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Identification and quantitation of free radicals and paramagnetic centers from complex multi-component EPR spectra
Identification and Quantitation of Free Radicals and Paramagnetic Centers from Complex Multi-Component EPR Spectra P. KUPPUSAMY and J. L. ZWEIER The Electron Paramagnetic Resonance Laboratories, Department of Medicine Division of Cardiology, The Johns Hopkins Medical Institutions 5501 Hopkins Bayview Circle, Baltimore, MD 21224 U.S.A.
We de&be a comprehensive generalized method which wss developed in rut effort to analyze and obtain quantitative informstion from EPR spectral data. This method consists of several routines including isotropic simulation, snisotropic simulation, computerized auto-simulation for component optimization, multi-component fitting, and qusntitstion. Several examples, which demonstrate that this program csn be applied to both isotropic and snisotropic systems, are shown.
KEYWORDS:
EPR; free radicals; simulation; qusntitation; fitting; software
INTRODUCTION The technological advances and availability of microcomputers in the past decade have had a tremendous impact on the ability to acquire, analyze and interpret EPR clata(VanCarnp and Heiss, 198 1). Automated data acquisition with fast real time analog-to-digital conversion enables one to acquire and store the data in digital form. This makes the post-acquisition processing of the data, such as digital filtering, spectral titration, extraction of static and dynamic information regarding the sample under investigation faster, easier and more precise. However, the validity of the results depends, apart from sample and instrumental parameters, upon the methodology used for the analysis. The primary part of analysis includes identification of the structure of the spectrum with the determination of the spin Hamiltonian parameters. In addition to identifying the structure of the spectrum it is often important to extract quantitative information regarding the amount of a given pammagnetic center or mixture of centers. Several different algorithms have been suggested and reported for simulating EPR spectra of spin systems ranging from simple isotropic first order Hamiltonian to complex anisotropic systems (Price et al., 1970, Ohler and Janzen, 1982, Nettar and Villafranca, 1985, Hyde and Subczynski, 1984, Motten and Schreiber, 1986, Motten et al., 1987, Beckwith and Brumby 1987, Duling et al., 1988). To date, however, there has been no comprehensive generalized program which has been developed that is capable of analyzing and providing quantitative information regarding this broad range of EPR data. We have developed a new program which uses a combined simulation and fitting procedure for obtaining reliable quantitative information from digital EPR data. We found this method to be accurate and reliable and demonstrated that it can easily be implemented on a microcomputer.
METHODS The present method of quantitation of EPR spectra involves the following steps: (1) The measured EPR spectrum must be in the digital form. This may require a real-time analog-to-digital conversion of the 36-l
ESR dosimetry
368
and applications
analog data from the signal channel of the EPR spectrometer or post-digitization of the analog plot. (2) The quality of the spectrum is then improved, if necessary, by correcting for large baseline drifts, and applications of digital filtering to suppress noise. (3) The components of interest in the spectra are then identified and an approximate estimate of the spin Hamiltonian parameters made for each component. (4) Each component signal is individually simulated to match to the corresponding component in the measured spectrum. (5) The simulated spectra are calibrated with respect to an appropriate free radical standard. (6) A least-squares fit of the simulated component spectra to the measured spectrum is performed to obtain the component weights. (7) Finally, the component weights, calibration and instrumental parameters are used to qua&ate the measured spectrum. The above scheme is general in nature and, in principle, one can program these steps for any type of computer. We have written and implemented these steps for an IBM PC microcomputer system. The program also contains routines for automation in data acquisition and analysis. All the sample computations reported in this article were performed using this program on an AT 80386/33 MHz computer equipped with a math coprocessor. A brief description of the routines relevant to quantitation is given in the following sections.
Isotropic Simulation A stick of unit height at the resonance field is split according to the coupling constants for each coupled nucleus taking into account the number equivalent nuclei, their coupling constant, isotopic abundance, weighting and their statistical distribution among equivalent set. The new stick coordinates (position with respect to resonance field and intensity) are mapped on to a 4k array. The stick array is then remapped on to a line shape array using standard derivative line shape IGnctions(Poole, 1983). The array(spectrum) is then normalized and the normalization factor is retained for later quantitation purposes. A maximum of 3 isotopes per nucleus can be included for simulation. The statistical distributions of couplings arising from nuclei with multiple isotopes are automatically computed. The simulated spectrum can be superimposed on a selected expcrimcntal spectrum for on-screen matching. The isotropic simulation routine is precise, fast and powerful. The computational time normally varies from fraction of a second to about a minute depending up on the complexity of the structure. Fig. 1 shows an example of an isotropic simulated spectrum. Anisotrooic
Simulation
The anisotropic simulation is written based on the procedure of Nettar and Villafranca(l985). The spin Hamiltonian includes anisotropic g, A, D, and Q tensors, isotropic nuclear Zeeman term, fourth- and sixth-order terms in S and first order anisotropic ligand hypertine terms. This program is versatile and can simulate anisotropic EPR spectrum of any molecule. Smaller systems, for example, an S=1/2 system with fewer coupling constants can be computed in a few minutes. A sample anisotropic spectrum, typically that of a [CuN,] paramagnetic moiety, simulated using this program is shown in Fig. I.
Auto-simulation Often it is not difficult to obtain the resonance field and coupling constants for a simulated spectrum. However accurate values for line width and line shape are difficult to obtain, particularly when the peaks are sharp or when there are overlapping peaks, For quantitation purposes it is very important that the later parameters are known precisely.We have implemented an auto-simulation routine, incorporated into the main program, to get the best possible parameters to match the experimental spectrum. The required inputs to this routine arc the experimental spectral data, guess values for center field, line width, line shape and coupling constants. The routine basically works in loops searching for the best value in each parameter in the following order: center field, coupling constants, line width and finally shape. In each loop, the spectrum is simulated and compared with the experimental spectrum in memory. The process repeats for a given number of cycles and at the completion of each cycle of refinement the current best simulated
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Fig. 1. (Let?) A sample isotropic simulation. The simulated spectrum(shown low-field one-halt) is that of di-p-anisylnitroxide radical. The simulation parameters were aN = 10 G, aJortho) = 1.92 G, a,(meta) = 0.90 G, a,(methoxy) = 0.18 G. line width = 0.10 G, line shape = 50% Lorentzian and x-resolution = 0.01 G. The computational time was 20 s. (Right) A sample anisotropic simulation for a system with S = l/2, I = 3/2, and four ligands (I = 1). The parameters were: g, = 2.006, gyy = 2.009, g,, = 2.29, A, = 12 MHz, 4, = 18 MHz, A,, = 380 MHz, Q, = Q,, = 1 MHz, Q,, = -2 MHz; four ligands, each with &= A,, = 16 G, A,, = 10 G; line width 3.0 G, x-resolution = 0.9 G, line shape = Lorentzian, orientations = 255. The computational time was 200 s.
spectrum will be displayed
along with the experimental spectrum. Examples of auto-simulated spectra along with experimental and initial guess spectra are shown in Fig. 2 which illustrate the power and value of auto-simulation. This routine refines only one component at a time. However it has the advantage that we can simulate and refine one specific component in a multi-component spectrum thus enabling each component to be simulated individually.
1
1
Fig. 2. Experimental, guess and auto-simulated spectra of 0.1 mM CTPO (3-Carbamoyl-2,2,5,5,-tetramethyl-3-pyrroline-yloxyl) in water(Hyde and Subczynski, 1984). Only the central, m, = 0, nitrogen hyperfine line is shown. A:Simulated spectrum with initial guess parameters. The parameters were: a,(methyl) (12) = 0.2 G, a,(vinyl, ring) (1) = 0.5 G, line with = 0.2 G, and shape = 1. B:Spectrum anaerobic conditions measured under at 25°C. C:Auto-simulated spectrum corresponding to B. The refined parameters were: hyperfine couplings 0.196 G, 0.515 G, line width = 0.125 G, shape=l. D:Spectrum aerobic conditions at measured under 25OC. E:Auto-simulated spectrum matching D. The refined parameters were identical to those of C except line width, which was 0.284 G.
Fitting At first the simulated components are matched to the experimental spectrum manually for base line and corresponding peak positions by moving the respective windows. The routine then does a first estimate for the contribution of each simulated component by using linear combinations of the components and by least squares minimization procedure. The values are refined in successive iterations, each time by narrowing down the search interval by half of previous iteration. The iteration is continued until a desired accuracy is reached. The accuracy of the fit can be judged from the least squares residual or by visual comparison of the experimental and calculated spectra on the same window and the difference spectrum. The fitting method has the following advantages: (1) The procedure is direct, informative and reliable. (2) Quantitation of a specific component in the midst of several other components can be performed under NC--M ,,I-->
ESR dosimetry and applications
370
favorable circumstances. (3) Since no integration of the experimental data is performed errors due to base line offset, base line drift and noise are considerably reduced. (4) The fitting takes only a couple of minutes on a PC-AT microcomputer for single spectrum with 4 components. (5) This procedure is not limited to isotropic spectrum alone. It works well with any type of curve provided the component curves are available. (6) With the use of an automation routine the analysis can be performed for a series of experimental spectra, for example, in a kinetic experiment, one can analyze and fit a series of spectra as long as the structure of the components does not change.
Ouantitation For a given EPR spectrometer, cavity, and type of free radical one must consider several factors in order to quantify the paramagnetic centers giving rise to the observed spectrum. First there must be a calibration standard, that is, a dependable radical standard of known concentration. The EPR spectrum of this standard sample must be obtained with the same or similar types of sample as well as certain instrument settings. The sample properties include similar concentration, identical solvent, type of sample holder and cavity, thus, filling factor and Q should be identical. Temperature, modulation field, time constant, scan time, and microwave power should also be identical or should be corrected for. Thus, the standard should ideally be run with the same settings of all parameters and sample conditions. Calibration of simulated spectra. The EPR spectrum of a suitable free radical standard of known concentration is run under conditions identical to that of experimental measurements. This spectrum is simulated and then the simulated spectrum is calibrated by matching to the measured spectrum by fitting. Since all the simulated spectra are correlated by their normalization factor or scale, this calibration of the simulated spectrum can then be extended to any other simulated component. Quantitation of exDerimental sDectra. If a given experimental spectrum has n components then all the n components are simulated and fitted to the experimental spectrum. If Xi is the weight obtained from fitting for the ith component then the concentration of the ith component in the experimental spectrum is given by C, = X, * Cisiln * Z, where Z is a correction term accounting for the differences in the instrumental settings, number of scans, etc. for the standard and experimental spectral measurements.
RESULTS
AND DISCUSSION
For digital EPR data, the intensity of the signal is usually obtained by double integration of the derivative curve. However, this method is prone to serious errors under the following circumstances: (i) baseline drift and baseline offset, (ii) overlapping signals and (iii) failure to extend the integration sufficiently far from the center of the peak(Wertz and Bolton, 1972). The baseline drift is often reduced by computing and subtracting the drift function from the measured spectrum. The baseline offset is a non-zero baseline error and is corrected by including a constant term. Overlapping signals will hamper the determination the intensity of a specific component signal. In such cases the integrations can not be carried out beyond the overlapping point and to correct for this error we have to extrapolate the curve beyond the cut-off point. However, the accuracy of such an extrapolation depends heavily on line shape and line width. Errors due to finite truncation of the integration are particularly large for line shapes with high Lorentzian contribution. All these factors make the integration method less reliable and can introduce large errors in the quantitation results. Quantitation by component fitting, on the other hand, does not involve integration of the experimental spectrum. It depends only on the accurate simulation of the component spectra and fitting to the experimental spectrum. When the components of interest are known and simulated, as is often the case with routine experiments, quantitation of all or individual components becomes easier and reliable. An example of component simulation and fitting for a 3 component isotropic experimental spectrum is shown in Fig. 3. The spectrum shown in Fig. 3A was obtained by using xanthine-xanthine oxidase system with spin trap 5,5’-dimethyl-I-pyrroline-N-oxide (DMPO) in aqueous solutions in presence of 5% ethanol (Kuppusamy and Zweier, 1989). The spectrum contained the DMPO adducts, namely, DMPO-OH,
ESR dosimetry and applications
371
Fig. 3. A sample three component measured isotropic spectrum (A) and the matching simulation (B). The final simulated spectrum was obtained by adding the three simulated component spectra I, II, and III with weighting factors 0.88, 2.36 and I.47 respectively. Simulated spectra. I: DMPO-OH; g = 2.0060, a,, = a,, = 14.90 G. line width = I.45 G, shape = 0.5 L. II: DMPO-OOH; g = 2.0061, aN = 14.15 G, a+, = 11.29 G, a,+ = I.25 G, line width = 1.50 G, shape = 0.67 L. I: g = 2.0057, aN = 15.89 G, a,., = 22.80 G, line width = 1.50 G, shape = 0.77 L. The spectra as displayed are normalized to unit area. DMPO-OOH and DMPO-CH(OH)CH,. These component spectra were simulated and the spectral parameters for each component were refined by auto-simulation routine by matching with the experimental spectrum. The three simulated component spectra were fixed on identical g-scale and fitted to the experimental spectrum. The line shapes observed with anisotropic or frozen glass EPR spectra are usually more complex and often it will be exceedingly diffkult to deconvolute the component shapes from a multi-component spectrum. In such cases selective removal of components from the mixture or spectral titration may be required to qualitatively identity as well as to qua&ate the components(Zweier et al., 1986). In the present method the number of components as well as their identity are guessed, simulated and fitted to the experimental spectrum. The parameters are refined and the procedure is repeated until the best matching is achieved. Fig. 4 shows the simulated components and fitting for a 4 component anisotropic spectrum (Zweier et al., 1989). The components were simulated individually using the anisotropic simulation routine. The Fig.
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4. A sample of frozen heart tissue which contains four component anisotropic experimental spectra (E), fitted spectrum (F) and the simulated components (A to D). The simulation parameters: A: g, = gyy = gU= 2.004, line width = 3.65 G, I orientation; B: g, = gYY= 2.005, g,, = 2.033, line width = 3.40 G, 63 orientations; C: g, = gyy = g,, = 2.000, one nucleus with I=1 and a = 24.00 G, line width = 3.40 G, 63 orientations; D: a digitized signal with g values 2.027 and I.936 attributable to an iron-sulphur protein with a Fe& cluster. The matching spectrum F was obtained by adding the four component spectra A to D.
parameters were adjusted manually to get the simulated spectra components that yielded the best fit to the measured spectrum. Finally, many repeated measurements were performed in order to test the accuracy of this method of quantitation. It was observed that the error with the present method of analysis was quite small, in the range of + 5 to 2 10%. Thus we find that the component optimization and fitting program is suitable for routine studies of EPR spectra of free radicals in a variety of biological or chemical systems.
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and applications
REFERENCES Be&with, A.L. and S. Brumby (1987). Numerical analysis of EPR spectra. 7. The simplex algorithm. J. Magn. Reson.,73,252-259. Duling, D.R., A.G. Motten and R.P. Mason (1988). Generation and evaluation of isotropic ESR spectrum simulations. J. Magn. Reson., 7l, 504-5 11. Hyde, J.S. and W.K. Subczynski (1984). Simulation of ESR spectra of the oxygen sensitive spin label probe CTPO. J. Magn. Reson., 5&, 125-130. Kuppusamy, P., and J.L. Zweier (1989). Characterization of free radical generation by xanthine oxidase. Evidence for hydroxyl radical generation. J. Biolog. Chem., =,9880-9884. Motten, A.G. and J. Schreiber (1986). Correlation analysis of ESR spectra on a small computer. J. Magn. Reson., 67,42-54. Motten, A.G., D.R. Duling and J. Schreiber (1987). Proton coupling constant extraction. A fast method for analyzing ESR spectra by computer. J. Magn. Reson., 7l, 33-44. Nettar, D. and J.J. Villafranca (1985). A program for EPR powder spectrum simulation. J. Magn. Reson., 64,61-65. Ohler, U.M. and G. Janzen (1982). Simulation of isotropic electron spin resonance spectra: A transportable basic program. Can. J. Chem., 60, 1542-1547. Poole Jr, C.P. (1983) Electron Spin Resonance: A Comprehensive Treatise on Experimental Techniques. John Wiley & Sons, New York. Price, J.H., J.R. Pilbrow, K.S. Murray and J. Smith (1970). J. Chem. Sot. A, 968. VanCamp, H.L., and A.H. Heiss (198 1). Computer Applications in Electron Paramagnetic Resonance. Magn. Reson. Rev., 1, 1. Wertz, J. E, and J.R. Bolton (1972). Electron Spin Resonance. McGraw-Gill, New York. Zweier, J.L., J.T. Flaherty and M.L. Weisfeldt (1986). Direct measurement of free radical generation following reperfusion of ischemic myocardium. Proc. Nat. Acad. Sci. USA, 84, 1404-1407. Zweier, J.L., P. Kuppusamy, R. Williams, B.K. Rayburn, D. Smith, M.L. Weisfeldt and J.T. Flaherty (1989). Measurement and characterization of postischemic free radical generation in the isolated perfused heart. J. Biolog. Chem., 264, 18890-18895.
We de&be a comprehensive generalized method which wss developed in rut effort to analyze and obtain quantitative informstion from EPR spectral data. This method consists of several routines including isotropic simulation, snisotropic simulation, computerized auto-simulation for component optimization, multi-component fitting, and qusntitstion. Several examples, which demonstrate that this program csn be applied to both isotropic and snisotropic systems, are shown.
KEYWORDS:
EPR; free radicals; simulation; qusntitation; fitting; software
INTRODUCTION The technological advances and availability of microcomputers in the past decade have had a tremendous impact on the ability to acquire, analyze and interpret EPR clata(VanCarnp and Heiss, 198 1). Automated data acquisition with fast real time analog-to-digital conversion enables one to acquire and store the data in digital form. This makes the post-acquisition processing of the data, such as digital filtering, spectral titration, extraction of static and dynamic information regarding the sample under investigation faster, easier and more precise. However, the validity of the results depends, apart from sample and instrumental parameters, upon the methodology used for the analysis. The primary part of analysis includes identification of the structure of the spectrum with the determination of the spin Hamiltonian parameters. In addition to identifying the structure of the spectrum it is often important to extract quantitative information regarding the amount of a given pammagnetic center or mixture of centers. Several different algorithms have been suggested and reported for simulating EPR spectra of spin systems ranging from simple isotropic first order Hamiltonian to complex anisotropic systems (Price et al., 1970, Ohler and Janzen, 1982, Nettar and Villafranca, 1985, Hyde and Subczynski, 1984, Motten and Schreiber, 1986, Motten et al., 1987, Beckwith and Brumby 1987, Duling et al., 1988). To date, however, there has been no comprehensive generalized program which has been developed that is capable of analyzing and providing quantitative information regarding this broad range of EPR data. We have developed a new program which uses a combined simulation and fitting procedure for obtaining reliable quantitative information from digital EPR data. We found this method to be accurate and reliable and demonstrated that it can easily be implemented on a microcomputer.
METHODS The present method of quantitation of EPR spectra involves the following steps: (1) The measured EPR spectrum must be in the digital form. This may require a real-time analog-to-digital conversion of the 36-l
ESR dosimetry
368
and applications
analog data from the signal channel of the EPR spectrometer or post-digitization of the analog plot. (2) The quality of the spectrum is then improved, if necessary, by correcting for large baseline drifts, and applications of digital filtering to suppress noise. (3) The components of interest in the spectra are then identified and an approximate estimate of the spin Hamiltonian parameters made for each component. (4) Each component signal is individually simulated to match to the corresponding component in the measured spectrum. (5) The simulated spectra are calibrated with respect to an appropriate free radical standard. (6) A least-squares fit of the simulated component spectra to the measured spectrum is performed to obtain the component weights. (7) Finally, the component weights, calibration and instrumental parameters are used to qua&ate the measured spectrum. The above scheme is general in nature and, in principle, one can program these steps for any type of computer. We have written and implemented these steps for an IBM PC microcomputer system. The program also contains routines for automation in data acquisition and analysis. All the sample computations reported in this article were performed using this program on an AT 80386/33 MHz computer equipped with a math coprocessor. A brief description of the routines relevant to quantitation is given in the following sections.
Isotropic Simulation A stick of unit height at the resonance field is split according to the coupling constants for each coupled nucleus taking into account the number equivalent nuclei, their coupling constant, isotopic abundance, weighting and their statistical distribution among equivalent set. The new stick coordinates (position with respect to resonance field and intensity) are mapped on to a 4k array. The stick array is then remapped on to a line shape array using standard derivative line shape IGnctions(Poole, 1983). The array(spectrum) is then normalized and the normalization factor is retained for later quantitation purposes. A maximum of 3 isotopes per nucleus can be included for simulation. The statistical distributions of couplings arising from nuclei with multiple isotopes are automatically computed. The simulated spectrum can be superimposed on a selected expcrimcntal spectrum for on-screen matching. The isotropic simulation routine is precise, fast and powerful. The computational time normally varies from fraction of a second to about a minute depending up on the complexity of the structure. Fig. 1 shows an example of an isotropic simulated spectrum. Anisotrooic
Simulation
The anisotropic simulation is written based on the procedure of Nettar and Villafranca(l985). The spin Hamiltonian includes anisotropic g, A, D, and Q tensors, isotropic nuclear Zeeman term, fourth- and sixth-order terms in S and first order anisotropic ligand hypertine terms. This program is versatile and can simulate anisotropic EPR spectrum of any molecule. Smaller systems, for example, an S=1/2 system with fewer coupling constants can be computed in a few minutes. A sample anisotropic spectrum, typically that of a [CuN,] paramagnetic moiety, simulated using this program is shown in Fig. I.
Auto-simulation Often it is not difficult to obtain the resonance field and coupling constants for a simulated spectrum. However accurate values for line width and line shape are difficult to obtain, particularly when the peaks are sharp or when there are overlapping peaks, For quantitation purposes it is very important that the later parameters are known precisely.We have implemented an auto-simulation routine, incorporated into the main program, to get the best possible parameters to match the experimental spectrum. The required inputs to this routine arc the experimental spectral data, guess values for center field, line width, line shape and coupling constants. The routine basically works in loops searching for the best value in each parameter in the following order: center field, coupling constants, line width and finally shape. In each loop, the spectrum is simulated and compared with the experimental spectrum in memory. The process repeats for a given number of cycles and at the completion of each cycle of refinement the current best simulated
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Fig. 1. (Let?) A sample isotropic simulation. The simulated spectrum(shown low-field one-halt) is that of di-p-anisylnitroxide radical. The simulation parameters were aN = 10 G, aJortho) = 1.92 G, a,(meta) = 0.90 G, a,(methoxy) = 0.18 G. line width = 0.10 G, line shape = 50% Lorentzian and x-resolution = 0.01 G. The computational time was 20 s. (Right) A sample anisotropic simulation for a system with S = l/2, I = 3/2, and four ligands (I = 1). The parameters were: g, = 2.006, gyy = 2.009, g,, = 2.29, A, = 12 MHz, 4, = 18 MHz, A,, = 380 MHz, Q, = Q,, = 1 MHz, Q,, = -2 MHz; four ligands, each with &= A,, = 16 G, A,, = 10 G; line width 3.0 G, x-resolution = 0.9 G, line shape = Lorentzian, orientations = 255. The computational time was 200 s.
spectrum will be displayed
along with the experimental spectrum. Examples of auto-simulated spectra along with experimental and initial guess spectra are shown in Fig. 2 which illustrate the power and value of auto-simulation. This routine refines only one component at a time. However it has the advantage that we can simulate and refine one specific component in a multi-component spectrum thus enabling each component to be simulated individually.
1
1
Fig. 2. Experimental, guess and auto-simulated spectra of 0.1 mM CTPO (3-Carbamoyl-2,2,5,5,-tetramethyl-3-pyrroline-yloxyl) in water(Hyde and Subczynski, 1984). Only the central, m, = 0, nitrogen hyperfine line is shown. A:Simulated spectrum with initial guess parameters. The parameters were: a,(methyl) (12) = 0.2 G, a,(vinyl, ring) (1) = 0.5 G, line with = 0.2 G, and shape = 1. B:Spectrum anaerobic conditions measured under at 25°C. C:Auto-simulated spectrum corresponding to B. The refined parameters were: hyperfine couplings 0.196 G, 0.515 G, line width = 0.125 G, shape=l. D:Spectrum aerobic conditions at measured under 25OC. E:Auto-simulated spectrum matching D. The refined parameters were identical to those of C except line width, which was 0.284 G.
Fitting At first the simulated components are matched to the experimental spectrum manually for base line and corresponding peak positions by moving the respective windows. The routine then does a first estimate for the contribution of each simulated component by using linear combinations of the components and by least squares minimization procedure. The values are refined in successive iterations, each time by narrowing down the search interval by half of previous iteration. The iteration is continued until a desired accuracy is reached. The accuracy of the fit can be judged from the least squares residual or by visual comparison of the experimental and calculated spectra on the same window and the difference spectrum. The fitting method has the following advantages: (1) The procedure is direct, informative and reliable. (2) Quantitation of a specific component in the midst of several other components can be performed under NC--M ,,I-->
ESR dosimetry and applications
370
favorable circumstances. (3) Since no integration of the experimental data is performed errors due to base line offset, base line drift and noise are considerably reduced. (4) The fitting takes only a couple of minutes on a PC-AT microcomputer for single spectrum with 4 components. (5) This procedure is not limited to isotropic spectrum alone. It works well with any type of curve provided the component curves are available. (6) With the use of an automation routine the analysis can be performed for a series of experimental spectra, for example, in a kinetic experiment, one can analyze and fit a series of spectra as long as the structure of the components does not change.
Ouantitation For a given EPR spectrometer, cavity, and type of free radical one must consider several factors in order to quantify the paramagnetic centers giving rise to the observed spectrum. First there must be a calibration standard, that is, a dependable radical standard of known concentration. The EPR spectrum of this standard sample must be obtained with the same or similar types of sample as well as certain instrument settings. The sample properties include similar concentration, identical solvent, type of sample holder and cavity, thus, filling factor and Q should be identical. Temperature, modulation field, time constant, scan time, and microwave power should also be identical or should be corrected for. Thus, the standard should ideally be run with the same settings of all parameters and sample conditions. Calibration of simulated spectra. The EPR spectrum of a suitable free radical standard of known concentration is run under conditions identical to that of experimental measurements. This spectrum is simulated and then the simulated spectrum is calibrated by matching to the measured spectrum by fitting. Since all the simulated spectra are correlated by their normalization factor or scale, this calibration of the simulated spectrum can then be extended to any other simulated component. Quantitation of exDerimental sDectra. If a given experimental spectrum has n components then all the n components are simulated and fitted to the experimental spectrum. If Xi is the weight obtained from fitting for the ith component then the concentration of the ith component in the experimental spectrum is given by C, = X, * Cisiln * Z, where Z is a correction term accounting for the differences in the instrumental settings, number of scans, etc. for the standard and experimental spectral measurements.
RESULTS
AND DISCUSSION
For digital EPR data, the intensity of the signal is usually obtained by double integration of the derivative curve. However, this method is prone to serious errors under the following circumstances: (i) baseline drift and baseline offset, (ii) overlapping signals and (iii) failure to extend the integration sufficiently far from the center of the peak(Wertz and Bolton, 1972). The baseline drift is often reduced by computing and subtracting the drift function from the measured spectrum. The baseline offset is a non-zero baseline error and is corrected by including a constant term. Overlapping signals will hamper the determination the intensity of a specific component signal. In such cases the integrations can not be carried out beyond the overlapping point and to correct for this error we have to extrapolate the curve beyond the cut-off point. However, the accuracy of such an extrapolation depends heavily on line shape and line width. Errors due to finite truncation of the integration are particularly large for line shapes with high Lorentzian contribution. All these factors make the integration method less reliable and can introduce large errors in the quantitation results. Quantitation by component fitting, on the other hand, does not involve integration of the experimental spectrum. It depends only on the accurate simulation of the component spectra and fitting to the experimental spectrum. When the components of interest are known and simulated, as is often the case with routine experiments, quantitation of all or individual components becomes easier and reliable. An example of component simulation and fitting for a 3 component isotropic experimental spectrum is shown in Fig. 3. The spectrum shown in Fig. 3A was obtained by using xanthine-xanthine oxidase system with spin trap 5,5’-dimethyl-I-pyrroline-N-oxide (DMPO) in aqueous solutions in presence of 5% ethanol (Kuppusamy and Zweier, 1989). The spectrum contained the DMPO adducts, namely, DMPO-OH,
ESR dosimetry and applications
371
Fig. 3. A sample three component measured isotropic spectrum (A) and the matching simulation (B). The final simulated spectrum was obtained by adding the three simulated component spectra I, II, and III with weighting factors 0.88, 2.36 and I.47 respectively. Simulated spectra. I: DMPO-OH; g = 2.0060, a,, = a,, = 14.90 G. line width = I.45 G, shape = 0.5 L. II: DMPO-OOH; g = 2.0061, aN = 14.15 G, a+, = 11.29 G, a,+ = I.25 G, line width = 1.50 G, shape = 0.67 L. I: g = 2.0057, aN = 15.89 G, a,., = 22.80 G, line width = 1.50 G, shape = 0.77 L. The spectra as displayed are normalized to unit area. DMPO-OOH and DMPO-CH(OH)CH,. These component spectra were simulated and the spectral parameters for each component were refined by auto-simulation routine by matching with the experimental spectrum. The three simulated component spectra were fixed on identical g-scale and fitted to the experimental spectrum. The line shapes observed with anisotropic or frozen glass EPR spectra are usually more complex and often it will be exceedingly diffkult to deconvolute the component shapes from a multi-component spectrum. In such cases selective removal of components from the mixture or spectral titration may be required to qualitatively identity as well as to qua&ate the components(Zweier et al., 1986). In the present method the number of components as well as their identity are guessed, simulated and fitted to the experimental spectrum. The parameters are refined and the procedure is repeated until the best matching is achieved. Fig. 4 shows the simulated components and fitting for a 4 component anisotropic spectrum (Zweier et al., 1989). The components were simulated individually using the anisotropic simulation routine. The Fig.
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.
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I . I &W, yet M~~naic F*ldIGI
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4. A sample of frozen heart tissue which contains four component anisotropic experimental spectra (E), fitted spectrum (F) and the simulated components (A to D). The simulation parameters: A: g, = gyy = gU= 2.004, line width = 3.65 G, I orientation; B: g, = gYY= 2.005, g,, = 2.033, line width = 3.40 G, 63 orientations; C: g, = gyy = g,, = 2.000, one nucleus with I=1 and a = 24.00 G, line width = 3.40 G, 63 orientations; D: a digitized signal with g values 2.027 and I.936 attributable to an iron-sulphur protein with a Fe& cluster. The matching spectrum F was obtained by adding the four component spectra A to D.
parameters were adjusted manually to get the simulated spectra components that yielded the best fit to the measured spectrum. Finally, many repeated measurements were performed in order to test the accuracy of this method of quantitation. It was observed that the error with the present method of analysis was quite small, in the range of + 5 to 2 10%. Thus we find that the component optimization and fitting program is suitable for routine studies of EPR spectra of free radicals in a variety of biological or chemical systems.
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and applications
REFERENCES Be&with, A.L. and S. Brumby (1987). Numerical analysis of EPR spectra. 7. The simplex algorithm. J. Magn. Reson.,73,252-259. Duling, D.R., A.G. Motten and R.P. Mason (1988). Generation and evaluation of isotropic ESR spectrum simulations. J. Magn. Reson., 7l, 504-5 11. Hyde, J.S. and W.K. Subczynski (1984). Simulation of ESR spectra of the oxygen sensitive spin label probe CTPO. J. Magn. Reson., 5&, 125-130. Kuppusamy, P., and J.L. Zweier (1989). Characterization of free radical generation by xanthine oxidase. Evidence for hydroxyl radical generation. J. Biolog. Chem., =,9880-9884. Motten, A.G. and J. Schreiber (1986). Correlation analysis of ESR spectra on a small computer. J. Magn. Reson., 67,42-54. Motten, A.G., D.R. Duling and J. Schreiber (1987). Proton coupling constant extraction. A fast method for analyzing ESR spectra by computer. J. Magn. Reson., 7l, 33-44. Nettar, D. and J.J. Villafranca (1985). A program for EPR powder spectrum simulation. J. Magn. Reson., 64,61-65. Ohler, U.M. and G. Janzen (1982). Simulation of isotropic electron spin resonance spectra: A transportable basic program. Can. J. Chem., 60, 1542-1547. Poole Jr, C.P. (1983) Electron Spin Resonance: A Comprehensive Treatise on Experimental Techniques. John Wiley & Sons, New York. Price, J.H., J.R. Pilbrow, K.S. Murray and J. Smith (1970). J. Chem. Sot. A, 968. VanCamp, H.L., and A.H. Heiss (198 1). Computer Applications in Electron Paramagnetic Resonance. Magn. Reson. Rev., 1, 1. Wertz, J. E, and J.R. Bolton (1972). Electron Spin Resonance. McGraw-Gill, New York. Zweier, J.L., J.T. Flaherty and M.L. Weisfeldt (1986). Direct measurement of free radical generation following reperfusion of ischemic myocardium. Proc. Nat. Acad. Sci. USA, 84, 1404-1407. Zweier, J.L., P. Kuppusamy, R. Williams, B.K. Rayburn, D. Smith, M.L. Weisfeldt and J.T. Flaherty (1989). Measurement and characterization of postischemic free radical generation in the isolated perfused heart. J. Biolog. Chem., 264, 18890-18895.