Integrated Transcriptomics
and Metabolomics Study ofRetinoblastoma Using Agilent
Microarrays and LC/MS/GC/MS
Platforms
Application Note
Authors

Abstract

Nilanjan Guha, Deepak S.A.,

This Application Note illustrates a multi-omics approach combining

Syed Lateef, Seetaraman Gundimeda,transcriptomics and metabolomics to study molecular events in the progression of

Arunkumar Padmanaban, andretinoblastoma (Rb). On a set of tissue samples affected with Rb, we performed

Upendra Simha

mRNA and miRNA gene expression using microarrays followed by pathway

Agilent Technologies, Inc.

analysis to identify gene enrichment that would enable functional characterization

Ashwin Mallipatna,
Vishnu Suresh Babu, andArkasubhra Ghosh
Narayana Nethralaya

of Rb. Transcriptomics data were collected using Agilent SurePrint G3 Human
microarrays and a SureScan microarray scanner. Metabolomics data wereobtained from aqueous humor, vitreous humor, and tear samples of Rb using
Agilent 7200 GC/Q-TOF and Agilent 6550 iFunnel Q-TOF LC/MS systems.
After feature extraction and processing using Agilent MassHunter Software,differential and multi-omics analyses were performed using Agilent GeneSpring
software suite. This study demonstrates how a comprehensive understanding ofa disease mechanism can be obtained using Agilent mass spectrometry (GC/MSand LC/MS) and microarray technologies along with Agilent Mass Profiler
Professional and GeneSpring GX software.

Introduction
Retinoblastoma occurs when both copiesof the RB1 gene in a child’s retina areinactivated. While much literature exists
regarding pathogenesis and genetic
changes in retinoblastoma, there is
still a lack of understanding of diseasemechanism. In this study, we acquired
multi-omics data using a combinationof an Agilent portfolio of instruments,
software tools, and sample preparation
kits to acquire, integrate, and analyze
multi-omics data.
The study samples were classified witha risk number based on the stage of
disease progression. Total RNA extracted

from nine fresh frozen Rb and two control
tissue samples were analyzed using
Agilent SurePrint G3 Gene Expression
microarrays to define a gene expression
pattern. The Rb sample expressions
were compared to two control samples.
Metabolite extraction was carried out
from aqueous humor, vitreous humor,
and tears of Rb and control samples. The
extracted metabolites were subjected
to analysis using LC/MS and GC/MS.
Figure 1 shows the various Agilent
products used in this multi-omic study.
Data generated from transcriptomics(mRNA and miRNA) and metabolomics
analyses were analyzed using
Agilent GeneSpring 13.1 software.

Genomics

Gene expression and miRNA
microarray analysis

Total RNA was extracted from
Rb and control tissues using theAgilent Absolutely RNA miRNA
Kit (Cat# 400814). The quality of
isolated RNA was determined onan Agilent 2200 TapeStation system(G2964AA) using an Agilent RNA
ScreenTape assay (5067‑5576). mRNAlabeling and microarray processing was
performed as detailed in the “One‑Color
Microarray‑Based Gene Expression
Analysis” (version 6.9, p/n G4140-90040).
miRNA labeling was done using an
Agilent miRNA Complete Labeling and
Hyb Kit (Cat# 5190-0456).

Metabolomics
Columns

Extraction kits

Electrophoresis platform

QPCR

Experimental

LC/MS

GC/MS

Microarray

GeneSpring Software

Figure1. Agilent products used in the study.

2

Biological pathwaysDifferential and pathway
analysis of gene expression andmiRNA data

The gene expression and miRNA data
were extracted using Agilent Feature
Extraction Software (11.5.1.1) andanalyzed using Agilent GeneSpring GX
13.1. In both mRNA and miRNA analyses,
transcripts exhibiting P ≤ 0.05 and foldchanges greater than or equal to twowere considered to be differentiallyexpressed.

Gene Expression microarray data wereanalyzed using the mRNA and miRNA
workflows in GeneSpring GX 13.1.
Signal intensities for each probe werenormalized to 75th percentile withoutbaseline transformation for gene
expression analysis. No normalization andbaseline transformation was performed
for miRNA analysis. Differential gene

Figure 2 outlines the genomics workflow.

and miRNA expression analysis was
performed using the Filter on Volcano
Plot option in GeneSpring. The analysis
was carried out using a Moderated
T-test unpaired statistical method with
the Benjamini Hochberg FDR method;p-values were computed asymptotically.
The differential miRNA list was used
to identify the gene targets by using
TargetScan, a miRNA target identification
database incorporated within GeneSpring.
The validated targets of the differential
miRNAs were identified using miRWalk1.

Control and Rb tissue

RNA QCRNA Extraction
Agilent 2100 Bioanalyzer
and RNA 6000 Nano kit

Agilent Absolutely RNA miRNA Kit

Agilent Low Input Quick Amp
Labeling Kit
Hybridization
Agilent SurePrint G3 Human
GE 8x60 K V2

miRNA

Labeling

Total RNA

Agilent miRNA Complete
Labeling and Hyb Kit

Agilent SureHyb
Microarray Hybridizationchamber and backing slides

Hybridization
Agilent SurePrint G3 Humanv16 miRNA 8x60K

Agilent Microarray Wash Buffer kits
Wash

Scanning andfeature extraction

Agilent Surescan Microarray scanner
Data analysis
GE Data analysis

Agilent GeneSpring 13.1

miRNA Data analysis

Figure 2. Schematic showing gene expression and miRNA workflow. Agilent products used in the workflow are highlighted in blue boxes.

3Pathway analysis was carried out
using the Pathway Architect Modulein GeneSpring 13.1. The differentiallyexpressed gene entity list (p ≤ 0.05 andfold change ≥ 2.0) was selected forpathway analysis. The list of validated
targets was used for miRNA pathway
analysis. Curated pathways from KEGG
were used for pathway analysis.

Metabolomics analysis using
LC/MS and GC/MS

The metabolites from each of the samples
were extracted using a mixture ofmethanol and ethanol (1:1 v/v). A 500 µL
aliquot of this solvent mixture was added
to 25 µL of each aqueous humor/vitreous

humor/tear sample with appropriate
amounts of internal standards, glucose(1-13C), tyrosine (1‑13C), cholesterol(3,4-13C2), and palmitic acid (1-13C). The
extract containing internal standards
was vortexed for 20 seconds followed byvigorous shaking for 15 minutes at 4 °C.
The samples were then centrifuged at
4 °C for 5 minutes at 10,000 rpm. Two
180 µL aliquots of this supernatant wereplaced into separate glass vials and dried.
One vial was used for GC/MS analysis,
and the other vial was used for LC/MS
analysis. Figure 3 shows a summary of
the workflow for monophasic extraction
followed by analysis using LC/MS and
GC/MS platforms.

25 µL of vitreous/aqueous humor
and tears

Agilent MassHunter Qualitative
Analysis Software andAgilent MassHunter Profinder Software• Agilent METLIN PCDL

To the dried sample, 60 µL of 0.5 ppm
epicatechin in methanol was added, andvortexed for 10 seconds followed bysonication for 30 seconds. Another 60 µL
of 0.2 % acetic acid in water was added
to the sample, vortexed for 10 seconds,
and sonicated for 30 seconds. The
reconstituted samples were subjected
to MS and data-dependent MS/MS
acquisition using an Agilent 1290 Infinity
LC System coupled to an Agilent 6550
Accurate mass QTOF LC-MS system witha dual Agilent Jet Stream source. The
Q‑TOF was tuned for low mass and fragile
molecule tune.

Metabolite extraction:
Monophasic
50:50 methanol:ethanol

Derivatization followed by
GC/MS analysis

LC/MS analysis

Agilent 6550 Q-TOF
Column 1 Agilent ZORBAX RRHD SB-Aq
Column 2 Agilent Poroshell 120 HILIC
Mode
Both positive and negative
MS and MS/MS analysis

LC/MS sample preparation anddata analysis

LC/MS

GC/MS

Data analysis

Data analysis

GC
MS
Column
MethodAgilent 7200
Agilent 5975C
Agilent DB-5MS (p/n 122-5532G)
Ref manual G1676-80000

Agilent Unknown Analysis Software(Fiehn RTL library)

Agilent GeneSpring 13.1-MPP• Differential Analysis• METLIN search using ID Browser• KEGG Pathway Searches using Pathway Architect
Figure 3. Summary of the workflow for monophasic extraction followed by LC/MS and GC/MS.

4

Statistics

The reference solution was preparedin ethanol:acetonitrile:water(750:200:50(v/v)) containing 0.1 %acetic acid by adding an appropriate
amount of Agilent HP921 reference
solution. The reference solution wasadded continuously during the run using
an external pump at the flow rate of
0.1 mL/min. Ions at m/z 64.0158 andm/z 922.0098 were used as reference
ions for calibration in positive mode.
Ions at m/z 68.9957, m/z 119.0363, andm/z 980.0164 were used as reference
ions in negative mode. Data wereacquired using electrospray ionization(ESI) in positive and negative ion modes
using modified polar reverse phase C18
and HILIC columns. Tables 1A and 1B
show the details of chromatographic andmass spectrometric parameters. Features
were found using Agilent MassHunter
Profinder, and were searched against
the Agilent METLIN database andconfirmed by matching them against theAgilent METLIN MS/MS library.
Agilent MassHunter Profinder (v. B.06.00)
software was used for processing
LC/MS data. The accurate mass MS
data were processed using the recursive
MFE tool in the Profinder software. The
aligned compounds were exported to an
Agilent Mass Profiler Professional moduleof the Agilent GeneSpring Multi-Omics
Analysis software. Agilent Mass Profiler
Professional software (MPP) was used
for statistical comparison of the LC/MS
data from Rb and control samples. The
differential list was exported as a .cef file.
The .cef file, which contains the mass
and retention time of the differential
compounds data, was used as a databaseto process the data-dependent MS/MS
files. The MS/MS data were processedusing the Find by Formula algorithmof the Agilent MassHunter Qualitative
Analysis Software (v B.07.00), pullingout those features found only in the
differential list. The spectral patterngenerated was searched against theAgilent METLIN MS/MS library.

Table 1A. The chromatographic parameters used in the LC/MS analysis.
LC parameters
ColumnAgilent ZORBAX RRHD SB-Aq
3.0 × 50 mm, 1.8 µm column(p/n 857700-314)

Agilent Poroshell 120 HILIC Plus,
3.0 × 50 mm, 2.7 µm column(p/n 699975-301)

Ionization mode

Positive and negative MS and MS/MS

Positive and negative MS and MS/MS

Mobile phase

A) Water with 0.2 % acetic acidB) Methanol with 0.2 % acetic acidA) (9:1) Acetonitrile:water with
50 mM ammonium acetateB) (5:4:1) Acetonitrile:water:water with
50 mM ammonium acetate

LC GradientTime (min)
1.0
12.0
13.0
15.0
15.1
20.0

Time (min)
3.0
12.0
15.0
15.1
20.0

% mobile phase B
5.0
35.0
95.0
95.0
5.0
5.0

Table 1B. The MS source parameters used in MS and MS/MS data acquisition.
Agilent 6550 MS parameters
Injection volume

5 µL

Flow rate

0.3 mL/min

Thermostated column temperature

40 °C

Gas temperature

175 °C

Drying gas flow

15 L/min

Nebulilzer

45 psig

Sheath gas temperature

200 °C

Sheath gas flow

12 L/minVCap

3,500 V

Nozzle voltage

1,000 V

Fragmentor

175

Skimmer65

Octopole RF peak750

Min range

50 m/z

Max range

1,200 m/z

MS Scan rate in MS mode

1 spectra/sec

MS Scan rate in MS/MS mode

6 spectra/sec

MS/MS scan rate

3 spectra/sec

Isolation width (MS/MS)

Medium (~4 amu)

Collision energy

10, 20, and 40 V

5

% mobile phase B
0.0
100.0
100.0
0.0
0.0

GC/MS sample preparation anddata analysis

The dried samples were subjected toderivatization as described elsewhere2.
An Agilent Fiehn GC/MS Metabolomics
Standards Kit (p/n 400505) was used toperform derivatization. An Agilent 7890C
GC coupled to an Agilent 7200 GC/Q-TOF
system was used for data acquisition.
D27 myristic acid was used for retention
time locking. Data were acquired using an
EI source and an Agilent DB-5ms column(p/n 122-5532G). Table 2 shows the
GC/MS chromatographic and acquisitions
parameters.
The 7200 GC/Q-TOF data were processedusing MassHunter Unknowns Analysis
Software (version B.07.00). This softwareuses mass spectral deconvolution,
which automatically finds peaks anddeconvolutes spectra from coeluting
compounds using model ion traces. The
spectral information was matched withthe retention time locked Agilent-Fiehn
library and retention time index withrespect to FAME mix (Agilent Fiehn
GC-MS Metabolomics Standards Kit,
p/n 400505). The matched compound list
was exported to MPP for further analysis.

Protein estimation

Three microliters of each ocular fluid
sample was added to 7 µL of watercontaining 0.9% sodium chloride toestimate the protein concentration
using the Bio-Rad DC Protein Assayfollowing the manufacturer guidelines.
A standard curve was generated using
BSA, and the total protein of the extract
was determined using UV measurement
at 750 nm. This protein concentration
was used to normalize the LC/MS
metabolomics data during MPP analysis.

Multi-omics data analysis

The metabolomics and gene microarray
results were combined and analyzed
using a pathway-centric approach.
Genomics and metabolomics data werecovisualized in the pathway context
using the Multi-Omics Analysis toolof GeneSpring 13.1. This enabledsimultaneous viewing of the differential
entities from both gene expression andmetabolomics. The data integrationof different omics data in GeneSpring
helps to gain a better understanding of
the interrelationship between changes
in expression of individual biochemical
entities.

Reagents and materials

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).

Table 2. The Agilent 7200 GC/Q-TOF chromatography and MS experimental parameters.
GC parameters
ColumnAgilent DB-5MS, 30 mm × 0.2 5mm, 0.25 µm,
Guard column 10 m (p/n 122-5532G)

2nd transfer column

Deactivated fused silica, 0.7 m × 150 µm, 0 µm (p/n 122-5532) atconstant pressure of 3 psi

Injection volume

1 µL

Inlet

Multimode operated in split less mode

Inlet temperature

250 °C

Carried gas and flow

Helium at 1.8636 mL/min, contact flow

Oven temperature program

60 °C for 1 minute
10 °C/min to 325 °C
10 minute holdTransfer line temperature
Agilent 7200 Q-TOF parameters

290 °C

Ionization mode

EI

Source temperature

230 °C

Quadrupole temperature

150 °Cm/z scan

50 to 600 m/z

Spectral acquisition rate

5 spectra/sec, 2,679 transients/spectrum

6Results and Discussion
mRNA

Approximately 1,600 genes weredifferentially expressed (p ≤ 0. 05 andFC ≥ 10) between control and Rb samples.
Pathway analysis revealed many key
pathways to be differentially altered in Rb

affected samples. Figure 4 displays many
key entities of the photo transduction
pathway to be differentially expressed.
The pathway in GeneSpring is illustrated
in two ways: first by coloring therectangles surrounding the differentiallyexpressed genes, and second by
displaying histograms near entities toindicate relative levels of genes between
Rb and controls. For example, Figure 4
shows down-regulation of transducine(Gt), with red and blue bars representing
the expression levels in control and Rb
samples, respectively.

Phototransduction
Plasma membrane
Control

Disc

Rb

Disc membrane

Dark
Rh

RK

Gt

Arr

Rec

Transduction

GC

PDE
cGMP

RGS9

Ca2+

Ca2+
Ca2+

Vertebrate rod photoreceptor cell

Dark adaptation

GC inhibition
GCAP

Ca2+

NCKX

4Na+

K+
CaM
CNG

Depolarization

Ca2++
Open Na

cGMP

Plasma membrane
Disc
Disc membrane

Light

Rh

Gt

P

+

Light
hn

Transduction

Arr
RK
cGMP
RGS9
Ca2+
Visual pigment Rhodopsin (Rh)Rechn
Rhodopsin

Opsin

PDE

Activation

Metarhodopsin II(Interaction with Gt)

Enzymatic
regeneration

Inactivation

11-cis-Retinal all-trans-Retinal
Retinol metabolism
in animals

cGMP

GC
GCAP

GC
activation

Light adaptation

Mg2+

Vertebrate rod
photoreceptor cell
RK
Arr

Opsin

NCKX

Ca2+
K+
CaM

Hyperpolarization(light response)

CNG
cGMP

Figure 4. Gene expression pathway analysis showing the photo transduction pathway to be significantly affected.

7

4Na+

We could identify 18 miRNAs to besignificantly regulated (p ≤ 0.05 andFC ≥ 10) in Rb samples relative to thecontrols. Figure 5 displays a volcano plot
showing differentially expressed miRNAs.

Metabolomics analysis

Metabolomics analysis was conductedusing both LC/MS (positive and negative,
HILIC and C18 columns) and GC/MS
methods. LC/MS and GC/MS produce
nonoverlapping results, allowing different
classes of compounds to be identified
with confidence. For LC/MS, the choiceof two different columns along withboth ionization modes gave broader
coverage of metabolites. HILIC columns
are an ideal choice for the analysis ofpolar compounds, and C18 columns for
nonpolar compounds. The metabolomics
experiments were conducted on aqueous
humor, vitreous humor, and tear samples
from the same set of samples used forgene expression and miRNA.

Of the 102, 117, and 75 total entities
identified from aqueous, vitreous, andtear samples, respectively by GC/MS,
32, 32, and 23 differential metabolites
were identified by spectral searchagainst the Agilent Fiehn GC/MS library.
The identified metabolites included
mostly carbohydrates and aminoacids. GC/MS and gene expression

multi‑omics analysis was performed
using the differential gene entity list
from Rb and control tissues samples, andidentified differential metabolite list from
aqueous humor, vitreous humor, and tearsamples independently. Table 3 shows
the predominate pathways revealed by
combined GC/MS and gene expression
multi-omics in tears.

43–Log10(corrected Pvalue)

miRNA

2

1

-6

-4

2
-2
0
Log2(fold change)

4

6

Figure 5. Volcano plot showing 18 differentially regulated miRNA between Rb and controls. Of the 1,000
validated and reported targets of these 18 miRNAs, 12 were found to be differentially expressed in themRNA data from this study.

Table 3. Predominant pathways revealed by combined GC-MS and gene expression multi-omic analysis in tears

Pathway

p-value (Geneexpression)

Matched entities(Gene expression)

Pathway entities
of experiment type
(Gene expression)

Matched entities(Tears_GC_
Normalization)

Pathway entities ofexperiment type (Tears_
GC_Normalization)

Fructose and mannose metabolism

0.0882

3

32

3

53

Starch and sucrose metabolism

0.0651

4

56

3

51

ABC transporters

0.1777

3

44

3

122

Aminoacyl-tRNA biosynthesis

0.7726

1

66

2

52

Protein digestion and absorption

0.3250

4

89

2

47

Carbohydrate digestion and absorption

0.0031

6

45

2

27

8

Pathway analysis and visualization can beextended to multi-omics analysis as well.
Figure 6 shows the combined analysisof transcriptomics data from tissues
and metabolomics using GC/MS from
aqueous humor of the same samples
visualized on the valine biosynthesis

pathway. The histograms next to theyellow bar and the blue bar show the
relative levels of genes and metabolites,
respectively. GC/MS studies showed
high levels of valine to be present
in aqueous humor Rb samples. The
valine synthesis pathway also shows

significantly low levels of the branchedchain amino-acid transaminase 1 enzyme(BCAT1)(EC 2.6.1.42), in Rb samples. The
deficiency of the enzyme is responsible
for the up‑regulation of valine, which isalso evident from the pathway figure.

Valine, leucine, and isoleucine biosynthesis
Glycine, serine, andthreonine metabolism

D-erythro3-methylmalate

Gene expression enrichment

2-Methylmaleate
4.2.1.35

1.1.1.85
Threonine

4.3.1.19(R)-2-Methylmalate

4.2.1.35

Gene expression

23.1.182
Acetyl-CoA

2-Oxobutanoate
Pyruvate
2.2.1.6

2.2.1.6(S)-2-Aceto2-hydroxybutanoate
1.1.1.86(R)-3-Hydroxy-3-methyl2-oxopentanoate(R)-2,3-Dihydroxy3-methylpentanoate

AH_GC_Normalized

5.4.99.3
Pyruvate
metabolism

1.1.1.86(R)-2,3-Dihydroxy3-methylbutanoate

4.2.1.9(S)-3-Methyl2-oxopentanoate

L-Isoleucine

Rb

Rb

3-Hydroxy-3-methyl2-oxobutanoate

1.1.1.86

2.6.1.42

1.1.1.86

Control

Control(S)-2-Acetolactate
5.4.99.3

Aqueous humor-GC normalized enrichment

4.2.1.92-Oxoisovalerate

1.4.1.9

2.6.1.42(2R,3S)-32-Isopropylmalate
Isopropylmalate
4.2.1.33
4.2.1.33

2.3.3.13
1.4.1.9(2S)-2Isopropylmalate

2.6.1.66

1.1.1.85(2S)-2-Isopropyl3-oxosuccinate
spontaneous

L-Valine

4-Methyl2-oxopentanoate
2.6.1.6
2.6.1.42
Valine, leucine, andisoleucine degradation

1.4.1.9
L-Leucine

Figure 6. Multi-omics analysis of aqueous humor GC/MS and tissue gene expression reveals the valine, leucine and isoleucine biosynthesis pathway to besignificantly down-regulated.

9

The differentially expressed entities
between Rb and control samples acrossaqueous humor, vitreous humor, and tearsamples were matched by searching
against the METLIN LC/MS accuratemass database, resulting in approximately
1,300 annotated metabolites. Figure 7

shows the combined multi-omics analysis
of LC/MS and gene expression datavisualized on the purine metabolism
pathway. LC/MS studies of the tearsamples showed significantly lower levels
of inosine and uric acid in the tears of Rb
versus the control group.

Figure 7. LC/MS and gene expression multi-omics analysis shows purine metabolism to be significantly affected in tumor samples.

10This study demonstrates the valueof using a multi-omics approachto understanding the biologicalpathways in disease progression. The
data were acquired using an Agilent
multi‑omics portfolio including reagents,
instrumentation, and software tools
designed for transcriptomics andmetabolomics. Agilent GeneSpring,
Agilent Mass Profiler Professional, andAgilent Pathway Architect software offer
new biological insights from complex
biological datasets. The software
enables researchers to perform discovery
experiments by co-analyzing data fromdifferent omics such as transcriptomics
and metabolomics.

Counts1.0

Cpd 40: -ESI Product ion
135.0312A

0.5
059.014292.9272

×10 2 Cpd 40: -ESI Product ion
135.0312
1 B
92.9272
59.01420CountsConclusions

×10 2

208.9266

208.9266

267.0737

267.0737314.3148

365.0170

414.2394

314.3148

365.0170

414.2394-1
×10 2 Inosine C10H 12N 4O 5 Product ion
135.0299
1.0 C
CountsFigure 8 shows the MS/MS spectra of
inosine as identified and confirmed using
the Agilent METLIN MS/MS library.
Several associated genes in the pathway
were also found to be differentiallyregulated.

0.5
92.02540
60

267.0735

80 100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440
Mass-to-charge (m/z)

Figure 8. MS/MS Library spectral match of inosine A) spectra taken from the sample, B) mirror plot
combining the MS/MS from the library and from the unknown sample C) spectra from Agilent METLIN
MS/MS library.

Combining data from transcriptomics
and metabolomics revealed previously
unknown pathways related toretinoblastoma. For example, the valine,
leucine, and isoleucine biosynthesis
pathway has never been implicated inretinoblastoma. In addition to the highlevels of valine detected by our GC/MS
experiments, gene expression results
also showed the enzyme branched chainamino-acid transaminase 1 (BCAT1), an
enzyme required for valine metabolism,
to be significantly lower in Rb samples.
Our study opens a possibility for follow‑up
studies, and demonstrates a unified
approach to discovering new biologicalinsights – an approach made possible byintegrating data from different omics inthe same sample.

11

References
1. Dweep, H., et al. miRWalk - database:prediction of possible miRNAbinding sites by “walking” the genesof 3 genomes, J. of Biomedical
Informatics 2011, 44, pp 839-7.
2. Palazoglu, M., and Fiehn, O.
Metabolite Identification in Blood
Plasma Using GC/MS and theAgilent Fiehn GC/MS Metabolomics
RTL Library. Agilent Technologies
Application Note, publication number
5990-3638EN, 2009.

www.agilent.com/chem
For Research Use Only. Not for use in diagnostic
procedures.
This information is subject to change without notice.
© Agilent Technologies, Inc., 2015
Published in the USA, October 1, 2015
5991-6215EN