Laboratory of Functional Genomics of Early Embryonic Development, Laval University, Pavillon Comtois, Local 4221 Université Laval, Québec, Québec, Canada.
Reproduction. 2010 Dec;140(6):787-801. doi: 10.1530/REP-10-0191. Epub 2010 Sep 10.
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.
“组学”技术的兴起始于近十年前，其中转录组学已成功用于对比哺乳动物卵母细胞和早期胚胎中的基因表达。早期发育阶段提供的生物材料稀缺是转录组学领域越来越受生殖生物学家欢迎的主要原因。能够扩增稀缺的 mRNA 样本并生成必要数量的起始材料，从而能够同时相对测量数千个候选物的 RNA 丰度。到目前为止，微阵列一直是该领域最常用的高通量方法。微阵列平台有多种格式，从 cDNA 集合到长或短的寡核苷酸探针集。这些平台生成了大量的数据，需要将比较 RNA 丰度值整合到早期发育的生理背景中，才能充分发挥其优势。不幸的是，数据集之间存在显著差异，这表明直接比较研究是困难的，而且通常是不可能的。我们研究了导致从小鸡胚样品中产生微阵列数据的样品处理步骤，并确定了对下游结果有重大影响的关键步骤。这篇综述讨论了用于微阵列分析的早期胚胎样品制备的最佳方法，并重点讨论了阻碍来自不同平台的数据集比较的挑战，以及为什么使用体细胞进行方法基准测试可能不适用于小鸡胚发育的非典型性质。
