GC-MS/MS analysis of metabolites derived from a single human blastocyst

Abstract

Introduction: The field of assisted reproductive technology (ART) has significantly advanced; however, morphological evaluation remains as the chosen method of assessment of embryo quality.

Objective: We aimed to examine metabolic changes in embryo culture medium to develop a non-invasive method for evaluation of embryo quality.

Methods: We performed metabolic analysis of culture medium obtained from a single blastocyst cultured for freezing.

Results: In total, 187 (39.8%) of the 469 detectable organic acid metabolites were identified. A significant change (p < 0.05) was observed in eight metabolites between the good-quality and poor-quality embryo groups. Differences were observed in several metabolic pathways between the good-quality and poor-quality embryo groups. Metabolites that showed significant changes were primarily involved in the metabolism of branched-chain amino acids.

Conclusion: The quantification of metabolism in human embryos may assist in identification and selection of good-quality embryos with high rates of survival before freezing and implantation in conjunction with morphological classification. This may help to identify embryos with high rates of survival.

Keywords: Branched-chain amino acids; Culture medium; Embryo quality; Metabolomics.

Conflict of interest statement

The authors of this study have no conflicts of interest to disclose.

Figures

Fig. 1

Results of the multiple classification analysis using SIMCA13, Umetrics, Inc., Sweden. a Distribution map of the four quality groups by OPLS-DA. All samples of each group show similar distribution. The x-axis t [1] and y-axis t [2] represent score vectors summarising all variables of the analysis. The goodness of prediction value R2 [1] = 0.391 and R2 [2] = 0.0501. The ellipse shows the 95% confidence interval using Hotelling’ T2 statistics. b 3D view of the four quality groups by OPLS-DA. The x-axis t [1], y-axis t [2], and z-axis t [3] represent score vectors summarising all variables of the analysis. The goodness of prediction value R2 [1] = 0.391, R2 [2] = 0.0501, and R2 [3] = 0.0302. The ellipse shows the 95% confidence interval using Hotelling’ T2 statistics. c Distribution map of the good-quality group vs poor-quality group by OPLS-DA. The distribution is remarkable between the good- and poor-quality groups. The x-axis t [1] and y-axis to [1] represent score vectors summarising all variables of the analysis. The goodness of prediction value R2 [1] = 0.123 and R2 to [1] = 0.528. The ellipse shows the 95% confidence interval using Hotelling’ T2 statistics. d S plot analysis of the good-quality group vs poor-quality group. OPLS-DA = orthogonal partial least square regression discriminant analysis. Positive or negative correlation levels are generally defined as  > 0.8 or  <  − 0.8, respectively

Fig. 2

Results of pathway analysis using MetaboAnalyst. a Result of metabolic set enrichment analysis (MSEA) showing threefold enrichment of the valine, leucine, and isoleucine degradation pathways. b Result of metabolic pathway analysis (MetPA). Pathway impact value (x-axis) from pathway topology analysis and p-values from the pathway enrichment analysis (y-axis) are shown. MetaboAnalyst was developed by Dr. Jianguo Xia of the Institute of Parasitology at McGill University. It is a free software for metabolic analysis. (http://www.metaboanalyst.ca/feces/home.xhtml)

Fig. 3

Variation in metabolomic changes between the poor-quality and good-quality embryo groups. Valine, leucine, and isoleucine levels are lower in the poor-quality group compared with those in the good-quality group. *p value < 0.05 compared with control. **p value < 0.01 compared with control