Asn3, a Reliable, Robust, and Universal Lock Mass for Improved

Oct 17, 2013 - The use of internal calibrants (the so-called lock mass approach) provides much greater accuracy in mass spectrometry based proteomics...
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Asn3, a Reliable, Robust, and Universal Lock Mass for Improved Accuracy in LC−MS and LC−MS/MS An Staes,†,‡ Jonathan Vandenbussche,†,‡ Hans Demol,†,‡ Marc Goethals,†,‡ Şule Yilmaz,†,‡ Niels Hulstaert,†,‡ Sven Degroeve,†,‡ Pieter Kelchtermans,†,‡ Lennart Martens,†,‡ and Kris Gevaert*,†,‡ †

Department of Medical Protein Research, VIB, B-9000 Ghent, Belgium Department of Biochemistry, Ghent University, B-9000 Ghent, Belgium

‡

S Supporting Information *

ABSTRACT: The use of internal calibrants (the so-called lock mass approach) provides much greater accuracy in mass spectrometry based proteomics. However, the polydimethylcyclosiloxane (PCM) peaks commonly used for this purpose are quite unreliable, leading to missing calibrant peaks in spectra and correspondingly lower mass measurement accuracy. Therefore, we here introduce a universally applicable and robust internal calibrant, the tripeptide Asn3. We show that Asn3 is a substantial improvement over PCM both in terms of consistent detection and resulting mass measurement accuracy. Asn3 is also very easy to adopt in the lab, as it requires only minor adjustments to the analytical setup.

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background components as internal calibrants. The most commonly used background-derived internal calibrant is one of the polydimethylcyclosiloxane (PCM) peaks.5,15 These PCM peaks are derived from airborne components present in the lab environment;16 however, their presence is highly variable between laboratories and even within the same lab over time.17 Furthermore, when many peptides are simultaneously entering the mass spectrometer source, the PCM peaks are easily suppressed,17 resulting in low signal and hence poor calibration and decreased mass accuracy in precisely those regions where most peptides will be analyzed. A simple solution to the suppression issue was mentioned by Lee and coauthors17 and consists of positioning a flask of deodorant next to the ion source. Although straightforward to execute, this approach does not allow fine-tuning of the amount of PCMs entering the source, often resulting in the presence of too many background ions that will in turn cause unwanted suppression of analyte ion formation. Despite its popularity, PCM therefore does not seem to be an ideal candidate for internal calibration. Apart from using PCM for online calibration, there are also several ways to recalibrate the acquired mass spectra offline with this approach. The most straightforward method is to simply use the PCM peaks present in the mass spectra for offline recalibration,5 but it is also possible to vary the PCM peak used for internal calibration, an approach called dynamic lock mass correction.11 Yet, all these types of PCM-based recalibration fail when no PCM peaks are present in the spectra. Therefore, the approach of software lock mass

ass measurement accuracy (MMA) is a key performance parameter in mass spectrometry based proteomics1−4 as this provides more confidence to peptide identifications3,5 and results in more reliable de novo sequencing.6 MS1 accuracy is important as a quality measure,3 as shown in the quality control displays of the PRIDE Inspector7 tool that analyzes data stored in the PRoteomics IDEntifications (PRIDE) repository8 and the pride-asap9 tool that is used to automatically annotate fragment ions in identified spectra stored in PRIDE. Increasingly, tandem mass spectrometry (MS/MS; MS2) mass accuracy is considered a key feature in differentiating signal from noise peaks, leading to a reduction in complexity of the scoring functions, as evidenced by the Morpheus search engine10 that was specifically designed for high-resolution peptide MS/MS spectra. Although several methods for mass calibration are available, internal calibration has proven to be most accurate.4,11−13 Indeed, internal calibration implicitly corrects for any sources of measurement error that the analyte may encounter during an LC−MS/MS run. Different approaches exist to obtain a suitable internal calibrant. A first approach consists of injecting a component through a dual electrospray source.12,13 This method requires an adjustment of the source to install the second sprayer, and maintaining a consistent second spray requires considerable skill. On instruments capable of electron transfer dissociation (ETD), a second approach is to use fluoranthene cations as an internal calibrant.4 This approach, however, suffers from laborious and costly fluoranthene filament maintenance and is therefore unsuited for routine operation. Flow injection of the calibrant was also proposed,14 but this again requires an adjustment of the source. In addition, various other approaches bypass the need to adjust the instrument invasively by using common © 2013 American Chemical Society

Received: August 27, 2013 Accepted: October 17, 2013 Published: October 17, 2013 11054

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correction, 1 implemented in MaxQuant, a quantitative proteomics software package, has been introduced. Here, the mass errors of the peptide ions are determined using a first-pass search with a broad mass tolerance, followed by a second, narrower search to correct for peptides eluting in a given time interval. Although the software lock mass approach gives good overall results, we will here show that the measurement accuracy is further improved when software-based recalibration can start from a more accurate data set. A particular issue arises when measuring low abundance samples where background suppression is often applied to maximize signals from analytes, for instance by applying a nitrogen flow16 or by using the Active Background ion reduction Device (ABIRD) system.18,19 While these approaches lower background interference including PCM peaks by 1 order of magnitude, they simultaneously undermine the ability to use PCM peaks as lock masses. Background suppression techniques therefore currently rely primarily on software lock mass for recalibration. Because of the obvious benefits of using a lock mass approach to increase mass accuracy and because of the inherent reliability issues that PCM peaks suffer from, the availability of a more consistent and fine-tunable lock mass would help the field by allowing for highly accurate measurements to be obtained throughout analysis. Here, we introduce a synthetic tripeptide Asn3 as internal calibrant that fulfills the requirements of a reliable and robust universal lock mass. By spiking this peptide directly into the LC solvents, the lock mass is guaranteed to be present at all times throughout a LC gradient. Because the spike-in can be dosed, the amount of Asn3 can be adjusted to suit the concentration of the ions present in the sample, thus correcting for possible lock mass or analyte ion suppression. Since the method can be applied in every laboratory without adjustments to the instrumentation, Asn3 serves as a truly universal lock mass. This internal calibrant is not restricted to one vendor either, as it can be spiked into the LC solvents in front of any mass spectrometer. With Asn3 ions consistently present alongside the analyte in the detector, mass accuracy correction can be performed using offline or online correction methods, depending on the options offered by the vendor acquisition software.

In total, 1.5 million cells were resuspended in 0.5 mL of 50 mM triethylamonium bicarbonate (Sigma-Aldrich, St. Louis, MO) and lysed by three repetitive freeze−thaw cycles. Protein concentrations were determined with BioRad’s Protein Assay (BioRad Laboratories, Munich, Germany). The resulting lysate was digested overnight at 37 °C with sequencing-grade modified trypsin (Promega, Madison, WI) in an enzyme/ substrate ratio of 1/100 (w/w). The peptide mixture was acidified with 5 μL of glacial acetic acid (Sigma-Aldrich). First Dimension Peptide Separation. An equivalent of 550 μg of protein material was oxidized by adding hydrogen peroxide to a final concentration of 0.6% and incubating for 30 min at 30 °C (this step ensures that all methionine residues are uniformly converted to methionine-sulfoxide20). Peptides were separated on a 2.1 mm i.d. × 150 mm column (Zorbax, 300 SBC18 Narrowbore, Agilent Technologies, Waldbronn Germany), fractionated in 1-min-wide fractions and finally pooled into 20 fractions for LC−MS/MS analysis. A 140 min gradient was used in which the peptides were loaded for 10 min in solvent A (10 mM ammonium acetate in water/acetonitrile (ACN), 98/2 (v/v), pH 5.5), next they were separated by a gradient of 1% solvent B (10 mM ammonium acetate in water/ACN, 30/70 (v/v), pH 5.5) per minute until 100% of solvent B was reached, following which the column was re-equilibrated for 20 min with solvent A. Each pooled fraction was vacuum-dried to complete dryness and redissolved in 100 μL of loading solvent (0.1% TFA in water/ACN, 98/2 (v/v)) for nano-LC−MS/MS analysis. LC−MS/MS Analysis. The obtained peptide mixtures were introduced into an LC−MS/MS system through a tandem configured21 Ultimate 3000 RSLC nano LC (Thermo Scientific, Bremen, Germany) in-line connected to an LTQOrbitrap Velos (Thermo Fisher Scientific). The sample mixture was first loaded on a trapping column (made in-house, 100 μm i.d. × 20 mm, 5 μm beads C18 Reprosil-HD, Dr. Maisch, Ammerbuch-Entringen, Germany). After flushing from the trapping column, the sample was loaded on an analytical column (made in-house, 75 μm i.d. × 150 mm, 5 μm beads C18 Reprosil-HD, Dr. Maisch). Peptides were loaded with loading solvent and separated with a linear gradient from 2% solvent A′ (0.1% formic acid in water) to 50% solvent B′ (0.1% formic acid in water/ACN, 98/2 (v/v)) at a flow rate of 300 nL/min followed by a wash reaching 99% solvent B′. The mass spectrometer was operated in data-dependent mode, automatically switching between MS and MS/MS acquisition for the 10 most abundant peaks in a given MS spectrum. In the LTQ-Orbitrap Velos, full scan MS spectra were acquired in the Orbitrap at a target value of 1 × 106 with a resolution of 60 000. The 10 most intense ions were then isolated in the linear ion trap with a target value of 5 × 104, with a dynamic exclusion of 20 s and fragmented in the HCD cell. The lock mass abundance (LMA) was set to zero for every analysis except for the space charge effect experiment where it was set to five. For MMA calculations and comparisons, the following Mascot workflow was used. From the MS/MS data in each LC run, Mascot Generic Files were created using Distiller software (version 2.4.3.3, Matrix Science, London, U.K., www. matrixscience.com/distiller.html). These peak lists were then searched with the Mascot22 search engine (Matrix Science) using the Mascot Daemon interface (version 2.4.0, Matrix Science). Spectra were searched against the Swiss-Prot database (version 13_04 of UniProtKB/Swiss-Prot protein database

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EXPERIMENTAL SECTION Asn3 Synthesis. The NH2-AsnAsnAsn-OH peptide (Asn3) was synthesized using standard solid-phase Fmoc chemistry on a SyroI peptide synthesizer (Biotage, Uppsala, Sweden). Synthesis was started on 25 μmol of preloaded FmocAsn(triethyl)-Wang resin (Novabiochem, Darmstadt, Germany). Amino acids were coupled in a 4-fold molar excess using 1-hydroxybenzotriazole/hexafluorophosphate activation. The second Asn was triethyl protected, whereas the N-terminal Asn was introduced as an unprotected variant. The peptide was cleaved with trifluoroacetic acid (TFA, Biosolve, Valkenswaard, The Netherlands) containing 2.5% ethanedithiol and 2.5% water for 4 h. The peptide was precipitated with tributylmethyl ether and recovered by centrifugation at 2000g. This ether washing and centrifugation step was repeated five times. The peptide was then purified by isocratic elution with 0.1% TFA in water on a reversed-phase C18 10 mm internal diameter (i.d.) × 250 mm column (Nucleosil 3-10 C18, Machery-Nagel, Düren, Germany). Sample Preparation. Jurkat cells were grown in RPMI 1640 medium (Invitrogen, Carlsbad, CA) at 37 °C in 5% CO2. 11055

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Figure 1. Discontinuous detection of PCM peaks leads to lower mass accuracies. The stacked bar plots in panels A and D show the percentage of scans where the lock mass (PCM in panel A and Asn3 in panel D) was found (blue) or absent (red) for each of 20 LC−MS/MS runs of a fractionated tryptic digest of Jurkat cells. Panels B and C show data on samples 11 (yellow box in panels A and D) and 7 (green box in panels A and D), respectively. Here, the scatter plots show per scan number whether the lock mass was found (blue spots shown at the top) or absent (red spots shown at the bottom), and this is shown when using the PCM peak (left plot) or using Asn3 as the lock mass (middle plot). The ppm errors derived after identification of the PSMs are shown in green. The plots on the right side in these panels show the overall mass error distributions of the PSMs for the PCM lock mass (red) and the Asn3 lock mass (blue). The calculated Huber scale is given together with the median shown in the color of the corresponding data set. For sample 11, the PCM based data show a bimodal distribution, and the densest part of this distribution shows a strong mass deviation, caused by the long time in which the PCM lock mass could not be measured. The much less prominent part of the PCM distribution shows a much lower mass deviation that is in line with the overall Asn3 distribution and corresponds to the minority of PSMs obtained where the lock mass was not suppressed. Panel E shows the mass error distributions of PSMs identified using the PCM lock mass of selected samples with a red line for PSMs of which the lock mass was absent in the preceding MS scan and a blue line for PSMs of which the lock mass was present in the preceding MS scan. Panel F shows again the percentage of the presence (dark blue) or absence (red) of the PCM lock mass for each of 20 LC−MS/ MS runs of a fractionated tryptic digest of Jurkat cells. Now, however, the percentages of PSMs are also depicted where a PCM lock mass correction was performed (light blue) or not (orange).

containing 20 232 sequence entries of human proteins) concatenated with its reversed sequence database. Variable modifications were set to pyro-glutamate formation of amino terminal glutamine and acetylation of the protein N-terminus, whereas fixed modifications only included oxidation of methionine. Mass tolerance on peptide ions was set to 10 ppm (with Mascot’s C13 option set to 1), and the mass tolerance on peptide fragment ions was set to 20 millimass units (mmu), except for the space-charge effect experiment where an extra search was done with a setting of 3 mmu. The peptide charge was set to 1+, 2+, 3+, and the instrument setting was put on ESI-QUAD. Enzyme was set to trypsin allowing for one missed cleavage, and cleavage was allowed when arginine or lysine is followed by proline. Only peptides that were ranked one and scored above the threshold score, set at 99%

confidence, were withheld. All data were processed and managed by ms_lims.23 The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange.org) via the PRIDE partner repository8,24 (PRIDE accession numbers 30654−30813) with the data set identifier PXD000426 and DOI 10.6019/PXD000426 (for reviewing, the following username and password are needed: username, review78079 and password, 9WhAaX2E). To obtain MaxQuant results, raw files were processed and searched by MaxQuant (version 1.4.0.3). All default settings were chosen for parts per million (ppm) tolerances, being 20 ppm on the first pass search and 4.5 ppm on the second pass search for MS1. Tolerances on the MS2 spectra were set to 20 ppm. Only methionine oxidation was set as a fixed modification. Variable modifications included acetylation on 11056

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Figure 2. Asn3 lock mass correction throughout an LC−MS/MS run. The absolute Asn3 lock mass correction (blue, left Y-axis) is shown for the raw data files plotted by scan number for samples 7 and 11. This is the correction that the instrument software calculates after measuring the lock mass, and this correction is then directly applied to all measured masses in the spectrum. The plots also show the superimposed density plot in green (right Y-axis) of the PSM distribution across the scans. At regions where the numbers of identified PSMs are at their highest density, the lock mass correction significantly drops compared to those regions in the chromatogram where the density of identified PSMs is low. As lock mass correction changes with the density of the (identified) PSMs, this strongly suggests a space charge effect.

interference. We found that the Asn3 tripeptide (m/z of 361.1466) fulfills all of these conditions. Indeed, Asn3 is not retained on nano-RP LC columns when using the ion-pairing agents TFA and formic acid, commonly used in MS-based proteomics. No retention time shift or peak broadening is observed when adding Asn3 to LC solvents in concentrations needed for continuous lock mass calibration (Figure S-3A, Supporting Information). Furthermore, Asn3 as lock mass performs almost equally well as the PCM peak at m/z 445.1200 in terms of the number of identified peptides when analyzing the same sample under otherwise identical circumstances (Figure S-3B, Supporting Information). Further, when analyzing low abundance, though still complex samples, only a moderate decrease in the number of identified peptides is observed with an adjusted amount of Asn3 (Figure S-3C, Supporting Information), hinting to moderate effects of Asn3 on ionization suppression of peptides. In the worst case observed, the number of identified peptides drops from 331 to 296 when 150 pmol Asn3/mL was added to the solvents, whereas in the best case there was an increase from 314 to 330 identified peptides when the same concentration of Asn3 was added. Importantly, Figure 1B,C also shows that while the median value of the ppm error is not significantly different when using the PCM peak as lock mass compared to the ppm error using Asn3 as a lock mass, the variation is a lot higher for the PCM lock mass and is often significantly different from that of the Asn3 lock mass (Table S-1, Supporting Information). The reason for this higher variation is the discontinuous presence of the PCM lock mass, rendering it quite unreliable as a lock mass. When splitting up the PCM lock mass data set in lock mass corrected PSMs and nonlock mass corrected PSMs, it is clear that the lock mass corrected PSMs have a significantly higher mass accuracy (Figure 1E). Interestingly, the bar plots shown in Figure 1A show that the lock mass for the PCM runs is absent for only about 11.8% of all scans, and the scatterplots shown in parts B and C of Figure 1 show that these scans are precisely the ones where most of the peptides elute. The PCM lock mass peaks are thus lost in precisely those scans where the analytes are most represented, thus amplifying the MMA issue. Indeed, a loss of lock mass in just under 12% of all scans affects about

protein N-terminus and pyro-glutamate formation of amino terminal glutamine. Enzyme setting was set to trypsin, allowing up to one missed cleavage. The spectra were again searched against the UniProKB/SwissProt database (version 13_04 of UniProtKB/Swiss-Prot protein database containing 20 232 human protein sequence entries). Data Analysis. Data analysis was performed using R (http://www.R-project.org) embedded in Knime.25 Publicly available data were collected from the PRIDE database.8 Through the ProteomeXchange Consortium Webpage, projects analyzed with an LTQ-Orbitrap were selected. All header information was extracted from the raw datafiles through Proteowizard.26 More specifically, the lines containing the information about the lock mass were extracted. In all mass measurement analyses, only ppm errors lower than 10 ppm were considered to avoid second isotope precursor selection errors. Furthermore, all peptide to spectrum matches (PSMs) obtained from the decoy database were omitted. Values of the mass errors over a complete data set were expressed by means of the median and the Huber scale.27 Extracted ion chromatograms were created through the unfinnigan tool (http://code. google.com/p/unfinnigan/).

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RESULTS AND DISCUSSION When using one of the PCM peaks as the internal calibrant, we noticed that the signal for the lock mass was often lost (Figure 1A). Analysis of publicly available LTQ-Orbitrap data confirmed our observation that the presence of a PCM lock mass has a high variability, not only within the same lab but also between laboratories (Figure S-1, Supporting Information). Since the lock mass has to be present at all times to properly compensate for the mass drifts within an LC−MS/MS run, its absence clearly reduces the MMA of the PSMs (Figure 1B,C). We were able to substantially increase the reliable presence of a lock mass by spiking the Asn3 tripeptide into the solvents of the nano-LC in front of the mass spectrometer (Figure 1D and Figure S-2 in the Supporting Information). Prerequisites for this tripeptide were that it should not have any retention on the column, not significantly suppress the ionization of the peptide analytes, and fall into the measured m/z range but outside the common m/z range of peptides to avoid m/z 11057

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Figure 3. Space charge effect on mass measurement accuracies. Panels A (LMA set to 0) and B (LMA set to 5) show the percentage of PSMs remaining when performing a database search with a fragment ion tolerance of 3 mmu as compared to 20 mmu MS2 tolerance. Numbers are shown for data where Asn3 (blue) or PCM (orange) was used as lock mass. Panel C shows the presence (blue) and absence (red) of the lock mass in each LC−MS/MS run for the PCM data set with LMA set to 5. Panel D shows the number of retained PSMs with 3 mmu MS2 tolerance compared to 20 mmu, but now only for Asn3 data with the LMA settings to 0 shown in light blue and for LMA set to 5 in dark blue.

MS1 and MS2 spectra. This is done through separate isolation of the lock mass in the C-trap by raising the LMA from 0 to 5.15 Because the lock mass is not present in MS2 spectra when the LMA is set to zero, the lock mass correction is taken from the preceding MS1 scan. However, since there is a large difference between the number of ions present in the Orbitrap for MS1 scans compared to MS2 scans, a space-charge effect can occur that negatively affects MS2 mass accuracy. This effect will thus necessitate a corresponding need for a separate lock mass correction in MS2 spectra. To investigate this, a comparison was made between the number of PSMs obtained from either a search using a very strict 3 mmu MS2 mass error tolerance or a more lenient 20 mmu MS2 tolerance. Since the more restrictive 3 mmu searches yield less identifications, the number of retained PSMs at 3 mmu is shown as a percentage of the number of PSMs at 20 mmu (Figure 3). With LMA set to zero no consistent large differences in the number of retained PSMs when either using PCM or Asn3 as a lock mass were found (Figure 3A), which hints to a similar mass accuracy on the MS2 level. There is however a large difference in mass accuracy on the MS1 level (Figure 1B,C), and it is worth noting that this difference is not propagated to the MS2 level despite the fact that, with LMA at zero, the lock mass correction from the MS1 level is used for the MS2 recalibration. With LMA set to 5, the PCM data set behaves very similarly, showing a significant drop

half (48%) of all PSMs (Figure 1F). This unfortunate loss of signal for the PCM lock mass is likely to be caused by ion suppression effects from the peptide analytes. Whenever the lock mass is not found, the Thermo acquisition software used in this study (Xcalibur v2.1) proceeds to adjust the mass with the deviation derived from the lock mass of the last scan in which it was detected. For the runs using PCM as a lock mass, the correction is therefore taken primarily from the beginning of the run. Looking closer at the adjusted ppm for the Asn3 lock mass (Figure 2, blue spots), it appears that the mass deviation correction and thus the actual instrument mass measurement error, fluctuates during the LC−MS/MS run, with the most substantial deviation measured in the middle part of the gradient where most of the peptides elute (Figure 2, green trace). Thus, as the PCM lock mass is not detected in the middle part of the gradient of the LC−MS/MS runs, the mass correction from the beginning of the gradient is used and thus mass correction in these middle parts of LC−MS/MS runs will be wrong. Next to the common explanation of time and temperature differences,1,11 this difference in measured mass deviation is probably also due to space-charge effects that occur when many peptides enter the ion source at one time and this despite the automatic gain control (AGC) which is intended to decrease such space charge effects.5,15 This was further investigated by allowing an injection of the lock mass in both 11058

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in retained PSMs at 3 mmu which is not the case for the Asn3 set (Figure 3B). This drop in PSMs for PCM data is a consequence of the rather rare occurrence of the lock mass in the MS2 spectra (Figure 3C) and the corresponding lower mass accuracy on the MS2 level in this data set compared to the Asn3 data set. In fact, as shown in Figure 3D, the Asn3 data set actually benefits from the use of the lock mass in the MS2 recalibration as more PSMs are retained with the LMA set to five and thus the MS2 gains a higher mass accuracy. This discrepancy in MS2 mass accuracy between LMA 0 and 5 shows that there is still a substantial space-charge effect despite the use of AGC. Accordingly, there will also be a space-charge effect when many ions enter the source at one time despite the use of AGC, and this space-charge effect negatively influences the mass accuracy. Therefore, the presence of a lock mass at all times during analysis is of high value. The use of Asn3 as lock mass over 20 LC−MS/MS runs rendered a median mass error of 0.09 ppm ±0.72 ppm bringing the mass accuracy to the subppm level. Software lock mass,1 used in MaxQuant, was shown to be a good alternative for PCM-based lock mass. Even though this approach is quite successful in correcting the mass deviations across a run, Figure 4 reveals that high MMA data created by the use of Asn3 delivers even better results. In fact, this is evident from calculating the differences of the areas under the curve (AUC) for the density curves of the Asn3 and PCM data sets between two fixed points, these being the 35 and 65 quantiles of the Asn3 data set. As the AUC is larger for the Asn3 data set in most cases (Figure S-4, Supporting Information), a bigger fraction of the Asn3 data set is measured at a high mass accuracy as compared to the PCM data set. This finding is contrary to the claim in Cox et al.1 due to the fact that the high MMA data used in their work were derived using PCM as a lock mass. As shown above, the almost complete suppression of PCM peaks in the most crucial parts of an LC−MS/MS run leads to quite poor measurement error correction. Dynamic lock mass correction also benefits from highly accurate data.11 A specific advantage of both these offline lock mass corrections could be that no time is lost during data acquisition of lock masses by a separate filling of the C-trap in LTQ-Orbitrap instruments. In our analysis however, this option was turned off by setting LMA to 0 and therefore does not have a negative effect on the cycle time, while maintaining full lock mass based mass error correction capabilities. Further, MS2 calibration is not possible with a software lock mass approach, although it is necessary for high MS2 mass accuracy as shown above. Furthermore, high mass accuracy on MS2 level is of high value when using isobaric mass tags.28,29

Figure 4. Recalibrated mass error distributions in MaxQuant. Each graph displays the recalibrated mass error distribution for the Asn3 data set (blue) overlaid with the PCM data set (red) for one sample. In each graph, the area under the curve (AUC, blue and red areas, see also Figure S-4, Supporting Information) between quantiles 35 and 65 of the Asn3 data set (vertical green lines), which represent the 30% most accurate data of the Asn3 data set, is clearly larger for the Asn3 data set (blue area) compared to the PCM data set (red area) as also indicated by the differences in the AUC values (ΔAUC). This shows that the recalibration of the mass error in MaxQuant benefits from accurate start data (Asn3), as a higher percentage of the identified PSMs are at a higher mass accuracy.

the sample analytes. Such dosage adjustments are impossible with the PCM lock mass as it is an environmental component and cannot be controlled. Indeed, while the amount of PCM and therefore the amount of suppression of sample ions can be reduced by the ABIRD18,19 or a nitrogen flow,16 both lead to a nearly complete suppression of PCM peaks. Furthermore, our Asn3 approach is compatible with a subsequent software lock mass correction, yielding additional improvements in mass accuracy. In summary, Asn3 does not interfere with the analytical system nor does it cause severe ion suppression, and it can be routinely employed in any laboratory with only minimal changes to the experimental setup.

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CONCLUSIONS We showed that Asn3 provides an extremely reliable, controllable spike-in lock mass for mass spectrometry based proteomics that greatly enhances the ability to obtain instrument-independent high mass accuracy measurements of peptides. A key advantage of spiking Asn3 into LC solvents is that it can be dosed to address possible ion suppression in peptide-dense regions, thus guaranteeing the presence of the lock mass at all times during an analysis. Moreover, lock mass peaks can also be guaranteed to be sufficiently intense, avoiding the pitfall that low intensity signals typically have low mass accuracy.2,11,15 Of course, by the same measure, one can also decrease the amount of spiked in Asn3 when a low abundance sample is loaded, which then ensures less ion suppression for

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ASSOCIATED CONTENT

S Supporting Information *

Additional information as noted in text. This material is available free of charge via the Internet at http://pubs.acs.org. 11059

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(23) Helsens, K.; Colaert, N.; Barsnes, H.; Muth, T.; Flikka, K.; Staes, A.; Timmerman, E.; Wortelkamp, S.; Sickmann, A.; Vandekerckhove, J.; Gevaert, K.; Martens, L. Proteomics 2010, 10, 1261−1264. (24) Vizcaino, J. A.; Cote, R. G.; Csordas, A.; Dianes, J. A.; Fabregat, A.; Foster, J. M.; Griss, J.; Alpi, E.; Birim, M.; Contell, J.; O’Kelly, G.; Schoenegger, A.; Ovelleiro, D.; Perez-Riverol, Y.; Reisinger, F.; Rios, D.; Wang, R.; Hermjakob, H. Nucleic Acids Res. 2013, 41, D1063− 1069. (25) Berthold, M. H.; Cebron, N.; Dill, F.; Di Fatta, G.; Gabriel, T. R.; Georg, F.; Moinl, T.; Ohl, P.; Sieb, C.; Wiswedol, B. Industrial Simulation Conference 2006 2006, 58−61. (26) Kessner, D.; Chambers, M.; Burke, R.; Agus, D.; Mallick, P. Bioinformatics 2008, 24, 2534−2536. (27) Huber, P. J. Ann. Math. Stat. 1964, 35, 73−101. (28) Werner, T.; Becher, I.; Sweetman, G.; Doce, C.; Savitski, M. M.; Bantscheff, M. Anal. Chem. 2012, 84, 7188−7194. (29) Pachl, F.; Fellenberg, K.; Wagner, C.; Kuster, B. Proteomics 2012, 12, 1328−1332.

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Fax: +32-92649496. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS L.M. and K.G. acknowledge the support of Ghent University (Multidisciplinary Research Partnership “Bioinformatics, from nucleotides to networks”) and the PRIME-XS and ProteomeXchange projects funded by the European Union 7th Framework Program under Grant Agreement Numbers 262067 and 260558, respectively. The data deposition to the ProteomeXchange Consortium was supported by the PRIDE Team of the EBI at Hinxton, U.K.

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dx.doi.org/10.1021/ac4027093 | Anal. Chem. 2013, 85, 11054−11060