Immune Cell Line Mixtures
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE11058 | GEO | 2009/07/07
SECONDARY ACCESSION(S): PRJNA108813
REPOSITORIES: GEO
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Project description:Microarray deconvolution is a technique for quantifying the relative abundance of constituent cells in a mixture based on that mixture's microarray signature and the signatures of the purified constituents. Its ability to discriminate related human cells is unknown. Here we test the ability of this technique to determine the fractions of transformed cells of immune origin in mixed samples. Experiment Overall Design: Four immune cell lines were grown and run on microarrays either by themselves or in mixtures of various relative proportions. Mixtures of cells were performed in triplicate. Experiment Overall Design: MixA (Jurkat: 2.5, IM-9: 1.25, Raji: 2.5, THP-1: 3.75) Experiment Overall Design: MixB (Jurkat: 0.5, IM-9: 3.17, Raji: 4.75, THP-1: 1.58) Experiment Overall Design: MixC (Jurkat: 0.1, IM-9: 4.95, Raji: 1.65, THP-1: 3.3) Experiment Overall Design: MixD (Jurkat: 0.02, IM-9: 3.33, Raji: 3.33, THP-1: 3.33)
2009-07-19 | E-GEOD-11058 | biostudies-arrayexpress
Project description:Microarray deconvolution is a technique for quantifying the relative abundance of constituent cells in a mixture based on that mixture's microarray signature and the signatures of the purified constituents. It has been applied to yeast and other systems but not to blood samples. Here we test the ability of this technique to determine the fractions of subsets of memory T cells in peripheral blood mononuclear cell (PBMC) samples. Keywords: cell type comparison
2009-07-07 | GSE11057 | GEO
Project description:Microarray deconvolution is a technique for quantifying the relative abundance of constituent cells in a mixture based on that mixture's microarray signature and the signatures of the purified constituents. It has been applied to yeast and other systems but not to blood samples. Here we test the ability of this technique to determine the fractions of subsets of memory T cells in peripheral blood mononuclear cell (PBMC) samples. Experiment Overall Design: PBMC samples from several donors were split. One portion of each PBMC sample was set aside, while the remainder of each was sorted into naive, central memory, and effector memory T cell subsets.
2009-07-19 | E-GEOD-11057 | biostudies-arrayexpress
Project description:Confounding due to cellular heterogeneity represents one of the foremost challenges currently facing Epigenome-Wide Association Studies (EWAS). Statistical methods leveraging the tissue-specificity of DNA methylation for deconvoluting the cellular mixture of heterogeneous biospecimens such as whole blood, offer a promising solution. However, their performance depends entirely on the library of DNA methylation markers being used as the basis for deconvolution. The objective of this study was to train and validate an algorithm for the identification of optimal DNA methylation libraries for the deconvolution of adult human whole blood.
2016-02-12 | GSE77797 | GEO
Project description:Small organisms can be used as biomonitoring tools to assess chemicals in the environment. Chemical stressors are especially hard to assess and monitor when present as complex mixtures. Here, Daphnia magna were exposed for 24 hours to five different munitions constituents 2,4,6-trinitrotoluene (TNT), 2,4-dinitrotoluene (2,4-DNT), 2,6-dinitrotoluene (2,6-DNT), trinitrobenzene (TNB), dinitrobenzene (DNB), or 1,3,5-trinitro-1,3,5-triazacyclohexane (RDX) as well as to 8 different munitions mixtures and ground water contaminated with munitions constituents. To better understand possible mixture effects, gene expression changes from all treatments were compared using high-density microarrays. While mixtures and ground water exposures had genes and gene functions in common with single chemical exposures, unique functions were also affected, which was consistent with the non-additivity of chemical effects in these mixtures.
2011-06-23 | GSE30163 | GEO
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2011-06-23 | GSE30163 | GEO
Project description:The cellular composition of heterogeneous samples can be predicted from reference gene expression profiles that represent the homogeneous, constituent populations of the heterogeneous samples. However, existing methods fail when the reference profiles are not representative of the constituent populations. We developed PERT, a new probabilistic expression deconvolution method, to address this limitation. PERT was used to deconvolve cellular composition of variably sourced and treated heterogeneous human blood samples. Our results indicate that even after correcting batch effects, cells presenting the same cell surface antigens display different transcriptional programs when they are uncultured versus culture-derived. Given gene expression profiles of culture-derived heterogeneous samples and profiles of uncultured reference populations, PERT was able to accurately recover proportions of pure populations composing the heterogeneous samples. We anticipate that PERT will be widely applicable to expression deconvolution problems using profiles from reference populations that vary from the corresponding constituent populations in cellular state but not cellular identity. Gene expression microarray to examine transcriptome variations between uncultured and culture-deried blood cells of the same phenotype as defined by the on and off expression of antigens. Fresh human umbilical cord blood-derived and serum free culture-derived colony-forming unit-monocytes (CFU-M) and megakaryocytes (MEGA) were compared respectively
2012-09-13 | E-GEOD-40830 | biostudies-arrayexpress
Project description:Confounding due to cellular heterogeneity represents one of the foremost challenges currently facing Epigenome-Wide Association Studies (EWAS). Statistical methods leveraging the tissue-specificity of DNA methylation for deconvoluting the cellular mixture of heterogeneous biospecimens such as whole blood, offer a promising solution. However, their performance depends entirely on the library of DNA methylation markers being used as the basis for deconvolution. The objective of this study was to train and validate an algorithm for the identification of optimal DNA methylation libraries for the deconvolution of adult human whole blood. Purified granulocytes, monocytes, CD4T, CD8T, natural killer cells, and B cells from normal human subjects were purchased from AllCells LLC (Emeryville, CA). DNA extracted from purified leukocyte subtypes were mixed in predetermined proportions to reconstruct two distinct sets of white blood cell (WBC) mixtures, each consisting of six samples. An additional six whole blood (WB) samples from disease-free adult donors with available immune cell profiling data from flow cytometry were purchased from All-Cells LLC and were included in this investigation. All DNA samples were bisulfite modified using the Zymo EZ DNA Methylation kit (Irvine, CA) and profiled for DNA methylation using the Illumina HumanMethylation450 array platform.
2016-02-12 | E-GEOD-77797 | biostudies-arrayexpress
Project description:The cellular composition of heterogeneous samples can be predicted from reference gene expression profiles that represent the homogeneous, constituent populations of the heterogeneous samples. However, existing methods fail when the reference profiles are not representative of the constituent populations. We developed PERT, a new probabilistic expression deconvolution method, to address this limitation. PERT was used to deconvolve cellular composition of variably sourced and treated heterogeneous human blood samples. Our results indicate that even after correcting batch effects, cells presenting the same cell surface antigens display different transcriptional programs when they are uncultured versus culture-derived. Given gene expression profiles of culture-derived heterogeneous samples and profiles of uncultured reference populations, PERT was able to accurately recover proportions of pure populations composing the heterogeneous samples. We anticipate that PERT will be widely applicable to expression deconvolution problems using profiles from reference populations that vary from the corresponding constituent populations in cellular state but not cellular identity. Human umbilical cord blood-derived lineage negative cells and mononucleated cells Cellular compositions of mononucleated cell and lineage negative cell compartments were deconvolved based on the gene expression profiles
2012-09-13 | E-GEOD-40829 | biostudies-arrayexpress
Project description:Difference in RNA content of different cell types introduces bias to gene expression deconvolution methods. If ERCC spike-ins are introduced into samples, predicted proportions of deconvolution methods can be corrected
2019-04-03 | GSE129240 | GEO