Transcriptomic signatures in trophectoderm and inner cell mass of human blastocysts with different expectations of pregnancy

doi:10.21203/rs.3.rs-1732754/v1

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Transcriptomic signatures in trophectoderm and inner cell mass of human blastocysts with different expectations of pregnancy

https://doi.org/10.21203/rs.3.rs-1732754/v1

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Selection of high-quality embryos is important to achieve successful pregnancy in assisted reproductive technology (ART). Recently, it has been debated whether RNA-sequencing (RNA-seq) should be applied to ART to predict embryo quality. However, the information on which genes can serve as markers for pregnant expectancy is limited. Furthermore, there is no information on which transcriptome of trophectoderm (TE) or inner cell mass (ICM) is more highly correlated with pregnant expectancy. Here we performed RNA-seq analysis for TE and ICM, respectively, in human blastocysts of which the expectation of pregnancy was retrospectively determined by the clinical outcomes of 1,890 cases of frozen-thawed blastocyst transfer. We identified dozens of genes that were correlated with the expected pregnancy rate in ICM and TE, respectively, with a larger number of genes identified in TE. Importantly, downregulated genes in TE of blastocysts that were estimated to have lower expectation of pregnancy included tight junction-related genes, such as CXADR, CLDN10, and ATP1B1, which were implicated in peri-implantation development. Additionally, we showed that aneuploidy estimation by RNA-seq datasets does not correlate with pregnancy expectation. Our study thus provides an expanded list of candidate genes for prediction of pregnancy in human blastocyst embryos.

In assisted reproductive technology (ART), cleavage-stage embryos (2–3 days post-fertilization) and blastocysts (5–6 days post-fertilization) are generally used for embryo transfer, with the latter exhibiting a higher ratio of pregnancy¹. With the advent of time-lapse imaging systems, temporal morphological assessment is currently the primary method for embryo quality evaluation². Reportedly, the degree of blastocoele expansion and re-expansion correlates with the ratio of live birth in fresh and frozen-thawed blastocyst transfer cycles^{3, 4}. In addition, time-lapse monitoring revealed that blastocyst collapse in vitro correlates with implantation and pregnancy outcomes⁵. Thus, temporal morphological parameters are considered beneficial for increasing pregnancy outcomes.

Nonetheless, it remains controversial whether the morphology of trophectoderm (TE) or inner cell mass (ICM) correlates better with pregnancy outcomes. Several studies have reported that the morphology of TE is more correlated with pregnancy outcomes than that of ICM^6–9. For example, Hill et al. 2013⁷ conducted a retrospective analysis of the pregnancy outcomes of 694 frozen-thawed blastocysts. They showed that the grading of TE, but not of ICM, was significantly correlated with the ratios of implantation and live birth. In contrast, other studies have revealed that ICM correlates better with pregnancy outcomes than TE^{10, 11}. For example, Richter et al.¹⁰ conducted a prospective observational study with 178 blastocysts and revealed that quantitative measurements of the size and shape of ICM were highly indicative of implantation potential, whereas the cell numbers of TE were unrelated to implantation rates. Therefore, it is essential to gain molecular insights into the ICM and TE of blastocysts using pregnancy expectation estimation.

Pre-implantation genetic testing for aneuploidy (PGT-A) has also been used in clinical practice to prevent miscarriages in patients with unexplained recurrent pregnancy loss, as it can detect aneuploid embryos. However, the inefficiency of PGT-A has been shown in recent studies considering the mosaicism of embryos and the heterogeneity of biopsy techniques among clinics^12–14. Additionally, embryo morphokinetics cannot accurately reflect the degree of chromosomal mosaicism¹⁵. Even diploid embryos with normal morphological parameters may result in miscarriages¹⁶.

The problems underlying the morphological parameters and PGT-A for blastocyst selection may be complemented by transcriptome analyses. A few studies reveal a correlation of the gene expression analysis of TE in human blastocysts with pregnancy^17–20. Jones et al.¹⁷ conducted a microarray analysis of biopsied TE of pooled viable and non-viable blastocysts. However, these data do not establish a direct correlation between gene expression and the implantation rate of individual embryos. Kirkegaard et al.¹⁸ and Ntostis et al.¹⁹ conducted RNA-seq for the biopsied TE of individual blastocysts. The embryo quality was prospectively estimated based on the outcome of live births. They identified dozens of genes significantly differentially expressed between embryos with pregnancy failure and those with success. Groff et al.²⁰ performed RNA-seq analysis to compare biopsied TEs and the remaining whole embryos to demonstrate that biopsied TEs can capture valuable information, including RNA-based digital karyotypes and several candidate genes associated with morphological superiority. Although these studies are helpful, information on the genes that can serve as markers for pregnancy expectancy is limited. In this study, we examined the correlation between expected pregnancy rate, gene expression profiles in ICM and TE, and karyotypes.

Classification of blastocysts based on the retrospective analysis of the pregnancy ratio

The speed of blastocyst development and blastocyst diameter at a given time point can be good parameters for pregnancy outcomes^{3, 21–23}; therefore, we retrospectively analyzed the clinical outcomes of 1,890 cases of vitrified-warmed blastocyst transfer performed at our clinic. Based on the diameter of blastocysts and the period to reach the blastocyst stage, they were classified into three groups (Fig. 1A): those that had taken less than 130 h to reach a diameter of > 170 µm (Group1, n = 676), those that had taken more than 140 h to reach a diameter of < 180 µm (Group2, n = 158), and those that had not (Group3, n = 1,056) (Fig. 1A). The pregnancy rates in Group1, 2, and 3 were 59.0%, 16.5%, and 34.2%, respectively (p < 0.001) (Fig. 1B). Given this high correlation with pregnancy outcome, we hereafter regarded Group1, 2, and 3 blastocysts as high, low, and intermediate expectations of pregnancy, respectively.

Validation Of Rna-seq Datasets

We used five blastocysts with high expectations of pregnancy (H1–H5), four with intermediate expectations of pregnancy (M1–M4), and four with low expectations of pregnancy (L1–L4) for RNA-seq analysis (Fig. 1C, Table 1). These blastocysts were derived from separate patients (Table 1). To understand which ICM or TE shows a greater correlation between their transcriptomes and the expectation of pregnancy, we dissected blastocysts into ICM and TE and performed RNA-seq analysis (Table S1).

First, we determined the quality of the RNA-seq data. Principal component analysis (PCA) confirmed that ICM and TE samples were well segregated in all groups, validating the success of ICM and TE dissection (Fig. 2A). We further confirmed that the number of expressed genes (transcripts per kilobase million [TPM] > 1) was approximately 10,000 in both the ICM and TE samples in all groups (Fig. 2B). In addition, plotting the number of expressed genes and transcriptome coverage confirmed that the complexity of the RNA-seq library of L1–L4 was not lower than that of H1–H5 (Fig. 2C, D). These results exclude the possibility that blastocysts with lower expectations of pregnancy have an aberrant quantity of whole transcripts in the ICM or TE.

Digital karyotyping by RNA-seq datasets

To examine whether lower expectations of pregnancy involve abnormalities in karyotypes, we conducted digital karyotyping using RNA-seq datasets. We first checked the expression levels of Y chromosome-linked genes to determine the embryo sex. Y-linked genes were expressed in 12 of 26 samples (Fig. 3A). The 12 samples comprised six pairs of ICM and TE from single blastocysts, indicating XY embryos. Next, we analyzed X chromosome-linked genes using a Z-score approach to estimate the copy numbers of X chromosomes, as described in a previous study²⁰. The results showed that the expression levels of X-linked genes were higher in 12 of the 26 samples (Fig. 3B). Again, these 12 samples comprised six pairs of ICM and TE from single blastocysts, indicating that they were XX embryos. We noticed that X-linked genes were intermediately expressed in L1 embryos (Fig. 3B). As the L1 embryo did not express Y-linked genes, we speculated that this embryo might be XO, which lacks one of the X chromosomes. Next, to estimate the karyotypes of the autosomal chromosomes, we performed RNA-based digital karyotyping. Based on a previous study²⁰, we considered chromosomes with Z-scores within ± 2 as euploid, those with Z-score > 2 as a potential gain of chromosomes, and those with Z-score <-2 as potential loss of chromosomes. Among the five blastocysts with high expectations of pregnancy (H1 to H5), four showed potential gain or loss of at least one chromosome in either ICM or TE (Fig. 3C). In four blastocysts with intermediate (M1 to M4) and low expectation of pregnancy (L1 to L4), only one (M2) and two (L1 and L4) showed potential gain or loss of at least one chromosome in either ICM or TE, respectively (Fig. 3C). Thus, these data suggest that RNA-based digital karyotyping alone does not allow the prediction of pregnancy expectations.

Identification of genes associated with pregnancy expectation

Having validated the quality of our RNA-seq datasets, we attempted to identify genes associated with the expectation of pregnancy. Using the criteria of q-value < 0.01 (calculated by DESeq2) comparing H1 to H5 and L1 to L4, we identified 141 and 263 DEGs in ICM and TE, respectively. To obtain the most reliable genes, we filtered out genes with high variability between individual blastocysts in each group by calculating the Z-score between all samples: The reliable DEGs were picked up if the deviation of Z-score between H1 to H5 and L1 to L4 was less than 0.5. Consequently, 26 and 67 DEGs were identified as the most reliable DEGs in the ICM and TE, respectively (Tables S2 and S3). Among the 26 DEGs identified in ICM, eight showed higher expression levels in blastocysts with high expectations of pregnancy than those with low expectations, and 18 showed the opposite trend (Fig. 4A). Among the 67 DEGs identified in TE, 11 showed higher expression levels in blastocysts with high expectations of pregnancy than those with low expectations, and 56 showed the opposite trend (Fig. 4B). Jitter plots for the gene expression levels (TPM) of the five representative DEGs in each group are shown (Fig. 4C). The blastocysts with the intermediate expectation of pregnancy (M1 to M4) showed intermediate expression levels of these DEGs (Fig. 4A-C), which further supported the idea that these DEGs could serve as valuable markers for pregnancy expectation. Gene Ontology (GO) analysis of the 56 DEGs whose expression levels were higher in the TE of blastocysts with lower expectations of pregnancy demonstrated that biological functions related to epithelial cell differentiation and negative regulation of the cell cycle were enriched (Fig. 4D), suggesting that excessive differentiation of epithelial cells and cell cycle defects in TE might cause pregnancy failure. The number of DEGs in the other groups was too small to perform GO analysis. Notably, among 11 DEGs whose expression levels were higher in the TE of blastocysts with higher expectations of pregnancy, CXADR, CLDN10, and ATP1B were known to be involved in normal development^24–26 (see Discussion). These results provide a valuable list of genes for predicting pregnancy expectations in blastocysts.

RNA-seq in human blastocysts has advanced our fundamental understanding of the molecular mechanisms underlying human preimplantation development^27–30. Previous studies linked the transcriptomes of biopsied TE using RNA-seq with the birth ratio after embryo transfer^18–20. Nevertheless, information on which genes can serve as markers of pregnancy expectancy is still limited. In addition, it remains unknown which transcriptomes of TE or ICM are more strongly correlated with pregnancy expectancy. The present study used the clinical pregnancies of 1,890 cases to establish novel criteria for evaluating pregnancy potential in blastocysts and identified several candidate genes that might be useful for pregnancy estimation. We also found that the number of DEGs in TE was larger than that in ICM, suggesting that genes expressed in TE, rather than ICM, might contribute more to the peri-implantation period and that TEs provide a better chance to predict pregnancy outcomes.

Although PGT-A is thought to increase the implantation rate, recent reports have argued against the positive effects of PGT-A^31–33. Digital karyotyping using our RNA-seq datasets allowed us to determine whether aneuploidy in blastocysts was correlated with pregnancy expectancy. The results did not show a correlation between the estimated karyotypic abnormalities and lowered expectation of pregnancy, supporting the notion that investigation of aneuploidy alone by PGT-A may not be sufficient to select high-quality embryos for transfer. Therefore, more comprehensive evaluations using simple karyotyping should contribute to the prediction of embryo quality.

The present study provides a list of genes that are potentially correlated with the expectation of pregnancy. Several genes downregulated in the TE of blastocysts with low expectations of pregnancy, such as CXADR, CLDN10, and ATP1B1, are known to be essential for human or mouse development. CXADR (CXADR Ig-like cell adhesion molecule) regulates tight junction stability, and its knockdown (KD) reduces implantation potential owing to tight junction defects in mice²⁴. CLDN10 (claudin 10) is a tight junction-associated gene known to cooperate with CXADR²⁴. ATP1B1 (ATPase Na⁺/K⁺ transporting subunit beta 1) also plays a vital role in forming tight junctions in the trophoblast membrane in mice²⁵. The uptake of Na⁺ ions into the blastocoel cavity by Na/K-ATPase participates in the expansion of human blastocysts²⁶. Lack of tight junctions or loss of function of the Na/K-ATPase pumps leads to a decrease in lumen size and cortical tension of trophectoderm cells³⁴. Thus, CXADR, CLDN10, and ATP1B1 likely play essential roles in development via the regulation of blastocyst collapse.

There is a limitation of this study. Although we established an expected pregnancy rate in relation to the degree of blastocyst development from retrospective clinical outcomes and used it as a surrogate marker for assigning biopsied blastocysts to different analysis groups, full-term development of the blastocysts used for RNA-seq were not monitored and the sample size was limited. Therefore, it remains unknown whether the gene expression profiles accurately reflects the pregnancy outcomes.

Nevertheless, our findings provide candidates for genes potentially helpful in establishing a non-invasive selection of high-quality blastocysts for ART. Future studies should reveal the detailed functions of these genes and determine whether genes identified in this study participate in human development. As in vitro implantation models of human embryos and artificial blastoids have recently been developed, these would contribute to understanding the functions of these genes in the near future^{35, 36}.

Institutional review board approvals

This study was approved by the Ethical Review Committees of the National Center for Global Health and Medicine and RIKEN Center for Integrative Medical Sciences for both the collection and experimental use of surplus blastocysts resulting from infertility treatment and donated for research (approval numbers: NCGM-A-003371-00 and 2020-8) and was registered with the Japan Society of Obstetrics and Gynecology (approval number: 195).

Consent Process

This study has been performed in accordance with the Declaration of Helsinki. All blastocysts used in this study were donated and stored at Yamashita Shonan Yume Clinic for research purposes. Couples who had blastocysts derived from c-IVF or ICSI donated surplus blastocysts for research after informed consent and signed an extensive consent form. These consent forms were approved by the Ethical Review Committees of the National Center for Global Health. We did not have access to any personal health information.

Blastocyst Evaluation Correlated With Pregnancy Rates

Blastocysts were classified according to their development speed and diameters, as described in Fig. 1A. In addition, Gardner scores are shown in Table 1 for comparison with conventional blastocyst evaluation methods³⁷. The correlation between blastocyst evaluation and pregnancy rate was retrospectively analyzed in 1,890 cases that underwent vitrified/warmed blastocyst transfer between March 2018 and December 2020 at Yamashita Shonan Yume Clinic. Pregnancy was defined as a confirmed gestational sac (GS).

Cryopreservation And Warming Of Blastocysts

Blastocysts were vitrified/warmed using a CRYOTOP SAFETY KIT (Kitazato Corporation, Japan). Briefly, this involved placing a single blastocyst on a sheet of 1 Cryotop ® (Kitazato Corporation) and immersed in liquid nitrogen. The blastocysts were subsequently warmed in specialized media (Kitazato Corporation) and re-expanded before the biopsy.

Biopsy

Biopsy was performed on the heated stage of an IX-73 microscope (Olympus, Japan), equipped with micromanipulation (Narishige Group, Japan) and an infrared diode laser (Saturn 5TM Laser system; CooperSurgical, Inc., Japan), in dishes prepared with droplets of HEPES-buffered calcium/magnesium-free medium (Sage In-Vitro Fertilization, Inc., USA) and biopsy medium (1062; Origio, Denmark). A diode laser was used to assist the opening of a 10–20 µm hole in the zona pellucida by 2–4 laser shots. ICM biopsy was performed using a glass pipette (PIN20-20FT; PRIME TECH LTD., Japan) either by laser-assisted removal of a small area of the TE layer on the opposite side of the ICM or by directly introducing the biopsy pipette into the blastocoel cavity and aspiration of the ICM cells on the TE layer. TE biopsies were performed by aspirating cells from the TE layer removed during ICM biopsy or newly collected cells from the TE layer after ICM removal using a pipette. In the biopsies used in this study, 10–20 cells were collected for both ICM and TE. Biopsy samples were transferred to a PCR tube (FG-021F; NIPPON Genetics Co., Ltd, Japan) containing 0.5 µL of phosphate-buffered saline (PBS) and frozen. The frozen samples were stored at -80°C until RNA-seq.

Library Preparation And Sequencing

RNA-seq libraries of all biopsy samples were generated using a SMART-Seq HT Kit according to the manufacturer’s instructions (TAKARA, 634436, Japan). Library construction was performed using a Nextera XT DNA Library Prep Kit (Illumina, FC-131-1024, USA) with 200 pg of cDNA. The libraries were sequenced on a NextSeq 500 with single-end 75-bp reads (Illumina).

Transcriptome Analysis

All sequencing reads were mapped to the human reference genome (hg19) using STAR (version 2.7.6a) with default parameters after evaluating the quality using FastQC (version 0.11.9) and trimming low-quality bases or adapter sequences using TrimGalore (version 0.6.4). RSEM (version 1.3.1) was used to calculate the gene expression level using the TPM (transcripts per million) value. To construct a high-quality reference transcriptome for downstream analyses, genes with TPM values greater than 1 in one or more samples were regarded as expressed genes. Genes with TPM values of less than one in all samples were filtered out. For transcriptome coverage analysis, genes with TPM values greater than 1 in 6 or more samples were used as the final filtered reference set. The number of expressed genes in the inner cell mass (ICM) and trophectoderm (TE) were 10,605 and 11,119, respectively. The coverage of each sample was defined as the proportion of genes expressed in the final reference set.

To identify differentially expressed genes (DEGs), featureCounts (version 2.0.0) was used to calculate the read count per gene. Afterward, DEGs (q-value < 0.01) were identified by R package “DESeq2” (version 1.32.0). Hierarchical clustering and visualization were performed using the R package “pheatmap” (version 1.0.12). The Gene Ontology enrichment analysis was conducted using Metascape.

Data Availability

All of the RNA-seq datasets generated in this study have been deposited to the Gene Expression Omnibus database under accession number GSE205171 (password for review: slcfiegihvgbdat).

Digital Karyotyping

According to Groff et al.²⁰, the Z-score approach was applied to assess RNA-based digital karyotypes of sex chromosomes and autosomes. To determine the copy number of the X chromosome, all reads mapped to the expressed genes on the X chromosome (except for genes located in pseudoautosomal regions 1 and 2) were summed per sample and normalized for sequencing depth. The Z-score was calculated across the ICM and TE samples. The mean and SD for the normalized read count were calculated across all samples per sample type (TE or ICM). Positive and negative Z-scores indicated the presence of two copies and one copy of the X chromosome, respectively. Furthermore, to examine the existence of the Y chromosome, the TPM values of three genes (ddx3y, rps4y1, and eif1ay) located on the Y chromosome were summed, and a total of > 250 TPMs provided a clear indication of the existence of the Y chromosome. To predict the copy number of each autosome, the Z-score cutoff was set at ± 2, and the out-layered chromosomes were identified as potential aneuploids, as described previously²⁰.

Statistical analysis

The chi-square with Bonferroni multiple comparison test was performed to identify potential relationships between morphological parameters of the vitrified blastocyst and clinical pregnancies. Statistical significance was set at p < 0.001. These statistical tests were conducted using EZR³⁸.

Author contributions

Y.K. and A.I. designed the study and prepared the manuscript. H.M. analyzed the sequencing data. H.K. performed the biopsies. N.N. and A.T. analyzed the clinical data. M.K. made the sequencing libraries. N.Y. supervised the study and obtained the informed consent. All authors reviewed the manuscript.

Additional information

Competing financial interests

The authors declare no competing financial interests.

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Table 1. List of blastocysts used for RNA-seq

	Sample ID	Maternal age at oocytes retrieval	Fertilization procedure	Gardner score	Number of sampling cells	Number of oocytes collected in the cycle	Number of frozen blastocysts in the cycle
Group1	H1	31	ICSI *	4BB	ICM: 10~15 cells, TE: 15~20 cells	11	5
	H2	42	c-IVF	4AA	ICM: 10~15 cells, TE: 10~15 cells	3	2
	H3	37	c-IVF	4AB	ICM: 5~10 cells, TE: 10~15 cells	6	5
	H4	29	c-IVF	4BB	ICM: 5~10 cells, TE: 10~15 cells	4	2
	H5	28	ICSI	4BB	ICM: 5~10 cells, TE: 10~15 cells	2	2
Group2	M1	38	ICSI	4CC	ICM: 10~15 cells, TE: 10~15 cells	6	2
	M2	37	ICSI	4CC	ICM: 15~20 cells, TE: 10~15 cells	2	1
	M3	39	ICSI	4CC	ICM: 30~40 cells, TE: 10~15 cells	5	4
	M4	39	ICSI	4CC	ICM: 30~40 cells, TE: 10~15 cells	4	2
Group3	L1	40	c-IVF	4CB	ICM: 15~20 cells, TE: 10~15 cells	5	2
	L2	33	c-IVF	4CC	ICM: 10~15 cells, TE: 10~15 cells	2	1
	L3	39	ICSI	4CC	ICM: 30~40 cells, TE: 10~15 cells	1	1
	L4	37	c-IVF	4BB	ICM: 30~40 cells, TE: 15~20 cells	1	1
* ICSI after oocyte activation treatment with electrical stimulation, ** All blastocysts were donated by different patients for this study

No competing interests reported.

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Transcriptomic signatures in trophectoderm and inner cell mass of human blastocysts with different expectations of pregnancy

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Abstract

Figures

Introduction

Results

Classification of blastocysts based on the retrospective analysis of the pregnancy ratio

Validation Of Rna-seq Datasets

Digital karyotyping by RNA-seq datasets

Identification of genes associated with pregnancy expectation

Discussion

Methods

Institutional review board approvals

Consent Process

Blastocyst Evaluation Correlated With Pregnancy Rates

Cryopreservation And Warming Of Blastocysts

Biopsy

Library Preparation And Sequencing

Transcriptome Analysis

Data Availability

Digital Karyotyping

Statistical analysis

Declarations

References

Table

Additional Declarations

Supplementary Files

Status:

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