1. Gene Expression Analysis

2. DNA Microarray First introduced in 1987
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.

3. Microarray Applications
Identify gene function
Similar expression can infer similar function
Find tissue/developmental specific genes
Different expression in different cells/tissues
Find genes affected by different conditions
Different expression under different conditions
Diagnostics
Different genes expression can indicate a disease state

4. Chips or Microarrays Different types of microarray technologies
Spotted Microarray
Two channel cDNA microarrays.
DNA chips- (Affymetrix, Agilent),
One channel oligonucleotide arrays

5. Microarray Experiment

6. Experimental Protocol Two Channel Arrays
Design an experiment
(probe design)
Extract RNA molecules from cell
Label molecules with fluorescent dye
Pour solution onto microarray
Then wash off excess molecules
Shine laser light onto array
Scan for presence of fluorescent dye
6. Analyze the microarray image

7. Analyzing Microarray Images
One gene or mRNA
One tissue or condition
Original Image

8. The ratio of expression is indicated by the intensity of the color
Red= High mRNA abundance in the experiment sample
Green= High mRNA abundance in the control sample
Transforming raw data to ratio of expression
Cy5
Cy3
Cy5
log2
Cy3
Cy3
Cy5

9. The ratio of expression is indicated by the intensity of the color
Red= High mRNA abundance in the experiment sample
Green= High mRNA abundance in the control sample
Transforming raw data to ratio of expression
Cy5
Cy3
Cy5
log2
Cy3
Cy3
Cy5

10. Expression Data Format
Conditions
normal hot cold
uch
gut
fip
msh
vma
meu
git
sec7b
apn
wos
Genes / mRNAs

11. One channel DNA chips Each sequence is represented by a probe set
1 probe set = N probes (Affymetrix 16 probes of length 25 mer).
Unknown sequence or mixture (target) colored with on\e fluorescent dye.
Target hybridizes to complimentary probes only
The fluorescence intensity is indicative of the expression of the target sequence

12. Affymetrix Chip

13. Designing probes for microarray experiments
Probe on DNA chip is shorter than target
Choice of which section to hybridize
Select a region which is unstructured
RNA folding, DNA stem-and-loop
Choose region which is target-specific
Avoid cross-hybridization with other DNA
Avoid regions containing variation
Minimize presence of mutation sites

14. Probe Design Two main factors to optimize Sensitivity Specificity
Strength of interaction with target sequence
Requires knowledge of target only
Specificity
Weakness of interaction with other sequences
Requires knowledge of ‘background’

15. Sources of Inaccuracy Some sequences bind better than others
Cross-hybridization, A–T versus G–C
Scanning of microarray images
Scratches, smears, cell spillage
Effects of experimental conditions
Point in cell cycle, temperature, density

16. Different types of probes
cDNA –
Longer probes (~70), more stable reactions
Readily available (by reverse transcription)
Specific
Oligonucleotides
20-60 mers
Allow higher density
Enable more flexible designs (e.g differentially measuring splice variants)

17. Splicing Specific Microarrays+
Pre-mRNA
mRNA
Total transcript level

18. Microarray Analysis Unsupervised Supervised Methods -Partion Methods
K-means
SOM (Self Organizing Maps)
-Hierarchical Clustering
Supervised Methods
-Analysis of variance
-Discriminate analysis
-Support Vector Machine (SVM)

19. Clustering
Grouping genes together according to their expression profiles.
Hierarchical clustering
Michael Eisen, 1998 :
Generate a tree based on similarity
(similar to a phylogenetic tree)
Each gene is a leaf on the tree
Distances reflect similarity of expression
Internal nodes represent functional groups

21. Clustering Self Organizing Maps
Genes are clustered according to similar expression patterns

22. What can we learn from clusters with similar gene expression ??
Similar expression between genes
One gene controls the other in a pathway
Both genes are controlled by another
Both genes required at the same time in cell cycle
Both genes have similar function
Clusters can help identify regulatory motifs
Search for motifs in upstream promoter regions of all the genes in a cluster

23. EXAMPLE
HNRPA1
SRp40
hnrnpA1
SRp40
hnrnpA1 binding sites

24. How can we use microarray for diagnostics?

25. + - How can microarrays be used as a basis for diagnostic ? patient 1
Gen1 + -
Gen2
Gen3
Gen4
Gen5

26. Informative Genes Differentially expressed in the two classes. Goal –
Identifying (statistically significant) informative genes

27. + - How can microarrays be used as a basis for diagnostic ? patinet1
patient 2
patient4
patient 3
patient 5
Gen1 + -
Gen3
Gen4
Gen2
Gen5
Informative
Genes

28. Specific Examples Cancer Research Hundreds of genes
that differentiate between
cancer tissues in different
stages of the tumor were found.
The arrow shows an example
of a tumor cells which
were not detected correctly by
histological or other
clinical parameters.
Ramaswamy et al, 2003
Nat Genet 33:49-54

29. Using SVMs to diagnose tumors based on expression data
Each dot represents a vector of the expression pattern taken from a microarray experiment . For example the expression pattern of all genes from a cancer patients.

30. How do SVM’s work with expression data?
In this example red dots can be primary tumors and blue are
from metastasis stage.
The SVM is trained on data which was classified based on histology. ?
After training the SVM we can use it to diagnose the
unknown tumor.

31. Gene Expression Databases and Resources on the Web
GEO Gene Expression Omnibus -
List of gene expression web resources
Another list with literature references
Cancer Gene Anatomy Project
Stanford Microarray Database

32. If time permits…..

33. Predicting function
Expression data
Structure

34. other RNA processing export transcription decay
splicing
export
transcription
splicing
IAI wt
decay
2.0
-2.0

35. Structural Genomics : a large scale structure determination project designed to cover all representative protein structures
ATP binding domain of protein MJ0577
Zarembinski, et al., Proc.Nat.Acad.Sci.USA, 99:15189 (1998)

36. Wanted ! As a result of the Structure Genomic
initiative many structures of proteins
with unknown function will be solved
Wanted !
Automated methods to predict function from the protein structures resulting from the structural genomic project.

37. Approaches for predicting function from structure
ConSurf - Mapping the evolution conservation on the protein structure

38. Approaches for predicting function from structure
PHPlus – Identifying positive electrostatic patches on the protein structure

39. Approaches for predicting function from structure
SHARP2 – Identifying positive electrostatic patches on the protein structure