Garosi Paola, De Filippo Carlotta, van Erk Marjan, Rocca-Serra Philippe, Sansone Susanna-Assunta, Elliott Ruan
Institute of Food Research, Norwich Research Park, Norwich NR5 7UA, UK.
Br J Nutr. 2005 Apr;93(4):425-32. doi: 10.1079/bjn20041385.
Microarrays represent a powerful tool for studies of diet-gene interactions. Their use is, however, associated with a number of technical challenges and potential pitfalls. The cost of microarrays continues to drop but is still comparatively high. This, coupled with the complex logistical issues associated with performing nutritional microarray studies, often means that compromises have to be made in the number and type of samples analysed. Additionally, technical variations between array platforms and analytical procedures will almost inevitably lead to differences in the transcriptional responses observed. Consequently, conflicting data may be produced, important effects may be missed and/or false leads generated (e.g. apparent patterns of differential gene regulation that ultimately prove to be incorrect or not significant). This is likely to be particularly true in the field of nutrition, in which we expect that many dietary bioactive agents at nutritionally relevant concentrations will elicit subtle changes in gene transcription that may be critically important in biological terms but will be difficult to detect reliably. Thus, great care should always be taken in designing and executing microarray studies. This article seeks to provide an overview of both the main practical and theoretical considerations in microarray use that represent potential sources of technical variation and error. Wherever possible, recommendations are made on what we propose to be the best approach. The overall aims are to provide a basic framework of advice for researchers who are new to the use of microarrays and to promote a discussion of standardisation and best practice in the field.
微阵列是研究饮食与基因相互作用的有力工具。然而，其使用伴随着许多技术挑战和潜在陷阱。微阵列的成本持续下降，但仍然相对较高。这一点，再加上与进行营养微阵列研究相关的复杂后勤问题，往往意味着必须在分析样本的数量和类型上做出妥协。此外，阵列平台和分析程序之间的技术差异几乎不可避免地会导致观察到的转录反应存在差异。因此，可能会产生相互矛盾的数据，重要的影响可能会被遗漏和/或产生错误线索（例如，最终被证明是不正确或不显著的差异基因调控的明显模式）。在营养领域可能尤其如此，我们预计许多营养相关浓度的膳食生物活性物质会引起基因转录的细微变化，这些变化在生物学上可能至关重要，但难以可靠检测。因此，在设计和执行微阵列研究时应始终格外谨慎。本文旨在概述微阵列使用中的主要实际和理论考虑因素，这些因素是技术变异和误差的潜在来源。只要有可能，我们就会对我们认为的最佳方法提出建议。总体目标是为刚接触微阵列使用的研究人员提供一个基本的建议框架，并促进该领域关于标准化和最佳实践的讨论。
