Anal. Chem. 1994,66, 1877-1883
Method for Estimating Molecular Mass from Electrospray Spectra Jeffrey J. Hagen and Curtis A. Monnig’ Department of Chemistry, Universiw of California, Riverside, California 9252 1-0403
A multiplicative correlation algorithm (MCA) is used to estimate the mass of macromoleculesfrom the mass-to-charge spectraproduced by electrosprayionization mass spectrometry. When compared with previous algorithms for processing electrospray spectra, the MCA allows accurate mass determinationswith a reduced quantity of sample. The performance of the MCA improves as the signal from the molecular ion is distributed across an increasing number of charge states, as the range of mass-to-charge values used to calculate the mass spectrum are constrained, and as noise is excluded from the calculations. The molecular ion peak in the mass spectrum increases exponentially with analyte concentration and ultimately limits the dynamic range of the measurement.
Mass spectrometry is a useful tool for estimating molecular mass and monitoring subtle structural modifications of macromolecules. The popularity of mass spectrometry for these studies can be attributed to the development of techniques which produce intact gas-phase molecular ions from highmass molecules. Although several ion sources are capable of producing molecular ions from large molecules, electrospray ionization (ESI) has gained notable popularity in recent years.’ The electrospray process was first reported and investigated by Dole et aL2 and Iribarne and Thomson3 but was largely ignored until Fenn and co-workers demonstrated its utility as an ion source for mass spectrometry of nonvolatile comp o u n d ~ . ~Since , ~ these initial reports, electrospray ionization has rapidly developed into a popular method for production of both positive and negative ions from recalcitrant samples.”1° The popularity of electrospray ionization can be traced to its simplicity and high ionization efficiency. For molecules which contain multiple groups capable of being ionized, the ESI source can produce highly charged molecules.l&l2 The deposition of multiple charges on a single molecule allows mass analyzers with a limited mass-to-charge range to record (1) Fenn, J. B.; Mann, M.; Meng,C. K.; Won& S. F.; Whitehouse, C. M. Science 1989, 246, 64-7 1. (2) Dole, M.; Mack, L. L.; Hines, R. L.; Mobley, R. C.; Ferguson, L. D.; Alice, M. B. J . Chem. Phys. 1968,49, 224W9. (3) Iribarne, J. V.; Thomson, B. A. J . Chem. Phys. 1976, 64, 2287-94. (4) Whitehouse, C. M.; Dreyer, R. N.; Yamashita, M.; Fenn, J. B. Anal. Chem. 1985, 57,675-679. ( 5 ) Wong, S. F.; Meng, C. K.; Fenn, J. B. J. Phys. Chem. 1988, 92, 546-550. (6) Liahtwahl, K. J.; Springer, D. L.; Winger, B. E.; Edmonds, C. G.; Smith, R. D.J. Am. Chem. SOC.1993, 115, 803% (7) Duffin, K. L.; Wepley, J. K.; Huang, E.; Henion, J. D. Anal. Chem. 1992,64, 1440-48. ( 8 ) Duffin, K. L.; Henion, J . D.; Shieh, J. J. Anal. Chem. 1991, 63, 1781-88. (9) Loo, J. A.; Loo, R. R. 0.;Light, K. J.; Edmonds, C. G.; Smith, R. D. Anal. Chem. 1992.64, 81-88. (10) Loo, J. A.; Edmonds, C. G.; Smith, R. D. Anal. Chem. 1991,63, 2488-99. (1 1) Smith, R. D.;Loo,J. A.; Edmonds, C. G.; Barinaga, C. J.; Udseth, H. R. Anal. Chem. 1990, 62, 882-99. (12) Mann, M.; Meng, C. K.; Fenn, J. B. Anal. Chem. 1989,61, 1702-1708. 0003-27OOf94f 0368-1877$04.50/0 0 1994 American Chemical Society
a spectrum for high molecular weight ions. In the extreme, spectra for molecules as large as 5 X lo6 Da have been recorded with a relatively modest quadrupole instrument.13 For those molecules which can sustain multiple charges, a distribution of charge states is often observed in the mass-to-charge spectrum (see Figure la). This multiplicity of states gives rise to an “envelope” of peaks in the spectrum from which the mass of the molecular ion can be determined. Although electrospray mass-to-charge spectra are usually simple to acquire, determining molecular mass from these spectra can sometimes be difficult. Mann et aL1*J4described two algorithms which addressed the problem of interpreting the ESI mass-to-charge spectrum. Both algorithms use the following expression to estimate the position of the peaks in the mass-to-charge spectrum:
M K i = -1+ m where Ki is the mass-to-charge ratio of potential peaks in the electrospray spectrum, Mis the molecular weight of the parent molecule (including neutral adducts), i is the number of charges on the molecule, and m is the mass of any adduct ions associated with the parent ion. Mann’s averaging algorithm14 requires knowledge of the mass-to-charge ratio for two peaks in the electrospray spectrum, the charge difference between these peaks, and the mass of the counterion. With this information, two expressions based on eq 1 can be quickly established and solved to estimate the mass of the molecule. Although the algorithm is capable of very accurate estimates of the molecular mass, selection of the peaks is often difficult in spectra with poor signal-to-noise characteristics or those which contain signals from multiple molecules. Mann’s deconvolution algorithm14 relies on a calculation which sums the intensities at mass-to-charge values predicted by eq 1 for a molecule of assumed mass M . By performing this calculation iteratively over a range of masses specified by the analyst, a mass spectrum such as the one shown in Figure 1b is generated. Although this algorithm is easily implemented with digital computers, it has significant limitations. One problem is the generation of spurious peaks in the calculated spectrum when a predicted peak for a mass not corresponding to one of the sample constituents is at the same mass-tocharge value as a peak from another mass. The resulting signal can approach the intensity of the “true” molecular signal and make spectral interpretation difficult. Labowski and co-workerslS extended the work of Mann by modifying the deconvolution algorithm so that it could (13) Nohmi, T.; Fenn, J. B. J . Am. Chem. SOC.1992, 114, 324146. (14) Fenn, J . B.; Mann, M.; Meng, C. K. US.Patent 5 130 538, 1992.
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Figure 1. Examples of different data analysis algorithms: (a) ESI spectrum for 1.72 pmol of myoglobin, (b)the output of the deconvolutlonalgorithm of Mann et al.,12.14(c) the output of the Relnhoid and (d) the output of the mukiplicathre correlation algortthm.
“deconvolute” spectra produced by adduct ion mixtures. This is accomplished by allowing the algorithm to consider more than one adduct ion mass in its calculation. The resulting three-dimensional surface allows easier identification of errors in mass scale calibration and permits an assessment of the accuracy of the molecular weight assignment, albeit at the expense of additional computation time. An alternative means of processing electrospray spectra is to employ a maximum entropy calculation16J7to reconstruct a mass spectrum from electrospray data. Mass spectra generated with a commercial maximum entropy algorithm were presented by Ferrige and c o - ~ o r k e r s . ~Although ~J~ this algorithm seems to provide enhanced resolution capabilities, a detailed description of the calculations was not provided so it is difficult to directly compare this data analysis procedure with other algorithms previously disclosed. More recently, Reinhold and ReinholdZ0 described an entropy-based algorithm for processing of electrospray data. This algorithm assumes a molecular weight, predicts the electrospray spectrum for a molecule of this mass, calculates the error between the predicted spectrum and the experimental data, and finally plots this error as a function of mass. An example of a mass spectrum generated with the Reinhold (15) Labowski,M.;Whitehouse,C.M.;Fcnn,J.B.RapidCommun.MassSpectrom. 1993, 7,71-84. (16) Daniell, G. J.; Gull, S. F. IEE Proc. 1980, 127E. 170. (17) Gull, S.F.; Skiling, J. IEE Proc. 1984, Z3ZF, 646. (18) Ferrige, A. G.;Seddon, M. J.; Grecn, B. N.; Jarvis, S.A.; Skilling, J. Rapid Commun. Mass. Spectrom. 1992, 6, 707-7 11. (19) Ferrige, A. G.; Seddon,M. J.; Jarvis, S. A. Rapid Commun. MassSpectrom.
1991,5, 374-379. (20) Reinhold, B. B.; Reinhold, V. N. J . Am. Soc. Mass Spectrom. 1992, 3, 207215.
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algorithm is shown in Figure IC. This algorithm discriminates against spurious peaks often generated by the Mann deconvolution algorithm, but the calculations can be slow and a priori knowledge of peaks shapes and relative intensities in the mass-to-charge spectrum is required for optimal processing of the data. Although each of the algorithms previously discussed offers unique advantages with respect to speed, accuracy, and ease of use, they all produce less than optimal mass spectra when the original data has poor signal-to-noise characteristics. In principle, it should be possible to develop an algorithm which makes better use of the known properties of electrospray spectra to produce improved quality spectra. Specifically, one characteristic of electrospray spectra which can be used for this purpose is that the ion signal for a high molecular weight molecule is usually distributed between two or more charge states. In this document, one algorithm is described which makes use of this property to enhance the quality of the calculated mass spectrum and allow accurate mass determinations from relatively noisy spectra.
EXPERIMENTAL SECTION Reagents. Horse heart myoglobin, horse heart cytochrome c (type 111), bovine pancreas trypsinogen, bovine milk @-lactoglobulinA, hen egg conalbumin (type I), and hen egg lysozyme standards were purchased from Sigma Corp. (St. Louis, MO) and used without further purification. Poly(ethylene glycol) standards were purchased from Aldrich Chemical Co. (Milwaukee, WI). Just prior to analysis, the samples were dissolved in a 1:l (v/v) solution of methanol and 3% acetic acid in deionized water.
Equipment. Electrospray spectra were acquired with a quadrupole mass spectrometer configured with an electrospray interface (Model 201 mass filter with options ES and E2000, Vestec Corp., Houston, TX). The quadrupole mass analyzer was controlled, and data were recorded by a dedicated processor (Model 900 DSP, Teknivent Corp., Maryland Heights, MO) monitored by a 80486-based computer running commercial software (Vector/Two version 1.4, Teknivent Corp.). Sample solutions were introduced into the electrospray interface by means of a syringe pump (Model 341B, Sage Instruments, Boston, MA) at flow rates between 0.96 and 1.83 pL/min. The electrospray needle was held between 2 and 3 kV for all analyses. The electrospray voltage, distance between the needle and the first skimmer cone, and sample flow rate were adjusted to achieve a stable spray current of approximately 0.2 pA. Instrument operating temperatures were maintained at the following values for all analyses; spray chamber, 70 "C; ion lenses, 150 OC; block, 250 OC. The repeller voltage was 20 V and the pressure in the analyzer 5 X 10" Torr. The quadrupole electronics were adjusted to enhance the throughput of the mass filter. Under these conditions, spectral peak widths were typically 3.5 mass-tocharge units (full width at half-maximum intensity). Calibration of the mass analyzer was performed while solutions of poly(ethy1ene glycol) with average molecular weights of 400, 900, and 1500 u were sequentially aspirated. Data were acquired by introduction of the analyte solution into the electrospray interface while the mass filter was scanned from 600 to 2000 mass-to-charge units at 0.2 unit intervals. Total protein quantities introduced into the ion source during the signal acquisition period are reported with each spectrum. Software. An Objective C program was developed locally to implement the deconv~lution,~~J~ Reinhold entropy-based?O and multiplicative correlation algorithms. ASCII files of the raw mass-to-charge spectra were transferred from the mass spectrometer's data system to a NeXTCube workstation (25 MHz, 68040 microprocessor, 16 MB RAM, NeXT Computer Inc., Redwood City, CA) for postacquisition processing. To ensure that the deconvolution and entropy-based algorithms were performing as expected, electrospray spectra were processed with our program and with commercial implementations of these algorithms (Mann Deconvolution Algorithm, Vector/Two version 1.3; Reinhold Entropy-Based Algorithm, Vector/Two version 1.4; Teknivent Corp., St. Louis, MO) and the resulting spectra compared. For all samples investigated, the programs generated nearly equivalent results. RESULTS AND DISCUSSION To address the problem of obtaining useful information from spectra with poor signal-to-noise characteristics, a new algorithm for processing electrospray mass-to-charge spectra was developed. This multiplicative correlation algorithm (MCA) exploits natural differences when numbers are multiplied versus summed together to enhance the electrospray signal from molecular species while suppressing the signal from other sources. A simple examplecan be used to illustrate the salient principles of this algorithm. Consider three positive, non-zero numbers whose sum is 100. Each of these numbers can be considered to be the intensity of an ion signal at a specific mass-to-charge value in an electrospray spectra. For
algorithms which sum the signal from the three channels, it does not matter how the signal is distributed between the three channels; the sum will always be 100. If a noise peak or the signal from another ion should fall at one of these positions, it will give rise to a significant signal in the calculated mass spectrum. Under unfavorable circumstances, this spurious signal can approach or even exceed the intensity of the "true" molecular signal. Now consider the result if the same column of numbers is multiplied instead of summed. If the signal has been selectively partitioned into one channel (e.g., (1, 1,98)), the product of these three numbers is small (Le., 98). If however, the intensity is more evenly distributed between the three numbers (Le., (33, 34, 33)), the product is dramatically enhanced (i.e., 37 026). Thus, multiplying a column of numbers will selectively enhance the signal which is distributed among several channels relative to a signal concentrated in one channel. Electrospra y spectra of macromolecules typically have the signal distributed among two or more charge states, while noise peaks and spurious correlations with other signals are typically associated with single, unrelated mass-to-charge values. Thus, a coincidental overlap between a predicted peak and noise spikes or signals from other molecules contributes little to the calculated spectrum. However, when a mass corresponding to a molecule in the sample is selected, a large fraction of the mass-to-charge positions examined will have a significant signal and thus generate a sizable response in the calculated spectrum. To implement the MCA, an algorithm conceptually similar to Mann's deconvolution procedure can be employed. The program used to generate the spectra shown in this document first reads the mass-to-charge data from a disk and queries the operator for the mass-to-charge range to be utilized in the calculations, the mass of the expected adduct ion, and the range of molecular weights to be searched. Starting with the first mass in the selected range, the program uses eq 1 to calculate the mass-to-charge positions for predicted ions for that mass and a specified adduct ion. Signal intensities at each of these positions are multiplied together to obtain a signal which can be associated with the molecular mass. The program selects the next mass to be considered and repeats the process until the entire mass range has been covered. One problem with the algorithm as outlined above is that, as the assumed mass of the ion increases, the number of massto-charge values multiplied together to obtain the signal intensity also increases. This has the effect of increasing the baseline signal and peak intensities as higher masses are examined. To correct for this problem, the product of signal intensities is divided by the root mean square (rms) intensity of the mass-to-charge spectrum raised to the number of positions in the mass-to-charge spectrum (n) used in the calculation. The following formula summarizes this calculation: I[lcll =
rmsn
(2) whereZ(M) is the calculted intensity in the spectrum for mass Analytical Chemism, Vol. 66,No. 11, June 1, 1994
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M , S is the intensity in the mass-to-charge spectrum, and X and Yare the minimum and maximum number of charges on the molecule, respectively. In practice, the values of X and Y are limited by the range of mass-to-charge values the operator elects to search and the assumed mass of the parent molecule. An important concern is to minimize the transfer of noise from the mass-to-charge spectrum to the calculated mass spectrum. Mann et al. report that restricting the mass-tocharge range searched by the algorithm limits the transfer of noise from the mass-to-charge spectrum to the calculated mass spectrum.12 This effect is also observed with the MCA. To obtain the optimumquality mass spectrum, the mass-to-charge utilized by the program should be restricted to only those regions of the spectrum which contain significant signal. A potentially significant problem associated with the MCA is the influence of very low intensity mass-to-charge signals on the calculated spectrum. If any data point used in the calculation has an intensity close to or equal to zero, the signal intensity in the mass spectrum will also be greatly suppressed. To minimize the transfer of noise between spectra and eliminate the possibility of multiplication by zero, the program establishes a threshold intensity below which the mass-tocharge data are ignored. Signals which fall below this threshold are judged to contain little useful information and are not used in the calculation. The quality of the calculated spectrum can be further enhanced when the mass-to-charge data are filtered prior to processing. Noise in the mass-to-charge spectra is usually high frequency, while the molecular signal is typically at lower frequency. Although many low-pass filters can be used to enhance the signal-to-noise ratio in the calculated mass spectrum, digital filtering via a “moving product” filter provides a convenient means of enhancing the signal quality of the mass-to-charge spectrum and thus enhancing the mass spectrum calculated from these data. This filter is implemented by calculating the product of the intensities over a small range of mass-to-charge values centered around the mass-to-charge value of interest. Maximum signal-to-noise enhancement occurs when the filter window is approximately the same as the width of the peak, albeit at the expense of some peak shape distortion. The following formulas summarizes the MCA calculation when a moving product filter is employed.
Z[W = JXJX+lJX+*
..‘ J,
(3)
where J is defined as J, =
s[M -
(31s[ - (3+ M
d ] s[ m -
(3+
2d]
...s[ m +
(31
For these calculations, w is the width of the filter window, m is the mass-to-charge value for the analyte as calculated by eq 1, and d is the mass difference between successive points in the mass-to-charge spectrum. An example of a mass spectrum generated with the MCA is shown in Figure Id. When compared with mass spectra 1880
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generated by other algorithms (Figures 1b,c), the molecular peak is much easier to identify. The time required tocalculate and display the spectrum in Figure Id was relatively modest, requiring approximately 60 s to process the data for the specified mass range. Figure Id also illustrates one of the limitations of the MCA: its inability to suppress spurious peaks at integer multiples of the molecular mass. Fortunately, the position of these peaks makes them easily recognizable so they do not interfere with spectrum interpretation. The ultimate goal of acquiring electrospray mass spectra is to obtain an accurate estimate of the analyte mass. The mass estimates generated by the Mann, Reinhold, and MCA programs and an independent estimate of the molecular mass (Mr)are summarized in Table 1. These mass estimates were obtained by aspirating solutions of the appropriate protein into the mass analyzer and processing the resulting spectra with the respective algorithms. To provide a good indication of the inherent mass accuracy of each of the algorithms and to minimize the influence of noise on these mass determinations, approximately 1 pmol of each protein was consumed to acquire the electrospray spectrum. In this table, the apex is the mass with the highest intensity in the spectrum, while the centroid is the statistical center of the peak. Thedata presented in Table 1 clearly indicate that the MCA provides equivalent estimates of the molecular mass when compared with the other algorithms. A second criteria for comparing algorithm performance is the ease with which a molecular signal can be identified. One difficulty with the direct comparison of spectra generated by different algorithms is the development of a quantitative measure of the “merit” of a spectrum. For purposes of comparison between the MCA and previously published algorithms, a new quantity, the quality factor (QF), is defined. The Q F is the signal intensity as the “true” molecular mass divided by the maximum signal intensity at any mass value other than the molecular mass. This value is analogous to a signal-to-noise ratio and thus provides a measure of the ease with which themolecular peakcan beidentified in the presence of a background signal (Le., spurious peaks). The larger the QF, the less likely a spurious peak will be mistaken for a molecular signal. All of the algorithms studied generate significant artifact peaks at integer multiples of the actual molecular mass unless procedures are used to limit artifacts (i.e., by restricting the charge states used in the calculations). To avoid skewing of the Q F values by these easily identifiable artifacts, signals corresponding to these peaks were ignored in the Q F calculations. In Figure 2, the relative performances of the Mann deconvolution algorithm and Reinhold’s entropy algorithm are compared with the MCA for analysis of an ESI spectrum of lysozyme. Figure 2a shows an electrospray mass spectrum generated when 3.51 fmol of lysozyme was introduced into the mass spectrometer. Two peaks corresponding to lysozyme are clearly evident at 1590 and 1789 mass-to-charge units, respectively. Figure 2b shows the spectrum generated when the Mann deconvolution algorithm was used to process the data from Figure 2a. Although a peak corresponding to the correct molecular mass is observed, many spurious peaks are also observed (QF = 1.1656). In Figure 2c, the mass spectrum produced by the Reinhold algorithm is displayed. For this
Table 1. Estimated Mass for Protelns from Electrospray Mats Spectra
protein
M/
cytochrome C lysozyme myoglobin @-lactoglobulinA trypsinogen conalbumin
12360 14306 16950 18 363 23918 17500
a
Mann algorithm apex centroid 12 360.3 14294.8 16 961.5 18 371.3 24001.5 77576.2
12352 14296 16944 18 360 23912 77520
Reinhold algorithm apex centroid 12 364 14 300 16 954 18 369 23 995 77 531
multiplicative correlation algorithm apex centroid
12 359.7 14 300.3 16 954.0 18 377.4 24 014.7 77 548.4
12 355 14 300 16 944 18 359 23 984 77 490
12 355.0 14 297.1 16 942.3 18 351.2 23 986.8 77 520.1
From ref 11.
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Figure 2. Influence of dlfferent data analysis algorlthms on calculated mass spectra: (a) ESI spectrum for 3.51 fmol of lysozyme, (b) the mass spectra calculated wlth the deconvolutlon algorithm of Mann et al., (c) the mass spectra calculated with the Reinhold algorithm, and (d) the mass spectra calculated wlth the multiplicative correlatlon algorithm.
example, the Reinhold algorithm provides little if any signal at the appropriate mass (QF < 1). The multiplicative correlation algorithm was used to calculate the spectrum shown in Figire 2d. Clearly the spectrum in Figure 2d is easier to interpret (QF = 1.558 X 10’) than either of the two previous spectra. Importantly, these data demonstrate that the MCA can provide significant enhancements even when the number of signal peaks is small. As previously discussed, the quality of a spectrum can be enhanced by establishing a threshold intensity below which data are excluded from the calculation. For the purpose of this discussion, the threshold will be defined in terms of its relative intensity (RI) where 0% RI and 100% R I are the lowest and the highest signal intensities in the mass-to-charge spectrum, respectively. Figure 3a shows the spectrum acquired when 13.6 fmol of cytochrome c was introduced into the mass analyzer. Figure 3b is a plot of the Q F of the spectrum generated by the MCA versus the threshold intensity expressed as % RI. The reduction in Q F between 0% R I and 10% RI results from the elimination of data with an intensity less than one while still including the contribution of the high-intensity noise. As expected, the greatest enhancement is seen when
the threshold is established at a level when most of the signal is above the cutoff while most of the noise is excluded. For the data in Figure 3a, a threshold of approximately 14% RI is optimal. Figure 3c shows the mass spectrum calculated with the MCA when the threshold was set at this value. Using a higher threshold diminishes the contribution of the signal while providing little additional discrimination against noise. Not surprisingly, the threshold necessary to obtain the maximal QFvaries between spectra. Fortunately, the range over which the threshold can be productively employed is broad and can usually be estimated by visual inspection of the data. Application of the moving product filter prior to analysis with the MCA has a significant effect on the quality of the calculated spectrum. Improvements in the Q F by factors of 1016-1017are often observed by application of this filter. The maximum enhancement is observed when the filter width matches the width of the peaks in the mass-to-charge spectrum. Unfortunately, as these peaks are often asymmetric, increasing the filter window can skew the mass-to-charge position of the filtered peak. This in turn can influence the position of the molecular peak in the mass spectrum. For myoglobin, the peaks in the mass spectrum were observed to shift apAnalytical Chemistry, Vol. 66, No. 11, June 1, 1994
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Figure 3. Effect of establishing a threshold level on the OF of the multlplicative correlation spectrum: (a) ESI spectrum for 13.6 fmol of cytochrome c, (b) the OF of spectrum calculated from data in a as a function of threshold level (expressed as percent of relative intensity), and (c) the output of the MCA with a baseline of 14% R I .
proximately 5-10 Da as the filter window was increased from 0 to 10 mass-to-charge units. For this reason, filtering of the data is only recommended when the poor signal-to-noise characteristics of the mass-to-charge spectrum significantly impact the mass spectrum. The ability of an algoritm to detect two or more compounds simultaneously is of great importance when analyzing samples where purity cannot be guaranteed. Figure 4a shows an electrospray spectrum where 74.3 fmol of horse heart myoglobin and 136 fmol of cytochrome c were introduced into the mass analyzer simultaneously. The spectrum generated by the MCA is shown in Figure 4b (QFmyodobin = 4087; QFcytochromec = 21 857). One problem associated with the analysis of mixtures is the relative positioning of peaks from different envelopes which can give rise to significant spurious signals in the mass spectrum. This can be particularly troublesome for trace component analysis as the intensity of 1882
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Figwe 4. Analysis of a multicomponent mixture: (a) ESI mass spectrum of 74.3 fmol of myoglobin and 136 fmol of cytochrome c and (b) the mass spectra generated by the multiplicative correlation algorithm.
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Analytical Chemistry, Vol. 66, No. 11, June 1, 1994
artifact peaks from major constituents will often exceed the intensity of peaks from the compounds of interest. Although the MCA will counteract these effects to some extent, the best solution to the problem is to isolate components prior to the analysis. One of the strengths of the MCA is its ability to generate mass spectra of high quality from poor-quality electrospray spectra. Figure 5a is the spectrum generated when 1.82 fmol of myoglobin was introduced into the mass spectrometer. The poor signal-to-noise ratio associated with this spectrum makes it difficult to identify the peak envelope associated with the molecular ion by visual inspection. Nevertheless, the MCA is able to extract a useful molecular signal from this data (Figure 5b, Q F = 8 540). A further demonstration of the ability of the MCA to process data over a broad range of concentrations was obtained by analyzing protein standards spanning 5 orders of magnitude in concentration (myoglobin, 7.1 pM to 71 pM; lysozyme, 6.3 pM to 63 pM; cytochrome c, 9.8 pM to 98 pM). In all cases, the Q F of the mass spectrum was greater than 1060(the dynamic range of the floating point numbers used in these calculations) until the concentration of the analyze was approximately 1 order of magnitude above the detection limit. Below this level, the Q F of the mass spectrum was rapidly reduced as the analyte concentration was decreased. For all samples investigated, the Q F of the multiplicative correlation spectrum greatly exceeded the Q F of the spectra generated by the deconvolution and Reinhold algorithms. Table 2 presents the Q F and mass estimate data for myoglobin as a function of concentration. From these data it is clear that the mass of the molecular ion as determined by the MCA is relatively independent of the mass of analyte
Table 2. Mass and QF Estimates as a Function of the QuantHy of Myoglobin Introduced Into the Mass Spectrometer
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1.0 x 1080 1.0 x 1080 1.0 x 1080 5.18 X loa1 8.98X 10% 8.65 X 1W
4Accepted mass is 16 950 (from ref 11). * Mass estimates are obtained from the peak apex.
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Figure 5. Electrospray ionization of myoglobin: (a) ESI spectrum of 1.82 fmol of myoglobinand (b) the mass spctrum calculated from the data in a with the multiplicative correlation algorithm.
introduced into the electrospray source. It is also interesting to note that, when 1.4 fmol of myoglobin was analyzed, an accurate estimate of the molecular mass was obtained even though this was not the most intense peak in the spectrum.
CONCLUSIONS Multiplicative correlation offers several significant advantages when compared with algorithms currently used to
interpret the mass-to-charge spectrum of multiply charged ions, the most important of which is the ability to obtain accurate estimates of molecular weight from spectra with poor signal-to-noise characteristics. These calculations consume a minimal amount of computing resources and are easily peformed by an analyst with a minimal amount of training. Despite these many advantages, the MCA does have several significant limitations. Most troublesome is the nonlinear dependence of the mass siginal intensity on the concentration of the analyte. This can cause significant problems when trying to visualize the signals in multicomponent solutions. Nevertheless, the MCA does demontrate the enhanced capabilities of algorithms which utilize more of the information contained in the electrospray spectrum. Alternative algorithms which exploit these characteristics while overcoming the limitations of the MCA should be possible.
ACKNOWLEDGMENT This work was supported by the National Science Foundation (CHE-91-08530), the Arnold and Mabel Beckman Foundation, and Lawrence Livermore National Laboratories (Contract B244803). Received for review November 24, 1993. Accepted March 11, 1994.@ e Abstract
published in Advance ACS Abstracts, April 15, 1994.
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