affymetrix case study

Affymetrix case study

Jesper JørgensenNsGene A/S

[email protected]

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

• 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, http://www.weizmann.ac.il/home/ligivol/research_interests.html

Gene 5’

Mulitple oligo probes

PMMM

3’

Probe synthesis on the array



Gene 5’


PMMM

3’

A probe set = 11-20 PM,MM pairs(Probe design is not optimized)

Probe set design



Gene 5’


PMMM

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)

U133AU133B

The hardware

Overview



• 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/A B/B

• Fold change = sample/control• Log transformation makes scale symmetric around 0• All data log2 transformed

Fold change (Log fold)

-4

-3

-2

-1

0

1

2

3

4

0 2 4 6 8 10 12

Fold changeLog

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 24.000 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 24.000 genes

Bonferroni correction

Overview






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. 2005 Jan;115(1):23-5.

Overview






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 VENTRALA 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<0.04, SLR>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






Verification of array dataArray 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

cDN

A#

25

7 (

DM

)

cDN

A#

25

6 (

VM

)

cDN

A#

24

5 (

DM

)

cDN

A#

24

4 (

VM

)

cDN

A#

25

4 (

DM

)

cDN

A#

25

3

(VM

)299bp

30x

299bp0

0,02

0,04

0,06

0,08

0,1

0,12

0,14

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43

Cycle

Flu

ore

sc

en

ce

30 35 40

Q-PCR verification of genes regulated on U133A

TH Q-PCR on a developmental series of subdissected human

embryonic and fetal brain material

OD260/280 were measured to 1.88 +/- 0.05 for all RNA samples

Q-PCR analysis and clustering

OD260/280 were measured to 1.88 +/- 0.05 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





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. 2005 May 11;25(19):4856-67.





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)

AcknowledgementsNsGene, Ballerup, Denmark (http://www.nsgene.com/)• 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

affymetrix case study

Documents