Microarray analysis of gene expression during early development: a cautionary overview

Abstract

The rise of the 'omics' technologies started nearly a decade ago and, among them, transcriptomics has been used successfully to contrast gene expression in mammalian oocytes and early embryos. The scarcity of biological material that early developmental stages provide is the prime reason why the field of transcriptomics is becoming more and more popular with reproductive biologists. The potential to amplify scarce mRNA samples and generate the necessary amounts of starting material enables the relative measurement of RNA abundance of thousands of candidates simultaneously. So far, microarrays have been the most commonly used high-throughput method in this field. Microarray platforms can be found in a wide variety of formats, from cDNA collections to long or short oligo probe sets. These platforms generate large amounts of data that require the integration of comparative RNA abundance values in the physiological context of early development for their full benefit to be appreciated. Unfortunately, significant discrepancies between datasets suggest that direct comparison between studies is difficult and often not possible. We have investigated the sample-handling steps leading to the generation of microarray data produced from prehatching embryo samples and have identified key steps that significantly impact the downstream results. This review provides a discussion on the best methods for the preparation of samples from early embryos for microarray analysis and focuses on the challenges that impede dataset comparisons from different platforms and the reasons why methodological benchmarking performed using somatic cells may not apply to the atypical nature of prehatching development.

Male fertility status is associated with DNA methylation signatures in sperm and transcriptomic profiles of bovine preimplantation embryos

“…The software empirically models the shift size of the tags and uses a dynamic Poisson distribution to account for local bias, generating more reliable estimates. The differentially methylated regions (DMRs) have been detected with the DiffBind package implemented in R [35, 36] which computes differentially bound sites using affinity data. The input for DiffBind consists of the set of peaks previously identified in MACS and the bam files containing aligned reads for each sample.…”

Comprehensive cross production system assessment of the impact of in vitro microenvironment on the expression of messengers and long non-coding RNAs in the bovine blastocyst

“…Based on the published gene expression studies, it should be possible to define the boundaries of this plasticity and identify key gene pathways involved in embryo development. However, large methodological differences among studies prevent the comparison of their gene lists and their integration into a comprehensive model of embryonic development (Gilbert et al 2010, Robert 2010, Seli et al 2010. Linking the observed variations in transcript abundance with the observed phenotypes has been extremely challenging.…”

Method to isolate polyribosomal mRNA from scarce samples such as mammalian oocytes and early embryos

“…total RNA, poly(A) bearing and polyribosomal mRNA), data normalization could not be performed across these groups. Furthermore, usual housekeeping gene candidates could not be used for data normalization across oocyte maturation stages since their respective transcript abundance been reported to be fluctuating [56,57]. As a consequence, absolute transcript measurements were considered where total variance includes both technical and biological variances.…”