AL, USA). 4-Di-16-ASP {4-[4-(dihexadecylamino)styryl]-N-methylpyridinium iodide} (DiA)was purchased from Thermo Fisher Scientific (Waltham, MA, USA).

NS fabrication. A total of 30 mg of polyvinyl chloride (PVC) was weighed into a 2-ml vial andcombined with 66 �l of BEHS. In a separate vial, 5 mg of Pt(II) meso-tetra(pentafluorophenyl)porphine(PtTFPP) and 0.2 mg 4-Di-16-ASP {4-[4-(dihexadecylamino)styryl]-N-methylpyridinium iodide} (DiA) weredissolved in 500 �l of tetrahydrofuran (THF). The THF and dissolved dyes were then added to thePVC/BEHS mixture and vortexed for 1 min until all solids were dissolved. A total of 750 �l of dichloro-methane (DCM) was then added to the vial, and the mixture was vortexed thoroughly. The optodecocktail was then stored at 4°C until use.

To fabricate nanosensors from the optode cocktail, 2 mg PEG-lipid (80 �l of a 25-mg/ml solution inchloroform) was dried in a 4-dram scintillation vial and then resuspended in 5 ml of PBS (pH 7.4) with aprobe tip sonicator (Branson Digital Sonifier 450; Branson Ultrasonics Corporation, Danbury, CT) for 30 sat 20% intensity. A total of 125 �l of the optode cocktail mixture was injected into the PBS/PEG-lipidsolution under probe tip sonication (3 min, 20% intensity). Following sonication, excess polymer wasremoved by filtration via a 0.22-�m syringe filter (Pall Corporation, Port Washington, NY).

NS characterization. Oxygen-sensitive nanosensors were calibrated on an Avantes AvaSpec-ULS2048L StarLine versatile fiber-optic spectrometer (Apeldoorn, the Netherlands) with a 100-�m slitwidth. O2NSs were sealed in an airtight screw top quartz cuvette (Starna Cells, Atascadero, CA), and theoxygen content of the sample was altered by bubbling a compressed air/N2 mixture through the cuvette

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for 30 min. The O2NS sample was then excited with a 450-nm laser diode, and spectra were collected atvarious concentrations of oxygen. A linear regression of ratiometric signals was performed in GraphPadPrism 7 software (La Jolla, CA). Values were fit to the Stern-Volmer relationship describing collisionalquenching of an excited species where the quencher Q is molecular oxygen. In the equation below, If

0

is the ratiometric fluorescence intensity in the absence of oxygen, If is the ratiometric fluorescenceintensity at the given concentration of oxygen [Q], and KSV is the Stern-Volmer constant.

If0

If� 1 � KSV�Q�

The ratiometric mode is a method of internal self-calibration that is made possible by the comparisonof emission at two different wavelengths (55). In this case, If refers to the ratio of the oxygen-sensitivefluorescence intensity of PtTFPP at 650 nm and the insensitive fluorescence intensity of DiA at 585 nm(I650/I585). This allows the sensors to overcome limitations, such as concentration variation or heteroge-neity of the local microenvironment, which will impact both fluorescent peaks similarly, while oxygen willonly impact one of the peaks (56, 57).

O2NSs were calibrated in alginate hydrogels on a Zeiss LSM780 confocal microscope. Hydrogels withembedded O2NS were fabricated according to Fletcher et al. (58). Briefly, 0.5 ml of 3 wt% alginate wasmixed with 200 �l of O2NS and 40 �l of 0.1 M CaSO4. The mixture was cast between two glass plates toproduce hydrogels of 380-�m thickness, which was measured with a micrometer. The resulting hydro-gels were placed into chamber slide wells and covered with 500 �l of PBS. Images were taken underambient conditions (21% O2 or 6.65 mg/liter dissolved O2 at the lab elevation of 5,750 ft above sea level)and under oxygen deficiency (0% O2 or 0 mg/liter dissolved O2). Chamber slide wells were deoxygenatedwith 10 mM glucose and 2 IU/ml glucose oxidase, as described by Baumann et al. (59). The averageintensities of the biofilms in the images at 0% O2 and 21% O2 were then used in a linear regression todetermine the Stern-Volmer constant KSV. In both calibration scenarios, the intensities of the PtTFPPsignal at 650 nm and the intensities of the reference dye (DiA) signal at 585 nm were divided to form aratiometric signal. The ratiometric signal at 0 mg/liter dissolved oxygen (DO) was then divided by theratiometric signal at each concentration to create a linear calibration curve. The linear Stern-Volmerrelationship between If

0/If and oxygen concentration was used to fit the calibration data (60).Image analysis was performed in Matlab using the Batch Image Processor. The image stack was

transformed into a 3D array in Matlab with dimensions of 1,024 by 1,024 by the number of slices in theimage stack containing 8-bit brightness values (intensity values, 0 to 255). These slices were convertedvia the Stern-Volmer calibration curve function into DO values in milligrams per liter, which could beextricated and graphed.

Dynamic light scattering (DLS), zeta potential via phase analysis light scattering (PALS), and mobilitymeasurements were performed on a Brookhaven zetaPALS instrument with Particle Solutions softwarev 2.2 (Brookhaven Instruments Corporation, Holtsville, NY).

Biofilm construction and growth. (i) P. aeruginosa PAO1 (ATCC 15692). Biofilm construction andgrowth procedures were adapted from methods by Kirchner et al. (61). Briefly, P. aeruginosa strains wereplated onto Luria Bertani (LB) agar (Sigma-Aldrich, St. Louis, MO, USA) from frozen stocks and incubatedat 37°C for 24 h. One colony was pulled and dispersed in 1 ml of LB broth and incubated for 24 h at 37°C.Liquid culture was diluted to an optical density at wavelength of 600 nm (OD600) of 0.05. Biofilms of PAO1were grown statically in a 16.7% vol/vol solution of oxygen nanosensors in PBS mixed with LB broth(Sigma-Aldrich). A total of 600 �l of O2NS � LB was added to each well of a 4-well LabTek chamber slideand then inoculated with 5 �l of liquid P. aeruginosa culture at an OD600 of 0.05. Chamber slides wereplaced in a humidity chamber and incubated for 72 h at 37°C.

(ii) P. aeruginosa clinical strains. Respiratory samples collected for clinical care were processed bythe clinical microbiology laboratory at Children’s Hospital Colorado using standard CF Foundationguidelines for culture. P. aeruginosa isolates were frozen and shipped to study investigators at theColorado School of Mines. All three clinical strains are deidentified and in the manuscript are labeled asclinical strain 1 (CS1), CS2, and CS3. With all three of the provided isolates, we expanded and grew thebiofilms as detailed above with the PAO1 samples.

(iii) Biofilm characterization. Three biofilms of PAO1 were grown as described above, namely, onecontaining no polymer nanoparticles, one containing polymer nanoparticles with no fluorescent com-ponents, and one containing O2NS. The FilmTracer LIVE/DEAD biofilm viability kit (Thermo FisherScientific) was used for biofilm viability. A total of 12 �l of SYTO9 green fluorescent nucleic acid stain(3.34 mM in dimethyl sulfoxide [DMSO]) and 12 �l of propidium iodide (20 mM in DMSO) were added to4 ml of PBS, and 400 �l of the resulting solution was added to each well of the chamber slide andincubated at room temperature for 15 min in the dark. The solution was then aspirated, and each wellwas washed 2 times with sterile PBS. A total of 400 �l of 10% formalin was added to each well andincubated for 30 min in the dark. Formalin was then aspirated, and each well was washed 2 times withPBS. A final volume of 200 �l PBS was added to each well before imaging. Three regions of interestcontaining the entirety of the biofilm were chosen for each sample containing no nanoparticles (n � 4images) or containing blank polymer nanoparticles (n � 3) or O2NS (n � 3).

(iv) Antibiotic testing. All antibiotic testing was performed on live biofilm samples that had notbeen stained or fixed prior to imaging. A total of 10 �l of 10 mg/ml tobramycin in PBS was added to the490 �l of PBS already present in the chamber slide well, for a final tobramycin concentration of 4 mg/ml(62). This was administered to each biofilm sample, and images were taken every 5 min for 2 h.

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For comparison, a final colistin concentration of 512 �g/ml was administered to each biofilm samplein a similar manner, and images were taken every 5 min for 2 h. Here, colistin was administered as colistinsulfate as opposed to the prodrug colistimethate sodium.

SUPPLEMENTAL MATERIALSupplemental material for this article may be found at https://doi.org/10.1128/AEM

.01116-19.SUPPLEMENTAL FILE 1, PDF file, 6.9 MB.SUPPLEMENTAL FILE 2, AVI file, 0.8 MB.SUPPLEMENTAL FILE 3, AVI file, 0.9 MB.SUPPLEMENTAL FILE 4, AVI file, 1 MB.SUPPLEMENTAL FILE 5, AVI file, 1.1 MB.

ACKNOWLEDGMENTSThis work was supported by Children’s Hospital of Colorado Research Institute, the

Office of Research and Technology Transfer at Colorado School of Mines, and start upfunds from Colorado School of Mines. Imaging experiments were performed in theUniversity of Colorado Anschutz Medical Campus Advance Light Microscopy Coresupported, in part, by NIH/NCATS Colorado CTSI grant number UL1 TR001082. Thecontents of the paper are the authors’ sole responsibility and do not necessarilyrepresent official NIH views.

We thank the Children’s Hospital of Colorado (CHCO) sample bank for providing theclinical isolates.

REFERENCES1. Nadell CD, Drescher K, Foster KR. 2016. Spatial structure, cooperation,

and competition in bacterial biofilms. Nat Rev Microbiol 14:589 – 600.https://doi.org/10.1038/nrmicro.2016.84.

2. Hall-Stoodley L, Costerton JW, Stoodley P. 2004. Bacterial biofilms: fromthe natural environment to infectious diseases. Nat Rev Microbiol2:95–108. https://doi.org/10.1038/nrmicro821.

3. Stewart PS, Franklin MJ. 2008. Physiological heterogeneity in biofilms.Nat Rev Microbiol 6:199 –210. https://doi.org/10.1038/nrmicro1838.

4. Hoiby N, Flensborg EW, Beck B, Friis B, Jacobsen SV, Jacobsen L. 1977.Pseudomonas aeruginosa infection in cystic fibrosis. Diagnostic andprognostic significance of Pseudomonas aeruginosa precipitins deter-mined by means of crossed immunoelectrophoresis. Scand J Respir Dis58:65–79.

5. Burns JL, Ramsey BW, Smith AL. 1993. Clinical manifestations and treat-ment of pulmonary infections in cystic fibrosis. Adv Pediatr Infect Dis8:53– 66.

6. Hamed K, Debonnett L. 2017. Tobramycin inhalation powder for thetreatment of pulmonary Pseudomonas aeruginosa infection in patientswith cystic fibrosis: a review based on clinical evidence. Ther Adv RespirDis 11:193–209. https://doi.org/10.1177/1753465817691239.

7. Macia MD, Rojo-Molinero E, Oliver A. 2014. Antimicrobial susceptibilitytesting in biofilm-growing bacteria. Clin Microbiol Infect 20:981–990.https://doi.org/10.1111/1469-0691.12651.

8. Douterelo I, Jackson M, Solomon C, Boxall J. 2017. Spatial and temporalanalogies in microbial communities in natural drinking water biofilms.Sci Total Environ 581–582:277–288. https://doi.org/10.1016/j.scitotenv.2016.12.118.

9. Besemer K, Singer G, Hödl I, Battin TJ. 2009. Bacterial community com-position of stream biofilms in spatially variable-flow environments. ApplEnviron Microbiol 75:7189 –7195. https://doi.org/10.1128/AEM.01284-09.

10. Drennan DM, Almstrand R, Ladderud J, Lee I, Landkamer L, Figueroa L,Sharp JO. 2017. Spatial impacts of inorganic ligand availability and localizedmicrobial community structure on mitigation of zinc laden mine water insulfate-reducing bioreactors. Water Res 115:50–59. https://doi.org/10.1016/j.watres.2017.02.037.

11. Osmond M, Bernier SM, Pantcheva MB, Krebs MD. 2017. Collagen andcollagen-chondroitin sulfate scaffolds with uniaxially aligned pores forthe biomimetic, three dimensional culture of trabecular meshwork cells.Biotechnol Bioeng 114:915–923. https://doi.org/10.1002/bit.26206.

12. Kim R, Wang YL, Hwang SHJ, Attayek PJ, Smiddy NM, Reed MI, Sims CE,Allbritton NL. 2018. Formation of arrays of planar, murine, intestinal crypts

possessing a stem/proliferative cell compartment and differentiated cellzone. Lab Chip 18:2202–2213. https://doi.org/10.1039/C8LC00332G.

13. Harris D, Ummadi JG, Thurber AR, Allau Y, Verba C, Colwell F, Torres ME,Koley D. 2016. Real-time monitoring of calcification process by Sporo-sarcina pasteurii biofilm. Analyst 141:2887–2895. https://doi.org/10.1039/c6an00007j.

14. Joshi VS, Sheet PS, Cullin N, Kreth J, Koley D. 2017. Real-time metabolicinteractions between two bacterial species using a carbon-based pHmicrosensor as a scanning electrochemical microscopy probe. AnalChem 89:11044 –11052. https://doi.org/10.1021/acs.analchem.7b03050.

15. Staal M, Prest EI, Vrouwenvelder JS, Rickelt LF, Kuhl M. 2011. A simpleoptode based method for imaging O-2 distribution and dynamics in tapwater biofilms. Water Res 45:5027–5037. https://doi.org/10.1016/j.watres.2011.07.007.

16. Walters MC, Roe F, Bugnicourt A, Franklin MJ, Stewart PS. 2003. Contri-butions of antibiotic penetration, oxygen limitation, and low metabolicactivity to tolerance of Pseudomonas aeruginosa biofilms to ciprofloxa-cin and tobramycin. Antimicrob Agents Chemother 47:317–323. https://doi.org/10.1128/aac.47.1.317-323.2003.

17. Whalen WJ, Bungay HR, III, Sanders WM, III. 1969. Microelectrode deter-mination of oxygen profiles in microbial slime systems. Environ SciTechnol 3:1297–1298. https://doi.org/10.1021/es60035a004.

18. Hao L, Li J, Kappler A, Obst M. 2013. Mapping of heavy metal ionsorption to cell-extracellular polymeric substance-mineral aggregates byusing metal-selective fluorescent probes and confocal laser scanningmicroscopy. Appl Environ Microbiol 79:6524 – 6534. https://doi.org/10.1128/AEM.02454-13.

19. Yu S, Wei Q, Zhao T, Guo Y, Ma LZ. 2016. A survival strategy forPseudomonas aeruginosa that uses exopolysaccharides to sequesterand store iron to stimulate Psl-dependent biofilm formation. Appl Envi-ron Microbiol 82:6403– 6413. https://doi.org/10.1128/AEM.01307-16.

20. Li J, Bishop PL. 2004. Time course observations of nitrifying biofilmdevelopment using microelectrodes. J Environ Eng Sci 3:523–528.https://doi.org/10.1139/s04-027.

21. Yu T, Bishop PL. 2001. Stratification and oxidation-reduction potentialchange in an aerobic and sulfate-reducing biofilm studied using micro-electrodes. Water Environ Res 73:368 –373. https://doi.org/10.2175/106143001X139399.

22. deBeer D, Schramm A, Santegoeds CM, Kuhl M. 1997. A nitrite micro-sensor for profiling environmental biofilms. Appl Environ Microbiol 63:973–977.

Nanosensors To Measure 3D O2 in P. aeruginosa Biofilms Applied and Environmental Microbiology

October 2019 Volume 85 Issue 20 e01116-19 aem.asm.org 11

on Septem

ber 12, 2020 by guesthttp://aem

.asm.org/

Dow

nloaded from

23. Billings N, Birjiniuk A, Samad TS, Doyle PS, Ribbeck K. 2015. Materialproperties of biofilms—a review of methods for understanding perme-ability and mechanics. Rep Prog Phys 78:36601. https://doi.org/10.1088/0034-4885/78/3/036601.

24. Bungay HR, III, Whalen WJ, Sanders WM, III. 1969. Microprobe techniquesfor determining diffusivities and respiration rates in microbial slimesystems. Biotechnol Bioeng 11:765–772. https://doi.org/10.1002/bit.260110505.

25. Hidalgo G, Burns A, Herz E, Hay AG, Houston PL, Wiesner U, Lion LW.2009. Functional tomographic fluorescence imaging of pH microenvi-ronments in microbial biofilms by use of silica nanoparticle sensors. ApplEnviron Microbiol 75:7426 –7435. https://doi.org/10.1128/AEM.01220-09.

26. Kenney RM, Boyce MW, Whitman NA, Kromhout BP, Lockett MR. 2018. ApH-sensing optode for mapping spatiotemporal gradients in 3D paper-based cell cultures. Anal Chem 90:2376 –2383. https://doi.org/10.1021/acs.analchem.7b05015.

27. Acosta MA, Ymele-Leki P, Kostov YV, Leach JB. 2009. Fluorescent micropar-ticles for sensing cell microenvironment oxygen levels within 3D scaffolds.Biomaterials 30:3068–3074. https://doi.org/10.1016/j.biomaterials.2009.02.021.

28. Rivera KR, Pozdin VA, Young AT, Erb PD, Wisniewski NA, Magness ST,Daniele M. 2018. Integrated phosphorescence-based photonic biosensor(iPOB) for monitoring oxygen levels in 3D cell culture systems. BiosensBioelectron 123:131–140. https://doi.org/10.1016/j.bios.2018.07.035.

29. Bland E, Burg K. 2013. Fluorescence ratio imaging for oxygen measure-ment in a tissue engineered construct. J Histotechnol 36:119 –127.https://doi.org/10.1179/2046023613Y.0000000033.

30. Rubol S, Freixa A, Sanchez-Vila X, Romaní AM. 2018. Linking biofilmspatial structure to real-time microscopic oxygen decay imaging. Bio-fouling 34:200 –211. https://doi.org/10.1080/08927014.2017.1423474.

31. Staal M, Borisov SM, Rickelt LF, Klimant I, Kühl M. 2011. Ultrabright planaroptodes for luminescence life-time based microscopic imaging of O2

dynamics in biofilms. J Microbiol Methods 85:67–74. https://doi.org/10.1016/j.mimet.2011.01.021.

32. Kühl M, Rickelt LF, Thar R. 2007. Combined imaging of bacteria andoxygen in biofilms. Appl Environ Microbiol 73:6289 – 6295. https://doi.org/10.1128/AEM.01574-07.

33. Holst G, Kohls O, Klimant I, König B, Kühl M, Richter T. 1998. A modularluminescence lifetime imaging system for mapping oxygen distributionin biological samples. Sensor Actuat B-Chem 51:163–170. https://doi.org/10.1016/S0925-4005(98)00232-9.

34. Kiamco MM, Atci E, Mohamed A, Call DR, Beyenal H. 2017. Hyperosmoticagents and antibiotics affect dissolved oxygen and pH concentrationgradients in Staphylococcus aureus biofilms. Appl Environ Microbiol83:e02783-16. https://doi.org/10.1128/AEM.02783-16.

35. Acosta MA, Velasquez M, Williams K, Ross JM, Leach JB. 2012. Fluores-cent silica particles for monitoring oxygen levels in three-dimensionalheterogeneous cellular structures. Biotechnol Bioeng 109:2663–2670.https://doi.org/10.1002/bit.24530.

36. Moßhammer M, Strobl M, Kühl M, Klimant I, Borisov SM, Koren K. 2016.Design and application of an optical sensor for simultaneous imaging ofpH and dissolved O2 with low cross-talk. ACS Sens 1:681– 687. https://doi.org/10.1021/acssensors.6b00071.

37. Cash KJ, Li C, Xia J, Wang LV, Clark HA. 2015. Optical drug monitoring:photoacoustic imaging of nanosensors to monitor therapeutic lithium invivo. ACS Nano 9:1692–1698. https://doi.org/10.1021/nn5064858.

38. Ruckh TT, Skipwith CG, Chang WD, Senko AW, Bulovic V, Anikeeva PO,Clark HA. 2016. Ion-switchable quantum dot forster resonance energytransfer rates in ratiometric potassium sensors. ACS Nano 10:4020 – 4030.https://doi.org/10.1021/acsnano.5b05396.

39. Galyean A, Behr M, Cash K. 2018. Ionophore-based optical nanosensorsincorporating hydrophobic carbon dots and a pH-sensitive quencherdye for sodium detection. Analyst 143:458 – 465. https://doi.org/10.1039/C7AN01382E.

40. Cash KJ, Clark HA. 2013. Phosphorescent nanosensors for in vivo trackingof histamine levels. Anal Chem 85:6312– 6318. https://doi.org/10.1021/ac400575u.

41. Ferris MS, Katageri AG, Gohring GM, Cash KJ. 2018. A dual-indicatorstrategy for controlling the response of ionophore-based optical nano-sensors. Sensor Actuat B-Chem 256:674 – 681. https://doi.org/10.1016/j.snb.2017.09.203.

42. Lee S-K, Okura I. 1997. Photostable optical oxygen sensing material:

platinum tetrakis(pentafluorophenyl)porphyrin immobilized in polysty-rene. Anal Commun 34:185–188. https://doi.org/10.1039/a701130j.

43. Lian J, Chan R, Lam K, Costerton JW. 1980. Production of mucoidmicrocolonies by Pseudomonas aeruginosa within infected lungs incystic fibrosis. Infect Immun 28:546 –556.

44. Worlitzsch D, Tarran R, Ulrich M, Schwab U, Cekici A, Meyer KC, Birrer P,Bellon G, Berger J, Weiss T, Botzenhart K, Yankaskas JR, Randell S,Boucher RC, Döring G. 2002. Effects of reduced mucus oxygen concen-tration in airway Pseudomonas infections of cystic fibrosis patients. J ClinInvest 109:317–325. https://doi.org/10.1172/JCI13870.

45. Singh PK, Schaefer AL, Parsek MR, Moninger TO, Welsh MJ, GreenbergEP. 2000. Quorum-sensing signals indicate that cystic fibrosis lungs areinfected with bacterial biofilms. Nature 407:762–764. https://doi.org/10.1038/35037627.

46. Wilking JN, Zaburdaev V, De Volder M, Losick R, Brenner MP, Weitz DA.2013. Liquid transport facilitated by channels in Bacillus subtilis biofilms.Proc Natl Acad Sci U S A 110:848–852. https://doi.org/10.1073/pnas.1216376110.

47. Zhang C, Li B, Tang J-Y, Wang X-L, Qin Z, Feng X-Q. 2017. Experimentaland theoretical studies on the morphogenesis of bacterial biofilms. SoftMatter 13:7389 –7397. https://doi.org/10.1039/c7sm01593c.

48. Dietrich LEP, Okegbe C, Price-Whelan A, Sakhtah H, Hunter RC, NewmanDK. 2013. Bacterial community morphogenesis is intimately linked to theintracellular redox state. J Bacteriol 195:1371–1380. https://doi.org/10.1128/JB.02273-12.

49. Wade KC, Benjamin DK. 2011. Clinical pharmacology of anti-infectivedrugs, p 1160 –1211. In Remington JS, Klein JO, Wilson CB, Nizet V,Maldonado YA (ed), Infectious diseases of the fetus and newborn, 7thed. W.B. Saunders, Philadelphia, PA.

50. Hill D, Rose B, Pajkos A, Robinson M, Bye P, Bell S, Elkins M, ThompsonB, MacLeod C, Aaron SD, Harbour C. 2005. Antibiotic susceptibilities ofPseudomonas aeruginosa isolates derived from patients with cysticfibrosis under aerobic, anaerobic, and biofilm conditions. J Clin Microbiol43:5085–5090. https://doi.org/10.1128/JCM.43.10.5085-5090.2005.

51. Satlin MJ, Jenkins SG. 2017. Polymyxins, chapter 151, p 1285–1288.e2. InCohen J, Powderly WG, Opal SM (ed), Infectious diseases, 4th ed.Elsevier, Amsterdam, the Netherlands.

52. Hengzhuang W, Wu H, Ciofu O, Song Z, Hoiby N. 2011. Pharmacokinetics/pharmacodynamics of colistin and imipenem on mucoid and nonmucoidPseudomonas aeruginosa biofilms. Antimicrob Agents Chemother 55:4469–4474. https://doi.org/10.1128/AAC.00126-11.

53. Stewart PS. 2003. Diffusion in biofilms. J Bacteriol 185:1485–1491.https://doi.org/10.1128/jb.185.5.1485-1491.2003.

54. Xu KD, Stewart PS, Xia F, Huang CT, McFeters GA. 1998. Spatial physio-logical heterogeneity in Pseudomonas aeruginosa biofilm is determinedby oxygen availability. Appl Environ Microbiol 64:4035– 4039.

55. Paredes RM, Etzler JC, Watts LT, Zheng W, Lechleiter JD. 2008. Chemicalcalcium indicators. Methods 46:143–151. https://doi.org/10.1016/j.ymeth.2008.09.025.

56. Yang ZG, Cao JF, He YX, Yang JH, Kim T, Peng XJ, Kim JS. 2014. Macro-/micro-environment-sensitive chemosensing and biological imaging. ChemSoc Rev 43:4563–4601. https://doi.org/10.1039/C4CS00051J.

57. Li X, Gao X, Shi W, Ma H. 2014. Design strategies for water-soluble smallmolecular chromogenic and fluorogenic probes. Chem Rev 114:590 – 659. https://doi.org/10.1021/cr300508p.

58. Fletcher NA, Babcock LR, Murray EA, Krebs MD. 2016. Controlled deliveryof antibodies from injectable hydrogels. Mater Sci Eng C Mater Biol Appl59:801– 806. https://doi.org/10.1016/j.msec.2015.10.096.

59. Baumann RP, Penketh PG, Seow HA, Shyam K, Sartorelli AC. 2008.Generation of oxygen deficiency in cell culture using a two-enzymesystem to evaluate agents targeting hypoxic tumor cells. Radiat Res170:651– 660. https://doi.org/10.1667/RR1431.1.

60. Keizer J. 1983. Nonlinear fluorescence quenching and the origin of positivecurvature in Stern-Volmer plots. J Am Chem Soc 105:1494–1498. https://doi.org/10.1021/ja00344a013.

61. Kirchner S, Fothergill JL, Wright EA, James CE, Mowat E, Winstanley C.2012. Use of artificial sputum medium to test antibiotic efficacy againstPseudomonas aeruginosa in conditions more relevant to the cysticfibrosis lung. J Vis Exp 5:e3857. https://doi.org/10.3791/3857.

62. Mouton JW, Vinks AA. 2005. Pharmacokinetic/pharmacodynamic mod-elling of antibacterials in vitro and in vivo using bacterial growth and killkinetics. Clin Pharmacokinet 44:201–210. https://doi.org/10.2165/00003088-200544020-00005.

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