Affymetrix case study Jesper Jørgensen NsGene A/S

Overview Affymetrix GeneChip technology Data processing –Expression level –Normalisation –Fold change –Statistics Parkinson disease Ventral versus dorsal midbrain (case study) Verification of array data –Q-PCR –In situ hybridization –Immunohistochemistry

Expression profiling –Investigate mRNA expression profile. –Compare gene expression between two or more situations. –Case versus control. Profiling methods –Differential display. –SAGE (Serial Analysis of Gene Expression) –Micro array (Custom spotted arrays / Affymetrix GeneChip).

Affymetrix GeneChip technology Figure adapted from: David Givol, Weizman Institute of Science, Gene 5’ Mulitple oligo probes PM MM 3’

Probe synthesis on the array

Affymetrix GeneChip technology Figure adapted from: David Givol, Weizman Institute of Science, Gene 5’ Mulitple oligo probes PM MM 3’

A probe set = PM,MM pairs (Probe design is not optimized) Probe set design

Affymetrix GeneChip technology Figure adapted from: David Givol, Weizman Institute of Science, Gene 5’ Mulitple oligo probes PM MM 3’

Preparation of samples for GeneChip Figure modified from: Knudsen (2002), “A Biologist's Guide to Analysis of DNA Microarray Data", Wiley. Amplification (T7 RNA polymerase) U133A U133B

The hardware

Overview Affymetrix GeneChip technology Data processing –Expression level –Normalisation –Fold change –Statistics Parkinson disease Ventral versus dorsal mesencephalon (case study) Verification of array data –Q-PCR –In situ hybridization –Immune histochemistry

Li-Wong model  n : scaling factor obtained by fitting Several other models exists. Irizarry et al. (2002) uses log transformed PM values after carrying out a global background adjustment and across array normalisation. Expression level (probe signal) Irrizary et al. (2002) Biostatistics

Workman et al., (2002) Genome Biology, vol. 3, No. 9. qspline normalisation (M/A plot) Assumption: Most genes are unchanged. M/A plot: Raw chip data are used to plot, for each probe, the logarithm of the ratio between two chips versus the logarithm of the mean expression for the two chips. Before After

Variation Two different amplifications of the same RNA applied to GeneChips A/AB/B

Fold change = sample/control Log transformation makes scale symmetric around 0 All data log2 transformed Fold change (Log fold) Fold change Log fold (2)

Student and Welch’s t-test ANOVA SAM Wilcoxon Kruskal-Wallis Westfall-Young ……….. Is the regulation significant? Statistical testing

5 false positives if you look at 100 genes 1200 false positives if you look at genes Increased likelihood of getting a significant result by chance alone At a P-value of 0.05 you expect: If you want 25% chance of having only one false positive in the list of regulated genes, you should only consider P-values more significant than the Bonferroni corrected cutoff. 2.5x10 -3 (0.25/100) if you look at 100 genes 1.0x10 -5 (0.25/24.000) if you look at genes Bonferroni correction

Overview Affymetrix GeneChip technology Data processing –Expression level –Normalisation –Fold change –Statistics Parkinson disease Ventral versus dorsal mesencephalon (case study) Verification of array data –Q-PCR –In situ hybridization –Immune histochemistry

Parkinson’s Disease (PD) A fairly common neurodegenerative disorder (app. 2 million in USA/Europe) Due to loss of the dopamine- producing neurons in the Substantia Nigra Cardinal motor symptoms: tremor, rigidity and bradykinesia Conventional treatment does not halt the progression nerve cell loss

Fetal Transplantation for PD Cells from the developing midbrain (A) –are collected and dissociated (B) –and transplanted into the striatum (C) The cells will integrate with the host brain and produce dopamine.

Stem cells in Parkinson disease Langston JW., J Clin Invest Jan;115(1):23-5.

Overview Affymetrix GeneChip technology Data processing –Expression level –Normalisation –Fold change –Statistics Parkinson disease Ventral versus dorsal mesencephalon (case study) Verification of array data –Q-PCR –In situ hybridization –Immune histochemistry

Aim * TH IHC In the human fetus, DA neurons can be found in the ventral part of the tegmentum (VT) from approximately 6 weeks. In contrast, no DA neurons can be found in the neighboring dorsal part (DT). We aim at finding genes associated with DA differentiation by using GeneChips to compare the expression profiles of VT and DT.

8wVT (B) 8wDT (A) 8wDT (B) 8wVT (A) High quality RNA from 8w GA human ventral midbrain

Experimental setup Compare VT against DT (3x3) Affymetrix Human Genome U133 Chip Set –HG-U133A: Well substantiated genes –HG-U133B: Mostly EST’s –Total: 45,000 probes (genome) A VENTRAL B VENTRAL C VENTRAL A DORSAL B DORSAL C DORSAL

U133A data permutations and filter Red: VM versus DM: VM ( A1 VENTRAL, A2 VENTRAL, B VENTRAL ) DM ( A1 DORSAL, A2 DORSAL, B DORSAL ) Other colors: Permutations Low-stringency filter as dotted line: Average expression > 50 P-value < 0.04 SLR>0.5 (42% up-regulation in VM) Arrange with descending fold change. SLR

Genes up-regulated in VM on U133A Low-stringency filter: Average expression > 50, P-value 0.5 arranged with descending fold change. Total list 107 probes. Only SLR>1 displayed.

Literature verification ALDH1A DAT1 VMAT2 TH Calbindin, 28kDa HNF3a 3x Nurr1 2x IGF 4x SNCA 4x DRD2 KCNJ6 (Girk2) Ret PITX3 BDNF DLK1 (FA1) SLC17A6 (VGLUT2) EPHA5 ERBB4

Overview Affymetrix GeneChip technology Data processing –Expression level –Normalisation –Fold change –Statistics Parkinson disease Ventral versus dorsal mesencephalon (case study) Verification of array data –Q-PCR –In situ hybridization –Immune histochemistry

Verification of array data Array Data (100 candiate genes) Validation on array material (confirmation) Validation on new samples (universality) Desk work Statistics Literature Bioinformatics RNA Q-PCR ISH Northerns Protein IHC ELISA Westerns

ALDH1A1 RT-PCR 35x cDNA#257 (DM)cDNA#256 (VM)cDNA#245 (DM)cDNA#244 (VM)cDNA#254 (DM) cDNA#253 (VM) 299bp 30x 299bp

Why Q-PCR Conservative (10-50 ng template) Sensitive Broad dynamic range Rapid (1-2 hrs) Gel-free Multiple samples can be processed simultaneously (1-96) How to do it Serial dilution of ’known’ standards (standard curve) From the standard curve, expression levels are calculated Then, data are ’normalized’ to housekeeping genes. Q-PCR

Q-PCR verification of genes regulated on U133A

TH Q-PCR on a developmental series of subdissected human embryonic and fetal brain material OD 260/280 were measured to / for all RNA samples

Q-PCR analysis and clustering OD 260/280 were measured to / for all RNA samples

1.5 fold up-regulation from no expression 1.5 fold up-regulation from some expression Fold change in a mixed population

Verification of array data Array Data (100 candiate genes) Validation on array material (confirmation) Validation on new samples (universality) Desk work Statistics Literature Bioinformatics RNA Q-PCR ISH Northerns Protein IHC ELISA Westerns

Organization of ISH procedure

GeneChip verification with ISH ISH from: Vernay et al., J Neurosci May 11;25(19):

Verification of array data Array Data (100 candiate genes) Validation on array material (confirmation) Validation on new samples (universality) Desk work Statistics Literature Bioinformatics RNA Q-PCR ISH Northerns Protein IHC ELISA Westerns

GeneChip verification with IHC Courtesy of Josephine Jensen

Conclusions Using arrays one will get at snapshot of the expression profile under the conditions investigated. –Careful experimental design –RNA quantity and quality are important Since a single array experiment generates thousands of data points, the primary challenge of the technique is to make sense of data. –Calculations/Statistics (back and forth) –Literature mining Independent methods are needed for verification –Q-PCR –In situ hybridization (ISH) –Immunohistochemistry (IHC)

Acknowledgements NsGene, Ballerup, Denmark ( Lars Wahlberg Bengt Juliusson Teit Johansen Neurotech, Huddinge University Hospital, Sweden Åke Seiger Department of Medical Genetics, IMBG, Panum Institute, Denmark Claus Hansen Karen Friis Wallenberg Neuroscience Center, Sweden Anders Björklund Josephine Jensen Elin Andersson CBS, DTU, Denmark Søren Brunak Steen Knudsen Nikolaj Blom Thomas Nordahl Petersen