the European Nutrigenomics Organisation

NuNuGOGONuNuGOGO

Deciding and acting on quality of microarray experiments in genomics

Chris EveloBiGCaT Bioinformatics Maastricht

the European Nutrigenomics Organisation

NuNuGOGONuNuGOGO

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

NuNuGOGONuNuGOGO First Example

Is red wine healthy?

Does it protect rats from eating the unhealthy stuff we usually eat?

the European Nutrigenomics Organisation

NuNuGOGONuNuGOGO

Pool of 10 controls

Cy 3

10 treated

50 mg/kg·day,

2 wks

Cy 5

• Control group:10 male F344 ratsDiet: high fat (23%), high sucrose, low fibre

• Experimental group: 10 male F344 ratsSame diet plus 50 mg/kg red wine polyphenols

Experimental design

DNA MicroarrayDNA Microarray

Rat genomeRat Genome Oligo Set Version 1.1™ (Operon Technologies)

5707 oligos

Omnigrid 100 microarrayer

poly-L-lysine glass slides

the European Nutrigenomics Organisation

NuNuGOGONuNuGOGO Microarray Principle

The The genomics genomics workflowworkflow

the European Nutrigenomics Organisation

NuNuGOGONuNuGOGO

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 controlQuality control-using Spotfire DecisionSite- (I)-using Spotfire DecisionSite- (I)

column for spotfire0 10 20 30 40 50 60 70 80

-80

-70

-60

-50

-40

-30

-20

-10

0

Microarray laser scan.16 Print blocks

Created with Spotfire DecisionSiteColors represent feature numbers of spots on microarray

Quality controlQuality control-using Spotfire DecisionSite- (II)-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

column for spotfire0 10 20 30 40 50 60 70 80

-80

-70

-60

-50

-40

-30

-20

-10

0

column for spotfire0 10 20 30 40 50 60 70 80

-80

-70

-60

-50

-40

-30

-20

-10

0

the European Nutrigenomics Organisation

NuNuGOGONuNuGOGOHierarchical Clustering

7.12E3 0

1 1706 2 2

rat 5 rat 12 rat 14 rat 13 rat 4 rat 3 rat 2 rat 1 rat 15 rat 11

Hierarchical Clustering

the European Nutrigenomics Organisation

NuNuGOGONuNuGOGO K-means Clustering

the European Nutrigenomics Organisation

NuNuGOGONuNuGOGO Dissimilar GenesHierarchical Clustering

5 12 14 13 4 3 2 1 15 11

690 genes

the European Nutrigenomics Organisation

NuNuGOGONuNuGOGO 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?

Scatter Plot

column for spotfire0 10 20 30 40 50 60 70 80

-80

-70

-60

-50

-40

-30

-20

-10

0

Localization of the differentially expressed genesLocalization of the differentially expressed genes-using Spotfire DecisionSite--using Spotfire DecisionSite-

Scatter Plot

column for spotfire0 10 20 30 40 50 60 70 80

-80

-70

-60

-50

-40

-30

-20

-10

0

Scatter Plot

column for spotfire0 10 20 30 40 50 60 70 80

-80

-70

-60

-50

-40

-30

-20

-10

0

Log ratio

3.48E3 0

1 2922 2 2 2

rat 14 rat 12 rat 5 rat 13 rat 1 rat 11 rat 15 rat 4 rat 3 rat 2

Visualize expression resultsVisualize expression results

SwissProt

Most important results of genMAPP Most important results of genMAPP

the European Nutrigenomics Organisation

NuNuGOGONuNuGOGO 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 MicroarrayExample 2: Antibody MicroarrayBD Biosciences (Clontech)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 arrayContent of antibody array

Two slides with flipped samplesTwo slides with flipped samples

Internally normalized resultsInternally 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)

Ratio2

Ratio1

First arrays did not look First arrays did not look good...good...

Array 2

Array 3

Technique improvement...Technique improvement...

Technique improvement...Technique improvement...

Less background problems but also less signal…

Spotfire analysis showed:Spotfire analysis showed:

Technique needs improvements!

Location of the antibodies on the Microarray

Some high background antibodiesProcedure Normalization method

the European Nutrigenomics Organisation

NuNuGOGONuNuGOGO Participants

BiGCaT Bioinformatics:

• Rachel van Haaften

• Arie van Erk

• Chris Evelo

Florence University

• Christina Luceri

Funding:

• NuGO (exchange)

• NBIC (Spotfire server)