Unknown,Transcriptomics,Genomics,Proteomics

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Expression data from pure/mixed blood and breast to test feasability of deconvolution of clinical samples

ABSTRACT: Samples collected from human subjects in clinical trials possess a level of complexity, arising from multiple cell types, that can obfuscate the analysis of data derived from them. Blood, for example, contains many different cell types that are derived from a distinct lineage and carry out a different immunological purpose. Failure to identify, quantify, and incorporate sources of heterogeneity into an analysis can have widespread and detrimental effects on subsequent statistical studies. We used microarrays to detail a statistical approach to model expression from a mixed cell population as the weighted average of expression from different cell types. Consequently, we can accurately and efficiently estimate the abundance of various cell populations. Favoring computation over manual purification has its advantages, such as measuring responses of multiple cell types simultaneously, keeping samples intact, and identifying biologically relevant differentially expressed genes. We mixed breast and blood biospecimens derived from female adults at the cRNA homogenate level in different proportions. Data was RMA normalized.

ORGANISM(S): Homo sapiens

SUBMITTER: Ting Gong

PROVIDER: E-GEOD-29832 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Tissues are often made up of multiple cell-types. Blood, for example, contains many different cell-types, each with its own functional attributes and molecular signature. In humans, because of its accessibility and immune functionality, blood cells have been used as a source for RNA-based biomarkers for many diseases. Yet, the proportions of any given cell-type in the blood can vary markedly, even between normal individuals. This results in a significant loss of sensitivity in gene expression studies of blood cells and great difficulty in identifying the cellular source of any perturbations. Ideally, one would like to perform differential expression analysis between patient groups for each of the cell-types within a tissue but this is impractical and prohibitively expensive. To test the relationship between measured gene expression in mixed samples and the expression of genes in the isolated pure subsets, we begin with a situation in which all factors are known. Tissue samples from the brain, liver and lung of a single rat were analyzed using expression arrays (Affymetrix) in triplicate. Homogenates of those three tissues were then mixed together at the cRNA level. We then measured the gene expression pattern of each mixed sample. Such mixtures mimic the common scenario in which biological samples in a dataset are heterogeneous and vary in the relative frequency of the component subsets from one another. We mixed rat brain, liver and lung biospecimens derived from one animal at the cRNA homogenate level in different proportions. 3 technical replicates each. Snap frozen rat liver and brain was kept frozen while cutting it into pieces. cDNA synthesis and labeling was done with a starting amount of 1 Î¼g, using the Affymetrix Eukaryotic One-Cycle Target Hybridization. Washing, Staining and scanning protocol for Eukaryotic Cartridge Arrays with User-Prepared Buffers and Sloutions according to the technical manuals (Affymetrix GeneChip Expression, Analysis for Cartridge Arrays using the GCAS version 1.4, Affymetrix GeneChip Expression Analysis (P/N 701021,Rev. 5) , Affymetrix GeneChip Expression Wash, Stain and Scan (P/N 702731, Rev. 3) , following the manufacturer/s instructions. Data was RMA normalized.

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Expression data from pure/mixed blood and breast to test feasability of deconvolution of clinical samples

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