Project description:Grob DA 280 Column Test Mix Liquid Injection Split Ratios 100:1 & 10:1 and Restek Fragrance Materials Test Mix spectra obtained with liquid injection at two split ratios over five concentrations in triplicate
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)
Project description:Mixtures of chlorinated persistent organic pollutants (C-POPs-Mix) are chemically related risk factors for type 2 diabetes mellitus (T2DM); however, the effects of chronic exposure to C-POPs-Mix on microbial dysbiosis remain poorly understood. Herein, male and female zebrafish were exposed to C-POPs-Mix at a 1:1 ratio of five organochlorine pesticides and Aroclor 1254 at concentrations of 0.02, 0.1, and 0.5 μg/L for 12 weeks. We measured T2DM indicators in blood and profiled microbial abundance, richness, and evenness in the gut as well as transcriptomic and metabolomic alterations in the liver. Exposure to C-POPs-Mix significantly increased blood glucose levels while decreasing the abundance and alpha diversity of microbial communities only in females at concentrations of 0.02 and 0.1 μg/L. The majorly identified microbial contributors to microbial dysbiosis were Bosea minatitlanensis, Rhizobium tibeticum, Bifidobacterium catenulatum, B. adolescentis, and Collinsella aerofaciens. PICRUSt results suggested that altered pathways were associated with glucose and lipid production and inflammation, which are linked to changes in the transcriptome and metabolome of the zebrafish liver. Metagenomics outcomes revealed close relationships between intestinal and liver disruptions to T2DM-related molecular pathways. Thus, microbial dysbiosis in T2DM-triggered zebrafish occurred as a result of chronic exposure to C-POPs-Mix, indicating strong host–microbiome interactions.
Project description:Mixtures of chlorinated persistent organic pollutants (C-POPs-Mix) are chemically related risk factors for type 2 diabetes mellitus (T2DM); however, the effects of chronic exposure to C-POPs-Mix on microbial dysbiosis remain poorly understood. Herein, male and female zebrafish were exposed to C-POPs-Mix at a 1:1 ratio of five organochlorine pesticides and Aroclor 1254 at concentrations of 0.02, 0.1, and 0.5 μg/L for 12 weeks. We measured T2DM indicators in blood and profiled microbial abundance, richness, and evenness in the gut as well as transcriptomic and metabolomic alterations in the liver. Exposure to C-POPs-Mix significantly increased blood glucose levels while decreasing the abundance and alpha diversity of microbial communities only in females at concentrations of 0.02 and 0.1 μg/L. The majorly identified microbial contributors to microbial dysbiosis were Bosea minatitlanensis, Rhizobium tibeticum, Bifidobacterium catenulatum, B. adolescentis, and Collinsella aerofaciens. PICRUSt results suggested that altered pathways were associated with glucose and lipid production and inflammation, which are linked to changes in the transcriptome and metabolome of the zebrafish liver. Metagenomics outcomes revealed close relationships between intestinal and liver disruptions to T2DM-related molecular pathways. Thus, microbial dysbiosis in T2DM-triggered zebrafish occurred as a result of chronic exposure to C-POPs-Mix, indicating strong host–microbiome interactions.
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.
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 μg, 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.
Project description:Quantitative analysis depends on pure-substance primary calibrators with known mass fractions of impurity. The datasets included were generated using mass spectrometry for the evaluation of label-free quantification (LFQ) as a method for determining the mass fraction of host-cell proteins (HCPs) in bioengineered proteins. For this purpose, hemoglobin-A2 (HbA2) was used, as obtained by overexpression in E.coli. Two different materials had been produced: natural, and U-15N-labeled HbA2. To quantify impurity, precursor ion (MS1-) intensities were integrated over all E.coli-proteins identified and divided by the intensities obtained for HbA2. This ratio was calibrated against the corresponding results for an E.coli-cell lysate, that had been spiked at known mass-ratios to pure HbA2. To demonstrate the universal applicability of LFQ, further proteomes (yeast and human K562) were then alternatively used for calibration and were found to produce comparable results. Valid results were also obtained when the complexity of the calibrator was reduced to a mix of nine proteins.