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      Identifying biomarkers for predicting successful embryo implantation: applying single to multi-OMICs to improve reproductive outcomes

      1 , 2 , 1 , 2 , 2
      Human Reproduction Update
      Oxford University Press (OUP)

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          Abstract

          BACKGROUND

          Successful embryo implantation is a complex process that requires the coordination of a series of events, involving both the embryo and the maternal endometrium. Key to this process is the intricate cascade of molecular mechanisms regulated by endocrine, paracrine and autocrine modulators of embryonic and maternal origin. Despite significant progress in ART, implantation failure still affects numerous infertile couples worldwide and fewer than 10% of embryos successfully implant. Improved selection of both the viable embryos and the optimal endometrial phenotype for transfer remains crucial to enhancing implantation chances. However, both classical morphological embryo selection and new strategies incorporated into clinical practice, such as embryonic genetic analysis, morphokinetics or ultrasound endometrial dating, remain insufficient to predict successful implantation. Additionally, no techniques are widely applied to analyse molecular signals involved in the embryo–uterine interaction. More reliable biological markers to predict embryo and uterine reproductive competence are needed to improve pregnancy outcomes. Recent years have seen a trend towards ‘omics’ methods, which enable the assessment of complete endometrial and embryonic molecular profiles during implantation. Omics have advanced our knowledge of the implantation process, identifying potential but rarely implemented biomarkers of successful implantation.

          OBJECTIVE AND RATIONALE

          Differences between the findings of published omics studies, and perhaps because embryonic and endometrial molecular signatures were often not investigated jointly, have prevented firm conclusions being reached. A timely review summarizing omics studies on the molecular determinants of human implantation in both the embryo and the endometrium will help facilitate integrative and reliable omics approaches to enhance ART outcomes.

          SEARCH METHODS

          In order to provide a comprehensive review of the literature published up to September 2019, Medline databases were searched using keywords pertaining to omics, including ‘transcriptome’, ‘proteome’, ‘secretome’, ‘metabolome’ and ‘expression profiles’, combined with terms related to implantation, such as ‘endometrial receptivity’, ‘embryo viability’ and ‘embryo implantation’. No language restrictions were imposed. References from articles were also used for additional literature.

          OUTCOMES

          Here we provide a complete summary of the major achievements in human implantation research supplied by omics approaches, highlighting their potential to improve reproductive outcomes while fully elucidating the implantation mechanism. The review highlights the existence of discrepancies among the postulated biomarkers from studies on embryo viability or endometrial receptivity, even using the same omic analysis.

          WIDER IMPLICATIONS

          Despite the huge amount of biomarker information provided by omics, we still do not have enough evidence to link data from all omics with an implantation outcome. However, in the foreseeable future, application of minimally or non-invasive omics tools, together with a more integrative interpretation of uniformly collected data, will help to overcome the difficulties for clinical implementation of omics tools. Omics assays of the embryo and endometrium are being proposed or already being used as diagnostic tools for personalised single-embryo transfer in the most favourable endometrial environment, avoiding the risk of multiple pregnancies and ensuring better pregnancy rates.

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          Single-cell reconstruction of the early maternal–fetal interface in humans

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            Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells.

            Measuring gene expression in individual cells is crucial for understanding the gene regulatory network controlling human embryonic development. Here we apply single-cell RNA sequencing (RNA-Seq) analysis to 124 individual cells from human preimplantation embryos and human embryonic stem cells (hESCs) at different passages. The number of maternally expressed genes detected in our data set is 22,687, including 8,701 long noncoding RNAs (lncRNAs), which represents a significant increase from 9,735 maternal genes detected previously by cDNA microarray. We discovered 2,733 novel lncRNAs, many of which are expressed in specific developmental stages. To address the long-standing question whether gene expression signatures of human epiblast (EPI) and in vitro hESCs are the same, we found that EPI cells and primary hESC outgrowth have dramatically different transcriptomes, with 1,498 genes showing differential expression between them. This work provides a comprehensive framework of the transcriptome landscapes of human early embryos and hESCs.
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              A new approach to decoding life: systems biology.

              Systems biology studies biological systems by systematically perturbing them (biologically, genetically, or chemically); monitoring the gene, protein, and informational pathway responses; integrating these data; and ultimately, formulating mathematical models that describe the structure of the system and its response to individual perturbations. The emergence of systems biology is described, as are several examples of specific systems approaches.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Human Reproduction Update
                Oxford University Press (OUP)
                1355-4786
                1460-2369
                March 2020
                February 28 2020
                February 25 2020
                March 2020
                February 28 2020
                February 25 2020
                : 26
                : 2
                : 264-301
                Affiliations
                [1 ]IVI-RMA Alicante, Innovation. Avda. de Denia 111, 03015 Alicante, Spain
                [2 ]Fundación IVI, Innovation-IIS La Fe, Avda. Fernando Abril Martorell 106, Torre A, 1° 1.23, 46026 Valencia, Spain
                Article
                10.1093/humupd/dmz042
                32096829
                7d2cf5af-8284-40a0-bf86-bdc8995459b6
                © 2020

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

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