Gene Expression Analysis

1. Gene Expression Analysis

2. DNA MicroarrayFirst 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 cDNA microarrays. • DNA chips- (Affymetrix, Agilent), Oligonucleotide or peptide arrays

5. Microarray Experiment http://www.bio.davidson.edu/Courses/genomics/chip/chip.html

6. Experimental Protocol Two channel cDNA arrays • Design an experiment (probe design) 2. Extract RNA molecules from cell • Label molecules with fluorescent dye • Pour solution onto microarray • Then wash off excess molecules 5. Shine laser light onto array • Scan for presence of fluorescent dye 6. Analyze the microarray image

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

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

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

10. Conditions Genes / mRNAs Expression Data Format normal hot cold uch1 -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

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 • 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 -Partion Methods K-means SOM (SelfOrganizing 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

20. Results of Clustering Gene Expression Limitations: • Hierarchical clustering in general is not robust • Genes may belong to more than one cluster

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. Finding Regulatory Motifs Within Expression Clusters Experiment 1 Experiment 2 Experiment 3 Normalized expression data from microarrays Search promoter regions for shared sequence motifs.

24. EXAMPLE HNRPA1 SRp40 hnrnpA1 SRp40 hnrnpA1 binding sites

25. How can we use microarray for diagnostics?

26. How can microarrays be used as a basis for diagnostic ?

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

28. How can microarrays be used as a basis for diagnostic ? Informative Genes

29. 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

30. Support Vector Machine (SVM)for predicting gene function based on microarray data • As applied to gene expression data, an SVM would begin with a set of genes that have a common function (red dots), In addition, a separate set of genes that are known not to be members of the functional class (blue dots) is specified.

31. ? • Using this training set, an SVM would learn to discriminate between the members and non-members of a given functional class based on expression data. • Having learned the expression features of the class, the SVM could recognize new genes as members or as non-members of the class based on their expression data.

32. 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.

33. ? 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.

34. Gene Expression Databasesand Resources on the Web • GEO Gene Expression Omnibus -http://www.ncbi.nlm.nih.gov/geo/ • List of gene expression web resources • http://industry.ebi.ac.uk/~alan/MicroArray/ • Another list with literature references • http://www.gene-chips.com/ • Cancer Gene Anatomy Project • http://cgap.nci.nih.gov/ • Stanford Microarray Database • http://genome-www.stanford.edu/microarray/