Noerholm et al. BMC Cancer 2012, 12:22 http://www.biomedcentral.com/1471-2407/12/22 Bioconductor and the packages limma, Agi4×44PrePro- cess and vsn (see R script in the Additional file 1 infor- mation for details). Where not mentioned in the text, the data from quartile normalization after background subtraction were used. To reduce the risk that the normalization procedure introduced unintended biases or artifacts, we normalized the data in three different ways using: 1) variance stabi- lized normalization (VSN); 2) quartile normalization with background subtraction; and 3) quartile normaliza- tion without background subtraction. Although there were small differences between the three methods many of the same genes turned out to be dysregulated regard- less of the applied normalization. Clustering analysis, heat maps and Linear Discrimi- nant Analysis of the normalized data was done using dChip (http://biosun1.harvard.edu/complab/dchip). Nor- malized data was transferred to Excel and filtered with various criteria, as described in the text. Gene lists of interest were uploaded and analyzed with the online Gene Ontology Tool DAVID 6.7 http://david.abcc. ncifcrf.gov/. The raw data and the quartile normalized mean data with background subtraction has been depos- ited in GEO with accession# GSE24084. Reverse transcription and qPCR analysis Twelve μL of the RNA isolated from 1 mL of serum were reverse transcribed using Superscript VILO cDNA synthesis kit (Invitrogen, Carlsbad, CA, USA), according to manufacturer’s recommendations. Samples were then preamplified using the TaqMan® PreAmp Master Mix (Applied Biosystems, Carlsbad, CA, USA). Briefly, 12.5 μL of the cDNA was added to the PreAmp Master Mix together with all the genes of interest and pre- amplified for 14 cycles, according to the manufacturer’s recom- mendations. The samples were then diluted 1:10 and TaqMan qRT-PCR was performed on all samples for all the selected genes. The amplification was performed using ABI PRISM 7500 with the following program: 50° C, 2 min; 95°C, 10 min; 40 cycles of 95°C, 15 s, 60°C, 1 min on standard mode. Additional file 2: Table S1 con- tains a list of all the commercially available and custom made probes used. Results Unsupervised clustering separates GBM patients from controls in expression array analysis Exosomes and other microvesicles (less than 0.8 μm in diameter, see methods) were isolated from serum sam- ples from 9 GBM patients immediately prior to tumour removal and from 7 normal healthy controls. RNA from this microvesicle fraction (exoRNA) was extracted, amplified by linear amplification, labeled and hybridized to Agilent microarrays containing 44,000 (44 K) capture Page 3 of 11 probes against essentially all genes in the human gen- ome. The raw data was background corrected and nor- malized between samples, as described in the Methods section. To investigate whether the two sets of samples could be separated by unsupervised clustering (without prior knowledge of the identity of each sample), we fil- tered the data to only include probes for which at least 4 out of 16 samples had a high signal intensity (intensity > 6) and for which the variation across all samples was high (Std. dev. > 0.8). This approach effectively excluded probes that displayed constant intensity in all samples and therefore did not contribute to distinguishing between the two groups. The selected subset of 206 probes thus displayed both variation and intensity, which are prerequisites for contributing to discrimina- tion between the two groups. When the signal from these probes was analyzed with unsupervised clustering it perfectly separated the GBMs from the controls as illustrated by the heat map and dendrogram in Figure 1A. The sample dendrogram at the top of the heat map has two primary branches illustrating the perfect separa- tion the GBM samples from the Normal Controls. The gene dendrogram to the left of the heat map separates the genes that are up-regulated from those that are down-regulated in GBM samples, respectively. The observation that the data clustered perfectly into two distinct groups without any pre-selection of genes based on t-test analysis indicates that there are significant dif- ferences in the expression profiles of the GBM exoRNA and the normal control exoRNA, even though the Figure 1 A total of 206 genes were selected without application of t-test by filtering for high signal intensity (> 6 in 30% of samples) and high variation between samples (stdev > 0.8 across all 16 samples). A) A heat map and dendrogram showing perfect unsupervised clustering of the samples based on these 206 genes. B) Principal Component Analysis (PCA) of the same 206 genes as in A.