Influence of Mass Resolving Power in Orbital Ion-Trap Mass

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Influence of mass resolving power in orbital iontrap mass spectrometry-based metabolomics Lukáš Najdekr, David Friedecký, Ralf Tautenhahn, Tomáš Pluskal, Junhua Wang, Yingying Huang, and Tomas Adam Anal. Chem., Just Accepted Manuscript • Publication Date (Web): 03 Nov 2016 Downloaded from http://pubs.acs.org on November 3, 2016

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Analytical Chemistry

1

Influence of mass resolving power in orbital ion-trap

2

mass spectrometry-based metabolomics

3

Lukáš Najdekr1,2, David Friedecký1,2, Ralf Tautenhahn3, Tomáš Pluskal4, Junhua Wang3,

4

Yingying Huang3, Tomáš Adam1,2

5

1

Laboratory of Metabolomics, Institute of Molecular and Translational medicine, Palacký University in

6

Olomouc, Hněvotínská 5, 775 15 Olomouc, Czech Republic 2

7

University Hospital Olomouc, I.P. Pavlova 185/6, 779 00 Olomouc, Czech Republic 3

8 9

4

Thermo Fisher Scientific, 355 River Oaks Parkway, San Jose, 95134 CA, USA

Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, MA 02142-1479, USA

10 11

Abstract

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Modern separation methods in conjunction with high resolution accurate mass (HRAM)

13

spectrometry can provide an enormous number of features characterized by exact mass and

14

chromatographic behavior. Higher mass resolving power usually requires longer scanning

15

times, and thus fewer data points are acquired across the target peak. This could cause

16

difficulties for quantification, feature detection and deconvolution. The aim of this work was

17

to describe the influence of mass spectrometry resolving power on profiling metabolomics

18

experiments.

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From metabolic databases (HMDB, LipidMaps, KEGG), a list of compounds (41 474) was

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compiled and potential adducts and isotopes were calculated (622 110 features). The number

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of distinguishable masses was calculated for up to 3840k resolution. To evaluate these

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models, human plasma samples were analyzed by LC-HRMS on an Orbitrap Elite hybrid

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mass spectrometer (Thermo Fisher Scientific, CA, USA) at resolving power settings of 15k

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(7.8 Hz) up to a maximum of 480k (1.2 Hz). Software XCMS 1.44, MZmine 2.13.1 and

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Compound Discoverer 2.0.0.303 were used for evaluation.

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In plasma samples, the number of detected features increased sharply up to 60k in both

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positive and negative mode. However, beyond these values, it either flattened out or

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decreased owing to technical limitations.

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In conclusion, the most effective mass resolving powers for profiling analyses of metabolite

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rich bio-fluids on the Orbitrap Elite were around 60 000 - 120 000 FWHM in order to retrieve

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the highest amount of information. The region between 400 – 800 m/z was influenced the

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most by resolution.

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Graphical Abstract

34 35

Introduction

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Analysis of complex samples by modern separation methods in conjunction with high

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resolution accurate mass (HRAM) spectrometry can yield an enormous number of features


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characterized by exact mass and chromatographic behavior. High resolution mass

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spectrometry analyzers are usually based on FT-ICR, double focused magnetic sectors,

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reflectron time-of-flight mass analyzers or ion traps. The last two techniques are

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predominantly used in analyses of biological samples. A resolution of several tens of

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thousands FWHM (full-width-at-half-maximum) with high speed data acquisition up to 100

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Hz can be achieved with current time-of-flight instruments (TOF), for which scan rate is

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independent of resolution. In contrast, mass spectrometers based on an orbital ion trap using

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fast Fourier transformation (FFT) allow a resolution of up to 500 000 FWHM (at 200 m/z) at

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the expense of lower acquisition rates. Hence, their higher mass resolving power usually

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requires a longer scanning times, and consequently fewer data points are acquired across the

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studied peak. This could cause problems for feature detection, the deconvolution of peaks and

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quantification. Mass spectrometry measurement with a precision of four decimal places is

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crucial for molecular formula prediction. With increasing resolution, the number of

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compounds with apparently identical m/z decreases owing to isobaric matrix interferences. In

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many analyses of highly complex samples (e.g., metabolomics, proteomics), the balance

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between speed of mass spectral acquisition and mass resolution is an issue.

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Chromatographic separation of complex biological matrices is still a considerable

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challenge. The human serum metabolome is chemically highly variable and consists of many

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classes of metabolites, including lipids (e.g., glycerolipids, phospholipids), amino acids,

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hydroxycarboxylic acids, purines, etc. Analysis of such complex matrices is usually very

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difficult and requires several different separation techniques (liquid chromatography, gas

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chromatography, capillary electrophoresis)

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metabolites can vary over six orders of magnitude. It has been reported in many studies that

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right choice of separation methods may significantly improve number of detected features (3-

(1-2)

. Furthermore, concentration levels of



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5

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spectrometry data is crucial.

). Despite the high efficiency and selectivity of available separation methods, HRAM

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The aim of this work was to describe this relationship by both theoretical and

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experimental methods. In the first part, we compiled a list of 41 474 metabolites available in

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public databases (HMDB, LipidMaps, KEGG) and calculated 622 110 potential adducts and

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isotopes. Values of the partition coefficient (LogP) for each metabolite were retrieved from

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the databases if available. The resulting lists were used for subsequent in silico calculations.

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In the second part of the study, human plasma was analyzed at different mass spectral

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resolutions and the experimental data was compared with the theoretically predicted behavior

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of a high resolution mass spectrometer.

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Materials and Methods

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Chemicals

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Solvents acetonitrile, methanol and water (all LC-MS quality) and acetone (HPLC

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quality) as well as formic acid were purchased from Sigma-Aldrich (St. Louis, USA).

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Samples

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Plasma samples from healthy volunteers were collected at the University Hospital

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Olomouc (Czech Republic). The samples were pooled and then stored at -80°C until analysis.

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Written informed consent according to the Declaration of Helsinki by the World Medical

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Association (WMA) was obtained from the volunteers for all samples used in the analyses.

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In silico calculations


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To obtain a comprehensive list of compounds known to constitute the human

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metabolome, a list of positively ionizable metabolites was compiled from the HMDB,

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LipidMaps and KEGG databases (41 474 metabolites in total after removing duplicates). All

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calculations were performed using R software (6) in conjunction with the package Rdisop (7-10).

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For each metabolite, the isotopic pattern based on the chemical formula was generated. From

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the database generated list, adducts for M, M+1 and M+2 isotopes ([M+H]+, [M+NH4]+,

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[M+Na]+, [M+K]+, [M+ACN+H]+) were calculated (622 110 features). Mass distribution

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graphs for 15 000, 30 000, 60 000, 120 000, 240 000, 480 000, 960 000, 1 920 000 and 3 840

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000 FWHM at 400 m/z were then plotted. The resolution in orbital ion trap based

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spectrometers is not constant through all mass range, thus the correction for each m/z was

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made (Figure S-6). By removing isobars from the metabolite list (41 474) based on m/z, a list

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of unique m/z was generated (15 722). For each unique m/z in the list, the theoretical mass

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spectrometry peak width [m/z - x; m/z + x] was calculated, where x = m/z mass/(resolving

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power*((400/(m/z mass))^(1/2))). Consequently, the entire final list of 622 110 features was

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searched against the interval defining the number of features not detectable due to isobaric

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matrix interferences within the calculated range of each unique m/z (15 722).

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The influence of resolution on the number of detected peaks was calculated for m/z up

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to 2000. The list of generated in silico features (622 110) was filtered to give unique m/z

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values (227 060). The first value from the list of unique m/z was taken and the peak width

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based on resolution and its m/z were calculated. All m/z values lying within the peak width

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were grouped and removed from the list. The final number of groups was considered to be the

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number of peaks detectable in the mass spectrum for the given resolution and mass range.

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Sample preparation and LC-MS method



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Samples were prepared using a method modified from Yuan et al. (11) Pooled human

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plasma sample (500 µL) was deproteinated by mixture of acetonitrile, acetone and methanol

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(v/v 1:1:1, 1500 µL, -80°C), vortex mixed and incubated overnight at -80°C. Samples were

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centrifuged (24 400 x g, 15 min, 4°C), freezed-dried and re-suspended in 1 mL of 10%

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methanol and 90% water. Before analyses, samples were centrifuged again in order to remove

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the debris and other solid objects.

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The LC method followed that of Wang, J. et al.

(12)

using a Dionex UltiMate 3000

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Rapid Separation LC system (Thermo Fisher Scientific, MA, USA). Samples were analyzed

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on an Acquity UPLC BEH C18, 2.1 x 100 mm, 1.7 µm column (Waters, MA, USA). The

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mobile phase consisted of water with 0.1% formic acid (mobile phase A) and methanol with

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0.1% formic acid (mobile phase B). A flow rate of 0.35 mL/min was used with the following

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elution gradient: t=0.0, 0.5% B; t=4.0, 70% B; t=4.5, 98% B; t=10.4, 98% B; t=10.6, 0.5% B;

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t=15.0 min, 0.5% B. The column temperature was set at 40°C and the injection volume was 2

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µL. Peaks in the retention window from 1 – 15 minutes were chosen for data processing.

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Same LC method was used for both ionization modes (13).

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An Orbitrap Elite hybrid mass spectrometer (Thermo Fisher Scientific, MA, USA)

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was operated in either positive or negative mode at 15 000 (transient = 24 ms; 7.8 Hz), 30 000

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(transient = 48 ms; 7.7 Hz), 60 000 (transient = 96 ms; 6.9 Hz), 120 000 (transient = 192 ms;

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4 Hz), 240 000 (transient = 384 ms; 2.3 Hz)and 480 000 (transient = 768 ms; 1.2 Hz)FWHM

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at 400 m/z over the ranges 70–500 m/z and 300–2000 m/z (acquisition at 480 000 FWHM was

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possible owing to the use of a Tune Plus Developer’s Kit, kindly provided by Thermo Fisher

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Scientific, MA, USA). Two mass range regions were chosen in order to increase sensitivity

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and ensure one scan per spectrum (according to Mathieu equation). To eliminate variances

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due to data acquisition, analyses of plasma samples were performed in sextuplicate for each

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mass spectrometry resolution. Settings of the electrospray ionization were as follows: heater 6 ACS Paragon Plus Environment

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temperature 250°C; sheath gas 35 arbitrary units; auxiliary gas 15 arbitrary units; capillary

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temperature 300°C and source voltage +3.0 kV. A Thermo Tune Plus 2.7.0.1103 SP1 was

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used as instrument control software and data were acquired in centroid mode using Thermo

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Excalibur 2.2 SP1.48 software (Thermo Fisher Scientific, MA, USA).

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LC-MS data processing

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The acquired dataset from the plasma samples was processed using the three most

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frequently used software based on different feature detection algorithms, i.e., XCMS 1.44 (in

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R software environment), Compound Discoverer 2.0.0.303 and MZmine 2.13.1 centWave

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algorithm in XCMS, to detect regions of interest (ROI) within the particular m/z value. The

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Continuous Wavelet Transform (CWT) was applied to the intensity values of the ROI and

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local maxima in the CWT coefficients for each scale were determined

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algorithms are mainly influenced by the parameters ppm mass error (ppm) and signal-to-noise

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ratio (snthresh). Various values of these parameters were tested (ppm = 2, 4, 6, 8, 10, 12, 14,

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16, 18, 20; snthresh = 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30) and after detailed study of the

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results, “ppm= 8” and “snthresh=20” were chosen as the best settings in order to obtain less

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false positive features (noisy features). These findings correspond to the recently published

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work by Glauser et al.

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grouping and retention time correction methods, see the Supporting Information. After

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processing, number of peaks was counted for each data file individually. In case of XCMS

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and MZmine no deisotoping module or package was used.

(15)

(14)

. Peak detection

. For details of the settings for each peak detection algorithm, peak

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Retention time correction in each software was performed for individual sextuplicates.

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The processed lists of features for the ranges 70–500 m/z and 300–2000 m/z for each

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resolving power were merged at m/z 400 in order to obtain the number of features in the

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spectra per resolving power. Coefficient of variance (CV) was calculated based on detected 7 ACS Paragon Plus Environment


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areas across six replicate injections. Peaks with CV > 30% were considered as noise and

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removed from further calculations.

156 157 158 159

Results and Discussion

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In this work, we employed both theoretical and experimental methods to investigate

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the relation between mass spectrometry resolving power, scan speed and capability of feature

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detection in a metabolomics study.

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The effect of separation could not be included in the calculations owing to the

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unpredictable behavior of compounds during separation (e.g., lipids with very similar exact

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mass but different formula and chromatographic behavior) and extensive variability of

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chromatographic methods. Thus, the presented calculations are only valid for flow injection

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analysis metabolomics experiments and “worst case scenarios” in separation methods.

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In silico calculations

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In silico calculations were performed to examine distributions of overlaps of m/z

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representing (622 110) metabolites, isotopes and adducts over the range 50 – 2000 m/z. The

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first step was filtering the combined metabolite list to identify unique m/z values. These

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unique m/z values are plotted on the X axis in Figure 1, whereas the Y axis shows the number

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of m/z values that lie within the interval [m/z - x; m/z + x], as described in the Materials and

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Methods. Hence, the coordinates of each dot shown in Figure 1 represents the unique m/z

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values (X axis) and the number of features that are apparently identical at a given resolution

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and not recognizable within the curve of mass spectrometry peak with Gaussian profile (Y 8 ACS Paragon Plus Environment

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axis). The scale variance (sigma squared) of the mass spectral peak was indirectly

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proportional to the resolving power. Thus, the number of indistinguishable features decreased

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with increasing resolving power. Two major regions with the highest number of m/z overlaps

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can be seen in Figure 1. The first one is the region between 400 m/z and 600 m/z, which

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corresponds to short peptides (di-, tri-, tetra-), secosteroids and partially to lipids with lower

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m/z (e.g., glycerophosphocholines, glycerophosphoethanolamines, long chain fatty acids). The

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second region was between 750 m/z to 1050 m/z, which corresponds mainly to lipids. The

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three colored lines shown in Figure 1 indicate three different quantiles (0.99; 0.75; 0.50) of

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the dot density distribution.

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Figure 2 depicts the maximum number and median of the calculated overlapping m/z

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values at a particular resolving power. The number of m/z masked by isobaric matrix

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interferences decreased according to a power function with limit at one. Above a resolving

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power of 240 000 FWHM, the maximum number of indistinguishable features did not

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decrease. Evaluation of the structure of the data revealed that it was caused by isobaric

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compounds with high structural diversity. For example, m/z 244.1549 corresponds to a M+K

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ion of mass 205.1951 (C15H24) which applies to a group of sesquiterpenes and prenols with

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130 possible overlaps. The other most abundant feature overlaps (m/z 205.1956, 298.2746,

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322.2746, 450.3219) can mostly be attributed to various lipid classes and adducts

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corresponding to those lipids (see Figure 1). The overall abundance of lipids in the compiled

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metabolite list is 37.23 % (Fatty acyls: 6.28%; Glycerolipids: 9.35%; Glycerophospholipids:

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10.65%; Polyketides: 3.46%; Prenol lipids: 1.85%; Saccharolipids: 0.03%; Sphingolipids:

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1.34%; Sterol lipids: 4.27%). The median values (dashed line) show that even with very high

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resolving power, it is not possible to separate all the features fully. At a resolving power of

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3 840 000 FWHM, a maximum of 35.2 % of features were represented by a specific m/z with



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no overlaps, whereas for a typical resolving power of 60 000 FWHM, only 3.63 % of features

202

could be separated.

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Comparison of individual increments of features generated in silico was made (Figure

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3). In the range 0 to 600 m/z (Figure 3a), there was a huge increase from 1.47 (0–100 m/z) up

205

to 23.11 (501–600 m/z). In the range 600–1400 m/z, the opposite trend was observed from

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14.43 to 2.80 (Figure 3b). The curves in Figure 3c show similar trends in the range 1400–

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2000 m/z (ratios 2.85 to 3.67) but a different dependence than observed at lower m/z because

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the data plateaued at high resolution above 960 000 FWHM. Thus, in these theoretical

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calculations, a resolution of millions FWHM still had an effect on the calculated number of

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detectable unique masses.

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LC-MS data

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We analyzed plasma samples at different resolutions up to 480 000 FWHM to

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investigate the influence of resolution on the number of detected features. The analysis lasted

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15 minutes with gradient elution and a peak capacity P=167 (N = 90 000 – 576 000 N/m).

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Total ion chromatograms and extracted ion chromatograms of selected isomeric compounds

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are provided in the Supporting Information (Figure S-1, Figure S-3). Three different software

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were used for processing the LC-MS data (data shown in Figure 4). Software XCMS,

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MZmine and Compound Discoverer yielded similar trends, i.e., sharp increase in the number

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of detected features with maximum at 60 000 FWHM in both positive and negative mode

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(120 000 FWHM for Compound Discoverer in positive mode). When all peaks considered,

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regardless the CV, the trends are peaking at 120 000 FWHM in positive and 60 000 FWHM

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in negative mode. This findings suggesting that many noise peaks are detected during the

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peak picking at resolution 120 000 FWHM in positive mode (See Supporting Information

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Figure S-7, S-8 and S-9). Each software is capable of producing different types of lists of 10 ACS Paragon Plus Environment

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features. XCMS is detecting all features without any further filtering, referred to as “raw

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features” (for isotope and/or adduct grouping, other software modules should be used).

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MZmine is capable of identifying “raw features” or if the deisotoping module is applied,

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“isotopic features”. Thus, for ready comparison, XCMS raw feature and MZmine raw feature

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lists were used to generate the plots shown in Figure 4. The “Unknown Detector” module in

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Compound Discoverer is capable of detecting features only with the minimum number of

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isotopes set to one or more, generating an “isotopic feature” list. Numbers obtained by

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software Compound Discoverer (Figure 4C) represent the sum of compounds present in the

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mass spectrum as several different ion species grouped as one (grouped isotopes and adducts).

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For the abovementioned reasons, the absolute numbers in Figure 4 are not strictly comparable

235

and only the trends should be considered. All the software predicted an approximately five

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times higher number of features for the positive mode compared to the negative mode. This

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observation may origin from fact that plasma metabolites are predominantly ionized in

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positive mode. The physical-chemical properties of the compounds and mobile phase

239

composition may also contribute to this observed phenomena

240

features observed in negative mode, the necessity for higher resolution is less crucial.

(16)

. Due to lower number of

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In plasma samples in positive mode at 60 000 FWHM, 6778 features (MZmine, Figure

242

4B) were detected (if all peaks considered, regardless the CV, at 120 000 FWHM, 10 168

243

features were detected (MZmine, Supporting Information Figure S-8a)). The error bars at

244

higher resolutions will be result of more individual ion signals, therefore presenting a

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challenge for the peak detection algorithms. In contrast, the number of features revealed by

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the in silico calculations at 60 000 FWHM was 49 529 (Figure S-4). Although both analyses

247

took account of metabolites, isotopes and most common adducts, the number of features for

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the plasma samples should be in theory even higher because it includes fragments, noise

249

features and other features possibly generated by the electrospray ionization. The discrepancy 11 ACS Paragon Plus Environment


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in number of features may appear from different reasons. A large number of compounds listed

251

in the databases are present in biological samples at concentrations below the limit of

252

detection for current profiling methods (e.g., hormones, neurotransmitters) and non-targeted

253

metabolite extract, as well as contains exogenic compounds (drugs, food metabolites,

254

xenobiotics, etc.). Other fragments may be chemically and/or biologically unstable, and thus

255

lost. Poor ionizability of certain compound classes may also decrease number of detected

256

features. Another limitation is that some compounds are not retained or are trapped on the

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column, thus undetectable. Further, isobars may show unpredictable behavior under the given

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separation modes (e.g., reverse phase, aqueous normal phase, HILIC).

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Figure 5 presents histograms of m/z values (by XCMS) from plasma samples showing

260

the distribution of individual data points in Figure 4. The overall trend in the curves mostly

261

follows that observed in the in silico calculations (Figure 1). The region 300–800 m/z showed

262

a strong dependence on resolution in positive mode (Figure 5a). In contrast, the region 800–

263

1400 m/z showed almost same number of feature for 60 000 and 120 000 FWHM (Figure 5a)

264

suggesting less need for the high resolution in this region. The resolution in orbital based ion

265

traps detectors is not linear (Figure S-6). This effect result in lower resolving power in region

266

with higher m/z values and thus less number of detected features. In the negative ionization

267

mode (Figure 5b), all curves at resolutions from 15 000 to 120 000 FWHM showed similar

268

profiles. The number of detected features with m/z above 400 at resolutions of 240 000 and

269

480 000 FWHM was significantly decreased due to insufficient scan frequency (data points).

270

This issue may be overcome by using Orbitrap mass spectrometer capable of higher scanning

271

speed.

272

In mass spectrometers based on an orbital ion trap, a high resolving power is achieved (17)

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by using longer acquisition of ions in the trap, thus lowering the frequency of data points

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(Figure S-2). It is generally accepted that there should be a minimum of four points per peak 12 ACS Paragon Plus Environment

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(14)

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for automatic feature detection algorithms

. In order to minimize the influence of this

276

parameter, we conducted an experiment where the minimum number of data points per peak

277

was set to 3 (centWave). Regardless of the resolving power, a higher number of features was

278

detected (Figure S-5). However, detailed inspection of the data revealed that most of the

279

reported peaks were false positive hits. This may suggest that 60 000 - 120 000 FHWM is a

280

good compromise in terms of resolution and scan speed for metabolomics on the mass

281

spectrometer used in this study.

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Although, very high resolution may not be suitable for general untargeted

283

metabolomics experiment, it can be very useful to define isotopic distribution and

284

determination of elemental composition.

285

Our compiled list covered metabolites present in a given biological system not taking

286

into account differences in tissue/bio-fluid distribution. It is also containing exogenic

287

compounds (drugs, xenobiotics, food and plant metabolites), which may be present to varying

288

degrees in biological samples depending on their nature. In silico calculations in this study

289

were focused on human plasma and it would be interesting to see its application in plant

290

metabolomics where many metabolites are preferably ionized in negative mode. Different

291

scenario may also appear in lipidomics or glycomics which are heavily influenced by high

292

number of structural isomers.

293

294

Conclusion

295

The aim of this work was to address theoretically and experimentally the relation

296

between mass spectrometry resolution and capability of feature detection in a metabolomics

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experiment. In silico calculations showed that with increasing resolution, more features can be



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detected (limited by the maximum number of features possible for the particular biological

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matrix). LCMS data showed that the best resolution was 60 000 - 120 000 FWHM in positive

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and 60 000 FWHM in negative ionization mode for ESI, thus our findings suggest that in

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current metabolomic studies, a resolution above 60 000 FWHM is necessary to retrieve the

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highest amount of information.

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Funding:

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The infrastructural part of this project (Institute of Molecular and Translational

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Medicine) was supported by a NPU I (LO1304) and Czech Science Foundation Grant 15-

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34613L. Tomáš Pluskal is a Simons Foundation Fellow of the Helen Hay Whitney

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Foundation.

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References

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Psychogios, N.; Hau, D. D.; Peng, J.; Guo, A. C.; Mandal, R.; Bouatra, S.; Sinelnikov, I.; Krishnamurthy, R.; Eisner, R.; Gautam, B.; Young, N.; Xia, J.; Knox, C.; Dong, E.; Huang, P.; Hollander, Z.; Pedersen, T. L.; Smith, S. R.; Bamforth, F.; Greiner, R.; McManus, B.; Newman, J. W.; Goodfriend, T.; Wishart, D. S. PLoS One 2011, 6, e16957.

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R Core Team (2015). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.Rproject.org/

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Böcker, S.; Letzel, M. C.; Lipták, Z.; Pervukhin, A. Bioinformatics 2009, 25, 218–224. 14 ACS Paragon Plus Environment

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Dunn, W. B.; Broadhurst, D.; Begley, P.; Zelena, E.; Francis-McIntyre, S.; Anderson, N.; Brown, M.; Knowles, J. D.; Halsall, A.; Haselden, J. N.; Nicholls, A. W.; Wilson, I. D.; Kell, D. B.; Goodacre, R. Nat. Protoc. 2011, 6, 1060–1083.

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Figure 1: In silico calculation of mass distribution at resolution of 15 000 (A), 120 000 (B)

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and 960 000 (C) FWHM. The X axis shows the number of unique m/z values filtered from

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the compiled list of metabolites, whereas the Y axis shows the number of m/z values that fit

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into the interval [m/z - x; m/z + x], where x is based on the resolution. The lines denote

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different quantiles (from the top 0.99, 0.75 and 0.50, respectively). The colors of the 16 ACS Paragon Plus Environment

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independent dots indicate polarity: green = polar, red = non-polar (based on their value of

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logP – octanol/water).

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Figure 2: Regression of overlapping features based on resolution (in silico calculation).

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The full line represents the maximum value of indistinguishable features according to the

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resolving power. The dashed line shows the median value of indistinguishable features in the

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list of m/z for each resolving power. The percentage of m/z values represented in mass spectra

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by a single value is shown by the red line.

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Figure 3: Relative increase of detected features from in silico calculations divided into

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100 m/z bins. The Y axis represents the ratio of detected features at the specific resolution

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standardized by value for 15 000 FWHM. The X axis represents the resolution from 15 000 to

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3 840 000 FWHM.

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Figure 4: Number of detected features in plasma samples. Each part of the picture

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represents results from different software in both positive (yellow line) and negative mode

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(blue line): A) XCMS (raw features), B) MZmine (raw features), C) Compound list (grouped

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features as a compounds).

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Figure 5: Histograms of m/z values in plasma samples in positive (A) and negative (B)

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mode (XCMS). Each point on the lines represents the frequency of m/z values within a 50 Da

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window. Numbers in the legend shows resolution and scan speed respectively.

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KEYwords: untargeted metabolomics, Orbitrap, high resolution, peak-picking, resolving

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power, mass spectrometry

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Shortcuts:

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CV

Coefficient of variance

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HRAM

High resolution accurate mass

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HMDB

Human Metabolome Database

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KEGG

Kyoto Encyclopaedia of Genes and Genomes

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LC

Liquid chromatography

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LC-HRMS

Liquid chromatography-high resolution mass spectrometry

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LC-MS

Liquid chromatography mass spectrometry

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HPLC

High performance liquid chromatography

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FWHM

Full-width-at-half-maximum

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FFT

Fast Fourier transformation

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TOF

Time-of-flight

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WMA

World Medical Association

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CWT

Continuous Wavelet Transform 19 ACS Paragon Plus Environment


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ROI

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Regions of interest

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Table of content: Sample preparation Peak detection algorithm settings Figure S-1: Total ion chromatograms Figure S-2: Lower number of datapoints Figure S-3: Separation of isomeric compounds on the column Figure S-4: Number of detectable compounds based on the list of unique masses Figure S-5: XCMS peak picking with 3 points/peak Figure S-6: Dependency of m/z and resolution in Orbitrap based mass spectrometers Figure S-7: All detected features by Compound Discoverer Figure S-8: All detected features by MZmine Figure S-9: All detected features by XCMS 394


page S-3 S-3 S-5 S-7 S-9 S - 10 S - 11 S - 12 S - 13 S - 14 S - 15

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Graphical abstrakt Graphical abstrakt

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Figure 1: In silico calculation of mass distribution at resolution of 15 000 (A), 120 000 (B) and 960 000 (C) FWHM. The X axis shows the number of unique m/z values filtered from the compiled list of metabolites, whereas the Y axis shows the number of m/z values that fit into the interval [m/z - x; m/z + x], where x is based on the resolution. The lines denote different quantiles (from the top 0.99, 0.75 and 0.50, respectively). The colors of the independent dots indicate polarity: green = polar, red = non-polar (based on their value of logP – octanol/water). Figure 1 241x291mm (300 x 300 DPI)


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Figure 2: Regression of overlapping features based on resolution (in silico calculation). The full line represents the maximum value of indistinguishable features according to the resolving power. The dashed line shows the median value of indistinguishable features in the list of m/z for each resolving power. The percentage of m/z values represented in mass spectra by a single value is shown by the red line. Figure 2 77x75mm (300 x 300 DPI)



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Figure 3: Relative increase of detected features from in silico calculations divided into 100 m/z bins. The Y axis represents the ratio of detected features at the specific resolution standardized by value for 15 000 FWHM. The X axis represents the resolution from 15 000 to 3 840 000 FWHM. Figure 3 76x24mm (300 x 300 DPI)


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Figure 4: Number of detected features in plasma samples. Each part of the picture represents results from different software in both positive (yellow line) and negative mode (blue line): A) XCMS (raw features), B) MZmine (raw features), C) Compound list (grouped features as a compounds). Figure 4 255x77mm (300 x 300 DPI)



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Figure 5: Histograms of m/z values in plasma samples in positive (A) and negative (B) mode (XCMS). Each point on the lines represents the frequency of m/z values within a 50 Da window. Numbers in the legend shows resolution and scan speed respectively. Figure 5 77x28mm (300 x 300 DPI)


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Influence of Mass Resolving Power in Orbital Ion-Trap Mass

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