Single-Cell Quantitative Proteomic Analysis of
Human Oocyte Maturation Revealed High
Heterogeneity in In Vitro–Matured Oocytes
Authors
Yueshuai Guo, Lingbo Cai, Xiaofei Liu, Long Ma, Hao Zhang, Bing Wang, Yaling Qi, Jiayin Liu,
Feiyang Diao, Jiahao Sha, and Xuejiang Guo
Correspondence Graphical Abstract
diaofeiyang@njmu.edu.cn;
shajh@njmu.edu.cn; guo_
xuejiang@njmu.edu.cn
In Brief
Here, we performed single-cell
quantitative proteomic analysis
of human germinal vesicle (GV),
in vivo (IVO), and in vitro matured
(IVM) oocytes and found low
correlation between protein and
mRNA levels. IVM oocytes
showed higher heterogeneity in
protein expression, which is
related to the levels of estradiol
per mature follicle on trigger day.
This study provides a rich
resource to characterize the
mechanisms of oocyte
maturation and to evaluate the
quality heterogeneity of IVM
oocytes at protein level.
Highlights
• Single-cell proteomic profiling of human oocytes matured in vitro and in vivo.
• Low correlation between protein and mRNA levels during human oocyte maturation.
• In vitro matured (IVM) oocytes exhibit higher heterogeneity at the proteome level.
• 45 differentially expressed proteins between IVM and in vivo matured (IVO) oocytes.
2022, Mol Cell Proteomics 21(8), 100267
© 2022 THE AUTHORS. Published by Elsevier Inc on behalf of American Society for Biochemistry and
Molecular Biology. This is an open access article under the CC BY-NC-ND license (http://
creativecommons.org/licenses/by-nc-nd/4.0/).
https://doi.org/10.1016/j.mcpro.2022.100267
RESEARCH
Single-Cell Quantitative Proteomic Analysis of
Human Oocyte Maturation Revealed High
Heterogeneity in In Vitro–Matured Oocytes
Yueshuai Guo1,‡
, Lingbo Cai2,‡
, Xiaofei Liu1,‡
, Long Ma2,‡
, Hao Zhang1,‡
,
Bing Wang1,3
, Yaling Qi1
, Jiayin Liu2
, Feiyang Diao2,*
, Jiahao Sha1,*
, and
Xuejiang Guo1,*
Oocyte maturation is pertinent to the success of in vitro
maturation (IVM), which is used to overcome female
infertility, and produced over 5000 live births worldwide.
However, the quality of human IVM oocytes has not been
investigated at single-cell proteome level. Here, we
quantified 2094 proteins in human oocytes during in vitro
and in vivo maturation (IVO) by single-cell proteomic
analysis and identified 176 differential proteins between
IVO and germinal vesicle oocytes and 45 between IVM and
IVO oocytes including maternal effect proteins, with potential
contribution to the clinically observed decreased
fertilization, implantation, and birth rates using human IVM
oocytes. IVM and IVO oocytes showed separate clusters in
principal component analysis, with higher inter-cell variability
among IVM oocytes, and have little correlation between
mRNA and protein changes during maturation. The
patients with the most aberrantly expressed proteins in
IVM oocytes had the lowest level of estradiol per mature
follicle on trigger day. Our data provide a rich resource to
evaluate effect of IVM on oocyte quality and study mechanism
of oocyte maturation.
Human oocyte maturation is a complex process that encompasses
the development from the germinal vesicle (GV)
stage through to the metaphase II (MII) stage, involving GV
breakdown (transition from prophase I to MII) and extrusion of
the first polar body (1). It involves several important structural
and biochemical events in both the cytoplasm and nucleus
that are necessary for achieving full maturity for fertilization
and early embryo development (2). Oocyte in vitro maturation
(IVM) is an assisted reproductive technology (ART) in clinics to
treat female infertility and involves IVM of GV oocytes
collected from antral follicles to MII stage (3). It is mainly used
as an alternative treatment option for women with polycystic
ovary syndrome to minimize the risk of ovarian hyperstimulation
syndrome, for patients with resistant ovary syndrome to
avoid repeated oocyte in vivo maturation (IVO) failure, and for
fertility preservation in women diagnosed with cancers (4, 5).
Although the success rates of IVM have significantly improved
during the last 3 decades, resulting in over 5000 live births
worldwide (6), structural and morphologic differences,
including oocyte size at different developmental stages, have
been observed when comparing IVO and IVM oocytes from
polycystic ovary syndrome patients (7). And comparative
studies have reported decreased fertilization rates, blastocyst
formation rates, implantation rates, clinical pregnancy rates,
and live birth rates following in vitro fertilization (IVF) of IVM
oocytes versus standard IVF using IVO oocytes (8–10). Thus,
the developmental potential of IVM oocytes may differ from
that of IVO oocytes.
As each individual is developed from a single fertilized egg,
and it is important to evaluate quality differences between
oocytes matured in vitro and in vivo at single-cell level. Singlecell
analysis of human oocytes was mainly performed at
transcriptome level, because single-cell RNA-seq has become
an established method in analyzing transcriptomic cell-to-cell
variation (11). Single-cell transcriptomic studies of human IVM
and IVO oocytes showed a cascade of competing and
compensatory actions driven by genes encoding enzymes
(12). Recent single-cell scTrioseq analysis further investigated
potential epigenetic differences between human IVM and IVO
oocytes (13). Considering the complex of transcriptional and
posttranscriptional regulation during oocyte maturation,
including early transcription, delayed translational activation,
and regulated protein degradation, the abundance of proteins
cannot be easily inferred from transcriptomic measurements
(14–17). Protein-level analysis can help better evaluate the
quality of human IVM oocytes.
With the development of single-cell proteomics, the analysis
of a limited number of samples has become more feasible,
From the 1
State Key Laboratory of Reproductive Medicine, Department of Histology and Embryology, Nanjing Medical University, Nanjing,
China; 2
State Key Laboratory of Reproductive Medicine, Clinical Center for Reproductive Medicine, The First Affiliated Hospital of Nanjing
Medical University, Nanjing, China; 3
School of Medicine, Southeast University, Nanjing, China
‡
These authors contributed equally to this work.
*For correspondence: Xuejiang Guo, guo_xuejiang@njmu.edu.cn; Jiahao Sha, shajh@njmu.edu.cn; Feiyang Diao, diaofeiyang@njmu.edu.cn.
RESEARCH
Mol Cell Proteomics (2022) 21(8) 100267 1
© 2022 THE AUTHORS. Published by Elsevier Inc on behalf of American Society for Biochemistry and Molecular Biology.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). https://doi.org/10.1016/j.mcpro.2022.100267
including approaches to profile the proteome of oocytes at
the single-cell level. Virant-Klun et al. applied single-pot
solid-phase–enhanced sample preparation technology and
identified ~450 proteins in a single human oocyte (18). Using
Oil-Air-Droplet Chip–based single-cell proteomics analysis, Li
et al. identified 355 proteins at the single mouse oocyte level
(19). However, the heterogeneity of human oocytes, especially
the comparison between IVO and IVM oocytes, has not been
investigated at the proteome level. Here, we report the singlecell
quantitative proteomic analysis of human oocytes
matured in vitro and in vivo. With identification of 2382 proteins
and quantification of 2094 proteins, we found complex
protein regulation during human oocyte maturation and higher
heterogeneity among human IVM oocytes.
EXPERIMENTAL PROCEDURES
Experimental Design and Statistical Rationale
To investigate the quality differences between oocytes matured
in vitro and in vivo, three types of human oocytes (GV, IVO oocytes:
retrieval after controlled ovarian hyperstimulation regimens; IVM oocytes:
cultured in vitro from GV) were used. Twelve biological replicates
of each type were analyzed using our single-cell proteomic
method (a total of 36 samples used).
The protein expression data were filtered according to the following
criteria: i) only proteins with label-free quantification (LFQ) values in at
least 50% of samples in any group were preserved for subsequent
analysis; ii) samples that identified less than 70% of the total proteins
were considered of low quality and discarded. Expression levels of
each protein plus one were log2 transformed in the following analysis
to avoid invalid values. To compensate for the missing quantitative
value caused by low protein abundance, we imputed the data with
quantile regression imputation of left-censored data algorithm (20).
Linear mixed effects model was conducted using lmerTest package
(21) to analyze the statistical significance of differentially expressed
proteins among three groups, the formula was set as followed:
y ∼ group + (1|group: individual)
The response is denoted y, group is a fixed effect, and individual is a
random effect which is nested within group. The types of oocytes were
defined as group, that is GV, IVM and IVO, while individual refers to
different donors. Only proteins with false discovery rate (FDR) q value
(Benjamini-Hochberg) lower than 0.05 as well as fold change >1.5 were
considered as differentially expressed among groups. Among the
differentially expressed proteins, those with fold change >1.5 and
Tukey’s posthoc p value <0.05 between GV and IVO or IVM and IVO
oocytes were considered significant between groups. For transcriptomic
data, the FDR q values between IVM and IVO groups were
obtained from Zhao et. al.’s work (12); and for GV and IVO dataset, both
the fold changes and FDR q values were calculated using deseq2
package (22). Only genes with fold change >1.5 and FDR q < 0.05 were
considered significant between GV oocytes and oocytes matured
in vivo (23) and between oocytes matured in vivo and in vitro (12).
Collection of GV, In vitro, and In vivo Matured Oocytes
This study included 36 human oocytes (12 GV oocytes, 12 IVM
oocytes, and 12 IVO oocytes) from 13 donors. Informed consents
were obtained from all donors in this study. This study has been
approved by the Ethical Committee of Jiangsu Province Hospital
(#2012-SR-128) and approved also by the Ethical Committee of
Nanjing Medical University (# (2018)648). Studies in this work abide by
the Declaration of Helsinki principles. All experiments and procedures
involving animals were approved by the Institutional Animal Care and
Use Committee of Nanjing Medical University.
Retrieval and culture of human oocytes were performed as previously
described (24). All donors underwent controlled ovarian hyperstimulation
regimens, including GnRH agonist and GnRH antagonist,
and B-ultrasound was used to monitor follicular growth and development.
The donors were triggered with 250 μg r-hCG (Ovidrel, Merck
Serono S.p.A.) or 0.2 mg GnRH agonist (Diphereline, Pharma Biotech)
if there were two follicles’ diameter ≥18 mm or four follicles’ diameter
≥16 mm. The number of follicles ≥14 mm in diameter and the levels of
E2 and progesterone (P) were assessed. The Estradiol/follicle ratio
(E2/fol) was defined as estradiol level per mature follicle ≥14 mm in
diameter. The oocytes were obtained by transvaginal puncture with an
18-gauge needle (Cook Medical) 36 to 38 h after the injection.
The oocytes and surrounding cumulus oophorus were treated with
80 U/ml prewarmed hyaluronidase (SAGE, In-vitro Fertilization, Inc) to
partially remove cumulus cells to facilitate the identification of GVstage
oocytes or IVO oocytes. To obtain IVM oocytes, GV oocytes
with associated cumulus cells were cultured in oocyte maturation
medium (IVM kit, SAGE) containing 0.075 IU/ml FSH and 0.075 IU/ml
LH (Ferring Pharmaceuticals) in an incubator in 6% CO2, 5% O2 at
37 ◦
C for 24 to 48 h by the presence of the first polar body in perivitelline
space. All oocytes were frozen by vitrification using a blastocyst
vitrification kit (COOK Medical) according to the manufacturer’s
instructions in liquid nitrogen.
Before proteomic processing, vitrified oocytes (GV, IVO, and IVM)
were warmed using a blastocyst warming kit (COOK Medical) and
transferred to human tubal fluid (HTF, COOK) and cultured in the
incubator of 6% CO2, 5% O2 at 37 ◦
C for 1 h. The zona pellucida of
each GV, IVO, and IVM oocyte was removed using Tyrode’s solution
(Sigma), and oocytes were washed three times in PBS to remove
culture media prior to proteomic analysis.
Proteomic Sample Preparation
To optimize the methods for single-oocyte proteomic analysis,
single mouse MII oocytes were lysed either in a urea lysis buffer
(8 M urea, 75 mM NaCl, 50 mM Tris, pH 8.2, 1% (vol/vol) EDTA-free
protease inhibitor, 1 mM NaF, 1 mM β-glycerophosphate, 1 mM
sodium orthovanadate, 10 mM sodium pyrophosphate) followed by
digestion with different amounts of trypsin (2 ng to 500 ng) according
to a previously published method (25) or by RapiGest SF (Waters)
buffer at different concentrations (0.01% to 0.1% RapiGest SF) followed
by digestion with trypsin (100 ng to 500 ng). Protein and
peptide yield from the different protocols were compared using LTQ
Orbitrap Velos mass spectrometer (ThermoFisher Scientific).
For single human oocyte proteomic analysis, each oocyte was
lysed in 10 μl lysis buffer containing 0.02% RapiGest SF and stored on
ice for 10 min. One microliter 50 mM DTT was added to a final concentration
of 5 mM, followed by incubation at 60 ◦
C for 40 min.
Following reduction, 3 μl of 33 mM IAA solution in 167 mM ammonium
bicarbonate was added to alkylate sulfhydryl groups by incubating for
40 min in the dark at room temperature. To avoid adhesion loss, a high
concentration of trypsin (2 μl, 100 ng/μl) (26) was added and incubated
at 37 ◦
C for 5 h. The digestion was stopped by adding 0.5 μl of formic
acid (FA), incubated at 37 ◦
C for 30 min to cleave RapiGest SF. Finally,
peptides were desalted by StageTips (ThermoFisher Scientific, SP301)
and dried with a SpeedVac concentrator before MS analysis.
LC-MS/MS Analysis
The purified peptides were resuspended in 0.1% FA and loaded on
an analytical column (75 μm × 25 cm, Acclaim PepMap RSLC C18
Single-Cell Proteomics of Human Oocytes
2 Mol Cell Proteomics (2022) 21(8) 100267
column, 2 μm, 100 Å; DIONEX) with direct injection mode based on
EASY-nLC 1200 System (ThermoFisher Scientific). The HPLC solvent
A was 0.1% FA, and solvent B was 80% acetonitrile, 0.1% FA. A 90min
linear gradient (3% to 8% buffer B for 4 min, 8% to 30% buffer B
for 77 min, 30% to 100% buffer B for 5 min, 100% buffer B for 4 min)
was applied.
For MS analyses, peptide analysis was performed on a Q Exactive
HF-X Hybrid Quadrupole-Orbitrap Mass Spectrometer (ThermoFisher
Scientific) in the data-dependent acquisition mode. A full survey scan
was obtained for the m/z range of 350 to 1500 at a resolution of
60,000. The automatic gain control target and maximum injection time
were set at 3E6 and 20 ms. MS/MS spectra were acquired from the
survey scan for the 40 most intense ions (as determined in real time by
Xcalibur mass spectrometer software, version 4.3) with a resolution of
30,000. The automatic gain control target and maximum injection time
were set at 5E4 and 100 ms. To minimize repeated sequencing, dynamic
exclusion was set to a duration of 40 s.
Data Analysis
All raw files were processed using Maxquant (25, 27, 28) (version
1.2.2.5) for feature detection, database searching, and protein quantification.
MS/MS spectra were searched against the UniProtKB human
database (downloaded on July 18, 2018; 76,117 protein
sequences) and commonly observed contaminants with N-terminal
protein acetylation and methionine oxidation as variable modifications,
and carbamidomethylation of cysteine residues as a fixed modification.
Trypsin/P was selected as a protease. The peptide mass tolerances
of the first search and main search (recalibrated) were <20 and
6 ppm, respectively. The match tolerance for MS/MS search was
20 ppm. The minimum peptide length was six amino acids, and the
allowed missed cleavage for each peptide was 2. Both peptides and
proteins were filtered with a maximum FDR of 0.01. To increase
peptide/protein identification, the MBR feature with a match window
of 0.7 min was used. LFQ was used to estimate protein abundance
according to the previous procedure (25). Both unique and razor
peptides were selected for protein quantification. All other parameters
were the default settings of the Maxquant software.
Bioinformatics Analysis
Correlation analysis across samples was performed on corrPlot (29)
package. The Principal Component Analysis (PCA) was performed
using the stats package. The top two principal components of IVM
group from PCA were used to build a two-dimensional space, the
median of each dimension was defined as MPC1 and MPC2, and group
median = [MPC1, MPC2], while IVMi = [IVMiPC1, IVMiPC2], then the
Euclidean distance Euci from IVMi to group median can be expressed
as:
Euci =
̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅
(IVMiPC1−MPC1)2
+ (IVMiPC2−MPC2)2
√
All heatmaps were derived using the ComplexHeatmap (30) package,
and Z-score was used for standardization between samples. The
volcano plots were by ggplot2 (31) package. ID conversion and
annotation were conducted using clusterProfiler (32) and org.Hs.eg.db
(33) packages. The annotation of transcriptional factors and cofactors
were according to the “HumanTFs” website (34) and AnimalTFDB 3.0
(35), respectively, and epigenetic factor annotation was according to
EpiFactors database (36). Gene ontology (GO), Kyoto Encyclopedia
Genes and Genomes (KEGG), and Gene Set Enrichment Analysis
analyses of differentially expressed proteins were also performed
using clusterProfiler package (32). Reproductive phenotypes and
embryo-related phenotypes were derived according to MGI database
(37). The gene-phenotype network was conducted using igraph
package (38). GRAVY scores were obtained against GRAVY calculator
(http://www.gravy-calculator.de).
RESULTS
Single-Cell Quantitative Proteomic Profiling of Human
Oocyte Maturation Showed High Heterogeneity of IVM
Oocytes
It is estimated the protein content in single human oocyte is
about 100 ng, which is about 1000 folds compared to a typical
human somatic cell (18). To optimize the method for singlecell
proteomic analysis of oocytes, we first tested different
conditions for protein lysis and trypsin digestion using mouse
single-oocyte samples. We found that protein lysis in 0.02%
RapiGest SF followed by protein digestion using 200 ng
trypsin allowed for identification of the largest number of
proteins and peptides (supplemental Fig. S1, A and B). Urea
lysis buffer had miss-cleavages higher than 20% and were
higher than those using RapiGest SF lysis buffer, and misscleavages
using RapiGest SF were all less than 20% except
that using 0.01% RapiGest SF (supplemental Fig. S1C). And
the best sequence coverages were detected using 0.02%
RapiGest SF lysis with 200 ng trypsin (supplemental Fig. S1D).
For the peptide GRAVY to show hydrophobicity, we found the
different concentrations of RapiGest SF and trypsin resulted in
similar distribution of GRAVY scores (supplemental Fig. S1E).
For the cellular component analysis, the terms enriched were
similar between Urea and 0.01% or 0.02% RapiGest SF, while
0.05% and 0.1% RapiGest SF had less enriched terms such
as “intracellular membrane-bounded organelle” or “cytoplasmic
vesicle” (supplemental Fig. S1F). For the peak width
analysis, RapiGest SF resulted in relatively wider peak widths
than the urea lysis buffer, except that 0.10% RapiGest SF had
significant wide peak widths (supplemental Fig. S1G). In view
of all above analysis, 0.02% RapiGest SF lysis combined with
200 ng trypsin showed good performances, resulted in the
largest number of protein and peptide identifications
(supplemental Fig. S1, A and B), and was used for our subsequent
human single-oocyte proteomic analysis.
To profile human oocyte maturation, we used a total of 36
oocytes collected from 13 donors undergoing assisted
reproductive therapy. The sample included 12 GV oocytes
from six donors, 12 IVM oocytes from eight donors, and 12
IVO oocytes from two donors (supplemental Table S1). For
donors 5689, 5898, and 9432, both GV and IVM oocytes were
collected. After removal of the zona pellucida and confirming
normal morphology, each oocyte was subjected to trypsin
digestion, LC-MS/MS analysis, and LFQ (Fig. 1, A and B).
Totally, 2382 proteins and 20,528 peptides were identified
(supplemental Table S2). Only proteins with expression in at
least 50% of samples in at least one group were considered in
the analysis, and oocyte samples with identification of greater
than 70% of the total proteins were considered of high quality.
After data filtering according to the standards mentioned in
Single-Cell Proteomics of Human Oocytes
Mol Cell Proteomics (2022) 21(8) 100267 3
Experimental Procedures, 2094 proteins in 34 oocytes (GV: 11
oocytes, IVM: 12 oocytes, IVO: 11 oocytes) were quantified
and subjected to the subsequent analysis (supplemental
Table S2). The average number of proteins/peptides quantified
per oocyte in GV, IVM, and IVO were 1947/12,589, 1900/
12,063, and 1836/11,374, respectively (supplemental
Fig. S2A). To assess the data quality, we compared our data
with Virant-Klun et al.’s single-cell proteomics data (18), which
identified 446 proteins in a single oocyte showed that 403
proteins (90.4%) were also identified in a single oocyte
(IVO.4152.2) from our data (supplemental Fig. S2B), indicating
consistent identification of human oocyte proteins.
Reproducibility analysis of protein quantification among the
oocyte samples indicated high reproducibility, with pairwise
Pearson’s correlation coefficients ranging from 0.893 to 0.935,
0.844 to 0.924, and 0.872 to 0.916 among oocytes in GV, IVM,
FIG. 1. Single-cell quantitative proteomic profiling of human oocytes maturation. A, schematic illustration of single-cell quantitative
proteomic profiling of human oocyte maturation in vivo and in vitro. B, representative images of GV, IVM, and IVO oocytes with (upper panel) and
without (lower panel) zona pellucida (ZP). The scale bar represents 100 μm. C, pairwise correlation analysis among the 34 oocytes using log2
transformed LFQ values. White boxes identify oocytes from the same individual within each group. GV, germinal vesicle; IVM, in vitro maturation;
IVO, in vivo maturation; LFQ, label-free quantification.
Single-Cell Proteomics of Human Oocytes
4 Mol Cell Proteomics (2022) 21(8) 100267
and IVO group, respectively (supplemental Fig. S2, C–E). The
analysis showed relatively higher Pearson’s correlation coefficients
within the GV samples, which appeared as a distinct
group. This was expected as IVM and IVO oocytes are both
MII oocytes and relatively close to each other. Additionally, for
some of the donors, oocytes from the same individual tended
to be more similar to each other with relatively higher Pearson’s
correlation coefficients; for example, IVO oocytes from
individual 4152 exhibited higher correlation coefficients to
each other (Fig. 1C).
To better evaluate the similarity among oocytes, PCA was
performed and outliers in each group were detected beyond
the 95% confidence interval. Three clusters of oocytes were
revealed, representing GV, IVM, and IVO oocyte clusters
(Fig. 2A), indicating distinct proteome profiles among groups.
Although GV oocytes were collected from six individuals, they
were closely clustered together. IVO oocytes were also relatively
closely clustered, indicating less heterogeneity among
different in vivo oocytes in protein expression patterns. IVM
oocytes were located between GV and IVO oocyte groups, but
closer to the IVO oocytes, presumably, because IVM and IVO
oocytes are both MII oocytes. The clustering of IVM and IVO
oocytes into two clusters suggested different protein
expression profiles between in vivo– and in vitro–matured
oocytes. Furthermore, in contrast to the closely clustered
patterns in GV and IVO groups, oocytes in the IVM group are
less closely clustered, exhibiting more heterogeneity.
IVM.5553.1 and IVM.5553.2 deviated most from other IVM
oocytes and were detected as outliers beyond the 95% confidence
interval. We found that oocytes from the same individual
were largely similar to each other, for example, the
oocytes from individual 4386 in the GV group, and those from
individual 5553 in the IVM group, are closer to each other
(Fig. 2A). Overall, our results show that the proteome profiles
of oocytes were less heterogeneous for oocytes from the
same donor than among different donors, and IVM oocytes
exhibited overall higher heterogeneity between oocytes at the
proteome level. It seems that oocytes cultured in vitro might
have different and varied protein profiles, possibly related to
the reported lower qualities of IVM oocytes.
To explore the factors contributing to the heterogeneity of
IVM oocytes, we analyzed the effects of the levels of estradiol
per mature follicle (≥14 mm) (E2/fol), the levels of P on trigger
day, the regimens of ovarian stimulation, and the ovulation
trigger strategies (Fig. 2, B–E). Correlation analysis between
the levels of E2/fol or P and the Euclidian distances from each
oocyte to the group median showed that E2/fol was significantly
correlated (p < 0.05, R = -0.625, supplemental Fig. S3A
and supplemental Table S1). The oocytes from individual 5553
in the IVM group, which deviated most from other IVM oocytes,
had the lowest levels of E2/fol (241.8 pmol/L for individual
5553) on trigger day (Fig. 2B and supplemental
Table S1). No significant difference of the Euclidian distances
from each oocyte to the group median was observed
for different regimens of ovarian stimulation or ovulation
trigger strategies (Figs 2, D and E, S3, C and D, supplemental
Table S1). For example, although individual 5553 was subjected
to GnRH agonist in ovarian stimulation, other individuals
with their oocytes closely clustered, such as
individuals 5193 and 4769, were also subjected to GnRH
agonist treatment (Fig. 2D). A similar phenomenon can be
observed in individuals subjected to different ovulation trigger
strategies. It seems that the regimens of ovarian stimulation
and the ovulation trigger strategies are not important factors
contributing to the heterogeneity of oocytes from
individual 5553.
Differential Protein Expression Analysis of Human Oocytes
Reveals Inconsistent mRNA and Protein Changes
As GV, IVM, and IVO oocytes form three clusters in PCA, we
quantified the expression levels of differentially expressed
proteins between groups and identified 243 proteins that
exhibited a fold change >1.5 and FDR q < 0.05 among the
three groups (supplemental Table S2), including 176 differential
proteins (supplemental Table S3) during maturation in vivo (IVO
group compared to GV group) and 45 differential proteins
(supplemental Table S4) in IVM versus IVO oocytes. Cluster
and heatmap analysis of the 243 differentially expressed proteins
showed five clusters (C1-C5) with different expression
patterns (Fig. 3, A and B). Cluster 1 to 4 showed relatively
decreased protein expression levels in IVO versus GV oocytes,
Cluster 5 showed increased protein expression levels in IVO
versus GV oocytes. Cluster 4 (59 proteins) and cluster 5 proteins
(95 proteins) exhibited similar expression levels in IVM
and IVO oocytes, suggesting similar regulation between IVM
and IVO. Cluster 4 showed enrichment of proteins in “translational
initiation”, and Cluster 5 in “mitotic nuclear division” as
well as “negative regulation of chromosome organization”.
Cluster 1 (64 proteins) had abnormally high levels in IVM oocytes
versus IVO oocytes, suggesting possible abnormalities in
protein degradation during IVM culture, and showed enrichment
in “proteasomal ubiquitin-independent protein catabolic
process”. Cluster 2 (11 proteins) and Cluster 3 proteins (14
proteins) exhibited lower expression levels in IVM oocytes
when compared with GV oocytes and IVO oocytes. Notably,
cluster 2 proteins had aberrantly low levels in three IVM oocytes
of individuals 9432 and 5553 compared with other IVM
and IVO oocytes, which may be related to the maturation
quality and should be investigated in further functional studies.
Posttranscriptional regulation during oocyte maturation is
complex. The quantitative proteome profile data obtained in
this study enabled a comparison of transcriptome and proteome
changes in human oocytes. Therefore, we compared our
data for proteins with quantified expression levels to the reported
transcriptome profile data for in vivo (39) and in vitro
oocyte maturation (12) in humans and found that the correlation
coefficient of fold changes between proteins and RNAs
was relatively low (R = 0.196, p < 0.0001). Among the 2066
Single-Cell Proteomics of Human Oocytes
Mol Cell Proteomics (2022) 21(8) 100267 5
FIG. 2. Principal component analysis and factors analysis of oocyte heterogeneity. PCA of the proteomic data from 34 human oocytes
obtained from 13 individuals (A) with different colors representing the levels of E2/fol (B) and progesterone (C) on trigger day, different regimens
Single-Cell Proteomics of Human Oocytes
6 Mol Cell Proteomics (2022) 21(8) 100267
genes identified in both proteome and transcriptome (IVO
group compared with GV group) during IVO, 174 were differentially
expressed at protein level and 1236 were differentially
expressed at mRNA level (IVO group compared with GV
group) during IVO, only 111 showed differential expression at
both the mRNA and protein level (Fig. 3C). Of 73 genes that
were upregulated at protein level in IVO oocytes versus GV
oocytes, 26 (36%) did not exhibit upregulation of the corresponding
mRNA and may therefore be subjected to translational
activation during oocyte maturation. Of 101 gene that
were downregulated at protein level in IVO oocytes versus GV
oocytes, 53 (52%) did not show downregulation of the corresponding
mRNA, suggesting that the levels of these proteins
are regulated via protein stability. For example, AURKA
(supplemental Fig. S4) was upregulated and TRIM28 (Fig. 3B)
was downregulated at the protein level in IVO oocytes but
neither showed a change at the mRNA level during maturation
in vivo. The correlation coefficient of fold changes between
proteins and RNAs following IVM and IVO was 0.028. Among
the overlapped 1925 genes, 45 showed differential expression
at protein level following IVM and IVO (IVM group versus IVO
group), only 5 (11.1%) were also differentially expressed at the
mRNA level (Fig. 3C). For example, FSCN1 and TRIM28 were
differentially expressed only at the protein level in IVM oocytes
(Fig. 3B). The mRNA and protein changes are inconsistent
during human oocyte maturation.
Protein Regulation During IVO of Human Oocytes
We used the single-cell protein expression data from GV
and IVO oocytes as a model to investigate changes in proteins
during oocyte maturation in physiological conditions and
identified a total of 176 differential proteins (supplemental
Table S3), including 75 upregulated proteins and 101 downregulated
proteins in IVO oocytes (Fig. 4, A and B). Analysis of
GO was performed (Fig. 4C and supplemental Table S5), and
biological process annotation showed enrichment of “translational
initiation (16 proteins)”, “mRNA transport (7 proteins)”,
“import into nucleus (7 proteins)”, “proteasomal
ubiquitin−independent protein catabolic process (4 proteins)”,
and “DNA methylation involved in embryo development (3
proteins)”. Cellular component annotation showed enrichment
of “chromosome, centromeric region (10 proteins)”, while
molecular function annotation showed enrichment of “mRNA
binding (12 proteins)”, “translation regulator activity (8 proteins)”,
“structural constituent of ribosome (7 proteins)”, and
“histone binding (7 proteins)”. The enriched terms indicate that
these differential proteins may have important roles in epigenetic,
transcriptional and protein translation, and degradation
regulation in oocyte maturation or early embryo development.
To evaluate the pathways regulated during oocyte maturation,
KEGG pathways were analyzed (Fig. 4C) and showed
enrichment of differential proteins in the “Ribosome (7 proteins)”
(supplemental Table S5); we observed that most identified
ribosome subunits were downregulated, including RPL37A,
RPS12, RPS3A, RPS13, RPS25, RPS27A, and RPS21. Meanwhile,
an upregulation of the translation initiation inhibitor,
TACC3 (supplemental Fig. S4), was upregulated, which is
consistent with the transcriptional quiescence and active
translation of specific transcripts in maturing oocytes (40).
Among proteins exhibiting differential expression during
oocyte maturation in vivo, we found 34 cell cycle proteins
(supplemental Table S6), mainly involved in translation initiation,
negative regulation of cell cycle phase transition, regulation
of RNA stability and cytoplasmic translation, according to
Cyclebase 3.0 database (Fig. 5A and supplemental Table S7)
(41), suggesting that these proteins may play regulatory roles
during the meiotic maturation of oocytes. Additionally, we
identified three transcription factors (TFs) and 15 TF cofactors
(supplemental Table S6) according to Lambert et al.’s and Hu
et al.’s data (34, 35) (Fig. 5B). These proteins have roles in the
regulation of DNA binding and protein stabilization according
to GO enrichment analysis (supplemental Table S7) and
including SUMO1 (supplemental Fig. S4), which is known to be
essential for oocyte maturation and female fertility in mice (42).
We found 12 differential epigenetic factors (supplemental
Table S6) involved in epigenetic regulation mechanisms such
as DNA methylation and histone modification according to the
Epifactors database (Fig. 5C and supplemental Table S7) (36),
including DPPA3/STELLA (supplemental Fig. S4) which is an
important maternal factor protecting the maternal genome in
mouse early embryo (43). These differential TFs/cofactors and
epigenetic factors may regulate the chromatin state of oocytes
and subsequent early embryonic development.
To analyze the in vivo functions of differentially expressed
proteins in GV versus IVO oocytes, the proteins were annotated
using the MGI database, identifying nine proteins with
reproductive phenotypes and 29 proteins with embryonic
phenotypes (Fig. 5D and supplemental Table S8). The reproductive
phenotypes were mainly female infertility and reduced
female fertility, and the embryonic phenotypes were mainly
related to decreased embryo size, embryonic growth retardation,
and embryonic growth arrest. Thus, differentially
expressed proteins were not only important for oocyte maturation
and functions but may function as maternal proteins
that regulate early embryonic development.
Oocytes Matured In vitro and In vivo Exhibit Different
Protein Expression Patterns
As the comparison of differential protein and mRNA expression
of in vitro– and in vivo–matured oocytes showed little
overlap (Fig. 3C), we evaluated the effects of IVM on the proteome
level. Of 45 proteins with differential expression in IVM
of ovarian stimulation (D), and different ovulation trigger strategies (E). Ellipses show the 95% confidence interval in each group. Dotted circles
denote oocytes obtained from the same individual. PCA, principal component analysis.
Single-Cell Proteomics of Human Oocytes
Mol Cell Proteomics (2022) 21(8) 100267 7
FIG. 3. Differential protein expression analysis of human oocytes showed inconsistent mRNA and protein changes. A, heatmap (left) of
243 differentially expressed proteins among GV, IVM, IVO oocytes and enriched biological processes (right) in each cluster. Clusters of proteins
(C1-C5) are indicated according to k-means. B, expression of representative proteins selected from each of the 5 clusters as interleaved boxes
and whiskers with minimum and maximum values displayed. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001 (linear mixed effects model with
Tukey’s posthoc test, fold change >1.5). C, scatter plot of the fold changes between the proteome and transcriptome during in vivo maturation
(IVO, left) and during in vitro maturation (IVM, right) and Venn diagram of comparison of genes differentially expressed at the proteome (DE
proteins) and transcriptome (DE genes) levels. Only genes identified in both transcriptome and proteome were considered. GV, germinal vesicle.
Single-Cell Proteomics of Human Oocytes
8 Mol Cell Proteomics (2022) 21(8) 100267
versus IVO oocytes (supplemental Table S4), seven were
downregulated and 38 were upregulated in IVM oocytes
(Fig. 6A). Heatmap analysis (Fig. 6B) showed that the expression
pattern of most of these differential proteins in IVM oocytes
(C1 and C2) resembled that of GV oocytes rather than that of
IVO oocytes, for example, CDCA3 was upregulated in IVO oocytes
but remained at a similarly low level in IVM oocytes to that
in GV oocytes, while HNRNPK and CASP3 were downregulated
in IVO oocytes but remained at a similarly high level in IVM
oocytes to those in GV oocytes (supplemental Fig. S4).
To further explore the functions of the proteins with
abnormal expression levels in IVM oocytes, Gene Set
Enrichment Analysis was applied. Among the enriched KEGG
pathway gene sets (supplemental Table S9), proteins in the
“spliceosome”, “proteasome”, “eukaryotic translation initiation
factor 3 complex”, “peroxisome”, “oxidative phosphorylation”,
and “apoptosis” pathways exhibited consistently
higher levels in IVM versus IVO oocytes (Fig. 6C). For example,
PSMA1, PSMA4, PSMA5, PSMB1, and PSMB5, which promote
assembly of the 20S proteasome (44–46) involved in the
proteolytic degradation of most intracellular proteins, exhibited
higher levels in IVM oocytes (Figs 6D and S4).
DISCUSSION
In clinics, even with ovarian stimulation, only around a
dozen oocytes from a single individual can be retrieved in ART
treatment (47). Single-cell global protein-level analysis of
FIG. 4. Protein regulation during in vivo maturation of human oocytes. A, volcano plot comparing proteins from IVO versus GV oocytes.
Differentially expressed proteins were defined by the following cutoff values: fold change >1.5, FDR q < 0.05, and posthoc p value <0.05.
B, heatmap of differentially expressed proteins during in vivo maturation. Euclidean dis metric and complete linkage clustering algorithms were
used. C, lollipop plot of enriched GO and KEGG pathway terms in differentially expressed proteins between GV and IVO oocytes. BP, biological
process, CC, cellular components, MF, molecular function. GV, germinal vesicle; IVO, in vivo maturation; KEGG, Kyoto Encyclopedia Genes and
Genomes.
Single-Cell Proteomics of Human Oocytes
Mol Cell Proteomics (2022) 21(8) 100267 9
FIG. 5. Functional annotations of differential human oocyte proteins during in vivo maturation. A-C, heatmap and GO annotations of cell
cycle proteins (A), transcriptional factors and cofactors (B), as well as epigenetic factors (C). D, network of in vivo differential proteins and mouse
reproductive and embryonic phenotypes. The color key ranging from blue to red identifies protein fold changes (IVO/GV). GV, germinal vesicle;
IVO, in vivo maturation.
Single-Cell Proteomics of Human Oocytes
10 Mol Cell Proteomics (2022) 21(8) 100267
FIG. 6. Protein expression patterns during in vitro oocyte maturation. A, volcano plot of comparing protein expression between IVM and
IVO oocytes. Differentially expressed proteins were defined by the following cutoff values: fold change >1.5, FDR q < 0.05, and posthoc p value
<0.05. B, heatmap of differentially expressed proteins between IVM and IVO oocytes. C, GSEA enrichment plots of KEGG pathways between
IVM and IVO oocytes. D, the expression values of PSMA4 and PSMB5 are presented as interleaved boxes and whiskers with minimum and
maximum values displayed. * p < 0.05, ** p < 0.01, (linear mixed effects model with Tukey’s posthoc test, fold change >1.5). IVM, in vitro
maturation; IVO, in vivo maturation; KEGG, Kyoto Encyclopedia Genes and Genomes.
Single-Cell Proteomics of Human Oocytes
Mol Cell Proteomics (2022) 21(8) 100267 11
oocyte maturation in vivo and in vitro can contribute to a better
understanding of oocyte development, providing a basis for
the evaluation of oocyte quality and subsequent optimization
of the IVM, a widely used ART technique. Due to the lack of
amplification methods for global protein analysis (48) and
limited human oocyte samples, only very few studies have
explored the proteome of human oocytes at the single-cell
level. Here, we have characterized the proteomes of GV,
IVM, and IVO oocytes in a total of 36 human oocytes at the
single-cell level, using optimized protein lysis and digestion
conditions. Among the 34 oocytes (GV: 11 oocytes, IVM: 12
oocytes, IVO: 11 oocytes) passing quality control, we identified
2382 proteins and quantified 2094 proteins. Our singleoocyte
proteomics data showed good reproducibility among
different individual oocytes by the pairwise Pearson’s correlation
coefficients analysis.
Single-cell proteomic analysis provides an approach to
dissect the heterogeneity of protein expression within groups
or even within individuals (49, 50), especially in rare cell
samples such as human oocytes. PCA analysis showed
relatively lower heterogeneity among GV and IVO oocytes than
among IVM oocytes, suggesting homogenous oocyte states
in vivo. However, IVM oocytes exhibited higher inter-cell
variability, suggesting a higher variability in the quality of
IVM oocytes than that of IVO oocytes, which is consistent with
the clinical observations of lower cumulative biochemical
pregnancy, clinical pregnancy, and live birth rates following
ART using IVM oocytes (8). The higher heterogeneity of IVM
oocytes is mainly caused by the deviation of two oocytes
matured in vitro from individual 5553 according to PCA analysis
and outlier detection. Additionally, three IVM oocytes from
individuals 5553 and 9432 exhibited aberrantly low levels of
the same subset of proteins (Cluster 2, Fig. 3A) that may be
related to oocyte quality and clinical outcome. We explored
the factors contributing to the variation of these oocytes and
found significant correlation of the levels of E2/fol with the
Euclidean distance from each oocyte to the group median,
suggesting that the variation in E2/fol levels may contribute to
the heterogeneity of in vitro–matured oocytes. Individual 5553
had the lowest level of E2/fol (241.8 pmol/L) on the hCG
trigger day, and individual 9432 had the second lowest level of
E2/fol (541.5 pmol/L). It is generally accepted that serum E2 is
necessary for follicle/oocyte maturation (51). Suneeta et al.
studied the level of E2/fol on trigger day and found that E2/fol
level is positively correlated with qualities of oocytes and
embryos (52). Hu et al. showed that implantation rate was
significantly lower in the group with E2/fol <279.83 pg/ml on
the hCG trigger day (51). Huang et al. provided evidence that
low E2/fol on trigger day might reduce the oocyte retrieval rate
and increase the risk of single and triple pronucleus (1PN and
3PN) formation and abortion (51). It is possible that the lowest
levels of E2/fol on trigger day is a cause of the abnormalities of
the two oocytes from individual 5553, which mainly showed
aberrantly low expression of proteins in Cluster 2 (Fig. 3A). For
ovarian stimulation regimens, compared to the GnRH agonist
protocol, the GnRH antagonist protocol is more recommended
given the comparable efficacy and higher safety (53),
but no statistically significant differences were found in
ovarian response or ongoing pregnancy rates between these
two regimens (54). In our dataset, individual 5553 using GnRH
agonists showed abnormal protein expression patterns, other
samples using the same strategy showed no significant difference
from those using GnRH antagonists, suggesting the
heterogeneity among IVM oocytes might be unrelated to
GnRH agonist. For oocyte ovulation triggering strategies, no
difference was observed for pregnancy and implantation rates
between the GnRH agonist triggering protocol and the hCG
triggering protocol (55–58). Although individual and 5553 was
triggered by GnRH agonists, IVM oocytes from other individuals
using GnRH agonists showed similar protein
changes compared with those IVM oocytes using the hCG
triggering protocol, indicating GnRH agonists might not be an
important contributing factor to the proteome level perturbation
observed in Cluster 2 (Fig. 3A). In addition, in our dataset,
IVM oocytes from the same individual exhibited higher Pearson’s
correlation coefficients, indicating higher inter-individual
variability. Taken together, this single-oocyte proteomic
quantification analysis enabled the evaluation of the quality of
individual oocytes matured in vitro at the protein expression
level and allowed for the evaluation of heterogeneity among
different oocytes.
Our quantitative analysis of oocyte proteins identified a total
of 176 proteins that were differentially expressed during
maturation in vivo (IVO group compared to GV group). However,
based on Yu et al.’s RNA-seq data, 1236 mRNAs are
differentially expressed in IVO versus GV oocytes (39), and
only 111 of them also had protein level changes. These
apparent inconsistencies between changes at protein and
mRNA levels imply complex posttranscriptional regulation.
Consistently, GO analysis of differentially expressed proteins
during human oocyte maturation in vivo identified enrichment
of 12, 4, and 8 proteins in “mRNA binding”, “ssRNA binding”,
and “cytoplasmic translation”, respectively. These differential
proteins included the RNA-binding protein HNRNPF, which
was upregulated during oocyte maturation. HNRNPF stabilizes
mRNAs by binding to the 3′
UTR (59) and may therefore
stabilize mRNAs for translation during the transcriptional
suppression phase of oocyte maturation.
Our data indicate that oocyte maturation also involves differential
expression of transcriptional and epigenetic factors,
including three TFs, 15 transcription cofactors, and 12
epigenetic factors. These proteins may regulate the chromatin
state of oocytes or regulate embryonic development. For
example, SUMO1, DNMT1, and SIN3A (supplemental Fig. S4)
were upregulated during oocyte maturation in vivo. As a
member of the ubiquitin-like modifier family, SUMO1 plays
Single-Cell Proteomics of Human Oocytes
12 Mol Cell Proteomics (2022) 21(8) 100267
crucial roles during meiotic oocyte maturation by regulating
spindle organization, chromosome congression, and chromosome
segregation (42). In addition to the role in oogenesis,
SUMOylation is also required for the communication of the
oocyte with the ovarian somatic cells (60). Deletion of the
maternal factor DNMT1 in mouse oocytes causes lethality
during the last third of gestation by affecting DNA methylation
of imprinted genes (61). Inhibition of oocyte SIN3A, a scaffolding
protein interacting with HDAC1/2, inhibits early embryonic
development beyond the 2-cell stage (62).
Furthermore, our results identified nine proteins with reproductive
phenotypes and 29 proteins with embryonic phenotypes,
also suggesting that the differential proteins may not
only regulate oocyte maturation but also play important roles
during early embryonic development. Maternal effect genes
are oocyte-expressed genes important for embryo development
(63). Among differential proteins with embryonic phenotypes,
TRIM28, DNMT1, ZAR1, and DPPA3 were reported
to be maternal effect proteins in mouse (64), while the differential
protein WEE2 was found to be a maternal effect protein
in human (65). Further studies of these factors are expected to
help elucidate the mechanisms of human oocyte maturation
and the roles of maternal proteins in early embryonic
development.
IVM is a promising approach to conventional ART for those
infertile women who cannot harvest in vivo–matured oocytes.
Our proteome-level characterization of IVM and IVO oocytes
showed that although IVM oocytes were more similar to IVO
oocytes than to GV oocytes, IVM and IVO oocytes formed two
distinct clusters and were not completely similar to each other.
The IVM oocyte cluster was located between GV and IVO
clusters, indicating that immature features were retained in IVM
oocytes. This is supported by the phenomenon that many
proteins retained expression patterns similar to GV oocytes
according to heatmap analysis. As many mRNAs are transcribed
at earlier stages of oocyte development and not
actively translated until oocyte maturation (17), abnormally low
levels of specific proteins in IVM oocytes retaining GV pattern
may result from abnormalities in translation during oocyte
maturation and might be related to the structural and
morphologic differences between IVM and IVO oocytes (7). We
also observed upregulation of translation initiation factor eIF3i,
and ribosomal proteins RPL37A and RPS27A, which might be
compensation for decreased translation activity in IVM oocytes.
Intriguingly, we observed that 40 of the 45 differential proteins
(overlapped with mRNA) abnormally expressed in IVM
oocytes showed no change at the corresponding mRNA level
based on previously published data, suggesting complex
translational regulation. Among the proteins aberrantly
expressed in IVM oocytes, TRIM28 is a maternal effect protein
in oocyte; its defective expression could lead to epigenetic
changes of blastocyst, partial postimplantation embryonic
loss, and no viable offspring (66). The abnormal expression of
TRIM28 in IVM oocytes retaining GV expression pattern might
also contribute to the reported lower live birth rates following
IVF of IVM oocytes (8–10).
Some limitations may weaken the integrative analysis of
RNA-seq and proteomics in this study. For example, the
samples used in the published RNA level and our proteomic
level analysis were not from the same individuals and may not
pass the same quality control, and the sampling time point
may impact on the RNA abundance during oocyte development.
Future RNA and protein level analysis of oocytes from
the same individuals or even the same cells could help better
reveal the relationship between transcriptome and proteome
during oocyte maturation. However, the maturing oocytes are
transcriptionally quiescent, and their mRNAs contributing to
growth, oocyte maturation, and downstream developmental
stages are produced, stored in GV oocytes, and are relatively
stable during maturation (40); the inconsistency between
protein and mRNA expression levels during oocyte maturation
in vivo and in vitro shown in our study indicates the importance
of studies at protein levels to some extent.
Taken together, our single-cell proteomic profiling of human
GV, IVM, and IVO oocytes has enabled a proteome-level
evaluation of the quality of IVM oocytes. IVM oocytes had
high inter-cell variation of protein expression patterns. Further
functional studies of differential proteins between IVM and IVO
oocytes may help identify approaches to improve the quality
of IVM oocytes. The proteome profiles of human oocyte
maturation will also be a rich resource to characterize the
mechanisms of decreased quality of human IVM oocytes and
identify the regulation of human oocyte maturation.
DATA AVAILABILITY
The proteomics data have been deposited in the ProteomeXchange
Consortium via the proteomics identifications
(PRIDE) database (Project accession: PXD023366).
Supplemental data—This article contains supplemental
data.
Acknowledgments—We thank Yang Yu (Peking University
Third Hospital, Beijing) for the statistical data of RNA-seq of
human oocyte maturation in vivo and in vitro. This work was
funded by National Key R&D Program of China (grant number
2021YFC2700200); Chinese National Natural Science Foundation
Grants (grant numbers 32071133, 81971439, and
81771641); The Fok Ying Tung Education Foundation (grant
number 161037), and the Open and Shared Research Projects
of Large-scale Scientific Instruments of Jiangsu Province
(grant number TC2021A010).
Author contributions—J. L., F. D., J. S., and X. G. conceptualization;
L. C., L. M., and J. L. resources; L. C., L. M., and J.
L. investigation; Y. G. and Y. Q. methodology; X. L., B. W., and
H. Z. data curation; X. L., B. W., and H. Z. formal analysis;
Single-Cell Proteomics of Human Oocytes
Mol Cell Proteomics (2022) 21(8) 100267 13
Y. G. and X. L. writing–original draft; F. D., J. S., and X. G.
writing–review and editing.
Conflict of interest—The authors declare that they have no
conflict of interest with the contents of this article.
Abbreviations—The abbreviations used are: ART, assisted
reproductive technology; FA, formic acid; FDR, false discovery
rate; GO, gene ontology; GV, germinal vesicle; IVM, in vitro
maturation; IVO, in vivo maturation; IVF, in vitro fertilization;
KEGG, Kyoto Encyclopedia Genes and Genomes; LFQ, labelfree
quantification; MII, metaphase II; PCA, principal component
analysis; P, progesterone; TFs, transcription factors.
Received October 26, 2021, and in revised from, June 29, 2022
Published, MCPRO Papers in Press, July 7, 2022, https://doi.org/
10.1016/j.mcpro.2022.100267
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