the European Nutrigenomics Organisation Deciding and acting on quality of microarray experiments in genomics Chris Evelo BiGCaT Bioinformatics Maastricht

the European Nutrigenomics Organisation The transfer of information from DNA to protein. The transfer proceeds by means of an RNA intermediate called messenger RNA (mRNA). In procaryotic cells the process is simpler than in eucaryotic cells. In eucaryotes the coding regions of the DNA (in the exons,shown in color) are separated by noncoding regions (the introns). As indicated, these introns must be removed by an enzymatically catalyzed RNA-splicing reaction to form the mRNA. From: Alberts et al. Molecular Biology of the Cell, 3rd edn. Gene Expression

the European Nutrigenomics Organisation First Example Is red wine healthy? Does it protect rats from eating the unhealthy stuff we usually eat?

the European Nutrigenomics Organisation Pool of 10 controls Cy 3 10 treated 50 mg/kg·day, 2 wks Cy 5 Control group:10 male F344 rats Diet: high fat (23%), high sucrose, low fibre Experimental group: 10 male F344 rats Same diet plus 50 mg/kg red wine polyphenols Experimental design

DNA Microarray Rat genome Rat Genome Oligo Set Version 1.1™ (Operon Technologies) 5707 oligos Omnigrid 100 microarrayer poly-L-lysine glass slides

the European Nutrigenomics Organisation Microarray Principle

The genomics workflow

the European Nutrigenomics Organisation Conclusions disagree with previous results –690 genes regulated genes –Involved in: cell adhesion and cell-cell communication –Instead of: e.g. antioxidant activity Before our analysis

Quality control -using Spotfire DecisionSite- (I) Microarray laser scan. 16 Print blocks Created with Spotfire DecisionSite Colors represent feature numbers of spots on microarray

Quality control -using Spotfire DecisionSite- (II) Localization of the flagged features (empty spots and bad spots (e.g. Signal < BG)) Flagged features are removed for further analysis

the European Nutrigenomics Organisation Hierarchical Clustering

the European Nutrigenomics Organisation K-means Clustering

the European Nutrigenomics Organisation Dissimilar Genes 690 genes

the European Nutrigenomics Organisation Dissimilar Genes

? Disagreement with biological data

Questions Differences due to the dietary treatment? Check on the rats growth during the experimental time and on their weight at sacrifice Differences due to the natural inter- individual variability? Fischer 344 are inbred rats, genetically very similar. A variability among rats is (of course) possible but unlikely in this case, due to the type of treatment and to the large amount of differences observed (more than 600 genes differentially expressed) Technical problem?

Localization of the differentially expressed genes -using Spotfire DecisionSite-

Log ratio

Visualize expression results SwissProt

Most important results of genMAPP

the European Nutrigenomics Organisation Conclusions Using Spotfire Decisionsite we can: see problems on microarrays see unexpected things using variable sliders group co-expressed genes (clustering, pca) see the location of specific genes or groups of genes immediately see the effects of alternative treatments combine with biological interpretation in GenMAPP

Example 2: Antibody Microarray BD Biosciences (Clontech) Chip-based technology Monoclonal antibodies printed at high density on a glass slide Profiling hundreds of proteins Analyses virtually any biological sample (cells, whole tissue and body fluids)

Content of antibody array

Two slides with flipped samples

Internally normalized results Sampling method controls for differences in labeling efficiency Internally Normalized Ratio can be calculated (represents the relative abundance of an antigen in sample A relative to that of sample B)

First arrays did not look good...

Array 2

Array 3

Technique improvement...

Less background problems but also less signal…

Spotfire analysis showed: Technique needs improvements! Location of the antibodies on the Microarray Some high background antibodies Procedure Normalization method

the European Nutrigenomics Organisation Participants BiGCaT Bioinformatics: Rachel van Haaften Arie van Erk Chris Evelo Florence University Christina Luceri Funding: NuGO (exchange) NBIC (Spotfire server)