Abstract
Gene expression profiling studies are commonly used to study signaling pathways and their impact on transcriptional regulation in plants. In some cases, a profiling study results in expression profiles in which most genes exhibit a small number of differentially expressed states among a large number of samples. In such instances, a pooling approach would help improve the efficiency of the profiling effort by employing fewer microarray chips and ensuring more robust measurement of transcript levels. Smart pooling involves pooling of mRNA samples in an information-efficient manner such that each sample is tested multiple times but always in pools with other samples. The resulting pooled measurements are then decoded to recover the expression profile of all samples in the study. In this protocol, we describe in detail the process of designing smart pooling experiments and decoding their results, which have been used for studying signaling in Arabidopsis root development. Heuristics are provided to select the design parameters that would ensure successful execution of smart pooling.
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Lucas M, Laplaze L, Bennett MJ (2011) Plant systems biology: network matters. Plant Cell Environ. doi:10.1111/j.1365-3040.2010.02273.x
Sreenivasulu N, Sunkar R, Wobus U, Strickert M (2010) Array platforms and bioinformatic tools for the analysis of plant transcriptome in response to abiotic stress. Methods Mol Biol 639:71–93
Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, Kim IF et al (2011) NCBI GEO: archive for functional genomics data sets—10 years on. Nucleic Acids Res 39:D1005–D1010
Kainkaryam RM, Bruex A, Gilbert AC, Schiefelbein J, Woolf PJ (2010) poolMC: smart pooling of mRNA samples in microarray experiments. BMC Bioinformatics 11:299
Candes EJ, Wakin MB (2008) An introduction to compressive sampling. Signal Process Mag IEEE 25(2):21–30
Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U et al (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostat 4(2):249–264
Dai M, Wang P, Boyd AD, Kostov G, Athey B, Jones EG et al (2005) Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res 33(20):e175–e179
Candes EJ, Romberg J (2005) l1-MAGIC: recovery of sparse signals via convex programming. http://www.acm.caltech.edu/l1magic
Lee MM, Schiefelbein J (1999) WEREWOLF, a MYB-related protein in Arabidopsis, is a position-dependent regulator of epidermal cell patterning. Cell 99:473–483
Acknowledgments
We thank Christa Barran for technical assistance with this research. This project has been financially supported by an NSF grant (IOS 0744599) to J.W.S.
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Kainkaryam, R.M., Bruex, A., Woolf, P.J., Schiefelbein, J. (2011). Smart Pooling of mRNA Samples for Efficient Transcript Profiling. In: Wang, ZY., Yang, Z. (eds) Plant Signalling Networks. Methods in Molecular Biology, vol 876. Humana Press. https://doi.org/10.1007/978-1-61779-809-2_15
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DOI: https://doi.org/10.1007/978-1-61779-809-2_15
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Publisher Name: Humana Press
Print ISBN: 978-1-61779-808-5
Online ISBN: 978-1-61779-809-2
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