Brief ReportHuman embryonic genome activation initiates at the
one-cell stageGraphical abstractHighlightsd Gene expression initiates at the one-cell stage in human
embryos
d Expression is of lowmagnitude but remains elevated until the
eight-cell stage
d Upregulated transcripts are spliced and correspond to
embryonic processes
d Upregulation is disrupted in morphologically abnormal one-
cell embryosAsami et al., 2022, Cell Stem Cell 29, 209–216
February 3, 2022 ª 2021 The Author(s). Published by Elsevier In
https://doi.org/10.1016/j.stem.2021.11.012Authors
Maki Asami, Brian Y.H. Lam,
Marcella K. Ma, ...,
Matthew D. VerMilyea, Giles S.H. Yeo,
Anthony C.F. Perry
Correspondence
mvermilyea@ovationfertility.com
(M.D.V.),
gshy2@cam.ac.uk (G.S.H.Y.),
perry135@aol.com (A.C.F.P.)
In brief
It is not known when genes are first active
in newly formed human embryos. Asami
and colleagues show that this activity
begins soon after fertilization, at the one-
cell stage. Gene activation mirrors early
embryonic processes, is disrupted in
abnormal embryos, and predicts links to
cancer-associated transcription
regulators.c. ll
OPEN ACCESS
llBrief Report
Human embryonic genome activation
initiates at the one-cell stage
Maki Asami,1,4 Brian Y.H. Lam,2,4 Marcella K. Ma,2 Kara Rainbow,2 Stefanie Braun,3 Matthew D. VerMilyea,3,*
Giles S.H. Yeo,2,* and Anthony C.F. Perry1,5,*
1Laboratory of Mammalian Molecular Embryology, Department of Biology and Biochemistry, University of Bath, Bath BA2 7AY, England
2MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, Addenbrooke’s Hospital, University of Cambridge,
Cambridge CB2 0QQ, England
3Ovation Fertility Austin, Embryology and Andrology Laboratories, Austin, TX 78731, USA
4These authors contributed equally
5Lead contact
*Correspondence: mvermilyea@ovationfertility.com (M.D.V.), gshy2@cam.ac.uk (G.S.H.Y.), perry135@aol.com (A.C.F.P.)
https://doi.org/10.1016/j.stem.2021.11.012SUMMARYIn human embryos, the initiation of transcription (embryonic genome activation [EGA]) occurs by the eight-
cell stage, but its exact timing and profile are unclear. To address this, we profiled gene expression at depth
in human metaphase II oocytes and bipronuclear (2PN) one-cell embryos. High-resolution single-cell RNA
sequencing revealed previously inaccessible oocyte-to-embryo gene expression changes. This confirmed
transcript depletion following fertilization (maternal RNA degradation) but also uncovered low-magnitude up-
regulation of hundreds of spliced transcripts. Gene expression analysis predicted embryonic processes
including cell-cycle progression and chromosome maintenance as well as transcriptional activators that
included cancer-associated gene regulators. Transcription was disrupted in abnormal monopronuclear
(1PN) and tripronuclear (3PN) one-cell embryos. These findings indicate that human embryonic transcription
initiates at the one-cell stage, sooner than previously thought. The pattern of gene upregulation promises to
illuminate processes involved at the onset of human development, with implications for epigenetic inheri-
tance, stem-cell-derived embryos, and cancer.INTRODUCTION
Fertilizing spermatozoa and metaphase II (mII) oocytes are
transcriptionally quiescent (Zhou and Dean, 2015). The first
transcription in newly formed embryos is known as embryonic
genome activation (EGA), but its onset, timing, and profile are
poorly understood (Jukam et al., 2017). In human embryos,
EGA is held to have occurred by the eight-cell stage, up to

68 h (
3 days) after fertilization (Braude et al., 1988; Leng
et al., 2019; Tesarı´k et al., 1988; Vassena et al., 2011; Xue
et al., 2013; Yan et al., 2013), but this model is likely incom-
plete. First, it does not accommodate hints that transcription
initiates earlier (Leng et al., 2019; Wu et al., 2018; Xue et al.,
2013; Yan et al., 2013), albeit previous analyses are restricted
by poor signal-to-noise ratios, low embryo or donor numbers,
and RNA-sequencing (RNA-seq) library preparation protocols
that reflect mRNA polyadenylation status (recruitment) (Blower
et al., 2013; Oh et al., 2000; Temeles and Schultz, 1997). Sec-
ond, the model does not explain how the embryo genome is
maintained in a transcriptionally silent state during cell prolifer-
ation to the eight-cell stage (Alpha Scientists in Reproductive
Medicine and ESHRE Special Interest Group of Embryology,
2011). Third, no mechanism has been proposed that explains
how maternal factor activity required for early development isCell Stem Cell 29, 209–216, Feb
This is an open access article undregulated at different phases of the first two-to-three cell cy-
cles in the absence of endogenous transcription. Fourth, the
model does not address the cue that instigates transcription,
which can be provided either in vivo or in vitro or whether it
is the cumulative consequence of transcription-independent
processes. We, therefore, evaluated the open possibility that
gene expression is triggered after fertilization in human one-
cell embryos.
RESULTS
We sought to determine the gene expression profile of human
one-cell embryos and relate it to development (Figure 1A). Hu-
man mII oocytes (n = 12, from seven donors, aged 22.5–31)
and bipronuclear (2PN) one-cell embryos (n = 12, from six cou-
ples, with no oocyte donor overlap) from various ethnic back-
grounds and that appeared healthy (Figure 1B) were subjected
to single-cell RNA-seq (scRNA-seq) that avoided poly(A) cap-
ture and its attendant potential library bias (Blower et al.,
2013; Oh et al., 2000; Temeles and Schultz, 1997). Whole-tran-
scriptome amplification produced indistinguishable yields
between oocytes and one-cell embryos (p = 0.595), and in-
depth scRNA-seq yielded a mean of 66.3 million reads per
cell. Principal component analysis, coupled with t-distributedruary 3, 2022 ª 2021 The Author(s). Published by Elsevier Inc. 209
er the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Figure 1. Human embryonic transcription initiates at the one-cell stage
(A) Schematic of human one-cell embryo development at the times after fertilization (Capmany et al., 1996). Pb1, first polar body; Pb2, second polar body; PN,
pronuclei; PNMB, pronuclear membrane breakdown.
(B) Brightfield images of representative human metaphase II oocytes (mII) and bipronuclear one-cell embryos (emb). Arrowheads indicate pronuclei. Pb1, first
polar body; Pb2, second polar body. Scale bar, 50 mm.
(C) t-SNE analysis (without filtering) of single-cell RNA-seq data for humanmII oocytes (mII, n = 12) and bipronuclear (2PN) one-cell embryos (emb, n = 12). Circles
correspond to oocytes from an African American/Hispanic donor and triangles correspond to embryos from Asian donors, with all other oocytes and embryos
being of Caucasian origin and filled triangles and circles corresponding to symbols of (D).
(D) Heatmap showing changes in gene expression levels (FDR< 0.1, log2FC> 0.58) in humanmII oocytes (mII) (n = 12) and bipronuclear (2PN) one-cell embryos (emb;
n = 12) of (C), indicating donors (top) and the Z score scale (4 to 4). Each patient is represented by a symbol to indicate the provenance of oocytes and embryos.
(E) Single-cell qPCR of upregulated DEG transcripts in individual human oocytes (mII; nR 3 independent biological replicates) and 2PN one-cell embryos (emb; n
R 3 independent biological replicates) (FDR < 0.1, log2FC > 0.5). Different oocytes and embryos were used to those of (D). Corresponding log2FC values from
RNA-seq are indicated beneath histograms. Primer pairs flanked exon junctions except in the cases of TIGD5 andNFKB1A (which have a single exon) andPIAS3.
Values are ± SEM and normalized against mII oocytes.
(F) Pie chart showing functional classes of upregulated DEGs (FDR < 0.1, cpm > 1.0). J, pseudogene; ERV, endogenous retrovirus; lncRNA, long non-coding
RNA; AS, antisense; miRNA, microRNA; si/so, sense-intronic/sense-overlapping; pt, processed transcript.
(G) Pie chart showing processing classes (FDR < 0.34) of upregulated DEGs (p < 0.05; cpm > 1.0). Letters indicate Ingenuity codes.
See also Figure S1 and Table S1.
ll
OPEN ACCESS Brief Reportstochastic neighbor embedding (t-SNE), segregated transcrip-
tomes into discrete oocyte and embryo groups (Figure 1C), and
comparison revealed 2,879 differentially expressed genes210 Cell Stem Cell 29, 209–216, February 3, 2022(DEGs; FDR < 0.1; Figures 1D and S1A). The number of
DEGs inversely correlated with fold-change (FC, log2FC; Fig-
ure S1B), and there were 1,395 DEGs with an absolute log2FC
Figure 2. Human one-cell upregulated gene characteristics and pathways
(A) Raw scRNA-seq density plots (Sashimi plots) along exons and exon junctions. Arcs representing splice junctions connecting exons and display the number of
reads split across the junction (junction depth) in mII oocytes (mII) and 2PN one-cell embryos (emb). Genomic coordinates (chrom) and gene annotation tracks are
aligned beneath each respective plot. Solid black bars above plots indicate regions of potential alternative splicing.
(legend continued on next page)
ll
OPEN ACCESSBrief Report
Cell Stem Cell 29, 209–216, February 3, 2022 211
ll
OPEN ACCESS Brief Reportof R0.5 (of which 1,081 survived an FDR cutoff of 0.05).
Expression levels of 1,557 genes (54.1% of DEGs; FDR <
0.1) decreased in one-cell embryos, reflecting maternal tran-
script degradation (Figures 1D and S1C) (Alizadeh et al.,
2005). These excluded orthologs of classically down-regulated
mouse transcripts, including MOS and GDF9, consistent with
relatively slow human maternal transcript degradation lasting
several cell cycles (i.e., days) (Leng et al., 2019; Paynton
et al., 1988; Petropoulos et al., 2016; Sha et al., 2020; Xue
et al., 2013). Outputs from Ingenuity pathway analysis (IPA)
(Kr€amer et al., 2014) corresponded to the initiation of embryo-
genesis and conclusion of gametogenesis (Figure 1A; Ta-
ble S1).
In addition to down-regulation, expression of 1,322 genes
increased in one-cell embryos compared with mII oocytes
(FDR < 0.1) (Figure 1D). To test this, we assessed the overlap
between upregulated DEGs and 657 transcripts common to
four datasets of stably expressed human genes (n = 6,040 ±
2,549 genes; Eisenberg and Levanon, 2013; Lin et al., 2019;
Petropoulos et al., 2016). Of these, 542 were expressed in
one-cell embryos, most (90.4%) at levels indistinguishable
from those in oocytes. Single-cell qPCR of upregulated DEGs
in different one-cell embryos mirrored increases revealed by
scRNA-seq (log2FC > 0.5; n R 3 different embryos per target;
n = 14 targets; Figure 1E). Most (76.3%) upregulated DEGs en-
coded annotated proteins (Figure 1F) and transcriptome re-as-
sembly de novo predicted the use of canonical transcription
start sites in 60.9% (n = 357) of cases and normative splicing
in 85.1% (Figure 1G). Sashimi plots demonstrated increases
in the levels of mature mRNA transcripts spliced at canonical
exon junctions in one-cell embryos, with evidence of embryo-
specific alternative splicing (Figure 2A). Exon-flanking qPCR
further corroborated mature, spliced mRNA level increases
(Figure 1E). These findings reveal the onset of human EGA in
one-cell embryos to produce canonically spliced mRNA
transcripts.
The degree of overlap between upregulated DEGs and previ-
ous expression data for cleavage-stage human embryos (Fig-
ures S1D and S1E) possibly reflected protocol differences
(e.g., poly(A) capture in library preparation), donor ethnicity,
and timing of gamete and embryo collection (Leng et al., 2019;
Xue et al., 2013). Only 3.8% of 3,476 human-sperm-intact
RNAs (Sun et al., 2021) corresponded to upregulated DEGs
(FDR < 0.1; Figure S1E), although sperm-associated RNA may
not enter mII oocytes (Amanai et al., 2006; Asami et al., 2020;
Zhou et al., 2019). Of the top 200 upregulated genes (FDR <
0.1), 153 were differentially expressed in two-, four-, and eight-
cell embryos (Leng et al., 2019) (Figure S1F). Most (119/153;
77.8%) exhibited sustained expression throughout two-to-four-
cell stages and markedly declined by the eight-cell stage (clus-
ters II and III, Figure S1F).(B) qPCR for transcripts in individual human monopronuclear (1PN) and tripronu
embryos per target). Values for metaphase II oocytes (mII) and bipronuclear one
SEM and normalized against mII oocytes (
1.0). Unpaired t tests indicate p < 0.
(C) Ingenuity pathway analysis (IPA) of gene networks upregulated (FDR < 0.1, lo
(D–F) Upstream transcription regulators inferred by IPA of upregulated gene netwo
MYCN (F).
See also Figure S2 and Table S1.
212 Cell Stem Cell 29, 209–216, February 3, 2022We investigated expression of upregulated transcripts in tri-
pronuclear (3PN) one-cell embryos, which have been adopted
for genome editing studies (Kang et al., 2016; Li et al., 2017;
Liang et al., 2015). Of 14 targets upregulated in 2PN embryos
that we evaluated, levels of 12 (85.7%) were lower (p < 0.2) in
3PN embryos (Figure 2B). This suggests that EGA initiation
was disrupted in most 3PN one-cell embryos, which undergo
developmental failure (Kola et al., 1987; Mutia et al., 2019).
Moreover, expression of 10 of the 14 targets (71.4%; p <
0.2) was lower in monopronuclear (1PN) one-cell embryos
(Figure 2B).
IPA of upregulated DEGs indicated that expressed gene func-
tion corresponded to developmental processes in healthy 2PN
embryos (Figures 1A and 2C; Table S1). These included ATM
activation, which induces G2-/M-phase arrest in response to
DNA damage in the mouse (Wang et al., 2013), protective
redox-dependent systems (Perreault et al., 1988; Yanagimachi,
1994), and chromosome segregation, consistent with the elimi-
nation of aneuploid two-cell embryos (Pauerova et al., 2020;
Schatten et al., 1988). Upregulated DEGs associated with early
developmental arrest included WEE2 (p = 0.006; Sang et al.,
2018) and BTG4 (p = 0.015; Zheng et al., 2020) as well as dis-
ease-associated nuclear genes involved in mitochondrial func-
tion (e.g., TFB2M, p = 6.71e-05;MFN1, p = 1.34e-05), consistent
with important early embryonic roles played by mitochondria
(Facucho-Oliveira and St John, 2009; McConnell and Petrie,
2004; Rusecka et al., 2018).
We found no evidence for major upregulation of LINE-1 retro-
transposons (Percharde et al., 2018) or transcription factors
(TFs) thought to drive cleavage-stage EGA, including OCT4
(Gao et al., 2018), LEUTX (Jouhilahti et al., 2016), and DUX4
(De Iaco et al., 2017; Hendrickson et al., 2017). However, upre-
gulated DEGs included 63 human endogenous retrovirus
(hERV) loci (Figures S2A and S2B). Trans-activators of EGA initi-
ation predicted by IPA (FDR < 0.1) included MYC (p = 1.94e-6),
MYCN (p = 4.54e-8), RABL6 (p = 0.02), FYN (p = 0.05), and
E2F4 (p = 6.48e-12) (Figures 2D–2F and S2C–S2I). Transcripts
for a third of these (33/97) were detected by scRNA-seq in mII
oocytes and down-regulation of E2F4 correlates with one-cell ar-
rest (Suo et al., 2018).
DISCUSSION
These findings have several implications. First, they suggest
that transcription is instated soon after fertilization, during
meiotic progression and gamete reprogramming, which
should illuminate mechanisms that coordinate chromatin re-
modeling, the cell cycle, and transcription complex assembly.
Second, they provide a readout of epigenetic states that sup-
port embryonic transcription and locate genomic addresses of
active chromatin. Third, they indicate that decoding gameteclear (3PN) one-cell embryos (3R nR 6 biologically independent oocytes or
-cell embryos (2PN) from Figure 1E are included for comparison. Values are ±
2.
g2FC > 0) in 2PN one-cell embryos.
rks (FDR < 0.1, log2FC > 0) in 2PN one-cell embryos for E2F4 (D), MYC (E), and
ll
OPEN ACCESSBrief Reportchromatin modifications acutely after fertilization might
mediate epigenetic inheritance. Fourth, commencement of
human gene expression in one-cell embryos accommodates
previous hints of early transcription (Wu et al., 2018; Yan
et al., 2013; Xue et al., 2013), circumvents protracted genome
latency and autonomous regulation, and suggests the trigger
for EGA: fertilization.
Other contexts relevant to cellular potency also manifest
small magnitude transcript-level changes that may be common
in cellular potency transitions. Trans-activation by MYC induces
relevant target gene expression less than two-fold (Baluapuri
et al., 2020; Nilsson et al., 2005) and mouse pluripotency factor;
Esrrb apparently causes less than two-fold upregulation of em-
bryonic-stem-cell-specific genes (Chronis et al., 2017). The
average negative effect of the transcription regulator, Poly-
comb, is about two-fold (Berrozpe et al., 2017), and its associ-
ated histone modification, H3K27me3, modulates cellular
potency and imprinting (Bernstein et al., 2006; Santini et al.,
2021). Several lines of evidence suggest that transcription at
the one-cell stage is functional. Upregulated transcripts ex-
hibited a stereotypical pattern that corresponded to early em-
bryonic processes (Perry and Verlhac, 2008; Yanagimachi,
1994; Zhou and Dean, 2015), encoded protein, and utilized ca-
nonical exons and transcriptional start sites. Predicted tran-
scription regulator functions mapped to one-cell embryonic
processes and the regulation of cancer (Figures 1A, 2C–2F,
and S2C–S2I). The switch at fertilization that activates pre-
sumptive maternal TFs may involve phospho-relay signaling
(Perry and Verlhac, 2008), as MYC, MYCN, FOXM1, E2F4,
and others are regulated by kinases (Joshi et al., 2013; Morillo
et al., 2012; Sjostrom et al., 2005; Vervoorts et al., 2006). Trajec-
tory analysis (Figure S1F) implied that TFs responsible for early
expression are deactivated during the four-to-eight-cell transi-
tion, coinciding with a major wave of EGA.
Transcriptional initiation in human one-cell embryos also has
clinical implications. Disruption of cancer-associated genes
might manifest as both impaired fertility and cancer, and female
infertility is indeed associated with 
15% elevated cancer risk
(Murugappan et al., 2019). It is also possible that parentally in-
herited epigenetic marks (e.g., those associated with acquired
obesogenic traits) affect gene expression immediately after
fertilization (Huypens et al., 2016; Samata et al., 2020). Maternal
factors required to initiate EGA may include polar body markers
of oocyte quality (Klatsky et al., 2010; Reich et al., 2011; Swain
and Pool, 2008; VerMilyea et al., 2011) and reveal processes in
artificial oocytes or embryos necessary for embryonic transcrip-
tion and totipotency (Yamashiro et al., 2018). Although stem-
cell-derived human embryos (blastoids) may one day bypass
embryonic totipotency (Liu et al., 2021; Yanagida et al., 2021;
Yu et al., 2021), EGA initiation may alternatively be indispensable
for normative development. Finally, evaluating genome editing in
3PN embryos (Kang et al., 2016; Liang et al., 2015; Li et al., 2017)
should accommodate disrupted gene expression to have clinical
utility (Perry, 2000; Yeh et al., 2019).
Limitations of the study
Human 2PN one-cell embryos that may legitimately be used for
research are extremely rare, in part reflecting current standard
practice among assisted reproduction facilities. This studysourced archival 2PN embryos, placing constraints on sample
availability that, inter alia, precluded corroborative analysis
orthogonal to high-resolution scRNA-seq, including inhibitor
studies (e.g., to block transcription by RNA polymerase II)
and confirmatory immunofluorescence (e.g., of predicted
maternal TFs). The findings are likely to be evaluated in more
tractable (e.g., mouse) models in the foreseeable future, and
perhaps eventually in closely related primate (e.g., baboon)
models.
STAR+METHODS
Detailed methods are provided in the online version of this paper
and include the following:
d KEY RESOURCES TABLE
d RESOURCE AVAILABILITYB Lead contact
B Materials availability
B Data and code availability
d EXPERIMENTAL MODEL AND SUBJECT DETAILS
B Human oocyte and embryo sample collection
d METHOD DETAILS
B Human metaphase II oocytes and one-cell embryos
(zygotes)
B Single-cell RNA sequencing (scRNA-seq) of oocytes
and one-cell embryos
B Ratiometric real-time-PCR (qPCR)
d QUANTIFICATION AND STATISTICAL ANALYSIS
B Bioinformatic analysis
B Statistical analysis
SUPPLEMENTAL INFORMATION
Supplemental information can be found online at https://doi.org/10.1016/j.
stem.2021.11.012.
ACKNOWLEDGMENTS
We are greatly indebted to Drs. C. Klein, A. Greenfield, and C. Tickle for
comments during manuscript preparation and thank J. Whitney for embryo
curation. We acknowledge support to A.C.F.P. from the Biotechnology and
Biological Sciences Research Council (BBSRC; BB/P009506/1) and Medi-
cal Research Council (MRC), UK (MR/N000080/1 and MR/N020294/1).
B.Y.H.L. is supported by BBSRC Project Grant BB/S017593/1 and
G.S.H.Y. by the MRC Metabolic Diseases Unit (MC_UU_00014/1). Next-
generation sequencing was performed by the IMS Genomics and transcrip-
tomics core facility and supported by the MRC (MC_UU_00014/5), the
Wellcome Trust (208363/Z/17/Z), and the Cancer Research UK Cambridge
Institute Genomics Core.
AUTHOR CONTRIBUTIONS
A.C.F.P. conceived the core idea. Embryo preparation was byM.D.V. and S.B.
Single-cell whole-transcriptome amplification was performed by B.Y.H.L.,
K.R., and M.K.M. B.Y.H.L. designed scRNA-seq analyses, performed and
evaluated by B.Y.H.L., M.K.M., and G.S.H.Y. M.A. performed qPCR. Data
analysis was by M.A., B.Y.H.L., G.S.H.Y., and A.C.F.P. A.C.F.P. wrote the
manuscript with input from B.Y.H.L. and M.A.
DECLARATION OF INTERESTS
The authors declare no competing interests.Cell Stem Cell 29, 209–216, February 3, 2022 213
ll
OPEN ACCESS Brief ReportReceived: April 8, 2021
Revised: August 24, 2021
Accepted: November 29, 2021
Published: December 21, 2021
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OPEN ACCESSBrief ReportSTAR+METHODSKEY RESOURCES TABLEREAGENT or RESOURCE SOURCE IDENTIFIER
Biological samples
Human metaphase II (mII) oocytes Ovation Fertility Austin, Embryology
and Andrology Laboratories, Austin,
TX 78731, USA
https://www.ovationfertility.com
Human bipronuclear (2PN) embryos Ovation Fertility Austin, Embryology
and Andrology Laboratories, Austin,
TX 78731, USA
https://www.ovationfertility.com
Human monopronuclear (1PN) embryos Ovation Fertility Austin, Embryology
and Andrology Laboratories, Austin,
TX 78731, USA
https://www.ovationfertility.com
Human tripronuclear (3PN) embryos Ovation Fertility Austin, Embryology
and Andrology Laboratories, Austin,
TX 78731, USA
https://www.ovationfertility.com
Critical commercial assays
Clontech SMARTer Total RNA-Seq Kit
Pico Input (V2) system
Takara Clontech Cat # 634412
Deposited data
RNA-seq data This paper GEO: GSE157834
RNA-seq data Leng et al., 2019 GEO: GSE133856
RNA-seq data Xue et al., 2013 GEO: GSE44183
RNA-seq data Wu et al., 2018 GEO: GSE101571
RNA-seq data Sun et al., 2021 GEO: GSE137490
Experimental models: Organisms/strains
Human (Homo sapiens) N/A
Oligonucleotides
Primers for qPCR: see Table S2 This paper N/A
Software and algorithms
Human GRCh38 genome and Ensembl
92 gene model using STAR (2.5.0a)
Dobin et al., 2013 N/A
Stringtie (90 1.3.6) Pertea et al., 2015 N/A
htseq-count (0.6.1p1) Anders et al., 2015 N/A
edgeR Robinson et al., 2010 N/A
trim-mean of M values (TMM) normalization
from the edgeR package
Robinson and Oshlack, 2010 N/A
Rtsne package https://cran.r-project.org/web/packages/
Rtsne/Rtsne.pdf
limma package Ritchie et al., 2015 N/A
Qiagen Ingenuity Pathway Analysis
(IPA) software
https://digitalinsights.qiagen.com/products/
qiagen-ipa/latest-improvements/current-line/
Sashimi plots Katz et al., 2010; Wang
et al., 2008
N/A
Integrative Genome Viewer (IGV)
version 2.4.19
Robinson et al., 2011 N/ARESOURCE AVAILABILITY
Lead contact
Further information and requests for resources and reagents should be directed to the lead contact, Tony Perry (perry135@aol.com).Cell Stem Cell 29, 209–216.e1–e4, February 3, 2022 e1
ll
OPEN ACCESS Brief ReportMaterials availability
This study did not generate new unique reagents.
Data and code availability
All data are available in the main text or the supplementary materials. Source data are provided for figures. Single-cell RNA
sequencing (scRNA-seq) data have been deposited into GEO with accession number GEO: GSE157834.
EXPERIMENTAL MODEL AND SUBJECT DETAILS
Human oocyte and embryo sample collection
Human oocytes and single monopronuclear (1PN), bipronuclear (2PN) and tripronuclear (3PN) one-cell embryos were supplied anon-
ymously subject to informed consent for use in research by couples who had finished family building or decided for other reasons to
discontinue fertility treatment. Consents strictly adhered to guidelines of the Ethics Committee of the American Society for Repro-
ductive Medicine. Embryos were cryopreserved and lysed on site at Ovation Fertility before being anonymized and shipped for
analysis.
METHOD DETAILS
Human metaphase II oocytes and one-cell embryos (zygotes)
Patients underwent ovarian stimulation according to guidelines of each clinic, where protocols included agonist luteal phase and
antagonist suppression. On the day of retrieval (day 0), mature, metaphase II (mII) oocytes were either cryopreserved by a slow freeze
method using propanediol (PROH) (Gook et al., 1993) 3-6 h post-collection, or used to produce embryos by in vitro fertilization (IVF) or
ICSI. One-cell embryos used here were morphologically assessed for pronuclear number 19-23 h post-fertilization, cryopreserved
1-5 h later using dimethylsulfoxide (Camus et al., 1989) and stored under liquid nitrogen. Some sibling embryos of morphologically
normal bipronuclear (2PN) one-cell embryos (i.e., each containing two pronuclei) gave rise to children. When required, cryopreserved
oocytes and one-cell embryos were thawed by rapid warming using a Vit-Warm Kit (FUJI Irvine Scientific, USA) according to the rec-
ommended protocol, and viability confirmed. All mII oocytes and one-cell embryos were washed in protein-free multi-purpose
handling medium (FUJI Irvine Scientific, USA) and each placed in a 0.2 ml PCR tube containing 0.8 ml 1x single-cell lysis buffer sup-
plemented with RNase inhibitor (Takara Clontech, USA). Oocyte and one-cell embryo (2PN; monopronuclear, 1PN; tripronuclear,
3PN) donor groups did not overlap; mII oocytes and one-cell embryos came from different individuals (Table S3). For scRNA-seq,
there were seven mII oocyte donors (six were aged 22.5, 24.5, 25, <30, 27 and 31 years); six were Caucasian and one African Amer-
ican/Hispanic. There were six 2PN one-cell embryo donor couples; for two, male and female ages were respectively 36 and 38, and
40 and 50 (data are unavailable for the other couples) and five of the couples were Caucasian, with one Asian couple.
Single-cell RNA sequencing (scRNA-seq) of oocytes and one-cell embryos
RNA sequencing libraries from 14 mII oocytes and 13 bipronuclear (2PN) one-cell embryos (Table S3) were prepared using the Clon-
tech SMARTer Total RNA-Seq Kit Pico Input (V2) system (Takara Clontech). Briefly, total RNAwas liberated by lysis of single oocytes
or one-embryos in 0.8 ml lysis buffer supplemented with RNase inhibitor (both from Takara Clontech). The RNA was incubated with
SMART Pico N6 primers at 72C for 3 min and then subjected to first-strand synthesis with SMARTScribe reverse transcription using
a Pico v2 SMART adapter (template-switching oligo, TSO). After first strand synthesis, cDNA amplification was performed using Se-
qAmp DNA polymerase with Illumina barcoded adapters for 5 cycles of 15 sec at 98C; 15 sec at 55C; 15 sec at 68C, followed by
final extension for 2 min at 68C. Ribosomal cDNA was removed using the ZapR v2 and R-Probes v2, after which there was a second
round of cDNA amplification with SeqAmp DNA Polymerase for 15 cycles of 15 sec at 98C, 15 sec at 55C, and 30 sec at 68C, to
generate the final sequencing libraries. Libraries were analyzed using High-sensitivity D1000 ScreenTape and Agilent TapeStation
4200 (Agilent, USA). All sampleswere subjected to the same amplification protocol and produced indistinguishable final library yields,
with mean±SD respectively of 54.9±25.3 nM and 50.7±9.7 nM for oocytes and one-cell embryos; p=0.595). For next-generation
sequencing, libraries were combined at equimolar concentrations before loading onto an Illumina NovaSeq 6000 instrument (Illu-
mina, USA) for paired-end 100 (PE100) sequencing to generate an average of 
88.2 million raw read pairs per sample.
Ratiometric real-time-PCR (qPCR)
For qPCR, total RNA was liberated by lysis of single oocytes or one-embryos in 0.8 ml lysis buffer supplemented with RNase inhibitor
(Takara Clontech). Synthesis of cDNA employed a method modified from the Clontech SMARTer protocol using a total RNA-Seq Kit
Pico Input (V2). In brief, total RNA was incubated with SMART Pico Oligos mix v2 at 72C for 3 min and immediately chilled on ice for
2 min. Samples were then subjected to first-strand synthesis with SMARTSCribe RT (Takara Clontech). First step cDNA amplification
was performed using SeqAmpDNA Polymerase, skipping the addition of Illumina barcode adapters and incubating for 1min at 94C,
followed by 5 cycles of (15 sec at 98C; 15 sec at 55C; 15 sec at 68C) and then for 2 min at 68C. Second step amplification was
performed with SeqAmp DNA Polymerase and PCR2 Primer 2v2, incubating for 1 min at 94C, followed by 16 cycles of (15 sec at
98C; 15 sec at 55C; 30 sec at 68C) and cDNA stored at -20C until required. qPCR reactions were performed in a QuantStudio
7 (Thermo Fisher Scientific, UK) or ABI 7500 Real Time PCR System (Applied Biosystems, CA) in reactions (20 ml) containinge2 Cell Stem Cell 29, 209–216.e1–e4, February 3, 2022
ll
OPEN ACCESSBrief Report1-2 ml template cDNA, forward and reverse primers (100 nM each) and 12.5 ml of Power SYBR (ABI), using standard parameters. Data
for each target were obtained from n=3 or 4 biological replicates (i.e., independent single cells) collected on at least two days, and
included technical replicates of each biological replicate. Primer sequences are given in Table S2. Primer sets (Sigma-Merck) were
non-dimerizing under the conditions employed. Reactions lacking input cDNAwere used to verify absence of contamination in cock-
tail components. Steady state transcript levels were normalized with respect to internal reference, RNA18s5, or in most cases,
H3F3A.H3F3A gave mean cycle threshold (Ct) values (±s.e.m.) of 24.51±0.30 for mII oocytes (n=45 replicates over all, n=5 biological
replicates) and 24.56±0.33 for 2PN one-cell embryos (n=51 replicates over all, n=8 biological replicates); p=0.829 for mII oocytes vs
embryos.
QUANTIFICATION AND STATISTICAL ANALYSIS
Bioinformatic analysis
Raw read pairs weremapped onto the Human GRCh38 genome and Ensembl 92 genemodel using STAR (2.5.0a) (Dobin et al. 2013),
employing the following parameters: (–outFilterScoreMinOverLread 0.3 –outFilterMatchNminOverLread 0.3 –outSAMstrandField in-
tronMotif –outSAMtype BAM SortedByCoordinate). Post-alignment reads (BAMs) were further processed by Stringtie (1.3.6) (Pertea
et al., 2015) to remodel the transcriptome using default parameters and the Ensembl 92 gene model as base reference. Once the re-
assembly was complete, a gene-level count was performed using htseq-count (0.6.1p1) (Anders et al., 2015). For transcript-level
analysis, Stringtie was used to generate estimated counts for all detected transcripts.
Gene- and transcript-level count tables were imported into edgeR (Robinson et al., 2010) for downstream differential gene expres-
sion analysis. For gene-level analysis, genes expressed at low levels (<1 count per million, >13 samples) were filtered, retaining a final
total of 16,625 genes. Due to minimal differences detected in the library yields (see above), samples were subjected to trim-mean of
M values (TMM) normalization from the edgeR package (for details, see Robinson and Oshlack, 2010), a common normalization
method where a weighted trimmed mean of the log expression ratios is used to normalize sequencing depth. This has been shown
to be among themost robust methods for RNA-sequencing differential expression analysis, including single-cell studieswhere global
gene expression differences are large (Evans et al., 2018; Soneson and Robinson, 2018). A subtle normalization factor of 1.00±0.07
(mean±SD) was applied. Normalized gene abundance was listed as read counts per million of mapped reads (CPM) and determined
using the formula:
CPM = raw count=sample library size  normalization factor
To generate a visual overview of transcriptome profiles, we employed the Rtsne package (https://cran.r-project.org/web/
packages/Rtsne/Rtsne.pdf) to perform dimensionality reduction via the principal component analysis (PCA, ndims=50) coupled
with t-distributed stochastic neighbor embedding (t-SNE), using the normalized abundance (CPM) of all genes as input with the
default parameters. The advantage of using t-SNE over PCA is that it provides superior resolution with which to differentiate samples
in fewer dimensions (two projections were used here) and it is commonly used in single-cell RNA sequencing studies. Two mII oo-
cytes were excluded as outliers, and one 2PN one-cell embryo failed to amplify and had to be abandoned (Table S3). The remaining
24 samples (n=12 each oocytes and one-cell embryos) were re-normalized (mean normalization factor ±SD: 1.03±0.02 for mII oo-
cytes and 0.97±0.03 for one-cell embryos) to give a final average of 66.3±9.5 (±SD) million usable read pairs. t-SNE was also re-per-
formed using the same parameters. For differential gene expression, a generalized linear model (GLM) was applied to determine the
common, trend and gene-wise dispersions, and likelihood-ratio tests were employed to detect differential gene expression. Genes
with a false discovery rate (FDR) of <0.1 were considered differentially expressed.
For transcript-level analysis, transcripts from the 24 samples with low expression were first removed and estimated transcript level
counts TMM-normalized (normalization factor = 1.00±0.05) and CPMdetermined by limma-voom (Law et al., 2014). The total number
of transcripts that remained in the analysis was 42,230. We then calculated a gene-variability statistic to adjust for themean-variance
relationship using limma-voom and differential expression was determined using empirical Bayesian t-test (eBayes) from the limma
package (Ritchie et al., 2015). Transcripts with an FDR <0.1 were considered differentially expressed.
Pathway and upstream regulator analyes were performed using Qiagen Ingenuity Pathway Analysis (IPA) software using HGNC
gene symbols and a raw cut-off of p<0.05. Loci encompassing multiple genes were split in the analysis. Sashimi plots (Katz et al.,
2010; Wang et al., 2008) were generated using all mapped reads accumulated from oocytes and one-cell embryos using Integrative
Genome Viewer (IGV) version 2.4.19 (Robinson et al., 2011).
For bioinformatic analysis of data from Leng et al. (2019), 65 raw fastq files for individual blastomeres of biparental two-, four- and
eight-cell embryo were downloaded via the Gene Expression Omnibus (accession number GSE133854) using the fastq-dump com-
mand from the SRA toolkit, with the –split-files argument to split reads 1 & 2. The fastq files were then mapped onto the Human
GRCh38 genome, and gene abundance (Ensembl V97) counted using standard STAR 2.5.0a, as described above. The average us-
able read pair counts (uniquelymapped read pairs to gene) per sample was 24.47 ± 1.2 (s.e.m.) million. Count datawere imported into
edgeR (3.32.1) and normalized via TMM. Samples were quality checked using the plotMDS() function and samples SRR9645989,
SRR9645990, SRR9645991, SRR9645992, SRR9645993, SRR9645994, SRR9645995, SRR9645996 and SRR9645997 were
removed, leaving a total of 56 samples. Genes with low expression (<1 CPM inR 46 samples) were removed and counts re-normal-
ized with an average normalization factor of 1.012 ± 0.020. Differential gene expression across all stages was determined by fitting a
generalized log linear model followed by likelihood ratio test with a false discovery rate (FDR) cutoff of <0.1; 11,083 (out of 16,810)Cell Stem Cell 29, 209–216.e1–e4, February 3, 2022 e3
ll
OPEN ACCESS Brief Reportgenes were detected as being differentially expressed. The intersection of the differentially expressed genes in this dataset and the
top 200 up-regulated genes (ranked by FDR) from ourmII vs one-cell embryo dataset were extracted andwe found themajority of the
genes (153/200; 76.5%) were differentially expressed in both datasets. A heatmap of the 153 genes was drawn using the pHeatmap
package (1.0.12), employing z-scoring to reveal clusters of gene expression trajectories.
Statistical analysis
Each experiment used nR3 experimental samples, as indicated in the text and figure legends, and are presented as mean ± s.e.m.
Statistical differences between pairs of qPCR datasets were analyzed by two-tailed unpaired t-tests. Values of p <0.05 were consid-
ered statistically significant unless stated otherwise.e4 Cell Stem Cell 29, 209–216.e1–e4, February 3, 2022