Project description:Gene expression profile studies have identified an interferon signature in whole blood or mononuclear cell samples from patients with systemic lupus erythematosus. This study was designed to determine whether specific lymphocyte and myeloid subsets freshly isolated from the blood of systemic lupus erythematosus patients demonstrated unique gene expression profiles compared to subsets isolated from healthy controls. Experiment Overall Design: The entire study included 67 samples. One CEL file was not available for SLE13_CD4
Project description:We focused on the major peripheral blood lymphocyte populations that may be involved in anti-tumor responses and negatively impacted by cancer, specifically CD8 T cells, CD4 T cells, B cells and CD56dim natural killer cells. The pure cell subsets were stringently sorted by flow cytometry from PBMC samples. Gene expression profiles of these cell populations from melanoma patients were compared to healthy controls. Experiment Overall Design: The raw data set contained 48 arrays: 6 healthy and 6 melanoma arrays for each of the 4 cell types. Two of the arrays had quality issues due to background noise and were excluded, leaving us with 46 arrays. Experiment Overall Design: On each array we used Cy3 to label a pool of two RNA samples from a pair of age and gender matched stage IV (American Joint Committee on Cancer) melanoma patients or from a pair of age and gender matched healthy donors. We used Cy5 to label the Total Lymphocyte Reference (TLR) RNA. The TLR is a common reference specifically created for this study using the total peripheral lymphocyte fraction from 20 healthy donors.
Project description:An early-differentiated CD8+ memory T cell subset with stem cell-like properties (TSCM) can be identified within the naïve-like T cell population by the expression of CD95/Fas. Based on experiments including exon- and gene-level expression analysis, we provide evidence that this subset of antigen-specific cells represents an early precursor of conventional central (TCM) and effector (TEM) memory CD8+ T cells with enhanced self-renewal capacity and proliferative potential. We identified 900 genes differentially expressed between major T cell subsets defined along with memory T cell commitment. Based on the analysis of these genes, CD95+ naïve T cells (TSCM) cluster closer to the CD8+ T memory compartment than to classical (CD95-) naïve T (TN) cells, and display an intermittent phenotype between classical TN and TCM cells in terms of all major T cell differentiation markers analyzed. Three healthy human blood donors provided lymphocyte-enriched apheresis blood for this study after informed consent. From all samples, total RNA was isolated using an RNEasy Micro kit (Qiagen), processed by Ambion’s WT expression kit, fragmented and labeled with a WT Terminal Labeling Kit (Affymetrix), hybridized to WT Human Gene 1.0 ST arrays (Affymetrix) and stained on a Genechip Fluidics Station 450 (Affymetrix), all according to the respective manufacturer's instructions. Samples represent "exon-level" and "gene-level" analyses.
Project description:A small subset of T cells also expresses kiler-cell immunoglobulin-like receptors (KIRs). We find that KIR+ T cells primarily reside in the CD56+ T population. However, little is known on how these cells are different from the conventional CD56- T, NK, and iNKT cells. We used microarray profiling to compare and determine the distinctive differences of CD56+ T cell and its KIR subsets when compared to the conventional CD56- T, NK and iNKT cells. Lymphocyte subsets were sorted from human peripheral blood mononuclear cells with FACSAriaII (BD Biosciences, San Jose, CA) using anti-CD3, anti-CD56, anti-CD14, anti-KIR2DL1, anti-KIR2DL2/3, anti-KIR3DL1 and anti-TCRValpha24 antibodies. The purity of CD3+CD56- T cells, CD3-CD56+ NK cells, CD3+CD56+ T cells, KIR-CD3+CD56+ T cells, and KIR+CD3+CD56+ T cells were more than 98% in all experiments. The purities of iNKT cells for TCRValpha24 and CD1d-tetramer were >95% and >90%, respectively. RNA pre-amplification, labeling and hybridization on Human Genome U133Plus 2.0 GeneChip array were performed in the St. Jude Hartwell Center for Bioinformatics & Biotechnology microarray core facility according to the manufacturer’s instructions (Affymetrix, Santa Clara, CA).
Project description:Little is known about alteration of the global gene expression by cigarette smoke (CS) and few biomarkers for smoking-related harm are available. We used Affymetrix HG-U133A GeneChips to measure the transcriptomes in eight cultured lymphocyte samples exposed to cigarette smoke condensate (CSC) in vitro . The in vitro exposure of lymphocytes to CSC significantly changed expression levels of 2,266 genes many of which biologically interacted. They included genes encoding for xenobiotic metabolism and oxidative stress-response (e.g. Nrf2 and AhR signaling pathways), inflammation/immune response (e.g. cytokines), apoptosis, cell cycle and tumorigenesis. However, the magnitude of expression responses for some genes showed high inter-individual variability. Experiment Overall Design: The goals of this study were to evaluate novel gene expression profiles and pathways affected by cigarette smoke condensate (CSC), and to identify potential biomarkers for cigarette smoke exposure and harm. To this end, we isolated the PBMC from eight light smokers, cultured the cells in vitro and exposed them to 2R4F CSC, then determined the gene expression profiles with Affymetrix microarray and analyzed alteration of global gene expression after exposure to CSC.
Project description:The recent discovery of the human B1 cells, identified by the expression of CD20, CD27 and CD43 in absence of expression of CD70 and CD69 has been subject of debate. Some studies have raised the possibility that these cells are B cells differentiating towards the plasmablast and plasma cell stage rather than being the human counterpart of murine B1 cells. No further in depth studies have been performed. Therefore, a functional comparison was made between, the proposed B1 cells and plasmablasts. We observed that for several functional characteristics (distribution of isotypes of spontaneously producted antibodies, production of antigen-specific antibodies after vaccination with both T-cell dependent as well as T-cell independent antigen, the proposed B1 cells behaved similar to plasmablasts. In addition, we were able to differentiate the proposed B1 cells in vitro, indicating that they are not from a distinct lineage as the murine B1 cells. Gene expression analysis revealed that these cells cluster between memory B cells and plasmablasts, contradicting them being the genuine human counterpart of murine B1 cells, rather revealing a preplasmablast phenotype. Different B cells subsets were isolated from PBMC from healthy donors by a combination of magnetic and fluorescence activated cell sorting
Project description:Baseline gene expression of human lymphocytes 24 hours after 5 Gy gamma irradiation We used microarrays to detail the global gene expression of human lymphocytes after irradiation Compare the gloable gene expression of human lymphocyte 24 hours after exposure to doses between 0 and 5 Gy γ rays
Project description:Monocytes are a heterogeneous cell population with subset-specific functions and phenotypes. The differential expression of CD14 and CD16 distinguishes classical CD14++CD16-, intermediate CD14++CD16+ and non-classical CD14+CD16++ monocytes. However, CD14++CD16+ monocytes remain the most poorly characterized subset so far. Therefore we analyzed the transcriptomes of the three monocyte subsets using SuperSAGE in combination with high-throughput sequencing. Analysis of 5,487,603 tags revealed unique identifiers of CD14++CD16+ monocytes, delineating these cells from the two other monocyte subsets. CD14++CD16+ monocytes were linked to antigen processing and presentation (e.g. CD74, HLA-DR, IFI30, CTSB), to inflammation and monocyte activation (e.g. TGFB1, AIF1, PTPN6), and to angiogenesis (e.g. TIE2, CD105). Therefore we provide genetic evidence for a distinct role of CD14++CD16+ monocytes in human immunity. Human monocyte subsets (CD14++CD16-, CD14++CD16+, CD14+CD16++) were isolated from 12 healthy volunteers based on MACS technology. Total RNA from monocyte subsets was isolated and same aliquots from each donor and monocyte subset were matched for SuperSAGE. Three SuperSAGE libraries (CD14++CD16-, CD14++CD16+ and CD14+CD16++) were generated.
Project description:Host defense against diverse pathogens involves the recruitment and differentiation of CD4+ T effector subsets including T helper 1 (Th1), Th2, Th17 and induced regulatory T (Treg) cells. Surface phenotype studies have revealed subset-specific surface markers for the identification and purification of human primary CD4+ T effector subsets. In the present study, we aimed to characterize the mRNA and large intergenic non-coding RNA (lincRNA) expression differences between human primary CD4+ T effector subsets and identify potential subset-specific genes. To achieve this goal, mRNA and lincRNA microarray profiling of flow cytometry-sorted human primary Th1, Th2, Th17 and Treg cells was performed. Principal component and pathway analyses revealed subset-specific gene expression patterns. A Th2-specific lincRNA, GATA3-AS1, also termed FLJ45983, was identified in primary immune cells and tissues, as well as in in vitro polarized CD4+ T effector subsets. Further analysis showed that GATA3-AS1 was a potential diagnostic marker in allergy, a Th2-associated disease. This first systematic genome-wide analysis of gene expression differences between primary CD4+ T effector subsets may help to identify novel regulatory protein-coding genes and lincRNAs regulating CD4+ T cell subset differentiation, as well as potential diagnostic markers. As an example, we identified a GATA3-associated Th2-specific marker lincRNA GATA3-AS1. Gene expression microarray analysis of flow-cytometry sorted human primary naïve CD4+ T cells, CD4+ T central memory cells, Th1, Th2, Th17 and Treg cells from buffy coat of four healthy controls Gene expression microarray analysis was performed using SurePrint G3 Human Gene Expression 8X60K microarray.
Project description:Different pathogens trigger naïve T cells to express distinct sets of effector proteins. To better understand the molecular mechanisms that drive this functional specification, we used high resolution, label-free mass spectrometry to measure proteomic differences between the seven largest circulating human CD8+ T cell subsets. Unsupervised hierarchical clustering of the proteomes placed naïve and CD45RA-expressing effector-type T cells at the extremes of the spectrum with central-memory and other effector-memory stages located in between. Prominent differences between the subsets included expression of various granzymes, signaling proteins and molecules involved in metabolic regulation. Remarkably, whereas most of the proteomic changes between the subsets were gradual, a small proportion of proteins were regulated only in discrete subsets. The data obtained from this proteome analysis correspond best to a progressive differentiation model in which specific stable traits are gradually acquired during pathogen-specific development.