Genomic Assessment of Human Cumulus Cell Marker
Genes as Predictors of Oocyte Developmental
Competence: Impact of Various Experimental Factors
Prisca Feuerstein1,2,3., Vincent Puard1,2,3., Catherine Chevalier5, Raluca Teusan5, Veronique Cadoret1,3,4,
Fabrice Guerif1,2,3,4, Remi Houlgatte5, Dominique Royere1,2,3,4*
1INRA, UMR85 Physiologie de la Reproduction et des Comportements, Nouzilly, France, 2Universite ´ de Tours, Tours, France, 3CNRS, UMR6175, Nouzilly, France, 4CHRU
de Tours, Laboratoire de Biologie de la Reproduction, Tours, France, 5Plateforme Puces a ` ADN de Nantes, Institut de Recherche The ´rapeutique de l’universite ´ de Nantes,
Background: Single embryo transfer (SET) is the most successful way to reduce the frequency of multiple pregnancies
following in vitro fertilisation. However, selecting the embryo for SET with the highest chances of pregnancy remains a
difficult challenge since morphological and kinetics criteria provide poor prediction of both developmental and
implantation ability. Partly through the expression of specific genes, the oocyte-cumulus interaction helps the oocyte to
acquire its developmental competence. Our aim was therefore to identify at the level of cumulus cells (CCs) genes related to
oocyte developmental competence.
Methodology/Principal Findings: 197 individual CCs were collected from 106 patients undergoing an intra-cytoplasmic
sperm injection procedure. Gene expression of CCs was studied using microarray according to the nuclear maturity of the
oocyte (immature vs. mature oocyte) and to the developmental competence of the oocyte (ability to reach the blastocyst
stage after fertilisation). Microarray study was followed by a meta-analysis of the behaviour of these genes in other datasets
available in Gene Expression Omnibus which showed the consistency of this list of genes. Finally, 8 genes were selected
according to oocyte developmental competence from the 308 differentially expressed genes (p,0.0001) for further
validation by quantitative PCR (qPCR). Three of these 8 selected genes were validated as potential biomarkers (PLIN2, RGS2
and ANG). Experimental factors such as inter-patient and qPCR series variability were then assessed using the Generalised
Linear Mixed Model procedure, and only the expression level of RGS2 was confirmed to be related to oocyte developmental
competence. The link between biomarkers and pregnancy was finally evaluated and level of RGS2 expression was also
correlated with clinical pregnancy.
Conclusion/Significance: RGS2, known as a regulator of G protein signalling, was the only gene among our 8 selected
candidates biomarkers of oocyte competence to cover many factors of variability, including inter-patient factors and
Citation: Feuerstein P, Puard V, Chevalier C, Teusan R, Cadoret V, et al. (2012) Genomic Assessment of Human Cumulus Cell Marker Genes as Predictors of Oocyte
Developmental Competence: Impact of Various Experimental Factors. PLoS ONE 7(7): e40449. doi:10.1371/journal.pone.0040449
Editor: Rodrigo Alexandre Panepucci, Hemocentro de Ribeira ˜o Preto, HC-FMRP-USP., Brazil
Received March 31, 2012; Accepted June 7, 2012; Published July 27, 2012
Copyright: ? 2012 Feuerstein et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was supported by a grant from the Institut National de la Sante ´ et de la Recherche Me ´dicale (Re ´seau de Recherche ‘Reproduction Humaine-
AMP’, contrat No. 4REO3H) and the Institut National de la Recherche Agronomique (INRA). P.F. is supported by an INRA/Ferring SA fellowship. V.P. is supported by
an INRA/Merck Serono fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: P.F. was supported by an INRA/Ferring SA fellowship. V.P. was supported by an INRA/Merck Serono fellowship. These fellowships were
shared between public funding (Institut National de Recherche Agronomique) and private funding without any interference with research programm. This does
not alter the authors’ adherence to all the PLoS ONE policies on sharing data and materials.
* E-mail: firstname.lastname@example.org
. These authors contributed equally to this work.
Despite its increasing use to alleviate human infertility, assisted
reproductive technology (ART) continues to face two major
challenges, the first being that it is relatively ineffective. The
second challenge is that multiple embryo transfer has often been
proposed in order to increase pregnancy rates and thus multiple
pregnancies remain a common and serious complication of in vitro
fertilisation (IVF) procedures. Moreover, the adverse outcomes
associated with high-order gestations include the increased
incidence of maternal, perinatal and neonatal morbidity and
mortality . Single embryo transfer (SET) is the most successful
way to reduce the frequency of multiple pregnancies in IVF 
but it may reduce the chance of getting pregnant. Defining the
developmental competence of one oocyte after fertilisation (its
ability to reach the blastocyst stage after 5/6 days of extended
culture after fertilisation) and the development ability of an
embryo and its implantation potential during IVF remain major
goals in order to select the most suitable embryo for transfer.
Morphological criteria are the most frequently used to evaluate the
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development potential and implantation ability of embryos in
human ART. However such morphological criteria (oocyte
morphology, zygote scoring, early cleavage and embryo morphol-
ogy at day 2 or 3) remain poorly predictive of development or
implantation ability [3–6]. Both genomic and proteomic analysis
are difficult in human embryos, since such an approach is invasive
and might affect embryo integrity . Several indirect and non-
invasive selection criteria focusing on oocyte or embryo quality
have been proposed in the last few years.
Various studies have focused on molecules inside the follicle or
the embryo microenvironment (see  for review). Proteomic
analysis of individual human embryos [9,10], metabolomic
analysis of oocytes and embryos [11,12] and oxygen consumption
at the oocyte level  have all been proposed as potential
biomarkers of oocyte or embryo quality.
Other studies have focused on the somatic cells (cumulus and/
or granulosa cells) surrounding the oocyte since their interactions
are involved in the acquisition of oocyte meiotic and develop-
mental competence [14,15]. Indeed specific oocyte factors are
involved in the differentiation and expansion of cumulus cells
(CCs) and prevent the apoptosis and luteinisation of the cumulus-
oocyte complex (COC) (see  for review). Via such interactions,
oocytes may promote specific patterns of gene expression and
protein synthesis in these somatic cells [17,18]. Several studies
have therefore focused on specific gene expression in CCs
according to oocyte quality in humans and animals (see  for
Developments in microarray technology have more recently
allowed a global transcriptomic approach to identify differentially
expressed genes according to the oocyte maturity. Studies showed
different expression profiles in follicular cells according to oocyte
nuclear maturity  or to oocyte developmental competence
(early cleavage of the embryo , embryo quality 3 days after
fertilisation  and implantation potential ).
Microarray analyses have to date focused on early embryo
development (early cleavage or embryo development at day 3) or
implantation ability. Early embryo development is highly depen-
dent on oocyte quality, but embryo genome activation takes place
beyond the 4 cell stage in the human . Moreover, implantation
involves both the development ability of the embryo and the
In an initial study, we evaluated the level of expression of 6
genes in human cumulus cells according to nuclear maturity and
the developmental competence of the oocyte . In this study, we
undertook a global assessment of gene expression in cumulus cells.
Our aim was thus to relate the transcriptome of individual human
CCs to the full competence of the oocyte for pre-implantation
development of the embryo as assessed by blastocyst stage
development by comparing in one hand CCs from mature oocyte
to immature oocyte and CCs from mature fertilised oocyte
yielding a blastocyst after 5/6 days of in vitro culture to CCs from
mature fertilised oocyte arresting development in other hand. We
then analysed the behaviour of the genes related to the oocyte
competence in a dataset of transcriptome of cumulus cells
available in the Gene Expression Omnibus (GEO) to determine
their consistency. Following this analysis, 8 genes were selected to
be validated by qPCR according to their differential expression.
To evaluate fully the validity of the genes as markers of oocyte
developmental competence, we investigated the impact of
technical and biological variability such as qPCR series and
patients on the level of gene expression. Finally, the gene selected
according to such criteria was investigated as a marker of
pregnancy outcome. All these requirements are needed before
any potential use of biomarkers to predict embryo developmental
ability and finally choose the embryo for transfer.
Materials and Methods
Patient Selection and IVF Treatment
One hundred and six patients were included in this study, all
undergoing an intracytoplasmic sperm injection (ICSI) procedure
for male infertility. The mean number of oocytes retrieved per
patient was 7 (range 3–15 oocytes). Average patient age was 33
years (range 21–42 years), 49 patients were included in the
microarray analysis and 36 patients in the qPCR analysis. To
further analyse variability between patients, 29 patients (21 new
patients and 8 patients from qPCR analysis) were selected on the
basis that at least one embryo had reached the blastocyst stage and
that there was at least one arrested embryo after 6 days of
extended culture. The patient groups are presented in Figure 1.
The ovarian stimulation protocol, the ICSI and the embryo
culture procedures have been described by Guerif et al. 2003 .
Cumulus Cell Recovery and Assessment of Oocyte and
Shortly before ICSI, individual COC were subjected to
dissociation, as already described by Feuerstein et al. 2007 .
CCs were washed in cold phosphate buffer saline (80 IU/ml,
SynVitro Hyadase, Medicult, Jyllinge, Denmark) then centrifuged
at 300 g for 5 minutes. The supernatant was removed and the
pellet was resuspended in 50 ml of RLT buffer of the RNeasyH
Micro Kit (Qiagen, Courtaboeuf, France) before storage at -80uC
until RNA extraction. Labelling allowed individual follow-up of
the whole process.
Follow-up of the morphological characteristics of the oocyte and
embryo were recorded on an individual basis. Assessment of
oocyte nuclear maturity and embryo quality has been described by
Feuerstein et al. 2007 . At the time of ICSI, the oocytes were
first classified into two categories on the basis of nuclear status:
mature oocyte with first polar body (metaphase II, MII) or
immature oocyte at the germinal vesicle (GV) stage. CCs from a
mature oocyte were denominated CCMII and CCs from an
immature oocyte CCGV. For mature and fertilised oocytes, we
evaluated the developmental competence of each oocyte according
to its ability to reach the blastocyst stage after extended culture (5
or 6 days after ICSI). As described by Feuerstein et al. 2007 ,
the blastocyst assessment score was based on the expansion of the
blastocoel cavity and the number and cohesiveness of the inner cell
mass and trophectodermal cells . MII COC were divided
retrospectively into two groups following the ICSI procedure on
this basis, CCs from an oocyte yielding a blastocyst after 5/6 days
of in vitro culture being denominated CCB+and CCs from an
oocyte arresting development at the embryo stage after 5/6 days of
in vitro culture being denominated CCB-.
Clinical pregnancy was defined as described by Guerif et al.
2007 , i.e. presence of a gestational sac with a foetal heartbeat
on ultrasound examination at 7 weeks of pregnancy, and the
implantation rate was defined as the number of gestational sacs per
number of embryos transferred. CCs from an oocyte yielding a
blastocyst after 5/6 days of in vitro culture resulting in a clinical
pregnancy were denominated P+ and CCs from an oocyte yielding
a blastocyst after 5/6 days of in vitro culture which did not lead to a
clinical pregnancy were denominated P-.
genomic DNA were performed using the RNeasyHMicro Kit
Total RNA extraction and removal of
Cumulus Cell Biomarkers of Oocyte Competence
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(Qiagen, Courtaboeuf, France) according to the manufacturer’s
recommendations. The quality and integrity of RNA samples used
for microarray analysis were assessed using the 2100 Bioanalyser
and RNA 6000 Nano LabChip kit series II (Agilent Technologies).
Total RNA was quantified using a NanodropH
spectrophotometer (Nyxor Biotech, Paris, France). The mean
quantity of RNA per cumulus was 217 ng (6 134 ng).
Ninety-six hybridisations were per-
formed with 10 CCs from immature oocytes (GV) and 60 CCs
from mature oocytes (MII), including 30 CCB+and 30 CCB-. As
far as nuclear maturity was concerned, hybridisations from 10 CCs
(GV) were compared to 60 CCs (MII). Regarding developmental
competence, hybridisations from 30 CCB+were compared to 30
CCB-, all issued from the 60 previous ones. Complementary RNA
samples were prepared according to the manufacturer’s protocol
(Two-Color Microarray-Based Gene Expression Analysis) and
hybridised on Whole Human Genome Oligo Microarray 4x44K
(Agilent Technologies). Each array contained 45,220 probes,
corresponding to 41,000 single human transcripts. Briefly, an
average of 72.6 ng of extracted RNA for each sample (range 65.5–
89.9 ng) was amplified with one round of amplification. Each
sample was labelled with cyanine 3 or cyanine 5. After purification
using the RNeasyHMicro Kit (Qiagen, Courtaboeuf, France), the
quantity of cRNA and the specific activity of the cyanine were
assessed using a NanodropHND-1000 spectrophotometer. Two
samples (825 ng of cRNA for each) were hybridised on each slot of
the 4x44K array, one sample labelled with cyanine 3 and one
sample labelled with cyanine 5. In order to validate the
microarray, some samples were labelled alternatively by cyanine
3 or 5, some samples were repeatedly introduced in each
microarray experiment (3 experiments). After 17 hours of
hybridisation, arrays were washed and scanned using the Agilent
Microarray Scanner. Finally results were extracted using Feature
Extraction software 9.5.1 (Agilent Technologies).
All quality controls were performed
according to the manufacturer’s recommendations.
Lowess fitness regression was applied for global normalisation
of raw expression ratios . Gene expression profiles were used
to classify genes, and biological samples were classified by a
hierarchical analysis method using Cluster software , and the
results of hierarchical clustering analysis were visualised using the
TreeView programme. A Student t-test was applied to determine
the differentially expressed genes, with a statistical significance
threshold of p,0.0001. Annotations of genes and functions were
performed using GoMiner software (http://discover.nci.nih.gov/
gominer). Following the functional annotation of the genes, we
calculated the enrichment of differentially expressed genes for
each function . Functions with .1.6 fold enrichment and p-
value,0.001 were considered as statistically regulated according
to the situation studied. The findings are accessible on the Gene
Expression Omnibus (GEO) through the series accession number
Figure 1. Distribution of patients included in study. Patients were separated into two main groups: microarray and qPCR. The variability group
was composed of patients who had one CCB+and at least one CCB-. The pregnancy group was composed of CCB+transferred from patients included
in the variability group. CCB+, cumulus cells from a mature oocyte yielding a blastocyst at day 5/6 of in vitro culture once fertilised; CCB-, cumulus cells
from mature oocyte which stopped developing at the embryo stage at day 5/6 of in vitro culture once fertilised; CCGV, cumulus cells from immature
oocyte at germinal vesicle stage; P+, pregnancy; P-, no pregnancy.
Cumulus Cell Biomarkers of Oocyte Competence
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Datasets were obtained from the GEO (http://www.ncbi.nlm.
nih.gov/geo/) and are presented in Table 1. In each dataset,
probes for the 308 genes differentially expressed between CCB-
and CCB+ were investigated using MADGene . Findings
corresponding to these probes were extracted from each dataset.
They were subjected to hierarchical clustering after log transfor-
mation and median centering of probes. The measurement used
was the distance of correlation, and the aggregation method was
the average linkage. The ability of these genes to discriminate
samples was measured by analyzing the composition of the main
separation on the sample dendrogram. Significance was calculated
by Fisher’s exact test.
Quantitative PCR Experiments
The following procedures were used in order to comply as far as
possible with the Minimum Information for Publication of
Quantitative PCR experiments MIQE guidelines.
RNA extraction and cDNA synthesis.
tion and genomic DNA removal were performed as already
described in microarray procedure. The quality and integrity of
RNA samples were further evaluated using the RNA 6000 Pico
LabChip kit series II (Agilent Technologies, Massy, France). Only
RNA samples that displayed a RIN (RNA integrity number)
greater than or equal to 7 were reverse transcribed to cDNA. The
mean quantity of RNA per cumulus was 99 ng (range 21–205 ng).
Total RNA from each sample was reverse transcribed into cDNA
using the iScriptTMcDNA Synthesis kit (Bio-Rad Laboratories,
Marnes-la-Coquette, France) with a blend of oligo(dT) and
random hexamer primers to provide complete RNA sequence
Quantitative PCR design.
validation stage were independent of samples used for microarray
hybridisations. CCs from a total of 56 mature oocytes were
analysed for the qPCR validation stage, including 28 oocytes
yielding a blastocyst at day 5/6 once fertilised (CCB+) and 28
oocytes arresting development at the embryo stage at day 5/6
once fertilised (CCB-).
To study the impact of patient variability and qPCR series on
the level of gene expression, CCs from 102 mature oocytes were
analysed, including 54 CCB+and 48 CCB-, of which 9 CCB+and 8
CCB-were from the cohort of the qPCR validation stage.
To study the relationship between the level of gene expression
and pregnancy, 22 patients were selected from the previous cohort
from variability study (Fig. 1). Only the CCB+ samples were
included, corresponding to 9 clinical pregnancies after transfer of a
single embryo, 18 pregnancy failures represented by 7 failures after
single embryo transfer and 11 after double embryo transfer.
Quantitative PCR was performed using a
Light Cycler apparatus with the iQ detection system and the iQTM
SYBRHGreen Supermix kit (Bio-Rad Laboratories). Each reaction
Total RNA extrac-
Samples used for the qPCR
mixture contained 10 ml 2x of iQ SYBR Green Supermix (dNTPs,
iTaq DNA polymerase, 6 mM MgCl2, SYBR Green I, fluorescein,
and stabilizers), 5 ml cDNA (25-fold, 125-fold or 250-fold dilution),
300 nM of each primer and 4.5 ml of RNase free water to a final
volume of 20 ml. Amplification was performed in triplicate in 96
well plates (ABgene Ltd, Epson, UK) with the following thermal
cycling conditions: initial activation at 95uC for 3 minutes,
followed by 40 cycles of 30 s at 95uC, 30 s at 60uC and 30 s at
72uC. A no template control (NTC) was included in all plates.
Dissociation analysis of PCR products was performed by using a
melting curve to confirm the absence of contaminants or primer
dimers. Four-fold serial dilutions of cDNA derived from pooled
human cumulus cells were used to establish the standard curve and
repeated for each run as described by Feuerstein et al. 2007 .
Primers were designed using the Beacon
Designer version 2.0 software (Bio-Rad Laboratories) to have a
melting temperature of 60uC, and if possible to cross an exon-exon
junction to avoid amplification of genomic DNA. Primers used for
qPCR experiments and qPCR parameters are listed in Table 2.
Data were normalized to RPL19 selected by
the GeNorm algorithm  as the most stable gene. The qPCR
data were recorded with iCycler IQ software version 3.1 (Bio-Rad
laboratories). Melting temperatures, mean efficiency values and
mean r2values for standard curves are presented in Table 2.
Outlier replicates of the triplicates with a variation greater than 1
quantification cycle (Cq) were excluded from the data analysis. For
each sample, detection was normalized for the mean of each
triplicate to RPL19. Each gene amplification for the qPCR
validation step was performed with 3 series and 7 series for the
Statistical analysis of qPCR results was performed on 26 data
points (after the deletion of outliers corresponding to the
maximum and the minimum values in each group) using variance
analysis followed by post-hoc comparison using the Scheffe ´ test
(Statview 4.1H, Abacus Concept, Berkeley, USA) with statistical
significance defined as p,0.05.
In order to evaluate the impact of developmental competence,
the patient variability and the qPCR series respectively on the level
of gene expression, an analysis of variance (Anova) with the
Generalised Linear Mixed Model (GLMM) procedure was
performed for each gene with Statistical Analysis System (SASH)
software. The model chosen was fitted for each gene indepen-
dently yijkn=m+Pi+Aj+Qk+PiAj+eijkn, where yijknis the gene expres-
sion level in the nth CC for a gene according to the ith phenotype
from the jth patient after the kth qPCR series, m is the gene
expression level mean, Pi the fixed effect of the ith phenotype (i=
CCB+or CCB-), Aj the random effect of the jth patient (j=1 to 29),
Qk the random effect of the kth qPCR series (k=1 to 7), (PA)ij the
first-order interaction between variables phenotype and patient,
and eijknthe residual random effect. The levels of significance of
Table 1. Dataset of transcriptome of cumulus cells used for meta-analysis.
Study Species GEO Accession Number
Influence of hCG on the transcriptome of CCsMouseGSE4260 
Comparison of transcriptome of mural granulosa cells with CCs transcriptomeHuman GSE18559 
Comparison of transcriptome of CCs from immature oocyte to CCs from mature oocyteBovine GSE21005 
CCs transcriptome according to embryo cleavageHuman GSE9526 
CCs, cumulus cells; GV, germinal vesicle stage; MII, metaphase II.
Cumulus Cell Biomarkers of Oocyte Competence
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the model and the different effects were set at p,0.01 and p,0.05,
The relationship between the level of expression of candidate
genes and pregnancy was assessed using one way analysis of
variance, Bartlett’s test to compare variances, followed by post-hoc
comparison using the Scheffe ´ test (p,0.05).
Initially, differentially expressed genes were analysed according
to the nuclear maturity of the oocyte. From the 45,220 probe sets
in the array, 15,531 unique genes were expressed in cumulus cells,
among which 724 unique genes (854 probes) were differentially
expressed between CCGVand CCMII. Six hundred and thirty-four
genes were upregulated and 90 genes were downregulated in
CCMIIcompared to CCGV. Hierarchical clustering based on the
132 most differentially expressed genes allowed separation of all
CCGV from other samples, corresponding to CCMII (Fig. 2).
Sixteen functions were upregulated in CCMII as compared to
CCGV,according to the calculated enrichment and p-value of each
function (Table 3). Among them the following annotations should
be emphasized: activation of MAPKK activity, positive regulation
of lipid biosynthesis process, caspase activator activity, caspase
regulator activity and apoptotic protease activator activity.
Following similar criteria, 37 functions were downregulated in
CCMIIcompared to CCGV(Table 4), among which the following
annotated functions should be emphasized: tRNA processing,
induction of apoptosis, induction of programmed cell death, tRNA
Differentially expressed genes were then analysed according to
the ability of the oocyte to yield a blastocyst. From the 45,220
probes set on the array, 354 were differentially expressed between
CCB+and CCB-. These 354 probes referred to 308 single genes,
with 133 genes downregulated and 175 genes upregulated in CCs
enclosing a mature oocyte yielding a blastocyst, compared to those
unable to reach this stage. The hierarchical clustering based on the
354 differentially expressed probes allowed separation of almost all
CCB+ from CCB- (Fig. 3). Upregulation of 23 functions was
observed in CCB+ compared to CCB-, including negative
regulation of cell differentiation, fatty acid biosynthesis, organic
acid biosynthesis, carboxylic acid biosynthesis and transcription
factor binding (Table 5). On the other hand, 31 functions such as
cell redox homeostasis, cyclin-dependent protein kinase regulator
activity, respiratory gaseous exchange and transporter activity
were downregulated in CCB+(Table 6).
We further analysed the behavior of our 308 genes discrimi-
nating CCB-and CCB+in other datasets available in the GEO
[17,21,33,34]. All the probes corresponding to the 308 genes using
MADGene  were extracted in each study. Data from these
probes were log-transformed and median centered and subjected
to hierarchical classification. The ability of these probes to
discriminate sample types was measured by Fisher’s exact test on
sample composition of the main separation on the sample
The results are shown in Figure 4. The 308 genes allowing
separation of almost all CCB+from CCB-(Fig. 4A) were under the
influence of hCG (Fig. 4C). These genes allowed discrimination of
mural granulosa cells from CCs, as expected of cumulus genes
(Fig. 4D). Moreover, these genes seemed to discriminate the
degree of nuclear maturity of the oocytes although without
statistical significance (Fig. 4E) while they allowed separation of
almost all CCB-from CCGVin our study (Fig 4B). However, they
did not discriminate the developmental stage of the embryo (early
Table 2. qPCR primer sequences, PCR efficiency, correlation coefficient of standard curves, Cq range of standard curves, amplicon
size and melting temperature.
GeneID Forward and Reverse primer (59 -39) E (%)r2
Cq rangeTm (6C)
size (bp) GeneBank No.
99.362.50.99560.002 23.9–33.982.5126 NM_001099
ANG F: CGAGCCACAGCGGGGTTC
103.1611.80.99460.003 27.8–34.4 *87 125NM_001097577
ANKRD22 F: GTGTATGTGTGTGGGCTTAGAGATTC
101.1616.20.99160.006 28.5–35.2 *81.5 187NM_144590
96.066 0.99360.00224.1–35.3 88 142NM_007021
108.962.1 0.99560.00327.7–33.8 *91.5 256NM_014214
92.660.07 0.99260.011 20.4–31.284.5 400 NM_001122
100.366.6 0.99560.00224.2–34.4 85170NM_002852
RGS2 F: CAGAACGCAAGAAGGGAATAGGTG
100.1610.3 0.99160.006 24.3–31 *83 392NM_002923
100.365.90.99060.00530.6–37.1 *88 225NM_024787
RPL19 F: TGAGACCAATGAAATCGCCAATGC
97.562.5 0.99760.002 20.3–30.686 94NM_000981
E, qPCR Efficiencies (mean 6 SEM); r2, correlation coefficient of standard curves (mean 6 SEM); Cq, quantification cycle; Tm, melting temperature; *, Standard curve
calculated with 3 points.
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Figure 2. Heat map and cluster dendograms of gene clusters differentially expressed according to oocyte nuclear maturity.
Hierarchical clustering of cumulus cell samples (columns) and the 132 most significant probes (rows). Upregulated genes are marked in red,
downregulated genes are marked in green. CCGV(blue), cumulus cells from immature oocyte at germinal vesicle stage; CCMII(pink), cumulus cells
from mature oocyte.
Cumulus Cell Biomarkers of Oocyte Competence
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vs. late cleaving embryo) (Fig. 4F). These results strongly support
the fact that our 308 genes expressed in CCs were presented in the
datasets previously reported and their expression well correlated
with the described conditions.
Comparison between microarray and qPCR.
of samples was used for qPCR validation.
Eight genes that were upregulated in CCB+as compared to
CCB-[ACPP (acid phosphatase prostate), ANG (angiogenin), ANKRD22
(ankyrin repeat domain 22), C10orf10 (chromosome 10 open reading frame
10, also known as DEPP or FIG), IMPA2 (inositol(myo)-1(or-4)-
monophosphatase 2), PLIN2 (perilipin 2), RGS2 (regulator of G-protein
signalling 2) and RNF122 (ring finger protein 122)] on the microarray
study were selected for qPCR validation. The 8 selected genes
revealed the same expression profile in qPCR as in microarray,
with statistical significance for three (PLIN2, RGS2 and ANG)
Assessment of the respective impact of patients and
qPCR series on the expression level of ANG, PLIN2 and
In order to further validate ANG, PLIN2 and RGS2 as
potential biomarkers of oocyte developmental competence, we
assessed the impact of patients and of qPCR series on the level of
gene expression according to oocyte developmental competence.
Using a Generalised Linear Mixed Model, we found the statistical
significance of the model for the 3 genes. Except for PLIN2, gene
expression was influenced by the qPCR series (Table 7). More-
over, the levels of expression of ANG and RGS2 were subjected to a
patient effect. Finally, the level of expression of RGS2 remained
clearly related to oocyte developmental competence, while taking
into account the influence of patient and qPCR series without any
interaction between patient and phenotype.
Further Validation Stage of RGS2: Link to Pregnancy
Having assessed the validity of RGS2 expression, we then tested
the hypothesis of a relationship between implantation and the level
of expression of this gene. Of the 22 patients who had a blastocyst
transfer in the previous cohort, 9 became pregnant (only after
single blastocyst transfer) whereas 13 did not (7 single blastocyst
transfers and 11 double blastocyst transfers). The level of
expression (mean 6 SEM) of RGS2 was significantly increased in
the pregnant compared to the non-pregnant group (4.7761.68 vs.
Table 3. Upregulated functions in CCMIIcompared to CCGV.
oxidoreductase activity 0.0011
lipid biosynthetic process0.0015
caspase regulator activity0.0018
phosphatidylcholine transmembrane transporter activity0.0019
caspase activator activity0.0038
activation of MAPKK activity 0.0054
neuron migration 0.0054
positive regulation of lipid biosynthesis process0.0054
organic acid biosynthesis process0.0065
carboxylic acid biosynthesis process0.0065
receptor signaling protein serine/threonine kinase activity0.0074
apoptotic protease activator activity0.0077
regulation of caspase activity0.0085
regulation of peptidase activity0.0085
regulation of endopeptidase activity 0.0085
CCMII, cumulus cells from mature oocyte; CCGV, cumulus cells from oocyte at
germinal vesicle stage.
Table 4. Downregulated functions in CCMIIcompared to
vitamin binding 0.0021
tRNA processing 0.0036
positive regulation of development process0.0068
4-aminobutyrate transaminase activity0.0071
N-acetyl-gamma-glutamyl-phosphate reductase activity0.0071
pseudouridylate synthase activity0.0071
vitamin D binding 0.0071
bleomycin hydrolase activity 0.0071
queuine tRNA-ribosyltransferase activity0.0071
7-methylguanosine metabolism process0.0071
nucleoside biosynthesis process 0.0071
response to tropane0.0071
tyrosyl-DNA phosphodiesterase activity 0.0071
stem cell maintenance 0.0071
calcium channel inhibitor activity 0.0071
pyridoxal phosphate binding 0.0071
troponin T binding0.0071
4-aminobutyrate transaminase complex0.0071
succinate-semialdehyde dehydrogenase binding 0.0071
response to cocaine0.0071
purine nucleoside biosynthesis process0.0071
ribonucleoside biosynthesis process0.0071
positive regulation of axon extension 0.0071
guanosine biosynthetic process0.0071
queuosine metabolic process 0.0071
7-methylguanosine biosynthesis process 0.0071
purine ribonucleoside biosynthesis process0.0071
behavioral response to cocaine0.0071
stem cell differentiation0.0071
stem cell development0.0071
cofactor binding 0.0078
induction of apoptosis0.0091
induction of programmed cell death 0.0093
tRNA metabolism process0.0094
CCMII, cumulus cells from mature oocyte;
CCGV, cumulus cells from oocyte at germinal vesicle stage.
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Comparison of RGS2 to a Known Potential Biomarker:
In addition we compared the relevance of our new potential
biomarker RGS2 to a known potential biomarker (Pentraxin 3,
compared to CCB- on microarray and qPCR analysis. In the
inCCB+comparedto CCB-(fold change=1.21, p=0.12). InqPCR
analysis, the level of expression of PTX3 was significantly higher in
CCB+than in CCB-(6.4465.71 vs. 3.0463.39, respectively) (Fig. 5).
Finally, as with the level of expression of RGS2, that of PTX3 was
significantly increased in the pregnant group compared to the non-
pregnant group (7.0163.64 vs. 1.2560.34). However, a significant
interaction between patient and phenotype factors wasobserved for
PTX3expression level (Table 7).
Following global genomic assessment of the human cumulus cell
transcriptome, 724 genes were found to be differentially expressed
Figure 3. Heat map and cluster dendograms of gene clusters
differentially expressed according to oocyte developmental
competence. Hierarchical clustering of cumulus cell samples (col-
umns) and the 354 most significant probes (rows). Upregulated genes
are marked in red, downregulated genes are marked in green. CCB-
(blue), cumulus cells from mature oocyte which arrested at the embryo
stage at day 5/6 of in vitro culture once fertilised; CCB+(red), cumulus
cells from mature oocyte yielding a blastocyst at day 5/6 of in vitro
culture once fertilised.
Table 5. Upregulated functions in CCB+compared to CCB-.
cytosolic large ribosomal subunit (sensu Eukaryota)0.0006
zinc ion binding0.0015
negative regulation of cell differentiation 0.0030
fatty acid biosynthesis0.0040
negative regulation of myeloid cell differentiation0.0045
cytosolic ribosome (sensu Eukaryota)0.0048
organic acid biosynthesis0.0060
carboxylic acid biosynthesis0.0060
ribosome biogenesis and assembly0.0063
large ribosomal subunit0.0064
metal ion binding0.0069
porphobilinogen synthase activity0.0073
creatine kinase activity 0.0073
male germ-line stem cell division 0.0073
transcription factor binding0.0093
transcription regulator activity0.0099
CCB+, cumulus cells from mature oocyte yielding a blastocyst at day 5/6 of in
vitro culture once fertilised;
CCB-, cumulus cells from mature oocyte which stopped developing at the
embryo stage at day 5/6 of in vitro culture once fertilised.
Cumulus Cell Biomarkers of Oocyte Competence
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according to the nuclear maturity of the oocyte and 308 other
genes according to the developmental competence of the oocyte.
These two series had no genes in common. Comparison of our list
of 308 genes with other datasets of CCs transcriptomes showed
that these genes were both under the influence of hCG, and they
discriminated mural granulosa cells from CCs. They also
demonstrated the degree of nuclear maturity of the oocytes as in
our study but were independent with regard to the early embryo
development at day 2/3. With qPCR experiments on 8 selected
genes, we validated 3 of them as potential biomarkers ANG, PLIN2
and RGS2. After further validation, RGS2 seemed to be the most
pertinent biomarker since its expression was correlated both with
oocyte developmental competence, despite the patient variability,
and with the clinical pregnancy.
Non-invasive assessment of embryo quality remains a major
goal, since the contribution of morphological evaluation of early
embryo development to prediction of further development or
implantation remains quite limited . Among the various ‘‘omic’’
approaches to the environment of the oocyte or the embryo,
studies on somatic cells in close contact with the oocyte represent
one alternative (see  for review). Indeed, the CCs surrounding
the oocyte contribute substantially to oocyte growth and matura-
tion. Gene expression at the level of CCs may reflect essential
stages in oocyte/cumulus interactions during oocyte maturation
and thus offers an indirect non-invasive way to assess oocyte
Studies in humans have to date focused either on the
developmental competence of the embryo (early cleavage at day
1 ; embryo quality at day 2 or 3 [22,36–38]) or on the
implantation ability of the embryo [22,23,38,39]. Embryonic
genome activation in the human occurs between the 4- and 8-cell
stages . However, reaching the 8-cell stage does not guarantee
further development to the blastocyst stage. Moreover, the
implantation ability of the embryo also depends on uterine
receptivity. Thus, as this parameter might create bias and lead to
overlooking of crucial factors for embryo quality, we chose to
Table 6. Downregulated functions in CCB+compared to CCB-.
cell redox homeostasis0.0022
cyclin-dependent protein kinase regulator activity 0.0025
transmembrane receptor protein serine/threonine kinase signalling pathway0.0026
respiratory gaseous exchange0.0048
thiamin diphosphokinase activity0.0048
regulation of border follicle cell delamination0.0048
border follicle cell delamination0.0048
thiamin diphosphate biosynthesis0.0048
thiamin diphosphate metabolism0.0048
septate junction assembly 0.0048
establishment and/or maintenance of neuroblast polarity0.0048
asymmetric protein localization during cell fate commitment 0.0048
positive regulation of developmental growth0.0048
regulation of developmental growth0.0048
G1/S transition checkpoint0.0048
alpha(1.6)-fucosyltransferase activity 0.0048
glycoprotein 6-alpha-L-fucosyltransferase activity0.0048
transporter activity 0.0060
hydroxymethylglutaryl-CoA synthase activity0.0095
thiamin and derivative biosynthesis0.0095
establishment and/or maintenance of polarity of larval imaginal disc epithelium0.0095
bisphosphoglycerate phosphatase activity 0.0095
bisphosphoglycerate mutase activity0.0095
zonula adherens assembly 0.0095
basal protein localization0.0095
collagen type VI 0.0095
phospholipase A1 activity 0.0095
rhythmic excitation 0.0095
outward rectifier potassium channel activity 0.0095
CCB+, cumulus cells from mature oocyte yielding a blastocyst at day 5/6 of in vitro culture once fertilised;
CCB-, cumulus cells from mature oocyte which stopped developing at the embryo stage at day 5/6 of in vitro culture once fertilised.
Cumulus Cell Biomarkers of Oocyte Competence
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relate the developmental competence of the oocyte at day 5/6
after ICSI to gene expression at the cumulus level before assessing
the ability to implant. Individual retrieval of CCs and individual
follow-up of oocyte maturity and embryo development and
pregnancy outcome allowed us to relate each cumulus expression
profile to the maturity of the oocyte of origin, subsequent embryo
quality and pregnancy outcome.
Our strategy was based on four stages: 1) transcriptomic
approach followed by a meta-analysis from other datasets of the
CCs transcriptome to validate the consistency of our list of
genes; 2) validation of potential biomarkers using qPCR in
relation to oocyte competence; 3) analysis of variability linked to
patient and/or qPCR series; and 4) evaluation of the predictive
value of biomarkers of clinical pregnancy. Three independent
sets of samples were used for the different stages of this study
(microarray and qPCR experiments). Indeed, Van Montfort et
al. have shown the importance of using independent samples for
validation to ensure that the expression profile was really
correlated with the situations being compared and was not due
to the samples .
At this point the considerable heterogeneity between the
potential biomarkers reported in the literature should be noted
Figure 4. Hierarchical clustering of microarray datasets. Measurements of the 308 genes discriminating CCB-and CCB+were extracted from
different datasets: our study (A, B), GSE4260 (C), GSE18559 (D), GSE21005 (E) and GSE9526 (F). They were subjected to hierarchical clustering after log
transformation and median centering of genes. The different types of sample are shown as coloured squares. The quality of the separation was
measured by Fisher’s exact test on the main branch. CCGV, cumulus cells from immature oocyte at germinal vesicle stage; CCMII, cumulus cells from
mature oocyte; CCB+,cumulus cells from mature oocyte yielding a blastocyst at day 5/6 of in vitro culture once fertilised; CCB-,cumulus cells from
mature oocyte which stopped developing at the embryo stage at day 5/6 of in vitro culture once fertilised.
Cumulus Cell Biomarkers of Oocyte Competence
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Figure 5. Relative expression level obtained by qPCR of 9 genes differentially expressed according to oocyte developmental
competence. Results were expressed as means 6 SEM of relative expression to the reference gene RPL19. CCB+,cumulus cells from mature oocyte
yielding a blastocyst at day 5/6 of in vitro culture once fertilised; CCB-,cumulus cells from mature oocyte which stopped developing at the embryo
stage at day 5/6 of in vitro culture once fertilised; *, significant difference (p,0.05).
Table 7. Test of hypotheses for Mixed Model Analysis of Variance, impact of developmental competence, patient variability and
experience (i.e. qPCR series) on the level of gene expression.
Effects / yijkn (Gene)Pi (Phenotype)Aj (Patient)Qk (qPCR series)(PA)ij (Patient*Phenotype)
PLIN2 0.31130.6204 0.43470.0550
PTX3 0.09910.9196 0.0229*
yijkngene expression level in nth cumulus cells for a gene according to the ith phenotype from jth patient after kth qPCR series; m gene expression level mean; Pi fixed
effect of ith phenotype (i= CCB+cumulus cells from mature oocyte yielding a blastocyst at day 5/6 of in vitro culture once fertilised and i= CCB-cumulus cells from
mature oocyte which stopped developing at the embryo stage at day 5/6 of in vitro culture once fertilised); Aj random effect of the jth patient (j=1 to 29); Qk random
effect of kth qPCR series (k=1 to 7); (PA)ij first-order interaction between variables phenotype and patient;
*Statistical significance of model factors for the levels of expression of the four genes (p,0.05).
Cumulus Cell Biomarkers of Oocyte Competence
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(see [40,41] for reviews). However such heterogeneity might at
least partly be explained by methodological differences. For
example, RNA extraction conditions, RNA sample amplification
depending on priming conditions, hybridisation sample labelling,
reverse transcriptase source, and finally level of significance for
gene selection may all have an impact on the genes listed .
O’Shea et al. showed the importance of cross comparison of
datasets to identify biomarkers . We therefore choose to
perform a meta-analysis by comparing our own list of genes in
terms of oocyte developmental competence to other datasets of
CCs from the mouse , humans [21,33] and bovines  with
regard to hCG influence, the difference in transcriptome between
mural granulosa cells and CCs, the degree of oocyte maturity and
early embryo development, respectively. Despite such heteroge-
neity, the results demonstrated a certain consistency over the
different conditions of these studies. Interestingly, these 308 genes
separated almost all the CCs according to oocyte maturity in our
study. However, the power of separation was weaker than for the
724 genes that we identified in our study. The meta-analysis
strongly supported the fact that the 308 genes were consistent with
results of published datasets.
One major problem may occur with the variability encountered
between the patients themselves and the experimental procedures.
Few reports have focused on these aspects, which are very
important if the use of such evaluations is to be promoted
prospectively, where the independence of the biomarker from the
patient and the experiment is essential [38,44]. Hamel et al.
reported a very interesting way of studying intra-patient variabil-
ity, considering both one or two embryos yielding pregnancy and
one arrested embryo for each patient, while inter-patient
experimentation focused on groups of embryos of different status
(pregnancy, non-pregnancy, embryo failure) which allowed
delineation of the impact of the status rather than the inter-
patient variability itself . Principal Component Analysis was
used to discriminate true positive embryos when two transferred
embryos yielded a single pregnancy. Interestingly, the same group
recently reported follicular marker genes as pregnancy predictors
for human IVF. While considering only the pregnancy as an
endpoint rather than both development characteristics and
pregnancy, they reported certain differences concerning expres-
sion levels of the same genes between the two experiments
reported to date [23,45]. Moreover, the second study emphasized
the predictive value of UDP-glucose pyrophosphorylase-2 and
pleckstrin homology-like domain, familyA, member1, which were
not mentioned in the previous report. In addition to the difference
in phenotype selection, the influence of experimental conditions
cannot be excluded.
To evaluate further the respective influences of developmental
competence and patient variability and qPCR series variability at
the level of gene expression, we studied a set of 29 patients with
at least one blastocyst and one arrested embryo per patient.
Analysis was based on an Anova test with GLMM to combine
the multiple factors, developmental competence as fixed variable,
and patient and qPCR series as random variables. Only 1 of the
8 candidate genes (i.e. RGS2) remained related to oocyte
developmental competence, independently of patient and qPCR
series variability. We can therefore assume some congruence with
the results of Hamel . Interestingly, this biomarker may also
be related to clinical pregnancy, since its level of expression has
been shown to be significantly increased in successful transfers
compared to implantation failures. As already mentioned, a
recent study showed that the expression level of RGS2 in human
follicular cells might be considered as a good predictor of
ongoing pregnancy .
The RGS2 gene encodes a GTPase-activating protein that
hydrolyses GTP to GDP on theasubunit of an activated G-protein
. Rgs2 is expressed in rat granulosa cells after hCG injection
prior to the ovulation  and is probably involved in the
regulation of granulosa cells response to gonadotrophins. Up
regulation of RGS2 by hCG in human and mouse granulosa cells
was recently confirmed with the ability of that protein to block
hCG induced downstream target gene COX2 trancription
through the Gas pathway . Another possibility might concern
a regulatory activity of RGS2 on ion channels. Recently RGS2
was reported to interact with a scaffolding protein spinophilin to
regulate calcium signalling in xenopus laevis oocytes [49,50].
If pregnancy is considered as the major endpoint, it is important
to note that, having applied statistical analysis to each of the eight
biomarkers investigated in our study, only one was selected, i.e.
RGS2. In addition we compared this gene to a known potential
biomarker PTX3. This finding is consistent with other studies
which identified RGS2 in follicular cells and PTX3 in CCs as
markers of pregnancy ( and , respectively). However, the
latter biomarker cannot be kept, since it was shown to involve a
significant interaction between patient and phenotype.
This study focused on 8 selected genes among 308 discrimina-
tive ones. The results presented do not exclude the presence of
biomarkers among other members of this list. However, we may
assume that, of all the putative candidate genes, the follicular
biomarker(s) of oocyte competence or pregnancy prediction
selected need to encompass all variability factors, including patient
and qPCR series factors. RGS2 was the only gene among our
selected candidates to fulfil these requirements in this study.
Understanding the biological role of RGS2 during the oocyte-
cumulus interaction and prospective double blind evaluation of
this biomarker (and others candidates) will be the next steps. This
latter condition has to concile with cost/effectiveness evaluation,
since time and cost constraints as well as routinely use may differ in
dedicated gene expression or eg. protein expression arrays before
any embryo transfer strategy based on biomarkers assessment
might be applied .
We thank R. Bidault, O. Gasnier, C. Jamet, M. Lemseffer and M.H.
Saussereau for collection of the human material and embryo observation.
Drs Couet, Gervereau, Lanoue, Lecomte and Ract, for referring the
couples and C. Hennequet-Antier and J. Le ´ger for help with the statistical
Conceived and designed the experiments: PF VP FG RH DR. Performed
the experiments: PF VP CC RT VC. Analyzed the data: PF VP FG RH
DR. Contributed reagents/materials/analysis tools: PF VP CC RT RH.
Wrote the paper: PF VP RH DR.
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