Microarrays and Gene Expression Analysis

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Gene Expression Data

• Microarray experiments

• Applications

• Data analysis

• Gene Expression Databases

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DNA Microrray

A microarray is a tool for analyzing gene expression in genomic scale.

The microarray consists of a small membrane or glass slide containing samples of many genes arranged in a regular pattern.

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DNA Microarrays

• First introduced in 1987– Still undergoing much development

• Small piece of glass– Thousands/millions of cells

• Each cell contains a DNA probe– Millions of copies bound to glass

• Cells capture their reverse complement– Ordinary DNA/RNA base pair hybridization

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Chips or Microarrays

Two types of microarray technologies

1. Spotted MicroarrayTraditionally called DNA microarrays.

2. Affymetrix-Developed at Affymetrix, Inc. Historically called DNA chips.

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Spotted Microarray

• probe cDNA (50~5,000 bases long) is immobilized to a solid surface such as glass using robot spotting and exposed to a set of targets either separately or in a mixture.

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http://www.bio.davidson.edu/biology/courses/genomics/chip/chip.html

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Experimental Protocol1. Identify RNA/DNA sequences of interest

– Design probes that are sequence-specific

2. Extract molecules from cell environment– Label molecules with fluorescent dye

3. Pour solution onto microarray– Then wash off excess molecules

4. Shine laser light onto array– Scan for presence of fluorescent dye

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Microarray Images

Original Image Summary

One geneor mRNA

One tissue or condition

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Cy3 Cy5Cy5Cy3

Cy5log2 Cy3

The ratio of expression is indicated by the intensity of the colorRed= High mRNA abundance in the experiment sample Green= High mRNA abundance in the control sample

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Expression Data Format

normal hot colduch1 -2.0 0.0 0.924 gut2 0.398 0.402 -1.329 fip1 0.225 0.225 -2.151 msh1 0.676 0.685 -0.564 vma2 0.41 0.414 -1.285 meu26 0.353 0.286 -1.503 git8 0.47 0.47 -1.088 sec7b 0.39 0.395 -1.358 apn1 0.681 0.636 -0.555 wos2 0.902 0.904 -0.149

Conditions

Gen

es /

mR

NA

s

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Microarray Applications

• Identify genes whose function is related– Similar expression in group in many cases

• Find genes expressed in specific tissues– Different expression in different cells

• Find genes affected by environment– Different expression under different conditions

• Distinguish different forms of a disease– Different expression in different patients

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Microarray ApplicationsSpecific Examples

• EvolutionChimpanzees and Human genomes are 98.7 % identical and therefore phylogenetic analysis can not separated them into different

species.

Most differences between human and chimpanzees have been detected at the level of gene expression in the brain.

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Microarray ApplicationsSpecific Examples

• Behaviour

Gene Expression Profiles in the Brain Predict Behavior in Individual Honey Bees

Charles W. Whitfield,1,2 Anne-Marie Cziko,1 Gene E. Robinson1,2 We show that the age-related transition by adult honey bees from hive work to foraging is associated with changes in messenger RNA abundance in the brain for 39% of 5500 genes tested. This result, discovered using a highly replicated experimental design involving 72 microarrays, demonstrates more extensive genomic plasticity in the adult brain than has yet been shown. Experimental manipulations that uncouple behavior and age revealed that messenger RNA changes were primarily associated with behavior. Individual brain messenger RNA profiles correctly predicted the behavior of 57 out of 60 bees, indicating a robust association between brain gene expression in the individual and naturally occurring behavioral plasticity.

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Microarray ApplicationsSpecific Examples

Cancer Research

Ramaswamy et al, 2003Nat Genet 33:49-54

Hundreds of genesthat differentiate betweencancer tissues in differentstages of the tumor were found.These different stageswere not detected byhistological or other clinical parameters.

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Microarray Analysis

GOALS

Sample classification- What are the set of genes that differentiate

between two or more groups of treatments

Gene Classification- What is the set of genes that have the same

expression profile along a set of treatments

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Microarray AnalysisHow do we answer them ?

• Supervised Methods-Analysis of variance-Discriminate analysis-Support Vector Machine (SVM)

• Unsupervised-Partion Methods

K-meansSOM (Self Organizing Maps

-Hierarchical Clustering

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TO BE CONTINUED

• Rest of the slides from Lecture12_05 where moved to Lecture13_05