From each frozen lung tissue 10-µm thick sections were prepared and transferred on polyethylene napthalate foil-covered slides (Zeiss, P.A.L.M. Microlaser Technologies GmbH, Bernried, Germany). The sections were fixed in methanol / acetic acid and stained in hematoxylin. The desired cells were microdissected using the PALM MicroLaser systems (Zeiss, P.A.L.M. Microlaser Technologies GmbH, Bernried, Germany) and collected in an adhesive cap (Zeiss, P.A.L.M. Microlaser Technologies GmbH, Bernried, Germany). Microdissected cells were resuspended in a guanidine isothiocyanate-containing buffer (RLT buffer from RNeasy MikroKit, Qiagen, Santa Clarita, CA, USA) with 10 µl/ml β-mercaptoethanol to ensure isolation of intact RNA. Approximately an area of 6 x 106 µm2 were pooled from a specific layer of interest in the same animal and used for RNA extraction. Following microdissection, total RNA-extraction was performed with the RNeasy Micro Kit (RNeasy MicroKit Qiagen, Santa Clarita, CA, USA) according to the manufacturer’s instruction. A standard quality control of the total RNA was performed using the Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, USA).
Label
Biotin
Label protocol
Total RNA (175ng) was used to generate biotin-labeled cRNA (10 µg) by means of Message Amp aRNA Premium Amplification Kit (Ambion, Austin, TX). Quality control of cRNA was performed using a bioanalyzer (Agilent 2001 Biosizing, Agilent Technologies).
Hybridization protocol
Following fragmentation, labeled cRNA of each sample was hybridized to Affymetrix GeneChip® Mouse Genome 430 2.0 Arrays covering over 34.000 genes and stained according to the manufacturer's instructions. The hybridization was performed for 16 hours at 60 rpm and 45°C in the GeneChip® Hybridization Oven 640 (Affymetrix). Washing and staining of the arrays were done on the Gene Chip® Fluidics Station 400 (Affymetrix) according to the manufacturers recommendation. The antibody signal amplification, washing and staining protocol (Affymetrix) was used to stain the arrays with streptavidin R-phycoerythrin (SAPE). To amplify staining, SAPE solution was added twice with a biotinylated anti-streptavidin antibody (Vector Laboratories, CA) staining step in between
Scan protocol
The arrays were scanned using the Affymetrix GeneChip® Scanner 7G. Scanned image files was visually inspected for artefacts and then analyzed, each image being scaled to the same target value for comparison between chips. The GeneChip® Operating Software (GCOS) was used to control the fluidics station and the scanner, to capture probe array data and to analyze hybridization intensity data.
Description
Lung samples were derived from SP-C/c-raf mice in the C57BL/6 background for at least five generations (aged 5 – 10 months); dysplastic and unaltered lung tissue were always isolated from the same dysplasia-bearing transgenic mouse (aged 5 – 7 months). Endogenous normal lung tissue was studied of non-transgenic mice (aged 7 – 10 months). The non-transgenic littermates (wild-type) served as control for transgenic effects. Mice were sacrificed and the lung tissues were immediately frozen on dry ice and stored at -80°C until further analysis.
Data processing
Array data was normalized using scaling or per-chip normalization to adjust the total or average intensity of each array to be approximately the same.
Microarray chips were analyzed by the GCOS (GeneChip Operating Software) from Affymetrix with the default settings except that the target signal was set to 500 and used to generate a microarray quality control and data report.
CEL files exported from GCOS were uploaded into ArrayTrack software (National Center for Toxicological Research, U.S. FDA, Jefferson, AR, USA (NCTR/FDA)) and normalized using Total Intensity Normalization after subtracting backgrounds for data management and analysis. ArrayTrack software includes some tools common to other bioinformatics software (e.g., ANOVA, T-test and SAM).To compare the normalized data from dysplasia, normal lung tissue from transgenic mouse, tumor cells and non-transgenic of different mice, we used the Significance Analysis of Microarrays (SAM) algorithm (ArrayTrack), which contains a sliding scale for false discovery rate (FDR) of significantly up- and down-regulated genes [70]. All data were permuted 100 cycles by using the two classes, unpaired data mode of the algorithm. As cut-off for significance an estimated FDR of 0.001 was chosen. Moreover, a cut-off for fold-change of differential expression of 2 was used.
Two clustering approaches were used to determine components of variation in the data in this study as follows.
A) Principal-component analysis (PCA) that was used to obtain a simplified visualization of entire datasets. PCA is a useful linear approach to obtain a simplified visualization of entire datasets, without losing experimental information (variance). PCA allowed the dimension of complex data to be reduced and highlights the most relevant features of a given dataset to be highlighted.
B) Hierarchical gene clustering (HCA) where the data points were organized in a phylogenetic tree in which the branch lengths represent the degree of similarity between the values.
