Investigating sample pooling strategies for DIGE experiments to address biological variability

• Published in: Proteomics (Weinheim) Vol. 9; no. 2; pp. 388 - 397
• Main Authors: Karp, Natasha A, Lilley, Kathryn S
• Format: Journal Article
• Language: English
• Published: Weinheim Wiley-VCH Verlag 01.01.2009; WILEY‐VCH Verlag; Wiley-VCH
• Subjects: Analysis of Variance; Analytical, structural and metabolic biochemistry; Animals; Biological and medical sciences; Biological variation; Brain Chemistry; Carbocyanines - chemistry; DIGE; Electrophoresis, Gel, Two-Dimensional; Electrophoresis, Polyacrylamide Gel - methods; Fluorescent Dyes - chemistry; Fundamental and applied biological sciences. Psychology; Gene Expression Profiling - methods; Genetic Variation; Humans; Mice; Miscellaneous; Models, Statistical; Oligonucleotide Array Sequence Analysis; Pooling; Power; Proteins; Reproducibility of Results; Statistics, Nonparametric; Two‐dimensional gel electrophoresis; Biological variation; DIGE; Proteomics; Two-dimensional gel electrophoresis; Two dimensional electrophoresis; Pooling; Power
• ISSN: 1615-9853; 1615-9861
• DOI: 10.1002/pmic.200800485

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.