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Big results from small samples: evaluation of amplification protocols for gene expression profiling.

ABSTRACT: Microarrays have revolutionized many areas of biology due to our technical ability to quantify tens of thousands of transcripts within a single experiment. However, there are still many areas that cannot benefit from this technology due to the amount of biological material needed for microarray analysis. In response to this demand, chemistries have been developed that boast the capability of generating targets from nanogram amounts of total RnA, reflecting minimal amounts of biological material, on the order of several hundred or thousand cells. Herein, we describe the evaluation of four chemistries for RnA amplification in terms of reproducibility, sensitivity, accuracy, and comparability to results from a single round of T7 amplification. No evidence for false-positive measurements of differential expression was observed. In contrast, clear differences between chemistries in sensitivity and accuracy were detected. PCR validation showed an interaction of probe sequence on the array and target labeling chemistry, resulting in a chemistry-dependent probe set sensitivity varying over an order of magnitude.

SUBMITTER: Viale A

PROVIDER: S-EPMC2062549 | biostudies-literature | 2007 Jul

REPOSITORIES: biostudies-literature

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Big results from small samples: evaluation of amplification protocols for gene expression profiling.

Viale Agnes A Li Juan J Tiesman Jay J Hester Susan S Massimi Aldo A Griffin Chandi C Grills George G Khitrov Greg G Lilley Kathryn K Knudtson Kevin K Ward Bill B Kornacker Karl K Chu Chin-Yi CY Auer Herbert H Brooks Andrew I AI

Journal of biomolecular techniques : JBT 20070701 3

Microarrays have revolutionized many areas of biology due to our technical ability to quantify tens of thousands of transcripts within a single experiment. However, there are still many areas that cannot benefit from this technology due to the amount of biological material needed for microarray analysis. In response to this demand, chemistries have been developed that boast the capability of generating targets from nanogram amounts of total RnA, reflecting minimal amounts of biological material, ...[more]

PMID: 17595311

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Project description:BackgroundCross-species gene expression analyses using oligonucleotide microarrays designed to evaluate a single species can provide spurious results due to mismatches between the interrogated transcriptome and arrayed probes. Based on the most recent human and chimpanzee genome assemblies, we developed updated and accessible probe masking methods that allow human Affymetrix oligonucleotide microarrays to be used for robust genome-wide expression analyses in both species. In this process, only data from oligonucleotide probes predicted to have robust hybridization sensitivity and specificity for both transcriptomes are retained for analysis.ResultsTo characterize the utility of this resource, we applied our mask protocols to existing expression data from brains, livers, hearts, testes, and kidneys derived from both species and determined the effects probe numbers have on expression scores of specific transcripts. In all five tissues, probe sets with decreasing numbers of probes showed non-linear trends towards increased variation in expression scores. The relationships between expression variation and probe number in brain data closely matched those observed in simulated expression data sets subjected to random probe masking. However, there is evidence that additional factors affect the observed relationships between gene expression scores and probe number in tissues such as liver and kidney. In parallel, we observed that decreasing the number of probes within probe sets lead to linear increases in both gained and lost inferences of differential cross-species expression in all five tissues, which will affect the interpretation of expression data subject to masking.ConclusionWe introduce a readily implemented and updated resource for human and chimpanzee transcriptome analysis through a commonly used microarray platform. Based on empirical observations derived from the analysis of five distinct data sets, we provide novel guidelines for the interpretation of masked data that take the number of probes present in a given probe set into consideration. These guidelines are applicable to other customized applications that involve masking data from specific subsets of probes.

Dataset Information

Big results from small samples: evaluation of amplification protocols for gene expression profiling.

Publications

Big results from small samples: evaluation of amplification protocols for gene expression profiling.

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