Division of Metabolism, Department of Clinical and Experimental Medicine, University of Padua, via Giustiniani 2, 35128, Padua, Italy.
Amino Acids. 2012 May;42(5):1583-90. doi: 10.1007/s00726-011-0873-7. Epub 2011 Mar 11.
In the field of proteomics, several approaches have been developed for separating proteins and analyzing their differential relative abundance. One of the oldest, yet still widely used, is 2-DE. Despite the continuous advance of new methods, which are less demanding from a technical standpoint, 2-DE is still compelling and has a lot of potential for improvement. The overall variability which affects 2-DE includes biological, experimental, and post-experimental (software-related) variance. It is important to highlight how much of the total variability of this technique is due to post-experimental variability, which, so far, has been largely neglected. In this short review, we have focused on this topic and explained that post-experimental variability and source of error can be further divided into those which are software-dependent and those which are operator-dependent. We discuss these issues in detail, offering suggestions for reducing errors that may affect the quality of results, summarizing the advantages and drawbacks of each approach.
在蛋白质组学领域，已经开发出几种分离蛋白质并分析其差异相对丰度的方法。其中最古老但仍然广泛使用的方法之一是 2-DE。尽管新方法不断进步，从技术角度来看要求较低，但 2-DE 仍然具有吸引力，并且具有很大的改进潜力。影响 2-DE 的总体可变性包括生物学、实验和实验后（与软件相关）的方差。重要的是要强调这种技术的总可变性中有多少是由于实验后可变性引起的，到目前为止，这在很大程度上被忽视了。在这篇简短的综述中，我们专注于这个主题，并解释了实验后可变性和误差源可以进一步分为软件相关和操作员相关的误差源。我们详细讨论了这些问题，提出了减少可能影响结果质量的错误的建议，总结了每种方法的优缺点。
