Enhanced Metabolite Proﬁling from
Bark of Alangium Salviifolium Using
LC/MS and GC/Q-TOF Techniques

Application Note
Metabolomics

Authors

Abstract

Chandranayak Siddaiah,

Traditional herbal remedies are used as alternative medicines for a number of diseases.
Alangium salviifolium is one such plant, used as traditional medicinal plant. Several
investigations have been performed using this plant extract to demonstrate its
therapeutic value. However, very few attempts have been made to identify the extensive
metabolite composition of this plant. In this Application Note, we performed metabolite
proﬁling and identiﬁcation from the bark of A. salviifolium by extracting the sample
in organic and aqueous solvents. The organic and aqueous extracts were fractioncollected using the Agilent 1260 Analytical Scale Fraction Collection System. Each of thefractions was analyzed by LC/MS and GC/Q-TOF techniques. The LC/MS/MS analyses
were performed using HILIC chromatography, as well as three separate, orthogonal
reverse phase columns. Data were collected using AJS source in both positive andnegative ionization modes, followed by METLIN database or MS/MS library searches.
Compounds from Alangium that could not be identiﬁed by database or library matchingwere subsequently searched against the ChemSpider (http://www.chemspider.
com/) database of over 30 million structures using Agilent MSC software. To identify
compounds generated by GC/Q-TOF, the data were searched against the Agilent-Fiehn
GC/MS Metabolomics Library and Wiley/NIST libraries. The results of the combined
GC libraries searches identiﬁed 62 compounds with a matching score > 70.

Harischandra Sripathi Prakash
DOS in Biotechnology,
University of Mysore
Mysore, India
Saligrama Adavigowda Deepak,
Syed Salman Lateef, andUpendra Simha
Agilent Technologies India Pvt. Ltd.
Bangalore, India

Using both techniques, a total of 1,016 compounds were detected, of which 511 wereidentiﬁed. A literature search revealed 81 out of 511 compounds had therapeutic
properties against traditionally reported diseases such as cancer, microbial infections,and so forth. Our study suggests that the use of fraction collection for metabolite
enrichment, biphasic solvent extraction, and orthogonal column chemistries formetabolite separation, as well as complementary LC/MS and GC/MS detection, leads togreater metabolite detection coverage in medicinal plants.

Introduction
Alangium salviifolium is a medicinal
plant reported in Ayruveda and Chinese
medicine. This plant is used traditionallyagainst several diseases such as cancer,
leprosy, diabetes, paralysis, microbial
infection, and others. Every part of theplant is used, either consumed orally, or
applied dermally, depending on the typeof disease that is treated. Experiments
correlating this medicinal plant withspeciﬁc diseases or activities have been
done previously1,2.
A comprehensive evaluation ofuntargeted metabolomics is an unbiased
analysis of biochemical intermediates in asample achieved by using complementary
universal analytical techniques such as
LC/MS, GC/MS, and NMR. The factorsthat can affect the comprehensive
evaluation of a metabolome depend on:•

The method used for sample
harvesting/extraction procedures•

Fractionation•

Chromatographic separation
chemistry•

Ionization techniques/modes•

Acquisition parameters•

Data processing/analysis•

Identiﬁcation databases/libraries5

We used orthogonal LC/MS
and GC/Q-TOF techniques for acomprehensive metabolite analysis in
the stem bark of this plant, including
identiﬁcation of the metabolites using
recent libraries and databases.

Experimental
Workﬂow
Table 1 shows an outline of the workﬂow
used in this study.

Table 1. Summary of the workﬂow for biphasic solvent extraction followed by analysis using LC/MS and
GC/MS platforms.
Collect & Flash freeze & Store
Aqueous extract
Organic extract
Fraction collection

Fraction collection

LC/MS and LC/MS/MS
Agilent Poroshell 120 HILIC Plus andAgilent ZORBAX RRHD SB-Aq columns

LC/MS and LC/MS/MS
Agilent ZORBAX RRHD Eclipse Plus Phenyl-Hexyl
and Agilent ZORBAX RRHD Eclipse Plus C18 columns

LC/MS/MS analysis
METLIN database/library

LC/MS/MS analysis
METLIN database/library

GC/MS
Agilent DB-5ms column

GC/MS
Agilent DB-5ms column

GC/MS analysis
Fiehn/Wiley/NIST library

GC/MS analysis
Fiehn/Wiley/NIST library

Reagents and materials

Fraction collection

LC/MS grade isopropanol, methanol, andacetonitrile were purchased from Fluka(Germany). Milli Q water (Millipore Elix 10model, USA) was used for mobile phase
preparation. The additives, ammonium
ﬂuoride, acetic acid, ammonium formate,formic acid, and ammonium acetate, were
procured from Fluka (Germany).

To the dried aqueous and organic layers,
200 μL of 50:50 and 30:70 mobile phase A
and B of respective fractionation method(Table 2) were added. The vials weresonicated to resuspend the compounds.
HPLC separation was performed byinjecting the resuspended mixturesfrom multiple vials of each extract to an
Agilent 1260 Inﬁnity analytical puriﬁcation
system equipped with a 1-mL Manual FLInjection valve (p/n 5067-4191) and thefractions were collected in 45 wells of a
96-well plate and dried in a speed vac.

Collection of plant material andextraction procedure
A. salviifolium bark was collectedfrom the plants near Mysore, Indiaand immediately transferred to liquid
nitrogen and stored at –80 °C until further
use. Two grams of bark tissue were
powdered using a mortar and pestle inliquid nitrogen. For extraction, 40 mLof degassed solution containing
chloroform:methanol:water in theratio of 1:2.5:1 (v/v/v) was added. The
undissolved sample was crushed for
5 minutes using mortar and pestle,transferred to 1.5 mL eppendorf tubes,
and vortexed for 5 minutes at 4 °C. The
tubes were centrifuged at 20,800 rpm
for 2 minutes and the supernatant waspooled from all the tubes into a glassvial. A 1-mL amount of the supernatant
was transferred to an eppendorf tube
and 400 μL of water was added. The
tubes were vortexed for 10 seconds
followed by centrifugation at 20,800 rpm
for 2 minutes. The aqueous (upper) andorganic (lower) layers were separated
and dried separately in a speed vac(Eppendorf).

2

Dual AJS-ESI-Q-TOF MS
conditions
The dried aqueous fractions were
resuspended in 250 μL of 50:50
methanol:water containing 0.2 % acetic
acid and sonicated for 10 seconds,
whereas the organic fractions weresuspended in 30:70 mobile phase
A:B (Table 3 – organic) followedby centrifugation at 3,000 rpm
for 10 minutes. Then, 5 μL of theresuspended fractions were injected
onto an Agilent 1260 Inﬁnity LC Systeminterfaced to an Agilent 6540 Accuratemass Q-TOF LC/MS system. Reference
solution was prepared using an
API-TOF Reference Mass Solution Kit(p/n G1969-85001). A 10-μL amount of
HP921 and 5 μL of purine was dissolvedin 1 L of methanol:acetonitrile:water(750:200:50) containing 0.1 % acetic acid,and was sprayed using an isocratic pumpat a ﬂow rate of 0.4 mL/min. The MS andchromatographic parameters are shownin Table 3A and 3B.

Table 3A. The MS source and chromatographic
parameters used in LC/MS and LC/MS/MS
analysis.

Table 2. Chromatographic parameters for fractionation.
Parameter

Aqueous extract

Organic extract

Mobile phases

A) Water + 10 mM
ammonium acetateB) 100 % acetonitrileA) 95:5 water:methanol with 0.1 % formic
acid and 5 mM ammonium formateB) 65:30:5 isopropanol:methanol:waterwith 0.1 % formic acid and 5 mM
ammonium formate

LC/MS parameters
Injection volume

5 µL

Flow rate

0.4 mL/min
40 °C

Flow rate

1.2 mL/min

1.2 mL/min

Thermostated column
temperature

Injection volume

1 mL

0.3 mLGas temperature

250 °C

Autosampler thermostat

4 °C

4 °C

Drying gas ﬂow

10 L/min

TCC temperature

25 °C

25 °C

Nebulizer

30 psig

DAD

210 and 254 nm

210 and 254 nm

Sheath gas temperature

350 °C

Peak width> 0.05 minutes> 0.05 minutes

Sheath gas ﬂow

11 L/minFraction collection mode

Time-based

Time-based

VCap

3,500 V

Total time

13 minutes

13 minutes

Nozzle voltage

1,000 V

ColumnAgilent ZORBAX SB-C18, 9.4 × 50 mm, 5 µm (p/n 846975-202)

Fragmentor

100 VTime slices

0.292 min/well0.292 min/well

Gradient

Time (min)
0.0
1.0
8.0
8.1
10.0
10.1
12.0

Time (min)
0.0
1.0
8.0
11.0
11.1
12.0

% Solvent B
5
5
35
95
95
5
5

% Solvent B
60
60
100
100
60
60

Table 3B. Chromatographic parameters used in the LC/MS and LC/MS/MS analysis.
Parameter

Aqueous fractions analyzed using an Agilent ZORBAX RRHD SB-Aq, 2.1 Aqueous fractions analyzed using an Agilent Poroshell 120 HILIC
× 50 mm, 1.8 µm column (p/n 857700-914)
Plus, 3.0 × 50 mm, 2.7 µm column (p/n 699975-301)

Ionization mode

Positive MS and positive
AutoMSMS

Negative MS and Negative
AutoMSMS

Positive MS and positive
AutoMSMS

Mobile phases

A) Water with 0.2 % acetic acidB) Methanol with 0.2 % acetic acidA) Water with
1 mM ammonium ﬂuorideB) 100% acetonitrileA) (90:10) acetonitrile:water with 50 mM ammonium acetateB) (50:40:10) acetonitrile:100% water:water with 50 mM
ammonium acetate

LC gradientTime (min) % mobile phase B
1.00
5.0
10.0
35.0
11.0
95.0
13.0
95.0
13.1
5.0
15.0
5.0
Organic fractions analyzed using an Agilent ZORBAX Eclipse Plus C18,
3.0 × 50 mm, 1.8 µm column (p/n 959757-302)

Time (min)
3.00
10.00
13.00
13.10
17.00

Ionization mode

Positive MS and positive
AutoMSMS

Positive MS and positive
AutoMSMS

Mobile phaseA) 95:5 water:methanol with 0.1 % formic acid and 5 mM ammonium formateB) 65:30:5 isopropanol:methanol:water with 0.1 % formic acid and 5 mM ammonium formate

Parameter

LC gradient

Negative MS and negative
AutoMSMS% of mobile phase B
0.0
100.0
100.0
0
0

Organic fractions analyzed using an Agilent ZORBAX Eclipse Plus
Phenyl-Hexyl 3.0 × 50 mm, 1.8 µm column (p/n 959757-312)

Time (min) % of mobile phase B
1.00
60.0
8.00
100.0
11.00
100.0
11.10
60.0
14.00
60.0

3

Negative MS and negative
AutoMSMS

Negative MS and negative
AutoMSMS

GC/Q-TOF conditions
The derivatization and experimental
parameters for both aqueous and organic
fractions were performed as described
elsewhere6. An Agilent 7200 GC/Q-TOF
was used for acquisition with absoluteretention times, which was locked to theinternal standard d27 myristic acid fromthe Agilent Fiehn GC/MS Metabolomics
Standards Kit (p/n 400505) with retention
time locking (RTL) software system. The
GC/Q-TOF conditions used are provided
in Table 4.

Table 4. Conditions used for GC/Q-TOF.
GC conditions
ColumnAgilent DB-5ms, 30 m × 0.25 mm, 0.25 µm,
Guard length 10 m (p/n 122-5532G)

Injection volume

1 µL

Split mode and ratio

Split 10:1

Split/Splitless inlet temperature

250 °C

Oven temperature program

60 °C for 1 minute,
10 °C/min to 325 °C,
10 minutes hold

Carrier gas

Helium at 1.2798 mL/min constant ﬂow

Transfer line temperature
Q-TOF conditions

290 °C

Data analysis

Ionization mode

EI

Agilent MassHunter Qualitative Analysis(v. B.06.00 SP1) software was used forprocessing MS, AutoMSMS data. The
accurate mass MS data were processedusing the Find by Molecular Feature tool
to export the compounds to Agilent Mass
Proﬁler Professional (MPP) Software. To
remove the molecular features arisingfrom the background, the data obtained
from each fraction were backgroundsubtracted using the blank data in MPP.
The ID browser was used to identify
putative compounds by searching
against the METLIN database comprising
64,092 compounds.

Source temperature

230 °C

Quadrupole temperature

150 °Cm/z scan

50 to 600 m/z

Spectral acquisition rate

5 spectra/s, 2,679 transients/spectrum, collecting both in centroid
and proﬁle modes

The LC/MS/MS data were processedusing the Find by AutoMSMS tool, andthe spectral pattern generated wascompared against the Metlin metabolite
library comprising accurate mass MS/
MS information for 19,714 compounds.
A few selected compounds foundin Alangium species detected in the
METLIN database, but not in Metlin

library, were processed using Molecular
Structure Correlation Software (MSC).
The MSC software (version B.05.00
Build 19) performed the systematic bondbreaking for the proposed structure
or from a database, and matched theobserved fragment ions, followed by
the assignment of an overall score. Theinterface provided the formula and overall
score, which was the combined MS
and MS/MS score, and the molecular
formulas for the fragment ions with ppm
m/z error.
The 7200 GC/Q-TOF data was processedusing MassHunter Unknown Analysis
Software (version B.06.00). This software4

uses mass spectral deconvolution,
which automatically ﬁnds peaks anddeconvolutes spectra from coeluting
compounds using model ion traces. The
spectral information was matched with
the Agilent-Fiehn library with retention
time index with respect to FAME mix(Agilent Fiehn GC/MS Metabolomics
Standards Kit, p/n 400505). The data
were also searched against NIST 11
and Wiley 9 mass spectral libraries. The
compounds with a library match score> 70 % were considered. The results from
LC/MS/MS and GC-QTOF were searchedin the literature for their therapeutic
importance.

Results and Discussion
In this Application Note, we performed acomprehensive analysis of A. salviifolium
bark metabolites using multiseparation
protocols/ionization modes andmultiplatform approaches. Initially,
we performed a fraction collectionof the aqueous and organic extracts
by injecting 1 mL of the extract for
preliminary separation and enrichmentof the metabolites. The Accurate Mass
MS results, when matched with the
METLIN database, tentatively found954 compounds with a databasematch score > 90 %. Literature search
revealed 81 of 954 compounds had
therapeutic properties. The majorityof these therapeutic compounds weresecondary metabolites that are reported
to have anticancer and anti-inﬂammatory
activities (Figure 1). These compounds
belonged to various plant secondary
metabolite classes such as terpenoids,
ﬂavonoid, saponins, alkaloids, glycosides,
and so forth. AutoMSMS analysis of all
fractions resulted in identiﬁcation of 449
compounds.

A
1

1

1

1
Antiviral
881Anticancer
Anti-inﬂammatory3

1

Anti-inociceptive
Antimicrobial
5
Antioxidant

28
5Antimalarial
Antidipressant
Antidiabetic

120

Antiulcer
Antiprotozoal

B
Terpenoids
Flavonoid

10

14
Saponins
Fatty acids

7
1
1
1

Alkaloids
10

Glycosides

3
1
1

Quinones
Polyphenols

5
1

4

Coumarins
18

Sugars1

Steriods

Figure 1. METLIN database matched compounds from A. salviifolium and grouped by therapeutic use (A)and compound class (B) based on literature reports.

5

Five compounds reported to be commonly
present in Alangium species could not
be identiﬁed in this study by LC/MS/MS
spectral matching since the spectra forthese compounds were not available in
the METLIN MS/MS library. The spectral
information was used to identify thecompounds using Agilent MassHunter
MSC software (Figure 2). The overall MSC

Possible molecular formula generated

score for all the compounds was > 97 %,except for cephaeline which was 80 %.
Using the accurate mass precursor andfragment ion information for cephaeline,
and the METLIN accurate mass database,
we were able to identify the putative
structures based on the MS/MS spectra
obtained for cephaeline (Figure 3). Thus,
this approach of using MSC for tentative

Parent compound

ID conﬁrmation can be a useful tool inshortlisting the number of compounds
for subsequent conﬁrmation using actual
standards. Table 5 shows the results ofAlangium compounds identiﬁed by MSC
software.

Substructure assignments

MFG result

Substructure assignments

Figure 2. Results from Agilent MSC software tool for identifying the compounds that did not have a spectral match in the
METLIN MS/MS library.

6

Error: 1 ppm
Score: 97.5
•
O
×10 2
7.5

O

7.0

CH3

5.0

Error: 0 ppm
Score: 97.7

4.5

HN

3

•

OH
164.0706
192.1015
178.0840

3.0
2.5
2.0
1.5

151.0739

1.0

O

H3C

N

O

CH3
CH3

220.0953
205.1082

3.5

CH3

Error: 0.9 ppm
Score: 97.5

OH

O•

4.0

O

HN

274.1801

O CH

HN

467.2909

OH

Error: 3.1 ppm
Score: 96.6
CH3

6.0

N

CH3

246.1486

5.5

Cephaeline
0.1 ppm

H3C O
H3C
O

N

•

6.5

Counts

The separation chemistries for
LC/MS/MS were performed using
Poroshell HILIC Plus and three orthogonal
reverse phase columns (ZORBAX Eclipse
Plus C18, ZORBAX Eclipse Plus SB-Aq,
and ZORBAX Eclipse Plus Phenyl Hexyl)
for the separation of hydrophilic andhydrophobic compounds, respectively.
The largest number of compounds wereidentiﬁed in ZORBAX Eclipse Plus C18(197), followed by ZORBAX Eclipse Plus
SB-Aq (187), Poroshell HILIC Plus (175),
and ZORBAX Eclipse Plus Phenyl-Hexyl(139) columns (Figure 4). Signiﬁcantcompound overlaps were found between
HILIC Plus/SB-AQ and Eclipse Plus C18/
Eclipse Plus Phenyl-Hexyl columns: 53
and 59 compounds, respectively. Only
10 compounds were common to all fourcolumn types. The three different reverse
phase columns, Eclipse Plus C18, Eclipse
Plus SB-AQ, and Eclipse Plus Phenyl
Hexyl separated 79, 73, and 28 uniquecompounds, respectively. Poroshell HILIC
Plus revealed 80 unique compounds.
Similar observations on enhanced
metabolite coverage have been madeearlier using HILIC and a reverse phase
Eclipse Plus C18 column7. Our results
using three different RP columns (fornonpolar and intermediate polar), alongwith an HILIC (for polar compounds)
clearly reveal the requirement fordifferent separation chemistries foruncompromised metabolomics study.

451.2676

110.0957

422.2324

0.5
080 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460
Mass-to-charge (m/z)

Figure 3. Proposed metabolite fragment structures for Cephaeline based on MSC analysis.
Table 5. Compounds found in Alangium species identiﬁed by Agilent MSC Software.

Metabolite

CAS/KEGG

FormulaMassdifference(ppm)

Overallscore

Ankorine

13849-54-2

C19H29NO4

-2.56

99.94

Deoxytubulosine

C11817

C29H37N3O2

-0.69

98.97

Ipecac (Cephaeline)

483-17-0

C28H38N2O4

-0.28

80.06

Lacinilene C 7-methyl ether

56362-72-2

C16H20O3

-1.84

98.95

Tubulosine

2632-29-3

C29H37N3O3

2.86

97.31

Entity list 1
C18
197 entries

Entity list 4
SB-AQ
187 entries

79

73
10

6

8

80

28
9

17
10

53

59
7

7
3

Entity list 2
HILIC
175 entries

Entity list 3
Phenyl Hexyl
139 entries

Figure 4. METLIN library matched compound distribution based on column chemistry. Figure drawn using
MPP Software.

7

The compounds obtained from
MS/MS analyses in positive and negative
ionization modes are summarized
in Figure 5. Clear differentiation ofcompounds for both ionization modes
was observed for all column chemistries
used in the study. More sugars andacidic amino acids were detected innegative mode ionization compared topositive mode ionization. Fewer than ninecompounds were common for positive
and negative ionization modes among
all the column types. This shows thatthe use of single ionization mode could
signiﬁcantly reduce the coverage of
metabolites.

99

9

98

Entity list 1
C18_Neg
108 entries

Entity list 2
C18_Pos
98 entries

77
Entity list 1
PH_Neg79 entries

95

2

7

73

Entity list 1
HILIC_Neg
102 entries

60

Entity list 2
HILIC_Pos
80 entries

118
Entity list 2
PH_Pos62 entries

Entity list 1
SB-AQ_Neg
122 entries

Figure 5. METLIN library matched compounds distribution based on ionization modes.

8

4

65
Entity list 2
SB-AQ_Pos
69 entries

A screenshot of MassHunter Unknown
Analysis Software is shown in Figure 6.
This software provides the features of
the chromatogramic comparison; queryversus database spectrum alignment,
molecular structure, and the components.
The components comprise details of eachcompound.

Samples

Chromatogram overlay

Compound spectrum

Library spectrum

Components

Ion peaks

Molecular structure

Figure 6. Fiehn/Wiley/NIST library matched analysis using Agilent MassHunter Unknown Analysis Software.

9The compounds found by GC/Q-TOF
were primarily ﬂavonoids, fatty acids,
sugars, terpenes, and so forth. For
example, D-lyxose identiﬁed from the
GC/Q-TOF analysis of aqueous extract
using MassHunter Unknown Analysis
Software is shown in Figure 7. The
acquired GC/Q-TOF spectrum is shownin Figure 7A, while Figure 7B shows theFiehn library spectrum. The matchingscore is 89.6. In addition, the retention
time (RT) in the library (14.74 minutes)
matches with the RT of the acquired
spectra (14.75 minutes).

Component RT: 14.7528
×10 2 A

73.0479

0.8 D-lyxose 1

217.1082

0.6

Acquired spectra
103.0582

0.4
0.2
0
-50

0

50

100

150

200

250

300

350

400

450

500

550

600

[65550] D-lyxose 1 [14.741]
×10 2

73.0

B

0.8

Library spectra

103.0

0.6

The LC/MS/MS and GC/Q-TOF
analysis resulted in identiﬁcation of
449 and 62 compounds, respectively.
The enhanced number of compounds
observed for LC/MS/MS was primarly
due to the use of orthogonoal columns.
It is well established that the LC/MS and
GC/Q-TOF are complementary techniques
for comprehensive metabolomics
to identify nonvolatile and volatilecompounds (Figure 8).

307.1547

189.0771

0.4

217.0

147.0
0.2

189.0

59.0 89.0

0
-50

0

50

100

150

200

307.0

393.0 436.0

233.0 277.0 335.0
250

300

350

400

467.0

450

Figure 7. GC/Q-TOF spectral search results.

443
LC entity list

654
GC entity list

Figure 8. Compounds identiﬁed using LC/MS/MS and GC/Q-TOF analysis.

10

500

550

600

Conclusions

References

This study demonstrates the utility ofapplying a comprehensive metabolite
separation and detection strategy toaid in identiﬁcation of metabolites
in A. salviifolium bark. Fractionation
was used for enrichment. In addition,a multiplatform approach was used
to detect compounds with different
degrees of polarity. Using four different
column chemistries, combined with two
ionization modes increased the total
number of metabolites identiﬁed. The
compounds that were not found in the
METLIN library were identiﬁed using MSC
software. Our results show that eighty
one secondary metabolites identiﬁedin this study are reported to havetherapeutic value.

1. Hung, T.M., et al. Phenolic glycosides
from Alangium salviifolium leaves
with inhibitory activity on LPS-induced
NO, PGE2, and TNF-a production.
Bioorganic & Medicinal Chemistry
Letters 2009, 19, pp 4389-4393.
2. Anjum, A., et al. Antibacterial
compounds from the ﬂowersof Alangium salviifolium.
Fitoterapia 2002, 73, pp 526-528.
3. Sharma, A.K., et al. Antidiabetic effectof bark of Alangium salvifolium inalloxan-induced diabetic rats.
J. of Global Pharma. Tech. 2011, 3(4),pp 26-32.
4. Murugan, V., et al. Anti-fertility
activity of the stem bark of Alangium
salviifolium (Linn. f.) Wang in wistar
female rats. Indian J. Pharmacol. 2000,
32, pp 388-9.

11

5. Commisso, M., et al. Untargeted
metabolomics: an emerging approachto determine the composition of
herbal products. Computational and
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4(5) e201301007.
6. Palazoglu, M., and Fiehn, O.
Metabolite Identiﬁcation in Blood
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www.agilent.com/chem
This information is subject to change without notice.
© Agilent Technologies, Inc., 2014
Published in the USA, June 1, 2014
5991-4663EN