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Development of an LC-HRMS metabolomics method with high specificity for metabolite identification using all ion fragmentation (AIF) acquisition Shama Naz, Hector Gallart-Ayala, Stacey Reinke, Caroline Mathon, Richard Blankley, Romanas Chaleckis, and Craig Edward Wheelock Anal. Chem., Just Accepted Manuscript • Publication Date (Web): 22 Jun 2017 Downloaded from http://pubs.acs.org on June 23, 2017
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Analytical Chemistry
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Development of an LC-HRMS metabolomics method with high specificity for metabolite
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identification using all ion fragmentation (AIF) acquisition
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Shama Naz‡ǂ, Hector Gallart-Ayala‡ǂ, Stacey N. Reinke‡, Caroline Mathon‡, Richard Blankley§,
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Romanas Chaleckis‡ ll, and Craig E. Wheelock‡ ll *
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‡
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Karolinska Institutet, Stockholm, Sweden
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§
Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics,
Agilent Technologies, Cheadle, Cheshire, UK
ll
Gunma University Initiative for Advanced Research (GIAR), Gunma University, Gunma, Japan
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ǂ
Authors equally contributed to the manuscript
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*
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Craig E. Wheelock, PhD
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Division of Physiological Chemistry 2
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Department of Medical Biochemistry and Biophysics
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Karolinska Institutet, 17177 Stockholm, Sweden
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Email:
[email protected] 18
Phone: +46 8 524 87630, Fax: +46 8 736 0439
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Correspondence to be addressed to:
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Abstract
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High-resolution mass spectrometry (HRMS)-based metabolomics approaches have made
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significant advances. However, metabolite identification is still a major challenge and significant
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bottleneck in translating metabolomics data into biological context. In the current study, a liquid
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chromatography (LC)–HRMS metabolomics method was developed using an all ion
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fragmentation (AIF) acquisition approach. In order to increase the specificity in metabolite
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annotation, four criteria were considered: i) accurate mass (AM), ii) retention time (RT), iii)
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MS/MS spectrum, and iv) product/precursor ion intensity ratios. We constructed an in-house
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mass spectral library of 413 metabolites containing AMRT and MS/MS spectra information at
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four collision energies. The % relative standard deviations between ion ratios of a metabolite in
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an analytical standard vs. sample matrix were used as an additional metric for establishing
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metabolite identity. A data processing method for targeted metabolite screening was then
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created, merging m/z, RT, MS/MS and ion ratio information for each of the 413 metabolites. In
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the data processing method, the precursor ion and product ion were considered as the quantifier
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and qualifier ion, respectively. We also included a scheme to distinguish co-eluting isobaric
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compounds by selecting a specific product ion as the quantifier ion instead of the precursor ion.
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An advantage of the current AIF approach is the concurrent collection of full scan data, enabling
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identification of metabolites not included in the database. Our data acquisition strategy enables a
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simultaneous mixture of database-dependent targeted and non-targeted metabolomics in
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combination with improved accuracy in metabolite identification, increasing the quality of the
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biological information acquired in a metabolomics experiment.
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High-resolution mass spectrometry (HRMS)-based metabolomics has become an integral method
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for understanding health and disease 1,2. Metabolomics has been used to obtain insight into
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multiple biochemical processes including biomarker discovery, food safety, and nutrition 1,3,4,
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and is considered an important component of precision medicine initiatives 5. These studies are
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often performed without a prior knowledge of metabolite identity, and compound identification
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is frequently based upon database searches (e.g., HMDB, Metlin, KEGG) 6-8. The accurate
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identification/annotation of metabolites is a vital component of transferring HRMS data into
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biological information; however, it remains a significant bottleneck in non-targeted
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metabolomics to correctly annotate the biological identity of a detected feature 9-12. Several
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guidelines have been proposed for metabolite identification to aid in the ability to directly
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compare data from different studies and laboratories 13-16, following upon the criteria proposed
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by the Metabolite Standard Initiative (MSI) 17.
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The MSI defined four levels of metabolite identification 17. The highest level (level 1) is
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based on matching two or more orthogonal properties [e.g., accurate mass (AM), retention time
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(RT)/index, isotopic pattern, MS/MS spectrum] of an authentic reference standard analyzed
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under the same condition as the metabolite of interest. This level of structural information
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provides a high level of confidence in metabolite identity, but is resource intensive. Attempts
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have also been made to develop targeted screening methods for a large number of metabolites 18-
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of targeted metabolites, which restricts discovery efforts to those metabolites included in the
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targeted list. While generating MS/MS data for all metabolites is challenging, a number of tools
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have been proposed for in silico MS/MS-based metabolite identification 21-23. However, even this
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level of annotation does not address the issue of identifying co-eluting isobaric compounds,
. However, while useful, these screening methods are by definition limited to a selected group
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which traditionally requires chromatographic resolution. To improve metabolite identification
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and reduce the requirement for multiple analytical runs for structural confirmation, two different
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MS/MS strategies have been implemented to date in non-targeted metabolomics: i) with
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selection of the precursor ion (data dependent acquisition), and ii) without selection of the
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precursor ion [all ion fragmentation (AIF), data independent acquisition (DIA), and MSE] 24-31.
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DIA-based MS generates MS/MS spectra that contain a mixed population of product ions
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together with their precursor ions and the extracted ion chromatogram (EIC) of each product ion
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needs to be mapped to its parent compound. This can be a challenging process; however, recent
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software developments have addressed some of these issues 32,33. The AIF and MSE approaches
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have been successfully used to conduct multiple fragmentation experiments in a single
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acquisition 30,31,34,35. The difficulty in identifying co-eluting isobaric compounds has been
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suggested to be solved using a DIA-based approach in combination with software deconvolution
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algorithms that merge precursor ions from low energy experiments and product ions from high
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energy experiments 32. This approach has been successfully applied in lipidomics 35,36.
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The aim of the current work was to establish a comprehensive analytical workflow for the
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application of liquid chromatography-mass spectrometry (LC-MS) to non-targeted
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metabolomics, with a high level of accuracy in metabolite identification employing the all ion
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fragmentation (AIF) approach. To accomplish this aim, we developed an HRMS-based
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metabolomics method coupled to both reversed phase (RP) and hydrophilic interaction liquid
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chromatography (HILIC) for metabolite screening. The applied AIF mode includes three
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sequential full scans at 0, 10 and 30 eV collision energies. In the subsequent data analysis, EIC
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from any precursor or associated product ions of interest can be extracted from the low or high
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energy scan data. One EIC is chosen for relative quantification (the quantifier ion) of the
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metabolite and further product ions from the same compound are used as qualifier ions. The
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ratios of qualifier/quantifier ion intensities are established from authentic analytical standards,
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and should be preserved when measured in a biological sample, increasing the accuracy of the
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identification. The same acquired data (0 eV) can be used in parallel for a global metabolite
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profiling workflow, enabling a combined database-dependent targeted and non-targeted
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metabolomics experiment. The combination of the AIF-based data acquisition with the ion ratio
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confirmation and deconvoluted co-eluting isobaric pairs provides a useful method for increasing
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the accuracy of metabolite identification in a metabolomics experiment.
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Materials and methods
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Reagents and Chemicals LC-MS grade water and formic acid were purchased from Sigma-Aldrich (St. Louis,
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USA). Acetonitrile (Optima ®-LC/MS), methanol (Optima ®-LC/MS) and 2-propanol (Optima
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®-LC/MS) were purchased from Fisher-Scientific (Loughborough, UK). The internal lock
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masses (purine and HP-0921) and tune mix for calibrating the TOF-MS (ESI-low concentration
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tuning mix) were purchased from Agilent Technologies (Santa Clara, USA). The analytical
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standards used to construct the compound spectral database, as well as the internal standards
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(Tables S1-S5), were purchased from Sigma Aldrich (St. Louis, USA), Cayman Chemical
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Company (Michigan, USA), Toronto Research Chemicals (Ontario, Canada), Zhejiang Ontores
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Biotechnologies Co., Ltd (Zhejiang, China) and Avanti Polar Lipids, Inc. (Alabama, USA)
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depending upon availability. The internal standards and standards were prepared at 1 mM
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concentrations in the appropriate solvent for dissolution, stored at −20 °C and diluted
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appropriately on the day of analysis.
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LC-HRMS instrumentation All experiments used a 1290 Infinity II ultra-high performance liquid chromatography
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(UHPLC) system coupled to a 6550 iFunnel quadrupole-time of flight (Q-TOF) mass
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spectrometer equipped with a dual AJS electrospray ionization source (Agilent Technologies,
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Santa Clara, CA, USA).
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Metabolite separation was performed with two complementary stationary phases. Polar
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metabolites were separated on a HILIC SeQuant® ZIC®-HILIC (Merck, Darmstadt, Germany)
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column 100 Å (100 mm × 2.1 mm, 3.5 µm particle size) coupled to a guard column (2.1 mm × 2
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mm, 3.5 µm particle size) and an inline-filter. Sample analysis in both positive and negative
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ionization mode was performed using water with 0.1 % formic acid (solvent A) and acetonitrile
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with 0.1 % formic acid (solvent B). The elution gradient used was as follows: isocratic step at 95
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% B for 1.5 min, 95 % B to 40 % B in 12 min and maintained at 40 % B for 2 min, then
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decreasing to 25% B at 14.2 min and maintained for 2.8 min, then returned to initial conditions
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over 1 min, and the column was equilibrated at initial conditions for 7 min. The flow rate was 0.3
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mL/min, injection volume was 2 µL and the column oven was maintained at 25 °C.
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Non-polar metabolites were separated on a RP Zorbax Eclipse Plus C18, RRHD (Agilent
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Technologies, USA) (100 mm × 2.1 mm, 1.8 µm particle size) column coupled to a guard
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column (5 mm × 2 mm, 1.8 µm particle size) and an inline-filter. Sample analysis in both
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positive and negative mode was performed using water with 0.1 % formic acid (solvent A) and
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2-propanol: acetonitrile (90:10, v/v) with 0.1 % formic acid (solvent B). The gradient elution was
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set as follows: isocratic step of 5 % B for 3 min, 5 % to 30 % B in 2 min, then B was increased to
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98 % in 13.5 min, maintained at 98 % B for 1.5 min, and returned to initial conditions over 0.5
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min, then held for a further 4.5 min. The flow rate was 0.4 mL/min, injection volume was 2 µL,
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and the column oven was maintained at 50 °C.
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Both chromatographic separations were coupled to an Agilent 6550 Q-TOF-MS system.
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The system was calibrated and tuned according to the protocols recommended by the
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manufacturer. Nitrogen (purity >99.999%) was used as a sheath gas and drying gas at a flow of 8
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L/min and 15 L/min, respectively. The drying and sheath gas temperature was set at 250 °C, with
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the nebulizer pressure at 35 psig and voltage 3000 V (+/- for positive and negative ionization
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mode). The fragmentor voltage was set at 380 V. The acquisition was obtained with a mass range
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of 40-1200 m/z for HILIC and 50-1200 m/z for RP in AIF mode, where a single high-resolution
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full scan is acquired including three sequential experiments at three alternating collision energies
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(one full-scan at 0 eV, followed by one MS/MS scan at 10 eV and then followed by one MS/MS
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scan at 30 eV). The data acquisition rate was 6 scans/s.
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An internal lock mass mixture (Agilent Technologies, Santa Clara, USA) was prepared at
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a final concentration of 2 µM purine (C5H4N4) and 2.5 µM HP-0921 (C18H18O6N3P3F24) in
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acetonitrile:water (19:1, v/v). The internal lock mass mixture was constantly infused at a flow
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rate of 1mL/min (split 1:100) using an isocratic pump together with the LC eluent for constant
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mass correction [positive ionization mode: purine (m/z 121.0509), HP-0921 (m/z 922.0098); and
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negative ionization mode: purine (m/z 119.0363), HP-0921 (m/z 966.0007, HP-0921+formate
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adduct)]. Although observed mass accuracy will depend upon the resolution, potential metabolite
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co-elution and isobaric compounds, a mass accuracy of
