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      Single-cell RNA sequencing identifies molecular targets associated with poor in vitro maturation performance of oocytes collected from ovarian stimulation

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          Abstract

          STUDY QUESTION

          What is the transcriptome signature associated with poor performance of rescue IVM (rIVM) oocytes and how can we rejuvenate them?

          SUMMARY ANSWER

          The GATA-1/CREB1/WNT signalling axis was repressed in rIVM oocytes, particularly those of poor quality; restoration of this axis may produce more usable rIVM oocytes.

          WHAT IS KNOWN ALREADY

          rIVM aims to produce mature oocytes (MII) for IVF through IVM of immature oocytes collected from stimulated ovaries. It is not popular due to limited success rate in infertility treatment. Genetic aberrations, cellular stress and the absence of cumulus cell support in oocytes could account for the failure of rIVM.

          STUDY DESIGN, SIZE, DURATION

          We applied single-cell RNA sequencing (scRNA-seq) to capture the transcriptomes of human in vivo oocytes (IVO) (n = 10) from 7 donors and rIVM oocytes (n = 10) from 10 donors. The effects of maternal age and ovarian responses on rIVM oocyte transcriptomes were also studied. In parallel, we studied the effect of gallic acid on the maturation rate of mouse oocytes cultured in IVM medium with (n = 84) and without (n = 85) gallic acid.

          PARTICIPANTS/MATERIALS, SETTING, METHODS

          Human oocytes were collected from donors aged 28–41 years with a body mass index of <30. RNA extraction, cDNA generation, library construction and sequencing were performed in one preparation. scRNA-seq data were then processed and analysed. Selected genes in the rIVM versus IVO comparison were validated by quantitative real-time PCR. For the gallic acid study, we collected immature oocytes from 5-month-old mice and studied the effect of 10-μM gallic acid on their maturation rate.

          MAIN RESULTS AND THE ROLE OF CHANCE

          The transcriptome profiles of rIVM/IVO oocytes showed distinctive differences. A total of 1559 differentially expressed genes (DEGs, genes with at least 2-fold change and adjusted P < 0.05) were found to be enriched in metabolic processes, biosynthesis and oxidative phosphorylation. Among these DEGs, we identified a repression of WNT/β-catenin signalling in rIVM when compared with IVO oocytes. We found that oestradiol levels exhibited a significant age-independent correlation with the IVO mature oocyte ratio (MII ratio) for each donor. rIVM oocytes from women with a high MII ratio were found to have over-represented cellular processes such as anti-apoptosis. To further identify targets that contribute to the poor clinical outcomes of rIVM, we compared oocytes collected from young donors with a high MII ratio with oocytes from donors of advanced maternal age and lower MII ratio, and revealed that CREB1 is an important regulator. Thus, our study identified that GATA-1/CREB1/WNT signalling was repressed in both rIVM oocytes versus IVO oocytes and in rIVM oocytes of lower versus higher quality. Consequently we investigated gallic acid, as a potential antioxidant substrate in human rIVM medium, and found that it increased the mouse oocyte maturation rate by 31.1%.

          LARGE SCALE DATA

          Raw data from this study can be accessed through GSE158539.

          LIMITATIONS, REASONS FOR CAUTION

          In the rIVM oocytes of the high- and low-quality comparison, the number of samples was limited after data filtering with stringent selection criteria. For the oocyte stage identification, we were unable to predict the presence of oocyte spindle, so polar body extrusion was the only indicator.

          WIDER IMPLICATIONS OF THE FINDINGS

          This study showed that GATA-1/CREB1/WNT signalling was repressed in rIVM oocytes compared with IVO oocytes and was further downregulated in low-quality rIVM oocytes, providing us the foundation of subsequent follow-up research on human oocytes and raising safety concerns about the clinical use of rescued oocytes.

          STUDY FUNDING/COMPETING INTEREST(S)

          This work was supported by the Collaborative Research Fund, Research Grants Council, C4054-16G, and Research Committee Funding (Research Sustainability of Major RGC Funding Schemes), The Chinese University of Hong Kong. The authors have no conflicts of interest to declare.

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          Most cited references70

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          Is Open Access

          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Is Open Access

            RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

            Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
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              Full-length RNA-seq from single cells using Smart-seq2.

              Emerging methods for the accurate quantification of gene expression in individual cells hold promise for revealing the extent, function and origins of cell-to-cell variability. Different high-throughput methods for single-cell RNA-seq have been introduced that vary in coverage, sensitivity and multiplexing ability. We recently introduced Smart-seq for transcriptome analysis from single cells, and we subsequently optimized the method for improved sensitivity, accuracy and full-length coverage across transcripts. Here we present a detailed protocol for Smart-seq2 that allows the generation of full-length cDNA and sequencing libraries by using standard reagents. The entire protocol takes ∼2 d from cell picking to having a final library ready for sequencing; sequencing will require an additional 1-3 d depending on the strategy and sequencer. The current limitations are the lack of strand specificity and the inability to detect nonpolyadenylated (polyA(-)) RNA.
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                Author and article information

                Contributors
                Journal
                Human Reproduction
                Oxford University Press (OUP)
                0268-1161
                1460-2350
                July 01 2021
                June 18 2021
                May 30 2021
                July 01 2021
                June 18 2021
                May 30 2021
                : 36
                : 7
                : 1907-1921
                Affiliations
                [1 ]Developmental and Regenerative Biology Program, School of Biomedical Sciences, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, PR China
                [2 ]Department of Chemical Pathology, and Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, PR China
                [3 ]Assisted Reproductive Technology Unit, Department of Obstetrics and Gynaecology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong SAR, PR China
                [4 ]Department of Medicine, The University of Hong Kong, Hong Kong SAR, PR China
                [5 ]Department of Obstetrics and Gynaecology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, N.T., Hong Kong SAR, PR China
                [6 ]Division of Life Science, Hong Kong University of Science and Technology, Shatin, N.T., Hong Kong SAR, PR China
                [7 ]Department of Obstetrics and Gynecology, IVF Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China
                [8 ]Institute of Molecular and Cellular Biology & Department of Medical Sciences, National Tsing Hua University, Hsinchu, Taiwan
                [9 ]Department of Infertility and Reproductive Medicine, Taiwan IVF Group Center, Hsinchu City, Taiwan
                Article
                10.1093/humrep/deab100
                34052851
                22b99729-968b-404b-a740-1a83b951599a
                © 2021

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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