Guidelines for the design, analysis and interpretation of 'omics' data: focus on human endometrium

Abstract

Background: 'Omics' high-throughput analyses, including genomics, epigenomics, transcriptomics, proteomics and metabolomics, are widely applied in human endometrial studies. Analysis of endometrial transcriptome patterns in physiological and pathophysiological conditions has been to date the most commonly applied 'omics' technique in human endometrium. As the technologies improve, proteomics holds the next big promise for this field. The 'omics' technologies have undoubtedly advanced our knowledge of human endometrium in relation to fertility and different diseases. Nevertheless, the challenges arising from the vast amount of data generated and the broad variation of 'omics' profiling according to different environments and stimuli make it difficult to assess the validity, reproducibility and interpretation of such 'omics' data. With the expansion of 'omics' analyses in the study of the endometrium, there is a growing need to develop guidelines for the design of studies, and the analysis and interpretation of 'omics' data.

Methods: Systematic review of the literature in PubMed, and references from relevant articles were investigated up to March 2013.

Results: The current review aims to provide guidelines for future 'omics' studies on human endometrium, together with a summary of the status and trends, promise and shortcomings in the high-throughput technologies. In addition, the approaches presented here can be adapted to other areas of high-throughput 'omics' studies.

Conclusion: A highly rigorous approach to future studies, based on the guidelines provided here, is a prerequisite for obtaining data on biological systems which can be shared among researchers worldwide and will ultimately be of clinical benefit.

Keywords: endometrium; epigenomics; genomics; metabolomics; proteomics.

Figure 1

‘Omics’ publications in human endometrium studies. Y-axis indicates the number of studies, and X-axis denotes the year of publication. The systematic review of the literature in PubMed was conducted up to March 2013. The eligible studies were additionally identified using reference lists of review articles and other relevant studies. Abstracts from conference proceedings were also considered. No language or any other restrictions were applied. Search terms are presented in detail in Supplementary data, Table S1, and the search results in Supplementary data, Figure S1. In short, keywords ‘endometrium’, ‘endometriosis’ and ‘embryo implantation’ were one-by-one searched with each paired term. After excluding duplicates, a total of 2478 manuscripts were identified and following critical selection 269 manuscripts of ‘omics’ studies in human endometrium remained (including studies on normal endometrium, endometrial receptivity, implantation and implantation failure, endometriosis and endometrial cancer): 23 of genomics, 164 of transcriptomics, 26 of epigenomics, 54 of proteomics and 2 of metabolomics.

Figure 2

The complexity of ‘omics’ fields in biological processes that contribute to the study and understanding of biological systems. There are more than 25 000 genes in the human genome, encoding ∼100 000–200 000 transcripts and 1 million proteins, whereas there are as few as 2500–3000 metabolites that make up the human metabolome (Botros et al., 2008). The genome is essentially invariant among cells and tissues, while the epigenome has a low/moderate temporal variance and influences both transcriptome and proteome. The transcriptome has a high temporal variance and is translated into the proteome differentially in different tissues and physiological states, affecting the metabolome in a tissue-specific manner. This ‘simple’ model is modulated by multiple factors: (A) differential splicing that can be affected by the proteome; (B) post-translational modification of proteins; (C) transcription factor binding; (D) receptor ligand binding and (E) environmentally induced factors (adapted from Gracie et al. 2011; Bellver et al. 2012).

Figure 3

High-confidence embryo-endometrium interaction network derived from protein–protein interaction data and literature curation. Node colour represents tissue-specific differential gene expression: blue, expressed in embryo; red, expressed in endometrium; grey, expressed in both tissues. The biggest interaction network highlights the importance of cell adhesion molecules, where integrins, collagens and laminins are present. The second largest interaction network represents proteins involved in cytokine–cytokine receptor interactions, where osteopontin, apolipoprotein D, leptin (LEP) and leukaemia inhibitory factor (LIF) pathways intertwine (from Altmäe et al., 2012; published with permission from Molecular Endocrinology).