SWATH data independent acquisition mass spectrometry for metabolomics

Accepted Manuscript SWATH data independent acquisition mass spectrometry for metabolomics Ron Bonner, Gérard Hopfgartner PII:

S0165-9936(18)30196-1

DOI:

https://doi.org/10.1016/j.trac.2018.10.014

Reference:

TRAC 15278

To appear in:

Trends in Analytical Chemistry

Received Date: 9 May 2018 Revised Date:

3 October 2018

Accepted Date: 15 October 2018

Please cite this article as: R. Bonner, G. Hopfgartner, SWATH data independent acquisition mass spectrometry for metabolomics, Trends in Analytical Chemistry (2018), doi: https://doi.org/10.1016/ j.trac.2018.10.014. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT 1

SWATH data independent acquisition mass spectrometry for metabolomics

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Ron Bonner1 and Gérard Hopfgartner2*

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1. Ron Bonner Consulting, Newmarket, ON, L3Y 3C7, Canada

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2. Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, 24 Quai Ernest Ansermet, CH-1211 Geneva 4, Switzerland

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8 9 Abstract:

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Systems Biology and ‘Omics’ require reproducible identification and quantitation of many

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compounds, preferably in large sample cohorts. Liquid chromatography-mass spectrometry is

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important since data generated can be used for structure elucidation and highly specific targeted

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quantitation. Despite great success, the technique has limitations such as: compound coverage in one

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analysis, method development time and single sample analysis time which determines throughput.

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New instrument capabilities have led to improved methods, including ‘Data Independent Acquisition’

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so-called because acquisition is not changed by acquired data. SWATH-MS is a specific example that

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has quickly become prominent in proteomics because of increased peptide coverage, high quantitation

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accuracy, excellent reproducibility and the generation of a ‘digital map’. These capabilities are

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important in small molecules analyses although uptake in these applications has been slower. We

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describe the SWATH-MS technique, review its use in applications such as metabolomics and

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forensics, and summarize on-going improvements and future prospects.

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Highlights

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Reproducible, high quality, high coverage quantitation

Data Independent Acquisition for QUAL/QUANT analysis of metabolites Post-acquisition data re-interrogation and quantitation Standardization of LC/MS metabolomics workflow

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1. Introduction and scope SWATH-MS[1] is one of the Data Independent Acquisition (DIA) liquid chromatography – mass

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spectrometry techniques (LC-MS), so-called to distinguish them from the widely used Data

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Dependent Acquisition (DDA). DIA methods are not new, but SWATH-MS has quickly become a

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widely used and powerful technique for quantitative proteomics for a variety of reasons including

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reproducible, specific quantitation; a reasonable analysis time; good dynamic range; and relatively

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little method development. Further, the technique has potential for simultaneous qualitative analyses,

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i.e. unknown compound identification, and allows retroactive data mining without re-analysis.

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These strengths are also attractive for small molecule applications, such as metabolite identification,

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forensic analysis and metabolomics, but acceptance has been slower in these areas and there are few

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reports using this technique. Here we summarize the current literature and applications for small

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molecule SWATH-MS and consider the future potential and required features, many of which are

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related to data analysis software.

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DIA methods evolved in proteomics from the need to reproducibly quantitate large numbers of

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proteins with good dynamic range in many samples. The techniques and proteomics applications have

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been thoroughly reviewed by several authors, for example: Tate et al. [2] reviewed the status of

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quantitative proteomics for affinity-purification experiments, while Chapman et al. [3] reviewed the

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development and applications of DIA and other multiplexed acquisition methods in depth. More

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recently, Anjo et al. [4] examined the role of SWATH-MS in biomarker discovery, Schubert et al. [5]

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considered the challenges of quantitative proteomics, and Fenaille et al.. [6] included DIA techniques

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in their review of LC-MS acquisition workflows for metabolomics. Thus we only briefly summarize

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the SWATH technique in order to provide context, compare it to other more well-known techniques,

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and underscore some of the important features.

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1.1 Background

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DIA and DDA are performed on tandem mass spectrometers that consist of two stages of mass

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analysis either physically separated by a collision cell or separated in time in a trapping instrument

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[7]. The first stage selects precursor ions that are fragmented and the products are analyzed in the

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second stage. The fragment ions provide information about the structure of the analyte and can be

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used for library searching or de novo identification. The instrument is often coupled to an LC

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separation system forming a powerful tool for the analysis of complex mixtures.

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The precursor-fragment ion pairs are very compound specific and form the basis of Selected Reaction

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Monitoring (SRM) that has long been the gold standard in quantitative analyses for drug development,

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ACCEPTED MANUSCRIPT environmental studies and forensics [8]. The instrument monitors the precursor-fragment ion pair

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during the LC run and the resulting signal is recorded. Quantitation is based on the area of peaks

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detected in the chromatogram obtained from the signal. Several compounds can be analyzed in the

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same run by switching between different ion pairs, but the number is limited by the amount of time

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required to measure each signal and the need for chromatographic fidelity [9]. This is known as

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Selected Reaction Monitoring mode or Multiple Reaction Monitoring (S/MRM). Method

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development requires optimization of the chromatography and chosen ion-pairs to minimize

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interferences and maximize sensitivity and linearity, and can be very time consuming even if the final

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method is short.

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Unlike S/MRM, which requires prior knowledge of the precursor and fragment ion masses, DDA

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techniques repeatedly 1) acquire a survey precursor ion spectrum, 2) select a number of target ions

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and 3) fragment the target ions and acquire fragment spectra. In proteomics the sample proteins are

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digested with an enzyme, typically trypsin, and the peptide mixture is subjected to LC-MS-MS by

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DDA. The resulting spectra are identified based on the peptide molecular weight, determined from the

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precursor spectrum, and comparison with real spectra or those predicted from a library of proteins.

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This is known as ‘shotgun’ proteomics. Continual development of the methodology, particularly the

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use of fractionation prior to the LC-MS-MS analysis and the use of long gradients, has allowed

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thousands of proteins to be identified from a single sample. However, the time required for one

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sample can be very long, for example, using a 4-hour LC gradient on each of 72 fractions needed 288

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hours (not counting re-equilibration time) [10].

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As the focus of proteomics shifted to quantitation it became clear that DDA had other significant

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limitations, often linked to the precursor ion selection process. Since an ion can be selected as soon as

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it crosses a threshold value, analysis will not occur at the top of the LC Peak and spectral quality will

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be sub-optimal. Survey scans are acquired frequently, to avoid missing compounds, which limits the

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number of precursor ions that can be selected. As a result, selection depends on all eluting ions and

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low intensity ions may never be selected. Further, selection is a stochastic process and different ions

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may be selected in different analyses, even in replicates of the same sample [11]. Finally,

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quantification must be based on MS data, since only single MS2 scans are acquired, rather than the

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more specific fragments, although the use of labelled compounds has allowed quantitation via spectral

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peak ratios rather than LC peak areas [12].

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The need for better quantitation of many proteins led to investigation of the applicability of MRM

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techniques [13]. Proteomics, however, is quite different to small molecule studies since digestion with

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trypsin produces highly redundant mixtures of chemically similar peptides. In contrast, small

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molecules are chemically diverse and one sample preparation process leads to a relatively small

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number of compounds which generate limited numbers of fragment ions. But proteins and peptides,

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ACCEPTED MANUSCRIPT unlike small molecules, are very predictable. For a set of proteins, the likely peptides, their retention

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times and fragment ion masses (but not intensities) can be simulated. This feature was exploited by

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several groups to find reduced numbers of ion pairs specific to given proteins. Sherman et al. [14]

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computed peptide-specific ion pairs, called Unique Ion Signatures, while Aebersold and co-workers

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determined the most specific (‘proteotypic’) peptides [15]. Since the intensity of any particular

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fragment ion cannot generally predicted, the group synthesized the peptides and analyzed them in

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batches by DDA to determine the most sensitive ion pairs [16]. This also provided accurate retention

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times which could be used to switch between sets of ions during analysis (scheduled MRM). The

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existence of these libraries was key to the development of SWATH-MS data analysis. Even for a

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small number of target proteins monitoring still required many ion pairs, for example, the analysis of

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108 proteins via 420 peptides required 1500 ion pairs [17]. Developing this method required several

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months which would have to be repeated if more proteins were added.

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DIA techniques were thus developed to provide a quantitation method with the reproducibility and

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specificity of MRM but with good coverage and reduced development time. In all cases, DIA

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techniques reproducibly cover a reasonable precursor mass range by repeatedly stepping through a

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sequence of precursor selection windows, fragmenting all the ions and collecting a composite

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spectrum. An extreme example is MSEverthing (MSE) which cycles between high and low

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fragmentation conditions of all masses and restores precursor-fragment relationships by correlation

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[18]. Of the four DIA techniques summarized by Chapman [3] three are implemented on trapping

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instruments while the fourth (SWATH) is implemented on a quadrupole-time-of-flight instrument (Q-

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TOF). The distinction is important as trapping instruments are not well suited to these analyses since

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they are sensitive to the number of stored fragment ions and too many will degrade resolution and

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mass accuracy. However, restricting the number of stored ions limits the dynamic range since the trap

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will tend to fill with the most intense ions at the expense of the less intense. Increasing the width of

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the ion window exacerbates this problem since there is a greater chance of storing high intensity ions.

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Further, trapping instruments require time to achieve full resolution. One of the techniques reviewed

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by Chapman [19] avoids this problem by using overlapping 2.5 amu windows, but multiple repeat

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analyses are required to cover the whole mass range so analyzing one sample may need several days.

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SWATH-MS. Acquisition and data processing

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MS/MS is based on the selection of a precursor ion and generation of fragment ions specific to the

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analyte. DIA methods systematically acquire fragment data from precursor ion ranges chosen to cover

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the mass range of interest. In many instruments precursor selection is performed by a quadrupole

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mass filter, since they can rapidly switch the selection window and have good transmission. Figure 1

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ACCEPTED MANUSCRIPT summarizes different options available for Q1 precursor window selection. Ideally the selection

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quadrupole would step 1 Da at a time and a product ion collected at each step (Figure 1A). But, for a

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mass range of 1000 Da with an acquisition time of 20 msec for each step, the total cycle time would

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be of 20 seconds which is incompatible with liquid chromatography. This mode of operation has been

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referenced as MSMSALL and has been successfully applied to lipids using flow injection analysis [20].

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A better fit with the LC time scale is to operate Q1 in RF mode (i.e. no selection) and to perform

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acquisition alternating two different collision energies, a low setting for molecular weight information

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and a high setting to generate fragments. The method has been described as MSEverthing (MSE) [18]

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or MSALL (Figure 2B) and is not a strictly an MS2 acquisition method as no precursor ion selection is

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performed. With SWATH, Q1 is operated in filtering mode and both fixed and variable Q1 windows

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can be used, potentially as many as 100 for an acquisition frequency of 100 Hz (Figure 1C and 1D).

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Finally, it is also possible to scan Q1 over the complete mass range using a fixed Q1 transmission

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window, e.g. 10-20 Da. This mode of operation offers an additional dimension of separation [21].

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Figure 2 compares different quantitation modes based on the MRM concept. With SRM quantification

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Q1 and Q3 are operated at unit mass resolution, typically a width of 0.7 Da at half-height, for good

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selectivity and sensitivity (Figure 2A). A second precursor ion in the selected unit mass window will

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only interfere with quantitation if it produces a fragment within the fragment selection window. In

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some cases this can be avoided by using a different fragment ion albeit with reduced sensitivity

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(Figure 2 B). If a high resolution second stage is used, such as in a QqTOF, it may be possible to use

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the original fragment for best response. This is sometimes known as MRM

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TOF always records all fragment ions, it is also possible to monitor several fragments to increase

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selectivity and response (Figure 2C) and to monitor different compounds in the same selection

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window based on their unique fragments. This is the basis of SWATH (Figure 2D) and leads to the

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question: what is the optimal selection window width to allow specific quantitation of multiple

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species? In the original SWATH paper, simulation for peptides suggested that 25 Da windows with a

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10,000 resolution second stage would produce the same specificity as MRM [1, 20].

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DIA workflows using sequential windows require a fast acquiring MS platform. QqTOF instruments

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operate with a fixed resolving power independent of the acquisition speed which can be as low as 10

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mSec seconds. Trapping instruments, such as the Q-orbitrap, require a trapping time and the resolving

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power (RP) decreases with cycle time so achieving a high acquisition frequency means compromising

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the RP. [9]. Furthermore, these instruments exhibit a decrease in resolution as the mass increases and

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are susceptible to space charge if many ions are stored. SWATH-MS involves the successive selection

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of overlapping precursor ion windows followed by fragmentation, which will occur even for ions

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below the selection threshold for DDA. The window widths can be varied to optimize specificity and

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acquisition time and even changed during an analysis [22]. Originally quantitation was based on peaks

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detected in chromatograms of fragment ions present in the spectral libraries of real peptides acquired

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. However, since the

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ACCEPTED MANUSCRIPT for MRM analyses [16]. The known intensities were part of a scoring system used to select high

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quality matches which also included: retention time, accurate mass (from the full spectra), isotope

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peaks and LC profile correlation. Comprehensive scoring is a strength of SWATH that cannot be

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matched even by MRM. Retention time standards, e.g. iRT peptides [23], have been used to allow for

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changes in chromatography and when transferring methods between instruments and laboratories.

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Spectral libraries can also be obtained from the study samples, for example, in proteomics it is

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common to analyze sample pools by DDA and base the SWATH data extraction on the measured

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masses. Note that the sample preparation conditions used to build the libraries do not have to be the

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same as those used in SWATH although the chromatographic system should be the same. For

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example, proteomics libraries have been generated from peptides enriched for phosphopeptides so that

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the quantitative SWATH method can monitor all of these along with other forms [24]. These authors

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also reported monitoring nearly 2000 proteins, c.f. the 90 reported by Bisson [17].

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Another strength of SWATH is that the data corresponds to a ‘digital map’ of all detectable

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fragments, i.e. a comprehensive electronic record of the sample. These maps can be mined at later

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stages, for example, if other compounds of interest are subsequently discovered, and also allow the

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samples to be part of different cohorts without the need for reanalysis [25].

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SWATH has thus emerged as a powerful technique, especially for the reproducible and accurate

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quantitation of many compounds. This has led to numerous reports of large-scale analyses for

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biomarkers and the development of clinical assays [4]. The important characteristics are also

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applicable to the analysis of small molecules in similar applications, i.e. reproducibility, speed,

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compound coverage, quantitation accuracy and minimal method development. The ability to

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retroactively mine the data and use it for qualitative analyses is an area of active research.

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Although SWATH was initially developed for quantitation, it has the possibility for use in qualitative

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analyses despite the lack of the precursor-fragment link. One approach, as used in MS , is to associate

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ions with similar chromatographic profiles although this may not be straightforward in very complex

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samples, if LC peak shape poor, or if there are completely co-eluting compounds. Another approach is

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to submit the spectra to library search algorithms although this suffers from the mixed spectra

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obtained when wide precursor windows are fragmented. However, the technique has great potential

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since all fragments are available. For example, if fragmentation occurs prior to the first analyzer the

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resulting fragments will themselves be fragmented in different precursor windows allowing a form of

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MS3. We previously [26] reported a manual example in which a prominent ion was identified as an

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[M+K]+ adduct which tend to fragment poorly; the precursor window containing the [M+H]+ ion was

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examined and revealed a strong ion corresponding to the loss of glucuronic acid (176 Da), and the

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window containing that fragment yielded a spectrum that could be successfully library searched.

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2. Data Analysis for Low Molecular Weight Compounds and Metabolites As noted above, SWATH-MS loses the direct link between precursor and fragment ions that is

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inherent to MRM. Correlation of chromatographic ion profiles can recover the link but may not be

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effective for co-eluting species, thus the qualitative analysis of unknown compounds can be

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challenging. However, the availability of information from all precursors, including fragments formed

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prior to the first mass analyzer, can be very useful [26]. For known compounds, data from the window

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containing the expected precursor can be interrogated for known fragment ions and quantitation can

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be based on the area of LC peaks in the resulting chromatograms. Scoring, which can utilize several

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parameters including measured intensity ratios and retention times, is important to ensure the best

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results. Consequently high quality spectral and retention time libraries are critical, especially for small

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organic molecules where fragment ion prediction is less reliable than in proteomics.

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Various software approaches to enhance Data-independent Acquisition for proteomics have been

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explored and were reviewed by Bilbao et al.. [27]. Metabolite data processing, however, depends

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mostly on proprietary vendor software such as PeakView, MasterView and MultiQuant in the case of

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Sciex. An open source software package ‘Mass Spectrometry – Data Independent AnaLysis’ (MS-

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DIAL) was developed by Tsugawa et al.. [28] for untargeted metabolomics. Raw data can come from

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different vendors (Bruker, Thermo) or mzXML files and are converted into Analysis Base File format

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(ABF). In a first step, precursor ions are detected based on retention time and accurate mass and 2 visualized as spots in retention time vs. m/z plots. For deconvolution MS-DIAL uses an MS Dec

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algorithm applied to the appropriate precursor ion windows for each precursor in all MS2

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chromatograms. MS-DIAL performs compound identification using MS1 and MS2 data and MS2

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spectral libraries (experimental and in silico) and quantifies the compound by area integration of MS1

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precursor extracted ion current chromatograms.

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MetDIA [29] is a data analysis package developed in the R environment, which enables the targeted 2 extraction of metabolites from multiplexed MS spectra generated by DIA following five steps: (1)

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peak detection and alignment, (2) targeted chromatogram extraction, (3) generation of peak-groups 2 and pseudo MS spectra, (4) metabolite centric identification and (5) statistical analysis and result

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output. A consensus spectral library of 786 analytes (765 positive mode, 757 negative mode and CE

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range +/- 20-50 eV) was constructed using chemical standards and the five most abundant fragment

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ions of each metabolite were selected. After MS1 peak detection and library matching, MS2 ions are

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extracted and compared using two orthogonal scores: peak-peak correlation and spectrum-spectrum

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ACCEPTED MANUSCRIPT match. Initially the MetDIA approach was evaluated with a set of 30 metabolites from the human

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metabolome database and all metabolites were identified with some additional false positives. The

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method was also compared with MS-DIAL and similar results were obtained. With biological samples

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(human serum, E coli bacteria, and rat liver tissue) however, the success rate of MS-DIAL was 20 to

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75% lower than that of MetDIA.

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Experimental and in-silico mass spectral libraries are versatile and have become essential tools for

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compound identification [30]. Collision induced spectra of singly changed low molecular weight

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compounds are generated at various discrete collision energies and depend on the instrument and

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laboratory conditions. This makes the transfer of libraries generated from one instrument to another

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difficult. Modern instruments also offer the possibility to use a collision energy ramp (e.g. 10 to 50

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eV) to acquire product ion spectra. Bruderer et al.. [31] described a workflow to build high quality

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mass spectral libraries for SWATH mass spectrometry data processing with discrete and composite

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collision energy values. Sixteen product ion spectra were collected at different collision energies (5-

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100 eV) for 532 metabolites from the human metabolome database in positive and negative mode.

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They found that in positive mode a relatively large collision energy range (10-70 eV) gave

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informative MS2 and more peaks which will generally result in more specific search spectra,

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supporting the use of composite spectra databases. Since SWATH acquisition uses a wide precursor

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ion window, complete isotope clusters are selected for fragmentation generating product ions with

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isotope peaks which can be exploited for identification based on the expected ratios. High quality

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libraries combining composite and discrete energy spectra are not only important for identification but

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also for building optimal HR-MRM assays for quantification and for method transfer to triple

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quadrupoles. Retention time is an important parameter in all LC-MS based metabolomics, i.e. MRM

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as well as SWATH, but is typically only available from in-house libraries with standardized LC

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conditions. The possibility to use a LC Calibration Mixture, similar to the iRT peptides for proteomics

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[23], and retention time modification to estimate the LC retention time windows for any metabolite

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and LC system is very attractive. [32]. Some indication of response factor would also be useful since

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it could help set reasonable expectations for compound detectability.

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The ultimate goal in metabolomics, in addition to metabolite identification, is sensitive, accurate and

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precise metabolite quantification. Triple quadrupole instruments are very effective for ultra-sensitive

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and accurate quantification but always require knowledge of the ion-pairs prior to acquisition. Unlike

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DDA acquisition, where quantification is only possible in MS1 mode, SWATH enables quantification

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in MS2 mode using HR-SRM for almost any precursor ions. Similar to proteomics, where spectral

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libraries based on study samples have been generated using either DDA or DIA[5], various

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approaches have been explored in metabolomics by using directly the user’s own SWATH data. A

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ACCEPTED MANUSCRIPT bioinformatics workflow to build customized MS2 spectral libraries for MS2 based quantification

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known as MetaboDIA was described by Chen et al.. [33]. With MetaboDIA the MS1 features are

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analyzed to generate a template for a Compound Identification Unit (CIU) which is characterized by a

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precursor m/z value and a retention time range. A consensus MS2 spectrum is also associated with

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each CIU. The emphasis of the workflow is that the user retains MS2 spectra that are present through

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the sample set even for metabolites where no publicly available reference spectrum exists. The

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approach was successfully evaluated using a clinical metabolomics data set.

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Zha et al.. [34] developed a SWATHtoMRM workflow R package to construct a large scale set of

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MRM transitions starting from SWATH files. Three initial steps are necessary to generate a consensus

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MS2 spectrum, which is used to construct the MRM transitions: 1) MS1 peak detection and alignment

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2) extraction of MS2 peaks and chromatograms, 3) MS1 and MS2 peak grouping. Three critieria

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define the selection of the transitions: 1) the m/z of fragment is smaller of that of the precursor ion by

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at least a CH2 group (14.0156 Da), 2) removal of common losses such as H2O, NH3 or CO2 and 3)

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selecting the remaining fragment ion with the highest intensity. The method was applied to build a

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targeted metabolomics method that enabled quantification of 1303 metabolites to profile issues from

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colorectal cancer (CRC) patients.

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Recently a design of experiment (DoE) study to optimize software (PeakView) parameters for a non-

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targeted peak finder approach combined with Formula Finder (Sciex) for a toxicological unknown

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screening was reported [35]. The use of the optimal determined parameter values enables to reduce by

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98.1% the amount of data necessary to identify drugs which significantly reduces the workload. The

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optimized parameters were tested on 22 drug spiked blood samples and 62 authentic forensic cases an

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in average 86.4% of analytes could be detected.

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While SWATH-mass spectrometry studies for the quantification of proteins haves shown high intra-

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laboratory and multi-laboratory reproducibility [36], limited work has been performed to evaluate the

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performance for low molecular weight compounds. In drug metabolism and pharmacokinetics

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quantification of the parent drug and identification of metabolites generally follow two different

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analytical processes using two different mass spectrometric platforms: low resolution triple

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quadrupole for quantification (MRM) and high resolution mass spectrometry for identification. Over

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two decades several approaches have been described enabling the integration of both qualitative and

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quantitative information (QUAL/QUAN) acquisition within a single analysis [37, 38]. Most of these

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had limited performance as they were based on a data dependent acquisition scheme although

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pharmaceutical method validation has to follow strict guidelines[39]. A QUAL/QUAN approach

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ACCEPTED MANUSCRIPT using MSE and HR-SRM was performed for the analysis of acetaminophen and metabolites in plasma

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samples. Similar accuracy and precision performances to the SRM/MS assay were obtained for

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APAP, APAP-CYS and APAP-GLUC using high resolution-selected reaction monitoring mode with

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LLOQ of 20, 50 and 50 ng/mL, respectively [40]. SWATH was shown to be powerful for integrating

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qualitative and quantitative analyses of bosentan and its metabolites in urine over a concentration

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range from 5 to 2,500 ng/mL [41]. Due to the large chemical space of pharmaceutical and endogenous

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metabolites far fewer analytes can be detected in a single LC-MS analysis compared to proteomics

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resulting in far better quality qualitative MS2 information. Absolute quantification requires the

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preparation of calibration samples and the use of internal standards spiked into the sample before

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analysis, while relative quantification can be performed post-acquisition. Standardizing the SWATH

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workflow offers the potential to perform absolute quantitation post-acquisition by including generic

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standards in the sample and generating calibration curves after identification. Forensic and

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Toxicology is also a field where QUAL/QUAN approaches are of interest. Arnhard et al. showed that

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even with relatively large Q1 windows (e.g. 21 amu) SWATH surpassed DDA for the identification of

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low molecular weight compounds of toxicological interest [42]. Validated quantification of 39

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antidepressants in whole blood was achieved based on a SWATH toxicological screening procedure

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Roemmelt, 2015 #32}. The performance of DDA, SWATH and MSALL (identical in this case to MSE)

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was evaluated for drug metabolite identification based on the quality of the metabolite fragment

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spectra generated [43]. Eight drugs with extensive oxidative metabolism after incubation in

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microsomes were investigated. Scores for MS2 quantity (QNS) and quality (QLS) were defined to

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evaluate the performance of the different acquisition approaches. Interestingly, in the microsomes

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analyzed only 4 and 5% of drug related metabolites were not selected for subsequent MS2 by DDA

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and mass defect DDA[44], respectively. However, the values increased to 29% and 18% when the

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metabolites were spiked in a more complex matrix such as urine. As expected, significantly better

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MS2 spectra quality was observed for SWATH as for MSALL. The consequence being that for

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endogenous metabolites compared to SWATH, MSALL will have limited sensitivity for the

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identification of metabolites and also a smaller dynamic range for quantification due to the high signal

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background .

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Polar metabolites such as carboxylic acid, phosphate, amines, or phenol hydroxyl-groups are

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challenging to analyze on reverse phase liquid chromatography due to their poor retention. Chemical

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derivatization can help to overcome this limitation and also enables additional features regarding

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ionization and identification. Siegel et al.. [45] proposed a strategy for integrated quantification and

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identification by SWATH/MS of aldehydes and ketones based on rapid autosampler-in-needle

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derivatization with p-toluenesulfonylhydrazine. The cycle time for one TOF experiment (range 50-

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1000) and two sets of seven Q1 windows(of 40 Da) with two collision energy conditions was about 1

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second which is essential to match UHPLC chromatographic performance. Beside improved LC

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ACCEPTED MANUSCRIPT retention, TSH-hydrazone ionized efficiently in positive and negative ESI. In ESI negative mode an

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abundant signature fragment at m/z 155.0172 due to toluene-sulfinate emerging from the

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derivatization agent provided an option of chemo selective screening. Applicability of the approach

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was demonstrated using 61 target metabolites in a yeast matrix.

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SWATH acquisition offers novel opportunities to combine drug metabolism and metabolomics after

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the administration of a single dose of vinpocetine to rats [26]. The SWATH data allows numerous

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data analysis approaches, including: detection of metabolites by prediction; metabolite detection by

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mass defect filtering; quantification from high resolution MS precursor chromatograms or fragment

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chromatograms. An acquisition scheme of two series of Q1 windows were defined, one with small

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windows of 15 Da specifically for drug metabolism and a second one with larger Q1 windows, 50 and

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100 Da, to cover endogenous metabolites. Multivariate analysis could be applied to the data from the

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full scan or SWATH windows and allows changes in endogenous metabolites as well as xenobiotic

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metabolites, to be detected. It was possible to characterize 28 vinpocetine metabolites mainly mono-,

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di- and tri-hydroxylated forms in urine, and detect endogenous metabolite expression changes.

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Data independent acquisition workflows such as SWATH for metabolomics are not limited to a

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QqTOF platform but can also be implemented on Q-orbitrap as illustrated by the work of Zhou et al..

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[46] for the analysis of metabolites in papillary thyroid carcinoma serum samples. HILIC and reversed

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phase chromatography were used for polar metabolites and lipids, respectively. An isolation window

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of 50 Da was used for polar metabolites (range 50-750 Da) and for lipids (200-1200 Da). These

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relatively large isolation windows, compared to 25 Da, were necessary to get an adequate number of

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data points over the LC peak.

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For proteomic analyses the SWATH precursor windows were originally fixed at 25 Da which was

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considered as equivalent to a triple quadrupole regarding selectivity to monitor peptides with at least

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five inference free transitions [1]. Of course, the optimal window size for best selectivity and

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sensitivity is sample and analyte dependent and an additional separation dimension can be achieved

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by using variable width precursor windows without compromising the total cycle time. Zhang et al.

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[22] compared the performance of 1) fixed Q1 windows where the mass range is equally divided

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(FIX), 2) variable window width chosen to equalize precursor ion population (PIP), i.e. the total

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number of precursor ions is divided by the number of windows, and 3) variable width windows that

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equalize precursor total ion current (TIC), where the intensity sum of all precursors is divided by the

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number of windows. The various windows were generated by an in-house open source software

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swathTUNER. Slightly different results were obtained for proteomic and metabolomics samples. In

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the analysis of pooled urine samples with fewer detectable precursor ions than proteomic samples, the

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TIC method appeared to provide slightly better spectra quality which is beneficial for metabolite

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identification. Considering the chemical space and the type of chromatographic separation applied in

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metabolite analyses, the use of scheduled SWATH [22] using variable isolation windows to match the

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ACCEPTED MANUSCRIPT chromatographic separation of analyte classes may be a more powerful way to optimize cycle time,

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quantification and identification.

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Mass spectrometry imaging (MSI) [47] can generate qualitative and quantitative molecular

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information directly from tissue samples without loss of spatial distribution information. In most cases

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MSI experiments are performed on high resolution instruments in full scan mode although DDA data

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can also be collected on almost any precursor but at the cost of analysis time. Therefore DIA

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workflows may also be attractive for direct surface analysis. Cahill et al. [48] investigated the use of

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laser microdissection-liquid vortex capture/electrospray ionization mass spectrometry for on-line

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classification of tissues using a SWATH acquisition scheme. The MS was set up to acquire mostly

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lipids with one TOF mass spectrum (m/z 600-1000) and three SWATH windows (m/z 700-775, 775-

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850 and 850-925) with a total acquisition time of 1-2 s. SWATH spectra in positive mode were

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dominated by the PC headgroup ions but in negative mode by PS, PE, PE and PI product ions.

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SWATH acquisition improved the accuracies of PCA-LDA model tissue region identification.

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LC-SWATH/MS acquisition was used for the temporal qualitative and quantitative analysis of

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pharmaceutical and illicit drugs in wastewater from southern Australia[49]. The TOF mass range was

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from m/z 50 to 600 and 34 SWATH Q1 windows (16 Da) were acquired with a total cycle time of 3.4

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sec. 53 samples were analyzed over a period of 14 months and a total of 100 compounds could be

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detected and confirmed.

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4. New developments

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While SWATH acquisition can currently be performed on high resolution QqTOF or Q-orbitrap

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platforms, the key parameters to fully benefit from the technique remain 1) the cycle time, which

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needs to be as short as possible to for use with ultra-high performance liquid chromatography, and 2)

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absence of saturation of the ions selected in the Q1 window which limits dynamic range. While it will

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take some additional for the use of SWATH as QUAL/QUANT tools for analysis of low molecular

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weight compounds to become widespread, some variations of the acquisition scheme are already

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emerging. Kaufman et al.. [50] propose using a systematic variation (nested design) versus the

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continuous variation of the Q1 windows. Preliminary experiments performed on a Q-orbitrap

404

compared an acquisition scheme of 18 fixed 48 Da Q1 windows for a total cycle of 1.51 second

405

versus a binary tree multiplex scan of various Q1 size (12 scans) with total cycle time of 1.31 s. The

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major benefit claimed being that smaller calculated mass windows, 13.4 Da versus 48 Da, can be used

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without increasing the total cycle time. Currently the major limitation is the lack of an adequate

408

computational framework.

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ACCEPTED MANUSCRIPT A current challenge in SWATH is correctly assigning precursor and fragment ions for qualitative

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analysis. Relying on the chromatography peak shape and performing feature alignment of extracted

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ion current profiles is not adequate as many LC peaks can show different behavior during the

412

chromatography (peak fronting, peak, splitting or peak tailing). Another interesting approach, where a

413

fixed Q1 window is scanned across the complete mass range, has been described on QqTOF

414

instruments by two different groups and referred to as scanning SWATH [51] and SONAR [21]. By

415

scanning the Q1 windows with and without collision energy, precursors and fragments ions can be

416

correlated both chromatographically and as a function of Q1 mass. This allows the use of much wider

417

Q1 windows for best sensitivity with correct assignment of different fragments in the m/z dimension

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even with coeluting precursors.

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In a relatively short time period (2010-2012) several novel DIA workflows were reported, primarily

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driven by proteomics while the use of DIA in metabolomics has only gained interest very recently. Of

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the four techniques reviewed by Chapman [3], SWATH has been mostly widely accepted with 710

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citations followed by PAcIFIC [19] 48 citations, FT-ARM [52] 48 citations and XDIA [53] 46

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citations. (Citation Index, web of Science, May 06 2018). Unlike the other methods, SWATH is well

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suited to QqTOF instruments since it relies on the shape of the Q1 isolation windows, the acquisition

427

speed and dynamic range of the mass spectrometer.

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The main reason for this acceptance is the capability of reproducible, high quality quantitation for a

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wide range of compounds. Unlike MRM, which still provides the best quantitation performance for a

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few compounds, SWATH requires little method development and does not require modification to

432

monitor additional compounds. For small molecule applications it seems likely that, as in proteomics,

433

methods will be standardized and reference compounds will be developed to calibrate retention times

434

and amounts. With this the ‘digital maps’ produced will also be standardized which will have

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numerous benefits. For example, inter-laboratory transfer of methods, which are related to data

436

processing not acquisition, will be straightforward and reproducible across laboratories. Further,

437

samples can be used in different studies that target different analytes, increasing the value of precious

438

samples.

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Currently the major challenge for routine use is the lack of software tools in two areas. First, data

440

processing tools to aid the identification of unknown compounds and, secondly, efficient and reliable

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data processing software which deals with complex acquisition schemes and is suitable for

442

simultaneous metabolite identification and quantification. This will become even more important, and

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difficult, with the integration of ion mobility spectrometry in the workflow as an additional separation

444

dimension. This is highly desirable for the analysis of very complex biological matrices.

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List of Figures

447 Figure 1: Various DIA workflows using different Q1 isolation window to cover the precursor mass

449

range of interest. A) MS/MSALL; a 1 Da window is stepped across the entire range. In practice this is

450

only useful with infusion analyses. B) MSe; the entire range is transmitted with alternating collision

451

energies, light blue = low energy, dark blue = high energy. C) SWATH with fixed windows; a set of

452

windows of the same width (e.g. 25 Da) covering the range are repeatedly analyzed. D) SWATH with

453

variable windows; as before but the window width is not uniform. E) SWATH with scanning

454

quadrupole; a wide selection window (e.g. 25 amu) is scanned across the range of interest generating

455

multiple overlapping windows.

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Figure 2: Comparison of MRM-like quantification techniques for fragment mass analyzers with

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different resolution and for different precursor selection window widths. A) SRM using a 1 amu

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precursor window and a 1 amu fragment selection window centred on the known fragment mass of

460

interest. Quantitation is achieved by monitoring the fragment intensity during an LCMS run. B) as A)

461

but with a different, less intense fragment, because the original has an interfering fragment from a

462

second co-selected precursor. C) As B) but using a high resolution second analyzer so that the

463

interfering fragments can be uniquely selected. For a TOF second stage all fragments are analyzed so

464

additional known fragments can also be monitored. As illustrated, both compounds could be analyzed.

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D) SWATH. Increasing the precursor window generates more fragments but if unique fragment

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chromatograms can be extracted multiple compounds can be analyzed.

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References

469

[1] L.C. Gillet, P. Navarro, S. Tate, H. Rost, N. Selevsek, L. Reiter, R. Bonner, R. Aebersold,

470

Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisition: A

471

New Concept for Consistent and Accurate Proteome Analysis, Mol. Cell. Proteomics, 11 (2012)

472

O111 016717.

475 476 477 478

protein–protein interactions, J. Proteom., 81 (2013) 91-101.

RI PT

474

[2] S. Tate, B. Larsen, R. Bonner, A.-C. Gingras, Label-free quantitative proteomics trends for

[3] J.D. Chapman, D.R. Goodlett, C.D. Masselon, Multiplexed and data-independent tandem mass spectrometry for global proteome profiling, Mass Spectrom. Rev., 33 (2014) 452-470.

[4] S.I. Anjo, C. Santa, B. Manadas, SWATH-MS as a tool for biomarker discovery: From basic research to clinical applications, Proteomics, 17 (2017) 23.

SC

473

[5] O.T. Schubert, H.L. Rost, B.C. Collins, G. Rosenberger, R. Aebersold, Quantitative proteomics:

480

challenges and opportunities in basic and applied research, Nat Protoc, 12 (2017) 1289-1294.

481

[6] F. Fenaille, P. Barbier Saint-Hilaire, K. Rousseau, C. Junot, Data acquisition workflows in liquid

482

chromatography coupled to high resolution mass spectrometry-based metabolomics: Where do

483

we stand?, J. Chromatogr. A, 1526 (2017) 1-12.

M AN U

479

484

[7] W.M.A. Niessen, MS–MS and MSn☆ A2 - Lindon, John C, in: G.E. Tranter, D.W. Koppenaal

485

(Eds.) Encyclopedia of Spectroscopy and Spectrometry (Third Edition), Academic Press, Oxford,

486

2017, pp. 936-941.

488 489 490

[8] G. Hopfgartner, E. Bourgogne, Quantitative high-throughput analysis of drugs in biological

TE D

487

matrices by mass spectrometry, Mass Spectrom. Rev., 22 (2003) 195-214. [9] G. Hopfgartner, Can MS fully exploit the benefits of fast chromatography?, Bioanalysis, 3 (2011) 121-123.

[10] N. Nagaraj, J.R. Wisniewski, T. Geiger, J. Cox, M. Kircher, J. Kelso, S. Pääbo, M. Mann, Deep

492

proteome and transcriptome mapping of a human cancer cell line, Mol. Syst. Biol., 7 (2011).

493

[11] B. Domon, R. Aebersold, Options and considerations when selecting a quantitative proteomics

495 496 497 498 499 500

AC C

494

EP

491

strategy, Nat. Biotechnol., 28 (2010) 710. [12] M. Mann, Functional and quantitative proteomics using SILAC, Nat. Rev. Mol. Cell Biol., 7 (2006) 952.

[13] L. Anderson, C.L. Hunter, Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins, Mol. Cell. Proteomics, 5 (2006) 573-588. [14] J. Sherman, M.M. J., A. Keith, M.M. P., How specific is my SRM?: The issue of precursor and product ion redundancy, Proteomics, 9 (2009) 1120-1123.

501

[15] P. Mallick, M. Schirle, S.S. Chen, M.R. Flory, H. Lee, D. Martin, J. Ranish, B. Raught, R.

502

Schmitt, T. Werner, B. Kuster, R. Aebersold, Computational prediction of proteotypic peptides

503

for quantitative proteomics, Nat. Biotechnol., 25 (2006) 125. 16

ACCEPTED MANUSCRIPT 504

[16] P. Picotti, O. Rinner, R. Stallmach, F. Dautel, T. Farrah, B. Domon, H. Wenschuh, R. Aebersold,

505

High-throughput generation of selected reaction-monitoring assays for proteins and proteomes,

506

Nat. Methods, 7 (2009) 43.

507

[17] N. Bisson, D.A. James, G. Ivosev, S.A. Tate, R. Bonner, L. Taylor, T. Pawson, Selected reaction

508

monitoring mass spectrometry reveals the dynamics of signaling through the GRB2 adaptor, Nat.

509

Biotechnol., 29 (2011) 653. [18] R.S. Plumb, K.A. Johnson, P. Rainville, B.W. Smith, I.D. Wilson, J.M. Castro‐Perez, J.K.

511

Nicholson, UPLC/MSE; a new approach for generating molecular fragment information for

512

biomarker structure elucidation, Rapid Commun. Mass Spectrom., 20 (2006) 1989-1994.

RI PT

510

[19] A. Panchaud, S. Jung, S.A. Shaffer, J.D. Aitchison, D.R. Goodlett, Faster, Quantitative, and

514

Accurate Precursor Acquisition Independent From Ion Count, Anal. Chem., 83 (2011) 2250-

515

2257.

SC

513

[20] B. Simons, D. Kauhanen, T. Sylvänne, K. Tarasov, E. Duchoslav, K. Ekroos, Shotgun

517

Lipidomics by Sequential Precursor Ion Fragmentation on a Hybrid Quadrupole Time-of-Flight

518

Mass Spectrometer, Metabolites, 2 (2012) 195-213.

M AN U

516

519

[21] M.A. Moseley, C.J. Hughes, P.R. Juvvadi, E.J. Soderblom, S. Lennon, S.R. Perkins, J.W.

520

Thompson, W.J. Steinbach, S.J. Geromanos, J. Wildgoose, J.I. Langridge, K. Richardson, J.P.C.

521

Vissers, Scanning Quadrupole Data-Independent Acquisition, Part A: Qualitative and

522

Quantitative Characterization, J. Proteome Res., 17 (2018) 770-779. [22] Y. Zhang, A. Bilbao, T. Bruderer, J. Luban, C. Strambio-De-Castillia, F. Lisacek, G.

524

Hopfgartner, E. Varesio, The Use of Variable Q1 Isolation Windows Improves Selectivity in LC-

525

SWATH-MS Acquisition, J. Proteome Res., 14 (2015) 4359-4371.

TE D

523

[23] C. Escher, L. Reiter, B. MacLean, R. Ossola, F. Herzog, J. Chilton, M.J. MacCoss, O. Rinner,

527

Using iRT, a normalized retention time for more targeted measurement of peptides, Proteomics,

528

12 (2012) 1111-1121.

EP

526

[24] B.C. Collins, L.C. Gillet, G. Rosenberger, H.L. Rost, A. Vichalkovski, M. Gstaiger, R.

530

Aebersold, Quantifying protein interaction dynamics by SWATH mass spectrometry: application

531

to the 14-3-3 system, Nat Methods, 10 (2013) 1246-1253.

532 533 534 535

AC C

529

[25] Y. Liu, R. Huttenhain, B. Collins, R. Aebersold, Mass spectrometric protein maps for biomarker discovery and clinical research, Expert Rev. Mol. Diagn., 13 (2013) 811-825. [26] R. Bonner, G. Hopfgartner, SWATH acquisition mode for drug metabolism and metabolomics investigations, Bioanalysis, 8 (2016) 1735-1750.

536

[27] A. Bilbao, E. Varesio, J. Luban, C. Strambio-De-Castillia, G. Hopfgartner, M. Muller, F.

537

Lisacek, Processing strategies and software solutions for data-independent acquisition in mass

538

spectrometry, Proteomics, 15 (2015) 964-980.

17

ACCEPTED MANUSCRIPT 539

[28] H. Tsugawa, T. Cajka, T. Kind, Y. Ma, B. Higgins, K. Ikeda, M. Kanazawa, J. VanderGheynst,

540

O. Fiehn, M. Arita, MS-DIAL: data-independent MS/MS deconvolution for comprehensive

541

metabolome analysis, Nat. Methods, 12 (2015) 523.

542

[29] H. Li, Y. Cai, Y. Guo, F. Chen, Z.-J. Zhu, MetDIA: Targeted Metabolite Extraction of

543

Multiplexed MS/MS Spectra Generated by Data-Independent Acquisition, Anal. Chem., 88

544

(2016) 8757-8764. [30] T. Kind, H. Tsugawa, T. Cajka, Y. Ma, Z. Lai, S.S. Mehta, G. Wohlgemuth, D.K. Barupal, M.R.

546

Showalter, M. Arita, O. Fiehn, Identification of small molecules using accurate mass MS/MS

547

search, Mass Spectrom. Rev., DOI 10.1002/mas.21535(2017).

RI PT

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[31] T. Bruderer, E. Varesio, A.O. Hidasi, E. Duchoslav, L. Burton, R. Bonner, G. Hopfgartner,

549

Metabolomic spectral libraries for data-independent SWATH liquid chromatography mass

550

spectrometry acquisition, Anal. Bioanal. Chem., 410 (2018) 1873-1884.

SC

548

[32] T. Bruderer, E. Varesio, G. Hopfgartner, The use of LC predicted retention times to extend

552

metabolites identification with SWATH data acquisition, J. Chromatogr. B, 1071 (2017) 3-10.

553

[33] G. Chen, S. Walmsley, G.C.M. Cheung, L. Chen, C.Y. Cheng, R.W. Beuerman, T.Y. Wong, L.

554

Zhou, H. Choi, Customized Consensus Spectral Library Building for Untargeted Quantitative

555

Metabolomics Analysis with Data Independent Acquisition Mass Spectrometry and MetaboDIA

556

Workflow, Anal. Chem., 89 (2017) 4897-4906.

M AN U

551

[34] H. Zha, Y. Cai, Y. Yin, Z. Wang, K. Li, Z.-J. Zhu, SWATHtoMRM: Development of High-

558

Coverage Targeted Metabolomics Method Using SWATH Technology for Biomarker Discovery,

559

Anal. Chem., 90 (2018) 4062-4070.

TE D

557

[35] M.P. Elmiger, M. Poetzsch, A.E. Steuer, T. Kraemer, Parameter Optimization for Feature and Hit

561

Generation in a General Unknown Screening Method—Proof of Concept Study Using a Design

562

of Experiment Approach for a High Resolution Mass Spectrometry Procedure after Data

563

Independent Acquisition, Anal. Chem., 90 (2018) 3531-3536.

EP

560

[36] B.C. Collins, C.L. Hunter, Y. Liu, B. Schilling, G. Rosenberger, S.L. Bader, D.W. Chan, B.W.

565

Gibson, A.-C. Gingras, J.M. Held, M. Hirayama-Kurogi, G. Hou, C. Krisp, B. Larsen, L. Lin, S.

566

Liu, M.P. Molloy, R.L. Moritz, S. Ohtsuki, R. Schlapbach, N. Selevsek, S.N. Thomas, S.-C.

567

Tzeng, H. Zhang, R. Aebersold, Multi-laboratory assessment of reproducibility, qualitative and

568

quantitative performance of SWATH-mass spectrometry, Nat. Commun., 8 (2017) 291.

AC C

564

569

[37] T.V. Olah, D.A. McLoughlin, J.D. Gilbert, The simultaneous determination of mixtures of drug

570

candidates by liquid chromatography/atmospheric pressure chemical ionization mass

571

spectrometry as an in vivo drug screening procedure, Rapid Commun. Mass Spectrom., 11

572

(1997) 17-23.

573 574

[38] N. Zhang, S.T. Fountain, H. Bi, D.T. Rossi, Quantification and Rapid Metabolite Identification in Drug Discovery Using API Time-of-Flight LC/MS, Anal. Chem., 72 (2000) 800-806.

18

ACCEPTED MANUSCRIPT 575

[39] C.T. Viswanathan, S. Bansal, B. Booth, A.J. DeStefano, M.J. Rose, J. Sailstad, V.P. Shah, J.P.

576

Skelly, P.G. Swann, R. Weiner, Quantitative bioanalytical methods validation and

577

implementation: best practices for chromatographic and ligand binding assays, Pharm. Res., 24

578

(2007) 1962-1973. [40] D. Tonoli, E. Varesio, G. Hopfgartner, Quantification of acetaminophen and two of its

580

metabolites in human plasma by ultra-high performance liquid chromatography-low and high

581

resolution tandem mass spectrometry, J. Chromatogr. B, 904 (2012) 42-50.

RI PT

579

582

[41] G. Hopfgartner, D. Tonoli, E. Varesio, High-resolution mass spectrometry for integrated

583

qualitative and quantitative analysis of pharmaceuticals in biological matrices, Anal. Bioanal.

584

Chem., 402 (2012) 2587-2596.

[42] K. Arnhard, A. Gottschall, F. Pitterl, H. Oberacher, Applying ‘Sequential Windowed Acquisition

586

of All Theoretical Fragment Ion Mass Spectra’ (SWATH) for systematic toxicological analysis

587

with liquid chromatography-high-resolution tandem mass spectrometry, Anal. Bioanal. Chem.,

588

407 (2015) 405-414.

M AN U

SC

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589

[43] X. Zhu, Y. Chen, R. Subramanian, Comparison of information-dependent acquisition, SWATH,

590

and MS(All) techniques in metabolite identification study employing ultrahigh-performance

591

liquid chromatography-quadrupole time-of-flight mass spectrometry, Anal. Chem., 86 (2014)

592

1202-1209.

594

[44] S.G. Ma, S.K. Chowdhury, Data acquisition and data mining techniques for metabolite identification using LC coupled to high-resolution MS, Bioanalysis, 5 (2013) 1285-1297.

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595

[45] D. Siegel, A.C. Meinema, H. Permentier, G. Hopfgartner, R. Bischoff, Integrated quantification

596

and identification of aldehydes and ketones in biological samples, Anal. Chem., 86 (2014) 5089-

597

5100.

[46] J. Zhou, Y. Li, X. Chen, L. Zhong, Y. Yin, Development of data-independent acquisition

599

workflows for metabolomic analysis on a quadrupole-orbitrap platform, Talanta, 164 (2017) 128-

600

136.

602

[47] A.R. Buchberger, K. DeLaney, J. Johnson, L. Li, Mass Spectrometry Imaging: A Review of

AC C

601

EP

598

Emerging Advancements and Future Insights, Anal. Chem., 90 (2018) 240-265.

603

[48] J.F. Cahill, V. Kertesz, T. Porta, J.C.Y. LeBlanc, R.M.A. Heeren, G.J. Van Berkel, Solvent

604

effects on differentiation of mouse brain tissue using laser microdissection 'cut and drop'

605

sampling with direct mass spectral analysis, Rapid Commun. Mass Spectrom., 32 (2018) 414-

606

422.

607

[49] R. Bade, J.M. White, C. Gerber, Qualitative and quantitative temporal analysis of licit and illicit

608

drugs in wastewater in Australia using liquid chromatography coupled to mass spectrometry,

609

Anal. Bioanal. Chem., 410 (2018) 529-542.

610 611

[50] A. Kaufmann, S. Walker, Nested data independent MS/MS acquisition, Anal. Bioanal. Chem., 408 (2016) 5031-5040. 19

ACCEPTED MANUSCRIPT 612

[51] M. Murphy, G. Ivosev, S. Tate, Y. Kang, N. Bloomfield, Using Scanning SWATH® Windows to

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Improve Both Quantitative and Qualitative Data Over Conventional SWATH and IDA

614

Methodologies, 64th ASMS Conference on Mass Spectrometry and Allied Topics, San Antonio,

615

TX, 2016. [52] C.R. Weisbrod, J.K. Eng, M.R. Hoopmann, T. Baker, J.E. Bruce, Accurate peptide fragment

617

mass analysis: multiplexed peptide identification and quantification, J. Proteome Res., 11 (2012)

618

1621-1632.

RI PT

616

619

[53] P.C. Carvalho, X. Han, T. Xu, D. Cociorva, M.d.G. Carvalho, V.C. Barbosa, I.I.I.J.R. Yates,

620

XDIA: improving on the label-free data-independent analysis, Bioinformatics, 26 (2010) 847-

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