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      Follicular fluid content and oocyte quality: from single biochemical markers to metabolomics

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          Abstract

          The assessment of oocyte quality in human in vitro fertilization (IVF) is getting increasing attention from embryologists. Oocyte selection and the identification of the best oocytes, in fact, would help to limit embryo overproduction and to improve the results of oocyte cryostorage programs. Follicular fluid (FF) is easily available during oocyte pick-up and theorically represents an optimal source on non-invasive biochemical predictors of oocyte quality. Unfortunately, however, the studies aiming to find a good molecular predictor of oocyte quality in FF were not able to identify substances that could be used as reliable markers of oocyte competence to fertilization, embryo development and pregnancy. In the last years, a well definite trend toward passing from the research of single molecular markers to more complex techniques that study all metabolites of FF has been observed. The metabolomic approach is a powerful tool to study biochemical predictors of oocyte quality in FF, but its application in this area is still at the beginning. This review provides an overview of the current knowledge about the biochemical predictors of oocyte quality in FF, describing both the results coming from studies on single biochemical markers and those deriving from the most recent studies of metabolomics

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          Most cited references146

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          Metabolomics by numbers: acquiring and understanding global metabolite data.

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            Current two-dimensional electrophoresis technology for proteomics.

            Two-dimensional gel electrophoresis (2-DE) with immobilized pH gradients (IPGs) combined with protein identification by mass spectrometry (MS) is currently the workhorse for proteomics. In spite of promising alternative or complementary technologies (e.g. multidimensional protein identification technology, stable isotope labelling, protein or antibody arrays) that have emerged recently, 2-DE is currently the only technique that can be routinely applied for parallel quantitative expression profiling of large sets of complex protein mixtures such as whole cell lysates. 2-DE enables the separation of complex mixtures of proteins according to isoelectric point (pI), molecular mass (Mr), solubility, and relative abundance. Furthermore, it delivers a map of intact proteins, which reflects changes in protein expression level, isoforms or post-translational modifications. This is in contrast to liquid chromatography-tandem mass spectrometry based methods, which perform analysis on peptides, where Mr and pI information is lost, and where stable isotope labelling is required for quantitative analysis. Today's 2-DE technology with IPGs (Görg et al., Electrophoresis 2000, 21, 1037-1053), has overcome the former limitations of carrier ampholyte based 2-DE (O'Farrell, J. Biol. Chem. 1975, 250, 4007-4021) with respect to reproducibility, handling, resolution, and separation of very acidic and/or basic proteins. The development of IPGs between pH 2.5-12 has enabled the analysis of very alkaline proteins and the construction of the corresponding databases. Narrow-overlapping IPGs provide increased resolution (delta pI = 0.001) and, in combination with prefractionation methods, the detection of low abundance proteins. Depending on the gel size and pH gradient used, 2-DE can resolve more than 5000 proteins simultaneously (approximately 2000 proteins routinely), and detect and quantify < 1 ng of protein per spot. In this article we describe the current 2-DE/MS workflow including the following topics: sample preparation, protein solubilization, and prefractionation; protein separation by 2-DE with IPGs; protein detection and quantitation; computer assisted analysis of 2-DE patterns; protein identification and characterization by MS; two-dimensional protein databases.
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              Measuring the metabolome: current analytical technologies.

              The post-genomics era has brought with it ever increasing demands to observe and characterise variation within biological systems. This variation has been studied at the genomic (gene function), proteomic (protein regulation) and the metabolomic (small molecular weight metabolite) levels. Whilst genomics and proteomics are generally studied using microarrays (genomics) and 2D-gels or mass spectrometry (proteomics), the technique of choice is less obvious in the area of metabolomics. Much work has been published employing mass spectrometry, NMR spectroscopy and vibrational spectroscopic techniques, amongst others, for the study of variations within the metabolome in many animal, plant and microbial systems. This review discusses the advantages and disadvantages of each technique, putting the current status of the field of metabolomics in context, and providing examples of applications for each technique employed.
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                Author and article information

                Journal
                Reprod Biol Endocrinol
                Reproductive Biology and Endocrinology : RB&E
                BioMed Central
                1477-7827
                2009
                4 May 2009
                : 7
                : 40
                Affiliations
                [1 ]Reproductive Medicine and IVF Unit, Department of Obstetrical and Gynecological Sciences, University of Torino, Via Ventimiglia 3, 10126 Torino, Italy
                [2 ]Department of Obstetrics, Gynecology, and Reproductive Sciences, University of California, San Francisco, USA
                Article
                1477-7827-7-40
                10.1186/1477-7827-7-40
                2685803
                19413899
                9fe43116-96c0-4298-985d-f167e05500a5
                Copyright © 2009 Revelli et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 25 August 2008
                : 4 May 2009
                Categories
                Review

                Human biology
                Human biology

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