HepG2 (liver carcinoma) cell line input total RNA (ug) = 5ug; cRNA yield (ug) = 56.2; Lot# = 035PO41658D
Extracted molecule
total RNA
Extraction protocol
The eight samples that were analysed during the training course were represented by MCF-7 (breast adenocarcinoma) and HepG2 (liver carcinoma) cell line total RNA (Ambion, Austin, TX, USA) with 1.0 µg to 8.0 µg input of total RNA (for labeling described below), and four leukaemia patient sample lysates prepared from mononuclear cells obtained after Ficoll density purification. The total RNA from the patient lysates was extracted at each centre as part of the training program, making these samples a test of the entire microarray process workflow post sample acquisition (RNeasy kit, Qiagen, Hilden, Germany).
Label
biotin
Label protocol
For each sample preparation, total RNA was converted into double-stranded cDNA by reverse transcription using a cDNA Synthesis System kit including an oligo(dT)24 T7 primer(Roche Applied Science, Mannheim, Germany) and Poly-A control transcripts (Affymetrix, Santa Clara, CA, USA). The generated cRNA was purified using the GeneChip Sample Cleanup Module (Affymetrix) and quantified using the NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA). The incubation steps during the cDNA synthesis, in vitro transcription reaction, and target fragmentation were performed using the Hybex Microarray Incubation System (SciGene, Sunnyvale, CA, USA) and Eppendorf ThermoStat plus instruments (Eppendorf, Hamburg, Germany).
Hybridization protocol
Hybridization, washing, and staining protocols, respectively, were performed on Affymetrix GeneChip instruments (Hybridization Oven 640, Fluidics Station FS450) as recommended by the manufacturer.
Scan protocol
Scanning was performed on Affymetrix GeneChip Scanner GCS3000 as recommended by the manufacturer.
Description
Performed by Operator 1 from Site9 at Training stage
Data processing
Data pre-processing included the summarization to generate probe set level signals for each microarray experiment and was performed using DQN algorithm.
