Transcript No Slide Title

DNA Microarrays
M. Ahmad Chaudhry, Ph. D.
Outline of the lecture
• Overview of Micoarray Technology
• Types of Microarrays
• Manufacturing
• Instrumentation and Softwares
• Data analysis
• Applications
Microarray Development
• Relatively young technology
• Widely adopted
• Mainly used in gene discovery
Evolution & 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
Approaches
• 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?
What are Microarrays?
• 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”
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 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 Probes
Set of oligos to be synthesized is defined, based on its ability to
hybridize to the target genes of interest
5’
3’
Sequence
Probes
Perfect Match
Mismatch
Chip
Computer algorithms are used to design photolithographic
masks for use in manufacturing
Each gene is represented on the probe array by multiple probe
pairs
Each probe pair consists of a perfect match and a mismatch
oligonucleotide.
Photolithographic Synthesis
Manufacturing Process
Probe arrays are manufactured by light-directed chemical
synthesis process which enables the synthesis of hundreds of
thousands of discrete compounds in precise locations
Lamp
Mask
Chip
Click here to launch the movie file
Affymetrix Wafer and Chip Format
20 - 50 µm
20 - 50 µm
Millions of identical
oligonucleotide
probes per feature
49 - 400
chips/wafer
1.28cm
up to ~ 400,000 features/chip
RNA-DNA Hybridization
Targets
RNA
probe sets
DNA
(25 base oligonucleotides of known sequence)
Non-Hybridized Targets are Washed Away
Targets
(fluorescently tagged)
“probe sets” (oligo’s)
Non-bound ones are washed away
Target Preparation
B
Biotin-labeled
transcripts
B
B
B
B
Fragment
(heat, Mg2+)
B
B
B
Fragmented cRNA
IVT
AAAA
mRNA
(Biotin-UTP
Biotin-CTP)
Wash & Stain
Scan
cDNA
Hybridize
(16 hours)
®
GeneChip Expression Analysis
Hybridization and Staining
Array
Hybridized Array
cRNA Target
Streptravidinphycoerythrin
conjugate
Instrumentation for Gene Chip
Affymetrix 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 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 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
steel
Spotted arrays
spotting pin
chemically modified slides
384 well source
plate
1 nanolitre spots
90-120 um diameter
Spotted cDNA microarrays
Advantages
• 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
Overexpressed
in normal
tissue
Gene E
Overexpressed
in tumour
• Biomarkers
of prognosis
• Genes
affecting
Treatment
Response
The 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 Design
• Choice of reference: Common (nonbiologically 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-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 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 analysis
Scatter plots
• Intensities of experimental samples versus
normal samples
• Quick look at the changes and overall quality of
microarray
UP
log/log
scatter plot
DOWN
Intensities scatter plot for normal sample Stratagene
Reference: Ambion normal ovary
Normalized
Normal ovary #1
versus normal ovary #2
10000
1000
100
Tumor ovary versus
normal ovary #1
10
10
100
1000
10000
100000
Cy3 intensity (Stratagene normal)
Intesities scatter plot for tumor sample OCA21B
Reference: Ambion normal ovary
Normalized
100000
Cy5 intensity (Ambion normal)
Cy5 intensity (Ambion normal)
100000
10000
1000
100
10
10
100
1000
Cy3 intensity (OCA21B)
10000
100000
Microarray 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 clustering
(Eisen et al, PNAS (1998) 95, 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
(“Eisen”) Clustering
Cluster analysis of genes in G1 and G2
Chaudhry et. al., 2002
Publicly Available Softwares
CLUSTER 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)
• 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
• ???
Applications
Clinical
PreClinical
Leads
Discovery
• Target
Discovery
• Target
Validation
• Genotyping
• Toxicology • ADE Screens
• Screening • Optimization
• Validation
• Optimization
Microarray 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 Expression
Kidney
Tumor
Lung
Tumor
Liver
Tumor
Normal vs. Normal
Normal vs. Tumor
Lung Tumor: Up-Regulated
Lung Tumor: Down-Regulated
Microarray Future
• Must go beyond describing differentially
expressed genes
• Inexpensive, high-throughput, genomewide scan is the end game for research
applications
• Protein microarrays beginning to be used
–Fundamentally change experimental design
–Will enhance protein dB construction
Microarray 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 Future
• Diagnostics
– Tumor classification
– Patient stratification
– Intervention therapeutics
Conclusion
• 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