Artificial neural networks for qualitative and quantitative analysis of target proteins with polymerized liposome vesicles

ANALYTICAL BIOCHEMISTRY Analytical Biochemistry 361 (2007) 109–119 www.elsevier.com/locate/yabio

Artificial neural networks for qualitative and quantitative analysis of target proteins with polymerized liposome vesicles Marina Santos, Suad Nadi, Hector C. Goicoechea 1, Manas K. Haldar, Andres D. Campiglia *, Sanku Mallik Department of Chemistry, University of Central Florida, Orlando, FL 32816, USA Received 28 August 2006 Available online 4 December 2006

Abstract We investigate the feasibility of using the luminescence response of polymerized liposomes incorporating ethylenediaminetetraacetate europium(III) (EDTA–Eu3+) for monitoring protein concentrations in aqueous media. Quantitative analysis is based on the linear relationship between the luminescence enhancement of the lanthanide ion and protein concentration. Analytical figures of merit are presented for carbonic anhydrase, human serum albumin, c-globulins, and thermolysin. Qualitative analysis is based on the luminescence lifetime of the liposome sensor. This parameter, which follows well-behaved single exponential decays and provides characteristic values for each of the four studied proteins, demonstrates the selective potential for protein identification. Then partial least squares-1 and artificial neural networks are compared toward the quantitative and qualitative analysis of human serum albumin and carbonic anhydrase in binary mixtures without previous separation at the concentration levels found in aqueous humor.  2006 Published by Elsevier Inc. Keywords: Luminescence; Lanthanide ions; Polymerized liposomes; Carbonic anhydrase; c-Globulins; Human serum albumin; Partial least squares; Artificial neural network

Development of sensors for the analysis of target proteins in complex biological matrices remains an analytical challenge. Clinical and laboratory tests such as the Lowry [1] and Bradford [2] methods, which currently are used to determine total protein content, lack the selectivity to target specific proteins in complex matrices. Enhanced selectivity and sensitivity have been obtained combining mass spectrometry to separation methods such as liquid chromatography [3,4] and electrophoresis [5,6]. Unfortunately, these powerful approaches lack the experimental simplicity for routine analysis of numerous samples. Recent efforts concerning simple protein assays have been based on syn*

Corresponding author. Fax: +1 407 823 2252. E-mail address: [email protected] (A.D. Campiglia). 1 Present address: Catedra de Quimica Analitica I, Facultad de Bioquimica y Ciencias Biologicas, Universidad Nacional del Litoral, Ciudad Universitaria, CC 242-S30001 Santa Fe, Argentina. 0003-2697/$ - see front matter  2006 Published by Elsevier Inc. doi:10.1016/j.ab.2006.11.019

chronous fluorescence spectroscopy [7], Rayleigh light scattering (RLS)2 spectroscopy [8,9], and near-infrared spectroscopy [10,11]. Fluorescence assays [7] rely on the spectral response of an organic fluorescence tag chemically attached to nanoparticles. Wavelength shifts on the fluorescence spectrum of the tag and intensity variations provide qualitative and quantitative information on the interacting protein, respectively. RLS methods are based on a similar 2 Abbreviations used: RLS, Rayleigh light scattering; 5As–EDTA–Eu3+, 5-aminosalicylic acid ethylenediaminetetraacetate europium(III); CA, carbonic anhydrase; HSA, human serum albumin; S/N, signal/noise ratio; LDR, linear dynamic range; R, correlation coefficient; c, analytical sensitivity; RSD, relative standard deviation; LOD, limit of detection; PLS-1, partial least squares-1; ANN, artificial neural networks; SS, steadystate; PMT, photomultiplier tube; TR, time-resolved; ICCD, intensified charged fiber-coupled device; CCD, charge-coupled device; TREEM, TR excitation–emission matrix; S/N, signal/noise; REP%, relative error of prediction.

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principle but extract their information from synchronous spectra, that is, spectra recorded at 0-nm difference between excitation and emission wavelengths [8]. The near-infrared approach [10,11] takes advantage of vibrationally resolved spectra with fingerprint information for protein identification. Because infrared transitions provide inherently weak spectral bands, peak assignment for qualitative and quantitative purposes is made possible with chemometric approaches that improve signal/noise ratio and minimize spectral interference from sample concomitants. Although these approaches are rapid, simple, and highly sensitive, their selectivity for the direct and accurate determination of target proteins in complex samples is still an open question. Biosensors based on antibody array technology have made rapid progress as a possible means of quick and direct protein detection [12]. The selectivity of this approach relies on the specific measurement of antigen–antibody interaction. Early reports have used thin metal films and glass slides to support the antibody array for high sample throughput [13]. Most recently, metallic films [14] and gold nanoparticles [15] have been proposed as sensing platforms to exploit the surface plasmon resonance phenomenon and its application to quartz crystal microbalance detection methods for biospecific interaction assays [16]. Although the antibody array methodology can provide quick and simultaneous protein detection with minimal sample volume, it has some limitations. In addition to the timeconsuming preparation procedure, many biological antibodies are difficult to grow and purify, particularly on a large scale. Because they are inherently sensitive to the surrounding environment, proper care must be taken in handling and storage (often at low temperature) to preserve their biological activity. Because metal and glass substrates are not the most desirable approach for in vivo applications, their use is restricted to in vitro applications. Our approach to protein detection takes advantage of the luminescence properties of lanthanide ions, particularly Eu3+ and Tb3+, incorporated into polymerized liposomes. Polymerized liposomes are spherically closed lipid bilayers with aqueous interior that offer an adequate lipophilic platform for protein interaction with the lanthanide ion [17]. Unlike unpolymerized vesicles, proteins cannot insert into the lipid bilayer of polymerized liposomes. Instead, they interact with the outer lipid layer of the vesicle via metal– ligand [18,19] and receptor–ligand [20,21] interactions. Because polymerized liposomes are appreciably more stable than their nonpolymerized counterparts, they provide more robust platforms for protein interaction. Protein sensing via polymerized liposomes has been reported, but their sensing ability has been based on the fluorescence properties of organic dyes [22,23]. The expectation from the lanthanide ion is to report qualitative and quantitative information on the interacting protein(s). Qualitative analysis is based on the luminescence lifetime of the liposome. Quantitative analysis is based on the linear relationship between the luminescence signal of the liposome and pro-

tein concentration. The long-lived luminescence of Eu3+ and Tb3+ is a good match to time-resolved techniques, which discriminate against the well-known short-lived fluorescence background of biological samples. Because their emission involves one of the shielded f-level electrons, their luminescence is less sensitive to oxygen quenching than are traditional organic dyes [24–26]. Analytical applications of our approach have been reported with 5-aminosalicylic acid ethylenediaminetetraacetate europium(III) (5As–EDTA–Eu3+) as the luminescence probe [27]. With this probe, sample excitation is accomplished at 316 nm, an appropriate wavelength to achieve efficient energy transfer from the antenna (5As) to the lanthanide ion. The presence of the antenna overcomes an inherent limitation of the lanthanide ion, namely, the rather weak absorption of excitation energy above 300 nm. Luminescence excitation above this wavelength is highly desirable in biological matrices because it avoids inner filter effects from main protein absorption. Excellent analytical figures of merit were reported for carbonic anhydrase (CA), human serum albumin (HSA), and c-globulins. The luminescence lifetime of the liposome showed sufficient sensitivity for sensing the presence of these three proteins on individual bases. In this article, we investigate the analytical potential of a polymerized liposome incorporating EDTA–Eu3+. Its sensing performance is studied for HSA, thermolysin, and c-globulins. We demonstrate that the liposome backbone provides a wide tunable excitation range for lanthanide excitation that extends all the way to approximately 400 nm. Although the luminescence intensity of Eu3+ is considerably lower in the absence of the antenna, liposome excitation above 320 nm still provides an analytically useful signal— signal/noise ratio (S/N) P3—for protein analysis. On sample excitation at wavelengths with minimum inner filter effects, excellent analytical figures of merit are presented for the four proteins: the linear dynamic range (LDR) of the calibration curve, the correlation coefficients (R), the analytical sensitivity (c), the relative standard deviation (RSD) at medium linear concentration, and the limit of detection (LOD). Distinct luminescence lifetimes on protein–liposome interaction demonstrate the feasibility of using the liposome sensor for qualitative analysis of proteins. Two chemometric approaches, partial least squares-1 (PLS-1) and artificial neural networks (ANN), are compared toward the quantitative and qualitative simultaneous analysis of HSA and CA in binary mixtures. Materials and methods Instrumentation A preliminary collection of excitation and emission spectra was carried out with a commercial spectrofluorimeter using standard quartz cuvettes (1 · 1 cm). No sample deoxygenation was attempted. For steady-state (SS) measurements, the excitation source was a continuous wave

Analysis of target proteins with polymerized liposome vesicles / M. Santos et al. / Anal. Biochem. 361 (2007) 109–119

75-W Xenon lamp with broadband illumination from 200 to 2000 nm. Detection was made with a photomultiplier tube (PMT) with a wavelength range from 185 to 650 nm. For time-resolved (TR) measurements, the excitation source was a pulsed 75-W Xenon lamp with a wavelength range from 200 to 2000 nm, a variable repetition rate from 0 to 100 pulses/s, and a pulse width of approximately 3 ls. Detection was made with a gated analog PMT (model 1527). Its spectral response extended from 185 to 900 nm. SS and TR spectra were recorded with excitation and emission monochromators having the same reciprocal linear dispersion (4 nm mm1) and accuracy (±1 nm with 0.25 nm resolution). Their 1200-grooves/mm gratings were blazed at 300 and 400 nm, respectively. The instrument was computer controlled using commercial software. Luminescence lifetimes were measured with an instrumental setup mounted in our laboratory [27–29]. Samples were excited at several excitation wavelengths. Excitation at 266 nm was accomplished with the fourth harmonic of a 10-Hz Nd:YAG Q-switched solid-state laser. Excitation above 270 was carried out directing the output of a tunable dye laser through a KDP frequency-doubling crystal. The dye laser was operated on Rhodamine 6G (Exciton) and was pumped with the second harmonic of the Nd:YAG laser. Excitation between 310 and 330 nm was made with the dye laser operating on DCM (Exciton). Luminescence was detected with a multichannel detector consisting of a front-illuminated intensified charge fiber-coupled device (ICCD). The minimum gate time (full width at half maximum) of the intensifier was 2 ns. The charge-coupled device (CCD) had the following specifications: active area = 690 · 256 pixels (26 mm2 pixel size photocathode), dark current = 0.002 electrons/pixel/s, and readout noise = 4 electrons at 20 KHz. The ICCD was mounted at the exit focal plane of a spectrograph equipped with a 1200-grooves/mm grating blazed at 500 nm. The system was used in the external trigger mode. The gating parameters (gate delay, gate width, and gate step) were controlled with a digital delay generator via a GPIB interface. Custom software was developed in-house for complete instrumental control and data collection. This system was also used to collect wavelength–time matrices for the analysis of proteins in binary mixtures. Reagents All reagents were purchased from commercial suppliers and used without further purification. Nanopure water was used throughout. Europium(III) chloride hexahydrate was obtained from Aldrich (Milwaukee, WI, USA). Ethylenediaminetetraacetic acid, Hepes, HSA, thermolysin, c-globulins, and CA were purchased from Sigma (Milwaukee, WI, USA). All solvents, including those used in the synthesis of liposomes, were of HPLC grade. Anhydrous solvents were obtained by distillation of HPLC-grade solvents over CaH2.

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Synthesis of polymerized liposomes The synthesis of polymerized liposomes has been described in detail elsewhere [28]. Briefly, liposomes are prepared in 25 mM Hepes buffer (pH 7.0) from Eu3+ complexes by mixing synthesized lipids (10 wt%) and commercially available polymerizable phosphocholine PC1 (90 wt%). The synthesized lipids have oligoethylene glycols as spacers and EDTA as the metal-chelating head group. Polymerization is carried out at 0 C with UV light (254 nm) and monitored via UV–vis spectrometry. Confirmation of liposome structure after polymerization is obtained via transmission electron microscopy.

Fluorescence measurements Measurements with the spectrofluorimeter were made with standard cuvettes (1 · 1 cm). Unless otherwise noted, all measurements were performed at neutral pH (25 mM Hepes buffer). Luminescence lifetimes were measured with the aid of a fiber-optic probe and the laser system described previously [27–29]. The probe assembly consisted of one excitation and six collection fibers fed into a 1.25-m long section of copper tubing. All of the fibers were 3 m long with 500-lm core diameter silica-clad silica with polyimide buffer coating. At the analysis end, the excitation and emission fibers were arranged in a conventional six-around-one configuration, bundled with vacuum epoxy, and fed into a metal sleeve for mechanical support. The copper tubing was flared stopping a swage nut tapped to allow for the threading of a 0.75-ml polypropylene sample vial. At the instrument end, the excitation fiber was aligned with the beam of the tunable dye laser, whereas the emission fibers were bundled with vacuum epoxy in a slit configuration, fed into a metal sleeve, and aligned with the entrance slit of the spectrometer. Lifetime determination followed a three-step procedure [27–29]: (i) collecting full sample and background wavelength–time matrices, (ii) subtracting background decay curve from the luminescence decay curve at the target wavelengths of the sensor, and (iii) fitting the background corrected data to single exponential decays. The decay curve data were collected with a minimum 150-ls interval between the opening of the ICCD gate and the rising edge of the laser pulse, an interval that was sufficient to avoid the need to consider convolution of the laser pulse with the analyte signal (laser pulse width = 5 ns). In addition, the 150-ls delay completely removed the fluorescence of the sample matrix from the measurement. Fitted decay curves ðy ¼ y 0 þ A1 expðxx0 Þt1 Þ were obtained with Origin software (version 5, Microcal Software) by fixing y0 and x0 at a value of zero. For chemometric analysis, all spectra were saved in ASCII format and transferred to a PC AMD (1200 MHz) for subsequent manipulation. All calculations were done using MATLAB 6.0 [30]. Routines for ANNs were written in our laboratory following previously known algorithms [31].

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PLS-1 was implemented using the MVC1 MATLAB toolbox [32]. Results and discussion Spectral characteristics of polymerized EDTA–Eu3+ liposome Fig. 1A shows the SS excitation and emission spectra of polymerized liposome incorporating EDTA–Eu3+. The broad excitation and emission bands are attributed mostly to the fluorescence of the liposome backbone. The relatively weak luminescence of Eu3+ is overwhelmed by the strong fluorescence of the liposome, and its contribution to the SS spectrum is unnoticeable. The luminescence of the lanthanide ion appears only in the TR spectrum of the liposome (Fig. 1B). A 90-ls delay after the excitation pulse completely removes the fluorescence contribution from the liposome,

providing a probe that relies solely on four characteristic peaks of Eu3+. The most intense bands correspond to the 5 D0–7F1(593 nm) and 5D0–7F2 (616 nm) transitions. The peaks at approximately 580 nm (5D0–7F0) and 698 nm (5D0–7F4) result from forbidden transitions and therefore are relatively weak. Fig. 2 depicts the TR excitation– emission matrix (TREEM) of the polymerized liposome. Although maximum excitation occurs at approximately 280 nm, a wide excitation range is still available to promote strong luminescence from Eu3+. Liposome excitation at 394 nm provides a reproducible reference signal (signal/ noise [S/N] = 3) for analytical use. Here it is important to point out that the delay needed to time-resolve the fluorescence of the EDTA–Eu3+ liposome was much shorter than the one used previously (150 ls) with the 5As–EDTA– Eu3+ liposome [27]. In the context of analytically useful S/ N ratios, shorter delays are comparatively advantageous because they collect a larger portion of the initial luminescence decay away from instrumental noise. Availability of coordination sites for protein interaction Qualitative and quantitative analysis of proteins via lanthanide–liposome sensing relies on the availability of ‘‘free’’ coordination sites in the first coordination sphere of the lanthanide ion [27,33]. In aqueous solvents, Eu3+ can accommodate up to eight or nine H2O molecules in its first coordination sphere. Coordination of H2O molecules to Eu3+ causes luminescence quenching as a result of weak vibronic coupling with the vibrational states of the O–H oscillators. Because the O–H oscillators act independently, the overall effect on the rate of radiationless deactivation is proportional to the number of O–H oscillators in the inner coordination sphere of Eu3+. On protein interaction, the replacement of O–H oscillators by low-frequency oscillators reduces the efficiency of the deactivation

Fig. 1. Excitation and luminescence spectra of polymerized liposome incorporating EDTA–Eu3+ in 25 mM Hepes recorded under SS (A) and TR (B) conditions. Instrumental parameters: (A) 2 nm excitation and emission bandpass; (B) 20 and 2 nm excitation and emission bandpass values, respectively; delay time = 0.15 ms; integration time = 1 ms.

Fig. 2. TREEM of polymerized liposomes incorporating EDTA–Eu3+ in 25 mM Hepes. Instrumental parameters: 20 and 2 nm excitation and emission bandpass values, respectively; delay time = 0.15 ms; integration time = 1 ms.

Analysis of target proteins with polymerized liposome vesicles / M. Santos et al. / Anal. Biochem. 361 (2007) 109–119

pathway. The intensity of the luminescence signal and the lifetime of the excited state become higher and longer, respectively [27,33]. The number of free coordination sites for protein interaction was determined with the following equation: q ¼ 1 ALN ðs1 H2 O  sD2 O Þ [34], where q is the number of H2O molecules in the first coordination sphere of Eu3+, ALN is the proportionality constant (1.05) for Eu3+, and sH2 O and sD2 O are the luminescence lifetimes of the lanthanide–liposome in H2O and D2O, respectively. Lifetime experiments were carried out with liposome solutions prepared in aqueous buffer (25 mM Hepes) or in aqueous buffer–D2O mixtures containing different volume ratios of H2O and D2O. The exponential decays were collected at kex/kem = 280/615 nm after approximately 15 min of H2O and D2O mixing.

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Fig. 3A shows two typical decays collected from solutions prepared with 100% and 25% of H2O. In both cases, the agreement between the calculated and the observed points over the two lifetimes of the decays falls within 1% and the residuals show no systematic trends. Single exponential decays with excellent fittings were also recorded from the other H2O–D2O mixtures. Fig. 3B presents the plot of the reciprocal lifetime (s1) as a function of molar fraction of H2O (vH2 O ). Each experimental data point plotted in the graph corresponds to the average of six lifetime measurements. s1 D2 O was obtained from extrapolation of the linear plot to vH2 O = 0. Substitution of any given pair of experimental values into the above equation yields a number of coordinated water molecules approximately equal to 3. This number is in good agreement with the fact that EDTA coordinates five sites of Eu3+ and that the lanthanide ion can take up to eight or nine water molecules in its first coordination sphere. Assuming that any given protein can replace up to three coordinated water molecules, the maximum number of available sites for protein interaction should be 3 per lanthanide ion. The displacement of a higher number of water molecules is unlikely to occur. EDTA forms a tightly bound complex with Eu3+ (with binding constants being reported as 1015 at pH 7) [27,33], ensuring the physical integrity of the probe in the presence of potentially competing proteins. Table 1 correlates the luminescence lifetime of the polymerized liposome to the number of water molecules displaced by D2O in the first coordination sphere of Eu3+. Each luminescence lifetime (sH2 O ) was calculated with the 1 1 1 equation q ¼ 1:05ðs1 H2 O  sD2 O Þ, where sH2 O ¼ 5:67 ms and q = 0, 1, 2, and 3. sH2 O was obtained by substituting vH2 O = 1 in the least squares fitting equation ðs1 ¼ 2:738vH2 O þ 2:905Þ of the plot in Fig. 3B. Assuming similar lifetime changes to those observed with D2O on protein interaction, the displacement of one H2O molecule by a protein oscillator should be enough for the liposome to sense the interaction of the protein with the first coordination sphere of Eu3+. Lanthanide concentration in the polymerized liposome Batch-to-batch variations of the concentration of lanthanide ion in the original liposome sample can seriously affect the reproducibility of measurements. A convenient way of avoiding this problem is to prepare liposome Table 1 Luminescence lifetime of polymerized liposome as a function of D2O molecules in the first coordination sphere of Eu3+

Fig. 3. (A). Fitted luminescence decay curves for EDTA–Eu3+ liposome in H2O and in H2O/D2O ratio (25:75). Experimental parameters for wavelength–time matrix collection: kex/kem = 266/616 nm; initial delay = 0.15 ms; gate width = 0.6 ms; gate step = 0.03 ms; number of accumulations per spectrum = 100; number of kinetic series per wavelength–time matrix = 40; slit width of spectrograph = 10 mm. (B) Reciprocal luminescence lifetime (s1, in ms1) as a function of mole fraction of water ðvH2 O Þ in D2O–H2O EDTA–Eu3+ liposome mixtures. 1 A ¼ 2:7381  0:0949; B ¼ s1 D2 O ¼ 2:9053 ms .

D2O molecules

Luminescence lifetimea (ls)

0 1 2 3

176 212 266 356

a Each luminescence lifetime ðsD2 O Þ was calculated with the equation 1 1 1 q ¼ 1:05ðs1 and q = 0, 1, 2, and 3. H2 O  sD2 O Þ, where sH2 O ¼ 5:67 ms ðsH2 O Þ was obtained by substituting ðvH2 O Þ = 1 in the least squares fitting equation ðs1 ¼ 2:738ðvH2 O Þ þ 2:905Þ of the plot in Fig. 3B.

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working solutions that yield the same concentration of Eu3+ in all analytical samples. (The term ‘‘analytical sample’’ here refers to the liposome solution presented to the instrument.) Then a simple method was developed to estimate the concentration of lanthanide ion incorporated into the original batch of liposome. The central idea was to build a calibration curve with standard solutions of known EDTA–Eu3+ concentrations using the original polymerized liposome solution as the ‘‘solvent’’. Fig. 4 shows the typical outcome of the multiple standard additions plot. The luminescence intensity of the lanthanide ion is graphed as a function of effective analyte standard concentration [nCs Vs/(Vx + Vs)], where Cs is the concentration of standard, Vs is the volume of standard addition, Vx is the volume of aliquot liposome, and n is the number of standard additions. The volumes of standard additions were negligible in comparison with the liposome volumes to ensure that the sample matrix was not significantly changed by dilution with standards. The extrapolation of the linear plot to y = 0 provides a good estimate of the concentration of EDTA–Eu3+ in the polymerized liposome. Previous knowledge of this concentration allows one to using appropriate dilution factors to compensate for batch-to-batch variations of luminescence signal. Then all analytical samples used for quantitative and qualitative measurements were prepared to contain approximately 5 · 106 M EDTA– Eu3+. This concentration of lanthanide ion provides a reproducible reference signal for analytical use with relative standard deviations below 5%.

a considerable enhancement. Within a certain range of protein concentrations, the magnitude of the luminescence enhancement correlates linearly with protein concentration. Figs. 5A and B show two types of titration curves observed when monitoring the luminescence signal versus protein concentration. All measurements were made inbatch after 15 min of protein mixing. In the case of HSA and thermolysin, the luminescence intensity of Eu3+ reached a plateau. In the case of c-globulins, the luminescence intensity dropped drastically after reaching the upper concentration limit. It is relevant to note that the analyst, when measuring the concentration of an unknown, always should measure an additional set of diluted unknown solutions to test whether the unknown concentration is within the ascending phase of the titration curve. Fig. 6 shows the least squares fitting of the linear portions of the two titration curves. The luminescence intensities plotted in the calibration graphs are the averages of

Quantitative analysis with the liposome sensor On protein interaction with the polymerized liposome, the luminescence intensity of the lanthanide ion experiences

Fig. 4. Luminescence intensity of polymerized EDTA-Eu3+ liposome as a function of standard addition concentration. All intensities were blank subtracted (25 mM Hepes buffer). Intensities were recorded at kex/ kem = 266/616 nm with 0.15- and 1-ms delay and gate times, respectively. Excitation and emission bandpass values were 20 and 5 nm, respectively. A cutoff filter at 450 nm was used.

Fig. 5. Titration curve for HSA (A) and c-globulins (B) obtained with polymerized liposome (estimated [EDTA–Eu3+] in liposome = 5 · 106 M). Intensity measurements were done at kex/kem = 266/ 616 nm using 0.15- and 1-ms delay and gate times, respectively. Excitation and emission bandpass values were 40 and 5 nm, respectively. A cutoff filter at 450 nm was used.

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Fig. 6. Least squares fitting of the linear portions of the titration curves for HSA (A) and c-globulins (B). The fitting parameters obtained were A = 3.76 (mg/L)1 cps and B = 207.9 cps for HSA (A) and A = 3.28 (mg/L)1 cps and B = 193.5 cps for c-globulins (B).

individual measurements taken from three aliquots of the same working solution. Excellent fittings were also observed with the other two proteins. Table 2 summarizes the analytical figures of merit obtained with the liposome sensor for the four proteins. The LDRs of the calibration

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curves are based on at least four protein concentrations. All correlation coefficients were close to unity, showing excellent potential for quantitative analysis of proteins. The LODs were calculated based on S/N = 3. The noise was estimated as the standard deviation (sR) of the average of 16 measurements of the reference signal, that is, the luminescence intensity of the polymerized liposome in the absence of protein. It is important to note that the reproducibility of the reference signal depends on its intensity, which is directly proportional to the concentration of lanthanide ion in the polymerized liposome. Because we time discriminate fluorescence background with an appropriate delay (150 ls), the main source of irreproducibility is instrumental noise, which becomes a problem only if the magnitude of the reference signal is close to instrumental noise. In optimizing sR with the appropriate liposome concentration, one should also keep in mind that there is a direct correlation between liposome and protein concentration and that protein traces are detected only with relatively low lanthanide concentrations. Careful consideration of these parameters led us to set the working concentration at 5.0 · 106 M EDTA–Eu3+. Two excitation wavelengths, 266 and 320 nm, were used for LOD determination. Excitation at 266 nm provides the highest intensity of the reference signal because it directly excites the lanthanide ion at its maximum excitation wavelength. Because proteins strongly absorb at this wavelength, its main disadvantage is the need to correct for protein absorption. Sample excitation at 320 nm avoids main protein absorption, but the intensity of the reference signal is considerably lower than the one resulting from excitation at 266 nm. For the four studied proteins, however, the LODs at 266 and 320 nm were of the same order of magnitude. This fact demonstrates the feasibility to perform sensitive protein detection at relatively long excitation wavelengths. Although a straightforward comparison with reported LODs for these four proteins is difficult because different instrumental setups, experimental approaches, and mathematical approaches have been used for their determination, we can safely state that our LODs are of the same order of magnitude as those reported previously with most sensitive methods [35–37].

Table 2 Analytical figures of merita for the four proteins obtained with polymerized EDTA–Eu3+ liposome Protein

LDRb (mg/L)1

R

cc (mg/L)1

RSD (%)

LOD (mg/L) (kex = 266 nm)

LOD (mg/L) (kex = 320 nm)

HSA CA c-Globulins Thermolysin

1.5–24.0 19.2–600.0 2.5–36.0 1.6–55.3

0.9997 0.9965 0.9989 0.9997

0.139 0.011 0.119 0.143

9.2 12.8 13.0 8.1

1.5 19.2 2.5 1.6

6.8 56.2 7.5 6.5

Protein solutions were mixed with a fixed final concentration of EDTA–Eu3+ liposome (5 · 106 M) using excitation and emission wavelengths of 266 and 616 nm, respectively, except in last column of table (excitation wavelength = 320 nm). Excitation and emission slits were 40 and 8 nm, respectively. Delay and gate times were 150 ls and 1 ms, respectively. b LDR of calibration curve defined from the LOD to the upper linear concentration. c Analytical sensitivity calculated with the formula c = m/sC, where m is the slope of the LDR of the calibration curve and sC is the standard deviation of the average of individual measurements taken from three aliquots of the same working solution. The concentration of the working solution corresponded to the centroid concentration of the LDR. a

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Qualitative analysis with the liposome sensor Previous work with polymerized liposome incorporating 5As–EDTA–Eu3+ has shown a significant change on the luminescence decay of the lanthanide ion on protein interaction with the liposome sensor. Similar to the effect observed with D2O, protein interaction increases the lifetime of the luminescence decay [33]. Because the luminescence lifetime is sensitive to the microenvironment of the lanthanide ion, there is a distinct possibility of using this parameter for qualitative analysis of proteins. Fig. 7 shows typical decays in the absence and presence of thermolysin, HSA, and c-globulins. Single exponential decays with excellent fittings are observed in all cases. The agreement between the calculated and observed points over the first two lifetimes of the decays agrees to within approximately 1%, and the residuals show no systematic trends. These facts suggest that only one type of microenvironment surrounds the lanthanide ion or that only one type of microenvironment contributes significantly to the measured lifetime. Table 3 compares the reference lifetime (absence of protein) to the lifetimes in the presence of target proteins. For a confidence level of 95% (a = 0.05, N1 = N2 = 6) [37], the reference value was statistically different from the lifetime in the presence of proteins, demonstrating that the lifetime of the liposome is sufficiently sensitive to probe the presence of a target protein on the basis of lifetime analysis. In addition, all of the lifetimes in the presence of proteins were statistically different (a = 0.05, N1 = N2 = 6) [37], showing the feasibility to differentiate these four proteins on the basis of lifetime analysis. These results show an advantage over the liposome incorporating 5As–EDTA–Eu3+, which was incapable of distinguishing between HSA and c-globulins [27]. Comparison of lifetime

Fig. 7. Fitted luminescence decay curves for EDTA–Eu3+ liposome alone (A) and in the presence of 4.80 g/L HSA (B), 0.14 g/L c-globulins (C), and 0.30 g/L thermolysin (D). Experimental parameters for wavelength–time matrix collection were as follows: kex/kem = 266/616 nm; initial delay = 0.15 ms; gate width = 0.6 ms; gate step = 0.05 ms; number of accumulations per spectrum = 100; number of kinetic series per wavelength–time matrix = 40; slit width of spectrograph = 10 mm.

Table 3 Comparison of luminescence lifetimes measured with EDTA–Eu3+ liposome in the absence and presence of proteins Proteina

Lifetimeb (ls)

RSD (%)

— HSA CA c-Globulins Thermolysin

177.3 ± 4.4 223.1 ± 4.0 276.7 ± 10.2 248.4 ± 5.2 370.1 ± 17.7

2.5 1.8 3.7 2.1 4.8

a Protein solutions were mixed with a fixed final concentration of EDTA–Eu3+ liposome (5 · 106 M) to provide the following final concentrations in lifetime measurements: 4.80 g/L HSA, 0.60 g/L CA, 0.14 g/L c-globulins, and 0.30 g/L thermolysin. b Each lifetime is the average of six measurements taken from six aliquots of sample solution. All measurements were made at kex/ kem = 266/615 nm.

values in Table 3 with those in Table 1 allows one to speculate on the number of water molecules each protein displaces from the first coordination sphere of Eu3+. Apparently, HSA and thermolysin substitute one and three water molecules, respectively. CA and c-globulins appear to displace two water molecules. The fact that these two proteins provide statistically different lifetimes is possibly due to chemically different protein oscillators.

Comparison of two chemometric models for the direct determination of CA and HSA in a binary mixture Our approach bases quantitative analysis on the linear relationship between signal intensity and protein concentration. Because there is no spectral shift on protein interaction, the qualitative parameter for protein identification is the luminescence lifetime. Unless the target protein is the only protein in the analytical sample, the luminescence intensity and the lifetime should be considered simultaneously to achieve accurate qualitative and quantitative analysis. In a previous article dealing with lanthanide complexes [27], we demonstrated the feasibility of using a multivariate calibration method, PLS-1, to process lifetime and intensity data simultaneously. HSA and c-globulins were determined accurately in synthetic mixtures without previous separation. This approach is applied here to the direct and simultaneous determination of HSA and CA in binary mixtures. Its ability to provide accurate determination of individual proteins in binary mixtures is compared with the performance of a nonlinear calibration technique, namely ANN. Both PLS-1 and ANN use the luminescence lifetimes as discriminatory parameters and regress the luminescence decays onto the concentrations of the standards. The first step for their implementation requires generating a training set of binary mixtures with the two target proteins: HSA and CA. The training set, which is statistically designed with the aid of simple software, provides the appropriate protein concentrations in each binary mixture. The second step consists of preparing the synthetic mixtures and

Analysis of target proteins with polymerized liposome vesicles / M. Santos et al. / Anal. Biochem. 361 (2007) 109–119

recording their instrumental responses, that is, the luminescence lifetimes and spectra. This information is back-fed to the software to obtain the regression coefficients of the statistical model that predicts the concentrations of the unknowns. The procedural steps are straightforward for both PLS-1 and ANN, and they present no particular advantage of one method over the other. In both cases, the training set and regression model are built to predict the unknown concentrations of a particular binary mixture. However, their application to different mixtures of the same proteins provides accurate predictions as well. So long as the target proteins are the same, there is no need to build a different training set for each unknown mixture. Unless deviations from linearity are suppressed by including additional modeling factors, PLS-1 tends to give large prediction errors and calls for more suitable models [37–39]. Similar to many other nonlinear calibration techniques [39–44], ANN is particularly useful when modeling complex and overlapped signals. Within the ANN context, the so-called multilayer feed-forward networks [31,42] often are used for prediction as well as for classification. Our approach to ANN modeling consisted of three layers of neurons or nodes: the basic computing units, the input layer with a number of active neurons corresponding to the predictor variables in regression, and one hidden layer with a number of active neurons. The input and hidden layer numbers are optimized during training, and the output layer has just one unit. The neurons are connected in a hierarchical manner; that is, the outputs of one layer of nodes are used as inputs for the next layer and so on. In the hidden layer, the sigmoid function f(x) = 1/(1 + ex) is used and the output of the hidden neuron j, Oj, is calculated as " # m X ðsi wij þ wbj Þ ; ð1Þ Oj ¼ f i¼1

where si is the input from neuron i in the layer above neuron j in the hidden layer, wij refers to the connection weight between neurons i and j, wbj is the bias to neuron j, and m is the total number of neurons in the layer. Linear functions are used in both the input and output layers. Learning is carried out through the back-propagation rule. The remarkable advantage of this rule is that there is no need to know the exact form of the analytical function on which the model should be built. Thus, neither the functional type nor the number of parameters in the model needs to be given to the program [31]. For the purpose of these studies, the calibration set was built with 13 samples performing 10 replicates for each sample (i.e., 130 luminescence decay curves). To obtain an orthogonal design, the component concentrations corresponded to a three-level full factorial design with 5 center samples. HSA and CA concentrations ranged from 2.30 · 107 to 1.80 · 106 mol L1 and from 2.07 · 107 to 2.50 · 107 mol L1, respectively. The validation set was built with 7 samples. The component concentrations

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were different from those used for the calibration set. The fact that the component concentrations spanned between the concentration ranges of the calibration set allowed us to draw conclusions on the predictive ability of the implemented models. Table 4 summarizes the optimal number of factors used for calibration and the relative error of prediction (REP%) for both calibration and validation sets. The luminescence decays for all sets were recorded in random order with respect to protein concentrations. Measurements were performed at kex/kem = 320/615 nm using the same time window (240–1390 ls, 24 points total/sample) for both methods. The optimal number of factors, allowing one to model the system with the optimal data volume and avoid overfitting, was determined with the cross-validation procedure. This procedure removes one training sample at a time and uses the remaining samples to build the latent factors and regression [45]. The large REP% values clearly show the difficulty of finding a common set of calibration parameters good enough for both proteins. A calibration set of 130 samples was used to train ANN. A randomized 30% of this 130-sample calibration set was used as the monitoring set. The 7-sample PLS-1 validation set was used as the test set for checking the predictive ability of ANN and for comparison between the two calibration models. The number of neurons in the input hidden layers was optimized by trial and error. The architectures selected for both components are displayed in Table 4. The numbers in parentheses indicate how many active neurons are employed in each layer. This means that the employed architecture has three input neurons, three hidden neurons, and a single output neuron for both components. To find the best model, each ANN was trained with the randomized 30% subset of the calibration set, but it subsequently was stopped before it learned the idiosyncrasies present in the training data. This was achieved by searching the minimum value of the root mean square error for the monitoring set. The number of adjustable weights was (4 · 4 · 1 = 16). These figures were obtained after considering the number of input and hidden layers plus one bias neuron on each layer. Table 5 compares the results obtained with PLS and ANN for the 7-sample validation set. The prediction improvement obtained with ANN Table 4 Statistical parameters when applying both PLS-1 and ANN analyses Figures of merit

CA

HSA

PLS-1

ANN

PLS-1

ANN

Region (ls) PLS-1 factors ANN model REP (CV) (%)a REP (Val) (%)a

240–1390 3 — 27.8 15.8

— (3,3,1) 12.1 8.4

3 — 29.3 17.4

— (3,3,1) 15.5 7.5

h P i1=2 I 2 x 1 REPð%Þ ¼ 100 , where (CV): cact corresponds to 1 ðcact  cpred Þ I the calibration set when cross-validation is applied, (Val): cact corresponds to the validation set, x is the average concentration of calibration or validation sets, and I is the number of samples. a

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Analysis of target proteins with polymerized liposome vesicles / M. Santos et al. / Anal. Biochem. 361 (2007) 109–119

Table 5 Predictions on the validation set when applying PLS-1 and ANN analyses Validation sample

1 2 3 4 5 6 7 Recovery average (%) Standard deviation a

Carbonic anhydrase (mol L1 · 106) Actual

PLS-1a

0.36 0.65 1.10 1.10 1.15 1.15 1.25

0.48 0.89 1.16 1.06 1.18 1.14 1.22 110.3 17.4

HSA (mol L1 · 106) ANNa

(0.07) (0.13) (0.05) (0.06) (0.11) (0.02) (0.03)

0.40 0.89 1.10 1.03 1.14 1.06 1.16 103.8 16.4

(0.02) (0.07) (0.03) (0.04) (0.07) (0.03) (0.03)

Actual

PLS-1a

0.85 0.85 1.33 1.33 1.50 1.70 1.70

0.92 1.16 1.51 1.70 1.60 1.77 1.70 113.8 13.4

ANNa (0.13) (0.10) (0.07) (0.14) (0.19) (0.06) (0.10)

0.82 0.87 1.48 1.39 1.46 1.61 1.55 99.8 6.9

(0.01) (0.03) (0.03) (0.04) (0.08) (0.03) (0.03)

Each value is the average of three replicates. Standard deviations are in parentheses.

(50%) demonstrates the power of this method for modeling nonlinear data and solving overlapped signals. The agreement between the predicted and actual protein concentrations demonstrates the potential of the method to simultaneously distinguish and quantify both proteins in the studied concentration range. Conclusion We have demonstrated the feasibility of using the luminescence response of polymerized liposome incorporating EDTA–Eu3+ for monitoring protein concentrations in aqueous media. Quantitative analysis is based on the linear relationship between the luminescence enhancement of the lanthanide ion and protein concentration. The luminescence enhancement is attributed to the removal of water molecules from the first coordination sphere of Eu3+. Excellent analytical figures of merit were obtained for the four studied proteins. Two excitation wavelengths, 266 and 320 nm, were used for LOD determination. Excitation at 266 nm directly excites the luminescence of the lanthanide ion at its maximum excitation wavelength and therefore provides the highest S/N ratio for the reference signal. Because there is a direct correlation between liposome and protein concentration, and because protein traces are detected only with relatively low lanthanide concentrations, this excitation wavelength provides the possibility of lowering the liposome concentration to improve LOD. The main disadvantage of sample excitation at 266 nm is the need to correct for protein absorption. In a matrix of unknown protein composition, the inadvertent use of inappropriate correction factors can cause significant error in the accuracy of analysis. Excitation at 320 nm provides an excitation wavelength above the main protein absorption region and therefore is extremely desirable for bioanalytical work. In this case, however, the relatively low intensity of the reference signal (S/N = 3) excludes the possibility of lowering the liposome concentration to improve LOD. Under the experimental conditions of this work, we can safely state that our levels of detection are of the same order of magnitude as those reported previously with most sensitive methods [35–37].

Qualitative analysis is based on the luminescence lifetime of the liposome sensor. This parameter follows well-behaved single exponential decays in the absence and presence of proteins. The shortest lifetime is observed in the absence of proteins, in agreement with the luminescence enhancement observed on liposome–protein interaction. Characteristic lifetimes were observed for the four studied proteins, demonstrating the selective potential of this parameter for protein identification. The combination of luminescence intensities and decays with PLS-1 and ANN calibration models makes feasible directly determining HSA and CA in binary mixtures. The considerable prediction improvement obtained with ANN (50%) is attributed to its ability to model nonlinear data and solve overlapped signals. For the direct analysis of HSA and CA in matrices of higher complexity, such as those typically found in aqueous humor [35,36], an additional parameter for selectivity might be necessary to reduce potential interference from other proteins. Current studies in this direction incorporate EDTA–Eu3+ into templated polymerized liposome capable of recognizing a target protein in a complex matrix. Acknowledgments This research was supported by the National Institutes of General Medicine Science (NIH 1 RO1 GM 6320401A1 to S.M. and A.D.C.) and the National Science Foundation (CHE-0138093 to A.D.C.). References [1] O.H. Lowry, N.J. Rosebrough, R.J. Randall, Protein measurement with the folin phenol reagent, J. Biol. Chem. 193 (1951) 265–275. [2] M.M. Bradford, A rapid and sensitive method for the quantification of microgram quantities of protein utilizing the principle of protein– dye binding, Anal. Biochem. 72 (1976) 248–254. [3] D.E. Terry, E. Umstot, D.M. Desiderio, Optimized sample-processing time and peptide recovery for the mass spectrometric analysis of protein digests, J. Am. Soc. Mass Spectrom. 15 (2004) 784–794. [4] A.M. Timperio, C.G. Huber, L. Zolla, Separation and identification of the light harvesting proteins contained in grana and stroma thylakoid membrane fractions, J. Chromatogr. A 10238 (2004) 77–84.

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