Anal. Chem. 1900, 60, 1415-1419
different than those of multiple photon absorption in the near-ultraviolet region (1). In this work we have studied several other interesting cases of MPI in aromatic-based peptides. One example is shown in Figure 3 which is a MPI mass spectrum of three isomeric peptides. In each cases a strong M'+ ion is clearly observed as well as an ion a t m / z 130 due to simple @-cleavagea t the tryptophan moiety. Although the preferred cleavage under the conditions of the experiment appears to be @ cleavage with a resulting acylium ion fragment, in each isomer a different fragment is obtained depending on the initial structure of the molecule. For example, the ion a t (M - 74) (244 u) is present in configurations 2 and 3 due to the cleavage as indicated. However, the same cleavage will not provide this ion when as in configuration 1in Figure 3 the tryptophan is on the other side of the bond. In general the fragments obtained from the three species are quite different. The MPI of the isomers of the tetrapeptide Phe-Gly-GlyPhe vs Phe-Gly-Phe-Gly also provides different fragmentation patterns as shown in Figure 4. In this case M'+ and (M COOH)+ peaks are observed at 426 and 381 u, respectively. The fragments arising from the Phe-Gly portion of the peptides are similar for both isomers. Ions at m / z 91 and (M 91) (335 u) are observed due to simple @-fragmentationat the phenylalanine side chain. Cleavage a t the CO-NH bonds produces acylium and aldimine ions at m / z values 148 and 205 (ac) and 278 and 221 (ald). Acylimmonium ions are observed at m/z 120 and 177. N-C bond cleavage occurs with charge retention on the C producing a m / z of 263 in both cases but charge retention on the N to produce a m / z of 163 occurs only in Phe-Gly-Phe-Gly. The difference between the two isomers occurs due to the third amino acid depending on whether it is Gly or Phe. The CO-NH cleavage produces an acylium ion of m / z 352 or 262 depending on whether Phe or Gly is present, respectively. The corresponding acylimmonium ions can be found a t 28 mass units lower at 324 and 234 respectively, thus providing distinctly different fragmentation patterns. Registry No. Leu-enkephalin, 58822-25-6;Met-enkephalin, 58569-55-4; Tyr-Gly, 673-08-5; Tyr-Gly-Gly, 21778-69-8; TyrGly-Gly-Phe, 60254-82-2; Phe-Leu, 3303-55-7; Gly-Phe-Leu,
1415
15373-56-5; Gly-Phe, 3321-03-7; Gly-Gly-Phe, 6234-26-0; GlyGly-Phe-Leu,60254-83-3;Phe-Met, 15080-84-9;Gly-Gly-Phe-Met, 61370-88-5;Gly-Gly-Trp, 20762-32-7; Gly-Trp-Gly, 57850-28-9; Trp-Gly-Gly, 20762-31-6; Phe-Gly-Gly-Phe, 40204-87-3; PheGly-Phe-Gly, 59005-83-3.
LITERATURE CITED Lubman, D. M. Anal. Chem. 1986,59,31A. Tembreull, R.; Lubman, D. M. Anal. Chem. 1986,58, 1299. Tembreull, R.; Lubman, D. M. Anal. Chem. 1987,59, 1003. Tembreull, R.; Lubman, D. M. Anal. Chem. 1987,59, 1082. Engelke, F.; Hahn, J. H.; Henke, W.; Zare. R. N. Anal. Chem. 1987, 59, 909. (6) Hahn, J. H.; Zenoba, R.; Zare, R. N. J. Am. Chem. Soc. 1987, 109, 2842. (7) Grotemeyer, J.; Boesl, U.; Walter, K.; Schlag, E. W. Org. Mass Spectrom. 1986.21.595. (8) Grotemeyer; J.; Boesl, U.;Walter, K.; Schlag, E. W. Org . Mass Spectrom. 1988,21, 645. (9) Rizzo, T. R.; Park, Y. D.; Peteanau, L.; Levy, D. H. J. Chem. Phys. 1985,83, 4819. (10) Posthumus, M. A.; Kistemaker, P. G.; Meuzelaar, H. L. C.; Ten Noever de Brauw, M. C. Anal. Chem. 1978,50, 985. (11) Wilkins, C. L.; Weil, D. A.; Yang, C. L. C.; Ijames, C. F. Anal. Chem. 1985. 57. 520. (12) McCrery, D.A.; Gross, M. L. Anal. Chim. Acta 1985, 778,91. (13) Shomo, R. E.; Marshall, A. G.; Wersenberger, C. R. Anal. Cbern. 1985.57. 2940. (14) Tembreuli, R.; Lubman, D. M. Appl. Spectrosc. 1987,47,431. (15) Lubman, D. M.; Rettner, C. T . ; Zare, R. N. J. Phys. Chem. 1982,86, 1129. (16) Li, L.; Lubman, D. M. Appl. Spectrosc. 1988,42,418. (17) Lynch, D. R.; Snyder, S. H. Annu. Rev. 6ioChem. 1986, 55, 773. (18) Roepstorff, P.; Fohlman, J. 6iorned. Mass. Spectrom. 1984, 11, 601. (19) Biemann, K.; Martin, S. A. Mass Spectrom. Rev. 1987, 6, 1. (20) Desiderio, D. M.; Sabbatini, J. 2. Biomed. Mass Spectrom. 1981,8, 565. (21) Katakuse, I.; Desiderio, D. M. I n t . J. Mass Spectrom. I o n Processes 1983,54, 1. (22) Lippstreu-Fisher, D. L.; Gross, M. L. Anal. Chem. 1985, 57, 1174. (23) Li, L.; Lubman, D. M., Appl. Spectrosc. 1988,42,411. (24) Boesl, U.; Neusser, H. J.; Schlag, E. W. J. Chem. Phys. 1980, 72, 4327. (25) Hughes, J.; Smith, 1.W.; Kosterlltz, H. W.; Fothergill. L. A,; Morgan, B. A.; Morris, H. R. Nature (London) 1975,258,577. (1) (2) (3) (4) (5)
RECEIVED for review May 18, 1987. Accepted March 1,1988. We acknowledge financial support of this work under NSF Grant CHE8419383 and NSF Grant DMR8418095 for the acquisition of the Chemistry and Materials Science Laser Spectroscopy Laboratory. David M. Lubman is an Alfred P. Sloan Foundation Research Fellow.
Computer-Based Linear Regression Analysis of Desorption Mass Spectra of Microorganisms J. A. Platt and 0. M. Uy T h e Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland 20707
D. N. Heller,* R. J. Cotter, and Catherine Fenselau' Department of Pharmacology and Molecular Sciences, T h e Johns Hopkins University School of Medicine, Baltimore, Maryland 21205 The deslgn and appllcations of a computer-based system for determlnlng relatlve proportions of library spectra in desorptlon mass spectra are reported. The system can perform correlation and llbrarymatchlngfunctions on slngle unknowns as well as deconvolute mMures of unknowns. The algorithms for thls system were developed for the rapid characterlzation of microorganlsms through desorptlon lonizatlon of their lipid blomarkers.
Current address: Chemistry Department, University of Maryland Baltimore County, Catonsville, MD 21228.
The discovery (1) that intact polar lipids can be desorbed selectively and reproducibly from microorganisms by laser desorption, plasma desorption, and fast atom bombardment provides a rapid and sensitive approach to characterizing these important biomarkers. The presence and relative molarities of the various acylglycerides, phospholipids, glycolipids, and other lipids have been shown by chemotaxonomic studies (2, 3) to be characteristic of microorganisms. Their observation from lysed cells by desorption techniques provides a profile analogous to that obtained by gas chromatography-mass spectrometry analysis of fatty acids ( 4 ) or pyrolysis-mass
0003-2700/88/0360-1415$01.50/00 1988 American Chemical Society
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ANALYTICAL CHEMISTRY, VOL. 60, NO. 14, JULY 15, 1988
spectrometry of cells (56). Thus the capability for computerized library matching of these desorption spectra, and for deconvolution and identification of spectra of mixtures, could also be useful in systems for identification of microorganisms. While multivariate statistical analysis (7, 8) and linear regression techniques (9) have been applied to pyrolysis mass spectra and for principal component analysis of desorption chemical ionization mass spectra (IO), the present work demonstrates the first use of computer programs to correlate or deconvolute desorption spectra. In this study we focused on the fast atom bombardment (FAB) mass spectra of microorganisms, obtained on a double-focusing sector mass spectrometer. While the desorption of polar lipids from micrqorganisms using laser desorption and plasma desorption (employing time-of-flight mass spectrometers) has also been reported ( I ) ,we have found that negative ion FAB-MS can produce more lipid biomarker signals from complex mixtures than other techniques. Also, the unit resolution of the sector instrument made it possible to use each nominal mass as the basic unit of comparison. By choosing FAB for this work, we accepted a unique feature of FAB spectra, the high level of “chemical noise” or “peak-at-every-mass” background (11)that occurs throughout the spectra.
THEORY A mass spectrum consists of ordered pairs of masses and positive intensities. For unit resolution spectra, it is convenient to divide the mass range into 1-amu segments, converting each measured.mass into its nominal mass. The mass spectrum can then be described as an ordered intensity vector in an N-dimensional feature space, where N is the mass range. The intensity vectors are normalized to compensate for variations in instrument gain, sample size, etc. N
[ C S , 2 ( k ) ] ’ /=~ 1 k=l
(1)
where S,(h) is the kth element of the ordered intensity vector corresponding to spectrum j . The cosine of the angle between two mass spectra (also called the correlation coefficient about the origin) in this feature space is given by the inner product of their intensity vectors: N
cos
(Yab
= k=l
s,(h)sb(h)
(2)
The correlation coefficient is a measure of the similarity of two mass spectra. The correlation coefficient of two identical spectra is one, while that for two completely dissimilar (orthogonal) spectra is zero. We have implemented a computer-based system to determine the relative proportions of known library substances in a mass spectrum of an unknown mixture. Our analysis is based upon the assumption of “linear superposition”,that is, the intensity at any one mass must be a linear sum of the intensities from each library member weighted by its relative proportion in the mixture M
S ( k ) = C amLm(h)
(3)
m=l
where L , is the mth member in a library of M spectra and a, is the relative proportion of the mth library component in the mixture. This means that when more than one microorganism is present in a mixture, the signals must combine additively and not interfere with one another. Equation 3 can be written in matrix notation
S = a x L (4) where S is the intensity vector of the unknown mixture spectrum, a is the matrix of relative proportions of library
elements in the unknown mixture, and L is the matrix of library spectra. The rows of L correspond to each library element’s intensity vector, while the columns correspond to each nominal mass in the mass range. The library matrix is known (consisting of spectra of lysed cells of single microorganisms), and the unknown mixture spectrum S is experimentally measured. Equation 4 must be solved for a to determine the relative proportions of the library substances in the unknown mixture. Direct inversion of eq 4 is not possible because L, in most cases, will not be invertible. Equation 4 can be solved by multiplying both sides of the equation by the transpose of the library matrix:
S x LT = a x L x LT
(5)
The relative proportions of the library substances in the unknown mixture are then
a = (S x LT) x (L x LT)-l (6) The matrix L X LT is invertible if all the rows of L (the library spectra) are linearly independent. In other words, no member of the library may be made up of a linear combination of other members in the library. The library members do not have to be orthogonal to each other. Equations 4,5, and 6 defiie the matrix formulation of linear regression. This method has also been termed “multiple linear regression” (12). The mixture proportions computed are those that minimize the square of the differences between the actual and fitted unknown mixture spectrum. Every computation is carried out by using each nominal mass in the chosen mass range; the only “weight” applied to a particular nominal mass in the unknown spectrum is due to the presence of an intense signal at that mass. The error between the actual and fitted mixture spectrum is
E = IS - a
X
LI
(7)
where E is the vector magnitude of the difference between the actual and fitted spectra. Errors in the library spectra (due to noise, mass assignment error, or variability in the spectra) are not reflected in the error of fit calculated by eq 7 , since linear regression analysis assumes that all errors are contained within the dependent parameter (in this case the unknown mixture spectrum). In particular, it does not account for the presence of substances in the mixture that are not contained in the library. If a nonlibrary substance correlates with any member of the library, an erroneous contribution will be made to the computed mixture proportions. The mass spectral mixture determination algorithm outlined above differs from factor analysis and target rotation techniques ( I 3 , 1 4 ) in that the factors are given a priori, i.e. the library spectra. The treatment of pyrolysis mass spectra of microorganisms generally employs factor analysis because the true fundamental factors in pyrolysis mass spectra have not been fully identified. Windig et al. (7) have applied discriminant analysis to the extracted factors, and their results, presented in the form of discriminant score plots, provide a quantitative measure of the differences between spectra. Similar techniques have been used successfully to distinguish taxonomic groupings of bacteria and yeasts (8, 15, 16). However, the component spectra themselves cannot be extracted from pyrolysis spectra by simple mathematical techniques because the structures of the mixture components and their origins cannot be fully assigned. On the other hand, our simpler mathematical approach is based on the assumptions that the identity and pattern of the peaks occurring in desorption spectra are known and specific for particular microorganisms, that they can be used to distinguish between microorganisms, that they can be observed in a reproducible fashion, and that intensities in the desorption mass spectrum of a mixture of microorganisms are additive.
ANALYTICAL CHEMISTRY, VOL. 60, NO. 14,JULY 15, 1988
1417
While such techniques have been used to determine (quantitatively) the partial pressures of volatile compounds in a mixture by electron impact at a standard (70 eV) ionization energy (17), it is the purpose of this work to test these assumptions for the desorption mass spectra ofmicroorganisms.
EXPERIMENTAL SECTION Negative ion fast atom bombardment (FAB) mass spectra were obtained on a KRATOS (Manchester, UK) MS50 double-focusing mass spectrometer, equipped with an ION-TECH (Teddington, UK) saddle field atom gun. Spectra were acquired with the KRATOS DS90 data system, and the mass/intensity files were transferred to an IBM PC/AT for off-line processing. Mass spectral files were stored on the 30-Mbyte hard disk of the IBM PC/AT in a relational database. The implemented system, coded in APL (APL'PLUS PC Systems, STSC Inc., Rockville, MD), consists of subsystems to format and load the magg spectral data into the database, read data from the database, build a spectral library of known substances, and determine the relative proportions of these library substances from the spectrum of an unknown mixture. Library interrogation may be repeated after limiting the next iteration to only those known spectra for which the found relative proportion is above a user-determined limit Library spectra were obtained by averaging replicate spectra of the same samples. Measured masses obtained from the DS90 mw/intensity files are converted to nominal masses by using a variation of the Hites-Biemann correction (18). For molecular'ions of pbospholipids and glycolipids in the mass range of 6W1500 m u , the mass defect correction is approximately 0.0007/amu. By comparison of the found and theoretical mass defect through the mass range (starting with the known mass of a matrix ion), the effect of mass assignment error on the computation can be eliminated. The chemical noise or peak-at-every-mass background in fast atom bombardment spectra can obscure biomarker signals and can artifactually alter the correlation between two spectra by disproportionate weighting of nonspecific peaks. Therefore, algorithms were developed to automatically define and subtract the chemical noise base line, taking into account that it varies in intensity throughout the spectrum and assuming that this background and sample ion signals are linearly superimposed. This was accomplished by searching for and connecting local "troughs" in the spectrum between the major ion signals, A third correction was made to apply a local =threshold"to the base-line-subtracted spectrum. This function is carried out to remove noise and thereby reduce the error of fit and is based on the rule-of-thumb that peaks must be twice the level of chemical noise to be significant. In a negative ion FAB spectrum of lysed bacteria, peaks satisfying this condition are assumed to be meaningful lipid biomarkers. The base-line-subtractedspectrum is again searched for local "troughs", except that masses at which no peaks occur are not considered. Peaks that are not twice the average intensity of their low-intensityneighbors,within a 31-amu window, are rejected. Protocols for sample preparation have been described (19). Negative ion fast atom bombardment mas4 spectra were obtained from solvent-lysed cells of the bacteria Escherichia coli (strains C-600 and ATCC 11303), Pseudomonas fluorescens, Proteus vulgaris, Bacillus subtilis, Micrococcus luteus, Staphylococcus aureus, the yeast Saccharomyces cereuisiae, and the alga Spirulina platensis. Of the E. coli samples, strain C-600 was cultured in complex LB broth for 15 h a t 37 "C in our lahmatory, and strain 11303 (strain B, purchased as lyophilized cells from the Sigma Chemical Co.) was cultured in casein medium for 18 h at 37 "C.
688
lEB0
M I Z
Figure 1. Portion of the anion FAB spectrum of M. luteus lysed cells: (a) starting data with calculated chemical base-line level: (b) baseline-subtracted data with calculated threshold line: (c) peaks above threshold.
1
0
1
I
b?!
iT
92
IOii
?,>>
M I 1
Figure 2. Portion of llbrary element created by averaging seven M . luteus lysed cell spectra (each scan base-line-subtracted and thresholded first) normalized so total intensity = 1. Table I. Interspectrum Correlations of Six Library Spectra' Gram-negative
N1 N2 N3
P1 P2 P3
N1
N2
N3
1 0.8931 0.8525 0.0800 0.0224 0.1326
0.8931 1 0.9274 0.0546
0.8525 0.9274 1 0.0769 0.0280 0.0666
0.0161 0.0570
Gram-positive P1 P2 P3 0.0800 0.0546 0.0769 1 0.6822 0.6996
0.0224 0.0161 0.0280
0.6822 1
0.2637
0.1326 0.0570 0.0666 0.6996 0.2637 1
RESULTS
'Abbreviations: N1, Escherichia coli; N2,Pseudomonas fluorescens; N3,Proteus uulgoris: P1,Bacillus subtilis; P2,Micmcoccus luteus: P3. Staohvlocoeeus ourius.
Figure 1shows a portion ofa spectrum of lysed M. luteus cells. The intensity scale has been plotted (XS) of the normalized intensity to reveal the details of the base line, so that several peaks appear off scale. The unprocessed spectrum in Figure l a shows a peak at nearly every mass and is overlayed with the calculated chemical base line. Subtraction of this base line gives the mass spectrum shown in Figure lb. Overlayed on this spectrum is a trace that represents the calculated local threshold line for each point in the spectrum.
Peaks whose intensities are greater than this threshold are included in the final spectrum plotted in Figure IC. Figure 2 shows the library spectrum for M. luteus that has been built from averaging seven spectra treated in this way. Application of the correlation function is illustrated in Table I, where library spectra from each of six lysed bacteria are cross-correlated with themselves. The correlation coefficients confirm the earlier visual interpretation (19) that the mass
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ANALYTICAL CHEMISTRY, VOL. 60,NO. 14, JULY 15, 1988
Table 11. Analysis of a Mixture of M . luteus and P . fluorescens Lysed Cells (4:l by Weight) re1 proportn
EOF"
Iteration 1, Eight Library Spectra included E. coli (average of 22 scans) -0.0489 0.9892 P. fluorescens (5 scans) 0.2626 P. oulgaris (5 scans) 0.0283 B. subtilis (4 scans) -0.0153 M . luteus (7 scans) 0.9639 S . aureus (2 scans) 0.0024 S. cerevisiae (3 scans) 0.0014 S. platensis (1 scan) -0.0012 Iteration 2, Retaining Spectra with Contribution > 0.0 P. fluorescens 0.2276 0.9860 P. vulgaris 0.0190 M . luteus 0.9566 S. aureus -0.0102 S . cerevisiae 0.0007
Table 111. Analyses of Mixtures of M . luteus and P . fluorescens Lysed Cells found ratio"
std dev
EOFb
1:8.4 (10)' 1:3.2 (3) 0.99 (1):l 4.4 (4):l 11 (1O):l
0.67 ( n = 2) 0.21 (n = 3) 0.15 ( n = 4) 0.37 (n = 3) 4.8 ( n = 4)
0.9893 0.9902 0.9847 0.9866 0.9913
Mean value for several scans from one run. Mean of correlation coefficients between actual and fitted spectra. Weight ratio in parentheses.
Table IV. Library Matching of a Spectrum from a Single Microorganism: E . coli (ATCC 11303) re1 proportn after 3 iteratns, retaining spectra with re1 proportn > 0.02
Iteration 3, Retaining Spectra with Contribution > 0.02 P. fluorescens 0.2447 0.9892 M . luteus
ATCC
library without ATCC
correlatn
11303"
11303b
0.9908 0.9397 0.8592 0.8548 0.2125 0.1597 0.2521 0.2707 0.2028
0.9437
library with
0.9544
"Error of fit in each computation, shown as correlation coefficient between actual and fitted spectrum. spectra arising from the desorption of lipids from Gramnegative bacteria are very similar to each other, that the spectra from the three Gram-positive bacteria are less similar to each other, and that the lowest similarity exists between the spectra of Gram-positive and Gram-negative bacteria. These differences between Gram-staining groups are readily observed in the table; where the correlation coefficient is not sufficient to differentiate larger number of groups in larger data sets, factor analysis might be applied. As indicated above, the computer-based system can be used to deconvolute mixtures as well as to correlate spectra. This is illustrated in Table 11, in which the relative proportions of each member of a library of eight spectra in a test mixture are evaluated. The test mixture was composed of a Gramnegative and a Gram-positive bacterium whose mass spectral correlation was low. In the second iteration, microorganism spectra that were found to contribute negatively to the mixture spectrum are eliminated and the proportions of the remaining library spectra are reweighted. In the third iteration, a relative proportion greater than 0.02 is required. At this point the two microorganisms in the test sample are correctly identified and the ratio of their relative proportions corresponds well to their composition in the mixture. In each iteration the error of fit is presented as the correlation coefficient between the actual and fitted spectra. Proportions determined in this way were in reasonable agreement with the actual compositions of standard mixtures from 1 O : l to 1 : l O by weight for these two species, although at one-tenth proportion, low, positive contributions from nonpresent library components approached the values found for the minor component. The average values determined for several scans of each mixture run are compared with the starting mixture ratio in Table 111. Another aspect of the deconvolution function is that calculated values do not necessarily add up to unity in the final iteration; this is an artifact of the procedure used to normalize the spectra. The term "relative proportion" distinguishes these values from percentages. If the mixture analysis program is applied to a spectrum of a single microorganism, the computed relative proportions can be a more powerful measure of similarity than the correlation coefficient. In Table IV a spectrum of lysed cells from E. coli strain (ATCC 11303) is used to challenge a library containing a different E. coli strain (C-600) as well as a library
E. coli ATCC 11303 C-600
P. fluorescens P. uulgaris R. subtilis M . luteus S. aureus S . cereuisiae S. platensis
0.8000 0.0544
0.1793
0.0212
0.0609 0.0216
EOF (correlationcoefficient between actual and fitted spectra) = 0.9913 bEOF = 0.9439.
containing both. When the sample spectrum is correlated with each of the library spectra, the best match occurs for E. coli (ATCC 11303), while a good correlation occurs as well for E. coli (C-600) and high correlations occur for the other two Gram-negative bacteria. When the deconvolution function is used with a library containing both E. coli strains, the sample is correctly identified from the large relative proportion for E. coli (ATCC 11303) with no contribution from the incorrect strain. Nevertheless, when the library element for the test E. coli strain is removed, the remaining E . coli strain is identified as the best match. The resolution of these two strains follows from differences in fatty acid distribution (evident in their spectra) that might result from strain differences or from the differing growth conditions of the samples; however, these differences were found to be less significant than the differences between E. coli and the other species in the library.
DISCUSSION Linear superposition of lipid signals is a critical assumption for the mixture analysis system described here. However, since the inception of FAB mass spectrometry, many examples of suppression of ion signals from one component of a mixture have been observed when combining several samples in the liquid matrix, most notably when mapping peptides obtained from protein digests (20). Relative surface activity has been implicated as a major factor in causing sample suppression in mixtures (11,20). Several strategies have been created to counteract these tendencies, two of which involve addition of surfactants of similar charge sign (22) or opposite sign (22) to that of the sample ions. These additives promote a more homogeneous surface layer. Lysed cell mixtures are very complex, consisting of various biopolymers and about 5% by weight of polar lipids, which
ANALYTICAL CHEMISTRY, VOL. 60, NO. 14, JULY 15, 1988
vary in relative charge and fatty acid composition. Because polar lipids are inherently surface active, they appear in the FAB spectra to the exclusion of other compounds. Negative ion FAB spectra from total lipid extracts correlate very well with the spectra of lysed cells, so one can infer that the presence of biopolymers does not strongly influence the negative ion FAB spectra. It appears that the differences in surface activities between the various lipids are not great enough to produce significant supression of one over the other. Even though relative abundances of lipid signals probably do not reflect their relative molarities in bacterial samples, it is not a requirement for linear superposition that they do so. If relative ionization efficiencies remain constant, then h e a r superposition will hold. It has been shown that samples with unequal but constant ionization efficiencies may be quantified by preparation of standard curves (23). In this study we hypothesized that linear addition of lipid signals would take place, designed a mass spectral analysis system based on that hypothesis, and tested it through the experiments shown in Tables 11-IV. The positive results seem to provide support for our hypothesis. The high level of reproducibility for repetitive probe loadings of the same sample (19) is also encouraging. A plausible model for this system would hold that if the surface activities of two compounds are quite similar, then the relative abundances of their signals will be guided by differences (or similarities) in the ability to sustain a charge and not by surface interference between the two. This sort of behavior has been observed for dansylated amino acids (24). Work with larger data sets and application to true unknowns would require investigation of some other points: the extent of variation in lipid profile with growth conditions; the similarity of lipid profiles among some Gram-negativespecies; and the need to include techniques for dealing with unknowns not in the library. CONCLUSIONS The use of FAB mass spectrometry to characterize microorganisms is feasible because the ionization process produces a polar lipid molecular anion profile that is related to the taxonomic grouping of the organism. The peaks observed represent unique and interpretable biomarkers whose pattern of mass and intensity values can be treated as a mathematical fingerprint. Although pyrolysis-mass spectrometry has also been applied to microorganisms, the peaks observed in pyrolysis spectra are nonspecific, lower mass peaks whose relative intensities reflect multiple origins. Statistical techniques for working with both types of mass spectra rely upon accurate and reproducible intensity measurements. When mixtures
1419
are analyzed by pyrolysis techniques, relative intensities at a given mass represent the cumulative effects of the components and the multiple pyrolytic reactions of each component. In desorption spectra, peaks observed at a particular mass arise almost exclusively from one component of the mixture but not others, and as a result, relative intensities may be used to separate the components of a mixture using the fairly simple linear regression analysis approach. LITERATURE CITED Heller, D. N.; Fenselau, C.; Cotter, R. J.; Demirev, P.; Olthoff, J. K.; Honovich, J.; Uy, M.; Tanaka, T.; Kishimoto, Y. Biochem. Siophys. Res. Commun. 1987, 142, 194-199. Kates, M. I n Advances in Lipid Research; Paoletti, R., Ed.; Academic: New York, 1964 Vol. 2, pp 17-90. Lechevalier, M. P. CRC Crit. Rev. Microbiol. 1977, 109-210. Moss, C. W. J. Chromafogr. 1981, 203, 337-347. Anhalt, J. P.; Fenselau, C. Anal. Chem. 1975, 4 7 , 219-225. Huff, S. M.; Matsen, J. M.; Windig, E.; Meuzelaar, H. L. C. Biomed. Mass Spechom. 1988, 13, 277-286. Windig, W.; Haverkamp, J.; Kistemaker, P. G. Anal. Chem. 1983, 55, 81-88. Shaw, 8. G.; Puckey, D. J.; MacFie, H. J. H.; Bolt, S . J. J. Appl. Bacferiol. 1985, 5 9 , 157-165. Voorhees, K. J.; Tsao, R. Anal. Chem. 1984, 5 6 , 368-373. Van der Greef, J.; Tas, A. C.; Bouwan, J.; ten Noever de Brauw, M. C. I n Advances in Mass Spectrometry, 1985; Todd, J. F. J., Ed.; Wiley: Chichester, UK, 1986; pp 1227-1228. Fenselau, C.; Cotter, R. J. Chem. Rev. 1987, 8 7 , 501-512. Beebe, K. R.; Kowalski, 8. R. Anal. Chem. 1987, 5 9 , 1007A-1017A. Chapman, J. R. Computers in Mass Spectrometry; Academic: London, 1978; p 90. Malinowski, E. R.; McCue, M. Anal. Chem. 1977, 4 9 , 204-287. Wleten, G.; Haverkamp, J.; Meuzelaar, H. L. C.; Engel, H. W. 8.; Berwald, L. G. J. Gen. Microbiol. 1981, 122, 109-118. Gutteridge, C. S.; Puckey, D. J. J. Gen. Microbiol. 1982, 728, 721-730. Meloan, C. E.; Kiser, R. W. Problems and Experiments in Instrumental Analysis; Charles, E. Merriii Publishing Co.: Columbus OH, 1983; pp 247-249. Hites, R. A.; Blemann, K. Anal. Chem. 1987, 3 9 , 965-970. Heller, D. N.; Cotter, R. J.; Fenselau, C.; Uy, 0.M. Anal. Chem. 1987, 5 9 , 2806-2809. Naylor, S.; Flndels, P.. F.; Gibson, B. W.; Williams, D. H. J. Am. Chem. SOC. 1988, 108, 6359-6363. De Pauw, E.; Pelzer, G.; Dao Vlet, D.; Marian, J. Siochem. Siophys. Res. Commun. 1984, 123, 27-32. Ligon, W. V.; Dorn, S . 8. Int. J. Mass Specfrom. Ion Processes 1984. -, 67. - . . 113-122. . .Townsend, R. R.; Heller, D. N.; Fenselau, C.; Lee, Y. C. Siochemistry 1984. 2 3 . 6389-6392. Beckner, 'C. F.; Caprioli, R. M. 6iOmed. Mass Specfrom. 1984, 1 1 , 60-62.
RECEIVED for review October 6,1987. Accepted March 1,1988. This work was supported by the U.S. Army CRDEC through US.Navy Contract N00024-85-C-5301 and by the APL Independent Research and Development Fund. Mass spectra were obtained at the Middle Atlantic Mass Spectrometry Laboratory, an NSF Shared Instrument Facility.
