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DNA MicroarraysDNA Microarrays
M. Ahmad Chaudhry, Ph. D.M. Ahmad Chaudhry, Ph. D.
Outline of the lectureOutline of the lecture
• Overview of Micoarray Technology• Types of Microarrays• Manufacturing
• Instrumentation and Softwares• Data analysis
• Applications
• Mainly used in gene discovery
Microarray DevelopmentMicroarray Development
• Widely adopted
• Relatively young technology
Evolution & IndustrializationEvolution & Industrialization• 1994- First cDNAs arrays are
developed at Stanford.• 1995- Quantitative Monitoring
of Gene Expression Patterns with a cDNA Microarray
• 1996- Commercialization of arrays
• 1996-Accessing Genetic Information with High Density DNA Arrays
• 1997-Genome-wide Expression Monitoring in S. cerevisiae
ApproachesApproaches• What genes are Present/Absent in a tissue?
• What genes are Present/Absent in the experiment vs. control?
• Which genes have increased/decreased expression in experiment vs. control?
• Which genes have biological significance based on my knowledge of the biological system under investigation?
• Microarrays are simply small glass or silicon slides upon the surface of which are arrayed thousands of genes (usually between 500-20,000)
• Via a conventional DNA hybridization process, the level of expression/activity of those genes is measured
• Data are read using laser-activated fluorescence readers
• The process is “ultra-high throughput”
What are Microarrays?What are Microarrays?
GENE EXPRESSION ANALYSIS WITH MICROARRAYS
DNA Chips
Miniaturized, high density arrays of oligos (Affymetrix Inc.)
Printed cDNA or Oligonucleotide Arrays Robotically spotted cDNAs or Oligonucleotides • Printed on Nylon, Plastic or Glass surface
Affymetrix MicroarraysAffymetrix Microarrays
Involves Fluorescently tagged cRNA • One chip per sample• One for control• One for each experiment
Glass Slide Microarrays Involves two dyes/one chip
• Red dye• Green dye• Control and experiment on same chip
Gene Chip Technology Affymetrix Inc
Miniaturized, high density arrays of oligos 1.28-cm by 1.28-cm (409,000 oligos)
Manufacturing Process
Solid-phase chemical synthesis and Photolithographic fabrication techniques employed in semiconductor industry
Selection of Expression ProbesSelection of Expression ProbesSet of oligos to be synthesized is defined, based on its ability to Set of oligos to be synthesized is defined, based on its ability to hybridize to the target genes of interesthybridize to the target genes of interest
Probes
Sequence
Perfect Match
MismatchChip
5’ 3’
Computer algorithms are used to design photolithographic masks for use in manufacturing
Each gene is represented on the probe array by multiple probe pairsEach probe pair consists of a perfect match and a mismatch oligonucleotide.
Photolithographic SynthesisPhotolithographic Synthesis
Manufacturing ProcessManufacturing ProcessProbe arrays are manufactured by light-directed chemical Probe arrays are manufactured by light-directed chemical synthesis process which enables the synthesis of hundreds of synthesis process which enables the synthesis of hundreds of thousands of discrete compounds in precise locationsthousands of discrete compounds in precise locations
Lamp
Mask Chip
Click here to launch the movie file
Affymetrix Wafer and Chip FormatAffymetrix Wafer and Chip Format
1.28cm
20 - 50 µm
20 - 50 µm
Millions of identical oligonucleotide
probes per feature
49 - 400 chips/wafer
up to ~ 400,000 features/chip
RNA-DNA HybridizationRNA-DNA Hybridization
probe setsDNA
(25 base oligonucleotides of known sequence)
TargetsRNA
Non-Hybridized Targets are Washed AwayNon-Hybridized Targets are Washed Away
“probe sets” (oligo’s)
Targets(fluorescently tagged)
Non-bound ones are washed away
Target PreparationTarget Preparation
cDNA
Wash & Stain
Scan
Hybridize
(16 hours)
mRNAAAAA
B B B B
Biotin-labeled transcripts Fragment
(heat, Mg2+)
Fragmented cRNA
B B
B
B
IVT(Biotin-UTPBiotin-CTP)
GeneChipGeneChip®® Expression Analysis Expression Analysis
Hybridization and StainingHybridization and Staining
Array
cRNA Target
Hybridized Array
Streptravidin-phycoerythrinconjugate
Instrumentation for Gene Chip
Affymetrix Gene ChipsAffymetrix Gene Chips• Human Genome U133 Chip Set
• 33,000 genes, 2 chip set• uses recent draft of human genome
•Arabidopsis Genome Chip: 24,000 genes• Murine Genome Chip: 36,000 genes• E. coli Genome Chip: 4,200 genes• C. elegans Genome Chip: 22,500 genes
Affymetrix Gene ChipsAffymetrix Gene Chips• Rat Toxicology Chip: 850 genes
• CYP450’s, Heat Shock proteins• Drug transporters• Stress-activated kinases
• Rat Neurobiology Chip: 1,200 genes• Synuclein 1, prion protein, Huntington’s disease
• Syntaxin, Neurexin, neurotransmitters
• Drosophila Genome Chip: 13,500 genes • Yeast Genome Chip: 6,400 genes
Quality Control IssuesQuality Control Issues
• RNA purity and integrity• cDNA synthesis efficiency• Efficient cRNA synthesis, labeling and
fragmentation• Target evaluation with Test Chips
GENE EXPRESSION ANALYSIS WITH MICROARRAYS
DNA Chips
Miniaturized, high density arrays of oligos (Affymetrix Inc.)
Printed cDNA or Oligonucleotide Arrays Robotically spotted cDNAs or Oligonucleotides • Printed on Nylon, Plastic or Glass surface
Microarray of thousands of genes on a glass slide
Spotted arraysSpotted arrays
1 nanolitre spots90-120 um diameter
384 well source plate
chemically modified slides
steel
spotting pin
Spotted cDNA microarraysSpotted cDNA microarraysAdvantages• Lower price and flexibility• Simultaneous comparison of two related
biological samples (tumor versus normal, treated versus untreated cells)
• ESTs allow discovery of new genes
Disadvantages• Needs sequence verification• Measures the relative level of expression
between 2 samples
Gene D Over-expressed in normal tissue
Gene E Over-expressed in tumour
• Biomarkersof prognosis
• Genes affecting Treatment
Response
The challenges of microarraysThe challenges of microarrays
• Acquisition of high quality clinical samples, tumor and normal tissues
• High Quality RNA• Experimental design: what to compare to what?• Data analysis -1: what to do with the data? • Data analysis -2: How to do it?
– Very large number of data points
– Size of data files
– Choice of data analysis strategy/algorithm/software
Experimental DesignExperimental Design
• Choice of reference: Common (non-biologically relevant) reference, or paired samples?
• Number of replicates: How many are needed? (How many are affordable?). Are the replicate results going to be
averaged or treated independently?• Choice of data base: Where and how to
store the data?
Data Pre-processingData Pre-processing
Filtering – Background subtraction – Low intensity spots– Saturated spots – Low quality spots (ghost spots, dust
spots etc)
Normalization– Housekeeping genes/ control genes
Affymetrix Software for Microarray Data Analysis
• Microarray Suite 5 • Micro DB • Data Mining Tool (DMT)• NetAffx
Affymetrix Microarray Suite - Data AnalysisAffymetrix Microarray Suite - Data Analysis
Absolute Analysis – used to determine whether transcripts represented on the probe array are detected or not within one sample (uses data from one probe array experiment).
Comparison Analysis – used to determine the relative change in abundance for each transcript between a baseline and an experimental sample (uses data from two probe array experiments). Intensities for each experiment are compared to a baseline/control.
Microarray data analysisMicroarray data analysis
Scatter plots
• Intensities of experimental samples versus normal samples
• Quick look at the changes and overall quality of microarray
log/log
scatter plot
UP
DOWN
Intesities scatter plot for tumor sample OCA21BReference: Ambion normal ovary
Normalized
10
100
1000
10000
100000
10 100 1000 10000 100000
Cy3 intensity (OCA21B)
Cy5
in
ten
sity
(A
mb
ion
no
rmal
)
Intensities scatter plot for normal sample StratageneReference: Ambion normal ovary
Normalized
10
100
1000
10000
100000
10 100 1000 10000 100000
Cy3 intensity (Stratagene normal)
Cy5
in
ten
sity
(A
mb
ion
no
rmal
)Normal ovary #1 versus normal ovary #2
Tumor ovary versus normal ovary #1
Microarray data analysisMicroarray data analysis
Supervised versus unsupervised analysis
– Clustering: organization of genes that are similar to each other and samples that are similar to each other using clustering algorithms
– Statistical analysis: how significant are the results?
Two dimensional hierarchical clusteringTwo dimensional hierarchical clustering (Eisen (Eisen et alet al, PNAS (1998) , PNAS (1998) 9595, p. 14863), p. 14863)
• Unsupervised: no assumption on samples
• The algorithm successively joins gene expression profiles to form a dendrogram based on their pair-wise similarities.
• Two-dimensional hierarchical clustering first reorders genes and then reorders tumors based on similarities of gene expression between samples.
Two dimensional hierarchical Two dimensional hierarchical (“Eisen”) Clustering(“Eisen”) Clustering
Cluster analysis of genes in G1 and G2
Chaudhry et. al., 2002
Publicly Available SoftwaresPublicly Available Softwares
CLUSTER and TREEVIEWCLUSTER and TREEVIEW
• Hierarchical Clustering
• K means Clustering
• Self Organizing Maps
Publicly Available Softwares
GenMAPP
Visualize gene expression data on maps representing biological pathways and groupings of genes.
Other Softwares
Extraction of information from DNA-chip with the technology of promoter analysis
Genomatix Software GmbH
Microarray Applications (some)Microarray Applications (some)• Identify new genes implicated in disease progression and
treatment response (90% of our genes have yet to be ascribed a function)
• Assess side-effects or drug reaction profiles
• Extract prognostic information, e.g. classify tumors based on hundreds of parameters rather than 2 or 3.
• Detect gene copy number changes in cancer (array CGH)
• Identify new drug targets and accelerate drug discovery and testing
• ???
ApplicationsApplications
Discovery
Leads
PreClinical
Clinical
• Target Discovery
• Target Validation
• Screening• Validation• Optimization
• Toxicology• Optimization
• Genotyping• ADE Screens
Microarray Technology - ApplicationsMicroarray Technology - Applications
• Gene Discovery-– Assigning function to sequence– Discovery of disease genes and drug targets– Target validation
• Genotyping– Patient stratification (pharmacogenomics)– Adverse drug effects (ADE)
• Microbial ID
The List Continues To Grow….
Profiling Gene ExpressionProfiling Gene Expression
LungTumor
LiverTumor
KidneyTumor
Normal vs. NormalNormal vs. Normal
Normal vs. TumorNormal vs. Tumor
Lung Tumor: Up-RegulatedLung Tumor: Up-Regulated
Lung Tumor: Down-RegulatedLung Tumor: Down-Regulated
Microarray FutureMicroarray Future
• Must go beyond describing differentially expressed genes
• Inexpensive, high-throughput, genome- wide scan is the end game for research applications
• Protein microarrays beginning to be used–Fundamentally change experimental design–Will enhance protein dB construction
Microarray FutureMicroarray Future
• Publications are now being focused on biology rather than technology
• SNP analysis –Faster, cheaper, as accurate as sequencing–Disease association studies–Population surveys
• Chemicogenomics–Dissection of pathways by compound application–Fundamental change to lead validation
Microarray FutureMicroarray Future
• Diagnostics– Tumor classification– Patient stratification– Intervention therapeutics
ConclusionConclusion
• Technology is evolving rapidly.• Blending of biology, automation, and
informatics.• New applications are being pursued
– Beyond gene discovery into screening, validation, clinical genotyping, etc.
• Microarrays are becoming more broadly available and accepted.– Protein Arrays– Diagnostic Applications