Mass Spectrometry Forum. Kenneth L. Busch

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1 Chemical Noise, Part IV: Reduction of Noise through Informatics The last installment of this series discusses informatics applications in MS-MS analyses that specifically allow greater differentiation of meaningful signal from chemical noise. Kenneth L. Busch Kenneth L. Busch recalls the days of mass spectra recorded with lightbeam oscillographs. Ink pen in hand, with the original spectral trace attached to the light box, he found that four cups of coffee consumed in rapid succession produced a hand jitter concordant with the level of chemical noise in the spectrum. Nowadays, he spends evenings playing with Photoshop, amused by the add noise filter function and yearning for the nonpixelated Tesla coil and sealing wax days of yore. He can be reached at buschken@hotmail. com. Views expressed in this column are those of the author and not those of the National Science Foundation. At a recent general chemistry conference, the session that drew my interest dealt with proteomics analysis by mass spectrometry (MS). The assembled experts debated various sophisticated issues related to the complex multistep analysis. A member of the audience asked about the signal-to-noise ratios in the mass spectrum: At what level of signal must an ion be identified in order for the analysis to be considered complete? The phrasing of the question was intriguing, referencing the overall analysis rather than the comparative magnitude of signals and noise in a single mass spectrum. Nevertheless, the answer offered by the presenter was predictably quick: signals are considered important when they appear in the mass spectrum at a signalto-noise ratio of 3:1. Discussion moved to other issues, but the basic question addressing the level of signal and the level of noise, and the connection with the completeness of the analysis, remained with me. On reflection, I recalled that the simple answer strictly applies only at the limit where the noise is purely electronic and random in nature, and not in the mass spectrum that was presented by the speaker. I prepared myself to explain how the overall process selectivity of the analysis plays a role in determining the relative significance of signal and of noise, and therefore also establishes the appropriate signal-tonoise ratio required for a complete analysis. But by this time, the meeting session was over, we were well into the second course of the evening banquet, and I decided that my dinner table companions would not welcome such a discourse. However, the question and my path to an answer provide the underlying rationale for this column, the last in a series on chemical noise in mass spectrometry. Process Selectivity Process selectivity is a cumulative property of the specific sequence of choices that culminate in a mass spectral measurement, which contains elements of both signal and noise. For example, we can measure a mass spectrum in both electron ionization and chemical ionization, but because the ionization processes are different, the mass spectra measured are expected to be different, and therefore the process selectivity is different. Further, not only is the signal different in each mass spectrum, but the expected noise is different as well. It often is difficult to quantify process selectivity except in terms of progressively higher resolution in one specific parameter that describes the analytical procedure. Generally, we choose our process to be less selective (positive ion electron ionization, for example) or more selective (negative ion chemical ionization), based upon our rational consideration of what we wish to measure and what we expect the contributors to noise will be. We invoked process selectivity, although not under that specific aegis, in our previous discussion of chemical noise in MS-MS spectra ( Chemical Noise in Mass Spectrometry, Part III: More Mass Spectrometry/Mass Spectrometry Spectroscopy, 18(5), 52 (2003)). There we linked the process selectivity of the analysis to the number of sequential independent stages of MS-MS. The absolute magnitude of the chemical noise in an MS-MS spectrum is reduced after each stage because the mass relationship specified in, for instance, a product ion scan, must be met for any signal that is measured. Additionally, the process selectivity imposes a requirement for the noise as well as for the signal itself. As the process selectivity increases, or in this case of MS-MS, as the number of sequential independent stages increases, the imposed require- 44 Spectroscopy 19(2) February 2004

2 Figure 1. Product ions formed by dissociation of any parent ion must exhibit the appropriate isotopic forms. For dissociation of a parent ion containing multiple chlorine atoms, a distribution of product ion intensities is imposed, as shown in the lower part of the figure. Specified mass Specified masses Specified intensities ments for both signal and chemical noise become more stringent. Because the chemical noise has its origins in compounds that should not meet the target-biased imposed requirements as well as the targeted compound itself, the chemical noise decreases faster in magnitude than the signal, and the signal-to-noise ratio consequently increases. At some ideal point, the chemical noise disappears entirely, to be replaced by electronic noise derived from measurement transducers. Until M 35 Cl M x Cl m/z m/z Loss of 35 Cl Loss of x Cl Selected M 35 Cl 35 Cl Selected M 35 Cl 37 Cl uses of informatics in MS that specifically allow greater differentiation of meaningful signal from chemical noise. Process selectivity is a subset of informatics, and process selectivity is related to intelligent experiment design. Informatics is defined broadly as the application of computer and statistical techniques to the management of information. We broaden the definition here to include assessment of the meaning of information; specifically, the use of information to aid in Clearing the Chemical Noise We start with simple examples of MS informatics, and then develop examples in which informatics is used for differentiating signal from noise. Isotoperesolved MS-MS provides our entrylevel example. Consider a simple product ion MS-MS spectrum. The parent ion selected for collisioninduced dissociation possesses a specified mass, which (in lower mass instances at least) reflects a certain isotopic composition. For the parent ion derived from a polychlorinated compound, selections of a parent ion could include an ion containing 35 Cl, 37 Cl, or a combination of both isotopes. The product ions observed in the MS- MS spectrum therefore would be expected to shift in mass as the combination changes. The appropriate mathematical relationships for simple MS-MS relationships were derived by Singleton and colleagues (3) in Rockwood and colleagues (4) described a general theory in Simple mathematical inspection provides the possible masses of the product ions formed from dissociations of parent ions of varying isotopic composition. For example, a parent ion that contains only 35 Cl can produce only 35 Cl-containing product PROCESS SELECTIVITY IS A CUMULATIVE PROPERTY OF THE SPECIFIC SEQUENCE OF CHOICES THAT CULMINATE IN A MASS SPECTRAL MEASUREMENT. that point, the nature of the chemical noise itself is changing continually. In passing through the multiple stages of MS-MS, it becomes less random, and, perhaps surprisingly, related increasingly to the signal itself. The concept of a definitive 3:1 ratio between meaningful signal and noise simply does not apply until the noise is no longer chemical in nature. We defer full examination of the relationship between process selectivity and relevant signal-to-chemical noise ratios for a later column, where we will explore the importance of the independence of sequential stages of an analysis. In this installment of Mass Spectrometry Forum, we highlight the differentiation of signal from noise in mass spectra. Careful continued attention to physical contaminants to decrease the level of noise in MS measurements represents a valuable and traditional noninformatics approach (1, 2), preserving the confidence in analyses with very low absolute signal levels, but still requiring that the signal be greater in magnitude than the noise. With consideration of MS informatics, as shown in the examples below, meaningful signals can be identified with signal-to-noise ratios far less than 1. Exploitation of these structured opportunities will enable the highest analytical performance of MS. ions, and the similar correspondence exists for any parent ion with 37 Cl atoms exclusively (see Figure 1). For parent ions that contain both 35 Cl and 37 Cl, however, the product ions can have either 35 Cl or 37 Cl, but the relative intensities of the product ions now are constrained. This is where informatics comes in. Not only are the masses of the product ions specified by the loss, but there is a required relationship between the measured intensities of the isotopic variants of the product ions. Excursions of measured ion intensity outside of this window must be attributed to noise. Conversely, the match in expected ion intensities adds to our 46 Spectroscopy 19(2) February 2004

3 Figure 2. The spacing of signal peaks for multiply charged ions is characteristic and can be used to differentiate low-intensity signals from highintensity noise. (M+3H) 3+ (M+Na+2H) confidence in the result. No change in the operation of the instrument was required to achieve this additional quality control element; the advantage is derived simply from MS informatics. Following this simple example, we now highlight several more involved informatics-based approaches. Kast and colleagues (5) described a noisefiltering technique applied to electrospray mass spectra recorded with a time-of-flight mass spectrometer. The goal of the filtering technique is to identify multiply charged ions in a ions must be accompanied by the isotopic peaks that appear in a denser spacing than the surrounding singly charged ions. In the case illustrated, the triply charged nature of the ion is reflected in the isotopic peaks that occur at every 1 3 mass unit interval. Knowing that a multiply charged ion of interest occurs at this mass, an MS- MS spectrum contains the product ions of lower charge states, which appear at higher m/z values than the selected parent ion. The knowledge of the process selectivity allows us to EXPLOITATION OF INFORMATICS WILL ENABLE THE HIGHEST ANALYTICAL PERFORMANCE OF MS. mass spectrum when the signals for such ions are very similar in intensity to the singly charged ions that constitute the chemical noise. The filtering uses Fourier transform-based filtering and a correlation algorithm that reflects the fact that a true ion count is accompanied by correlated signals in adjacent measurement channels. The mathematical treatment is discussed in detail in the original paper, but Figure 2 illustrates the point graphically. Two multiply charged ions can be identified, but in neither case is the signalto-noise ratio greater than 2:1. Certainty of identification is provided by the fact that the multiply charged assign a meaningful signal, and choose a parent ion of interest, even when the measured signal-to-noise ratio is below the 3:1 requirement. Cannon and Jarman (6) have demonstrated the use of the naturally occurring isotopic patterns observed in MS-MS spectra to deduce a sequence of amino acids without explicit identification of the origin of the ions themselves. The sequences predicted from the match in predicted isotopic signatures correlate with those established by identification of the types of fragmentation ions and the differences in mass between adjacent product ions in the series. This 48 Spectroscopy 19(2) February 2004 Circle 45

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5 Figure 3. The use of a derivatizing tag that has a unique mass defect allows signals corresponding to the derivatized ions to be differentiated from higher intensity noise. The tag in this case contains bromine, so additional value is derived from the isotopic signature. application is yet another example of informatics, here used not to differentiate a signal from noise, but to derive information from signals that were thought to contain none. The algorithm is also applicable to the more targeted case in which explicit isotopic labeling in a growth medium is used in proteomic studies for quantifying peptides. The conclusions of this report harken back to the question asked in that meeting session and the question of what information can be deduced even at trace signal levels to support the completeness of an analysis. The characteristic appearance of isotopic envelopes the spacing and intensities of ions that together comprise the isotopic signature of an ion has been a fruitful ground for informatics. Szymura and Lamkiewicz (7) present a general model useful as an aid in the interpretation of electron ionization mass spectra of organometallic compounds using a polynomial regression that minimizes the variance between measured and predicted spectra, including those with overlapping ion signals. The model is general, and can be applied to mass spectra generated by any ionization method. In the present context, by virtue of the breadth of the isotopic envelope, it provides a means to assign significance to a signal whose measured magnitude is not distinct from that of the background signal. 50 Spectroscopy 19(2) February 2004 Circle 47

6 Informatics by Design The summaries above show that after 50 years of analytical MS, we still are learning how to exploit even the most fundamental information available in a mass spectrum mass and intensity information. However, there is no reason to simply accept such information as it naturally presents itself. Hall and co-authors (8) use informatics to highlight signals observed in mass spectra generated from derivatized molecules, using the mass defect of the derivatizing tags in a strategy of protein sequencing. Incorporation into the targeted molecule of one or more elements chosen from the range between chlorine and iridium places a channel for maximum graphical impact. Most informatics approaches developed for the enhanced differentiation of signal from chemical noise, and the examples presented above, are designed through a detailed consideration of the mass properties of ions, either directly or through consideration of the distribution of ions in the isotopic envelope. However, other characteristics of signals in mass spectra also are potentially useful. The detailed time evolution of a signal is used in reconstructed ion chromatograms in gas chromatography MS (GC MS) for example, but we have made little progress to a more sophisticated use of time-resolved AFTER 50 YEARS OF ANALYTICAL MASS SPECTROMETRY, lower threshold, which zeroed out the signal for much of the recorded traces, can be seen in the figure, undermining the accurate calculation of the true signal-to-noise ratio in both cases. Such carelessness must be avoided. Similarly, overly simplistic answers provided to questions in meetings undermine our own understanding of our results. Accurate determination of noise, and understanding of what noise represents, demands the same attention to detail as our determination of signal. We must also remain vigilant to basic practices that minimize contamination and result in chemical noise in mass spectra, with careful experiments for newer ionization methods (10) that WE STILL ARE LEARNING HOW TO EXPLOIT EVEN THE MOST FUNDAMENTAL INFORMATION AVAILABLE IN A MASS SPECTRUM. mass-defect tag on the molecule the added moiety includes an atom with a distinctive mass defect. These ions can be differentiated clearly from background ions of the same mass because of the difference in mass using a moderate resolution mass spectrometer. Figure 3 illustrates this strategy applied to a fragment ion derived from labeled myoglobin. The peak-at-everymass signals that constitute the chemical noise are of a significantly higher absolute magnitude than the side peaks indicated by the arrows that correspond to the signals from the mass-defect tags. Here, signal has meaning even though the signal-to-noise ratio is far less than 1; process selectivity allows this assignment, and informatics confirms the significance. Additionally, since the mass-defect tag is derived from bromine, the 1:1 ratio of the signals from the two bromine isotopes ( 79 Br and 81 Br) is evident in the matched intensity of the side peaks. The basic tenets of isotope-resolved MS-MS recur in this venue. This requisite match (another element in the process selectivity) can be used to first match data within a quality control window and then report the signal in a single signals in designed attempts to reduce chemical noise. The work by Stein (9) is noteworthy for its elegant use of time-resolved information. We normally eschew the rigorous use of intensity data in mass spectral measurements, but high-resolution intensity data hold as much promise as mass-resolved data for informatics processing that reduces chemical noise. We routinely apply a simple form of atomic amplification in our use of electron multipliers as detectors in MS, but we see only vaguely the potential of molecular amplification at surfaces, or in the gas-phase itself. Prognostications in this area await yet another future column. Conclusion This conclusion to the series on chemical noise in MS deals with a topic that many might consider elementary, and already well understood. However, a recent publication included a figure (not reproduced here) that purported to document an improvement in signal-to-noise ratios attained with a change in experimental procedure. Despite the confident claim made in the paper, the influence of an arbitrary mirror our past experiences and discoveries (11), while using informatics tools to preserve and extend our analytical capabilities. References 1. K.M. Verge and G.R. Agnes, J. Amer. Soc. Mass Spectrom. 13, (2002). 2. C.R. Gibson and C.M. Brown, J. Amer. Soc. Mass Spectrom. 14, (2003). 3. K.E. Singleton, R.G. Cooks, and K.V. Wood, Anal. Chem. 55, (1983). 4. A.L. Rockwood, M.M. Kushnir, and G.J. Nelson, J. Amer. Soc. Mass Spectrom. 14, (2002). 5. J. Kast, M. Gentzel, M. Mann, and K. Richardson, J. Amer. Soc. Mass Spectrom. 14, (2003). 6. W.R. Cannon and K.D. Jarman, Rapid Commun. Mass Spectrom. 17, (2003). 7. J.A. Szymura and J. Lamkiewicz, J. Mass Spectrom. 38, (2003). 8. M.P. Hall, S. Ashrafi, A. Obegi, R. Petesch, J.N. Peterson, and L.V. Scneider, J. Mass Spectrom. 38, (2003). 9. S.E. Stein, J. Amer. Soc. Mass Spectrom. 10, (1999). 10. A.N. Krutchinsky and B.T. Chait, J. Amer. Soc. Mass Spectrom. 13, (2002). 11. M. Ende and G. Spiteller, Mass Spectrom. Rev. 1, (1982). 52 Spectroscopy 19(2) February 2004