Dual-Probe Luminescence Lifetime Measurements for the Oxygen Compensation in Enzymatic Biosensors Bradley Collier, Ruiqi Long, and Mike McShane Department of Biomedical Engineering Texas A&M University College Station, TX, U.S.A.
[email protected] Abstract—An optical system for monitoring glucose with a means of compensating for local oxygen variations is described. Measurements of sensor response to glucose under varying oxygen levels indicate a strong dependence of the optical response on ambient oxygen. The two-probe approach enables simultaneous monitoring of luminescence lifetime of microparticle sensors with a compact optical system. Glucose tracking experiments with both probes reveal a modulation of oxygen in the vicinity of sensors, even when external oxygen is constant. These results prove the concept of simultaneous monitoring of multiple sensor types in the same matrix.
I.
INTRODUCTION
Numerous biosensing systems have been developed based on the enzymatic oxidation of analytes, with the end goal often being in vivo measurements. With certain transduction approaches, particularly those based on monitoring oxygen consumption, accurate measurements rely heavily on local oxygen concentrations remaining constant. As oxygen deviates from the level at calibration, the error in the predicted analyte concentration increases. Compensation for such changes is particularly important when the local oxygen concentration is expected to vary and the catalytic action of the sensors leads to oxygen depletion. This latter influence is of special concern when using distributed microparticles, where regions of oxygen depletion in intervening spaces may be observed when reaction rates are high. By monitoring the local oxygen levels, one should be able to mathematically adjust the calibration curve to compensate for oxygen variation. To date, only one report of compensating for oxygen variation can be found in the literature [1]. Glucose sensing has received a particularly large amount of attention in recent years due to the increasing worldwide incidence of diabetes mellitus. We have been developing an approach for in vivo optical glucose monitoring using implanted microparticle glucose sensors utilizing glucose oxidase [2, 3]. These sensors provide a viable option for an implantable continuous glucose monitoring system due to the ability to precisely tailor their sensitivity and response range. However, the effects of ambient tissue oxygen on the accuracy
of these sensors have become a concern, as the oxygen distribution in tissue is naturally inhomogeneous and may vary even more widely in the presence of reactive particles. In this situation, oxygen monitoring for compensation is essential, and requires the introduction of a second luminescent particle to optically transduce the local oxygen concentration. Currently there are few luminescence-based sensing systems capable of measuring multiple analytes simultaneously and these schemes are largely based on intensity measurements [4-6]. Although lifetime measurements offer many advantages over intensity measurements [6], particularly minimal susceptibility to photobleaching, resolving multiple lifetimes in a sensing system is not common and can require complicated and/or expensive schemes [7]. In this paper, we report a straightforward and affordable solution for dual lifetime measurements to compensate for oxygen levels during enzymatic sensing. The approach relies on sensing dyes with overlapping excitation spectra and complementary emission spectra, enabling simultaneous excitation, spectral discrimination via low-cost optical components, and lifetime measurements using a two-channel phase fluorimeter.
Figure 1. Illustration of the luminescent microparticle sensor concept: (left) implanted microparticles with transdermal monitoring and (right) diagram of the structure of an individual microparticle glucose sensor (right). The core is a porous silicate particle with immobilized enzyme (GOx) and oxygensensitive phosphor (PtOEP), and the outer coating is a transport-controlling nanofilm comprised of polyelectrolyte multilayers.
This work was supported by the National Institutes of Health (R01 EB000739) and the Texas Engineering Experiment Station (TEES).
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IEEE SENSORS 2009 Conference
A.
Materials Mesoporous alginate-silica (“algilica”) microspheres were created using sodium alginate (low viscosity, 250 centipoise, MW 12-80 kDa), (3-glycidyloxypropyl) trimethoxysilane (GPTS), and ammonium hydroxide (Sigma). Pt(II) octaethylporphine (PtOEP, Frontier Scientific), tetrahydrofuran (THF, Fluka), glucose oxidase (GOx, type VII from Aspergillus niger, 198k units/g of solid, Sigma), N-(3dimethylaminopropyl)-N´-ethylcarbodiimide hydrochloride (EDC, Fluka), N-hydroxysulfosuccinimide sodium salt (NHSS, Toronto Research Chemicals Inc.), and sodium acetate (Sigma) were used to prepare PtOEP/GOx-doped algilica particles. Poly(allylamine hydrochloride) (PAH, MW 70 kDa, Aldrich), poly(sodium 4-styrenesulfonate) (PSS, MW 70 kDa, Aldrich), and sodium chloride (Sigma) were used during the deposition of nanofilms. Additional algilica microspheres were loaded with oxygen sensitive Pd(II) mesoTetra (pentafluorophenyl) porphine (PdTFPP, Frontier Scientific). Gels with immobilized sensor particles were prepared by mixing poly(ethylene glycol) (PEG, 1000 MW, Polysciences) and water with an initiator solution composed of Irgacure 184 (Ciba) dissolved in dimethyl sulfoxide (SigmaAldrich). A silanol solution consisting of pure ethanol, water, and 3-(Trimethoxysilyl)propyl methacrylate was used to attach the PEG gel to a glass slide. β-D-glucose (MP Biomedicals, Inc.), oxygen and nitrogen gas (PraxAir), and PBS (Sigma) were used during dynamic testing. All necessary pH adjustments were performed using titrations of 1.0 M HCl and 1.0 M NaOH (Fluka). All chemicals listed above were reagent grade and used as received. Ultrapure water with a resistivity of greater than 18 MΩ-cm was used to prepare all aqueous solutions.
B. Sensor Preparation Previously-described procedures [2] were used to synthesize algilica particles and prepare glucose sensors. Briefly, ammonium hydroxide was added to a mixture of water, alginate, and GPTS to initiate particle condensation, which was followed by the addition of more water. The oxygen-sensitive porphines were introduced into the particles using insolubility-induced precipitation. PtOEP was utilized for the glucose sensors, while PdTFPP was used in the oxygen sensors. These dyes can both be excited by a green LED due to similar absorption wavelengths (519 and 536 nm, respectively) and can be spectrally separated due to larger Stokes’ shifts yielding unique emission spectra (peaks occurring at 647 and 670 nm, respectively) [8] as seen in Fig. 2. Enzyme dissolved in an acetate buffer was then mixed with PtOEP-containing particles. Covalent coupling of the enzyme to alginate moieties was then performed using an EDC/NHS solution. Finally, these glucose-reactive particles were then coated with the polyelectrolytes PAH and PSS using the layerby-layer self-assembly technique of alternating electrostatic adsorption. These nanofilms are used to tune the sensors to the desired range and sensitivity [3]. The PEG gel presents an additional diffusion barrier to glucose and oxygen reaching the sensors, which results in a delay in the response but has no apparent effect on the sensitivity and response range.
C. Custom Testing Setup A dynamic testing setup similar to that reported elsewhere [2] was constructed, with overall system control provided by a custom virtual instrument software program (LabVIEW, National Instruments). The oxygen concentration in the reservoirs was controlled using mass flow controllers (type 1179A, MKS) and a pressure gauge controller (model PR4000F, MKS). An oxygen microelectrode (model PA 2000, Unisense) was used to determine the dissolved oxygen concentration in the solution. The software also controls the glucose concentration exposed to the sensors using peristaltic pumps (model 7550-30, Masterflex) that mix solution from the reservoirs containing PBS buffer and concentrated glucose. A custom-designed lens system was used to deliver and collect signals from the sensors within the reaction chamber (Fig. 3). The green excitation light (523 nm) was passed through a 560 nm short pass optical filter to minimize spectral bleedthrough. The light was collimated and directed to a long-pass dichroic mirror that reflects all wavelengths shorter than 570 nm. Lenses were used to focus this light onto the sensors being exposed and fluorescence emitted was collected by these lenses. The emitted fluorescence above 570 nm is then able to pass through the first dichroic mirror. A 570 nm long pass filter is used to reduce scattered excitation light prior to dividing the emission spectrum with a second longpass dichroic mirror with a cut-on wavelength of 660 nm. The PtOEP signal (647 nm peak emission) is reflected while the PdTFPP signal (670 nm peak emission) is transmitted. Each signal passes through another filter (645 band pass for PtOEP and 670 long pass for PdTFPP) to limit spectral bleedthrough from the other luminophore. The LED driving signal was generated by a multifrequency phase fluorometer (MFPF-100, TauTheta Instruments) set at 1kHz modulation frequency. The MFPF was also used to determine the phase shift, demodulation, and corresponding luminescence lifetime from the emission signals. The custom virtual instrument read the data from the TauTheta Host Program and then stored and displayed it. Normalized Intensity
EXPERIMENTAL
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Figure 2. Spectral properties of luminescent oxygen probes. (A) Individual dye spectra and (B) emitted signals from suspensions containing a mixture of particles with PtOEP and particles with PdTFPP. The spectra shown are: after the 570nm longpass filter in the center of the optical train (570lp), reflected from the second dichroic and passed through the 645nm bandpass filter (645bp) and passed through the second dichroic and the 670nm longpass filter (670lp).
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IV.
RESULTS AND DISCUSSION
A. Glucose Response over a 24 Hr Period After obtaining the 24 hr response, the data were manipulated to correct for photobleaching of the longer lifetime dye species. The calibration curve was then applied to convert the lifetimes into predicted glucose concentrations (Fig. 4). While the sensor signal tracks the input very closely, the predicted concentrations exhibit errors particularly near the peaks and valleys of the profile. This can be partially attributed to the lack of compensation for local oxygen concentrations. Because the ambient oxygen was maintained constant during this experiment, the errors due to external oxygen variation were minimal. When ambient oxygen varies significantly, more substantial errors arise, as seen in the next section. 180
Figure 3. Custom-designed lens system for simultaneous measurement of luminescence lifetime of two oxygen probes. Concentration (mg/dL)
PROCEDURES
A 24-hr experiment was performed to test the effectiveness of pre-calibrated glucose sensors in tracking dynamic concentration changes. The gel was exposed to concentrations similar to a glucose profile seen in a patient with Type II diabetes taken every ten minutes over a 24 hr period [9]. The dissolved oxygen concentration was held constant at 276 μM. A similar version of the device seen in Fig. 3 was utilized to excite the sample and collect fluorescence emission. The frequency of the lifetime system was set to 40 kHz and integration was performed for one second every two seconds. After data collection, the measured lifetime was corrected for photobleaching by linear fitting of data points collected at repeated concentrations. These data were then used to predict concentration by application of a linear transformation. To assess the effect of ambient oxygen concentration on the glucose calibration curve, a second gel containing only glucose sensors was immobilized inside the reaction chamber and exposed to randomly-ordered glucose concentrations at three different oxygen concentrations. An oxygen response experiment was also performed with a third sensor gel containing particles loaded with PdTFPP. A PBS buffer solution was flowed over the sensors with oxygen concentrations near those expected in our glucose gel. These data were used to obtain a calibration curve for oxygen. The excitation frequency for both of these experiments was 1 kHz, with integration period of one second every ten seconds.
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Figure 4. Calibrated glucose 24 hr response.
B. Glucose and Oxygen Sensor Response with Different Ambient Oxygen Concentrations Glucose response curves were obtained for three different dissolved oxygen levels (Fig. 5). The decreasing sensitivity and increasing range at higher oxygen levels is consistent with expectations, due to the higher relative sensitivity of the oxygen probes at lower oxygen levels (Fig. 6). The SternVolmer plot (Fig. 6, inset) exhibits linearity and high sensitivity (KSV=59.49). These data confirm the reliance of our enzymatic sensor accuracy on dissolved oxygen concentration.
Finally, a second 24-hr experiment was performed using a gel containing a mixture of glucose and oxygen sensors. For this test, the frequency was reduced to 1 kHz and integration was performed for one second every second. The shorter integration time was used to reduce photobleaching of the longer lifetime species. The primary goal of this experiment was to assess whether oxygen variation can be observed in between the glucose sensors within the hydrogel, and then determine whether measurements of oxygen will allow mathematical adjustment of the calibration equation to maintain accurate predictions.
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Figure 5. Glucose calibration curves at different ambient oxygen levels. Error bars represent standard error.
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Figure 6. Response of PdTFPP-doped particles to oxygen. Error bars represent one standard error. Inset shows Stern-Volmer plot.
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Figure 7. Response of dual-sensor system to 24-hour glucose profile. The top two curves (PdTFPP, top; PtOEP, middle) are plotted on the right y-axis.
C. Dual Lifetime Measurements Glucose tracking performed with both glucose and oxygen sensors revealed significant oxygen variation internal to the hydrogel. As can be seen from Fig. 7, the changes in the oxygen sensors mimic the response of the glucose sensors. There are two reasons for this: (1) there is some spectral crosstalk between the two channels, resulting in a longer measured lifetime than is expected for PtOEP (should be in the range of 10μs) as well as a glucose-dependent signal in the PdTFPP channel; and (2) the glucose sensors cause a change in the local oxygen concentration within the hydrogel, such that the PdTFPP sensors observe fluctuating oxygen, even though the enzyme is only encapsulated in the PtOEP sensors. The first factor must be corrected for accurate glucose and oxygen measurements; the optical system must be modified to provide improved spectral discrimination. The second factor indicates that even with a constant ambient oxygen concentration, the depleted oxygen inside the gels results in significant drift from calibration values, and this can be tracked with the oxygen sensors. If ambient oxygen levels also fluctuate, the co-immobilized oxygen probes will enable tracking of both sources of variation, yielding excellent parallel data for calibration compensation. V.
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VI.
B.C. gratefully acknowledges the Texas A&M Vice President for Research for financial support in the form of a Graduate Merit Fellowship.
VII. REFERENCES [1]
[2]
[3]
[4]
[5]
CONCLUSIONS
It has been shown that PtOEP lifetime response of glucose-sensitive, enzymatic microparticles is greatly affected by changes in the ambient oxygen concentration. Oxygen sensors utilizing the dye PdTFPP, which can be spectrally differentiated from PtOEP, were developed and characterized to aid in an oxygen compensation system. This multi-analyte scheme also required the development of a novel system capable of making dual-luminescent lifetime measurements. The new device has shown the ability to make lifetime measurements of both glucose and oxygen sensors simultaneously. The lifetime response of the oxygen sensors can now be used to mathematically compensate for error seen in the raw glucose response data.
ACKNOWLEDGMENT
[6] [7]
[8]
[9]
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