1. A snapshot that captures the activity
Microarrays
A snapshot that captures the activity
pattern of thousands of genes at once.  
Custom spotted arrays
Affymetrix GeneChip

2. Microarray Process

3. Practical Applications of Microarrays
Gene Target Discovery
By allowing scientists to compare diseased cells with normal cells, arrays can
be used to discover sets of genes that play key roles in diseases. Genes that
are either overexpressed or underexpressed in the diseased cells often
present excellent targets for therapeutic drugs.
Pharmacology and Toxicology
Arrays can provide a highly sensitive indicator of a drug’s activity
(pharmacology) and toxicity (toxicology) in cell culture or test animals.
This information can then be used to screen or optimize drug candidates
prior to launching costly clinical trials.
Diagnostics
Array technology can be used to diagnose clinical conditions by detecting
gene expression patterns associated with disease states in either biopsy
samples or peripheral blood cells.

4. Microarray Platforms Oligonucleotide-based arrays
25mers spotted on a glass wafer, Affymetrix GeneChip arrays
Custom spotted 50-80mers generated from
known sequences.
cDNA
Inserts from cDNA libraries
PCR products generated from gene specific or
universal primers

5. GeneChip® Instrument System
Fluidics
Station
Scanner made by
Hewlett-Packard
Computer
Workstation

6. GeneChip® Probe Array

7. GeneChip® Probe Arrays*
Hybridized Probe Cell
GeneChip Probe Array
Single stranded, fluorescently
labeled DNA target
Oligonucleotide probe
24µm
1.28cm
Each probe cell or feature contains
millions of copies of a specific
oligonucleotide probe
Over 250,000 different probes
complementary to genetic
information of interest
Image of Hybridized Probe Array

8. Synthesis of Ordered Oligonucleotide Arrays
O O O O O
Light
(deprotection)
HO HO O O O
T T O O O
T T C C O
C A T A T
A G C T G
T T C C G
Mask
Substrate
T –
C –
REPEAT
Light removes protecting groups at defined positions. Single nucleotide washed over the chip, binds where the protecting group removed. Through successive steps, any sequence can be built up in any position on the chip. The number of steps corresponds with length of oligo, so can increase # of genes without # of steps

9. Probe Tiling Strategy Gene Expression
(25-mer)

10. Gene Expression Tiling Strategy
Magnified image from a human chip set. You can see genes that are upregulated, downregulated, or not affected. Note the reproducibility of the pattern of hybridization across the gene.
Uninduced
Induced
40 separate hybridization events are involved in determining the presence or absence of a transcript
80 separate hybridization events are involved determining differential gene expression of a transcript between two samples

11. Starting material for Microarrays
Platform
Affymetrix
Poly (A)+ mRNA ~2 mg
Total RNA ~10 mg
Spotted arrays
Poly (A) + ~0.4 – 2 mg
Total RNA mg

12. Experimental Design Biotin - labeled cRNA transcript Cells Poly (A)+Or
Total RNA B
IVT
Biotin-UTP
Biotin-CTP B B B B
Fragment
heat, Mg2+
cDNA B B
Hybridize
(16 hours) B B
Target Preparation
RNA isolation is the first step. 1-2 hours
The messenger RNA is then reverse transcribed into cDNA (we then go on to make the second strand of cDNA).
4 hours
An in vitro transcription reaction using biotinylated nucleotides is then done to both amplify and label the transcripts hours
These are then fragmented in order to get a more efficient hybridization ( bases pairs is the goal).
1.0 hours
The fragmented target is then hybridized overnight to a GeneChip expression array. 16 hours
When washing and staining of the array is complete it can then be scanned hours B
Wash & Stain
Biotin - labeled cRNA fragments
Scan
(75 minutes)
Add Oligo B2 & Staggered Spike
Controls
(8 minutes)

13. Normalization and Scaling
Non-biological factors can contribute to the variability of data
in many biological assays, therefore it is important to minimize
the non-biological differences. Factors that may contribute to
variation include:
Amount and quality of target hybridized to array
Amount of stain applied
Experimental variables
The data can be normalized from:
a limited group of probe sets
all probe sets
Thus the normalization of the array is multiplied by a
Normalization Factor (NF) to make its Average Intensity
equivalent to the Average Intensity of the baseline array.

14. Normalization and Scaling
Average intensity of an array is calculated by averaging all
the Average Difference values of every probe set on the array,
excluding the highest 2% and lowest 2% of the values.

15. Data Processing:
Analytical Spreadsheet can Handle Millions of Rows or Columns
Scaling & Normalization (e.g. standardize, log-scale, log & linear scale, power)
Sort rows by Value or by Similarity to Prototype (find genes most similar to
specified prototype)
Missing Data Handling (e.g. analysis, casewise deletion, imputation)

16. Cluster and Tree View

17. Microarray Process

18. Products used for spotting
Easy-To-Spot™ Products (Incyte Genomics)
Every clone is sequence-verified prior to PCR
PCR products are purified to remove excess salts,
unincorporated nucleotides, primers, and particulates
Quality controlled production process with failure rate1 of less
than 10%
8,734 PCR products from sequenced-verified clones from the
UniGene database from NCBI, average length is greater than
500 nucleotides
Between 1-3 ug of DNA per well. Enough to fabricate 500 to 1,000
arrays
Corresponding clones available for purchase for further research

19. Indirect labeling
Simple, highly sensitive technique requires less starting RNA, and creates evenly labeled DNA without dye bias.
Uniform incorporation of fluorescent dyes produces more reliable signals
High sensitivity to detect low-copy signals
Requires only 10 to 20 µg of total RNA or 0.4 to 1 µg of polyA RNA
Clontech
Atlas™ Glass Fluorescent Labeling Kit
Stratagene
FairPlay™ Microarray Labeling Kit

20. (Operon Technologies)
Array Ready Oligo set
(Operon Technologies)
Complete Yeast Genome Oligo Set
Optimized 70-mer oligonucleotides for each of the 6,307 open reading frames (ORFs) of Saccharomyces cerevisiae from the Saccharomyces Genome Database (SGD) at Stanford University
The amount of sample provided with each set is sufficient to print between 2000 and 6000 slides, depending on the printing procedure used.
Human Genome Oligo Set
This Array-Ready Oligo Set™ contains arrayable 70-mers representing 13,971 well-characterized human genes from the UniGene database. This database is located at the National Center for Biotechnology Information.
All 70-mer oligonucleotides in the Human Genome Oligo Set were designed from the representative sequences in the UniGene database, Hs build #119. The set also contains 29 controls.

21. GeneMachine Omni Grid Arrayer
Printing Pin

22. Axon GenePix4000A Scanner 10mm pixel size Simultaneously scans array
slides at two wavelengths
User-selectable laser power
User-selectable focus poisitions

24. GenePix Pro Features
Auto Align
After Auto Align
Before Auto Align

25. GenePix Pro Features Feature Viewer P = pixel intensity
F = feature intensity
B = background intensity
Rp = ratio of pixel intensities
Rm = ratio of means
mR = median of ratios
rR = regression ratio

26. GenePix Pro Features
Feature Pixel Plot

27. GenePix Pro Features
Histogram

28. GenePix Pro Features
Scatter Plot

29. Competitive hybridization
Spotted glass slide microarrays
Advantages
Low cost per array
Custom gene selection
Any species
Competitive hybridization
Open architecture
Disadvantages
Clone management
Clone cost
Quality control

30. Affymetrix GeneChip system
Advantages
Stream line production
Large number of genes and ESTs/chip
Several number of species
Disadvantages
System cost
GeneChip cost
Propietary system
Limits on customizing

32. GeneCards Database

34. Challenges in analyzing Microarray Data
Amount of DNA in spot is not consistant
Spot contamination
cDNA may not be proportional to that in the tissue
Low hybridization quality
Measurement errors
Spliced variants
Outliers
Data are high-dimensional “multi-variant”
Biological signal may be subtle, complex, non linear,
and buried in a cloud of noise
Normalization
Comparison across multiple arrays, time points, tissues,
treatments
How do you reveal biological relationships among genes?
How do you distinguish real effect from artifact?

35. Factors to consider in designing microarray experiments
Need to do lots of control experiments-validate method
Do replicate spotting, replicate chips, and reverse labeling
for custom spotted chips
Do pilot studies before doing “mega chip” experiments
Don’t design experiment without replication; nothing will
be learned from a single failed experiment
Design simple (one-two factor) experiments,
i.e. treatment vs. untreatment
Understand measurement errors
In designing Databases; they are useful ONLY if quality
of data is assured
Involve statistical colleagues in the design stages of your studies

36. Once you have identified an interesting expression pattern, what comes next?
With some arrays it is possible to purchase clones of interest for further experimentation.
Confirm that the particular clone you now have in your hand shows the expression pattern so indicated by the array, quantitating individual mRNA species.
RT-PCR, Relative, quantitative RT-PCR uses an internal standard to monitor each reaction and allow comparisons between different reactions to be made.
Competitive RT-PCR --a competition between a known amount of a template and an unknown target.
Northern analysis