ORIGINAL PAPER

Transcriptome Profiling of Neuronal Model Cell PC12from Rat Pheochromocytoma

Ramasamy Saminathan Æ Arjunan Pachiappan ÆLuo Feng Æ Edward G. Rowan ÆPonnampalam Gopalakrishnakone

Received: 27 October 2008 / Accepted: 5 January 2009 / Published online: 5 February 2009

� Springer Science+Business Media, LLC 2009

Abstract GeneChip� microarray is a cutting-edge tech-

nology being used to study the expression patterns of genes

with in a particular cell type. In this study, the Affymetrix�

RAE230A platform was used to profile stably expressed

mRNA transcripts from PC12 cells at passage 5 and 15.

The whole-cell PC12 transcriptome revealed that a total of

7,531 stable transcripts (P \ 0.05), corresponding to 6,785

genes, were found to be consistently expressed between

passage 5 and 15. The data analysis revealed 3,080 func-

tional proteins, belonging to 13 families, which indicate

that about 65% of the proteins expressed in PC12 cells are

uncharacterized. By using our custom-built rat neuronal

reference genome database, we mapped endogenously

expressed stable neuronal transcripts from PC12 cells

comprising about 765 genes responsible for neuronal

function and disease. These neuronal transcripts were fur-

ther analyzed to provide a genetic blueprint that can be

used by neurobiologist to unravel the complex cellular and

molecular mechanisms underlying biological functions and

their associated signalling networks for diseases affecting

the nervous system.

Keywords PC12 cells � Microarray � Gene expression �Rat pheochromocytoma � PC12 transcriptome

Abbreviations

ERK Extracelular signal-regulated kinase

EST Expressed sequence tag

GABA c-Aminobutyric acid

GAPDH Glyceraldehyde-3-phosphate dehydrogenase

GCOS GeneChip� operating software

GenMAPP Gene map annotator and pathway profiler

GEO Gene expression omnibus

IPA Ingenuity� pathway analysis

JNK c-Jun N-terminal kinase

KEGG Kyoto encyclopaedia of genes and genomes

MAPK Mitogen-activated protein kinase

NGF Nerve growth factor

PACAP Pituitary adenylate cyclase-activating

polypeptide

PKA Protein kinase A

qRT-PCR Quantitative reverse

transcription-polymerase chain reaction

RAE230A Rat expression 230A

Introduction

PC12 cells are a neoplastic counterpart of chromaffin cells

originally derived from transplantable pheochromocytoma

of the irradiated rat adrenal medulla (Greene and Tischler

1976). They are a well established cell line commonly

used as an in vitro neuronal model for studying signalling

pathways (both constitutive and regulated) activated by

Electronic supplementary material The online version of thisarticle (doi:10.1007/s10571-009-9345-y) contains supplementarymaterial, which is available to authorized users.

R. Saminathan � A. Pachiappan � L. Feng �P. Gopalakrishnakone (&)

Venom and Toxin Research Programme, Department of

Anatomy, Yong Loo Lin School of Medicine, National

University of Singapore, Singapore 117597, Singapore

e-mail: antgopal@nus.edu.sg

R. Saminathan

e-mail: drsam72@hotmail.com

R. Saminathan � E. G. Rowan

Division of Physiology and Pharmacology, Strathclyde Institute

of Pharmacy and Biomedical Sciences, University of

Strathclyde, Glasgow G4 0NR, UK

123

Cell Mol Neurobiol (2009) 29:533–548

DOI 10.1007/s10571-009-9345-y

neurotrophic factor(s), as well as to study vesicular neu-

rotransmitter (quantal) release (Sombers and Ewing 2002;

Martin and Grishanin 2003). In normal culture conditions,

PC12 cells synthesize and release various neurotransmitters

(acetylcholine, dopamine and norepinephrine, but not epi-

nephrine), neuromodulators (endorphin, dynorphins,

enkephalin, neuropeptide Y, neurotensin and somatostatin),

secretory proteins and enzymes (secretogranins or chro-

mogranins, tyrosine hydroxylase, dopa decarboxylase,

dopamine b-hydroxylase, choline acetyltransferase, protein

kinases, phospholipases) and nucleotides (Shafer and At-

chison 1991; Margioris et al. 1992; Solem et al. 1997;

McCullough et al. 1998; Traina and Bagnoli 1999). As with

the most central and peripheral neurons, PC12 cells express

different types of cell surface receptors: voltage-gated ion

channels (Na?, K? and Ca2?), voltage and Ca2?-activated

K? channels, proton-gated channels, and ATP-evoked ionic

pumps; neurotransmitter receptors such as cholinergic

(muscarinic and nicotinic) receptors, dopamine receptors,

adenosine and P2 receptors, serotonin-gated receptors,

glutamate (metabotropic) receptors; neuromodulator

receptors such as, opioid receptors, neuropeptide Y recep-

tors and transporters such as catecholamine transporters,

glucose transporters and divalent metal transporter etc.

(Shafer and Atchison 1991; Clementi et al. 1992; Margioris

et al. 1995; Park et al. 1998; Kobayashi et al. 1999;

McCullough et al. 1998; Arslan and Fredholm 1999; Kane

et al. 1998; Roth et al. 2000; Ardizzone et al. 2002; Chu et al.

2002). Because of their unique properties and suitability for

genetic manipulations, PC12 cells are regarded to be a

convenient alternative to endogenous neuronal cells, and a

model system for a range of studies such as, neuronal dif-

ferentiation, neurotransmitter synthesis and exocytosis

kinetics, mechanism of synaptotagmin, neurotrophin action,

monoamine biosynthesis, ion channel and receptors modu-

lations, protein trafficking, secretory vesicle dynamics,

oxygen sensing, opioids and their receptor interaction

mechanisms, cellular necrosis and apoptosis, cell signalling,

neural plasticity, neuronal sprouting, gene expression, and

neuronal diseases such as, Alzheimer’s diseases, Hunting-

ton’s disease, peripheral neuropathy and neurotoxicology

(Greene and Rein 1977; Anderson et al. 1991; Burgoyne

1991; Keilbaugh et al. 1991; Isom et al. 1998; Margioris

et al. 1995; Bal-Price and Brown 2000; Vaudry et al. 2002a;

Spicer and Millhorn 2003; Igarashi et al. 2003; Martin and

Grishanin 2003; Fukuda and Yamamoto 2004).

In this study, we demonstrate genetic signature under-

lying complex cellular and molecular mechanisms and

their associated signalling networks in PC12 cells. In order

to achieve our objectives, we profiled stably expressed low-

to high-abundance mRNA transcripts of the PC12 cells

cultured at different serial passages. cRNA synthesized by

in vitro transcription belonging to the passages 5 and 15

were used to high-throughput oligonucleotide microarray,

using GeneChip� RAE230A (Affymetrix�, Inc., Santa

Clara, USA), to obtain global pattern of transcriptome of

either passage. Using the RAE230A platform, a total of

15,866 probe sets (excluding internal controls) representing

14,619 unique genes are subjected to analyse. Of the

14,619 genes represented, about 8,725 are well-substanti-

ated to date, in which 5,020 are functionally annotated. The

global pattern of PC12 gene expression profile are acquired

by GeneChip� operating software v1.4 (GCOS) (Affyme-

trix� Inc., Santa Clara, CA, USA) and analyzed further

using GeneSpring GX 7.3.1 expression analysis software

(Agilent Technologies, Inc., Palo Alto, CA, USA). Further,

to study the endogenous signalling networks and molecular

mechanisms involved in neuronal functions and diseases,

about 940 neuronal transcripts representing 765 genes are

mapped by use of our custom build rat neuroscience gen-

ome, which comprise 3,010 transcripts corresponding

1,941 genes. Overall analysis bestowed the studies with

array of 3,080 functional proteins, in which 65% are not

known by any of proteomic studies to date, belong to 13

families are taken further to explore the appropriate use of

the PC12 cells in neuroscience research, using Ingenuity�

knowledge base facilitated IPA v4.0 software (Ingenuity

Systems, Inc., Redwood City, CA, USA).

Experimental Procedures

Cell Culture

PC12 cells (CRL-1721; A.T.C�C, Manassas, VA, USA)

were grown by serial passage in Ham’s F12-K medium

(GIBCO, Grand Island, NY, USA) supplemented with 15%

(v/v) heat-inactivated horse serum (Hyclone, Longan, UT,

USA), 2.5% (v/v) foetal bovine serum (Hyclone), 2 mM

L-glutamine and adjusted to contain 1.5 g/l sodium bicar-

bonate in a humidified atmosphere at 37�C and 5% CO2.

The cells were maintained in poly-L-lysine coated 75-cm2

cell culture flasks at a density of *105/ml and the cells

were sub-cultured once per week with medium changes

every 2–3 days.

Preparation of Target cRNA

Total RNA from PC12 cells of passage 5 and 15 were

extracted using RNeasy Mini Kit following the manufac-

turers’ protocol (Qiagen, Maryland, USA). The eluted

RNA samples were subsequently treated with the RNase-

free DNAse I at room temperature for 20 min and stored

at -80�C until use. The quality and quantity of extracted

RNA was determined by SmartSpec 3000 spectrophotom-

eter (Bio-Rad, Hercules, CA, USA). With highest purity at

534 Cell Mol Neurobiol (2009) 29:533–548

123

A260/A280 ratios of 1.8–2.0, the RNA samples were used

for microarray hybridization. The integrity and relative

contamination with residual genomic DNA was assessed

by 1% agarose gel electrophoresis.

The preparation and processing of labelled fragmented

cRNA for oligonucleotide microarray hybridization were

performed according to the protocol described in Gene-

Chip� expression analysis technical manual (Affymetrix�

Inc., Santa Clara, CA, USA). Briefly, double-stranded

cDNA was synthesized from total RNA using GeneChip

T7-oligo (dT) promoter primer (Gibco BRL Superscript

Choice System; Life Technologies). Subsequently, biotin-

ylated cRNA was synthesized by in vitro transcription

using the Enzo� BioarrayTM

HighYieldTM

RNA Transcript

Labelling Kit (Enzo Lifesciences Inc., Farmingdale, NY,

USA). The biotin-labelled cRNA was cleaned using

RNeasy Mini Kit (Qiagen) and quantitated and fragmented

for hybridization. The purity was then confirmed by 1%

agarose gel electrophoresis.

GeneChip� Hybridization

The GeneChip� RAE230A contains 11 pairs per 25-mer

oligonucleotide probe set with optimal hybridization

behaviour enabled most accurate independent measure-

ments of every transcript. Before hybridizing GeneChip�

RAE230A, the quality of the fragmented labelled cRNA

was assessed additionally using GeneChip� Test3 arrays.

After confirming that the fragmented cRNA samples are in

good quality, the RAE230A gene chips (one chip per

passage, n = 2) were hybridized for 16 h at 45�C with

60 rpm in a hybridization oven. After the hybridization, the

arrays were washed in nonstringent buffer (69 SSPE buf-

fer, 0.01% Tween20, 0.005% antifoam) at 25�C and

stringent wash buffer (100 mM MES buffer, 0.1 M [Na?],

0.01% Tween20) at 50�C, and stained with streptavidin–

phycoerythrin (10 lg/ml) together with an antibody

amplification step using automatic procedure performed on

a GeneChip� Fluidics Station 400 (Affymetrix� Inc.),

according to the protocols described in the Affymetrix�

GeneChip� expression analysis technical manual.

Data Acquisition, Evaluation and Normalization

The hybridized RAE230A arrays were scanned using a

GeneArray confocal scanner (Agilent, Palo Alto, CA). The

data scaled to an average intensity of 800 were qualified and

quantified by Affymetrix� GCOS system as referred in

GeneChip� expression analysis-data analysis fundamentals

(Affymetrix� Inc.). To assess the quality of the hybridiza-

tion artefacts of every RAE230A array, single-array image

file and its grid alignment and intensity of the control Oligo

B2 at the four corners were inspected essentially. Secondly,

house-keeping/control genes (GAPDH, b-actin and hexo-

kinase 1) were reviewed for positive controls. Extremely

spiked bacterial bioB, bioC, bioD and cre genes were served

as positive hybridization controls. Poly-A controls, such as

dap, lys, phe, and thr, genes were served to assess in vitro

transcription. In addition, a set of maintenance genes (100

probe sets) were served as normalization controls to

facilitate scaling of array experiments. Detection and

quantification of expressed transcripts were accomplished

by the detection algorithm, which uses the hybridization

signal of the 11 probe pairs against the strategy of perfect

match (PM) and mismatch (MM) analysis, and the relative

level of expression for each probe set was calculated by the

signal algorithm (Lescallet et al. 2004). The each probe set

has a reference probe (central nucleotide containing a single

base mismatch) constructed adjacent to it, which allows

closely related sequences to be clearly identified. This per-

fect and mismatch strategy accounts for possible cross-

hybridization and controls for non-specific binding and

location dependent hybridization differences (Yanagawa

et al. 2005). Therefore, the target (cRNA) hybridization

signals are determined to be present if the hybridization

intensities of the perfect match probes are significantly

above the background signal after subtraction of the mis-

match hybridization signal.

The ‘.CHP’ files generated by GCOS were directly

loaded into GeneSpring GX 7.3.1 software (Agilent

Technologies, Inc.) using enhanced pre-processor plug-in.

Using a per-chip 50th percentile procedure, raw signal

intensity and expression values were normalized across the

experimental set, passage 5 and 15. The expression of each

transcript was generated by normalization over the median

of the experiment set. The stable expression of individual

transcript was obtained based on the filter criteria present

call in both (P/P) or present with marginal (P/M). Addi-

tionally, the filter on confidence has to pass t-test with false

discovery rate P-value cut-off = 0.05.

Construction of a Reference Neurogenome Database

For this work, we have constructed a rat reference genome

database relevant to neuroscience research. This database

comprises over 2,158 annotated genes from various resour-

ces, such as the annotated files of Affymetrix GeneChip� Rat

Neurobiology U34 array (http://www.affymetrix.com), Rat

Genome Database (RGD)––Neurological Disease Portal

(http://rgd.mcw.edu), SuperArray Bioscience Corpora-

tion––Neuroscience Arrays Portal (http://www.superarray.

com), European Molecular Biology Laboratory––European

Bioinformatics Institute (EMBL-EBI)––InterPro v13.0

database (http://www.ebi.ac.uk/interpro/) and Ingenuity

Pathways Knowledge Base (IPKB)––IPA v4.0 software

(Ingenuity Systems, Inc.). The lists of selected mammalian

Cell Mol Neurobiol (2009) 29:533–548 535

123

neuronal genes from the above resources were then further

evaluated by the RGD and NetAffxTM

analysis centre (http://

www.affymetrix.com/analysis/index.affx) for their rat

equivalents, synonyms and mRNA transcripts. Of the 2,158

mammalian genes selected, 1,941 genes of rat equivalents

containing 3,010 transcripts were identified by NetAffxTM

analysis centre, whereas ca. 217 genes were not identified to

their respective probe sets or mRNA transcripts. The curetted

3,010 mRNA transcripts (1,941 genes) were referred to map

the endogenously expressed mRNA encoding neuronal

genes of PC12 cells.

Quantitative Real-Time PCR

Total RNA isolated from the passages 5 and 15 (n = 3 each)

using RNeasy Mini Kit (QIAGEN) was used in reverse

transcription qRT-PCR using LightCycler (Roche Diag-

nostics, Penzberg, Germany), for assessing relative mRNA

expression and validation of target genes. DNA content was

measured by real-time fluorimetric intensity of SYBR green

I incorporation after completion of the primer extension step

in each cycle. The expression of b-actin was used as internal

controls for equal RNA loading and to normalize relative

expression data for all other genes analyzed. Melting curves

of the PCR products were generated and referred to as

quality control, indicating the presence of specific and non-

specific PCR fragments. The gene specific primers with

Genebank ID are: (1). NM_012517 (CACNA1C): (154 bp;

Forward 50-gttgccctgggtgtattttg-30, Reverse 50-tagctgctgct

tctcacgaa-30), (2). NM_017298 (CACNA1D): (159 bp;

Forward 50-aaggctaaagcacgtggaga-30, Reverse 50-gggcatgct

agtgtttcgtt-30), (3). NM_147141 (CACNA1B): (215 bp;

Forward 50-tttccctgttccatcctctg-30, Reverse 50-gccacgttcta-

gaggtgctc-30), (4). NM_012918 (CACNA1A): (250 bp;

Forward 50-gctgtgctcactgttttcca-30, Reverse 50-tgagctcac

gttcaatctgc-30). The data were quantified using relative

quantification (2-DDCT) method described by Livak and

Schmittgen (2001).

Results

Profiling of Stable mRNA Transcripts in PC12 Cells

To obtain the stably expressed mRNA transcripts of PC12

cells from passage 5 and 15, stringent filtering criteria were

applied as stated in the experimental methods section

‘‘Data Acquisition, Evaluation and Normalization.’’ From

the normalized two independent experiments of passage 5

and 15, a total of 7,630 probe sets contain 7,531 transcripts,

excluding internal control genes, were identified to be

expressed between the normalized values of 0.195–74.365

with P-value \ 0.05. The single-array data analysis of the

passage 5 and 15 were identified 7,892 and 8,914 expressed

probe sets/transcripts, respectively, with detection call ‘P’

(present) or ‘M’ (marginal). Upon normalization, the

passage 5 lost its 262 expressed probe sets/transcripts,

whereas the passage 15 lost its 1,284 probe sets/transcripts.

In comparison, about 1,022 probe sets/transcripts were not

expressed stably between the passages, 5 and 15.

PC12 Transcriptome Analysis

The stably expressed 7,630 probe sets from the PC12 cells

were subject to transcriptome analysis. The 7,630 probe

sets belong to 6,785 genes, including 937 ESTs, were

classified based on the gene ontology (GO) annotations

using GeneSpring GX 7.3.1 software. The results were

obtained based on a hypergeometric P-value that passed

the maximum P-value filter 1.0. With reference to the GO

annotations in the RAE230A reference genome, about

4,355 and 4,518 transcripts were classified into biological

process (47.51%) and molecular function (47.35%),

respectively. The genes of significant major GO classes

(i.e. restricted subclasses) of biological process and

molecular functions were presented in Table 1. There were

no unknown annotations reported in the classification of

biological process and cellular component, whereas two

transcripts (Rn.22467.1 and Rn.13242.1) in the molecular

function have no GO annotations at this time.

In order to obtain classification based on protein families,

the same set of 7,630 probe sets were loaded into IPA v4.0

software (Ingenuity Systems, Inc.). Of the 7,630 probe sets,

5,561 were reported to be mapped out and 2,069 reported as

unmapped. In other terms, the 7,630 probe sets representing

3,209 genes/proteins were eligible for 169 networks and

3,087 proteins were eligible for functions and canonical

pathways. The results of IPA classifications of protein

families have been presented in Fig. 1. All of the 7,630

probe sets contain 7,531 transcripts were classified into 13

classes: (1). Enzyme (1,070); (2). Transcription regulator

(439); (3). Transporter (410); (4). Kinase (277); (5). Pepti-

dase (159); (6). Phosphatase (116); (7). Transmembrane

receptor (75); (8). Translation regulator (65); (9). Ion

channel (62); (10). G protein-coupled receptor (48); (11).

Growth factor (24); (12). Ligand-dependent nuclear recep-

tor (18); (13). Cytokine (16) and (14). Others (2,782).

To translate the expressed mRNA transcripts of the

PC12 cells into the context of finding genes that are

functionally connected through the biological pathways,

the same set of 7,531 transcripts were analyzed against the

following databases: KEGG (http://www.genome.jp/kegg),

BIOCARTA (http://www.biocarta.com/) and GenMAPP

(http://www.genmapp.org/). The subsets of genes identified

to particular pathways (KEGG or GenMAPP) were ana-

lyzed using GeneSpring GX 7.3.1 software against an

536 Cell Mol Neurobiol (2009) 29:533–548

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Table 1 Gene ontology (GO) classification of stably expressed whole-cell transcriptome of PC12 cells

Category GO no. Genes

in categorya% of genes

in categorybPC12 genes

in category

% of PC12 genes

in category

Significance

P-value

Biological Process 8150 9167 99.96 4355 47.51 0.943

Cellular process 9987 8701 94.88 4183 48.07 2.49E-06

Physiological process 7582 8473 92.39 4089 48.26 4.15E-07

Cellular physiological process 50875 7861 85.72 3922 49.89 1.50E-29

Metabolism 8152 5649 61.6 2980 52.75 2.15E-37

Macromolecule metabolism 43170 3396 37.03 1954 57.54 2.10E-49

Regulation of biological process 50789 3217 35.08 1494 46.44 0.939

Cell communication 7154 3143 34.27 1186 37.73 1.0

Transport 6810 2553 27.84 1181 46.26 0.936

Development 7275 2336 25.47 896 38.36 1.0

Response to stimulus 50896 1813 19.77 693 38.22 1.0

Biosynthesis 9058 1138 12.41 683 60.02 1.14E-19

Cell differentiation 30154 927 10.11 368 39.70 1.0

Cell death 8219 670 7.306 353 52.69 0.00307

Catabolism 9056 558 6.084 316 56.63 5.35E-06

Behaviour 7610 636 6.935 227 35.69 1.0

Electron transport 6118 372 4.056 174 46.77 0.635

Cell motility 6928 427 4.656 162 37.94 1.0

Extracellular structure

organization and biogenesis

43062 150 1.636 48 32.00 1.0

Membrane fusion 6944 46 0.502 30 65.22 0.0116

Molecular function 3674 9542 100 4518 47.35 1.0

Binding 5488 7409 77.65 3531 47.66 0.135

Catalytic activity 3824 3920 41.08 2051 52.32 2.66E-16

Protein binding 5515 4271 44.76 2050 48.00 0.131

Nucleic acid binding 3676 1927 20.19 1089 56.51 1.21E-19

Hydrolase activity 16787 1569 16.44 815 51.94 3.78E-05

Signal transducer activity 4871 2098 21.99 713 33.98 1.0

Transferase activity 16740 1360 14.25 665 48.90 0.114

Transporter activity 5215 1590 16.66 660 41.51 1.0

Transcription regulator activity 30528 994 10.42 468 47.08 0.583

Receptor activity 4872 1280 13.41 383 29.92 1.0

Structural molecule activity 5198 642 6.728 315 49.07 0.195

Oxidoreductase activity 16491 562 5.89 291 51.78 0.0169

Enzyme regulator activity 30234 623 6.529 277 44.46 0.938

Ligase activity 16874 376 3.94 256 68.09 1.01E-16

Carrier activity 5386 499 5.23 239 47.90 0.418

Ion transporter activity 15075 714 7.483 225 31.51 1.0

Protein transporter activity 8565 202 2.117 150 74.26 3.43E-15

Electron transporter activity 5489 237 2.484 131 55.27 0.00803

Translation regulator activity 45182 126 1.32 92 73.02 3.46E-09

Channel or pore class transporter activity 15267 432 4.527 88 20.37 1.0

Lyase activity 16829 159 1.666 77 48.43 0.422

Isomerase activity 16853 119 1.247 68 57.14 0.0197

Motor activity 3774 137 1.436 63 45.99 0.658

Helicase activity 4386 91 0.954 57 62.64 0.00229

Antioxidant activity 16209 47 0.493 21 44.68 0.695

Integrase activity 8907 5 0.0524 4 80.00 0.156

Cell Mol Neurobiol (2009) 29:533–548 537

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annotated reference rat genome RAE230A (Affymetrix).

The BIOCARTA pathways were analyzed manually by

gene names using an online browser (http://www.biocarta.

com/genes/index.asp). As a result, a comprehensive con-

solidated list of 171 pathways at a threshold ratio of[ 0.5

was selected and those are represented in a pictogram

(Fig. 2).

Mapping Neuronal Genome in PC12 Cells

Before analyzing the neuronal transcripts from the PC12

cells, we first analyzed the neuronal genome in RAE230A

GeneChip� expression array. In comparison with our cur-

etted 3,010 transcripts from the rat reference neuronal

genome, the RAE230A array was able to reveal about

2,440 transcripts, which was 81.06% of the total tran-

scripts. To facilitate further filtering of the expression data

for high priority and confidence mapping, the 2,440 iden-

tified transcripts were then taken as reference data set for

the following comparison analysis of PC12 expressed

neuronal genes. With reference to the 2,440 transcripts of

the RAE230A, the comparative analysis was carried out

against the 7,630 probe sets for the occurrence of neuronal

genes in PC12 cells. Of the 7,630 probe sets mapped, only

about 940 transcripts were identified to be identical tran-

scripts of neuronal genes of PC12 cells. The 940 transcripts

belong to 765 genes were then analyzed using IPA v4.0

software (Ingenuity Systems, Inc.) in an attempt to further

characterize the neuronal transcripts and there associated

signalling pathways. According to the IPA annotations, 697

genes were reported to be eligible for generating networks

and 727 genes were eligible for functional and canonical

pathways. The analysis of the results of the significant

neuronal functions and signalling pathways are presented

in Tables 2 and 3, respectively.

Analysis of Plasma Membrane Genome Complex

In order to map out receptors, ion channels and accessory

proteins located to the plasma membrane of PC12 cells, the

619 plasma membrane transcripts were analyzed further

using IPA v4.0 software (Ingenuity Systems, Inc.). The 619

transcripts, comprising 583 GeneBankTM

accession num-

bers, belong to 513 genes were classified into 10 classes: (1)

Ion channels (45 of 513); (2) Transporters (99 of 513); (3) G

protein-coupled receptors (46 of 513); (4) Transmembrane

receptors (66 of 513); (5) Enzymes (47 of 513); (6) Kinases

(29 of 513); (7) Peptidases (12 of 513); (8) Phosphatases (16

of 513); (9) Transcription regulators (2 of 513) and (10)

Others (221 of 513). The analysis results of the plasma

membrane transcripts of the PC12 cells are presented in

Supplementary Table 1, along with a total of 70 redundan-

cies assorted out of the 583 GeneBankTM numbers.

Real-Time qRT-PCR Validation for VACCs

The microarray results of mRNA expressions of four

representative VACCs a-subunits such as, CACNA1A

Cytokines (0.3%)Ligand-dependent nuclear receptors (0.32%)

Growth factors (0.43%)

G-protein coupled receptors (0.86%)Ion channels (1.11%)

Translation regulators (1.17%)Transmembrane receptors (1.34%)

Phosphatases (2.08%)

Peptidases (2.85%)Kinases (5%)

Transporters (7.37%)

Transcription regulators (7.9%)

Other enzymes (19.24%)

Others (50.03%)

Unmapped (27.12%)

Fig. 1 The pie-diagram

depicting the protein family

classification of PC12 cells by

IPA system

Table 1 continued

Category GO no. Genes

in categorya% of genes

in categorybPC12 genes

in category

% of PC12 genes

in category

Significance

P-value

Chaperone regulator activity 30188 7 0.0734 3 42.86 0.728

Permease activity 15646 3 0.0314 1 33.33 0.854

Molecular function unknown 5554 4 0.0419 2 50.00 0.647

a The total number of annotated genes in the reference genome of RAE230A and bthe percentage of the total genes in the RAE230A genome that

has been assigned to this category. The subsets of significant ontological classification have been considered based on the P-value B 1.0

538 Cell Mol Neurobiol (2009) 29:533–548

123

(P/Q-type), CACNA1B (N-type), CACNA1C (L-type) and

CACNA1D (L-type) were validated by using real-time

qRT-PCR. The results of qRT-PCR expression data shows

the mRNA existence of a1B and a1C in 5 and 15 passages,

whereas decreased level of a1D in both passages based on

expression of gene copy numbers with the reference of

house keeping genes. This indicates that the presence and

absence of the genes in the corresponding passages com-

pare with one another (Fig. 3). In addition, the decreased

expression of a1A in passage 5 followed by gradual

increase of expression in passage 15, which demonstrates

the later on development of the neuritis and channels in the

PC12 cells. This assay could also confirm overall reliability

of microarray data, in other terms, obtained to be used in

genome-wide analysis to decipher endogenously encoded

significant biological/functional cascades.

Discussion

Over the past two decades PC12 cells have been exten-

sively used in neuroscience and molecular biology

laboratories as a model of a neuronal or neurosecretory

cell. In order to facilitate our comprehensive understanding

of this cell line we have, for the first time, attempted to

explore the whole-cell mRNA transcriptome of PC12 at

different passages. Although there are few studies on

genome expressions of PC12 cells in response to neuro-

trophic factors (Vaudry et al. 2002b; Grundschober et al.

2002; Grumolato et al. 2003; Konu et al. 2004; Lattanzi

et al. 2007), no attempt has so far been made to determine

stable whole-cell transcriptome, along with the results of

influence of passage and clonal variations. To analyse

heterogeneity of the PC12 transcriptome, we have com-

pared our results with a similar profile obtained from an

European PC12 clone (NCBI GEO Dataset Repository

numbers: GSM114191, GSM114198 and GSM 114199)

(Lattanzi et al. 2007). The analysis revealed that 7,918

transcripts at passage 4 were consistently expressed among

the three experiments, which are about 26 transcripts

higher than our passage 5 expressions. Further, comparison

between the stably expressed transcripts of these two

clones, i.e. passage 4 (of European; earlier studies) and

passage 5 (of ATCC; present study), revealed about 6,793

identical expressions, irrespective of their clonal, culture

and experimental variations. The 6,793 identically

expressed transcripts were then compared with our cells at

passage 15 in order to determine the number of stable

transcripts. Of the 6,793 transcripts, 6,702 were shown to

be stably expressed, regardless of the clonal variations,

culture and experimental conditions and passage variations.

This data explains 87.84% of transcripts were consistently

expressed among the profiles of these two independently

analyzed clones. The difference (12.16%) could be con-

sidered due to certain factors involved in cell culture and

experimental conditions, reference database, analysis

algorithms and filter criteria stringency used on those

individual clones studied.

Ontologizing the stably expressed 7,531 transcripts has

revealed a significant number of clusters in biological

process and molecular functions (Ref. Table 1). The results

of ontological classification of biological process deci-

phered multiple facets of the PC12 cells. This in other

words reveals the possible use of PC12 cells in the studies

of various cellular and physiological processes such as,

metabolism, cell signalling, cell differentiation, cell

motility, cell death/apoptosis, electron transport, biosyn-

thesis etc. Among the classes of the biological process

revealed by ontology, cell differentiation remarkably

accounted for about 368 candidate genes, representing

8.44% of the PC12 genome (Ref. Table 1). Differentiating

PC12 cells with neurotrophic factors (e.g. NGF, EGF,

PACAP etc.) are known to cause neuronal-like phenotype

through specific signal transduction induced by trophic

factor. Earlier studies on PC12 cells differentiated with

NGF and PACAP have been shown to significantly express

66 and 73 marker genes, respectively (Vaudry et al. 2002a;

Marek et al. 2004). Functional classifications of the NGF-

induced transcriptome analysis involved in four significant

ontological classes, (1) Signalling proteins (21), (2) Neural

and synaptic vesicle proteins (13), (3) Ca2? binding and

cytoskeletal proteins (8), (4) Transcription factors (13), and

(5) Miscellaneous and unknown functions (11) (Marek

et al. 2004). Likewise, PACAP induced expressions were

classified into seven classes, (1) Neurogenesis (14), (2)

Growth arrest (3), (3) Cell growth (12), (4) Drug resistance

(6), (5) Intracellular traffic (4), (6) Metabolism (8), and (7)

Miscellaneous and unknown functions (26) (Vaudry et al.

2002a). These two study results lead to an assumption that

neuronal properties, particularly electrical, signalling and

metabolic properties elucidated in differentiated PC12 cells

could be neurotrophic factor specific genetic programme.

Notably, 1% DMSO, a non-neurotrophic factor, induced

differentiation in PC12 cells have been shown to be with

distinct neurite outgrowth. However, the differentiation did

not accompany with increased electrical properties unlike

with the NGF or PACAP induced differentiation (our

unpublished results). In addition, chronic exposure of eth-

anol (150 mM), another non-neurotrophic factor, have

been shown to increase ionic currents in PC12 cells pri-

marily through induction of protein kinase cascade (Gerstin

et al. 1998; McMahon et al. 2000). Whereas, the neuronal

differentiation by NGF were mediated by integrated ERK

and JNK signalling in association with Ras signalling

(Marek et al. 2004). These studies on neuronal differenti-

ation indicates that displaying neural morphology need not

Cell Mol Neurobiol (2009) 29:533–548 539

123

necessarily account for neuronal excitability or electrical

properties, which are rather determined by the conduits of

genetic signalling programmed due to induction of that

specific trophic factors.

A study on large-scale 2D electrophoresis on undiffer-

entiated PC12 cells documented a total of 1,080 proteins,

represented by 474 gene products, and was categorized into

10 major GO functional classes (Yang et al. 2006); this data

is agreement with our data set of GO classifications (Ref.

Table 1). The majority of the proteins/genes belong to

binding proteins and enzymes, which indicates that complex

signalling networks precede cellular processes in PC12 cells.

Of the 1,080 proteins reported, about 68.98% of proteins

(745) had isoelectric points (pI) in the range of 3.77–7.0 and

31.02% of proteins (335) were between pI 7.0 and 10.44. It is

likely that acidic proteins were predominantly taking key

role during the events of cellular functions (Peyrl et al. 2003;

Yang et al. 2006). The detailed GO assignments (e.g.

receptor activity, 383 genes (8.47%); transporter activity,

660 genes (14.61%); signal transducer activity, 713 genes

(15.78%) of PC12 cells are listed in Table 1. The functional

proteins encoded by the mRNA transcripts of the PC12 cells,

are in accord with IPA knowledgebase (Ingenuity Systems,

Inc.), have identified about 3,080 encoded functional pro-

teins out of 5,561 genes studied, indicating that about*65%

of the functional proteins are remaining to be defined.

.

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 6163 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 101 103 105 107 109 111 113 115 117 119 121 123

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61125 128 131 134 137 140 143 146 149 152 155 158 161 164 167 170 173 176 179 182 185

Rat

ioR

atio

Rat

io

Pathways

Pathways

Pathways

Thr

esho

ldT

hres

hold

Thr

esho

ld

(9/9

)(4

/4)

(6/6

)

(3/3

)(1

2/12

)(1

5/15

)(4

/4)

(23/

23)

(6/6

)(1

2/12

)(5

/5)

(4/4

)(5

/5)

(7/7

)(2

1/22

)(1

9/20

)(1

7/18

)(1

3/14

)(9

/10)

(17/

19)

(15/

17)

(14/

16)

(7/8

)(1

3/15

)(1

3/15

)(6

/7)

(17/

20)

(11/

13)

(11/

13)

(11/

13)

(5/6

)(1

0/12

)(2

0/24

)(5

/6)

(5/6

)(5

/6)

(19/

23)

(9/1

1)(1

8/22

)(1

3/16

)(2

1/26

)(2

5/31

)(4

/5)

(4/5

)(2

4/30

)(1

1/14

)(1

8/23

)(1

4/18

)(1

7/22

)(1

0/13

)(1

6/21

)(1

9/25

)(2

2/29

)(2

7/36

)(3

/4)

(6/8

)(6

/8)

(6/8

)(1

7/23

)(1

4/19

)(1

4/19

)(1

1/15

)

22/3

011

/15

11/1

522

/30

19/2

616

/22

16/2

218

/25

25/3

55/

710

/14

5/7

19/2

79/

1328

/41

15/2

224

/36

14/2

120

/30

4/6

20/3

016

/24

8/12

12/1

819

/29

15/2

311

/17

9/14

9/14

25/3

914

/22

7/11

14/2

217

/27

32/5

115

/24

10/1

610

/16

5/8

15/2

45/

823

/37

13/2

18/

1316

/26

32/5

211

/18

11/1

811

/18

28/4

617

/28

17/2

83/

512

/20

3/5

3/5

12/2

034

/57

22/3

713

/22

13/2

210

/17

35/6

014

/24

7/12

22/3

811

/19

12/2

144

/77

4/7

4/7

12/2

125

/44

21/3

713

/23

13/2

314

/25

5/9

5/9

5/9

12/2

26/

1126

/48

20/3

77/

1314

/26

31/5

816

/30

8/15

8/15

17/3

217

/32

18/3

412

/23

28/5

447

/91

16/3

117

/33

25/4

92/

41/

28/

166/

1216

/32

6/12 3/6

13/2

62/

43/

623

/47

27/5

611

/23

10/2

128

/59

7/15

18/3

96/

136/

1311

/24

10/2

25/

1110

/22

5/11

5/11

BIOCARTA KEGG GenMAPP

540 Cell Mol Neurobiol (2009) 29:533–548

123

Fig. 2 Pathways analysis for the stably expressed genes of PC12 cells of passages 5 and 15 using network databases. Note: The significant

pathways among overlapping pathways of the three databases were selected based on the higher value of their threshold ratio. The threshold

value is to limit the display of the pathways to only those whose ratio was above 0.5. The list of the selected significant pathways are follows; 1.

Activation of PKC through G protein-coupled receptor, 2. Benzoate degradation via hydroxylation 00362, 3. b-Oxidation of fatty acids, 4.

Degradation of the RAR and RXR by the proteasome, 5. Eph kinases and ephrins support platelet aggregation, 6. Erythropoietin mediated

neuroprotection through NF-jB, 7. Generation of amyloid b-peptide by PS1, 8. Influence of Ras and Rho proteins on G1 to S transition, 9. Low-

density lipoprotein (LDL) pathway during atherogenesis, 10. MAP kinase inactivation of SMRT corepressor, 11. Opposing roles of AIF in

apoptosis and cell survival, 12. Phosphatidylcholine biosynthesis pathway, 13. Phospholipase Cd1 in phospholipid associated cell signaling, 14.

Reductive carboxylate cycle (CO2 fixation) 00720, 15. Ras signaling pathway, 16. Ras-independent pathway in NK cell-mediated cytotoxicity,

17. PTEN dependent cell cycle arrest and apoptosis, 18. ATP synthesis 00193, 19. Free radical induced apoptosis, 20. AKT signaling pathway,

21. Cholesterol biosynthesis, 22. p53 signaling pathway, 23. Regulation of cell cycle progression by Plk3, 24. Multiple anti-apoptotic pathways

from IGF-1R signaling lead to BAD phosphorylation, 25. Role of PI3K subunit p85 in regulation of actin organization and cell migration, 26.

TSP-1 induced apoptosis in microvascular endothelial cell, 27. ATM signaling pathway, 28. Amyotrophic lateral sclerosis (ALS) 05030, 29.

Phosphorylation of MEK1 by cdk5/p35 down regulates the MAP kinase pathway, 30. RB Tumor suppressor/checkpoint signaling in response to

DNA damage, 31. Activation of cAMP-dependent protein kinase, PKA, 32. Biosynthesis of steroids 00100, 33. CXCR4 signaling pathway, 34.

Overview of telomerase RNA component gene hTerc transcriptional regulation, 35. Prion disease 05060, 36. Proteolysis and signaling pathway

of notch, 37. Erk and PI-3 kinase are necessary for collagen binding in corneal epithelia, 38. Electron transport reaction in mitochondria, 39.

Inhibition of cellular proliferation by Gleevec, 40. Tumor suppressor Arf inhibits ribosomal biogenesis, 41. p38 MAPK signaling pathway, 42.

Links between PYK2 and MAP kinases, 43. Blockade of neurotransmitter release by Botulinum toxin, 44. Nucleotide sugars metabolism 00520,

45. Oxidative phosphorylation 00190, 46. Presenilin action in Notch and Wnt signaling, 47. PDGF signaling pathway, 48. Role of EGF Receptor

transactivation by GPCRs in cardiac hypertrophy, 49. Ceramide signaling pathway, 50. Attenuation of GPCR signaling, 51. Corticosteroids and

cardio protection, 52. mCalpain and friends in cell motility, 53. G1 to S cell cycle_Reactome, 54. Cell cycle 04110, 55. Mitochondrial carnitine

palmitoyltransferase (CPT) system, 56. Overview of telomerase protein component gene hTert transcriptional regulation, 57. Phospholipase C

signaling pathway, 58. STAT3 signaling pathway, 59. TPO signaling pathway, 60. Chaperones modulate interferon signaling Pathway, 61. EPO

signaling pathway, 62. CD40L signaling pathway, 63. FAS signaling pathway (CD95), 64. IL-3 signaling pathway, 65. Role of nicotinic

acetylcholine receptors in the regulation of apoptosis, 66. TNFR1 signaling pathway, 67. Phospholipids as signaling intermediaries, 68.

Apoptotic signaling in response to DNA damage, 69. RAC1 cell motility signaling pathway, 70. Krebs-TCA cycle, 71. Electron transport chain,

72. c-Secretase mediated ErbB4 signaling pathway, 73. Repression of pain sensation by the transcriptional regulator DREAM, 74. The role of

FYVE-finger proteins in vesicle transport, 75. Inactivation of Gsk3 by AKT causes accumulation of b-catenin in alveolar macrophages, 76. D4-

GDI signaling pathway, 77. Bioactive peptide induced signaling pathway, 78. Aspirin blocks signaling pathway involved in platelet activation,

79. Angiotensin II mediated activation of JNK pathway via Pyk2 dependent signaling, 80. Endocytotic role of NDK, phosphins and dynamin, 81.

Erk1/Erk2 MAPK signaling pathway, 82. Formation of ketone bodies from acetyl-CoA, 83. MAPK cascade, 84. Mitochondrial fatty acid b-

oxidation, 85. Pentose phosphate pathway 00030, 86. uCalpain and friends in cell spread, 87. Neuropeptides VIP and PACAP inhibit the

apoptosis of activated T cells, 88. Hypoxia and p53 in the cardiovascular system, 89. Huntington’s disease 05040, 90. The IGF-1 receptor and

longevity, 91. Trka receptor signaling pathway, 92. VEGF signaling pathway 04370, 93. Phosphoinositides and their downstream targets, 94.

Rho-selective guanine exchange factor AKAP13 mediates stress fiber formation, 95. SNARE interactions in vesicular transport 04130, 96.

Growth hormone signaling pathway, 97. Colorectal cancer 05210, 98. Cell cycle: G2/M checkpoint, 99. IL-7 signal transduction, 100. Multi-step

regulation of transcription by Pitx2, 101. Neutrophil and its surface molecules, 102. Regulation of eIF4e and p70 S6 kinase, 103. Shuttle for

transfer of acetyl groups from mitochondria to the cytosol, 104. BCR signaling pathway, 105. IL-6 signaling pathway, 106. CBL mediated

ligand-induced down regulation of EGF receptors, 107. mTOR signaling pathway 04150, 108. TGF-beta signaling pathway, 109. Sprouty

regulation of tyrosine kinase signals, 110. The 4-1BB-dependent immune response, 111. TNFR2 signaling pathway, 112. Tight junction 04530,

113. Cell cycle: G1/S check point, 114. EGF signaling pathway, 115. Benzoate degradation via CoA ligation 00632, 116. IGF-1 signaling

pathway, 117. Ion channel and phorbal esters signaling pathway, 118. Neuroregulin receptor degradation protein-1 controls ErbB3 receptor

recycling, 119. Pertussis toxin-insensitive CCR5 signaling in macrophage, 120. Mechanism of gene regulation by peroxisome proliferators via

PPARa, 121. ALK in cardiac myocytes, 122. Regulation of BAD phosphorylation, 123. S1P signaling, 124. Role of Erk5 in neuronal survival,

125. GnRH signaling pathway 04912, 126. Nitric oxide signaling pathway, 127. Phospholipase C-epsilon pathway, 128. Integrin signaling

pathway, 129. Nerve growth factor pathway (NGF), 130. Effects of calcineurin in keratinocyte differentiation, 131. Insulin signaling pathway

04910, 132. Parkinson’s disease 05020, 133. Reelin signaling pathway, 134. Thrombin signaling and protease-activated receptors, 135. Longterm

potentiation 04720, 136. IL-2 receptor b-chain in T-cell activation, 137. Caspase cascade in apoptosis, 138. CCR3 signaling in eosinophils, 139.

TNF/Stress related signaling, 140. Cardiac protection against ROS, 141. CDC25 and chk1 regulatory pathway in response to DNA damage, 142.

Role of PPAR-gamma co-activators in obesity and thermogenesis, 143. Role of mitochondria in apoptotic signaling, 144. Vitamin C in the brain,

145. T-cell receptor signaling pathway, 146. Signaling pathway from G-protein families, 147. Cell to cell adhesion signaling, 148. Regulation of

PGC-1a, 149. Wnt signaling pathway 04310, 150. B-cell receptor signaling pathway 04662, 151. Hypoxia-inducible factor in the cardiovascular

system, 152. Stress induction of HSP regulation, 153. Adherens junction 04520, 154. Oxidative stress, 155. Signal transduction through IL1R,

156. Cyclins and cell cycle regulation, 157. Long-term depression 04730, 158. G-protein signaling, 159. G13 signaling pathway, 160. Induction

of apoptosis through DR3 and DR4/5 death receptors, 161. Axon guidance 04360, 162. Ahr signal transduction pathway, 163. Alpha-synuclein

and parkin-mediated proteolysis in Parkinson’s disease, 164. Ca2?/calmodulin-dependent protein kinase activation, 165. Circadian rhythm

04710, 166. Control of skeletal myogenesis by HDAC & calcium/calmodulin-dependent kinase (CaMK), 167. Cystic fibrosis transmembrane

conductance regulator (CFTR) and b2-adrenergic receptor pathway, 168. IL-18 signaling pathway, 169. Inositol phosphate metabolism 00562,

170. Malate-aspartate shuttle, 171. One carbon pool by folate 00670, 172. Fc epsilon RI signaling pathway 04664, 173. NFAT and hypertrophy

of the heart (transcription in the broken heart), 174. HSP70 and apoptosis, 175. Role of Tob in T-cell activation, 176. GAP junction 04540, 177.

PKC-catalyzed phosphorylation of inhibitory phosphoprotein of myosin phosphatase, 178. Adipocytokine signaling pathway 04920, 179.

Cysteine metabolism 00272, 180. Deregulation of CDK5 in Alzheimers Disease, 181. Neurodegenerative disorders 01510, 182. Glutathione

metabolism 00480, 183. Granzyme A mediated apoptosis pathway, 184. IL-2 signaling pathway, 185. IL-4 signaling pathway, 186. Regulation of

eIF2

b

Cell Mol Neurobiol (2009) 29:533–548 541

123

Table 2 Functional

categorization of proteins

encoded by neuronal transcripts

of PC12 cells

Function and diseases Associated

genes

Significance

P \ 0.005

Molecular and Cellular Functions

Signalling pathway 143 2.93E-07

Post translational modification of protein 90 1.35E-07

Phosphorylation of protein 53 7.02E-09

Neurotransmission 45 8.44E-09

Synaptic transmission 44 8.22E-09

Stress of cells 16 6.84E-08

Quantity of reactive oxygen species 16 4.05E-06

Generation of reactive oxygen species 15 1.05E-07

Oxidative stress 13 1.21E-06

Synaptic transmission of cells 12 7.98E-07

Exocytosis of vesicles 11 1.78E-06

Disease and Disorders

Neurological disorder 213 2.62E-67

Cell death of neurons 132 2.39E-68

Apoptosis of neurons 84 5.37E-43

Metabolic disorders 82 5.04E-12

Cell death of fibroblast cell line 70 4.64E-21

Cell death of central nervous system cells 58 1.72E-30

Cell death of connective tissue cells 58 7.09E-17

Cell death of brain cells 52 1.40E-27

Apoptosis of connective tissue cells 47 1.05E-13

Apoptosis of fibroblast 43 7.56E-16

Cell death of cerebral cortex cells 40 8.38E-22

Cell death of muscle cells 39 1.79E-15

Apoptosis of central nervous system cells 37 1.32E-20

Neuropathy 37 1.53E-13

Neurodegeneration 33 2.95E-16

Apoptosis of muscle cells 32 8.73E-13

Apoptosis of brain cells 31 4.84E-17

Brain tumour 29 2.76E-14

Motor neuron disease 29 2.05E-10

Cell death of brain cancer cell line 28 6.17E-14

Progressive motor neuropathy 28 7.97E-10

Cell death of neuroblastoma cell line 26 8.19E-12

Degeneration of neurons 25 1.06E-12

Necrosis 25 7.26E-07

Neurodegeneration of neurons 24 9.33E-13

Seizures 24 1.54E-06

Cell death of cortical neurons 23 1.24E-12

Apoptosis of brain cancer cell line 23 5.22E-12

Apoptosis of neuroblastoma cell line 23 6.64E-11

Glioma 22 1.15E-11

Cell death of hippocampal cell 21 1.07E-12

Cell death of sympathetic neuron 20 3.56E-16

Seizures of rodents 20 8.69E-08

Cell death of hippocampal neuron 18 1.50E-10

Cell death of granule cells 17 1.78E-10

Cell death of neuroglia 17 4.41E-07

542 Cell Mol Neurobiol (2009) 29:533–548

123

Table 2 continuedFunction and diseases Associated

genes

Significance

P \ 0.005

Apoptosis of cerebral cortex cells 17 5.88E-09

Apoptosis of parenchymal cells 17 4.34E-08

Primitive neuroectodermal tumour 17 1.24E-07

Gliosis 16 1.91E-10

Cell death of motor neuron 15 2.30E-09

Cell death of nervous tissue cell line 15 3.30E-07

Apoptosis of granule cells 15 3.59E-10

Survival of brain cells 14 5.26E-07

Apoptosis of sympathetic neuron 14 1.37E-10

Apoptosis of nervous tissue cell lines 13 5.45E-07

Cell death of ganglion cell 12 8.18E-09

Cell death of neuroblastoma cells 11 2.92E-08

Apoptosis of ganglionic cells 11 2.92E-08

Degeneration of neuritis 11 6.02E-08

Astrocytoma 11 6.02ZE-8

Glioblastoma multiform 11 6.02E-08

Myelodysplastic syndrome 11 1.11E-06

Cell death of glioma cell 10 4.27E-07

Neurodegenerative disorder 10 4.61E-08

Apoptosis of glioma cells 9 7.87E-07

Degeneration of axons 9 2.89E-06

Leigh syndrome 9 5.44E-09

Damage of mitochondria 9 1.56E-06

Adrenoleukodystrophy 7 4.65E-06

Leukocephalopathy with vanishing white matter 5 3.04E-06

Ovarioleukodystrophy 5 3.04E-06

Tumorigenesis of neuroepithelial tumour 5 3.04E-06

Hypertrophy of central nervous system cells 5 3.04E-06

Physiological System and Development

Differentiation of cells 163 4.55E-19

Neurogenesis 79 6.48E-17

Growth of neuritis 76 6.25E-28

Outgrowth of neuritis 71 1.37E-27

Behaviour 65 1.25E-13

Development of nervous system 47 1.29E-08

Survival of neurons 42 9.20E-18

Proliferation of connective tissue cells 41 1.42E-09

Differentiation of neuron 39 8.46E-12

Growth of fibroblast cell lines 38 2.02E-08

Proliferation of fibroblast cell line 36 2.00E-09

Development of neuritis 35 1.90E-12

Quantity of neurons 35 3.18E-10

Transmembrane potential 34 5.76E-11

Cell movement of neuron 32 6.56E-11

Migration of neurons 31 1.15E-10

Cell movement of fibroblast 30 1.61E-11

Growth of connective tissue cells 29 1.25E-09

Proliferation of fibroblast 28 1.09E-07

Cell Mol Neurobiol (2009) 29:533–548 543

123

The IPA cell signalling analysis on the whole-cell

transcriptome of the PC12 cells were found to express 169

networks. A parallel analysis with most popular online

network pathway databases such as, KEGG, BioCarta and

GenMAPP demonstrated about 171 significant networks

(Fig. 2). These two independent analyses together strongly

suggest that the PC12 cells can be an instrumental model

system for defining or dissecting multiple signalling cas-

cades underlying particular biological function. In addition,

the present study demonstrates the use of PC12 cells for

intracellular signalling research. So far, few reports from

PC12 cells that have been documented for intracellular

signalling such as, apoptotic pathway, PKA signalling,

ERK/MAP kinase signalling cascade, cellular homeostatic

pathway (Pittman and DiBenedetto 1996; Lazarovici et al.

1998; Vaudry et al. 2002b; Konu et al. 2004) indicate that

*97% of the signalling networks have not yet been

demonstrated from PC12 cells. Furthermore, signalling

networks analyses of the endogenously encoded neuronal

transcripts unveiled *765 neuronal genes underlying

significant neuronal functions and associated signalling

pathways (Table 2 and 3). The results of the analysis fur-

ther indicate that the PC12 cells are an ideal tool for

studying regulatory genes implicated in neurological dis-

eases and disorders (Table 2). In addition, we strongly

believe that this study would also provide baseline data for

elucidating possible downstream signalling convergences

and/or abnormalities (otherwise known as altered signal-

ling) associated with regulatory genes and their implicated

signal transduction.

Interestingly, analysis of plasma membrane genome in

the PC12 cells revealed significant number of transcripts

encoded for various receptors, ion channels, and their

associated proteins (Ref. Supplementary Table 1). This

information provides a molecular blue print of membrane

architecture for those seeking a cellular model to charac-

terize novel molecules or drugs or drug-leads. Among the

various receptors (including ion channels, transporters and

enzymes) reported in both differentiated and undifferenti-

ated PC12 cells, the Ca2? channels, particularly voltage-

sensitive Ca2? channels (VSCCs) have been extensively

studied using PC12 cells. In PC12 cells, the plasma mem-

brane genome analysis has brought out an array of G

protein-coupled receptors, ion channels, transporters and

enzymes etc. (Ref. Supplementary Table 1). The existence

of types of voltage-sensitive ion channels in both differen-

tiated and non-differentiated PC12 cells have been studied

extensively by electrophysiological techniques. It has been

reported that undifferentiated PC12 cells expressed Cav1.2

(L-type), Cav2.2 (N-type) and Cav2.1 (P/Q–type) Ca2?

Table 2 continuedFunction and diseases Associated

genes

Significance

P \ 0.005

Growth of fibroblast 26 1.85E-09

Proliferation of muscle cells 23 3.88E-06

Extension of neuritis 23 2.07E-10

Differentiation of fibroblast cell lines 23 7.24E-09

Extension of neuritis 23 2.07E-10

Migration of connective tissue cells 23 1.16E-09

Development of axons 22 2.90E-08

Migration of fibroblast 22 1.51E-10

Cognition 22 2.90E-08

Long-term potentiation 20 2.62E-06

Differentiation of muscle cells 20 4.83E-06

Learning 20 1.97E-07

Motor function 19 1.93E-10

Growth of axons 18 4.55E-07

Differentiation of neuroglia 18 3.84E-08

Survival of fibroblast cell lines 16 2.03E-08

Growth of brain cancer cell line 16 2.04E-07

Proliferation of central nervous system cells 15 9.22E-07

Proliferation of neurons 15 4.14E-06

Length of neuritis 13 2.27E-07

Survival of fibroblasts 12 4.06E-06

Survival of cerebral cortex cells 10 3.94E-06

544 Cell Mol Neurobiol (2009) 29:533–548

123

Table 3 IPA canonical

pathways analysis of the

endogenous neuronal transcripts

of PC12 cells

Signalling pathways Associated

genes

Total

genes

Significance

P \ 0.05

Actin cytoskeleton signalling 29 205 4.36E-02

Amyloid processing 17 36 1.54E-08

Amyotrophic lateral sclerosis signalling 27 72 5.22E-10

Apoptosis signalling 30 69 5.66E-13

Axonal guidance signalling 55 261 6.08E-08

B-cell receptor signalling 35 115 1.05E-09

Calcium signalling 34 172 9.96E-05

cAMP-mediated signalling 28 152 1.33E-03

Cell cycle:G1/S checkpoint regulation 15 49 6.78E-05

Chemokine signalling 25 36 1.28E-09

Death receptor signalling 13 53 2.11E-03

Dopamine receptor signalling 10 36 2.55E-03

EGF signalling 12 40 4.49E-04

Ephinin receptor signalling 29 117 3.72E-06

ERK/MAPK signalling 28 124 3.62E-05

Fc Epsilon RI signalling 21 72 5.63E-06

FGF signalling 12 55 8.50E-03

G protein-coupled receptor signalling 39 159 9.68E-08

GM-CSF signalling 21 51 6.51E-09

Huntington’s disease signalling 43 43 1.72E-11

Hypoxia signalling in the cardiovascular system 10 46 1.61E-02

IGF-1 signalling 24 68 2.01E-08

IL-10 signalling 19 60 4.14E-06

IL-2 signalling 14 40 2.20E-05

IL-4 signalling 9 36 8.80E-03

IL-6 signalling 21 69 2.63E-06

Insulin receptor signalling 35 105 6.15E-11

Integrin signalling 30 167 1.38E-03

JAK/SAT signalling 19 47 5.02E-08

Leukocyte extravasations signalling 22 132 1.41E-02

Neuregulin signalling 24 69 2.80E-08

Neurotrophin/TRK signalling 14 50 3.39E-04

NF-jB signalling 20 110 7.38E-03

Nitric oxide signalling in the cardiovascular system 11 44 3.91E-03

P13 K/AKT signalling 29 94 2.81E-08

P38 MAPK signalling 12 63 2.44E-02

Parkinson’s signalling 5 19 3.86E-02

PDGF signalling 17 55 1.92E-05

PPAR signalling 19 65 1.53E-05

PTEN signalling 27 71 3.60E-10

SAPK/JNK signalling 16 73 2.41E-03

Serotonin receptor signalling 7 30 2.89E-02

Sonic Hedgehog signalling 6 23 2.51E-02

Synaptic long term depression 15 62 1.14E-03

Synaptic long term potentiation 23 67 7.30E-08

T cell receptor signalling 21 72 5.63E-06

TGF-b signalling 20 61 1.24E-06

VEGF signalling 24 70 3.86E-08

Xenobiotic metabolism signalling 33 199 3.20E-03

Cell Mol Neurobiol (2009) 29:533–548 545

123

channels (Usowicz et al. 1990; Reber and Reuter 1991; Liu

et al. 1996). It has further been postulated that these ion

channel densities could be increased depending upon the

induction of appropriate neurotrophic factors. Additionally,

the increased expression/density of particular ion channels

and its functional characteristics could be associated with

existence appropriate transcripts in that particular passage.

Further to reveal the passage variations in expressing ion

channel transcript, as an example, we have attempted to

analyse the stably expressed mRNA transcripts of the

plasma membrane genes of passage 5 and 15. The analysis

revealed existence of Cav1.2 and Cav2.2 channels along

with their multimeric subunits complexes, a2d1, b2/3 and c4

(Ref. Supplementary Table 1). Notably, there were no

Cav2.1 and Cav1.3 subtypes in our studies. Their absences

were predicted to be due to low-abundance of transcripts or

the cells may have hardly expressed relative transcripts or

there were no consistent expression between the passages.

To address these issues further, the passages 5 and 15 were

individually analyzed (before normalization) using GCOS

software. Interestingly, the Cav2.1 was found to be

expressed in passage 15 (signal: 268.6; P \ 0.037), but not

in the passage 5. The results indicate that passage status

may have influence over the expression of Cav2.1 subtype

and its associated functional characteristics. Further, the

expression of Cav2.1 subtype only in passage 15 could be

due to the distinguished ability of the PC12 cells to undergo

differentiation towards the passages, without any differen-

tiating factors (Clementi et al. 1992; Arslan and Fredholm

1999). Additionally, the analysis indicated that transcript of

Cav1.3 subtype was not found in the analysis of these

passages.

In summary, Affymetrix� GeneChip� microarray based

translational approach has fetched out an array of complex

cellular and molecular mechanisms and their implicated

signalling networks in PC12 cells. The expression analysis

on the stably expressed 7,531 mRNA transcripts belonging

to 6,785 genes have been analysed to evaluate the PC12

cells to be used as models in molecular and cell biological

research. The unveiled genetic signatures underlying neu-

ronal functions, diseases and disorders delineated neuronal

specific transcripts from *765 genes, which are derived

from our custom built rat neuroscience reference database.

Further, analysis of these genes has bestowed various sig-

natures that could be useful to neuroscientists, who are

interested in using the PC12 cells as a model for diseases

affecting the nervous system. This study has also identified

about 65% of functional proteins and 97% of signalling

networks, which have not been demonstrated to date from

any kind of studies. Further more, the analysis of plasma

membrane receptome in the PC12 cells revealed existence

of various receptors, ion channels, and their associated

membrane proteins, which could certainly be useful for

scientists seeking a cellular model system to characterize

novel molecule for known/unknown functional mechanism

and target/s and mediated signal transduction.

Acknowledgements We are thankful to National University of

Singapore for funding [R-181-000-089-112] and facilities to carry out

this work. And, we are also grateful to Mr. Akira Niwayama,

Director, Commercial Operations (Asia Pacific), Redwood City, CA,

USA, for his extended support in providing IPA software (Ingenuity�

Systems) and Mr. Len Sheng Wong, Genomax Technologies Pte Ltd,

Singapore, for his technical support in analyzing Affymetrix�

Genechip� expression data using GeneSpring GX 7.3.1 software.

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