|
| Status |
Public on Mar 31, 2015 |
| Title |
S9_KO_2 |
| Sample type |
RNA |
| |
|
| Source name |
cell line S9
|
| Organism |
Homo sapiens |
| Characteristics |
origin: human bronchium
cell line: S9
condition: control
|
| Extracted molecule |
total RNA |
| Extraction protocol |
Total RNA was isolated using the TRIzol reagent (Invitrogen). RNA was purified using the RNA Clean-Up and Concentration Micro Kit (Norgen) and concentrations were measured using a ND-1000 spectrophotometer (Thermo Fisher Scientific Inc). RNA integrity was validated by means of the lab-on-chip capillary electrophoresis technology (Bioanalyzer 2100, Agilent Technologies). Only RNA samples with an RNA integrity number (RIN)>9.5 [34], 260/280 nm≥1.8, 260/230 nm≥1.9 were used for microarray analyses.
|
| Label |
biotin
|
| Label protocol |
For each sample, 200 ng of total RNA was reverse transcribed into cDNA, amplified, and in vitro transcribed to cRNA. Sense-strand cDNA was generated from 10 µg of purified cRNA using random primers, followed by fragmentation and labelling using 5.5 µg of purified sense-strand DNA.
|
| |
|
| Hybridization protocol |
Biotinylated sense-strand DNA was then hybridized onto the Affymetrix GeneChip Human Gene 1.0 ST arrays for 16 h. Arrays were washed and stained using the Fluidics Station 450.
|
| Scan protocol |
Scanning was performed by GeneChip Scanner 3000 7G (Affymetrix); raw CEL files were generated using the GCOS software. Quality assessment of all hybridizations was carried out by inspecting scan images and by reviewing external and endogenous controls using the Expression Console software (Affymetrix).
|
| Description |
Gene expression data from S9 cells treated as control.
|
| Data processing |
Data analysis was carried out using Rosetta Resolver system for gene expression data analysis (Rosetta Bio software). In brief, the raw signals of the gene-specific probes were summarized using the Robust Multi-array Average algorithm and data transformation for array comparability was achieved by performing quantile normalization. Genes exhibiting significantly different expression on the RNA level were identified using the following cut-off criteria: one-way analysis of variance with subsequent Benjamini and Hochberg false discovery rate multiple-testing correction on pair-wise comparisons (ANOVA, p≤0.05), signal correction statistics (Ratio Builder, p≤0.05) and fold-change≥1.5-fold. Probe-set transformation into genes was performed by using the Rosetta Resolver transformation tool based on the Entrez Genes/Unigenes search engine (NCBI).
processed_data.txt: Columns A to C: Gene Identification; Columns D to J: Summarized Intensity signals; Column L & O: Fold Change, Column M,N and P, Q: p-value and post hoc p-value, respectively
|
| |
|
| Submission date |
Jan 15, 2015 |
| Last update date |
Mar 31, 2015 |
| Contact name |
Joerg Mostertz |
| E-mail(s) |
mostertz@uni-greifswald.de
|
| Organization name |
Greifswald University
|
| Department |
Competence Center Functional Genomics
|
| Lab |
Pathoproteomics
|
| Street address |
F.-L.-Jahnstr 15
|
| City |
Greifswald |
| ZIP/Postal code |
17489 |
| Country |
Germany |
| |
|
| Platform ID |
GPL6244 |
| Series (1) |
| GSE65018 |
The epithelial cell transcriptome after alpha-toxin treatment |
|
