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Microarray Technology and Applications

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Presentation on theme: "Microarray Technology and Applications"— 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
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
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

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

12 Oligoarrays vs. Spotted Arrays
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









21 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.

22 cDNA Array Sample Preparation



25 Axon Instruments Scanner
GenPix 4000

26 Choice of Microarray System
cDNA arrays (Affymetrix) Oligonucleotide chips cRNA arrays (Applied Biosystems) 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


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

33 Affymetrix Probe Array (Photolithography) Synthesis of probe

34 Affymetrix System


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 ALL AML 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 Possible outliers ALL-related

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 Training set Genes Learner Model experiments
labels Predictor Genes Labels experiments Test set

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

43 Spotted cDNA Array Production

44 Hybridization Process

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

46 Microarray Data Processing
normalization quality & intensity filtering 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 What genes are up-regulated, down-regulated, co-regulated, not-regulated? Gene discovery Pattern discovery Inferences about biological processes Classification of biological processes

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

55 Monitoring Cell Differentiation
Diauxic shift respiration <- fermentation


57 Gene Prediction and Annotation
raw sequence ...TTCTCCTAGTTCTAGAGCGTTCGGCACGAGCAAATCTTAAACGTTTTACTTTGTGCT... predicted gene protein sequence database predicted mRNA or EST 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 Reaction(Droplet or Pin Spotting) Scanning
Target DNA (reference and test samples) Slides Reaction(Droplet or Pin Spotting) Hybridization Scanning Analysis Image processing Data mining Modeling Arrayer Hardware Scanner 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 Deposition of oligo probes on GeneChip Synthesis of cDNA & addition of T7 promoter Biotin-labeling of cRNA & purification Hybridization to GeneChip Data Mining Interesting genes Image acquisition & analysis 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)
Perfect match: ATGTTTGACGCAGCGTAGATCCGAG Mismatch: ATGTTTGACGCAACGTAGATCCGAG Oligos are selected from a region of the gene that has low similarity to other genes.

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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