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Microarray Technology and Applications Introduction to Nanotechnology Foothill College.

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Presentation on theme: "Microarray Technology and Applications Introduction to Nanotechnology Foothill College."— Presentation transcript:

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 Surface Chemistry Robotics Computing Power & Bioinformatics Genomics

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 base calling quality clipping vector clipping contig assembly chromatogram files automated sequencer software pipeline >GENE01 ACCTGTCAGTGTCAACTGCTTCAATAGCTAATGCTAGGCTCGATAATCGCTGGC CTCAGCTCAGTCTAGCATTACGATTACGGAGACCTATGCTTTAGCTAGTAGGAA CCTCAGCTCAGTACCTGTCAGTGTCAACTGCTTCAATAGCTAATGCTACTC >GENE01 ACCTGTCAGTGTCAACTGCTTCAATAGCTAATGCTAGGCTCGATAATCGCTGGC CTCAGCTCAGTCTAGCATTACGATTACGGAGACCTATGCTTTAGCTAGTAGGAA CCTCAGCTCAGTACCTGTCAGTGTCAACTGCTTCAATAGCTAATGCTACTC output

11 What Is Microarray Technology? Different Approaches Stanford/ Pat Brown Affymetrix How DNA sequences are laid down SpottingPhotolithography Length of DNA sequences cDNA(Complet e sequences) Oligonucleotides

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

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

22 cDNA Array Sample Preparation

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25 Axon Instruments Scanner GenPix 4000

26 Choice of Microarray System 1.cDNA arrays (Affymetrix) 2.Oligonucleotide chips 3.cRNA arrays (Applied Biosystems) 4.SNP arrays Applied Biosystems)

27 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

28 Membrane cDNA microarray

29 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

30 cDNA Microarray Slide

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32 Affymetrix Microarrays 50um 1.28cm ~10 7 oligonucleotides, half Perfectly Match mRNA (PM), half have one Mismatch (MM) Raw gene expression is intensity difference: PM - MM Raw image

33 Affymetrix Probe Array (Photolithography) –Synthesis of probe

34 Affymetrix System

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36 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 ALLAML Visually similar, but genetically very different

37 Tumor Cell Analysis

38 Heatmap Visualization of Selected Fields ALL AML 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 Possible outliers

39 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.

40 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

41 Learning Gene Classes Predictor LearnerModel Labels labels experiments Genes Training set Test set

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

43 Spotted cDNA Array Production

44 Hybridization Process

45 Sources of Error Systematic Random log signal intensity log RNA abundance

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

47 Data Filtering -- Intensity

48 Data Normalization Issues Normalization of data from different chips –MGED normalization standards – Natural biological variation is large Technical variation is small ~ 98% auto- correlation MIT approach -- raw gene expression values Stanford approach -- ratios

49 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

50 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

51 Identifying Co-Expressed Genes Cluster Treeview Eisen et al., PNAS 1998

52 SOM Clustering SOM - self organizing maps Preprocessing –filter away genes with insufficient biological variation –normalize gene expression (across samples) to mean 0, st. dev 1, for each gene separately. Run SOM for many iterations Plot the results

53 Microarray Analysis Gene discovery Pattern discovery Inferences about biological processes Classification of biological processes What genes are up-regulated, down-regulated, co-regulated, not-regulated?

54 Identifying Differential Expression SAM Significance Analysis of Microarrays Tusher et al., PNAS 2001

55 Diauxic shift respiration <-fermentation Monitoring Cell Differentiation

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57 Gene Prediction and Annotation...TTCTCCTAGTTCTAGAGCGTTCGGCACGAGCAAATCTTAAACGTTTTACTTTGTGCT... raw sequence predicted gene predicted mRNA or EST protein sequence database presumed identity/function

58 Functional Genomics: High Throughput Platforms Microarrays –spotted cDNAs/ESTs –spotted oligonucleotides –in situ synthesized oligos SAGE GeneCalling® Megasort MAPs (High Throughput Genomics, Tucson)

59 Advanced Questions in Microarray Analysis e.g. can we correlate patterns in other types of data with the microarray results? -cis-elements –protein domains –protein-protein interactions –orthologous genes –gene ontologies –textual associations

60 Procedures Preparation –Target DNA (reference and test samples) –Slides Reaction(Droplet or Pin Spotting) –Hybridization Scanning Analysis –Image processing –Data mining –Modeling Arrayer Scanner Hardware Software

61 The Basic System Computational Tasks Gene set selection Probe design Image analysis Normalization of chip, sample…. …more….

62 A Typical GeneChip Array Experiment Target Preparation Isolate total RNA from tissue Synthesis of cDNA & addition of T7 promoter Biotin-labeling of cRNA & purification Hybridization to GeneChip Deposition of oligo probes on GeneChip Image acquisition & analysis Data Mining Interesting genes Pattern Recognition Pathways

63 Probe Design System A method of hybridization of short synthetic oligonucleotide probes to cloned DNA sequences to derive genetic sequence information. Application field –oligonucleotide array –Polymerase Chain Reaction : primer design

64 Probe Design System Probe design issue –Unique probe for each gene –Different probe sets for each genes : fingerprints –Probes (or a unique probe) of each gene should have the ability of representing the gene. –The similarities between probes should very low.

65 The Analysis - Computational Tasks Clustering genes: which genes seem to be regulated together Classifying genes: which functional class does a given gene fall into Classifying cell samples: does this patient have ALL or AML Inferring regulatory networks: what is the circuitry of the cell

66 Oligo Probe Pairs (SNPs) Oligos are selected from a region of the gene that has low similarity to other genes. Perfect match: ATGTTTGACGCAGCGTAGATCCGAG Mismatch: ATGTTTGACGCAACGTAGATCCGAG

67 SNPs – Single Nucleotide Polymorphisms

68 A GeneChip Spot is Composed of Numerous Cells and Includes Controls

69 SNPs and Alleles

70 SNPs and Intervals

71 Haplotype Mapping of Chromosome 21 Seven haplotypes make up 93% of the genetic sampling

72 Summary Microarray process Microarray applications From genes to pathways Haplotyping and SNP mapping Using microarrays and medicine


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