Microarray Technique Some background M. Nath
Outline Introduction Spotting Array Technique GeneChip Technique Data analysis Applications Conclusion
Now
Blind Guess?
Functional Pathway
Microarray Technique
Principle Comprehensive functional analysis of genome Simultaneous analysis of patterns of gene expression Genome > Transcriptome > Proteome
Types of Microarray cdna Array (Brown et. al., 1995) Genomic DNA Array (DeRisi et. al., 1997) Oligonucleotide Array (Morton et. al., 1998)
Spotted Array Technology
Library Spotted Array Technology
Printing Slides Spotted Array Technology An array of slides is printed Slides can be glass or nylon
Spotted Array Technology
Hybridisation of Slides Spotted Array Technology Slide developer Up to 48 slides are developed under uniform conditions
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
Results Spotted Array Technology Green = Active in Sample 1 Red = Active in Sample 2 Yellow = Active in both samples Black = Active in neither
Affymetrix chip GeneChip Technology Oligos of 25 nt long 40 oligos for detection of each gene 11-20 oligos as Perfect Match (PM) 11-20 oligos as Mismatch (MM) at position 13
GeneChip Technology
GeneChip Technology
Spotted Array Technology Features Routine Starting material Probes pair per gene No. of genes / array 10-20 µg total RNA 1 10000
Spotted Array Technology Laborious Inexpensive Moderate specificity Moderate representation Low Density Cannot detect polymorphism
GeneChip Technology Features Starting material Detection specificity Discrimination of related genes Probes pair per gene No. of genes / array Routine 5 µg total RNA 1:10 5 70-80% identity 20 12000 Limit 2 ng total RNA 1:10 6 93% identity 4 40000
GeneChip Technology Easy Expensive High specificity High representation High density Can detect polymorphism
Data Analysis
Gene intensity Chip 2 Scaling Data Analysis Linearity Gene intensity Chip 1
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
Outlier Data Analysis Two chips may differ in expression for same gene If one replicate deviates several standard deviation from mean, remove it
Data Analysis Absolute measurements AvgDiff Σ ( PM n MM n ) / N Weighted AvgDiff Σ ( PM n MM n ) φ n / N
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)
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
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
Cluster Analysis Data Analysis Cluster analysis Hierarchical clustering K-means clustering Self Organising Maps Distance measures
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
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
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
Applications Simultaneous detection of many genes Gene discovery Pathway analysis Molecular basis of disease progression
Applications Molecular signatures of pathogens Comparative genomic studies of pathogens Virulence difference Pathogen genetics and manifestation Life cycle Replication, translational control
Applications Host-parasite interaction Pathogen establishment Host cell recognition Host cell response Parasite response to host immune response
Applications
Constraints Complex system of eukaryotes & multicellular organisms Transcriptome analysis Developing technology Many stages Design of experiment
Constraints Array quality Highly variable data Analysis of data Published experiments Cost
From Here to Tomorrow Recent & Powerful More improvement Protocol Hardware Experimental design Computational technique Integrate with other data Reproducible, fast, sensitive & economic