RESEARCH ARTICLE

Comparative proteome and transcriptome analyses

of embryonic stem cells during embryoid body-based

differentiation

Ali Fathi1, Mohammad Pakzad2, Adele Taei2, Thore C. Brink3, Leila Pirhaji1, Guifre Ruiz3,Mohammad Sharif Tabe Bordbar1, Hamid Gourabi4, James Adjaye3, Hossein Baharvand 2,5�

and Ghasem Hosseini Salekdeh1,6

1 Department of Molecular Systems Biology, Royan Institute for Stem Cell Biology and Technology,ACECR, Tehran, Iran

2 Department of Stem Cells and Developmental Biology, Royan Institute for Stem Cell Biology and Technology,ACECR, Tehran, Iran

3 Department of Vertebrate Genomics, Max-Planck Institute for Molecular Genetics, Berlin, Germany4 Department of Genetics, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran5 Department of Developmental Biology, University of Science and Culture, ACECR, Tehran, Iran6 Department of Systems Biology, Agricultural Biotechnology Research Institute of Iran, Karaj, Iran

Received: January 5, 2009

Revised: July 12, 2009

Accepted: July 22, 2009

Gene expression analyses of embryonic stem cells (ESCs) will help to uncover or further

define signaling pathways and molecular mechanisms involved in the maintenance of self-

renewal and pluripotency. We employed a 2-DE-based proteomics approach to analyze

human ESC line, Royan H5, in undifferentiated cells and different stages of spontaneous

differentiation (days 3, 6, 12, and 20) by embryoid body formation. Out of 945 proteins

reproducibly detected on gels, the expression of 96 spots changed during differentiation.

Using MS, 87 ESC-associated proteins were identified including several proteins involved in

cell proliferation, cell apoptosis, transcription, translation, mRNA processing, and protein

folding. Transcriptional changes accompanying differentiation of Royan H5 were also

analyzed using microarrays. We developed a comprehensive data set that shows the use of

human ESC lines in vitro to mimic gastrulation and organogenesis. Our results showed that

proteomics and transcriptomics data are complementary rather than duplicative. Although

regulation of many genes during differentiation were observed only at transcript level,

modulation of several proteins was revealed only by proteome analysis.

Keywords:

Cell biology / Differentiation / Embryonic stem cells / Human / Microarray

1 Introduction

Human embryonic stem cells (hESCs) are undifferentiated

cells generally characterized by their functional capacity to

both self-renew and to form all embryonic germ layer deri-

vatives, except extracellular tissues (e.g. placenta) [1].

Because of their exceptional properties, they have enormous

potential to be used for developmental biology studies, drug

screening, tissue engineering, and transplantation therapy.

However, a detailed understanding of signaling pathways

and molecular mechanisms involved in the maintenance of

the undifferentiated state and initial loss of pluripotency will

be essential before embryonic stem cell (ESC)-based thera-

pies can safely be applied in the clinics.

Abbreviations: CPP, component plane presentation; dif-ESC,

differentiated embryonic stem cell; EB, embryoid body; ESC,

embryonic stem cell; GO, gene ontology; hESC, human embryo-

nic stem cell; SOM, self-organizing map

�Additional corresponding author: Dr. Hossein Baharvand

E-mail: Baharvand@RoyanInstitute.org

Correspondence: Dr. Ghasem Hosseini Salekdeh, Department of

Molecular Systems Biology, Royan Institute for Stem Cell Biol-

ogy and Technology, P. O. Box 19395-4644, Tehran, Iran

E-mail: Salekdeh@Royaninstitute.org

Fax: 198-21-22414532

& 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

Proteomics 2009, 9, 4859–4870 4859DOI 10.1002/pmic.200900003

Over the past few years, there has been a growing interest

in applying transcriptomics and proteomics to study differ-

ential expression of SC genes in different developmental

stages, thereby specifically aiming at unraveling the regu-

latory networks active during differentiation of ESCs.

Microarray analysis is a powerful transcriptomics tool to

study the differential expression of a large number of genes

and the whole genome under any given condition. Several

groups have applied wide-scale transcriptome profiling

approaches to identify molecular signatures in hESCs (e.g.[2–4]). These studies have generated a wealth of data and

several mechanisms and genes emerged as key participants

in ESC proliferation and differentiation. Although tran-

scriptomics analyses can provide wide coverage of RNA, it

has been concluded that the notion was expressed that

mRNA levels are only a partial reflection of the functional

state of an organism [5]. However, the amount of protein is

often not predictable from mRNA abundance [6].

Expression of many proteins may be regulated at the level

of translation and the rates of degradation of mRNAs and

proteins differ markedly between genes. Post-translational

modifications are often extremely important for the function

of many proteins, but most of these modifications cannot

yet be predicted from genomic or mRNA sequences. It is

becoming increasingly important to know how proteins in a

cell interact with each other and how these interactions

respond to internal and external signals. These important

issues should be addressed at the proteome level. Proteo-

mics has proven to be a powerful approach to address these

issues [7]. Changes in ESCs proteome during differentiation

to embryoid body (EB) have been studied in mouse [8, 9],

monkey [10], and human [11]. To identify human and

mouse ESC-specific proteins, Van Hoof et al. [11] used a

wide-scale proteomics approach to compare the proteomes

of undifferentiated ESCs and their early spontaneous

differentiated derivatives using FT-ICR-MS/MS. They

distinguished 191 proteins were exclusively identified in

both human and mouse ESCs but not in their differentiated

derivatives. Many of the ESC-specific proteins were known

to be involved in cell proliferation.

Although most studies used either transcriptomics or

proteomics approaches to study ESCs, combined proteome

and transcriptome analyses enables us to unveil important

transcriptional and post-transcriptional regulatory mechan-

isms during both ESC proliferation and differentiation that

would not be evident by examining either mRNA or protein

[12, 13]. It has been suggested that an integrated analysis of

both mRNAs and proteins is very important to gain further

insights into complex biological systems [13].

In the current study, we employed 2-DE-based proteo-

mics approach to analyze an hESC line, Royan H5, in

undifferentiated and different uncommitted differentiation

stages represented as EBs. We further analyzed transcrip-

tional changes accompanying differentiation of Royan H5

using microarrays. The comparative analysis the tran-

scriptomes and proteomes has revealed several genes and

the encoded proteins as key players than can serve as

potential markers of stem cell self-renewal and differentia-

tion.

2 Materials and methods

2.1 hESC culture and sample preparation

The hESC line, Royan H5, at passages 40–50 used in these

experiments was first cultured on mouse embryonic fibro-

blasts, inactivated by mitomycin C (Sigma; M0503) [14]. The

cells were then passaged and maintained under feeder-free

conditions for 25–30 passages as described previously [15].

Briefly, ideal colonies were mechanically dissected into

small pieces and re-plated on matrigel-coated dishes and the

medium changed every day. The cells were cultured in

hESC medium: DMEM/F12 medium (Gibco; 21331-020)

supplemented with 20% knock-out serum replacement

(Gibco; 10828-028), 2 mM L-glutamine (Gibco; 25030-024),

0.1 mM b-mercaptoethanol (Sigma; M7522), 1% nones-

sential amino acid (Gibco; 11140-035), 100 U/mL penicillin

and 100 mg/mL streptomycin (Gibco; 15070-063), 100 ng/mL

basic-fibroblast growth factor (Sigma; F0291). Cells were

grown in 5% CO2 and 95% humidity, and they were further

passaged as small clumps (100–500 cells) every 6–7 days

mechanical and enzymatic with 2 mg/mL of dispase and

using a cell scraper by gently pipetting.

To promote differentiation, hESCs were first cultured in

suspension in ESC medium without knock-out serum

replacement and containing FBS (ES-qualified; Gibco

16141-079), where they developed into multicellular aggre-

gates called EBs. The EBs were cultured in suspension for a

further 12 days and then plated on gelatin-coated dishes for

8 days in the same medium to form a pool of spontaneously

differentiated cells. We used the term nonlineage-differ-

entiated cells to highlight the fact that these spontaneously

differentiated cells represent a mixture of various cell types

in the outgrowths of the EBs. For microarray and proteo-

mics analysis, we collected cells from three independent

replications from hESCs and differentiating embryonic

stem cells (dif-ESCs) at day 3 (EB3), day 6 (EB6), day 12

(EB12), and day 1218 (EB20). The cell pellet was washed

with 10 mL of PBS and centrifuged and then washed with

NaCl 0.9 mg/mL. After discarding, the cell pellet was frozen

in liquid nitrogen, and the samples were stored at �801C for

sample preparation and proteomic analysis.

2.2 Karyotype analysis

For karyotype analysis, cells were treated with thymidin

(0.01 g/mL, Sigma) for 16 h at 371C in 5% CO2. After

washing, the cells were left for 5 h and then treated with

Colcemid (Gibco, 0.15 mg/mL, 30 min) and then ESCs were

isolated from mouse embryonic fibroblasts as described

4860 A. Fathi et al. Proteomics 2009, 9, 4859–4870

& 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

above. Isolated ESCs were exposed to 0.075 M KCl at room

temperature for 16 min. The cells were then fixed with ice-

cold 3:1 methanol:glacial acetic acid (repeated three times)

and dropped onto pre-cleaned chilled slides. Chromosome

spreads were Giemsa banded and analyzed for chromoso-

mal status. At least 20 metaphase spreads were screened

and five banded karyotypes were evaluated for chromosomal

rearrangements.

2.3 Flow cytometric analysis of ESCs

All staining was performed in staining buffer consisting of

PBS supplemented with 1% heat-inactivated FBS, 0.1%

sodium azide, and 2 mM EDTA. After determination of the

viability of the cells by trypan blue exclusion, cells were

washed two times in staining buffer and fixed in 4%

paraformaldehyde 15 min at room temperature. For

permeabilization Triton X-100 0.5% were used for 5 min.

Non-specific antibody binding was blocked for 15 min at 41C

with a combination of 10% heat-inactivated rat and goat

serum (prepared in our laboratory) in staining buffer, and

3–5� 105 cells were used per sample. Cells were incubated

with appropriate primary antibodies or appropriate isotype

matched controls (eBioscience or Dako Cytomation) for

45 min at 41C. Primary antibodies used were anti-SSEA-4

(1:50, ChemiconMAB4304) hOct-4 (1:50, R&D MAB1759),

Tra-1-60 (1:20, Chemicon MAB4360), Tra-1-81 (1:20,

Chemicon MAB4381) and Nanog (1:100, R&D MAB1994).

The cells were washed two times in staining buffer and

incubated for 30 min at 41C with FITC-conjugated goat

F(ab0)2 anti-rat Ig G2 (1:100, Sigma Immunochemical,

F6252), FITC-conjugated goat F(ab0)2 anti-mouse immu-

noglobulin (1:200, Chemicon, AP308F), and PE-conjugated

rat F(ab0)2 anti-mouse IgM (0.06 mg per million cells in a

100 mL total staining volume, eBioscience, 12–5790) as

appropriate. Cells were washed as before and fixed with 2%

paraformaldehyde. Flow cytometric analysis was performed

with a BD-FACS Caliber Flow Cytometer (Becton

Dickinson). The experiments were replicated at least three

times. Acquired data were analyzed by using WinMDI

software.

2.4 2-DE analyses

Triplicate cell line samples (at least 106 cells per replicate)

from hESCs and differentiated derivatives at 3, 6, 12, and 20

days after the initiation of differentiation were homogenized

in Trizol reagent (Invitrogen) and protein extraction

performed according to the manufacturer’s instruction.

Proteins were then solubilized in lysis buffer (9.5 M urea,

2% w/v CHAPS, 0.8% w/v Pharmalyte, pH 3–10, 1% w/v

DTT).

The total protein concentration was quantified by the

Bradford assay (Bio-Rad, Hercules, CA, USA) with BSA as

the standard and 2-DE was carried out as previously

described [9]. For the first dimension, 24 cm IPG strips

(GE healthcare) with a linear gradient (pH 4–7) and for the

second dimension 11.5% SDS-polyacrylamide gel were

applied. For analytical and preparative gels, 125 mg and

1.5 mg protein were loaded, respectively. The analytical 2-D

gels were stained with silver nitrate as described by Blum

et al. [16]. Preparative gels were stained with colloidal CBB G

250 [17].

The silver stained gels were scanned at a resolution of

600 dots per inch on a GS-800 densitometer (Bio-Rad). The

scanned gels saved as TIF images for subsequent analysis.

Spot quantization was carried out using the Melanie 3

software (GeneBio, Geneva, Switzerland). After image

treatment, spot detection, protein quantification, and spot

pairing were carried out based on Melanie 3 default settings.

Then, spot pairs were investigated visually and the scatter

plots between gels of each data point were displayed to

estimate gel similarity and experimental errors. The mole-

cular masses of proteins resolved in the gels were deter-

mined by co-electrophoresis of standard protein markers

(GE healthcare) and the pI of the proteins were determined

by migration of the protein spots on 24 cm IPG (pH 4–7

linear) strips.

Three of 2-D gels per cell line were run and the percen-

tage volume of each spot was estimated and analyzed by

one-way ANOVA. Only those statistically significant spots

(pr0.05) that had to be consistently present in all replicates

were scored for further analysis.

2.5 Protein identification and database search

Protein spots were excised from CBB and silver stained gels,

and analyzed using Applied Biosystems 4700 Proteomics

Analyzer (Protein and Proteomics Centre, Department of

Biological sciences, National University of Singapore).

Protein digestion, desalting, and concentration of samples

were carried out using Montages In-Gel Digestion Kits

(Millipore and Applied Biosystems, Foster City, CA, USA).

The samples were dissolved in solvent consisting of 0.1%

trifluroacetate and 50% ACN in MilliQ Water. An aliquot of

0.5 mL of peptide mixtures were spotted on a 192-well target

plate and crystallized with 0.5 mL of CHCA matrix solution

(5 mg/mL). Peptides were analyzed with MALDI-TOF/TOF

Mass Spectrometer (S/N 34 700 098, production year 2004,

Applied Biosystems, Framingham, MA, USA). MS data

were automatically acquired using Exclusion List containing

trypsin auto-digestion peaks and selecting the ten most

intense ions for MS/MS. The collision gas used was

Nitrogen with the collision energy setting of 1 kV. GPS

ExplorerTM software version 3.5 (Applied Biosystems) was

used to create and search files with MASCOT search engine

(version 2.0; Matrix Science) for peptide and protein iden-

tification. S/N ratio in MS/MS mode for peak identification

was greater than 40.

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& 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

Combined MS-MS/MS searches were conducted with the

selection of the following criteria: NCBInr database 060427

(3 525 863 sequences; 1 211 011 241 residues), all entries,

parent ion mass tolerance at 50 ppm, MS/MS mass toler-

ance of 0.2 Da, carbamidomethylation of cysteine (fixed

modification), and methionine oxidation (variable modifi-

cation).

The threshold for positive identification was a MOWSE

score of 478 (po0.05). Each candidate ID derived from the

above search was then manually examined in the Swiss-Prot

database to eliminate redundancy of synonymous proteins.

A protein’s name and accession number were reported

based on Swiss-Prot except for proteins that are only

deposited in the NCBI database. The single-protein member

of a multi-protein family were singled out by comparing

experimental pI and MW with theoretical pI and MW of

different members of gene family, the sequenced covered by

PMF and MS/MS, and ion-score of MS/MS data.

2.6 Western blot analysis

Fifty micrograms of proteins separated by 12% SDS-PAGE

electrophoresis (120 V for 1 h) using a Mini-PROTEAN 3

electrophoresis cell (Bio-Rad) and proteins were transferred

to PVDF membrane (Amersham) by semi-dry blotting (Bio-

Rad) using Dunn carbonate transfer buffer (10 mM

NaCHO3, 3 mM Na2CO3, 20% methanol). Membranes

were blocked for 1.5 h using Western blocker solution

(Sigma, W0138) and incubated overnight at 41C with the

respective primary monoclonal antibodies, anti-ERP29

(abcam 1:4000), anti-NPM1 (sigma 1:1000), anti-Hsc70

(stressgen 1:10 000), anti-EBP1 (Santa Cruz 1:4000), anti-

SGT1 (abcam 1:2000). At the end of the incubation time,

membranes incubated with the peroxidase-conjugated

secondary antibodies, anti-mouse (1:180 000, Sigma,

A9044), anti-rat (1:160 000, Sigma, A5795) and anti-Rabbit

(1:160 000, Sigma, A2074) as appropriate for 2 h at room

temperature. Finally, the blots were visualized using ECL

detection reagent (Sigma, CPS-1-120). Subsequently, the

films were scanned with densitometer (GS-800, Bio-Rad)

and quantitative analysis was performed using UVI band-

map software (UVItec, Cambridge, UK). To investigate the

uniformity of proteins amount loaded on gels, the

membranes were stained by Fast Green (FCF, Sigma,

F7252).

2.7 Illumina bead chip hybridizations and analysis of

expression data

EBs representative of the following time points, days 3, 6, 12,

and 20 (EB3, EB6, EB12, and EB20) were generated in

triplicate from Royan H5. Total RNA was then isolated using

Trizol reagent (Invitrogen). Approximately 400 ng of total

RNA from three biological replicates per time point served as

input to generate biotin-labeled cRNA employing a linear

amplification kit (Ambion, Austin, TX, USA). An aliquot of

750 ng of cRNA was used for the hybridization reaction.

Washing, Cy3-streptavidin staining, and scanning were

performed on the Illumina BeadStation 500 (Illumina, San

Diego, CA, USA) platform using reagents and following

protocols supplied by the manufacturer. cRNA samples were

hybridized onto Illumina human-8 BeadChips. All basic

expression data analysis was carried out using the manu-

facturer’s software BeadStudio 1.0. Raw data were back-

ground-subtracted and normalized using the ‘‘rank invariant’’

algorithm. Normalized data were then filtered for significant

expression on the basis of negative control beads. Selection

for differentially expressed genes was performed on the basis

of arbitrary thresholds for fold changes plus statistical

significance according to the Illumina t-test error model.

2.8 Statistical analysis

The Pearson correlation coefficient (r) of each gene’s mRNA

and protein expression profile was calculated using the

following equations:

rXY ¼sXY

sXsY

and

rXY ¼

PN

i¼1

½Xi � mx�½Yi � my�

N

where X is the mRNA expression level and Y is the protein

expression level for each gene, sX is the standard deviation

of X,sY is the standard deviation of Y, N is the total number

of genes, mX is the mean of X, and mY is the mean of Y.

Genes were excluded if mRNA or protein expression level of

first stage was zero. p-Values were calculated to infer

significant correlation between mRNA and protein expres-

sion level, in which p-values less than 0.05 indicate that the

correlation was significantly different from zero.

2.9 Data mining

Total significant proteins were clustered by k-means cluster-

ing method. Determination of the Correct Number of Clus-

ters is based on measuring of how similar the gene was to

genes in its own cluster compared with genes in other clus-

ters, which were measured by the average of intra-cluster and

inter-cluster distance [18, 19]. MATLAB software, 7.3 version,

was used for both k-means clustering and k-means clustering

profile figures. Heatmaps of k-means clustering are repre-

sented using MeV (MultiExperiment Viewer) software.

A software package of the self-organizing map (SOM)

algorithm, implemented with the MATLAB 7.3

environment (www.cis.hut.fi/projects/somtoolbox/) was

4862 A. Fathi et al. Proteomics 2009, 9, 4859–4870

& 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

used for SOM training. Illustration of SOM outputs was

eventually formed in the format that inputs with similar

features are mapped to the same map unit or nearby

neighboring units, creating a smooth transition of related

individuals over the entire map. Component plane presen-

tation (CPP) was performed in MATLAB environment.

2.10 Enrichment analysis of gene categories

We used BiNGO [20] with the Cytoscape plugin to find

statistically over- or under-represented gene ontology (GO)

categories in biologic data as the tool for enrichment

analysis of our proteome and transcriptome data sets.

Enrichment was determined in reference to all human

Entrez GeneIDs annotated in the Biological Process branch

(14 394 genes total). p-Values are derived from a hypergeo-

metric test followed by Benjamini and Hochberg false

discovery rate correction. A p-value cutoff of 0.01 was used to

identify significantly enriched categories.

2.11 Real-time PCR

Reverse transcription of the isolated RNA was carried out

using the MMLV reverse transcriptase (USB) and oligo-dT

priming following the manufacturer’s instructions. Real

time was carried out on an Applied Biosystems 7900

instrument in 10 mL reactions containing 5mL of SYBR

Green PCR mix (Applied Biosystems) and 0.375 mM of each

primer. All primers used for these assays were tested for

specificity and amplification efficiency. The sequence of the

primers used is listed in Supporting Information Table 1.

Relative mRNA levels were calculated using the comparative

CT method (ABI handbook) with beta actin as internal

control for normalization.

3 Results

3.1 Characterization of hESCs

The hESCs were propagated feeder-free on matrigel in the

presence of noggin and basic-fibroblast growth factor

(Fig. 1A) and grow as compact colonies with a high nuclear

to cytoplasmic ratio and prominent nucleoli (Fig. 1B).

Moreover, the hESC line had a normal karyotype (46 XX)

(Fig. 1C). To evaluate the percentage of undifferentiated

hESCs, we analyzed the expression of key hESC markers

including Nanog (Fig. 1D), Oct-4 (Fig. 1E), SSEA-4 (Fig. 1F),

Tra-1-60 (Fig. 1G), Tra-1-81 (Fig. 1H), and SSEA-4 and Tra-

1-60 or Tra-1-81 using two-color flow cytometry (Figs. 1I and

J). Under these conditions, the cells expressed Oct-4

(93.8178.08%), Nanog (91.9276.71%), SSEA-4

(98.3972.30%), Tra-1-60 (94.478.32%), and Tra-1-81

(92.52712.09%) (Fig. 1K). The cells were also double posi-

tive for SSEA4/Tra-1-60 (97.2974.53%) and SSEA4/Tra-1-

81 (96.0776.50%) (Fig. 1K). To induce differentiation,

hESCs were cultured as EBs. The EBs were cultured in

suspension for 12 days (Fig. 1L 5 day3, 1M 5 day6, and

1N 5 day12) and then plated onto gelatin-coated dishes for 8

days in the same medium to form a pool of spontaneously

differentiated cells (Fig. 1O).

3.2 Proteome pattern

Proteomics analysis has been shown to be a powerful

approach to discover the regulatory networks driving

differentiation of ESCs (for review see [7]). In the present

study, we applied a 2-DE-based proteomics approach to

discover ESC-associated proteins by comparing ESCs and

dif-ESCs (Fig. 2). Using the Melanie 4 software, we could

reproducibly detect 979 protein spots in three replications of

ESC and four time courses of differentiation (787 in ESC,

936 in EB3, 925 in EB6, 915 in EB12, and 912 in EB20). In

total, 96 proteins showed significant changes during differ-

entiation of which 58 spots were down-regulated and 38

spots were up-regulated in at least one EB compared with

ESCs. Proteins with fluctuated expression levels along the

differentiation were excluded from further analysis.

These 96 protein spots were categorized in six expression

groups (Fig. 3). The largest group was composed of proteins

down-regulated during differentiation. Functional analysis of

expression clusters using BiNGO revealed that proteins

involved in metabolisms were enriched in both down-regulated

(D, E, and F groups) and up-regulated (A, B, and C groups)

protein clusters. In addition, clusters D, E, and F were enri-

ched in proteins involved in cell-cycle, proteolysis, regulation of

developmental process, and apoptosis. Ninety-two proteins

(up- or down-regulated during differentiation) out of 96

proteins, which could be detected on the CBB-stained gels,

were excised and subsequently subjected to MS analysis. The

protein spots were analyzed by MALDI TOF-TOF MS/MS on

the basis of a combined peptide mass fingerprinting and MS/

MS analysis, leading to the identification of 87 proteins (Fig. 3

and Supporting Information Tables 2 and 3).

The identified proteins belonged to different biological

processes including development (13.9%), RNA processing

(13.6%), regulation of cell cycle (12.5%), neurogenesis

(10.1%), amino acid phosphorylation (8.5%), protein folding

(4.8%), cell cycle (4.5%), transporter activity (4%), and

translation regulator activity (3.9%).

3.3 Verification of differentially expressed proteins

by Western blot analysis

To further verify the proteome data, we examined the expres-

sion levels of five proteins, CALU, ERP29, EBP1, HSC70,

SUGT1, by Western blotting, using protein extracts derived

from the Royan H5 cell line. The results are shown in Fig. 4

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& 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

and Supporting Information Fig. 1. The level of expression of

EBP1, HSC70, and SUGT1 was very similar in Western blot

and 2-DE results. Although, the expression level of CALU and

ERP29 was slightly different in Western blot analysis

compared with proteome result, the down-regulation of

proteins was observed in both methods. This difference could

be attributed to possible presence of different forms of proteins

on 2-DE gels. The expression levels of these proteins were

further analyzed in another hESC line, Royan H6, which

showed similar results to Royan H5.

3.4 Transcriptional dynamics of differentiation EB

formation

As a first level of quality control of the hybridized RNA

samples, the transcriptional profiles of all the replicate samples

were assessed for their correlation with respect to biological

reproducibility (Supporting Information Fig. 2). All triplicate

mRNA samples cluster together with the exception of one of

the EB3 samples, which shares a correlation coefficient of 0.94

with EB6 samples. In general, all replicates of a single stage

A

F

G IH

J

D E

K

0

20

40

60

80

100

120

0 d 3 d 6 d 12 d 12+8 d

% P

ositi

ve C

ells

Nanog Oct4SSEA-4 Tra 1-60SSEA-4/Tra 1-60 Tra 1-81SSEA-4/Tra 1-81

B

L

M N O

C Figure 1. Morphological and

flow cytometry analyses of

undifferentiated and differ-

entiated hESCs. (A) Phase

contrast photomicrographs of

a colony of hESC grown under

feeder-free conditions. The

cells possess a typical undif-

ferentiated morphology with a

clear border. (B) High magni-

fication of hESC cells. Each cell

displays a compact morphol-

ogy and a high nucleus to

cytoplasmic ratio, containing

prominent nucleoli typical of

undifferentiated hESC. (C) The

hESC line had a normal

karyotype (46 XX). Repre-

sentative flow cytometric

analysis of key hESC markers

including NANOG (D), OCT-4

(E), SSEA-4 (F), Tra-1-60 (G),

Tra-1-81 (H), expression on

hESCs. The cells expressing

markers compared with

isotype control (white peaks)

were termed marker-positive

population. Percentages of

double positive for SSEA-4/

TRA-1-60 (I) and SSEA-4/TRA-

1-81 (J) were indicated in the

dot plots. The percentages of

undifferentiated and differ-

entiated hESCs were presen-

ted in (K). We analyzed the

expression of key hESC

markers including NANOG

(D), OCT-4 (E), SSEA-4 (F), Tra-

1-60 (G), Tra-1-81 (H), and

SSEA-4 and Tra-1-60 or Tra-1-

81 using two-color flow cyto-

metry (I and J). The differ-

entiating EBs were cultured in

suspension for 12 days (L 5

day3, M 5 day6, and N 5

day12) and then plated onto

gelatin-coated dishes for a

further 8 days in the same

medium to form a pool of

spontaneously differentiated

cells (O).

4864 A. Fathi et al. Proteomics 2009, 9, 4859–4870

& 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

have a correlation coefficient between 0.98 and 1.0. A total of

6187 transcripts were found modulated during differentiation

(Supporting Information Table 4).

A comparison between ESCs and dif-ESCs revealed that

the changes in expression pattern were more pronounced in

EBs compared with ESCs and the number of differentiation

regulated protein increased during differentiation

(Supporting Information Fig. 3). Unlike proteome response,

the number of up-regulated transcripts during differentia-

tion was higher than down-regulated ones. The expression

pattern of ectodermal, mesodermal, and endodermal

markers in EBs showed that spontaneously differentiated

cells represented a mixture of various cell types in the

outgrowths of the EBs (Supporting Information Table 5).

3.5 Verification of differentially expressed mRNA

Real-time PCR confirmations were carried out for genes

selected from four different groups: (i) pluripotency markers

OCT4 and NANOG, (ii) genes where the expression of the

mRNA is up-regulated but the protein down-regulated

(e.g. S100A10, HSPA5, ZNF664), (iii) genes where the

expression of the mRNA is down-regulated but the protein

up-regulated (e.g. PHB, DDAH1, ATIC), and (iv) both

mRNA and protein are up-regulated upon differentiation

(e.g. CAPNS1, CALU).

The RNA samples considered for confirmation were

undifferentiated ES cells and EB20. In nine out of ten

reactions the microarray-derived differential expression

could be confirmed at a confidence level of 90%. In just one

case, namely HSPA5, the up-regulation of the mRNA at day

20 detected by microarray could not be confirmed by real-

time PCR (Supporting Information Fig. 4).

3.6 GO of transcriptome and proteome data

Enrichment analysis of GO categories was performed

using BiNGO. Supporting Information Fig. 5 represents

significantly over- and under-represented GO cellular

component, molecular function, and biological process

terms for the set of differentially expressed transcripts and

proteins.

177

54

395

400

638

840

10

68

8691

186

471

485

755

795

872

889

216222

290 134

325

582

643

314

797

875

808

817

931

932

573

pI=4-7

Mr(KD)

97

67

43

30

20

14

53

131

141

167

194

221

223

267

542723730

313

393461

46446

562 639641

727757

821

828

854

920

912291

320 327425

426

445 528

551

623

848

876 877878

882

944

945

611

ESC

ESC EB3 EB6 EB12 EB20 ESC EB3 EB6 EB12 EB20

Figure 2. 2-D gel analysis of

proteins extracted from

hESCs. The regions of the

gel, which are surrounded by

boxes, were delimited and

enlarged in panels. In the

first dimension, 125 mg of

protein was loaded on a

24 cm IPG strip with a linear

gradient of pH 4–7. In the

second dimension, 11.5%

SDS-PAGE gels were used,

with a well for Mr standards.

Proteins were visualized by

silver staining. Numbered

spots correspond to identi-

fied differentiation asso-

ciated proteins. Examples of

changes in protein abun-

dance during differentiation

corresponding to boxes in

2-DE gel have also been

presented in panels.

Proteomics 2009, 9, 4859–4870 4865

& 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

In the biological process category, a comparison between

transcriptome and proteome data sets revealed that the

primary metabolisms and protein metabolisms were over-

represented in transcriptome and proteome data sets,

respectively. However, regulatory genes categories such as

regulation of biological and cellular processes, regulation of

signal transduction, and cell differentiation were specifically

over-represented in transcriptome data sets (Supporting

Information Figs. 5A and D). In the molecular function

category, protein binding and RNA binding proteins were

over-represented in the proteome data set (Supporting

Information Fig. 5B). At the transcript level (Supporting

Information Fig. 5D), in addition to protein binding and

catalytic activities, genes involved in transcription regulation

were over-represented as expected.

As shown in Supporting Information Fig. 5C, in the

cellular component category for proteome data set, GO

terms related to intracellular and cytoplasm were over-

represented whereas membrane proteins were under-

represented. However, in the cellular component category

for the transcriptome data set (Supporting Information

Fig. 5F), cytoplasm, nucleus, and membrane proteins were

over-represented and ribosomal and ribonucleoprotein

complexes, and cystoskeleton were under-represented. Out

of 6187 transcripts differentially expressed during differ-

entiation, 75 and 177 plasma membrane transcripts were

hESCs and EBs enriched, respectively (Supporting Infor-

mation Table 6). These findings suggested that tran-

scriptome analysis may provide valuable information about

the expression profile of plasma membrane genes during

differentiation, which hypothetically might be investigated

in future studies as surface markers of differentiation.

4 Discussion

4.1 Proteome analysis

In this study, we report an extensive comparative analysis of

the transcriptome and proteome of hESCs during prolif-

eration and at different stages of differentiation defined by

821 YWHAE

EB3 EB6 EB12 EB20Spotno

AB

CD

FE

Genesymbol

AccessionnoC

lust

er

920 PSMB2825 KRT19461 CASP3716 KRT1787 N.I 528 ALB 828 ALB 757 HNRPK 221 KRT1 744 N.I 799 N.I 739 N.I 727 N.I 167 CAPNS154 CBX5 53 SERPINB1324 VDAC1 755 C6ORF108541 N.I 908 TATDN1730 CALR 877 KRT1 466 TUBA1B 177 SFRS1 854 TRAF1 393 ATIC 945 EIF1A 425 HDGF 267 TXNDC5 464 TUBA1C 542 N.I 638 PPIA 887 HSPA8 400 CALU 795 RNH1 395 STIP1 872 KHSRP 633 S100A10 729 PSMG2 912 CNN2 944 UCHL3 314 EIF2S2 611 HNRNPL 134 EEF1D 68 PPA1 216 EIF3S3 91 EEF1B2 131 SUGT1 141 PHB 723 KRT1 222 CAPZA1 31 ECHS1 10 ERP29 485 GRSF1 641 METAP2 327 MRPS22 588 NDUFS1 808 NSFL1C 738 PPAPDC1A 471 PSMC4 817 TRB 797 TUBB 840 UBE2C 562 UBQLN1 86 YWHAG 737 ZNF664 150 HSPA5 582 HSPA8 194 HNRPC 889 NPM1 573 PA2G4 426 NPM1 223 AHCY 325 GALKL1 643 N.I 639 ALB 878 VCL 931 ALDH1B1 445 ATP5B 882 ENO1 717 NAP1L4 320 PPP1CA 186 PSMD13 623 SFRS7 886 VCP 848 VIL2 551 NPM1 878 VCL 875 VCL 64 KRT1 313 DDAH

291 NPM1 932 HNRPC 290 NPM1

P62258

P49721P08727 Q5HYI3 P04264

P02769 P02769 P61978 P04264

P04632 P45973 Q9UIV8 P21796 O43598

Q9BY40 P27797 P04264 P68363 Q07955 Q13077 P31939 P41567 P51858 Q8NBS9 Q9BQE3

P62937 P11142.2 O43852 Q96FD7 Q5TZU9 Q92945 Q6FGE5 Q9P1R6 Q99439 P15374 Q5TDH5 P14866 P29692 Q5SQT7 Q5BKY2 P24534 Q9Y2Z0 P35232 P04264 P52907 P30084 P30040 Q12849 P50579 P82650 P28331 Q9UNZ2 Q5VZY2 P43686 P04435 P07437 O00762 Q9UMX0 P61981 Q8N3J9 P11021 P11142.2 P07910 P06748 Q9UQ80 P06748 P23526 P51570

P02769 P18206 Q9BV45 P06576 Q53HR3 Q99733 P62136 Q53XU2 Q16629 Q96IF9 P15311 P06748 P18206 P18206 P04264 O94760

P06748 P07910 P06748

719 N.I

Figure 3. K-means clustering of protein expression pattern of 96

differential expressed at ESCs, EB3, EB6, EB12, and EB20. Input

data for pre-processing was the induction factor that was

calculated by dividing the percentage volume of each protein

spot at the defined EB stage by the percentage volume of the

same protein spot at the undifferentiated stage. One-dimen-

sional K-means gene clustering was performed and proteins

were clustered in six groups (A–F). MS identified proteins are

shown on the right. Proteins that were analyzed by MS but did

not significantly match with any characterized protein in the

various databases are presented as not identified (N.I.).

Sampling stages are shown on the top. All quantitative infor-

mation is transmitted using a color scale in which the

color ranges from green (�1) for the highest down-regulation to

red (11) for the highest up-regulation. Dark boxes (0) indicate no

changes in expression pattern of EBs compared with ESC.

3

4866 A. Fathi et al. Proteomics 2009, 9, 4859–4870

& 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

EB formation. We identified 87 proteins differentially

expressed in these stages of hESC differentiation.

A 2-DE coupled with MS approach enabled us to identify

a wide variety of proteins.

GO database searches revealed that the differentially regu-

lated genes and proteins identified in our study are involved in

various biological processes within hESCs. This further reflects

the changes that occur in the transition from the undiffer-

entiated state of hESCs to differentiation at day 20. The

expression of proteins involved in cell growth and cell cycle

progression are expected to change during differentiation.

Some of the proteins enriched in proliferating hESCs have

these properties. For example, hepatoma-derived growth factor

has mitogenic and DNA-binding activity and may play a role in

cellular proliferation and differentiation [21].

Another down-regulated protein during differentiation

was SUGT1, a homologous to the yeast gene SGT1, which

encodes a protein involved in kinetochore function and is

required for cell cycle progression at both the G1/S and G2/

M transitions [22]. Furthermore, down-regulation of its

expression in differentiated hESCs was confirmed using

Western blot analysis.

Another hESC-enriched protein was proliferation-asso-

ciated 2G4 also known as ERB3-binding protein (EBP1).

Down-regulation of this protein was also observed in

Western blot analysis. EBP1 appears in the nuclei from late

G1 to early S phase and diminish at the late G2 phase of the

cell cycle. Therefore, it is thought to play an important role

in DNA replication or cell cycle progression [23]. NPM1 was

also more abundant in hESCs compared with differentiated

cells. Using a (Tc)-inducible small interference RNA, it has

been shown that Npm1 is an essential gene for mouse ESC

proliferation [24]. The down-regulation of Ebp1, Sugt1, and

Npm1 has been reported during mouse ESC differentiation

[9]. Another down-regulated protein was S100A10, which

contains 2 EF-hand calcium-binding motifs and is involved

in the regulation of a number of cellular processes such as

cell cycle progression and differentiation [25].

Of particular interest are proteins involved in transcrip-

tional control and signal transduction. Amongst the proteins

involved in transcription regulation, we found ZFP664 and

CALR. The latter protein can modulate gene expression by

binding to the glucocorticoid receptor [26]. Proteins involved

in signal transduction are YWHAE and YWHAG. YWHAE

is known to involved in the linkage between mitogenic

signaling and the cell cycle machinery [27]. YWHAG

provides a link between Raf-1 and PKC and may represent a

signal transduction protein that is regulated both tran-

scriptionally and post-transcriptionally by growth factors

[28]. Down-regulated expression of YWHAG was also

observed during mouse EB-mediated differentiation of

ESCs [9].

In addition to these mechanisms, cell differentiation is

determined by spatial and temporal control of expression of

many other genes and mechanisms. Our results suggest

that the expression levels of proteins that have regulatory

functions key to maintaining self-renewal and pluripotency

are down-regulated upon differentiation. Most importantly

there are sets of genes whose protein levels are regulated in

a similar manner upon differentiation (e.g. OCT4 and

NANOG) and those having contrasting expression at the

mRNA and protein levels (e.g. DDAH1 and PHB).

We have identified several genes involved in transcription

(proteasome subunits, HNRNPL, HNRPC, and hnRNPK),

RNA splicing (KHSRP, SFRS1, SFRS7, and hnRNPs), and

translation (EIF1A, EEF1B2, EIF2S2, EIF3S3, EEF1D, and

hnRNPK) that are preferentially expressed in undiffer-

entiated cells and may have key roles in the proliferation of

WB/H6 WB/ H5 Pr/H5

Figure 4. The expression pattern of proteins in Western blot analysis of total protein extracts derived from Royan H5 (WB/H5) and H6 (WB/

H6) as well as expression level of corresponding proteins at proteome level (Pr/H5). Equal amounts of protein in total cell lysates of hESCs

and dif-ESCs were subjected to SDS-PAGE followed by Western blotting. ESCs and dif-ESCs were analyzed with antibodies against five

proteins (CALU, ERP29, EBP1, HSC70, and SUGT1).

Proteomics 2009, 9, 4859–4870 4867

& 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

ESCs. Further studies are needed to elucidate the detailed

and specific roles of these proteins in maintaining self-

renewal of hESCs. Changes in expression of several of these

genes including hnRNPK, EEF1B2, and EEF1D during

differentiation have been reported in mouse ESCs [9, 29].

We observed higher expression of HSPA5 and HSPA8 in

undifferentiated ESCs. The heat shock proteins serve as

chaperones, being involved in protein folding, assembly,

and transportation [30]. Although the biological role of these

HSPs in hESC differentiation is not yet known, there is a

growing body of evidence linking chaperone-like molecules

to the regulation of cell proliferation and differentiation [31,

32]. Interestingly, HSPA8 protein has been suggested as a

novel cell-surface marker for undifferentiated hESCs [33].

Although its biological role is currently unknown, it has

been speculated that it might be associated with immune

responses [33]. The mouse ortholog of HSPA5 has also been

shown to be down-regulated upon differentiation of mouse

ES cells [29].

Changes in expression of several proteins related to

cytoskeleton and cell shape including Keratin 1, Keratin 19,

Tubulin, and Calponin 2, and Vinculin were observed.

While differentiation may cause changes in cell shape,

several studies have noted that changes in cell shape

themselves can regulate biological processes such as

proliferation [34] and differentiation [35].

4.2 Transcriptomics

A unique feature of our study is the comparative analysis of

the proteome and transcriptome of the same batch of

cultured cells. The analysis revealed that the complete loss

of expression of the self-renewal factors OCT4 and NANOGoccurred on day 6 and beyond (EB6 till EB20). This is

further manifested by the gradual reduction in the correla-

tion coefficients between the undifferentiated cells and the

derived EBs. For example, the correlation between undif-

ferentiated H5A and EB3 is 0.98 and as low as 0.80 when

compared with EB20.

4.3 Integration of transcriptomics and proteomics

data

In the present study, we have shown both global and indi-

vidual (single genes and proteins) regulation during differ-

entiation of hESCs by transcriptome and proteome analyses.

A total of 6187 transcripts and 97 proteins were found

modulated during differentiation. This clearly points to the

technical challenges one faces with proteomic analysis. In

the future more sensitive proteomic platforms such as

SILAC will have to be employed for studies such as we have

presented here. There is emerging evidence that suggests

that combining proteomics and transcriptomics analysis of

the same samples can provide further insights into complex

mechanisms.

We analyzed the correlations between mRNA and protein

levels at different stages.

The protein spots for correlation analysis were selected

from proteome map generated by Baharvand et al. [36] and

the current study. The expression levels of a large number of

proteins that migrated to more than one spot (presumably

due to differential protein processing or modifications) were

calculated by integrating the intensities of the different

spots. The 262 proteins on 2-DE were correlated to corre-

1 2 3 4 5 6 7

8 9 10 11 12 13 14

15 16 17 18 19 20 21

22 23 24 25 26 27 28

29 30 31 32 33 34 35

36 37 38 39 40 41 42

43 44 45 46 47 48 49

50 51 52 53 54 55 56

1

0

-1

Pro

tein

mR

NA

EB3/ESC EB6/ESC EB12/ESC EB20/ESCA

C(1) CBS, CNN2, CTBP2, FARSLB, HNRPDL, NUDT9. (2) ANXA3 KRT18, PSMC4, TALDO1. (3) PPA1, SUGT1, UCHL3. (4) AHCY, EIF2S2,VCP. (5) ALDH1B1, ATP5B, ENO1, ERP29, GRSF1, PSMD13. (6) GALK1. (7) CAPZA1, HNRPC, MRPS12, NSFL1C. (8) VCL, YWHAB. (9) NDUFS1, TPMT. (10) LDHB, PCNA, PSMD14, PSME3. (13) EEF1D, VIL2. (14) ECH1. (15) FABP5, NACA, PDIA6, PDLIM1, SMS. (16) GART, PHB. (17) ACTR3, CLIC1, FKBP5, HSPA8, KPNA2, YWHAG. (18) MCM7, NPM1, PA2G4, PPP1CA, TUBB. (20) ACY1, CALU, CKS,HSPA5, KRT10, RAD23A, S100A10, SERPINB6. (21) ADA, FKBP10. (22) C7ORF24, HNRPK. (23) NP, RANBP1. (24) C14ORF166. (25)ECHS1, KRT1. (26) APOE, ARCN1, ARHGDIA. (28) C6ORF108, HDGF, HIBADH, PGLS, RNH1. (29) MRPS12, SRI, TXNDC12, UCHL1. (30) ATPBD4, GARS. (31) TATDN1, VDAC1. (32) LAP3, PPIA, SFRS1, UBQLN1. (34) EIF3S3. (35) POLR2C, RAD23B, SERPINB9. (36) CCT6A,PSMB6. (37) SET. (38) TXNDC5, XRCC6. (39)CASP3, STIP1. (40) KHSRP, NAP1L4, RPA2. (41) SMARCB1. (42) CNN3, DDAH2, EEF1B2,IDI1, PPAPDC1A, SFRS7, ZNF664. (43) ATIC, DDAH1. (44) PSMA2. (45) APRT, HMGB1, METAP2, STMN1. (48) COPE. (49) ALDH2, KRT8.

(50) TRAF1. (51) CALR. (52) CBX5, SERPINB13, YWHAE. (53) KRT19, PSMB2. (54) CRABP2. (55) CAPNS1. (56) ALB, CRABP1.

B

Figure 5. (A) SOM clustering

of 136 genes modulated

during differentiation at

protein and/or mRNA levels.

SOM outputs were visualized

by CPP. Each presentation

illustrates a sample-specific

proteomics and tran-

scriptomic map in which all

up-regulated (red) and down-

regulated (blue) are well

delineated. (B) Each group

created by SOM clustering is

numerically labeled. (C) The

genes corresponding to

groups in (B) and genes

modulated at the mRNA or

protein levels are depicted in

red and blue, respectively.

Several modulated genes at

the mRNA level and also

regulated at the protein level

in similar (purple) or opposite

(black) directions are shown.

4868 A. Fathi et al. Proteomics 2009, 9, 4859–4870

& 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.proteomics-journal.com

sponding mRNA in microarray analysis. Of them, 136

changed significantly during differentiation at mRNA and/

or protein levels. Genes were excluded from the correlation

analysis if no mRNA expression data were available for the

protein or more than a single mRNA was presented in

microarray analysis for a single gene. The correlations were

measured by Pearson’s method between 262 paired protein

and transcript as well as 136 differentiation expressed

mRNA and/or proteins. As shown in Supporting Informa-

tion Fig. 6, there was a low correlation between RNA and

protein abundance levels.

To find out links between transcriptome and proteome, the

expression of 136 differentially expressed genes at the level of

proteins and/or mRNAs were simplified and visualized by

CPP-SOM analyses (Fig. 5 and Supporting Information Table

7). As shown in Fig. 5A, the CPP-SOM outputs offered a

global view of mRNA and protein clustering, particularly with

respect to the expression patterns of clustered genes. This

approach allowed the illustration of SOM outputs in multiple,

vector component specific presentations. Each of these

presentations illustrated values of a single vector component

in all map units. For instance, the first presentation (EB3/

ESC protein expression) in Fig. 5A shows the SOM values of

clustered proteins of all map units (136 transcript or protein)

at the EB3 and last one (EB20/ESC mRNA expression) shows

the SOM values of clustered mRNA of all map units at the

EB20. Each of these presentations also presented sample

specific expression, in which all up-regulated units (hexagons

in red), down-regulated units (hexagons in blue), and

moderately transcribed units (hexagons in green and yellow)

were well delineated. Therefore, it was straightforward to

determine functional significances of genes regulated at

protein and/or transcript level at each sampling point during

differentiation. By comparing these presentations, we could

correlate the expression pattern of protein with mRNA. The

expression pattern of mRNA and protein of genes mapped at

two upper corners suggested that the genes on the left were

down-regulated mainly at mRNA level whereas the genes on

the right were mainly down-regulated at protein level. The

expression of genes mapped to the two bottom corners

demonstrated that protein levels of corresponding genes in

the bottom left corner were particularly increased at the end

of differentiation (EB20), whereas the progressively increased

expression level of mRNAs in the right corner showed that

these genes were modulated mainly at mRNA. Overall, the

comparative analysis revealed that proteomic and tran-

scriptomic data are complementary rather than duplicative.

Overall, the differentially expressed genes could be clus-

tered in four different groups (Figs. 5B and C): (i) Both

mRNA and protein were modulated upon differentiation in

the same direction. This group was composed of only 12

genes mainly involved in cytoskeleton organization and

biogenesis. (ii) Thirteen mRNAs and their corresponding

proteins were modulated upon differentiation but in oppo-

site directions. This category was enriched in genes involved

in primary metabolisms and RNA processing. (iii) The

expression levels of 40 proteins changed during differ-

entiation but the level of corresponding mRNAs did not

change significantly. Proteins involved in metabolic process

and protein metabolism and processing were over-repre-

sented in this category. (vi) The expression levels of 71

mRNAs were modulated but the level of the corresponding

proteins did not change significantly. Genes involved in

metabolisms and regulations of cellular processes were over-

represented in this analysis.

However, application of current technologies to establish

a direct correlation between transcriptomics and proteomics

data is challenging due to multiple layers of discrepancies

such as the distinct sensitivities of microarray and 2-DE,

differences in mRNA stability and the translational effi-

ciency of different genes, posttranslational modifications of

proteins, and timing discordance of modulation at mRNA

and protein levels. Our results showed that proteomics and

transcriptomics data are complementary rather than dupli-

cative. For example, modulation of several proteins involved

in cell growth and cell cycle (e.g. hepatoma-derived growth

factor, proliferation-associated 2G4, STIP1), transcription

regulation (CALR), signal transduction (YWHAE and

YWHAG), transcription (PSMD13 and HNRP), and trans-

lation (EEF1B2, EIF2S2, EIF3S3, EEF1D) was revealed only

by proteome analysis.

This project was partially funded by grants from RoyanInstitute, Iran to B.H. and G.H.S. and from the Max PlanckSociety to J.A.

The authors have declared no conflict of interest.

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