Unknown,Transcriptomics,Genomics,Proteomics

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T helper lymphocyte- and monocyte-specific type I interferon (IFN) signatures in autoimmunity and viral infection.

ABSTRACT: This study demonstrates quantitative and qualitative differences between type I IFN signatures in autoimmunity and viral infection using purified CD4pos T cells and CD16pos- and CD16neg-monocyte subsets. We were able to discriminate between cell-specific viral response signatures and the pathogenically amplified IFN signatures observed in autoimmunity. The differences were of both a qualitative and quantitative nature, as the signatures in the patients with SLE were characterized by much more complexly compiled gene patterns with increased absolute gene expression levels. Collection of cells from Systemic Lupus Erythematosus (SLE) patients: For CD4pos T cells, six patients with SLE (average age: 29.0 +/- 7.6) and four normal healthy donors (ND; 24.8 +/- 0.5) were recruited. For CD16neg monocytes, four patients with SLE (26.5 +/- 1.7) and four NDs (24.8 +/- 0.5) were recruited. For CD16pos monocytes, four patients with SLE (26.5 +/- 1.7) and three NDs (24.7 +/- 0.6) were recruited. All patients and NDs were female. The same NDs were examined before and after immunisation with yellow fever vaccine (YFV). Collection of cells from yellow-fever vaccinated individuals: ND were immunised with a vaccine against the wild-type YF virus, which is a single-stranded RNA virus without adjuvants. This vaccine consists of a live but attenuated strain of the yellow fever virus (YFV-17D). Based on its vaccination-associated clinical and serological manifestations, this immunisation can be regarded as a real viral infection. A total of 50 ml peripheral blood was taken 7 days after immunisation, when sufficient numbers of CD19pos/CD27high plasmablasts were detected by flow cytometry. Cell sorting: A total of 50 ml peripheral blood was collected in Vacutainer heparin tubes and erythrocytes were lysed in EL buffer. Subsequently, granulocytes were depleted using CD15-conjugated microbeads (MACS). The CD15-depleted fraction was stained with a CD14-fluorescein isothiocyanate (FITC) antibody, a CD16-APC-Cy7 antibody, a CD3-Vioblue antibody and a CD4-FITC antibody. Using a FACSAria cell sorter, CD4pos T cells, CD16neg monocytes and CD16pos monocytes were isolated with purities and viabilities of >97%. After sorting, the cells were immediately lysed with RLT buffer and frozen at -70 M-BM-0C. Total RNA was isolated using an RNeasy mini kit, and quality control was ensured by Bioanalyser measurements.Total RNA was extracted using the RNeasy Mini kit. The integrity and amount of isolated RNA was assessed for each sample using an Agilent 2100 Bioanalyzer and a NanoDrop ND-1000 spectrophotometer. Biotinylated complementary RNA (cRNA) was synthesized from 100 ng total RNA, using reagents as recommended in the technical manual from Affymetrix. Fifteen micrograms of fragmented cRNA of each sample were hybridized to HG-U133 plus 2.0 arrays. Hybridization was performed according to procedure 2 as described in the technical manual. Finally, the arrays were scanned with a GeneChip Scanner 3000 using the GCOS software. All relevant GCOS data of quality checked microarrays were analyzed with High Performance Chip Data Analysis (HPCDA, unpublished), using the BioRetis database (www.bioretis-analysis.de), as described and validated previously.

ORGANISM(S): Homo sapiens

SUBMITTER: Joachim GrM-CM-<n

PROVIDER: E-GEOD-51997 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Gene expression profiling of cells isolated ex vivo is a unique tool to assess gene expression in vivo. Exemplified for CD4+CD45RO+ effector/memory T helper (T E/M) lymphocytes of human peripheral blood, we have analyzed different isolation procedures and storage conditions for the introduction of bias. Cells from clinical samples can be sorted to high purity by a flow cytometric sorter and assessed for their gene expression pattern but storage/shipping conditions and often necessary pre-enrichment due to low initial population frequencies bears the danger of introducing artificial changes. To test for changes introduced by different procedures, cells were enriched either by magnetic cell sorting (MACS) with Whole-Blood CD4 beads, density gradient centrifugation, alone or in combination with CD3 MACS, or by lysis of erythrocytes followed by MACS depletion of CD15+ cells prior to fluorescence-activated cell sorting (FACS) as CD3+CD4+CD45RO+ cells. A total of 7 different protocols were compared among each other and to T cells stimulated for 3 hours after isolation with Phorbol-12-myristate-13-acetate and Ionomycin (PMA/Iono). Preparation-induced de novo transcription was blocked in one of the protocols by addition of 2 M-BM-5g/ml Actinomycin D (ActD). To test for changes introduced by delays in sample preparation blood samples were drawn and either processed directly or kept at 4 M-BM-0C or room temperature (RT) for 2 or 6 hours before T E/M cells were isolated. Gene expression was compared among the various cell preparations and to T E/M cells isolated from 1 day old buffy coat fractions stored at RT. Sorted cells were lysed in TRIzol (Invitrogen) and frozen at -80M-BM-0C until used for RNA extraction. Total RNA was extracted using the miRNeasy kit (Qiagen). The integrity and amount of isolated RNA was assessed for each sample using an Agilent 2100 Bioanalyzer (Agilent, Waldbronn, Germany) and a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE). Double-stranded complementary RNA was synthesized from 1 M-BM-5g total RNA using Message AmpII Biotin (Ambion, USA). Fifteen micrograms of fragmented cRNA of each sample were hybridized to 26 HG-U133A plus 2.0 GeneChips (Affymetrix). Hybridization was performed in a Hybridization Oven 640, and chips were washed and stained in the Fluidics Station 400 (both Affymetrix). Finally, the arrays were scanned with a GeneChip Scanner 3000 using the GCOS software, version 1.4, both Affymetrix. All relevant GCOS data of quality checked microarrays were analyzed with High Performance Chip Data Analysis (HPCDA, unpublished), using the BioRetis database (www.bioretis-analysis.de), as described and validated previously.

Project description:In order to identify genes that are activated in differentiating WT ESCs, but are missing in Tal-1-/- and Runx1-/- ESCs, and which might be involved in the generation of definitive hematopoietic progenitors and their specification thereafter, we performed microarray analyses on purified Flk-1+ cells, differentiated from these ESCs for 4, 5, and 6 days M-bM-^@M-^\in vitroM-bM-^@M-^]. Gene-expression profiling of three biological replicates was performed at days 4, 5, and 6 during the differentiation process of WT J1 ESCs (9 samples), and at day 6 during the differentiation process of either Runx1-/- J1 or Tal-1-/- J1 ESCs (3 samples each). Total RNA was extracted using the RNeasy Mini kit (Qiagen). The integrity and amount of isolated RNA was assessed for each sample using an Agilent 2100 Bioanalyzer (Agilent, Waldbronn, Germany) and a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE). Complementary DNA was synthesized from 3-5 M-NM-<g total RNA, using reagents recommended in the technical manual GeneChip Expression Analysis (Affymetrix, Santa Clara, CA). The in vitro transcription, necessary for the synthesis of biotinylated complementary RNA (cRNA) was performed using the Enzo RNA Transcript Labeling kit (Affymetrix). Fifteen micrograms of fragmented cRNA of each sample were hybridized to nine Mouse Genome 430-2 arrays (Affymetrix). Hybridization was performed in a Hybridization Oven 640, and chips were washed and stained in the Fluidics Station 400 (both Affymetrix), according to procedure 2 as described in the technical manual. Finally, the arrays were scanned with a GeneChip Scanner 3000 using the GCOS software, both Affymetrix. All relevant GCOS data of quality checked microarrays were analyzed with High Performance Chip Data Analysis (HPCDA, unpublished), using the BioRetis database (www.bioretis-analysis.de), as described and validated previously (55). Used query parameters for database filtering process was described earlier several times (56). For hierarchical cluster analysis, we used the program Genes@Work (57) with gene vectors for normalization and Pearson w/mean for similarity measure. As cluster type, we used center of mass.

Project description:Objective: In idiopathic inflammatory myopathies (IIM) infiltration of immune cells into muscle and upregulation of MHC-I expression implies increased antigen presentation and involvement of the proteasome system. To decipher the role of immunoproteasomes in myositis, we investigated individual cell types and muscle tissues and focused on possible immune triggers. Methods: Expression of constitutive (PSMB5, -6, -7) and corresponding immunoproteasomal subunits (PSMB8, -9, -10) was analyzed by real-time RT-PCR in muscle biopsies and sorted peripheral blood cells of patients with IIM, non-inflammatory myopathies (NIM) and healthy donors (HD). Protein analysis in muscle biopsies was performed by western blot. Affymetrix HG-U133 platform derived transcriptome data from biopsies of different muscle diseases and from immune cell types as well as monocyte stimulation experiments were used for validation, coregulation and coexpression analyses. Results: Real-time RT-PCR revealed significantly increased expression of immunoproteasomal subunits (PSMB8/-9/-10) in DC, monocytes and CD8+ T-cells in IIM. In muscle biopsies, the immunosubunits were elevated in IIM compared to NIM and exceeded levels of matched blood samples. Proteins of PSMB8 and -9 were found only in IIM but not NIM muscle biopsies. Reanalysis of 78 myositis and 20 healthy muscle transcriptomes confirmed these results and revealed involvement of the antigen processing and presentation pathway. Comparison with reference profiles of sorted immune cells and healthy muscle confirmed upregulation of PSMB8 and -9 in myositis biopsies beyond infiltration related changes. This upregulation correlated highest with STAT1, IRF1 and IFNM-NM-3 expression. Elevation of T-cell specific transcripts in active IIM muscles was accompanied by increased expression of DC and monocyte marker genes and thus reflects the cell type specific involvement observed in peripheral blood. Conclusions: Immunoproteasomes seem to indicate IIM activity and suggest that dominant involvement of antigen processing and presentation may qualify these diseases exemplarily for the evolving therapeutic concepts of immunoproteasome specific inhibition. Investigation of constitutive and immunoproteasomal subunit expression in muscle tissue of patients with inflammatory and non-inflammatory myopathies These transcriptomes were used as reference signatures of different immune cell types in order to estimate the contribution of immune cell transcripts to the muscle transcriptomes investigated in the datasets GSE2044, GSE3112, GSE5370, GSE39454, GSE3307, GSE13205, GSE10685.

Project description:To compare human memory CD4+ T cell subsets in peripheral blood (PB) and bone marrow (BM) of healthy individuals at transcriptional level, we analyzed the global gene expression of ex vivo PB CD69- as well as BM CD69- and CD69+ memory CD4+ T cells from 4 paired PB and BM samples. The gene expression of these subsets was additionally compared to the transcriptional profile of 8 PB samples taken ex vivo or stimulated with phorbol myristate acetate (PMA) and Ionomycin for 3 hours. Three ex vivo memory CD4+ T cell subsets (CD4+CD45RO+CD69+ and CD4+CD45RO+CD69- cells from bone marrow; CD4+CD45RO+CD69- cells from peripheral blood) were isolated from 4 paired bone marrow and blood samples (two males and two females) or from a different cohort of 8 blood samples (3 males and five females). The purity of sorted cells was higher than 95% as assessed by FACS. Subsequently, a fraction of purified cells from the 8 blood samples were stimulated with phorbol-myristic-acid/ionomycin (PMA/iono) for 3 h and used as high controls. Total RNA of each cell subset was extracted using a NucleoSpin RNA XS Kit (Macherey-Nagel) or RNeasy Mini kit (Qiagen). The integrity and amount of isolated RNA was assessed for each sample using an Agilent 2100 Bioanalyzer (Agilent, Waldbronn, Germany) and a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE). Double-stranded complementary RNA was synthesized from 1 M-BM-5g total RNA using Message AmpII Biotin (Ambion, USA). Fifteen micrograms of fragmented cRNA of each sample were hybridized to 28 HG-U133A plus 2.0 GeneChips (Affymetrix). Hybridization was performed in a Hybridization Oven 640, and chips were washed and stained in the Fluidics Station 400 (both Affymetrix). Finally, the arrays were scanned with a GeneChip Scanner 3000 using the GCOS software, version 1.4, both Affymetrix. All relevant GCOS data of quality checked microarrays were analyzed with High Performance Chip Data Analysis (HPCDA, unpublished), using the BioRetis database (www.bioretis-analysis.de), as described and validated previously.

Project description:To determine characterize human B cells that express IL-10 on a molecular level, we compared the global gene expression of primary CD19pos B cells secreting IL-10 or not, upon activation with anti-CD40, IL-4 and CpG for 2 days. Human B cells from healthy donors were sorted according to CD19 expression by magnetic cell separation. The cells were stimulated at 2.5E06 cells/ml by activating anti-CD40 mAb (1 M-BM-5g/ml, clone 82111, RnD Systems), recombinant human IL-4 (10 ng/ml, Immunotools) and CpG2006 (3 M-BM-5g/ml) for 2 days following restimulation for 3 h with phorbol myristate acetate (PMA, 10 ng/ml) and ionomycin (1 M-BM-5g/ml). Staining of viable IL-10 secreting human CD19pos B cells was performed by the IL-10 capture assay according the manufacturers instructions (Miltenyi Biotec, n=2). Finally, the cells were sorted into IL-10posCD69pos and IL-10negCD69pos cells by flow cytometric cell sorting on a BD Aria II sorter (Becton Dickinson). Dead cells were excluded using propidium iodide. Total RNA was extracted using the RNeasy Mini kit (Qiagen). The integrity and amount of isolated RNA was assessed for each sample using an Agilent 2100 Bioanalyzer (Agilent, Waldbronn, Germany) and a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE). Double-stranded complementary RNA was synthesized from 1 M-BM-5g total RNA using Message AmpII Biotin (Ambion, USA). Fifteen micrograms of fragmented cRNA of each sample were hybridized to four HG-U133A plus 2.0 GeneChips (Affymetrix). Hybridization was performed in a Hybridization Oven 640, and chips were washed and stained in the Fluidics Station 400 (both Affymetrix). Finally, the arrays were scanned with a GeneChip Scanner 3000 using the GCOS software, version 1.4, both Affymetrix. All relevant GCOS data of quality checked microarrays were analyzed with High Performance Chip Data Analysis (HPCDA, unpublished), using the BioRetis database (www.bioretis-analysis.de), as described and validated previously.

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T helper lymphocyte- and monocyte-specific type I interferon (IFN) signatures in autoimmunity and viral infection.

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