RNA isolation and microarray analysis

About 2 million of GFP positive (two groups of samples from E14 hGFAP-eGFP cortex, GFPhigh and GFPlow) or GFP/prominin-double positive sorted cells (two groups of samples from E14 cortex, GFPhigh/prominin positive cells and GFPlow/prominin positive cells; one group GFP/prominin positive from E18 cortex) were each split into three groups of biological replicates, re-suspended in lysis buffer and dissociated with a 0.6mm gauge needle for subsequent RNeasy RNA purification (RNeasy kit, QIAGEN). The same procedure was done for microarray analysis of embryonic stem cell derived radial glia (WT and Pax6-deficient (Bibel, 2007; Bibel et al., 2004; Nikoletopoulou et al., 2007)). The resulting RNA concentration was measured by the 260nm/280nm ratio (Nanodrop). RNA quality was examined in the Agilent 2100 Bioanalyser and revealed high purity of all RNA preparations. Agilent has developed software that will assign a specific quality number to the RNA sample based on its electrophoretic profile. The RNA Integrity Number (RIN) ranges from 1 (totally degraded RNA) to 10 (completely intact RNA). Only high quality RNA, with RIN greater than 8 and A_260/280 greater than 1.8 was considered for microarray analysis.

In a total of seventeen samples, RNA was prepared and used for microarray hybridization. From three replicates of E14 GFPlow and three from GFPhigh sorted cells 1µg of RNA was used for each microarray chip. From the three replicates of E14 GFPlow/prominin positive,

four of E14 GFPhigh/prominin positive and four of E18 GFPlow/prominin+ sorted cells only 0.5µg was used due to the smaller number of cellular yield. Generation of cDNA, production of labelled cRNA and hybridization to Affymetrix MOE4302.0 GeneChip (46 k probesets) were performed according to standard protocols provided by Affymetrix (www.affymetrix.com). The RNA amplification was performed with MessageAmp II-Biotin Enhanced (Ambion, 1791). The single round aRNA Amplification kit was used similarly for all samples to avoid variations resulting from multiple rounds of amplification. All housekeeping genes were present and the number of present calls was determined as 40% or higher. To process the data we calculated probe set summaries according to the three most popular algorithms – MAS 5.0, dChip, RMA and normalized the data - a nonlinear transformation employing the loess smoother (Cleveland, 1981). To test the quality and reproducibility of the samples, hierarchical clustering was used to find (dis)similarities between the samples, showing that the replicates of each group of cells analyzed were clustering together. Probe-sets (genes) were selected as significantly differentially expressed if they had a p-value <0.05 (i.e. 5% significance level) to reduce the level of false positives. Different false discovery rate controlling algorithms were applied such as the Bonferroni and Benjamim-Hochberg (Benjamini, 1997). We applied statistical tests on the probe set summaries, the Wech’s t-test, Bayesian t-test and the Wilcoxon paired rank test on the single probes. Data presented here are the differentially expressed probe-sets that showed a reproducible fold-change above threshold (2-fold in all biological replicates), exhibited a reasonably high expression level (equal or above 50; see Holm et al. 2007 for justification) in both sets of comparisons of cells sorted from E14 (GFPlow::GFPhigh and GFPlow/prominin+:: GFPhigh/prominin+) and E18 cortices as the most stringent approach. Confirmation of gene expression differences by real-time RT-PCR supported the reliability of this analysis.

The same microarray data were also analyzed for membrane proteins among the GFPlow and GFPhigh sorted cells. Data were analyzed on chip quality controls, including MAplot, boxplot and density expression values histograms that demonstrated the absence of significant defects. LIMMA package (Bioconductor 2.3) was used to build the “gene by gene” linear model to fit log (2) transformed intensities. The parameters considered to determine differential expression were: two-fold change between gene expression among the two cell lines and an adjusted P-value <0.01. For the GFPlow fraction out of the 907 up-regulated nucleotide sequences 285 aminoacid sequences were present in GeneBank, while 387 aminoacid sequences were available in GeneBank, out of 1159 upregulated nucleotide sequences in the GFPhigh fraction. For each of the 1394 nucleotide sequences with no

aminoacidic translation available, a tblastx query (Gentilini, 2006) has been performed in order to associate the best scoring aminoacidic sequence (e-value<1e-70). The protein databases Non-redundant GenBank CDS translations, PDB, SwissProt, PIR and PRF have been used and protein sub-localization has been performed using the WoLF PSORT (Paul Horton, 2006).

The following link has been created to allow review of all microarray data with the series entry GSE8034:

http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?token=tbylhsqmyikogpk&acc=GSE8034 Total RNA was also isolated from cortical tissue of WT and AP2γ conditional embryos at E14 using the RNeasy Mini kit (Qiagen) including DNase treatment. RNA quality was examined in the Agilent 2100 Bioanalyzer and Pico Labchip kit. RNA of WT and AP2γ

conditional animals from three different litters was amplified using the MessageAmp II- Biotin Enhanced Single Round aRNA Amplification Kit (Ambion) and hybridized on Affymetrix MOE430 2.0 arrays containing about 46k probe sets according to standard protocols provided by Affymetrix (www.affymetrix.com). Staining and scanning was done according to the Affymetrix expression protocol. For statistical analysis of the expression data the Bioconductor software package implemented in Carma web (Rainer et al., 2006) was employed using RMA preprocessing and the paired moderated limma test. The Benjamini- Hochberg algorithm was used to identify genes with a false discovery rate < 5 %.