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Metabolomics
ISSN: 2153-0769 JOM an open access journal
Research Article Open Access
Metabolomics : Open Access
Evans et al., Metabolomics 2014, 4:2
http://dx.doi.org/10.4172/2153-0769.1000132
Volume 4 • Issue 2 • 1000132
includes the precursor unit mass prole (including adducts, in-source
fragments, isotopes, etc.), retention time, and MS/MS spectra on the
ions from the authentic standard. Experimental data is then searched
against this library and detected compounds are rapidly identied. is
multi-criteria authentic standard library dramatically diminishes the
need for HRAM data for compound identication since the multiple
data streams (i.e., mass, retention and fragmentation pattern), provide
the needed specicity to make identications. Using this library, our
methodology monitors each sample for over 3200 endogenous and
exogenous metabolites. In addition, this library includes over 4000
chemicals whose identities have yet to be determined (unknowns). It is
important to note that while this library consists of such a large number
of compounds, not all compounds are detected in each experimental
analysis on a routine basis. Many are matrix specic; for example, found
in cells or urine only. Others may be species specic or disease specic.
e eld at large has yet to agree on the number of metabolites that are
routinely detected in mammalian or plant species and there is much
debate about how many should, can or will ultimately be detectable;
estimates range from the low hundreds to many thousands [4, 27, 28].
*Corresponding author: Evans AM, Metabolon, Inc., 617 Davis Drive, Suite
400, Durham, NC, USA, Tel: 919-287-3358; Fax: 919-572-1721; E-mail: aevans@
metabolon.com
Received: August 20, 2014; Accepted: September 24, 2014; Published:
September 26, 2014
Citation: Evans AM, Bridgewater BR, Liu Q, Mitchell MW, Robinson RJ, et al.
(2014) High Resolution Mass Spectrometry Improves Data Quantity and Quality
as Compared to Unit Mass Resolution Mass Spectrometry in High-Throughput
Proling Metabolomics. Metabolomics 4: 132. doi:10.4172/2153-0769.1000132
Copyright: © 2014 Evans AM, et al. This is an open-access article distributed
under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the
original author and source are credited.
High Resolution Mass Spectrometry Improves Data Quantity and Quality
as Compared to Unit Mass Resolution Mass Spectrometry in High-
Throughput Profiling Metabolomics
Evans AM
1*
, Bridgewater BR
1
, Liu Q
2
, Mitchell MW
1
, Robinson RJ
1
, Dai H, Stewart SJ
1
, DeHaven CD
1
and Miller LAD
1
1
Metabolon, Inc. 617 Davis Drive, Suite 400, Durham, NC 27713, USA
2
Analytical Research Laboratories, 840 Research Parkway, Suite 546, Oklahoma City, OK, 73104, USA
Keywords: Metabolomics; Accurate mass; High resolution; Liquid
chromatography
Introduction
Metabolomics has repeatedly demonstrated utility in identifying
biomarkers, elucidating disease and treatment mode of action,
bioprocess improvement and other areas of study [1-7]. e
instrumentation applied to metabolomics varies widely depending on
the approach used and the desired properties of the nal dataset. NMR
is oen utilized when rapid classication of study samples is needed;
however, NMR is limited by low sensitivity [8-13]. Triple quadrupole
mass spectrometers are oen used when the desired output is the
quantication of a specic subset of known biochemicals, oen referred
to as targeted metabolomics [14-16]; however, this approach is blind to
novel changes and novel biochemicals. Finally, there is small molecule
proling, otherwise known as non-targeted metabolomics, which
aims to detect and semi-quantify as many biochemicals, both known
and unknown, as possible. is approach is oen used to discover
new insights into biological phenomenon, but presents challenges in
compound identication and data processing. It is oen necessary
to utilize several of the above noted techniques in combination. For
example, non-targeted metabolomics techniques may be used to
discover biomarkers followed by targeted metabolomics, based on
standard analytical chemistry techniques, to validate the biomarkers
[17-19].
e current non-targeted high-throughput biochemical proling
approach utilized by our group diers from many other methodologies
in the eld, which typically rely on High Resolution Accurate Mass
(HRAM) data output to drive compound identication. A great deal of
literature has been focused on how to best utilize these data streams for
compound identication [20-25]. In our approach, rather than relying
on HRAM data to identify biochemicals, identications are based on
multiple orthogonal criteria to a unit mass spectral library built from
authentic standards, so called tier 1 identications [26]. is library
Abstract
Metabolomics is a technique in which the small molecule component from a biological source material is analyzed
for changes resulting from some set of test conditions. Liquid chromatography tandem mass spectrometry (LC/MS/MS)
methods are commonly used because of the sensitivity and specicity of the data collected. The sensitivity of these methods
permit the detection of a large number of small molecules, leading to greater coverage of the biochemical pathways
involved in the system being tested. The success of metabolomic studies are partially reliant upon instrumentation, but
to what extent? Here we present an evaluation of the analytical attributes of a high resolution accurate mass (HRAM)
orbitrap based mass spectrometer compared to a unit mass resolution (UMR) ion-trap mass spectrometer as applied to
high-throughput, non-targeted metabolomics. To carry out this evaluation, different sets of samples were analyzed and
the data evaluated for analytical performance. Two dilution series of authentic standards demonstrated that the HRAM
data stream improved the limit of detection from several fold to several orders of magnitude and showed an increased
linear dynamic range of an order of magnitude over the UMR data stream. Analysis of a biological serum sample set
demonstrated that the HRAM data stream enabled the detection of 118 additional named/known compounds, leading to
the detection of 531 tier 1 and tier 2 identied compounds in human serum, with decreased process variability, increased
consistency and accuracy of detection and integration.
Citation: Evans AM, Bridgewater BR, Liu Q, Mitchell MW, Robinson RJ, et al. (2014) High Resolution Mass Spectrometry Improves Data Quantity
and Quality as Compared to Unit Mass Resolution Mass Spectrometry in High-Throughput Proling Metabolomics. Metabolomics 4: 132.
doi:10.4172/2153-0769.1000132
Page 2 of 7
Volume 4 • Issue 2 • 1000132
Metabolomics
ISSN: 2153-0769 JOM an open access journal
Even though, given our methodology, accurate mass
instrumentation is not necessary for compound identication, we
wanted to assess the other potential analytical benets, above and beyond
compound identication, from the use of HRAM data on our non-
targeted metabolomics methodology [29]. e goal of this evaluation
was to compare and contrast the analytical performance characteristics
of HRAM data to Unit Mass Resolution (UMR) data. e analyses
included assessing Linear Dynamic Range (LDR), Limit Of Detection
(LOD)/sensitivity, scan rate, and mass accuracy, then determining how
these dierent factors impacted the process variability, the number of
compounds and features detected, and the overall quality of data in a
biological non-targeted metabolomics analysis.
To perform this evaluation, separate sets of data were analyzed. To
compare the limit of detection/sensitivity and linear dynamic range for
the dierent instrument data streams two dierent dilution series of
isotopically labeled standards, ranging from 0.05 ng/mL to 250,000 ng/
mL, and spanning almost seven orders of magnitude, were analyzed.
e dierent dilution series were designed to assess dierent aspects
of the sensitivity prole of the instruments. One series contained
standards which spanned chromatographic time and was analyzed
using reverse-phase chromatography while the other spanned a wider
mass range and was analyzed using Hydrophilic Interaction Liquid
Chromatography (HILIC). e next set of data analyzed was from
30 individual human serum samples, and 11 Quality Control (QC)
samples, which included six technical replicates of a pool of aliquots
from each of the 30 serum samples [30] to assess process variability,
and ve water blanks used to identify process contributed artifacts. e
individual serum and QC samples were used to compare the analytical
performance of the instrument data streams based on the total number
of chromatographic peaks detected, the total number of named/
known compounds that were detected and identied, scan speed, mass
accuracy and precision, process variability, reproducibility/consistency
and accuracy of detection and integration.
While we have not focused on data processing soware in this
manuscript, without such tools and methodologies the robust analysis
of the data would not have been possible. It is well established that
one challenge of any high-throughput screening methodology is data
processing. A great deal of previous work has established the necessary
soware applications, tools and methodologies in order to permit rapid
compound detection, integration, identication and QC of the data
streams being analyzed in this study [31-33].
Experimental Section
Sample Material
Found in Supplementary Information 1.
Sample Preparation
Dilution Series: e aliquots were analyzed on two separate
ermoFisher Scientic (Waltham, MA) mass spectrometers; a Linear
Ion-Trap (LTQ) and an Orbitrap (Q-Exactive), to determine the
limit of detection and the linear dynamic range of each instrument
for each standard. e two dierent series dilutions were prepared;
one destined for a reverse-phase chromatographic method and the
other for a Hydrophilic Interaction Liquid Chromatographic (HILIC)
method. e dilution series of standards ranged from 0.05 ng/mL to
250,000 ng/mL and included one blank. For the reverse-phase dilution
series, aliquots were dried and then reconstituted with 100 µL 0.1%
formic acid in water. e list of standards in the reverse-phase dilution
series can be found in Supplementary Information 2. For the HILIC
dilution series of energy metabolites, 50 µL aliquots were plated into
two 96-well PCR plates each at twice the nal concentration in 60/40
acetonitrile/10mM ammonium formate buer (pH 10.6) and brought
to nal concentration with 50 µL acetonitrile. e list of standards in
the HILIC dilution series can be found in Table 1 .
Biological Samples
Biological samples were stored at -80°C until needed and then
thawed on ice just prior to extraction. Extraction of samples was
executed using an automated liquid handling robot (Hamilton LabStar,
Hamilton Robotics, Inc., Reno, NV), where 450 µL of methanol was
added to 100 µl of sample to precipitate proteins. e methanol
contained four recovery standards, DL-2-uorophenylglycine,
tridecanoic acid, d6-cholesterol and 4-chlorophenylalanine to
allow conrmation of extraction eciency. Four aliquots of each
sample were taken from the extract and dried. For serum samples,
two aliquots of each sample were reconstituted in 50 µL of 6.5 mM
ammonium bicarbonate in water (pH 8) for the negative ion analysis
and another two aliquots of each were reconstituted using 50 µL 0.1%
formic acid in water (pH ~3.5) for the positive ion method. Urine
samples were extracted similarly but reconstituted with 100 µL of
reconstitution solvent. Reconstitution solvents contained instrument
internal standards (listed in Supplementary Information 2) to assess
instrument performance and to serve as retention index markers for
chromatographic alignment. Extracts of a pooled serum sample were
injected six times for each data set on each instrument to assess process
variability and ve water aliquots were also extracted and analyzed to
serve as process blanks for artifact determination.
UPLC Method
Separations were performed using a Waters Acquity UPLC
(Waters, Milford, MA). Reverse-phase (RP) positive ion method
analysis used mobile phases consisting of 0.1% formic acid in water
(A) and 0.1% formic acid in methanol (B). Reverse-phase negative
ion analysis used mobile phases consisting of 6.5 mM ammonium
bicarbonate in water, pH 8 (A) and 6.5 mM ammonium bicarbonate
in 95% methanol/ 5% water (B). e gradient proles can be found in
Supplementary Information 3. e sample injection volume was 5 µL
and a 2x needle loop overll was used. Separations utilized separate
acid and base-dedicated 2.1 mm × 100 mm Waters BEH C18 1.7 µm
columns held at 40°C.
HILIC used mobile phases consisting of 10 mM ammonium
formate in 15% water, 5% methanol, 80% acetonitrile (eective pH
10.16 with NH
4
OH) (A) and 10 mM ammonium formate in 50% water,
50% acetonitrile (eective pH 10.60 with NH
4
OH) (B). e gradient
proles can be found in Supplementary Information 3. e sample
injection volume was identical to the RP method. e stationary phase
consisted of a 2.1 mm × 150 mm Waters BEH Amide 1.7 µm column
Standard HRAM LOD ng/ml UMR LOD ng/ml Nominal m/z
Succinate 0.5 5 117
Malate 0.5 25 133
Alpha-ketoglutarate 0.1 25 145
pyruvate 10 100 175
*
ATP 2500 5000 506
NAD
+
250 250 540
NADH 500 500 664
*
dimer used for quantication
Table 1: Limit of Detection (LOD) for a Dilution Series of Standards Ranging in
Mass Using HILIC Chromatography.
Citation: Evans AM, Bridgewater BR, Liu Q, Mitchell MW, Robinson RJ, et al. (2014) High Resolution Mass Spectrometry Improves Data Quantity
and Quality as Compared to Unit Mass Resolution Mass Spectrometry in High-Throughput Proling Metabolomics. Metabolomics 4: 132.
doi:10.4172/2153-0769.1000132
Page 3 of 7
Volume 4 • Issue 2 • 1000132
Metabolomics
ISSN: 2153-0769 JOM an open access journal
held at 40°C.
Unit Mass Resolution (UMR) Method
A ermoFisher Scientic (Waltham, MA) LTQ was the unit mass
resolution instrument tested. Detailed source, MS and MS/MS settings
can be found in Supplementary Information 4. For all methods, the
scan range was 80-1000 m/z with a scan speed of ~4.5 scans per second
(alternating between MS and MS/MS scans). e MS/MS dynamic
exclusion option was enabled with the user-set exclusion duration time
of 3.5 s. Calibration of the LTQ instrument was performed as needed.
High Resolution Accurate Mass (HRAM) Method
A ermoFisher Scientic (Waltham, MA) Q-Exactive [34] was
the HRAM instrument tested. Detailed source, MS and MS
n
settings
can be found in Supplementary Information 4. e scan range was 80-
1000 m/z with a scan speed of ~9 scans per second (alternating between
MS and MS/MS scans), and the resolution was set to 35,000 (measured
at 200 m/z). Mass calibration was performed as needed to maintain <5
ppm mass error for all standards monitored.
Data Processing and Analysis
A detailed description of data processing including chromatographic
alignment, QC practices and compound identication can be found in
references [31-33]. A brief description is provided below.
Dilution Series Analysis
To analyze the data from the dilution series experiment, the
ermoFisher Scientic soware Xcalibur QuanBrowser was used
for peak detection and integration. is soware package targeted the
specic compounds in the dilution series and permitted optimization
of peak detection and integration criteria on a per-compound and a
per-sample basis. e integration of each individual chromatographic
peak was manually approved and the integration rened, if necessary,
for each standard in each step of the series to ensure an accurate
comparison of instrument performance.
Biological Sample Analysis
In-house peak detection and integration soware was used whose
data output was a list of m/z ratios, retention indices and area under
the curve (AUC) values. User specied criteria for peak detection
included thresholds for signal to noise ratio, area and width. Relative
standard deviations (RSDs) of peak area were determined for each
internal and recovery standard to conrm extraction eciency,
instrument performance, column integrity, chromatography and
mass calibration. e biological data sets, including QC samples, were
chromatographically aligned based on a retention index that utilized
internal standards assigned a xed RI value [35,36]. e RI of the
experimental peak was determined by assuming a linear t between
anking RI markers whose RI values are set.
Peaks were matched against an in-house library of authentic
standards and routinely detected unknown compounds specic to
the respective method. Identications were based on retention index
values within 150 RI units (~10 s), experimental precursor mass match
to the library authentic standard within 0.4 m/z for the LTQ or 0.005
m/z for the accurate mass instrument and quality of MS/MS match.
All proposed identications were then manually reviewed and hand
curated by an analyst who approved or rejected each identication
based on the criteria above [31,32].
Results and Discussion
Dilution Series Limit of Detection (LOD)/Sensitivity
e LOD of an instrument is a direct measurement of an
instrument’s capability to distinguish a compound’s signal from any
noise present in the mass channel. e lower the limit of detection,
the more sensitive the instrument is and therefore the more signals
can be detected and/or distinguished from noise. In the application
of non-targeted metabolomics, it is critical to be able to detect as
many compounds as possible and therefore any technology that oers
lower limits of detection and improved sensitivity provides increased
compound detection. To compare the limits of detection and therefore
sensitivity of each instrument, two separate dilution series, one using
reverse-phase chromatography and the other hydrophilic interaction
liquid chromatography (HILIC), were run. Each dilution series
contained a unique set of compounds used to assess dierent aspects
of instrument sensitivity; the reverse-phase dilution series standards
spanned the chromatographic time window and the HILIC dilution
series standards covered a wider mass range. e reverse-phase dilution
series included nine labeled standards ranging in concentration from
0.05 ng/mL to 250,000 ng/mL, with each concentration being run in
triplicate.
is dilution series demonstrated that the HRAM data stream had
consistently lower LODs than the UMR data stream (Supplementary
Information 2) in scanning mode. e degree of improved sensitivity
ranged from several fold to several orders of magnitude. e LOD
was determined as the lowest concentration where a discernible and
reproducible peak could be detected and/or distinguished from the
background in all technical replicates and demonstrated dilution from
the next higher concentration.
e improvement in sensitivity is likely a result of the decreased
noise associated with the smaller isolation window utilized with the
HRAM data. e HRAM data demonstrated better than 3 ppm mass
accuracy for the dilution standards and therefore, when integrating
peaks, a 5 ppm mass window could condently be used to detect and
quantify these standards. is meant that instead of having to use a
total mass window of 0.4 m/z, which was used for the UMR analysis, a
much smaller mass window could have been used to isolate the same
analytical signature. As an example, the mass window of 0.001 m/z
could be used to isolate the analytical signature for d3-leucine on the
HRAM data, while for this same signature in the UMR data one would
have to use a 0.4 m/z window. Ultimately, using smaller mass windows
included signicantly less noise thus improving the signal/noise ratio.
e reasoning for assessing the dierence in sensitivity of these two
instruments using dilution series was that oen the noise associated
with the HRAM data stream was minimal to non-existent, therefore
sensitivity was determined as the rst concentration where a signal was
detected.
It should be noted that the HRAM instrument tested contained
a dierent and newer source design than the UMR instrument. It is
possible that the new source design increased signal, which in addition
to the reduction in noise due to tighter mass tolerance, improved the
sensitivities of these standards as well. In order to better assess the
relative contribution of improved sensitivity resulting from an increase
in signal or a decrease in noise we performed another dilution series
which included several higher mass energy metabolites analyzed using
Citation: Evans AM, Bridgewater BR, Liu Q, Mitchell MW, Robinson RJ, et al. (2014) High Resolution Mass Spectrometry Improves Data Quantity
and Quality as Compared to Unit Mass Resolution Mass Spectrometry in High-Throughput Proling Metabolomics. Metabolomics 4: 132.
doi:10.4172/2153-0769.1000132
Page 4 of 7
Volume 4 • Issue 2 • 1000132
Metabolomics
ISSN: 2153-0769 JOM an open access journal
hydrophilic interaction liquid chromatography (HILIC). In general,
the amount of background noise in a mass spectrometer is higher at
lower masses and decreases with higher masses. is low mass noise
is primarily from solvent clusters and contaminants. If the gain in
sensitivity seen is driven by a reduction of noise, then at higher masses
the dierence between the HRAM data and the UMR data would be
less pronounced as there is less noise in the higher mass region. Table
1 shows that the LOD/sensitivity for the higher mass standards are the
same or very similar between the HRAM and UMR instruments. is
data supports the theory that the improvement in sensitivity between
these instruments is primarily due to the reduction of noise provided
by the tighter mass window tolerance.
Number of Compounds and Peaks Detected in a Biological
Sample Set
e dilution series data demonstrated the improved sensitivity of
the HRAM data over UMR data using standards in a neat environment.
In order to understand the implications of improved sensitivity on
biologically variable and complex samples, a sample set consisting
of 30 individual human serum samples, along with QC samples, was
analyzed. is sample set was run on both instruments and the data
monitored for approximately 3200 known compounds using an in-
house authentic standards library. Identication of compounds was
based on three criteria: 1) mass match within 0.4 m/z for the UMR
data and a very conservative value of 0.005 m/z for the HRAM data,
2) fragmentation spectral match, and 3) retention time/index match,
all to the authentic standard library entry for each compound. Data
was manually inspected to remove compounds not present with at least
3x greater concentration than the corresponding peaks found in the
water blanks and to assess quality of fragmentation spectral match and
consistency of integration of peaks sample to sample [31,32].
Figure 1 shows the number of named/known compounds detected
from the individual serum samples (list of all detected compounds
are in Supplementary Information 13 and 14). is data shows that
the HRAM data enabled the detection of more compounds when
monitoring for positive or negative ions as compared to UMR data. In
total, the HRAM data stream permitted the detection of an additional
118 unique compounds over the UMR data stream.
e major factors contributing to the increase in the number of
compounds detected by using HRAM data over UMR data are the
added mass resolution and sensitivity permitting the detection of
metabolites whose masses could not be resolved previously, either from
other compounds or from noise. Many examples of these phenomena
were seen in the data. Figure 2 demonstrates the ability of the improved
mass resolution to separate two known and co-eluting compounds. In
this example, the HRAM data stream (Figure 2A) permitted the clean
detection of the signicantly lower intensity N-acetylglutamine peak
underneath the N6-acetyllysine peak, whereas N-acetylglutamine
was masked by N6-acetyllysine in the UMR data (Figure 2B). ere
were also examples in the data where a metabolite could not be
dierentiated from the noise in the UMR data but could reproducibly
be detected using HRAM data (Figure 3). In Figure 3, the HRAM data
stream (Figure 3A) is able to distinguish the family of methylxanthine
compounds from a noisy mass channel that masks the family almost
entirely in the UMR data (Figure 3B). In addition to detecting more
unique/new compounds, the HRAM data also showed improved
consistency of detection, with more compounds being detected in
100% of the experimental samples in the HRAM data than the UMR
data (Supplementary Information 5).
e number of named compounds varies in dierent matrices. e
data presented here is from human serum, which is a relatively simple
matrix, in terms of number of compounds, when compared to other
matrices like urine or feces. For example, initial data using HRAM
instrumentation have demonstrated that we were able to detect almost
800 named compounds in feces (data not shown). It is important to
note that this large increase in detected compounds is accompanied by
a large increase in the number of chromatographic/mass peaks (ion-
features) detected. e HRAM data stream produced three to four
times more ion features than the UMR data stream (Supplementary
Information 6). e ~3-4x increase in ion-features did not translate
into 3-4x the number of compounds that were detected, because these
additional peaks had multiple sources. Some of these additional peaks
were from the detection of newly detectable known compounds as a
result of the improved sensitivity and mass resolution, as evidenced
by the increased number of named compounds detected. However, in
addition to these newly detected compounds, some of these new peaks
are simply new redundant measurements of the same parent compound
in the form of new adducts, in-source fragments, and multimers not
previously detected for an individual metabolite [20, 37,38]. Finally,
some of the additional peaks detected could represent compounds not
previously detected or characterized. As a result, data mining will likely
add to the number of named compounds.
Process Variability
e overall process variability of an analytical method contributes
signicantly to the ability to eectively detect changes in concentrations
of compounds within a biologically variable sample set. e lower
the process variability of the measurement for any given compound
the smaller the biologically relevant concentration shi which can be
accurately and reproducibly detected. In this way, it becomes imperative
to continually reduce the process variability in order to detect smaller
yet statistically signicant biological concentration changes.
Biological variability is routinely much higher than process
variability. erefore the technical replicates of the serum samples
Figure 1: Shows the number of named/known compounds identied per instru-
ment used and per chromatographic method used (Positive Ion and Negative
Ion Method). Many compounds were redundantly detected in both the positive
and negative ion methods. The total unique compound totals remove these
redundancies and provides a more accurate representation of the number of
unique named/known compounds identied.
Citation: Evans AM, Bridgewater BR, Liu Q, Mitchell MW, Robinson RJ, et al. (2014) High Resolution Mass Spectrometry Improves Data Quantity
and Quality as Compared to Unit Mass Resolution Mass Spectrometry in High-Throughput Proling Metabolomics. Metabolomics 4: 132.
doi:10.4172/2153-0769.1000132
Page 5 of 7
Volume 4 • Issue 2 • 1000132
Metabolomics
ISSN: 2153-0769 JOM an open access journal
were used to assess the process variability. In order to assess the eect
these two data streams had on overall process variability, the median
Relative Standard Deviation (RSD) for the compounds (excluding
internal standards) detected in 100% of the technical replicates (6 total)
was determined. e total process variability of the HRAM data set
was reduced 50% as compared to the UMR data for both the positive
and negative ion methods. e median RSD went from 13 in the
UMR data to 6 in the HRAM data, even though the HRAM data set
detected more total compounds (Supplementary Information 7). is
reduction in process variability seems to be mostly a result of improved
consistency and quality of peak detection and integration, again due
to the reduction of noise associated with the tighter mass tolerance
that can be utilized in the HRAM data. In Figure 4, the selected ion
chromatogram for N-acetylhistidine is shown from two individual
human urine samples (black and red traces respectively) from UMR
data (Figure 4A) and from HRAM data (Figure 4B). Given the more
clearly dened peak start and stop in the HRAM data (Figure 4B),
automated soware could more readily detect and integrate peaks as
compared to the UMR data shown in Figure 4A, thus driving reduced
overall process variability.
Another observation from this data is that the noise associated
UMR data can mask the dierences in concentration between samples
as seen in Figure 4. In this example, the reduced noise of the HRAM
data stream (Figure 4B) permitted the detection of the dierence in
concentration of N-acetylhistidine between these two dierent urine
samples that was completely masked in the noisier UMR data (Figure
4A). In this way, the use of HRAM data permitted the detection of
potentially signicant biological changes in concentration that was
obscured in the UMR data.
Dilution Series Linear Linear Dynamic Range (LDR)
e LDR of an instrument is a measurement of an instrument’s
ability to accurately represent concentration changes seen in
experimental samples. To compare the LDRs of the HRAM and UMR
instruments, the reverse-phase dilution series data was used. For this
analysis, the data from the nine labeled standards in the dilution series
were tted with a linear trend line. When the area response for each
concentration deviated enough to shi the linear t beyond an R
2
of
0.98 the concentration was considered to have become non-linear
(Supplementary Information 8). Comparing the data streams the
HRAM data demonstrated an overall increased LDR compared to the
UMR data. e average LDR for the HRAM data was four orders of
magnitude, while the UMR data had an average LDR of three orders
of magnitude (Supplementary Information 9). Interestingly, the UMR
data was capable, in several cases, of improved linear behavior at higher
concentrations while still demonstrating an overall reduced LDR as
compared to the HRAM dataset. is overall reduced LDR was a result
of the decreased sensitivity at low concentrations for the UMR data
(Supplementary Information 2). A summary of all LOD and LDR data
for each standard can be found in Supplementary Information 10.
As expected, all standards on both instruments demonstrated
deviations from linear area response behavior at high concentrations
(Supplementary Information 8-11). is deviation from linearity likely
derives from electrospray saturation. Even though both instruments
Figure 2: ESI+ extracted ion chromatogram of the masses for N6-acetyllysine
and N-acetylglutamine from a human serum extract from the HRAM instrument
(A) and from the UMR instrument (B). Precise masses and mass windows
used are labeled on each panel. The added mass resolution and accuracy
permitted N6-acetyllysine to be fully resolved from N-acetylglutamine in HRAM
data streams, thus permitting the accurate detection of both molecules, which
was not possible in the UMR data stream.
Figure 3: ESI+ extracted ion chromatogram of the mass for the methylxanthine
family of isomers from a human serum extract from the HRAM instrument (A)
and from the UMR instrument (B). Precise mass and mass windows used are
labeled on each panel. The added sensitivity provided by increased mass reso-
lution and accuracy permitted the detection of all three methylxanthine isomers
in the HRAM data streams from the inherent background noise, which was not
possible with the UMR data. Vertical lines indicate full MS scans taken during
analysis. Note the faster scan speed of the HRAM instrument.
Figure 4: ESI+ extracted ion chromatogram of the mass for N-acetylhistidine
from two different human urine extracts (black and red traces respectively)
from UMR data (A) and HRAM data (B). Precise mass and mass windows used
are labeled on each panel. The added mass resolution and accuracy of the
HRAM data stream permitted the detection of the difference in concentration
of N-acetylhistidine between these two samples that was masked in the noisier
UMR data. This data was reproduced on an alternate HRAM data stream in-
strument to conrm nding (data not shown).
Citation: Evans AM, Bridgewater BR, Liu Q, Mitchell MW, Robinson RJ, et al. (2014) High Resolution Mass Spectrometry Improves Data Quantity
and Quality as Compared to Unit Mass Resolution Mass Spectrometry in High-Throughput Proling Metabolomics. Metabolomics 4: 132.
doi:10.4172/2153-0769.1000132
Page 6 of 7
Volume 4 • Issue 2 • 1000132
Metabolomics
ISSN: 2153-0769 JOM an open access journal
displayed deviations from linear behavior, the replicate injections
demonstrated a high degree of precision in the determination of area,
with the HRAM instrument demonstrating tighter precision than
the UMR instrument (Supplementary Information 11). In addition,
while the area response deviated from linear behavior, even at high
concentration the mass measurements were still within a 5ppm
tolerance (data not shown) for the HRAM instrument.
Scan Speed
e scan speed comparison of these instruments is highly dependent
on the methods and instrument settings used. e UMR instrument
scanned approximately 4.5 scans per second, which permitted adequate
sensitivity and UHPLC peak sampling. When operated at 35,000
resolution (measured at 200 m/z) the HRAM instrument scanned two
times faster than the UMR instrument (Supplementary Information 12
and Figure 3, vertical lines). is increased scan speed likely had some
positive inuence on the observed reduction of process variability,
but more practically, this improved scan speed opened a great deal
of exibility around method development. For example, given our
requirement of MS/MS spectrum match for compound identication,
the HRAM instrument scanned fast enough that one could choose
to take one full mass MS scan followed by two or even three data
dependent MS/MS scans (top 2 or 3, respectively) and still obtain
more full mass MS scans in a second, which are used for determining
the area under the curve for quantication purposes (Supplementary
Information 12). Others might nd the ability to increase the mass
resolution of the instrument, thus lowering the scan speed in order
to gain mass resolution, more important to their specic application.
Either way the added scan capacity opens up more options for the user.
For our instrument comparison the same scan prole was maintained,
specically one MS scan followed by one MS/MS scan (top 1), for all of
the reported comparisons.
Accurate Mass for Unknown Identication
While the presented work focused on the known or named
compounds detected, a large component of any global screening
metabolomics method is the detection of unknowns. Unknowns or
unnamed compounds are compounds with reproducible mass, retention
and fragmentation characteristics but for which a precise identity has
yet to be determined. While the majority of compounds detected in
the serum sample set were named/known compounds matched to
authentic standard library entries, many were unknown compounds.
ere is certainly a great deal of work by the community focused on
identifying similar unknowns found in various biological data since
these can oen be distinguishing biomarkers or display important
correlations with the study design at hand. By using the HRAM stream
of data the mass assignments of these unknowns can be automatically
used to identify or aid in identication of these unknowns. For many
of the lower mass unknowns detected in biological data the accurate
mass data can lead to a unique molecular formula, particularly when
fragmentation and isotope ratios are included in the determination
[21,22]. erefore these instruments also provide powerful additional
information to aid in the identication of unknowns without need
for additional sample analyses. In the time between the analysis and
data processing of the data presented in this manuscript and more
recent data analyzed in human serum the number of named/known
compounds has increased to over 600 (data not shown) as a result of
the accurate mass data stream permitting us to identify and annotate
unknowns.
Conclusion
Our results demonstrate the utility of HRAM data above and
beyond its use for compound identication. e HRAM data
oered signicant analytical benets to every aspect of data quality
investigated and improved downstream data processing of high
throughput metabolomics data. e HRAM data, mostly through the
reduction of noise and interferences, demonstrated greater sensitivity,
wider dynamic range, reduced process variability and permitted
the detection of more compounds than the UMR data without
detrimental eect on scan speed. While only orbitrap-based HRAM
instrumentation was directly evaluated, it is likely that other accurate
mass instrumentation, such as ToF, will demonstrate similar analytical
benets [39]. While metabolomics, as a whole, is far from being only
an instrumentation problem, our results indicate that the HRAM data
stream demonstrated signicant analytical improvement. In addition
to the benet of accurate mass for compound identication, this type
of instrumentation is likely to be extremely benecial to practitioners
of non-targeted metabolomics.
Acknowledgment
The authors would like to thank Metabolon’s sample preparation team,
especially Don Harvan, for their assistance in preparing samples for instrument
analysis. We thank the IT team, especially Sarada Tanikella and Herb Lowe,
for their work in software development and quality control. Finally, we thank
Metabolon’s CEO and CSO, John Ryals and Mike Milburn, respectively, for their
continued commitment and support of research.
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