Microarray Technique. Some background. M. Nath

Transcription

1 Microarray Technique Some background M. Nath

2 Outline Introduction Spotting Array Technique GeneChip Technique Data analysis Applications Conclusion

3 Now

4 Blind Guess?

5 Functional Pathway

6 Microarray Technique

7 Principle Comprehensive functional analysis of genome Simultaneous analysis of patterns of gene expression Genome > Transcriptome > Proteome

8 Types of Microarray cdna Array (Brown et. al., 1995) Genomic DNA Array (DeRisi et. al., 1997) Oligonucleotide Array (Morton et. al., 1998)

9 Spotted Array Technology

10 Library Spotted Array Technology

11 Printing Slides Spotted Array Technology An array of slides is printed Slides can be glass or nylon

12 Spotted Array Technology

13 Hybridisation of Slides Spotted Array Technology Slide developer Up to 48 slides are developed under uniform conditions

14 Scanning Spotted Array Technology Confocal laser scanner is used Two different lasers to read Red and Green dye intensities A Graphic image is saved Laser Scanner Imaging software reads Red & Green intensity for each dot applied

15 Results Spotted Array Technology Green = Active in Sample 1 Red = Active in Sample 2 Yellow = Active in both samples Black = Active in neither

16 Affymetrix chip GeneChip Technology Oligos of 25 nt long 40 oligos for detection of each gene oligos as Perfect Match (PM) oligos as Mismatch (MM) at position 13

17 GeneChip Technology

18 GeneChip Technology

19 Spotted Array Technology Features Routine Starting material Probes pair per gene No. of genes / array µg total RNA

20 Spotted Array Technology Laborious Inexpensive Moderate specificity Moderate representation Low Density Cannot detect polymorphism

21 GeneChip Technology Features Starting material Detection specificity Discrimination of related genes Probes pair per gene No. of genes / array Routine 5 µg total RNA 1: % identity Limit 2 ng total RNA 1: % identity

22 GeneChip Technology Easy Expensive High specificity High representation High density Can detect polymorphism

23 Data Analysis

24 Gene intensity Chip 2 Scaling Data Analysis Linearity Gene intensity Chip 1

25 Gene intensity Chip 2 Scaling Data Analysis Linear and non-linear models Constitutively and constantly expressed Maintenance gene More genes on chip Gene intensity Chip 1

26 Outlier Data Analysis Two chips may differ in expression for same gene If one replicate deviates several standard deviation from mean, remove it

27 Data Analysis Absolute measurements AvgDiff Σ ( PM n MM n ) / N Weighted AvgDiff Σ ( PM n MM n ) φ n / N

28 Fold Change Data Analysis Log 2 of ratio of intensities after being corrected for background E.g. Log 2 (Sample / Control) = Log 2 (Red / Green) =1 : unchanged; >1 : upregulated; <1 : downregulated Affymetrix chip (AffyFold) (Sample - Control) / Min (Sample, Control)

29 Test of significance Significance Test t-test with unequal variance ANOVA and F test REML Data Analysis Non-parametric tests Wilcoxon test Mann-Whitney rank sum test Correction for multiple testing Bonferroni correction

30 Cluster Analysis Data Analysis Single array not suitable Functional analysis Co-regulation New gene discovery Samples collected temporally, spatially Multiple array & Cluster analysis Clustering of similarly behaving genes Genes with similar functions generally cluster together

31 Cluster Analysis Data Analysis Cluster analysis Hierarchical clustering K-means clustering Self Organising Maps Distance measures

32 Beyond Clustering Data Analysis Discovery of regulatory elements in promoter region Identifying regulatory networks Time series approach Steady-state approach Neural network technique Selection of genes Gene finding Selection of regions within the genes Selection of PCR primers Selection of unique oligomer probes

33 Software Package Data Analysis Affymetrix Data Mining Tool Affymetrix NetAffx Biomax Gene Expression Analysis Suite GeneData Expressionist Informax Xpression Invitrogen Corp. ResGen Pathways Rosetta Resolver Gene Expression System Silicon Genetics GeneSpring Spotfire

34 Applications Analysis of patterns of gene expression Functional relationship between genes Expression in coregulatory gene group Monitoring changes in genomic DNA Cellular pathways affected by mutation Changes in expression profiles of mutants

35 Applications Simultaneous detection of many genes Gene discovery Pathway analysis Molecular basis of disease progression

36 Applications Molecular signatures of pathogens Comparative genomic studies of pathogens Virulence difference Pathogen genetics and manifestation Life cycle Replication, translational control

37 Applications Host-parasite interaction Pathogen establishment Host cell recognition Host cell response Parasite response to host immune response

38 Applications

39 Constraints Complex system of eukaryotes & multicellular organisms Transcriptome analysis Developing technology Many stages Design of experiment

40 Constraints Array quality Highly variable data Analysis of data Published experiments Cost

41 From Here to Tomorrow Recent & Powerful More improvement Protocol Hardware Experimental design Computational technique Integrate with other data Reproducible, fast, sensitive & economic

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