Investigating sample pooling strategies for DIGE experiments to address biological variability
Investigating sample pooling strategies for DIGE experiments to address biological variability
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DOI：
10.1002/pmic.200800485
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发表时间：
2009-01-01
期刊：
影响因子：
3.4
通讯作者：
Lilley, Kathryn S.
中科院分区：
文献类型：
--
作者：
Karp, Natasha A.;Lilley, Kathryn S.
如果要用定量蛋白质组学来回答生物学问题，就必须设计出具有足够能力的实验来检测表达的变化。样本子合并是一种可用于减少方差但仍允许研究涵盖生物学变异的策略。潜在的样本池策略是生物平均假设，即对池进行的测量等于对个体进行的测量的平均值。本研究未发现DIGE样本合并引发系统偏倚的证据，合并可用于减少生物学变异。在定量蛋白质组学中，这两种差异来源首次被分离，发现小鼠大脑的技术差异占主导地位，而人类大脑的生物差异占主导地位。功效分析发现，随着合并的个体数量增加，所需的重复次数减少，但生物样本的数量增加。生物样品的重复测量减少了所需的样品数量，但增加了所需的凝胶数量。一个成本效益分析的例子展示了研究人员如何在考虑可用资源的同时优化他们的实验。
If biological questions are to be answered using quantitative proteomics, it is essential to design experiments which have sufficient power to be able to detect changes in expression. Sample subpooling is a strategy that can be used to reduce the variance but still allow studies to encompass biological variation. Underlying sample pooling strategies is the biological averaging assumption that the measurements taken on the pool are equal to the average of the measurements taken on the individuals. This study finds no evidence of a systematic bias triggered by sample pooling for DIGE and that pooling can be useful in reducing biological variation. For the first time in quantitative proteomics, the two sources of variance were decoupled and it was found that technical variance predominates for mouse brain, while biological variance predominates for human brain. A power analysis found that as the number of individuals pooled increased, then the number of replicates needed declined but the number of biological samples increased. Repeat measures of biological samples decreased the numbers of samples required but increased the number of gels needed. An example cost benefit analysis demonstrates how researchers can optimise their experiments while taking into account the available resources.