Journal of Assisted Reproduction and Genetics (2024) 41:3357–3370
https://doi.org/10.1007/s10815-024-03278-4
GENETICS
Incidence of haploidy and triploidy in trophectoderm biopsies
of blastocysts derived from normally and abnormally fertilized
oocytes
Laura Girardi1 · Cristina Patassini1 · Jose Miravet Valenciano2 · Yoshimi Sato3 · Natalia Fagundes Cagnin4 ·
Jose Antonio Castellón5 · Francesco Cogo1 · Paola Zambon1 · David Blesa2 · Jorge Jimenez Almazan2 ·
Adedoyin Akinwole6 · Bruno Coprerski4 · Carmen Rubio2
Received: 25 July 2024 / Accepted: 24 September 2024 / Published online: 8 October 2024
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024
Abstract
Purpose We aimed to identify the correlation between morphological pronuclear (PN) status and the genetically determined
ploidy configuration in preimplantation embryos.
Methods A retrospective observational study was conducted on 1982 embryos displaying normal fertilization and 380
embryos showing an atypical PN pattern, tested for aneuploidies and ploidy status via preimplantation genetic testing (PGT)
between May 2019 and May 2024. Ploidy prediction was performed using a validated targeted-NGS approach and a proprietary bioinformatic pipeline analyzing SNPs B-allele frequency information. Ploidy results were obtained in relation to the
morphological PN pattern and further stratified by mode of PN observation, maternal age, and embryo quality parameters.
Results Abnormal ploidy results in 2PN-derived embryos were 1% (n = 20/1982): 0.8% showed triploidy and 0.2% haploidy.
Ploidy results in relation to PN number in atypical fertilization were as follows: 0PN (n = 150/380) associated with 87.3%
of diploidy, 8.7% of haploidy, and 4.0% of triploidy; 1PN-derived blastocysts (n = 73/153) were haploid in 47.7% of cases,
6.5% were triploid, and 45.7% diploid; 2.1PN (n = 23/280) and 3PN patterns (n = 54/280) predicted a triploid result in 34.8%
and 74.1% of cases, respectively. PN observation with time-lapse increased ploidy status predictivity from 28.3% to 80.4%
(p < 0.01) and reduced expected diploid rates to 19.6% (p < 0.01). Diploidy rate was higher for maternal age ≤ 35 years and
for morphologically high-grade embryos.
Conclusion Morphological PN check can be improved by incorporating ploidy analysis within the conventional PGT workflow. Euploid 2PN-derived embryos can be further selected removing haploids and triploids, and some atypical PN pattern
can be better classified.
Keywords Ploidy · Pronuclei · PGT · NGS · Fertilization · Time-lapse
Introduction
* Laura Girardi
laura.girardi@igenomix.com
1
Igenomix Italy, Reproductive Genetics, Marostica, Italy
2
Igenomix Spain Lab S.L.U., Research and Development,
Paterna, Spain
3
Igenomix Japan, Laboratory of Reproductive Genetics,
Tokyo, Japan
4
Igenomix Brazil, Laboratory of Genetic Medicine, São Paulo,
Brazil
5
Igenomix R&D, Medical Department, Paterna, Spain
6
Igenomix USA, Laboratory of Genetic Medicine, Miami, FL,
USA
Normal fertilization, defined as the presence of two pronuclei and two polar bodies, is generally assessed in the
in vitro fertilization (IVF) setting by microscopic evaluation of inseminated oocytes between 16- and 18-h postfertilization [1]. Oocytes without exactly two pronuclei
(PN)—0PN (non-pronuclear), 1PN (mono-pronuclear), or
more than 2PN configurations—are considered abnormally
fertilized (https://​atlas.​eshre.​eu/​es/​14546​65024​18111​90)
and not recommended for clinical use [1]. The practice of
excluding embryos with abnormal PN patterns is usually
justified based on the increased risk of unbalanced genomic
contribution from the gametes, uniquely predicted by PN
Vol.:(0123456789)
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number. The 1PN pattern outlines a single genomic contribution (haploid genome) and 3PN zygotes associate with
multiple genomic contributions (triploidy); however, the
increased practice of extended culture until the blastocyst
stage in all embryos, regardless of their fertilization status
[2], has revealed that many zygotes derived from abnormally fertilized oocytes (AFOs) form high-quality blastocysts [3–5]. When tested, these blastocysts can display a
diploid genetic composition [3, 6, 7] and can even result
in healthy pregnancies [5, 8, 9]. Of note, most of previous
studies were not supported by the use of time-lapse systems;
thus, the low concordance between PN number and ploidy
constitution could be also explained by PN misclassification. Similarly, previous studies showed that the presence
of 2PN does not guarantee a normal diploid composition,
reporting between 1 and 2% of non-diploid results at preimplantation stage [2, 9–13]. These studies highlight a limitation of standard static microscopic assessment in precisely
predicting embryo ploidy status from PN patterns [8, 14, 15]
and suggest the need to incorporate highly accurate genetic
methodologies able to detect ploidy defects in routine clinical practice. Remarkably, triploids can represent up to 10%
of abnormal products of conception following natural pregnancies [16, 17]. Therefore, the identification of abnormal
ploidy configurations at preimplantation level would avoid
the transfer of altered embryos that can lead to implantation
failure and miscarriages, significantly improving IVF outcomes. Moreover, the application of a molecular assessment
to complement morphological fertilization evaluations could
potentially reduce the rate of ploidy misclassification when
presumed atypical PN patterns are observed. Considering
that the reported incidence of 1PN oocytes varies between
1 and 8% and 3PN between 1 and 7% [2] (with some differences in PN configuration reportedly influenced by the
fertilization method, timing, and manner of observation),
it has been estimated that the exclusion of these zygotes
from clinical use reduces the pool of available zygotes in
a cohort by 10% [8, 11]. Unfortunately, limitations in the
use of zygotes with atypical PN have not allowed to collect large datasets from the application of ploidy testing so
far. Consequently, the incidence of ploidy abnormalities in
pre-implantation embryos displaying presumed abnormal
fertilization and the effective diploid rate remains relatively
unknown.
From a technical and analytical perspective, the recent
development of high-resolution pre-implantation genetic
testing (PGT) methods—including single nucleotide polymorphism (SNP) array [10, 11] and genotyping-by-sequencing approaches [7]—has contributed the expansion of PGT’s
clinical utility, which also includes ploidy status evaluation
[11, 18]. The level of information provided by incorporating
SNP analysis now supports the identification of anomalies
involving the entire complement of chromosomes, such as
Journal of Assisted Reproduction and Genetics (2024) 41:3357–3370
haploidy (one complete chromosome set lost) or triploidy
(one complete chromosome set gained). This methodological improvement may overcome technical limitations associated with standard PGT for aneuploidy (PGT-A) methods
based on low-pass genome sequencing, which identify the
presence of one or more extra or missing chromosomes (trisomy or monosomy) but cannot distinguish variations in the
whole chromosome complement using copy number analysis
only. In a previous study, we have validated a cost-effective
targeted NGS strategy able to detect ploidy defects within
the standard PGT-A workflow, without the need of additional samples to be analyzed [18].
In the present study, we present the clinical application
of the integrated PGT-A and ploidy strategy in a cohort of
apparently normally fertilized zygotes. Moreover, to shed
light on the correlation between PN number and ploidy
results, the same strategy was employed on a large dataset
of euploid embryos showing atypical pronuclear patterns,
providing additional useful information to guide the potential clinical use of these embryos.
Material and methods
Ethical approval
The patient signed consent for PGT-A and ploidy status
evaluation, and ethical approval for retrospective analysis
of completely anonymized data was obtained from the Drug
Research Ethics Committee at Valencia University Clinical
Hospital (project number: 2024/10).
Experimental design
This study evaluated the incidence of different ploidy configurations in normally- and abnormally fertilized blastocysts defined through morphological PN examination. After
fertilization assessment (Fig. 1a), samples reaching the blastocyst stage were biopsied and subjected to PGT-A analysis
to detect the presence (aneuploid) or absence (euploid) of
extra or missing chromosomes in the embryo (Fig. 1b). Only
samples initially designated as euploid were subjected to the
subsequent ploidy level assessment and further classified in
diploid, haploid, or triploid (Fig. 1c) [18].
IVF procedures
Oocytes fertilized by intracytoplasmic sperm injection
(ICSI) were individually cultured in standard benchtop
incubators or EmbryoScope™ time-lapse monitoring systems (EmbryoScope™, Vitrolife, Gothenburg, Sweden)
according to IVF clinic criteria. For standard and timelapse cultures, fertilization was assessed at 16–18 h as
Journal of Assisted Reproduction and Genetics (2024) 41:3357–3370
a
3359
FERTILIZATION ASSESSMENT WITH PN ANNOTATION
ABNORMAL FERTILIZATION
NORMAL FERTILIZATION
2PN
b
1PN
0PN
non-pronuclear monopronuclear
3PN
three equally
sized PN
FIRST LEVEL OF ANALYSIS: PGT FOR ANEUPLOIDIES
EUPLOID
c
2.1PN
one smaller
additional PN
ANEUPLOID
SECOND LEVEL OF ANALYSIS: PLOIDY STATUS EVALUATION
DIPLOID
HAPLOID
TRIPLOID
Fig. 1 Study design. Genetic evaluation of embryos in normal and
abnormal fertilization. a Fertilization assessment and pronuclear
(PN) annotation. Normal or abnormal fertilization status was defined
using microscopic PN examination. 2PN outlines normal fertilization. Atypical PN status, including the absence of PN (0PN), monopronuclear (1PN), or more than a 2PN pattern (2.1 with a smaller
additional PN or 3 evenly size PN), indicates abnormal fertilization.
b First level of genetic analysis: pre-implantation genetic testing for
aneuploidies (PGT-A). All zygotes reaching the blastocyst stage, irrespective of PN status, undergo analysis for aneuploidies detection via
trophectoderm biopsy and PGT analysis. c Second level of genetic
analysis: ploidy status evaluation. Samples displaying euploidy after
PGT-A undergo further analysis using a ploidy test to detect non-diploid configurations
recommended [1]. After the evaluation of fertilization,
all zygotes were classified as follows (Fig. 1a):
PGT‑A analysis
• Normally fertilized zygotes when presenting with two
equally sized PN
• Non-pronuclear or 0PN zygotes when static or time-
lapse observation failed to identify any PN
• Mono-pronuclear zygotes when only 1PN was present
• Tri-pronuclear zygotes when three equally sized PN
were identified (3PN), or a smaller additional PN was
detected (2.1PN)
Polar body annotation was not routinely performed
in all centers; thus, this information was not included in
this study. Embryos were cultured according to the standard practice of each clinic, and blastocyst quality was
assessed and defined according to the Gardner embryo
grading system (Gardner and Schoolcraft 1999). Trophectoderm biopsies were performed according to standard
IVF practice, irrespective of PN configuration.
The standard PGT-A protocol employed in this study was
previously validated for the detection of whole chromosome and segmental aneuploidies and mosaicism [19]. In
brief, individual biopsies were analyzed using a semiautomated protocol, which employs the Ion Chef™ system
and the Ion S5 sequencer (Thermo Fisher Scientific, MA,
USA). All blastocyst biopsies were subjected to DNA
extraction and whole-genome amplification (WGA) using
an Ion Reproseq PGS kit (Thermo Fisher). Template
preparation and chip loading were performed using the
Ion Chef system according to the manufacturer’s instructions. Sequencing data obtained by the S5™ Sequencer
were processed and sent to the Ion Reporter software 5.4
(Thermo Fisher Scientific) for analysis.
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Journal of Assisted Reproduction and Genetics (2024) 41:3357–3370
Ploidy detection with a target NGS protocol
and combined algorithm strategy
Ploidy detection with SNP array and a custom‑made
algorithm
Ploidy prediction with the target NGS protocol and a custom bioinformatic pipeline employed in this study was previously validated [18] obtaining 100% accuracy. Briefly,
trophectoderm biopsies were firstly amplified by employing the WGA protocol for shallow sequencing using an Ion
Reproseq PGS kit and then processed for library preparation and target enrichment with a specifically designed
multiplex PCR panel using the Ion Ampliseq Library kit
(Thermo Fisher Scientific). Template preparation, chip
loading, and sequencing were performed on the Ion ChefIon S5 XL system. Sequencing data processing with statistical modeling allowed ploidy classification, aggregating the statistical signal of BAFs from variants genotyped
across the whole genome. In addition, a second independent bioinformatic analysis was performed for each sample to obtain more reliable ploidy prediction, comparing
ploidy results between the two approaches. This second
methodology considers the distribution of the alternate
allele frequency (variant allele fraction: VAF) of the
detected variants. VAF is the percentage of sequencing
reads observed matching a specific DNA variant divided
by the overall coverage at that locus, thus a surrogate
measure of the proportion of DNA molecules carrying
the variant. For a diploid sample, the VAF density should
accumulate at values 0.5 and 1, detecting two peaks. For
a haploid sample, all variants should be homozygous with
a VAF peak at the value of 1. For a triploid sample, the
VAF would be distributed across 3 peaks at 0.33, 0.66,
and 1. The implementation of peak number detection was
carried out using the KDEUnivariate function from the
statsmodels.api library in Python, which is a univariate
kernel density estimator, using the VAF distribution as
input. This function allows us to calculate the number of
peaks in the density distribution. The values from the distributions of samples with known ploidy enabled us to set
the VAF values and the number of peaks associated with
each ploidy status, as well as for uncertain samples. If the
sample has 2 peaks and the maximum of one of them falls
within the VAF values of 0.4 and 0.6, the result is diploid.
If the sample has more than 2 peaks and has 1 peak with
a maximum between the VAF values of 0.2 and 0.4, and
another peak with a maximum between 0.55 and 0.85, the
result is triploid. If the sample has 1 peak with a maximum greater than 0.9, the result is haploid. If none of the
above conditions are met, the result is uncertain. Double
algorithm strategy accuracy was further validated on 100
samples with known triploid and diploid status, obtaining
a 100% sensitivity and 98.8% specificity, which leads to a
global accuracy of 99.0%. Intra-run and inter-run reproducibility resulted in 100% concordance.
Previous approach for aneuploidy and ploidy analysis
employed a common WGA step (REPLI-g® kit, QIAGEN)
followed by standard PGT-A for the detection of aneuploidies (see “PGT-A analysis” section) and Infinium Karyomapping protocol (Illumina) for ploidy. BeadChips scanning
with the NextSeq550 system (Illumina) defined B-allele frequency (BAF) data from 300,000 SNPs in the array. Custom algorithm criteria included the definition of BAF values
expected for diploid, haploid, or polyploid configurations
and associations with the presence of each of the five possible genotypes obtained for different ploidy results: BAF in
the ranges 0–0.1 and 0.9–1.0 for “AAA” and “BBB”; BAF in
the ranges 0.1–0.4 for “AAB,” BAF in the ranges 0.4–0.6 for
“AB,” and BAF in the ranges 0.6–0.9 for “ABB” genotypes,
respectively. The validity of the SNP array technique was
previously demonstrated by analyzing raw BAF data from
samples with known aneuploidy and ploidy configurations,
resulting in 100% accuracy (n = 26/26; 95%CI = 86.8–100).
Statistical analysis
Categorical variables were expressed in percentages and
compared using the Chi-square or Fisher exact test (as
corresponds). Nominal variables were analyzed using the
Cochran-Armitage test to detect trends, and the Wilcoxon
rank-sum test was used to compare continuous variables.
Multivariable logistic regression was used to analyze the
variables affecting ploidy status and predictivity; female
age, male age, number of pronuclei, platform, and incubator were used as covariates. The adjusted odds ratio and the
corresponding confidence interval for each coefficient were
computed. Statistical software R (version 4.2) was used to
perform the statistical analysis. The main libraries used were
tidyverse (version 2.0.0) [20], DescTools (version 0.99.54)
[21] for the Cochran test, and rms (version 6.8–0) [22] for
the multivariate analysis.
Results
Incidence of ploidy alterations in blastocysts
from normally fertilized oocytes
We collected 1982 samples from 2PN-derived blastocysts
or presumed normally fertilized embryos labeled as euploids
after aneuploidy screening for ploidy analysis. This dataset
possessed a mean female age of 36.20 ± 4.44. The target
NGS-based workflow revealed a frequency of abnormal
ploidy results detected for euploid normally fertilized blastocyst of 1.0% (n = 20/1982; 95% CI = 0.62–1.55), 0.2%
Journal of Assisted Reproduction and Genetics (2024) 41:3357–3370
0.8%
(16/1982)
100
0.2%
(4/1982)
80
Proportion (%)
2PN-derived zygotes
N=1982
60
99.0%
(1962/1982)
40
20
0
DIPLOID
HAPLOID
TRIPLOID
Fig. 2 Global distribution of ploidy results in normally fertilized
zygotes. Ploidy status distribution in 1982 2PN-derived blastocysts or
presumed normally fertilized embryos
displayed haploidy (n = 4/1982; 95% CI = 0.05–0.52), and
0.8% triploidy (n = 16/1982; 95% CI = 0.46–1.31) (Fig. 2).
Genetic characterization of embryos with atypical
PN patterns
Ploidy status detection with target NGS and inter‑platform
comparison with SNP‑array
A total of 508 AFO-derived embryos were initially collected
for PGT-A analysis, of these 246 (41.1%) displayed euploidy
and were further tested for ploidy abnormalities via targeted
NGS analysis (Fig. 3A).
0PN-derived blastocysts (n = 106) displayed diploidy in
84.9% of cases (n = 90/106; 95% CI = 76.65–91.12), haploidy
in 10.4% of cases (n = 11/106; 95% CI = 5.30–17.81), and
triploidy in 4.7% of cases (n = 5/106; 95% CI = 1.55–10.67)
(Fig. 3A). 1PN-derived blastocysts (n = 94) displayed diploidy in 48.9% of cases (n = 46/94; 95% CI = 385–59.5), haploidy in 44.7% of cases (n = 42/94; 95% CI = 34.41–55.29),
and triploidy in 6.4% (n = 6/94; 95% CI = 2.4–13.4). Finally,
33.3% (n = 5/15; 95% CI = 11.8–61.6) and 74.2% (n = 23/31;
95% CI = 55.4–88.1) of polyploid profiles were reported
when detecting 2.1PN and 3PN, respectively; the remaining showed diploidy (Fig. 3A). As previously performed in
the technical validation phase of the target NGS panel [18],
we confirmed the validity of the rates reported for AFOderived embryos in an inter-platform comparison with a
validated SNP-array platform composed of 300,000 SNPs.
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An independent dataset of 319 AFO-derived blastocysts
was collected, resulting in 42.0% of euploid samples, which
were additionally tested for ploidy status characterization
(Fig. 3B). In this group, 0PN-derived blastocysts (n = 44)
displayed diploidy in 93.2% of cases (n = 41/44; 95%
CI = 81.34–98.57), haploidy in only 4.5% of cases (n = 2/44;
95% CI = 0.56–15.47), and triploidy in 2.3% of cases
(n = 1/44; 95% CI = 0.06–12.02) (Fig. 3B). 1PN-derived
blastocysts (n = 59) displayed all possible ploidy configurations; only 52.5% displayed haploidy (n = 31/59; 95%
CI = 39.12–65.70) (Fig. 3B). 2.1PN (n = 8) and 3PN (n = 23)
blastocysts comprised 37.5% (n = 3/8; 95% CI = 8.52–75.51)
and 73.9% (n = 17/23; 95% CI = 51.59–89.77) of polyploid
configurations, respectively (Fig. 3B). Figure 3A and B provide also examples of SNP array and NGS profiles displaying the ploidy configurations detected in this study.
The Chi-square test used to compare ploidy rates in each
abnormally fertilized category provided no significant differences between the SNP-array and NGS platform, enabling a
more comprehensive analysis of a single dataset. Moreover,
logistic regression analysis performed on normal and abnormal results demonstrated PN number as the only variable
significantly correlated with the ploidy status; female age,
male age, and the platform employed did not have any association with ploidy results (Online Resource 1).
Global distribution of ploidy configurations in abnormally
fertilized zygotes
To provide a comprehensive view of the predictive value of
embryo ploidy status when assessing PN configuration, we
evaluated genetic ploidy results on a large dataset of 380
embryos, designated as euploid, obtained by merging data
from the SNP array and NGS platforms (Fig. 4). This group
possessed a mean female age of 37.34 ± 4.38 years.
When conventional or time-lapse observation failed
to identify any PN (0PN), we observed a rate of genetic
diploidy of 87.3% (n = 131/150; 95% CI = 80.93–92.20),
haploidy of 8.7% (n = 13/150; 95% CI = 4.70–14.36), and
triploidy of 4.0% (n = 6/150; 95% CI = 1.48–8.50) (Fig. 4A).
When identifying a 1PN pattern with standard or timelapse observation, we observed an association with an
expected haploid result of only 47.7% (n = 73/153; 95%
CI = 39.58–55.93) and a value of 6.5% for abnormal triploid
configurations (n = 10/153; 95% CI = 3.18–11.69). Remarkably, 45.8% (n = 70/153; 95% CI = 37.68–53.99) of 1PNderived embryos displayed diploidy (Fig. 4B).
The presence of 1 smaller PN, in addition to 2 equally
sized PNs, indicated the presence of an extrachromosomal
set in only 34.8% of cases (n = 8/23; 95% CI = 16.38–57.27),
and we observed a significantly higher expected diploid rate for 2.1PN-derived zygotes compared to the 3PN
group, reaching 65.2% (n = 15/23; 95% CI = 42.27–83.62)
3362
Journal of Assisted Reproduction and Genetics (2024) 41:3357–3370
a
b
OPN
1PN
2.1PN
3PN
OPN
1PN
2.1PN
3PN
DIPLOID
84.9%
n=90/106
48.9%
n=46/94
66.7%
n=10/15
25.8%
n=8/31
DIPLOID
93.2%
n=41/44
40.7%
n=24/59
62.5%
n=5/8
26.1%
n=6/23
HAPLOID
10.4%
n=11/106
44.7%
n=42/94
0%
n=0
0%
n=0
HAPLOID
4.5%
n=2/44
52.5%
n=31/59
0%
n=0
0%
n=0
TRIPLOID
4.7%
n=5/106
6.4%
n=6/94
33.3%
n=5/15
74.2%
n=23/31
TRIPLOID
2.3%
n=1/44
6.8%
n=4/59
37.5%
n=3/8
73.9%
n=17/23
SNP-array profiles
NGS profiles
Diploid
BAF
Density
Diploid
0.5
1.0
0.5
1.0
0.5
1.0
Haploid
BAF
Density
Haploid
Triploid
BAF
Density
Triploid
VAF variant allele fraction
chromosomes
Fig. 3 Interplatform comparison of ploidy results distribution. Ploidy
configurations (diploid, haploid, and triploid) obtained with the two
technologies employed in the study reported according to embryo
pronuclear (PN) status: 0PN, 1PN, 2.1PN, and 3PN. a Top: next-generation sequencing (NGS)-based ploidy results obtained using a 357
SNPs panel reported according to embryo PN pattern. Bottom: exam-
ples of NGS profiles displaying variant allele fraction (VAF) distribution obtained for different ploidy configurations reported in the study.
b Top: SNP array-based ploidy results obtained by analyzing 300,000
SNPs and reported according to embryo PN status. Bottom: examples
of SNP array profiles displaying B-allele frequency (BAF) distributions obtained for different ploidy configurations reported in the study
(Fig. 4C). Conversely, the detection of 3 evenly-sized PNs
accurately predicted a triploid configuration in 74.1% of
cases (n = 40/54; 95% CI = 60.35–85.04) (Fig. 4D). The
Fisher test confirmed a significant association between
ploidy results and the presence of 2.1PN or 3PN (p < 0.01).
2.1PN pattern, and 24 displaying 3 evenly sized PNs—was
assessed and compared to the same PN pattern examined
through static or unknown observations. Confirmed triploid configurations, obtained from 3PN-derived embryos,
increased from 63.3% (n = 19/30; 95% CI = 43.86–80.07)
using static/unknown PN observation to 87.5% (n = 21/24;
95% CI = 67.64–97.34) in the time-lapse group (p = 0.09).
Similarly, confirmed haploid results increased to 68.7%
(n = 11/16; 95% CI = 41.34–88.98) in 1PN-derived
embryos defined by time-lapse, much higher when compared to non-time-lapse embryos (46.4%, n = 58/125; 95%
CI = 37.44–55.53) (p = 0.16). In addition, unlike static
observation, we reported no triploid results when visualizing only 1PNs. Finally, PN prediction power towards a
haploid configuration increased from 3.5% (n = 5/142; 95%
CI = 1.15–8.03) to 100% (n = 8/8; 95% CI = 63.06–100.00)
in static/unknown vs. time-lapse observation, respectively,
in 0PN-derived blastocysts (p < 0.01) (Table 1).
Overall, when confirming PN status by time-lapse, the
accuracy in predicting the correspondent abnormal ploidy
Impact of time‑lapse culture on the correlation
with ploidy results
Ploidy predictivity from pronuclear status
After obtaining the expected global ploidy results in a typical IVF scenario where both conventional and time-lapse
observation are adopted, we highlighted how time-lapse
incubators could influence fertilization assessment and
ploidy correlation by evaluating a small subset of AFOsderived embryos, where the assistance of time-lapse systems
confirmed PN visualization.
Using our targeted NGS platform, the ploidy status of
51 zygotes—8 without PNs (0PN), 16 with 1PN, 3 with
Journal of Assisted Reproduction and Genetics (2024) 41:3357–3370
3363
b
a
0PN embryos
N=150
8.7%
(13/150)
c
1PN embryos
N= 153
d
2.1PN embryos
N=23
3PN embryos
N=54
6.5%
4.0%
(10/153)
(6/150)
45.8%
(70/153)
65.2%
(73/153)
DIPLOID
(14/54)
(8/23)
47.7%
87.3%
(131/150)
25.9%
34.8%
(15/23)
HAPLOID
Fig. 4 Global distribution of ploidy configurations in abnormally fertilized zygotes with different pronuclear statuses. Overview of ploidy
results distribution (diploid, haploid, and triploid) reported for each
74.1%
(40/54)
TRIPLOID
group of embryos concerning the number of pronuclei (PN) detected.
Ploidy results in a 0PN-derived embryos, b 1PN-derived embryos, c
2.1PN-derived embryos, and d 3PN-derived embryos
Table 1 Ploidy predictivity for
pronuclear status according to
the type of PN observation
Type of observation
Confirmed triploidy from 3PN-derived embryos % (n)
Confirmed haploidy from 1PN-derived embryos % (n)
Confirmed triploidy from 2.1PN-derived embryos % (n)
Confirmed haploidy from 0PN-derived embryos % (n)
Total PN prediction power % (n)
p-value
Static or unknown
Time-lapse
63.3% (19/30)
45.2% (62/137)
35.0% (7/20)
3.5% (5/142)
28.3% (93/329)
87.5% (21/24)
68.8% (11/16)
33.3% (1/3)
100.0% (8/8)
80.4% (41/51)
NS
NS
NS
< 0.01*
< 0.01*
PN pronuclear
*
p-values are significant below 0.05
status significantly increased from 28.3% (n = 93/329;
95% CI = 23.46–33.47) to 80.4% (n = 40/51; 95%
CI = 66.88–90.18) (p < 0.01) (Table 1). Multivariate logistic regression analysis reported the PN number and the type
of PN observation (time-lapse or conventional) as variables
significantly associated with ploidy status predictivity
(Online Resource 2).
Expected diploidy rates
Time-lapse PN observation also impacted the overall expected
diploidy rates of abnormally fertilized zygotes, with a significant overall decrease from 66.9% with static/unknown evaluation (n = 220/329; 95% CI = 61.50–71.94) to only 19.6%
with time-lapse (n = 10/51; 95% CI = 9.82–33.12) (p < 0.01).
In detail, when visualizing 0PN with time-lapse, no diploid
results were reported, significantly different to the diploidy rate
of 92.3% (n = 131/142) expected for static/unknown observation (p < 0.01) (Fig. 5). Similarly, diploidy rates of 1PN and
3PN-derived zygotes decreased from 47.5% (n = 65/137;
95% CI = 38.86–56.15) and 36.7% (n = 11/30; 95%
CI = 19.93–56.14) to 31.3% (n = 5/16; 95% CI = 11.02–58.66)
and 12.5% (n = 3/24; 95% CI = 2.66–32.36), respectively
(Fig. 5); however, differences reported in this dataset did not
reach statistical significance. Regarding 2.1PN, comparable diploid rates have been reported with (66.7%, 2/3; 95%
CI = 9.43–99.16) or without time-lapse observation (65.0%,
13/20; 95% CI = 40.78–84.61).
Impact of maternal age and embryo morphology
on ploidy results
With the aim of providing additional useful elements
to guide IVF clinics in the management of atypical
3364
Journal of Assisted Reproduction and Genetics (2024) 41:3357–3370
b
a
0PN embryos
N=8
N=44
c
d
2.1PN embryos
N=3
1PN embryos
N=16
3PN embryos
N=24
12.5%
31.3%
(5/16)
100.0%
(8/8)
(1/3)
68.7%
66.7%
(11/16)
DIPLOID
(3/24)
33.3%
(2/3)
HAPLOID
87.5%
(21/24)
TRIPLOID
Fig. 5 Ploidy constitution of abnormally fertilized zygotes confirmed
by time-lapse observation. Overview of ploidy result distribution
(diploid, haploid, and triploid) reported for a 0PN, b 1PN, c 2.1PN,
and d 3PN-derived embryos when identifying pronuclear (PN) status
with time-lapse instruments
pronucleated embryos, we also evaluated the potential effect
of female age on expected diploid results. When exploring
the distribution of ploidy results according to a single cutoff at 35 years, the chi-square test revealed a significantly
higher proportion of diploidy for the group aged ≤ 35 years
(74.8%, n = 92/123; 95% CI = 66.17–82.19) compared
to the group aged > 35 years (53.7%, n = 138/257; 95%
CI = 47.39–59.91) (p < 0.01) (Fig. 6 and Online Resource
3). Further analysis confirmed the association between
ploidy results and advanced female age within the main age
classes: ≤ 35 years, between 36 and 38, between 39 and 40,
and above 40 years old. Despite the slight increase for the
39–40 age class, the Cochran Armitage test displayed a significant decrease in the diploidy rate with increasing female
age, going from 74.8% in patients ≤ 35 years (n = 92/123;
95% CI = 66.17–82.19) to 46.2% in patients over 40 years
(n = 36/78; 95% CI = 34.79–57.81) (p-value < 0.01) (Online
Resource 3).
When considering embryo morphological evaluations,
Cochran Armitage test revealed a significant link between
diploidy rates and TE quality with a decreasing trend going
from 74.2% (n = 95/128; 95% CI = 65.74–81.54) to 55.9%
(n = 109/195; 95% CI = 48.63–62.99) and 45.5% (n = 26/57;
95% CI = 32.36–59.34) for embryos classified with TE
grade as A, B, and C, respectively (p < 0.01). Similarly, a
decreasing trend was reported in relation to inner cell mass
(ICM) quality, moving from 68.3% in grade A (n = 110/161;
95% CI = 60.54–75.42) to 55.0% (n = 100/182; 95%
CI = 47.41–62.31) in grade B and 54.1% (n = 20/37; 95%
CI = 36.92–70.51) in grade C (p = 0.02). No clear association
was reported for embryo expansion degree (Online Resource
3).
A complete overview of the samples included in the study
is reported in Online Resource 4.
Discussion
In this work, we report the clinical application of a validated
targeted sequencing protocol to detect ploidy abnormalities
in human blastocysts as a complementary strategy to conventional PGT-A analysis. In addition to normally fertilized
zygotes, we analyzed one of the largest datasets of atypical
pronucleated zygotes, providing a comprehensive description of abnormal ploidy rates for each category in relation
to PN number. Unlike other studies, two independently validated methodologies support our findings (targeted NGS and
SNP array platforms), demonstrating consistent proportions
of ploidy abnormalities in equivalent abnormal PN categories. Aggregated data from both datasets provided meaningful data regarding the expected correlation rates of PN
configuration and embryo ploidy status, adding new insights
thanks to time-lapse assistance.
We first applied the NGS-based ploidy strategy to a
dataset of normally fertilized blastocysts. As previously
suggested [9], our study confirmed that two equally sized
PNs cannot guarantee a diploid chromosome constitution
composed of two balanced sets of chromosomes. Indeed,
we obtained abnormal ploidy results in 1% of cases despite
a 2PN morphological observation, being triploidy the most
Journal of Assisted Reproduction and Genetics (2024) 41:3357–3370
a
100
80
Proportion (%)
Fig. 6 Maternal age and embryo
morphological grade correlation
with ploidy results. a Ploidy
result distribution in abnormally
fertilized zygotes according
to a maternal age, above and
below a cut-off at 35 years. b
Trophectoderm quality and c
inner cell mass quality. Samples
were divided into grades: A,
good; B, fair; C, poor, according
to Gardner’s embryo grading
system
3365
60
40
20
0
N=257
N=123
Maternal age
b
100
80
Proportion (%)
60
40
20
0
N=128
N=195
N=57
A
B
C
Trophectoderm grade
c
100
80
Proportion (%)
60
40
20
0
N=161
N=182
N=37
A
B
C
Inner cell mass grade
DIPLOID
HAPLOID
3366
prevalent ploidy abnormality among normally fertilized
oocytes (0.8%). Regarding possible explanations, mechanisms of diandry or digyny could give rise to triploidy associated with a normal 2PN composition [23]. Consequently,
a small proportion of ploidy anomalies would remain undetected without using an advanced SNP-based methodology
that can detect non-diploid configurations.
As far as the overall abnormal ploidy rates, our results
agree with previous studies performed with different SNPbased platforms, displaying similar rates of ploidy abnormalities [11, 13]. Furthermore, the rate of abnormalities we
detected at the blastocysts stage is similar to the obtained in
natural pregnancies, where triploidy accounts for 1–3% of all
recognized conceptuses [24–26]. These findings confirm the
elevated level of accuracy of the proposed methodology in
detecting aberrant ploidy status without overestimating their
incidence in IVF embryos and offering an additional level
of clinical utility in IVF cycles already directed to PGT-A.
Indeed, from a clinical standpoint, the impact of ploidy
abnormalities on pregnancy outcomes is well known, particularly their association with unfavorable events like
implantation failure, miscarriage, and molar pregnancy [27].
Of note, triploidy represents one of the most frequent chromosome abnormalities detected in early pregnancy losses,
ranging from 9–10% in products of conception analysis [16,
28] to 13.1% in chorionic villus sampling [29] from natural
conceptions. Interestingly, ploidy status assessment would
allow the detection of these abnormalities at the pre-implantation level, providing a deselection option before embryo
transfer, potentially decreasing the rate of miscarriages and
improving overall IVF treatment outcomes.
Using the same combined PGT-A and ploidy approach,
we further explored the correlation between PN status and
ploidy configuration in 380 zygotes, previously reported
as euploid, showing an atypical PN number. As previously
reported, an initial classification of abnormal fertilization
based exclusively on PN observation does not always predict
an abnormal ploidy configuration. 0PN-derived blastocysts
classified mainly according to conventional PN observation
displayed the highest rate of diploidy after ploidy status
evaluation. We reported more than 87% of 0PN-derived
blastocysts as diploid, in line with previous genetic findings
reporting more than 75% of 0PN embryos as bi-parentally
diploid, potentially able to increase live birth rates in PGT
cycles for monogenic disorders (PGT-M) [7]. These results
are also compatible with clinical outcomes reported from the
transfer of these embryos which showed cumulative pregnancy rates, live birth rates, and neonatal outcomes comparable to 2PN embryos [30, 31]. However, it is interesting
to note that when we isolated authentic 0PN embryos, confirmed by time-lapse observation, none of them produced
a diploid profile (8/8 haploids), confirming the ability of
time-lapse monitoring systems in improving fertilization
Journal of Assisted Reproduction and Genetics (2024) 41:3357–3370
assessment [32] and potentially reducing the rate of 0PN
embryos reported in the IVF lab [33].
Indeed, from our and previous evidence combining
static 0PN observation and molecular analysis, we can now
assume that most presumed 0PN embryos produced in the
IVF setting represent normally fertilized zygotes carrying
2PN that miss detection during fertilization evaluations,
due to delayed time of PN observation or earlier pronuclear
breakdown [33, 34]. This context of static PN assessment
can be improved by the incorporation of an additional level
of SNP-based genetic testing, if 0PN zygotes are intended
for transfer, offering the possibility to confirm that PN check
was missed and excluding ploidy abnormalities which are
in any case higher compared to confirmed 2PN embryos.
Focusing on 1PN-derived blastocysts, we found them
associated with all possible ploidy profiles, with a moderate diploidy rate of 43%. Again, our data agree with previous studies reporting diploid results and healthy live births
from these putative abnormally fertilized embryos [8, 9,
30, 35, 36]. In addition to available evidence, we reported
a higher sample size, strength by independent confirmation using a robust SNP-array platform, and separated in
conventional and time-lapse observation, overall providing
realistic expected ploidy results. Indeed, a different scenario
is observed both in terms of type and rates of ploidy configurations in 1PN zygotes cultured in time-lapse, showing
no triploidy, a decrease in diploidy from 44 to 31%, and an
overall improvement in the association between ploidy in
relation to PN status. In terms of possible reasons for the
1PN pattern observations, various studies have reported that
the 1PN pattern can originate from parthenogenic oocyte
activation, asynchronous PN formation [37, 38], early pronuclei fusion [39], or premature PN breakdown [40]; however, in terms of ploidy results (except for parthenogenic
activation), all mechanisms can potentially result in diploid
embryos identified with genetic testing. Conversely, asynchronous PN appearance could explain the small percentage
of triploids that are detected in static, but not in time-lapse
observation. Considering the moderate diploidy rate and live
birth rate reported so far, some attempts suggesting revision of current guidelines on the clinical use on 0PN and
1PN embryos have been made by several authors [2, 30, 36]
and scientific societies [41]. Considering the moderate diploidy rate reported for 1PN confirmed by time-lapse, ploidy
evaluation can be considered appropriate, however, even if
not routinely performed in an IVF/PGT setting, bi-parental
diploid confirmation is advisable before embryo transfer, to
exclude the risk of uniparental disomy.
Focusing on atypical configurations displaying more than
2PN, we showed that the appearance of 3PNs detected with
both conventional and time-lapse observation was associated with a higher prediction of triploid zygotes compared
to 2.1PN, even if between 14 and 26% of 3PN embryos
Journal of Assisted Reproduction and Genetics (2024) 41:3357–3370
display diploidy, similar to the few previously reported
findings [15]. From a mechanistic point of view, self-correcting events arising during in vitro embryo development
could explain these findings [15, 42, 43], especially for
3PN embryos derived from ICSI [12]. However, based on
the high rate of triploids we reported both with static and
time-lapse observation, the chance of having a diploid result
from these embryos is extremely low and may not justify
their routine testing, even more so in the advanced maternal
age population who could suffer from a combined effect of
aneuploidy and ploidy abnormalities. Until more evidence
regarding their clinical outcomes will be available [44],
these embryos should be treated with caution and separately
from the other abnormally fertilized categories.
Different considerations should be made for 2.1PN
zygotes, which behave differently from 3PNs in terms of ability to predict a triploid result. Indeed, most displayed diploidy
(65%) after genetic assessment, similar to what was reported
in previous studies [8]. Our and previous data would confirm
that the identification of an additional smaller PN does not
perfectly correlate with the detection of an additional set of
chromosomes at the blastocyst stage [45] and that the atypical pattern may become corrected through the “extrusion” of
the micro-pronucleus or by other self-correcting mechanisms
or could correspond to the presence of a vacuole. It is worth
noting that this scenario is supported also by the few diploid
cases confirmed by time-lapse, which should be however confirmed in future studies with higher sample size. On the other
side, we cannot exclude that a proportion of 3PN embryos
could have been wrongly annotated as 2.1PN, explaining the
higher triploid rate reported compared with time-lapse studies
[46] and raising hopes of even higher diploidy rates. Overall,
considering our and other diploidy rates and previous clinical
outcomes [8, 45–47], 2.1PN embryos should be considered
separately from 3PN embryos and subjected to additional levels of genetic assessment instead of being directly discarded.
Furthermore, to increase the comfort level in transferring
these embryos, additional confirmatory analysis using parental DNA can confirm the bi-parental origin of diploid results.
Looking at additional patient and embryo features which
could help in predicting expected diploidy rates in abnormally fertilized zygotes, we reported a significant decrease
in diploid results in older patients and poor-quality embryos.
While previous studies had highlighted the predominant
maternal origin of the extra haploid genome in triploids [10,
11, 24, 48] and higher female age in 3PN fertilization [49],
we believe we now report one of the first evidence of correlation between ploidy abnormalities and advanced female
age in pre-implantation embryos with abnormal fertilization, suggesting a possible age-dependent mechanism behind
haploidy and triploidy. Regarding trophectoderm and ICM
morphology, we found them significantly associated with the
diploidy rates, with grade A embryos displaying the highest
3367
number of diploid configurations followed by grades B and
C. The correlation between blastocyst quality and aneuploidies has been well documented, with previous studies reporting morphological features as predictive of an altered chromosomal constitution [50] and the presence of aneuploidies
as strongly associated with blastocyst morphology [51, 52].
Likewise, we speculate that whole genome ploidy alterations
could impact ICM and trophectoderm quality.
Taken together, our results provide supporting evidence
for the incorporation of an additional molecular approach
into the standard PGT-A workflow complementing conventional fertilization check to confirm embryo ploidy status.
This strategy would overall improve clinical outcomes of
2PN euploid embryos, by deselecting non-diploid results and
avoiding unfavorable outcomes related to triploid and haploid pregnancies. Regarding embryos showing atypical PN
numbers, we showed that discordance with the genetically
determined ploidy constitution is higher with static visualization, compared to time-lapse, suggesting that PN misclassification, rather than other biological mechanisms, could
be the major cause of the high rate of diploidy reported.
Consequently, molecular ploidy check could be beneficial in
this setting giving the opportunity to rescue some categories
of atypical pronucleated embryos in particular 0PN, 1PN,
and 2.1. On the other hand, the higher correlation between
ploidy status and PN number reported with time-lapse suggests a lower benefit of ploidy testing confined to a minimal
proportion of diploid zygotes in 1PN and 2.1PN categories,
where diploid results persist. Despite this, specific categories of patients which suffer of diminished availability of
embryos, including poor prognosis patients, or couples with
monogenic conditions and in general when no euploid/diploid or unaffected embryos are available, could benefit of the
molecular testing of abnormally fertilized embryos.
Regarding the main limitations of this study, some rare
forms of ploidy abnormalities like uniparental disomy, when
two different chromosomes set are inherited from one parent, cannot be distinguished with our and other NGS platform using only SNP BAF variations and without parental
DNA analysis. Similarly, extremely rare polyploid conditions
where multiple identical chromosome sets are present cannot be distinguished from diploidy neither by genome-wide
haplotyping. In addition, another potential limitation of
ploidy testing is represented by a rare biological phenomenon called mixoploidy or ploidy mosaicism [53, 54]. It has
been reported that aberrant segregation of an entire parental
genome into distinct cell lineages during zygotic divisions
(“heterogoneic division”), can result in a mixture of cells
with different ploidy constitution in the embryo (e.g., diploid/
triploid mixoploidy) [53, 55]. From a technical perspective,
the accurate detection of ploidy mosaicism with PGT methods would require further preclinical and clinical validations,
which are not available so far. Moreover, any PGT test would
3368
inevitably remain affected by sampling bias, where the ploidy
status of single trophectoderm biopsy may not be representative of the entire embryo, especially if ploidy mosaicism is
present. From an embryological perspective, for those centers
lacking time-lapse observation, embryo fertilization assessment was based uniquely on embryologist experience. Even
though we reported new meaningful information regarding
PN predictivity when PN check is performed via time-lapse,
future studies should confirm these results using higher samples size. Finally, we only addressed the characterization of
ploidy rates after normal and abnormal fertilization; future
studies should focus on the clinical follow-up after the transfer of euploid-diploid embryos.
In conclusion, as reported in the present study, the transition towards more sensitive molecular strategies based
on SNP information allowed the incorporation of ploidy
assessment into the PGT setting, where it displayed evidence of improved clinical utility and benefit at several levels. Regarding the possibility of testing and using embryos
with PN number other than 2, IVF clinics can benefit from
the significant information we have produced to guide reproductive counselling on expected diploidy rates based on the
methodology of PN assessment, and considering also maternal age and embryo morphological grade, with the final aim
of improving patients’ reproductive options and decisionmaking in the use of abnormally fertilized embryos. However, considering the heterogeneity of techniques and settings, it is advisable that IVF clinics that want to establish
an internal program for rescuing embryos with atypical PN
numbers determine their own rate of diploidy for different
PN conditions and set specific indications for ploidy testing.
Supplementary Information The online version contains supplementary material available at https://d​ oi.o​ rg/1​ 0.1​ 007/s​ 10815-0​ 24-0​ 3278-4.
Acknowledgements The authors acknowledge Igenomix staff from
the R&D, laboratory and diagnosis departments of Italy, Japan, USA,
Brazil, and Spain.
Funding This work was supported by Igenomix.
Data Availability The data underlying this article are available in the
article and its online supplementary material.
Declarations
Conflict of interest All authors are employees of Igenomix, a company
providing reproductive genetic services, part of Vitrolife Group.
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