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Microarray Technology and Applications

Microarray Technology and Applications . Introduction to Nanotechnology Foothill College. Outline. Gene expression Microarray technology Microarray process Applications in research / medicine Haplotyping (SNP) genotyping projects Future directions

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Microarray Technology and Applications

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  1. Microarray Technology and Applications Introduction to Nanotechnology Foothill College

  2. Outline • Gene expression • Microarray technology • Microarray process • Applications in research / medicine • Haplotyping (SNP) genotyping projects • Future directions • New technologies / open source science

  3. Several Advances in Technology Make Microarrays Possible Genomics Surface Chemistry Robotics Computing Power & Bioinformatics

  4. Gene Expression • Cells are different because of differential gene expression. • About 40% of human genes are expressed at any one time. • Gene is expressed by transcribing DNA exons into single-stranded mRNA • mRNA is later translated into a protein • Microarrays measure the level of mRNA expression by analyzing cDNA binding

  5. Analysis of Gene Expression • Examine expression during development or in different tissues • Compare genes expressed in normal vs. diseased states • Analyze response of cells exposed to drugs or different physiological conditions

  6. Monitoring Changes in Genomic DNA • Identify mutations • Examine genomic instability such as in certain cancers and tumors (gene amplifications, translocations, deletions) • Identify polymorphisms (SNPs) • Diagnosis: chips have been designed to detect mutations in p53, HIV, and the breast cancer gene BRCA-1

  7. Applications in Medicine • Gene expression studies • Gene function for cell state change in various conditions (clustering, classification) • Disease diagnosis (classification) • Inferring regulatory networks • Pathogen analysis (rapid genotyping)

  8. Applications in Drug Discovery • Drug Discovery • Identify appropriate molecular targets for therapeutic intervention (small molecule / proteins) • Monitor changes in gene expression in response to drug treatments (up / down regulation) • Analyze patient populations (SNPs) and response • Targeted Drug Treatment • Pharmacogenomics: individualized treatments • Choosing drugs with the least probable side effects

  9. Many Genes Have Unknown Function • Of the 25,498 predicted Arabidopsis genes: • 30% have unknown function • Only 9% are experimentally verified The Arabidopsis Genome Initiative, Nature 2000

  10. Generating DNA Sequence automated sequencer chromatogram files software pipeline base calling quality clipping vector clipping contig assembly output >GENE01 ACCTGTCAGTGTCAACTGCTTCAATAGCTAATGCTAGGCTCGATAATCGCTGGCCTCAGCTCAGTCTAGCATTACGATTACGGAGACCTATGCTTTAGCTAGTAGGAACCTCAGCTCAGTACCTGTCAGTGTCAACTGCTTCAATAGCTAATGCTACTC

  11. What Is Microarray Technology? • Different Approaches

  12. Oligoarrays vs. Spotted Arrays • Oligoarrays • Shorter nucleotides • Higher feature density • Used for SNP detection • Perfect match and mismatch (A,T,C,G) • More expensive to prepare • Higher per unit cost production • Not manufactured in as high numbers

  13. Spotted Array Experiment 1. Prepare sample. 4. Print microarray. Test Reference 2. Label with fluorescent dyes. 5. Hybridize to microarray. 3. Combine cDNAs. 6. Scan.

  14. cDNA Array Sample Preparation

  15. Axon Instruments Scanner • GenPix 4000

  16. Choice of Microarray System • cDNA arrays (Affymetrix) • Oligonucleotide chips • cRNA arrays (Applied Biosystems) • SNP arrays Applied Biosystems)

  17. cDNA Arrays: Advantages • Non-redundant clone sets are available for numerous organisms (humans, mouse, rats, drosophila, yeast, c.elegans, arabidopsis) • Prior knowledge of gene sequence is not necessary: good choice for gene discovery • Large cDNA size is great for hybridization • Glass or membrane spotting technology is readily available

  18. Membrane cDNA microarray

  19. cDNA Arrays: Disadvantages • Processing cDNAs to generate “spotting-ready” material is cumbersome • Low density compared to oligonucleotide arrays • cDNAs may contain repetitive sequences (like Alu in humans) • Common sequences from gene families (ex: zinc fingers) are present in all cDNAs from these genes: potential for cross-hybridization • Clone authentication can be difficult

  20. cDNA Microarray Slide

  21. 50um Affymetrix Microarrays Raw image 1.28cm ~107 oligonucleotides, half Perfectly Match mRNA (PM), half have one Mismatch (MM) Raw gene expression is intensity difference: PM - MM

  22. Affymetrix • Probe Array (Photolithography) • Synthesis of probe

  23. Affymetrix System

  24. Microarrays: An Example • Leukemia: Acute Lymphoblastic (ALL) vs Acute Myeloid (AML), Golub et al, Science, v.286, 1999 • 72 examples (38 train, 34 test), about 7,000 genes • well-studied (CAMDA-2000), good test example ALL AML Visually similar, but genetically very different

  25. Tumor Cell Analysis

  26. ALL AML Possible outliers Heatmap Visualization of Selected Fields Heatmap visualization is done by normalizing each gene to mean 0, std. 1 to get a picture like this. Good correlation overall AML-related ALL-related

  27. Analysis Tasks • Identify up- and down-regulated genes. • Find groups of genes with similar expression profiles (++ / -- , fold change). • Find groups of experiments (tissues) with similar expression profiles (++ / -- genes). • Find genes that explain observed differences among tissues (feature selection), and new pathways.

  28. Types of Analysis • Unsupervised learning: learn from data only • visualization: find structure in data • clustering: find clusters / classes in data • Supervised learning: learn from data plus prior knowledge • classification: predict discrete classes • regression: predict a real value

  29. Learning Gene Classes Training set Genes Learner Model experiments labels Predictor Genes Labels experiments Test set

  30. Microarray Data Life Cycle Biological Question Sample Preparation Data Analysis & Modeling Microarray Detection Microarray Reaction

  31. Spotted cDNA Array Production

  32. Hybridization Process

  33. logsignal intensity log RNA abundance Sources of Error • Systematic • Random

  34. Microarray Data Processing normalization quality & intensity filtering background correction expression ratios (treated / control)

  35. Data Filtering -- Intensity

  36. Data Normalization Issues • Normalization of data from different chips • MGED normalization standards – http://www.mged.org/ • Natural biological variation is large • Technical variation is small ~ 98% auto-correlation • MIT approach -- raw gene expression values • Stanford approach -- ratios

  37. Data Preparation • Thresholding: usually min 20, max 16,000 • For older Affy chips (new Affy chips do not have negative values) • Filtering - remove genes with insufficient variation • e.g. MaxVal - MinVal < 500 and MaxVal/MinVal < 5 • biological reasons • feature reduction for algorithmic • For clustering, normalize each gene separately to • Mean = 0, Std. Dev = 1

  38. Clustering Goals • Find natural classes in the data • Identify new classes / gene correlations • Refine existing taxonomies • Support biological analysis / discovery • Different Methods • Hierarchical clustering, SOM's, etc

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