Resolving isobaric interferences in direct infusion tandem mass spectrometry

Rationale The co‐fragmentation of precursors in direct infusion (DI) tandem high‐resolution mass spectrometry (HRMS) can complicate the fragment spectra and consequently lead to false hits during compound identification. Methods The method herein described, termed IQAROS (incremental quadrupole acquisition to resolve overlapping spectra), modulates the intensities of precursors and fragments by stepwise movement of the quadrupole isolation window over the mass‐to‐charge (m/z) range of the precursors. The modulated signals are then deconvoluted by a linear regression model to reconstruct the fragment spectra with less interference. The hardware to demonstrate the use of IQAROS was an orbitrap with electrospray ionization (ESI) or secondary electrospray ionization (SESI), although the method can also be applied to other ionization techniques or mass analyzers. Results Assessing the performance of IQAROS with isobaric standards revealed that the reconstructed fragment spectra match with spectra acquired from the pure standards and that more compounds were correctly identified compared with the classical approach with the quadrupole centered at the m/z value of the precursor of interest. Moreover, the strength of IQAROS is exemplified by the identification of two isobaric biomarkers directly from a breath sample with SESI‐HRMS. Conclusions With IQAROS, cleaner fragment spectra of co‐fragmenting isobars during DI‐HRMS analysis can be obtained. IQAROS can easily be set up by the standard graphical user interface of the instrument. Therefore, it facilitates the characterization of features of interest in samples analyzed by DI‐HRMS, for example, in high‐throughput or real‐time metabolomics.


| INTRODUCTION
Given its speed and sensitivity, direct infusion (DI) mass spectrometry (MS) is the ideal technique for high-throughput analysis 1 and for realtime on-line monitoring. 2  (HRMS) has proven to compensate at least partially for the missing chromatographic separation. 5,6 In a typical DI-HRMS metabolomics workflow, samples are analyzed on an MS 1 level, followed by statistical analysis which yields MS 1 signals of interest, which in turn have to be annotated to chemical structures in the next step. 4 For sufficient confidence, this identification step requires characterization by tandem mass spectrometry (MS 2 ). 7 While molecules with differences of a few millidaltons can be distinguished on the MS 1 level with HRMS, their MS 2 characterization is more challenging because the quadrupole (Q) isolation window for precursor selection typically has a width of a few hundred millidaltons to a few daltons. Thus, isobaric precursors are often co-fragmented and interfere with each another's MS 2 spectra. In the literature, the resulting complicated spectrum is often referred to as a chimeric MS 2 spectrum or chimera. 8 If a feature of interest makes up ≤50% of the total MS 1 intensity in the isolation window, a rule of thumb says that spectral matching is unrealiable. 9 Thus, for DI-MS metabolomics, where hundreds of features are detected in the low-mass region, 10,11 spectral matching is often problematic.
For DI-MS, options (1) and (2) are not feasible because they rely on chromatography. Option (3) could be applied to DI-MS, but it assumes that the interfering isobars are reported in a spectral library.
However, this might often not be the case for adducts and in-source fragments [26][27][28][29][30] or for ionization techniques other than ESI. Option (4) can have similar limitations, but is still very helpful because the focus on reasonable mass losses is independent of the ionization technique. Nonetheless, it might be that a fragment is consistent with two isobars simultaneously from a pure mass loss perspective, e.g., the benzyl fragment C 6 H 5 + could arise from either one (or both) of two co-fragmented aromatic precursors. Only a few studies have explicitly addressed the problem for DI-MS, e.g., it is reported that ion mobility 31 or "collisional purification" 32,33 on MS 3 -or pseudo-MS 3capable instruments can resolve chimeras.
Here, we report an alternative method dubbed IQAROS (incremental quadrupole acquisition to resolve overlapping spectra) to resolve co-fragmented precursors in DI-HRMS. It relies on small, s c a n n u m b e r s m/z  F I G U R E 1 Overview of IQAROS: a, Two co-isolated, co-fragmented and co-detected isobars result in a chimeric MS 2 spectrum. b, The basic principle of IQAROS. The isolation window is stepwise moved over the m/z range of the two isobaric precursors. On the narrowest isolation width setting, the isobars cannot be isolated individually, but they can be modulated distinctively. c, Moving the isolation window through the precursor region modulates the intensities of the two isobaric precursors. Their scan s dependent intensities are denoted P 1,s and P 2,s . Likewise, the fragment intensities are also modulated. d, The observed fragment intensity F i,s of fragment i in scan s can be expressed by a linear combination of the precursor intensities P j,s in the same scan s and a contribution coefficient β i,j . The estimatesβ are obtained with two methods here: a non-negative multi-linear regression (method 1) or a non-negative simple-linear regression (method 2). The green rectangle highlights the difference between the two methods [Color figure can be viewed at wileyonlinelibrary.com] millidalton differences between the accurate masses of the precursors. The method is based upon the fact that the width of the Q isolation window is relatively broad, but the window's center can be set quite precisely. Thus, by moving the Q over the precursor range in a stepwise manner, the precursor's transmission through the Q is regulated and the intensities of the precursors and their associated fragments are therefore modulated (Figures 1a-1c).
Through visual interpretation of the modulation behavior or by mathematical deconvolution, the fragments can then be assigned to the correct precursor(s).
In related work, it was previously recognized that the problems associated with chimeras can be addressed by Q modulation. A binary alternation of the Q position was implemented in a DIA LC/MS 2 method 34 and in a precursor-dependent LC/MS 2 method. 17 7 5 C H NO 7 5 C H N O 6 5 3 C H N 5 5 5 C H NO 8 9 C H N

| Mass spectrometry
The mass spectrometer used in this study was an orbitrap Q-Exactive Plus (Thermo Scientific) operated with the manufacturer's standard control software (ExactiveTune, version 2.9, Thermo Scientific) and Xcalibur (version 4.1.31.9, Thermo Scientific). Mass calibration was performed according to the instrument manual and was always more recent than 7 days according to specifications. Direct ESI-MS 2 data for standards and the blank were averaged over all scans for each selected precursor and peak-picked with the Matlab function mspeaks. The MS 2 spectra of the standards were then blank subtracted. The resulting averaged and blank-subtracted MS 2 spectra were written into a new.mgf file for every precursor and used to compare the results against the IQAROS output. The direct SESI-MS 2 was averaged during exhalation and baseline-subtracted.
Then, the same processing as for the ESI standards was applied.
An overview of the IQAROS code is shown in Figure S2  F ¼ β Á P with the n Â p matrix F containing the fragment intensities for n fragments and p scans, the m Â p matrix P containing the precursors intensities for m precursors and p scans, and the n Â m matrix β describing the contribution to fragment i from every precursor j. The column j of matrix β represents the MS 2 spectrum for precursor j. For example, β i,j ¼ 0 means that fragment i is not part of the MS 2 spectrum of precursor j. Likewise, β i,j ¼ 1 or β i,j ¼ 2 signifies that fragment i appears in the MS 2 spectrum of precursor j with the same or double the intensity of precursor j, respectively.
To obtain the estimates b β i,j , two methods were used, as depicted in Figure 1d: method 1 is identical to that of Nikolskiy et al, 17 i.e., b β i,j are estimated by running a non-negative multiple linear regression with the Matlab function lsqnonneg for every fragment i. Performing n times a non-negative m-multiple linear regression yields matrixβ.
For method 2, it is assumed that every fragment is the product of only one precursor. The aim is to find the single precursor j which alone best describes the observation of fragment i. Thus, for every ith fragment m, non-negative simple linear regressions are run with every jth precursor XIC as independent variable yielding in m b β i,j per fragment i. For every jth regression, the coefficient of determination i,j is calculated. Only the regression coefficient b β i,j with the highest R 2 i,j is written into matrixβ for fragment i, all other values of b β i,j are set to zero. Thus, every row i ofβ contains only one element >0.
From theβ matrix, the mass spectra of the precursors are reconstructed, i.e. for a column j inβ the entry b β i,j represents the intensity of fragment i when precursor j has a normalized intensity of 1. Finally, the reconstructed mass spectra are saved as.mgf files.

| Spectral matching
The deconvoluted spectra were compared with the blank-subtracted direct MS 2 spectra. For this purpose, peaks with higher m/z values than the precursor were omitted and the intensities were filled into two incremental vectors I To further assess the performance of IQAROS, the direct and the deconvoluted MS 2 spectra were processed with SIRIUS 24    While m/z 0.4 is the narrowest Q isolation width, the Q isolation center can be entered in the instrument control software with a theoretical accuracy of m/z 0.00001. However, we found on our instrument that the Q hardware reacts to input by the software with an accuracy of approximately m/z 0.06, although this does not limit the resolving power of IQAROS, as will be discussed below.
Thus, we set up IQAROS with a software-defined arbitrary increment of m/z 0.02 ( Figure S1, supporting information).
Important is that the software-defined increment is smaller than the hardware increment; here 0.02 ≤0.06, but different values are expected on other instruments. A larger step size within the hardware limit could make IQAROS even faster, but this was not optimized here. Therefore, the actual modulation corresponds to the one depicted in Figure 1b, i.e., the software moves the Q window after every scan by m/z 0.02 ( Figure S1, supporting information) but the hardware reacts only after every third scan by moving m/z 0.06 ahead. In principle, an alternation between MS 1 and MS 2 is also possible, which would result in a method as depicted in Figure S5a (supporting information) and would be better to detect lowabundance interfering isobars in the MS 1 scan. However, due to an instrument-specific technical restriction, which does not allow the orbitrap scan range and the Q isolation range for MS 1 to be set independently from each another (details in Figure S5b and S5c, supporting information), the modulation here was only performed on the MS 2 level, as shown in Figure 1b.  To extract this relationship quantitatively, the two deconvolution methods were applied: method 1 establishes a fragment-precursor 16  relationship by describing every fragment intensity as a positive linear combination of precursor intensities. In contrast, method 2 assigns every fragment to that single precursor which best explains the intensity of the fragment. Mathematically, method 1 is based on a non-negative multi-linear regression. The reconstructed spectra of mixtures II, III and VI are compared with the pure standards in Figure S4, S8 and S9 (supporting information), respectively. For benchmarking with the standard method, the pure standards are also compared with the direct MS 2 measurements of the isobaric mixtures.
The direct and the IQAROS spectra are compared with the pure standards by calculating the MS 2 match score, which is plotted in For mixture VI, the VIFs lie between 16 and 1060. Consequently, the poor result for 5 in mixture VI can be attributed to issues with multicollinearity of the method and the deconvolution model. Multicollinearity can be tackled by alternative regression methods like ridge or lasso (least absolute shrinkage and selection operator). 43,44 However, these methods cannot be run on the standard numerical computing software used here with the nonnegativity restriction. Yet, determiningβ in this study is restricted to non-negativity because it is assumed that a precursor never leads to a "negative intensity" fragment. This assumption is important, because it already eliminates some mathematical linear combinations which would otherwise be troublesome when dealing with the multicollinear data. Thus, an alternative to ridge or lasso regressions when dealing with multicollinearity is variable elimination in the model. 43 This is implemented in method 2, where every fragment is assigned to the single precursor which best explains the XIC of the given fragment.
Mathematically, this is done by picking the non-negative simple linear regression model with the best fit, i.e., with the highest coefficient of determination. Method 2 will not suffer from multicollinearity as method 1. However, as a disadvantage, method 2 will not correctly describe when two precursors lead to the same fragment. Instead, the fragment will be assigned to only one precursor as, for example, depicted for fragment F 3 in Figures 1c and 1d. The IQAROS spectra deconvoluted by method 2 are shown for the three isobaric mixtures in Figures S10, S11 and S12 (supporting information) and the MS 2 match scores comparison in Figure 4. With method 2, IQAROS yields an average MS 2 match score of 0.99 and therefore the multicollinearity and the problem with compound 5 in mixture VI is eliminated.
T A B L E 1 Compound identification of the standards using SIRIUS. 24,25 The first column shows the structures of the six isobaric standards. The second column shows the most likely structures according to the output of SIRIUS when the pure blank-subtracted standards are processed. If the true structure is not the top hit, the position of the correct hit is listed instead. The third column shows the SIRIUS output when processing the direct MS 2 targeted mixture VI. The fourth and fifth columns show the SIRIUS output of mixture VI when analyzed by IQAROS with method 1 and method 2, respectively

| Spectral search with isobaric standards
In the previous section, it was demonstrated that IQAROS leads to fragment spectra comparable with spectra of the pure standards.
Here, the discussion focuses on how helpful this is for compound identification. For this purpose, the previously discussed spectra were processed with the spectral interpretation software SIRIUS. 24,25 The most challenging sample, i.e., mixture VI, is addressed.
The first column in Table 1 shows the structures of the six isobaric standards. When the pure standards are analyzed by direct MS 2 , blank-subtracted and processed with SIRIUS, the structures shown in the second column in Table 1  If IQAROS with method 2 is applied, the true structure of 5 is at the 3rd hit position. As a disadvantage, method 2 eliminates too many features for 6. While it correctly assigns the main fragment [6 -C 2     In conclusion, both IQAROS methods 1 and 2 lead to better formula assignments when analyzing isobaric mixtures compared with the classical direct MS 2 analysis of the mixed isobars. For structure assignment of the mixed isobars, IQAROS performs in most cases better or comparable to direct MS 2 analysis. However, the structure assignment does not meet the quality of the pure standards analyzed individually by direct MS 2 .

| Example: Identification of isobars in breath
An exemplary application will now be discussed where IQAROS turns out to be very useful. Secondary electrospray ionization (SESI)-MS is an enormously sensitive and rapid technique for real-time DI analysis of volatile metabolites and therefore is typically applied for breath analysis. 4 Depending on the acquisition technique and instrument, a few hundred to a few thousand features are found in the mass range from m/z 50 to 500 during exhalation. 10,11 Taking into account that there are also some background peaks, it becomes apparent that many features must have neighboring isobars and thus often account for ≤50% of the isolated total intensity, which makes MS 2 spectral matching ambiguous. 9 Hence, reliable biomarker identification is usually done by collecting breath condensate followed by LC/ESI-MS. 38 However, this method is time-consuming and can lead to contamination/analyte loss during sample preparation. As a consequence, it is highly desirable to obtain higher quality MS 2 spectra of biomarkers without the need for condensation and LC separation.
As an example, isobaric signals around m/z 187 acquired by SESI-MS during the exhalation of a volunteer are shown in Figure 5a. In a previous study by Gaugg et al, 38 two of the isobars were identified by breath condensation and LC/MS to correspond to fatty acids 7 and 8.
Both in the study of Gaugg et al 38 as well as here (Figures 5b and 5c in blue), a direct MS 2 experiment acquired over five exhalations leads to non-interpretable fragment spectra due to the co-fragmented isobars. In fact, if the direct MS 2 spectra are processed by SIRIUS, incorrect structural assignments are obtained (Figure 5f, second column). Conversely, with IQAROS over five exhalations and the more stringent method 2, the spectra (Figures 5d and 5e in blue) fit well with the ESI-MS 2 spectra obtained from the pure standards (Figures 5d and 5e) in red). If the IQAROS spectra are processed with SIRIUS, they yield the same structures as the ones reported by Gaugg et al. 38 In conclusion, direct SESI-MS 2 can lead to incorrect assignments due to co-fragmented isobars. In contrast, with IQAROS biomarkers in SESI-MS can be accurately identified without the need to collect breath condensate followed by LC/MS. Comparable results are obtained within minutes instead of several hours of sample preparation and analysis.

| CONCLUSIONS
IQAROS (incremental quadrupole acquisition to resolve overlapping spectra) is an approach to disentangle chimeric MS 2 spectra that arise when a precursor of interest is co-fragmented with neighboring isobars in DI-HRMS. The method modulates precursor and fragment intensities by moving the isolation quadrupole's center over the precursor range in a stepwise fashion, followed by a mathematical deconvolution to reconstruct the MS 2 spectra of the individual precursors. The method can easily be implemented by simply creating a list with incremental precursor isolation center masses using the graphical user interface of the instrument.
For the analysis of a mixture containing up to six isobaric standards confined within a range of m/z 0.09, IQAROS delivered higher quality MS 2 data compared with a direct MS 2 approach. The match between the deconvoluted spectra and the spectra of pure standards was significantly higher and compound search with the spectra interpretation software SIRIUS led to a better hit rate.
Depending on the mathematical model, problems might arise from multicollinearity (method 1multi linear regression) or from the incapability of the deconvolution algorithm to assign a fragment to more than one precursor (method 2simple linear regression). As an exemplary application, IQAROS was able to directly identify two biomarkers from on-line breath measurements yielding the same structures as in a previous study, 38 but without the need for timeconsuming breath condensate collection and LC/MS analysis. In general, IQAROS might be useful for all sorts of ambient or desorption ionization methods like DART, DBDI, DESI, EESI, DAPCI, and many more. 48 For these techniques, hyphenation with chromatography to resolve interferences is often difficult or impossible. In contrast, IQAROS allows to mitigate the problem of cofragmentation without the need for chromatography or changes in the ionization process.
In this study, an isolation quadrupole in tandem with an orbitrap was used together with ESI-based ionization sources. The deconvolution was performed by a non-negative multi-or simplelinear regression. It is important to note that IQAROS can be used with other setups as well: (1) it can be used for variable fragmentation techniques or ionization methods; (2) it can also be employed for other high-resolution MS instruments such as time-of-flight or Fourier transform ion cyclotron resonance analyzers, and (3) other mathematical methods such as ridge, lasso or many more could possibly be used for deconvolution.
Limitations arise from multicollinearity when many precursors are deconvoluted. This problem can possibly be tackled with more advanced statistical methods. Moreover, the method needs sufficiently intense precursors, i.e., if a precursor is modulated to 10% of its maximum intensity, a fragment must still be abundant enough to be detected. An additional limitation, unique to the instrument used in this study, is that at 10 V collision energy (CE) the precursors must still be detectable. However, this criterion is repealed if modulation can be performed while alternating between MS 1 and MS 2 .

PEER REVIEW
The peer review history for this article is available at https://publons. com/publon/10.1002/rcm.9266.

DATA AVAILABILITY STATEMENT
The original data used in this publication are made available in a curated data archive at ETH Zürich (https://www.research-collection. There, the fully commented IQAROS deconvolution Matlab code is provided. Additionally, the modulation experiments of mixtures II and III are provided as exemplary.mgf files such that the user can familiarize with the processing.