Creating High Quality Metabolite Libraries for
Fast Metabolomics Screening and Identification
Poster Note 64467
the field of metabolomics, especially in the area of life sciences research.
Compared to genomics and proteomics, identification of endogenous
metabolites continues to pose great limitations and challenges to many
researchers. This is due to the absence of a reliable screening library to provide
accurate information and high quality data needed for metabolic profiling
studies. Multiple online databases are available for metabolite identification,
but often provide numerous candidates that require further analysis to remove
redundancy. Most of the early metabolite libraries were derived from gaschromatography mass spectrometry (GC-MS) based methods. Application of
liquid-chromatography mass spectrometry (LC-MS) in the field of
metabolomics research has grown exponentially in recent years and has
become the method of choice. The number metabolite entries in LC-MS based
metabolite libraries however remains limited. In order to overcome the
obstacles in metabolite identification, we present a compound spectral library
of endogenous metabolites. This library contains a repository of accurate
masses, retention times and LC-MS2 spectra information, which are acceptance
criteria that can be used to improve the confidence in metabolite identification.
Gina Tan,High
Junhua Wang,Quality
Yingying Huang, Andreas
FR Hühmer1
Creating
Metabolite
Libraries for Fast Metabolo
Thermo Fisher Scientific, San Jose, CA, USA
Methods
Gina Tan, Junhua Wang, Yingying Huang,
Andreas FR Hühmer1
Sample Preparation
1Thermo Fisher Scientific, San Jose, CA,
300 commercially available metabolite standards were used for this
USA
experiment. 20 to 25 standards were combined into a single batch and a total
of 15 batches were prepared. Standards were individually weighed at 1mg each
in water; B: 0.1% Formic acid in methanol
and gave a final concentration of 0.5mg/ml after constitution with 50/50
methanol/water. The standard mixtures for all 15 batches were put through
sonication followed by a filtration step to remove any undissolved salt
particles. Mass Chromatography
Spectrometry
Liquid
Overview
Purpose: To create a comprehensive and reliable metabolites screening library
of 300 commercially available metabolite standards, containing retention time
and high quality MS2 spectra information. Methods: 300 metabolite standards were prepared in mixtures and analyzed
on a Thermo Scientific Q ExactiveTM mass spectrometer coupled to an Ultimate
3000 UHPLC system. Full MS and full MS – data dependent MS/MS analysis in
both positive and negative polarities was performed to obtain retention time
information and MS2 spectra of each individual compound. All data were
processed using the Thermo Scientific TraceFinder 3.2 software. The
compound database, MS2 spectra library and screening methods were also
created with the TraceFinder software. (Figure 1.)
TM Two
Allliquid
samples
were analyzed on
a Thermo
Scientific
Exactive
TM
The
chromatographic
separation
was
done onQthe
Thermo. Scientific
TM UltiMate
TM 3000
acquisition
modes
were used.
A full. MS
scan atTM
35,000
Dionex
RS system
A Hypersil
GOLD resolution
C18, 150 Xwith
2.1,positive
and negative
switching.
The was
other
acquisition
mode was of
a full
at 70,000
1.9μm
reverse-phase
column
used
for the separation
theMS
metabolite
resolution
MS2 in both positive and negative
analytes
at afollowed
flow rateby
ofdata-dependent
450 μl/min.
polarities. An inclusion list for all the respective batches was included in the
full MS data-dependent MS2 method. All MS2 spectra of the compounds were
acquired
at three
fixed collision energies, 10, 30 and 45.
1. UHPLC
Conditions.
TABLE
Data Analysis
Time
%A
%B
All data were was processed using TraceFinder 3.2. A compound database was
99.5
0.5
created that records0.0
compound information,
chemical formulas
and retention
times. A screening method for processing the data was also created in
5.5
50.0
50.02
MS spectra of the
1
TraceFinder 3.2. Raw files were imported and individual
6.0
2.0 Manager 2.0 98.0
compounds were extracted
using Library
(part of TraceFinder 3.2)
to generate the MS2 spectra library.
12.0
2.0
98.0
13.0
99.5
0.5
FIGURE1. Overview of experimental workflow from sample preparation,
15.0
99.5 library creation
0.5 for metabolites
data acquisition, compound
and spectra
screening
Creating High Quality Metabolite Libraries for Fast Metabolo
Gina
Yingying
spectral library
of 300 endogenousHuang, Andreas FR Hühmer
Results: ATan,
compoundJunhua
database and MSWang,
metabolites was created and incorporated into a metabolite screening
1workflow.
A ZDF rat plasma dataset was screened against the library and a total
Thermo
Fisher Scientific, San Jose, CA, USA
of 85 metabolites were confidently identified based on the accurate mass,
retention time and MS spectra.
in water; B: 0.1% Formic acid in methanol
2
2
Introduction
The study of endogenous metabolites has brought about new possibilities in
the field of metabolomics, especially in the area of life sciences research.
Compared to genomics and proteomics, identification of endogenous
metabolites
continues
to pose greatand
limitations
challenges
to manylibrary
Purpose:
To create
a comprehensive
reliable and
metabolites
screening
This is available
due to the
absence of
a reliablecontaining
screening retention
library to time
provide
ofresearchers.
300 commercially
metabolite
standards,
accurate
information
and high
quality data needed for metabolic profiling
information.
and
high quality
MS2 spectra
studies. Multiple online databases are available for metabolite identification,
Methods:
metabolite
standards
were that
prepared
in mixtures
and analyzed
but often300
provide
numerous
candidates
require
further analysis
to remove
TM mass spectrometer coupled to an Ultimate
onredundancy.
a Thermo Scientific
Q Exactive
Most of the
early metabolite
libraries were derived from gas3000
UHPLC system.
Fullspectrometry
MS and full MS
– data based
dependent
MS/MS
analysis inof
chromatography
mass
(GC-MS)
methods.
Application
both
positive and negativemass
polarities
was performed
retention
time
liquid-chromatography
spectrometry
(LC-MS)toinobtain
the field
of
2 spectra of each individual compound. All data were
information
and
MS
metabolomics research has grown exponentially in recent years and has
processed
using
the
Thermo
Scientific
TraceFinder
3.2
software.
The
become the method of 2choice. The number metabolite entries in LC-MS based
spectra
librarylimited.
and screening
werethe
also
compound
metabolitedatabase,
libraries MS
however
remains
In ordermethods
to overcome
created
withinthe
TraceFinder
software. (Figure
1.) a compound spectral library
obstacles
metabolite
identification,
we present
Overview
of endogenous
metabolites.
This
contains
a repository
of accurate
library
of 300 endogenous
Results:
A compound
database
andlibrary
MS2 spectral
are acceptance
masses, retention
timesand
andincorporated
LC-MS2 spectra
metabolites
was created
intoinformation,
a metabolitewhich
screening
criteria that
canrat
beplasma
used todataset
improve
thescreened
confidence
in metabolite
identification.
workflow.
A ZDF
was
against
the library
and a total
of 85 metabolites were confidently identified based on the accurate mass,
retention time and MS2 spectra.
Methods Sample Preparation
Introduction
300 commercially available metabolite standards were used for this
The
study of endogenous
metabolites
broughtinto
about
new possibilities
experiment.
20 to 25 standards
werehas
combined
a single
batch and a in
total
the
of metabolomics,
especially
in thewere
areaindividually
of life sciences
research.
of field
15 batches
were prepared.
Standards
weighed
at 1mg each
Compared
genomics
and proteomics,
identification
of endogenous
and gave to
a final
concentration
of 0.5mg/ml
after constitution
with 50/50
metabolites
continues
pose great
limitations
challenges
to put
many
methanol/water.
The to
standard
mixtures
for alland
15 batches
were
through
researchers.
This is due
the absence
reliableany
screening
librarysalt
to provide
sonication followed
by to
a filtration
stepoftoaremove
undissolved
accurate
information
and high quality data needed for metabolic profiling
particles.
studies. Multiple online databases are available for metabolite identification,
Liquid
Chromatography
but often provide numerous candidates that require further analysis to remove
redundancy.
Most of the early metabolite
derived
from
gas- TM
The liquid chromatographic
separation libraries
was donewere
on the
Thermo
Scientific
TM 3000
TMmethods.
chromatography
mass
spectrometry
(GC-MS)
based
Application
DionexTM UltiMate
RS system
. A Hypersil
GOLD C18,
150 X 2.1,of
liquid-chromatography
mass spectrometry
in the field
of metabolite
1.9μm reverse-phase column
was used for(LC-MS)
the separation
of the
metabolomics
grown
exponentially in recent years and has
analytes at a research
flow rate has
of 450
μl/min.
become the method of choice. The number metabolite entries in LC-MS based
metabolite libraries however remains limited. In order to overcome the
obstacles
metabolite
identification, we present a compound spectral library
TABLE 1.inUHPLC
Conditions.
of endogenous metabolites. This library contains a repository of accurate
masses, retention times
and LC-MS2 spectra
Time
%A information, which
%B are acceptance
criteria that can be used to improve the confidence in metabolite identification.
0.0
99.5
0.5
Methods
Sample Preparation
5.5
6.0
50.0
2.0
50.0
98.0
12.0
2.0
98.0
300 commercially available metabolite standards were used for this
13.0
99.5
0.5batch and a total
experiment. 20 to 25 standards
were combined
into a single
A:0.1%
Formic acid
Compound
name,
chemical formula
Mass Spectrometry
Check online
databases
All samples were analyzed on a Thermo Scientific Q ExactiveTM. Two
Compound List
acquisition modes were used. A full MS scan at 35,000 resolution with positive
and negative switching. The other acquisition mode was a full MS at 70,000
resolution followed by data-dependent MS2 in both positive and negative
polarities. An inclusion list for all thePrepare
respective batches was included in the
All MS2 spectra of the compounds were
full MS data-dependent MS2 method.
standards
acquired at three fixed collision energies, 10, 30 and 45.
Data Analysis
Compound database
LCMS
full scan 3.2. A compound
All data were was processed using
TraceFinder
database
with RT was
created that records compound information, chemical formulas and retention
times. A screening method for processing the data was also created in
TraceFinder 3.2. Raw files were imported and individual MS2 spectra of the
Target Inclusion
compounds were extracted using Library
ListManager 2.0 (part of TraceFinder 3.2)
to generate the MS2 spectra library.
FIGURE 1. Overview of experimental workflow from sample preparation,
LC-MSMS
data acquisition, compound and spectra library creation for metabolites
screening
Compound name,
chemical formula
Results
Generate Spectra
Library
Check
online(Isotope,
Screening
databases
AM, RT, MSMS)
Compound List
Prepare
Generation of The Metabolites List
standards
A preliminary list of endogenous, non-lipid metabolites was compiled from
important metabolic pathways, which include the catabolic metabolism
pathway, glycolysis and citric acid (Krebs’) cycle to nameCompound
a few. The database
shortlisted
LCMS online
full scan
compounds were validated against
databases to ensure their
with RT
endogenous nature. Final choice on the metabolites list was also dependent on
the commercial availability of the metabolite standards.
Target
Inclusion
Compound Database Creation
With
Inclusion of Retention Time
List
Information
From the first set of full MS scans acquired, the retention time of each
compound was recorded based on the standardized reverse-phase
LC-MSMS
chromatographic method used for
this experiment. All the retention time
information was combined with the related chemical information and used to
create the endogenous metabolite compound database within TraceFinder
(Figure 2.). The compound repository includes the 300 metabolite compound
Screening (Isotope,
Generate Spectra
names, chemical formulas, positive and negative m/z values,
as RT,
wellMSMS)
as CAS
AM,
Check
online
Compound
compoundsname,
were extracted using Library Manager 2.0 (part
of TraceFinder
3.2)
databases
chemical
formula
to generate
the MS2 spectra library.
FIGURE 1. Overview of experimental
from sample preparation,
Compoundworkflow
List
data acquisition, compound and spectra library creation for metabolites
screening
Compound name,
Prepare
standards
chemical formula
Compound List
LCMS full scan
Check online
databases
Compound database
with RT
Prepare
Targetstandards
Inclusion
List
LCMS full scan
LC-MSMS
Target Inclusion
Generate List
Spectra
Library
Compound database
with RT
Screening (Isotope,
AM, RT, MSMS)
LC-MSMS
Results
Generation
of The Metabolites Generate
List
Spectra
Library
Screening (Isotope,
AM, RT, MSMS)
A preliminary list of endogenous, non-lipid metabolites was compiled from
important metabolic pathways, which include the catabolic metabolism
pathway, glycolysis and citric acid (Krebs’) cycle to name a few. The shortlisted
compounds were validated against online databases to ensure their
endogenous nature.
Final choice on the metabolites list was also dependent on
the commercial availability of the metabolite standards.
Generation of The Metabolites List
Compound Database Creation With Inclusion of Retention Time
A preliminary list of endogenous, non-lipid metabolites was compiled from
Information
important metabolic pathways, which include the catabolic metabolism
From the first set of full MS scans acquired, the retention time of each
pathway, glycolysis and citric acid (Krebs’) cycle to name a few. The shortlisted
compound
waswere
recorded
based
on theonline
standardized
reverse-phase
compounds
validated
against
databases
to ensure their
chromatographic
method
used
for this
experiment.
All list
the was
retention
time
endogenous nature.
Final
choice
on the
metabolites
also dependent
on
information
was
combined
with
the
related
chemical
information
and used to
the commercial availability of the metabolite standards.
create the endogenous metabolite compound database within TraceFinder
Compound
Database
Creation
Withincludes
Inclusion
Retention
Time
(Figure
2.). The
compound
repository
theof
300
metabolite
compound
Information
names,
chemical formulas, positive and negative m/z values, as well as CAS
IDs.
Thisthe
database
good starting
point
for metabolite
profiling
From
first setserves
of fullas
MSa scans
acquired,
the retention
time of
each
experiments
screen
for possible
metabolites
that could
be present. Samples
compoundto
was
recorded
based on
the standardized
reverse-phase
willchromatographic
be screened against
this
compound
database
and
the
likely
metabolite
method used for this experiment. All the retention time
candidates
willwas
be identified,
by the monoisotopic
mass.
information
combined first
withand
the foremost
related chemical
information and
used to
Results
F
T
MS2 Spectra Library Creation
The second part of the experiment was to acquire MS2 information of the
metabolites to build the high quality accurate mass MS2 spectral library.
Individual MS2 spectra of the metabolites at the three collision energies 10, 30
and 45 in the respective polarities were extracted to give wider coverage for
profiling experiments. The data analysis software has the added functionality
to aid in the creation of a MS2 spectral library. A combination of approaches
MS2
Spectra
Library
Creation
were
used
to build
the library.
The first approach was to use the compound list
that
consisted
information
of the specified
.raw
files where
the spectraofscans
the
The
second part
of the experiment
was to
acquire
MS2 information
of metabolites
the compounds
werethe
present.
The chemical
the adduct
type
2 spectral
to build
high quality
accurateformulae
mass MSand
library.
was
supplemented
as
well.
With
the
import
function,
the
software
was
able
to 30
of the metabolites at the three collision energies 10,
Individual MS2 spectra
with reference
to the compound
list and
add it for
pick
the
MS2 spectra
and
45desired
in the respective
polarities
were extracted
to give wider
coverage
into
the spectral
library. The
approach
used was
addition
of
profiling
experiments.
Thesecond
data analysis
software
has for
the the
added
functionality
spectra
and
the .rawlibrary.
file in the
library creation
software.
A combination
of approaches
to aidby
in importing
the creation
ofviewing
a MS2 spectral
Theoretical
values The
of metabolites
of interest
werethe
specified
and list
were usedaccurate
to buildmass
the library.
first approach
was to use
compound
thethat
corresponding
scan withinofthe
.raw
file would
be
displayed
(Figure
3.).scans
The
consisted
information
the
specified
.raw
files
where
the
spectra
wouldpresent.
next beThe
added
into the
library. and
Relevant
required
MS2 spectra were
of the compounds
chemical
formulae
the adduct type
information
such as the
value and
scan filterwas able to
was supplemented
as extracted
well. With precursor
the importm/z
function,
the software
information
were automatically
populated
fromtothe
.raw
file
into
theit
reference
theacquired
compound
list
and
add
pick the desired
MS2 spectra with
library.
spectral library. The second approach used was for the addition of
into the
spectra
by importing
and viewing
the .rawinfile
the librarymethod
creationand
software.
The
complete
spectral library
was specified
theinprocessing
used
accurate
massfor
values
of metabolites
of interest were
specified
as Theoretical
an additional
dimension
confirmation
of the metabolites’
identity.
MS2and
the corresponding
scan
within the
.raw file would
displayed (Figure
3.). The
spectra
matching allows
confident
identification
and be
complements
the result
2 spectra would next be added into the library. Relevant
required
matches
by MS
filtering
the redundancy otherwise generated only by precursor m/
2 spectra
information
such of
as 1500
the extracted
precursor
m/z
value
scan filter
for the 300
z matching.
A total
high quality
accurate
mass
MSand
information
were
automatically
theas
acquired
file into the
metabolites
were
collated
into thispopulated
metabolitefrom
library
part of .raw
the screening
library.for metabolic profiling workflow.
solution
2 spectra
2 spectra
FIGURE
3. Addition
of individual
MSspecified
create
the MSmethod
library
The complete
spectral
library was
into
the
processing
and
used
F
c
forasmetabolites
screening
using
Library Manager
TraceFinder
3.2 MS2
an additional
dimension
for confirmation
of the2.0,
metabolites’
identity.
spectra matching allows confident identification and complements the result
matches by filtering the redundancy otherwise generated only by precursor m/
z matching. A total of 1500 high quality accurate mass MS2 spectra for the 300
metabolites were collated into this metabolite library as part of the screening
solution for metabolic profiling workflow.
FIGURE 3. Addition of individual MS2 spectra to create the MS2 spectra library
for metabolites screening using Library Manager 2.0, TraceFinder 3.2
olomics Screening and Identification
olomics Screening and Identification
create the endogenous metabolite compound database within TraceFinder
(Figure 2.). The compound repository includes the 300 metabolite compound
The
retention
timeformulas,
is a secondary
criteria
taken into
consideration
in order
to
names,
chemical
positive
and negative
m/z
values, as well
as CAS
provide
additional
for a starting
metabolite’s
in the sample.
The
IDs. This
databaseconfirmation
serves as a good
pointidentity
for metabolite
profiling
use
of retention
selection
criteria
removes the
often Samples
experiments
to time
screen
for possible
metabolites
thatredundancy
could be present.
associated
with results
obtained
from larger
databases.
Greater
will be screened
against
this compound
database
and the
likelyconfidence
metaboliteis
achieved
as the
of likely
and
relevant
candidates
are narrowedmass.
down.
candidates
will number
be identified,
first
and
foremost
by the monoisotopic
The retention time information of each metabolite was also subsequently
included in the target inclusion list to be used for the full MS data-dependent
The retention time is a secondary criteria taken into consideration in order to
MS2 acquisition method.
provide additional confirmation for a metabolite’s identity in the sample. The
use of retention time selection criteria removes the redundancy often
associated
with results
obtainedcontaining
from largerinformation
databases. Greater
confidence is
FIGURE
2. Compound
database
of compound
achieved
as the number
of likely
andm/z
relevant
candidates
are narrowed
down.
names,
chemical
formulas,
CAS IDs,
values
and retention
times in
the
table
grid viewtime
and information
the detailedofview
The retention
each metabolite was also subsequently
included in the target inclusion list to be used for the full MS data-dependent
MS2 acquisition method.
FIGURE 2. Compound database containing information of compound
names, chemical formulas, CAS IDs, m/z values and retention times in the
table grid view and the detailed view
MS2 Spectra Library Creation
The second part of the experiment was to acquire MS2 information of the
metabolites to build the high quality accurate mass MS2 spectral library.
Individual MS2 spectra of the metabolites at the three collision energies 10, 30
and 45 in the respective polarities were extracted to give wider coverage for
profiling experiments. The data analysis software has the added functionality
to aid in the creation of a MS2 spectral library. A combination of approaches
MS2 Spectra Library Creation
were used to build the library. The first approach was to use the compound list
2 information
of the
Theconsisted
second part
of the experiment
was to .raw
acquire
that
information
of the specified
filesMS
where
the spectra
scans
to build
thepresent.
high quality
accurate formulae
mass MS2and
spectral
library.type
ofmetabolites
the compounds
were
The chemical
the adduct
of the
metabolites
the three
collision
energies
10,to30
Individual
MS2 spectra
was
supplemented
as well.
With
the importatfunction,
the
software
was able
andthe
45 desired
in the respective
polarities
were extracted
to give wider
for
with reference
to the compound
listcoverage
and add it
pick
MS2 spectra
profiling
experiments.
analysis
software
functionality
into
the spectral
library.The
Thedata
second
approach
usedhas
wasthe
foradded
the addition
of
library.
combination
of approaches
to aid in
creationand
of aviewing
MS2 spectral
spectra
bythe
importing
the .raw
file inAthe
library creation
software.
were used accurate
to build the
library.
The
approach
to use
thespecified
compound
Theoretical
mass
values
of first
metabolites
of was
interest
were
andlist
that
consisted information
of the specified
.raw files
where the(Figure
spectra3.).
scans
the
corresponding
scan within
.raw file would
be displayed
The
Creating
High
Quality
Metabolite
Libraries
for
Fast
Metabolomics
Screening
and
2
of the compounds
present.
The
chemical
formulae
the adduct type
would
next be
added
into the
library.and
Relevant
required
MS2 spectrawere
was supplemented
With the
import m/z
function,
was able to
information
such as as
thewell.
extracted
precursor
valuethe
andsoftware
scan filter
reference
to the compound
listfile
andinto
addthe
it
pick the desired
MS2 spectra with
information
were automatically
populated
from
acquired .raw
Performance Validation of the Compound Database and MS2 Spectral
Library
The performance of the metabolite screening workflow, which comprised of
the compound database and the high quality accurate mass MS2 spectral
library, was validated against a ZDF rat plasma dataset. The ZDF rat plasma
sample was ran under the same LC gradient conditions as stated in the
“Methods” section. The sample dataset was processed to identify the possible
Performance Validation of the Compound Database and MS2 Spectral
endogenous metabolites present based on the criteria of accurate mass,
Library
retention time and MS2 spectra matches. Of the 300 metabolites, 252
The performance
of the metabolite
screening
workflow,
of
metabolites
were identified
with matching
accurate
mass which
values.comprised
152
spectral
the compound
and
theisotopic
high quality
accurate
mass
MS2 of
metabolites
weredatabase
identified
with
pattern
matched
scores
100%. With
library, was
against
ZDF rat plasma
dataset.
The ZDF
rat plasma
retention
timevalidated
information,
130a metabolites
showed
positive
identification
sample
ranmass
under
theretention
same LCtime
gradient
conditions
the
with
bothwas
exact
and
matches.
Lastly,as
ofstated
these in
metabolites,
2
“Methods”
section.
sample
dataset
to identify
the possible
matched
85
metabolites
were The
further
identified
to was
haveprocessed
the MS spectra
endogenous
metabolites
on the criteria
of accurate
mass,
exactly.
An overview
of thepresent
numberbased
of metabolites
identified
is shown
in Figure
2 spectra
matches.
Of the results
300 metabolites,
252 that the
time
and a
MS
6.retention
Generated
within
short
processing
time,
demonstrated
metabolites
were
identified
with matching
mass values.
152
use
of multiple
screening
features
narrowedaccurate
the identification
of the
metabolites metabolites
were identified
with
isotopic
pattern
matched scores
of 100%.
With
endogenous
in the
ZDF
rat plasma
considerably.
Additional
search
retention
time information,
130and
metabolites
showed
positive identification
criteria
reduces
the redundancy
gave improved
confidence
to the profiling
with both exact
mass
and retention
time matches.
of these
metabolites,
experiment.
Figure
4. shows
the metabolite
profilingLastly,
screening
result
pbtained
85 metabolites
weresoftware.
further identified to have the MS2 spectra matched
from
the processing
exactly. An overview of the number of metabolites identified is shown in Figure
6. Generated
within aresults
short processing
thedataset
results demonstrated
that the
FIGURE
4. Screening
of ZDF rattime,
plasma
processed using
use of multiple
TraceFinder
3.2 screening features narrowed the identification of the
endogenous metabolites in the ZDF rat plasma considerably. Additional search
criteria reduces the redundancy and gave improved confidence to the profiling
experiment. Figure 4. shows the metabolite profiling screening result pbtained
from the processing software.
T
a
b
m
o
o
f
F
z
FIGURE 4. Screening results of ZDF rat plasma dataset processed using
TraceFinder 3.2
C
Identification
A
c
m
ry
y
experiment. Figure 4. shows the metabolite profiling screening result pbtained
from the processing software.
FIGURE 4. Screening
results
ZDF rat plasma
dataset
using
Performance
Validation
of theofCompound
Database
andprocessed
MS2 Spectral
TraceFinder
3.2
Library
The performance of the metabolite screening workflow, which comprised of
the compound database and the high quality accurate mass MS2 spectral
library, was validated against a ZDF rat plasma dataset. The ZDF rat plasma
sample was ran under the same LC gradient conditions as stated in the
“Methods” section. The sample dataset was processed to identify the possible
endogenous metabolites present based on the criteria of accurate mass,
retention time and MS2 spectra matches. Of the 300 metabolites, 252
metabolites were identified with matching accurate mass values. 152
metabolites were identified with isotopic pattern matched scores of 100%. With
retention time information, 130 metabolites showed positive identification
with both exact mass and retention time matches. Lastly, of these metabolites,
85 metabolites were further identified to have the MS2 spectra matched
exactly. An overview of the number of metabolites identified is shown in Figure
6. Generated within a short processing time, the results demonstrated that the
use of multiple screening features narrowed the identification of the
endogenous metabolites in the ZDF rat plasma considerably. Additional search
criteria reduces the redundancy and gave improved confidence to the profiling
experiment. Figure 4. shows the metabolite profiling screening result pbtained
from the processing software.
The screening results demonstrate confident metabolite identification. Here,
an example of Tryptophan is shown (Figure 5.). Tryptophan was matched
based on all the search criteria with a mass accuracy of 1.7ppm and a
maximum score of 100 for the MS2 spectra library matching. The MS2 spectra
of the sample was matched against the standard library spectra with emphasis
on the mass accuracy of the individual fragment masses and the overall
fragmentation pattern unique to the metabolite.
FIGURE 6. Coverage of identification results using the 3 search criteria m/
z, RT and MS2 spectra.
Conclusion
A metabolomics library comprising of high quality accurate mass MS2 spectra
combined with retention time information enables fast and confident
metabolite screening with the use of multiple search criteria.
Improved confidence in metabolite identification and reduced occurrence
of redundancy often associated with current metabolomics databases
search methodology.
!
FIGURE 4. Screening results of ZDF rat plasma dataset processed using
TraceFinder 3.2
HRAM quality MS2 spectral entries obtained using a high resolution mass
spectrometer.
!
Future addition of more metabolites to the existing library to increase the
identification coverage in screening workflows for metabolic profiling
experiments.
!
FIGURE 5. Identification of Tryptophan that has matched by screening
criteria of m/z, RT and MS2 spectra library matching.
References
1. Metabolomics Fiehn Lab,
http://fiehnlab.ucdavis.edu/Metabolite-Library-2007/.
2. Xu, G., Yin P. Journal of Chromatography A. 2014, 1374, 1-13
3. Human Metabolome Library, http://www.hmdb.ca/hml/metabolites
Conclusion
A metabolomics library comprising of high quality accurate mass MS2 spectra
combined with retention time information enables fast and confident
metabolite screening with the use of multiple search criteria.
!
!
Improved confidence in metabolite identification and reduced occurrence
of redundancy often associated with current metabolomics databases
search methodology.
HRAM quality MS2 spectral entries obtained using a high resolution mass
spectrometer.
©! 2015
Thermoaddition
Fisher Scientific
Inc. All
rights reserved.
trademarks
the property
of Thermo
library
to increase
the
Future
of more
metabolites
to All
the
existingare
Fisher identification
Scientific and its subsidiaries.
This
informationworkflows
is not intended
encourage use
of these
coverage in
screening
fortometabolic
profiling
products in any manner that might infringe the intellectual property rights of others.
experiments.
FIGURE 5. Identification of Tryptophan that has matched by screening
criteria of m/z, RT and MS2 spectra library matching.
References
1. Metabolomics Fiehn Lab,
http://fiehnlab.ucdavis.edu/Metabolite-Library-2007/.
2. Xu, G., Yin P. Journal of Chromatography A. 2014, 1374, 1-13
3. Human Metabolome Library, http://www.hmdb.ca/hml/metabolites
© 2015 Thermo Fisher Scientific Inc. All rights reserved. All trademarks are the property of Thermo
Fisher Scientific and its subsidiaries. This information is not intended to encourage use of these
products in any manner that might infringe the intellectual property rights of others.
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