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

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