Microarray: an introduction and their applications to plant and medical research

Microarray: an introduction and their applications to plant and medical research
Nelzo C. Ereful
A presentation on Microarray
No copyright is attached with this presentation,
just acknowledge the author and the project.
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Outline
1. “Omics” organization overview
3. Steps in Microarray analysis
4. Applications of microarray
2. A partial view of transcriptomics
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Omics organization: an overview
Citrate Cycle
Genome
Transcriptome
Proteome
Metabolome
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Another way of looking at it
Taken from Goodacre (2005)
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Transcriptomics
global analysis of gene expression

transcriptome, complete set of RNA transcripts produced by the genome at any one time
examines the expression level of mRNAs in a given cell population, often using high-throughput techniques such DNA microarray technology.
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Transcriptomics
Analysis techniques
SAGE
cDNA
fingerprinting
MPSS
Microarray
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What is microarray?
Typically a glass slide where DNA molecules are fixed
Allows simultaneous measurement the level of expression of thousands of genes
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Landscape
subarray
Slide
gene
spot
pingroup
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Affymetrix GeneChip
Oligonucleotide:
11-25 mer (short oligo)
50-70 mer (long oligo)
http://en.wikipedia.org/wiki/Image:Affymetrix-microarray.jpg
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Applications of microarray
Drug
Discovery and
development
Evolutionary
and ecological
genomics
Tiling array
Tumor
classification
Gene
expression
(stress)
Microarray
SNP
detection
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Pre-microarray analysis questions
NO
YES
Consider other options/ techniques
Proceed to experimental design
Is Microarray the
best technique to meet
your research
objectives?
Which genes are down-regulated (repressed)
or up-regulated (induced)
at a particular condition or time?
(Gene expression analysis)
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General Scheme
Frame a biological
question
Choose a microarray
platform
Decide on biological
and technical replicates
Design the series
of hybridization
Experimental design
Technical
performance
Obtain the samples
Isolate total RNA
Label cDNA or mRNA
Perform the
hybridizations
Scan the slides
or chips
Statistical
analysis
Extract fluorescence
intensities
Normalize data to
remove biases
t-tests for pairwise
comparisons
ANOVA for multifactorial
designs
Data
mining
Cluster analysis
and pattern recognition
Study lists of
genome ontologies
Design validation
and follow-up experiments
Search for regulatory motifs
Gibson, G and SV Muse, 2004.
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Another way of looking at it
Joseph Clarke and Tong Zhu, 2006
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Experimental design
Reference design
Loop design
Split plot
contrast each experimental sample against a common reference sample
ex: cancer type 1, type 2, etc. vs. normal person
(modeled after agricultural field trials) useful when an experiment already involves multiple factors. ex: variety and stress
(with dye swap) for three or more conditions.
ex: well-watered, control and no water (rice)
Simple design
Classical design
Original design for microarray for comparing two conditions
Ex: aerobic vs. anaerobic yeast; drought vs. control
Bartolome (personal comm); Kerr and Churchill (2001); Churchill (2002); Gibson and Muse. 2004.
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Reference sample design
Loop design
Split plot design.
Experimental design
Gibson, G and SV Muse. 2004.
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CONSULT YOUR
STATISTICIAN!
Experimental design
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Experimental design
Drought stress
(Katherisan, 2005)
Define objective:
Assess drought-induced expression among three varieties (IR64, Apo, Azucena)
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Sample questions
What particular tissue?
What stage of the plant?
What variety (ies)?
What statistical design?
Sampling time?
What conditions?
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General scheme
Varieties:
Azucena
Apo
IR 64
(Katherisan, 2005).
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Field experimental procedure
Azucena
Apo
IR64
(Katherisan, 2005).
Apo, IR64
Azucena
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mRNA extraction
Images by Microarray Mediabook. http://gcat.davidson.edu/Pirelli/index.htm
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cDNA synthesis and fluorescent dye labeling
1. cDNA Synthesis
2. Dyes are incorporated
Images by Microarray Mediabook. http://gcat.davidson.edu/Pirelli/index.htm
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mRNA degradation
3. RNAse degrades mRNA
Images by Microarray Mediabook. http://gcat.davidson.edu/Pirelli/index.htm
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cDNA are mixed
Images by Microarray Mediabook. http://gcat.davidson.edu/Pirelli/index.htm
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Construction of cDNA library
(Katherisan, 2005).
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Microarray analysis
Hypothetical genome profile
Images by Microarray Mediabook. http://gcat.davidson.edu/Pirelli/index.htm
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Slides printing using GeneTAC printer
Microtiter plates
Glass slides
3000 spots per slide
PCR products
from >9000 genes
Images by Dr. John Bennett, IRRI
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Hybridize overnight
Images by Microarray Mediabook. http://gcat.davidson.edu/Pirelli/index.htm
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GeneTAC Hyb station
Automated and manual hybridization chambers
Manual hyb chamber
in water bath
Images by Dr. John Bennett, IRRI
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The hybridization process
Quackenbush, 2006
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Image processing by Laser scanning
Microarray Mediabook on scanning
Images by Dr. John Bennett, IRRI
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Images captured by scanner
Images by Dr. John Bennett, IRRI
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Slides Scanning
10K rice panicle cDNA library printed at IRRI
59 K oligo array from BGI, Beijing
22K chips from Agilent
Images by Dr. John Bennett, IRRI
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Data analysis
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Data analysis
Merged images
Induced
Repressed
Expressed in both conditions
R
G
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Expression ratio
R
G
T=
Reference/ Control
Test/ Experimental
18000
11600
13000
15500
5600
1200
600
800
2500
6500
10900
13500
16500
17500
22.5
6.2
2.0
1.0
0.4
0.07
0.03
R
G
0/0
∆ Gene expression
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Fold change
18000
11600
13000
15500
5600
1200
600
800
2500
6500
10900
13500
16500
17500
22.5
6.2
2.0
1.0
0.4
0.07
0.03
R
G
2.5
14.3
33.3
Reciprocal
Fold Induction
Fold Repression
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Asymmetrical distribution
Image by Microarray Mediabook. http://gcat.davidson.edu/Pirelli/index.htm
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log2 transform
Transformation
22.5
6.2
2.0
1.0
0.4
0.07
0.03
-5.1
-3.8
-1.3
0
1.0
2.6
4.5
Induction
Repression
Image by Microarray Mediabook. http://gcat.davidson.edu/Pirelli/index.htm
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Normalization
To eliminate variations
Process of correcting
Why normalize?
Differential labeling
Differences in the amount of mRNA samples
Spatial positions of the spots
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Reporting your results
Images by Microarray Mediabook. http://gcat.davidson.edu/Pirelli/index.htm
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Transformed colors
Blue-yellow
Red- green
Gray scale
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Hierarchal clustering
r = 0.89
r = 0.88
r = 0.88
Truncated image taken from Kathiresan et al., 2005
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Dendogram
Higher correlation between genes
Lower correlation between genes
0.8 0.85 0.9
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Results
Another slide
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Medical application of microarray analysis
treatment of cancer may be tailored to the needs of that individual’s gene expression profile.
Figure take from
http://en.wikipedia.org/wiki/Image:Microarray-schema.jpg
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Tumor classification
Quackenbush 2006; Retrieved from http://content.nejm.org/cgi/content/full/354/23/2463
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Expression matrix
Quackenbush 2006; Retrieved from http://content.nejm.org/cgi/content/full/354/23/2463
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Cluster Analysis
Quackenbush 2006; Retrieved from http://content.nejm.org/cgi/content/full/354/23/2463
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Acknowledgement
Dr. John Bennett
GCP Microarray Workshop, SA
Dr. Ramil Mauleon
Dr. Richard Bruskiewich
Teresa De Leon
Icoy Mercado
Beng Bartolome
Microarray mediabook for images


Kindly see your notes. Thanks!
References
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Crop Bioinformatics
Microarray and asthma
Something more
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tolerant ATGTGTGGAGGAGAAGTGATCCCCGCCGACATGCCGGCGGCGCCGTTCACGCCACGCCAC 60 susceptible ------------------------------ATGCCGGCGGCGCCGTTCACGCCACGCCAC 30 ******************************
tolerant GGCGACGGCGAGACATGGGTTGACAGAAAGAGGAGGAACAAGAAGAAGAGGAAGCGCGGC 120 susceptible GGCGACGGCGAGACATGGGTTGACAGAAAGAGGAGGAACAAGAAGAAGAGGAAGCGCGGC 90 ************************************************************
tolerant GCCGACGAAGAATGGGAGGCCGCCTTCCAGGAGTTCATGGCTGCTGACGACGACGACGAC 180 susceptible GCCGACGAAGAATGGGAGGCCGCCTTCCAGGAGTTCATGGCTGCTGACGACGACGACGAC 150 ************************************************************
tolerant GGCGGCGGACTCGTGTTAAGTAGTAAATCTTTGGTGTTGAGGTCACCAGGTGAAAATGAT 240 susceptible GGCGGCGGACTCGTGTTAAGTAGTAAATCTTTGGTGTTGAGGTCACCAGGTGAAAATGAT 210 ************************************************************
tolerant GCAGGCCGGGGCGCCGCCGCCACCATGTCCATGCCGCTGGACCCCGTGACCGAGGAGGCC 300 susceptible GCAGGCCGGGGCGCCGCCGCCACCATGTCCATGCCGCTGGACCCCGTGACCGAGGAGGCC 270 ************************************************************
tolerant GAGCCGGCGGTGGCTGAGAAGCCTCGCCGGCGCCGGCCGAGGCGGAGCTACGAGTACCAC 360 susceptible GAGCCGGCGGTGGCTGAGAAGCCTCGCCGGCGCCGGCCGAGGCGGAGCTACGAGTACCAC 330 ************************************************************
tolerant GGCATCCGGCAGCGGCCGTGGGGGCGGTGGTCGTCGGAGATCCGCGACCCCGTCAAGGGC 420 susceptible GGCATCCGGCAGCGGCCGTGGGGGCGGTGGTCGTCGGAGATCCGCGACCCCGTCAAGGGC 390 ************************************************************
tolerant GTCCGCCTCTGGCTCGGCACCTTCGACACCGCCGTCGAAGCCGCGCTCGCCTACGACGCC 480 susceptible GTCCGCCTCTGGCTCGGCACCTTCGACACCGCCGTCGAAGCCGCGCTCGCCTACGACGCC 450 ************************************************************
tolerant GAGGCCCGCCGCATCCACGGCTGGAAAGCCCGGACAAACTTCCCACCCGCCGATCTTTCT 540 susceptible GAGGCCCGCCGCATCCACGGCTGGAAAGCCCGGACAAACTTCCCACCCGCCGATCTTTCT 510 ************************************************************
tolerant TCGCCGCCGCCGCCGTCGCAGCCGCTCTGCTTCTTGCTCAACGACAACGGCCTCATCACA 600 susceptible TCGCCGCCGCCGCCGCCGCAGCCGCTCTGCTTCTTGCTCAACGACAACGGCCTCATCACA 570 *************** ********************************************
tolerant ATCGGAGAAGCGCCGACCGACGACGCCGCGTCGACGTCGACGTCGACGACGGAGGCGTCC 660 susceptible ATCGGAGAAGCGCCGACCGACGACGCCGCGTCGACGTCGACGTCGACGACGGAGGCGTCC 630 ************************************************************
tolerant GGCGACGCGCGCATACAACTGGAGTGCTGCTCGGACGACGTGATGGACAGCCTCCTCGCC 720 susceptible GGCGACGCGCGCATACAGCTGGAGTGCTGCTCGGACGACGTGATGGACAGCCTCCTCGCC 690 ***************** ******************************************
tolerant GGCTACGACGTGGCCAGCGGCGACGACATATGGACATGGACATCTGGAGCCTCCTCCACC 780 susceptible GGCTACGACGTGGCCAGCGGCGACGACATATGGACATGGACATCTGGAGCCTCCTCCACC 750 ************************************************************
tolerant TCTGTTAACCAAGAGATCAAGACCCCATCGATCCACCAAAACATATCATATGCAGGTGCC 840 susceptible TCTGTTAACCAAGAGATCAAGACCCCATCGATCCACCAAAACATATCATATGCAGGTGCC 810 ************************************************************
tolerant CGCCCCATGACTTGTCACTTTAAGAATCATAAAAACACTTTTGTACAAATGGAGTGCTCA 900 susceptible CGCCCCATGACTTGTCACTTTAAGAATCATAAAAACACTTTTGTACAAATGGAGTGCTCA 870 ************************************************************
tolerant ACCATGCTAAACTTACTCAAAGGCCACAAACAATAA 936 susceptible ACCATGCTAAACTTACTCAAAGGCCACAAACAATAA 906 ************************************
CCGTCGC
CCGCCGC
tolerant ATGTGTGGAGGAGAAGTGATCCCCGCCGACATGCCGGCGGCGCCGTTCACGCCACGCCAC 60 susceptible ------------------------------ATGCCGGCGGCGCCGTTCACGCCACGCCAC 30 ******************************
tolerant GGCGACGGCGAGACATGGGTTGACAGAAAGAGGAGGAACAAGAAGAAGAGGAAGCGCGGC 120 susceptible GGCGACGGCGAGACATGGGTTGACAGAAAGAGGAGGAACAAGAAGAAGAGGAAGCGCGGC 90 ************************************************************
tolerant GCCGACGAAGAATGGGAGGCCGCCTTCCAGGAGTTCATGGCTGCTGACGACGACGACGAC 180 susceptible GCCGACGAAGAATGGGAGGCCGCCTTCCAGGAGTTCATGGCTGCTGACGACGACGACGAC 150 ************************************************************
tolerant GGCGGCGGACTCGTGTTAAGTAGTAAATCTTTGGTGTTGAGGTCACCAGGTGAAAATGAT 240 susceptible GGCGGCGGACTCGTGTTAAGTAGTAAATCTTTGGTGTTGAGGTCACCAGGTGAAAATGAT 210 ************************************************************
tolerant GCAGGCCGGGGCGCCGCCGCCACCATGTCCATGCCGCTGGACCCCGTGACCGAGGAGGCC 300 susceptible GCAGGCCGGGGCGCCGCCGCCACCATGTCCATGCCGCTGGACCCCGTGACCGAGGAGGCC 270 ************************************************************
tolerant GAGCCGGCGGTGGCTGAGAAGCCTCGCCGGCGCCGGCCGAGGCGGAGCTACGAGTACCAC 360 susceptible GAGCCGGCGGTGGCTGAGAAGCCTCGCCGGCGCCGGCCGAGGCGGAGCTACGAGTACCAC 330 ************************************************************
tolerant GGCATCCGGCAGCGGCCGTGGGGGCGGTGGTCGTCGGAGATCCGCGACCCCGTCAAGGGC 420 susceptible GGCATCCGGCAGCGGCCGTGGGGGCGGTGGTCGTCGGAGATCCGCGACCCCGTCAAGGGC 390 ************************************************************
tolerant GTCCGCCTCTGGCTCGGCACCTTCGACACCGCCGTCGAAGCCGCGCTCGCCTACGACGCC 480 susceptible GTCCGCCTCTGGCTCGGCACCTTCGACACCGCCGTCGAAGCCGCGCTCGCCTACGACGCC 450 ************************************************************
tolerant GAGGCCCGCCGCATCCACGGCTGGAAAGCCCGGACAAACTTCCCACCCGCCGATCTTTCT 540 susceptible GAGGCCCGCCGCATCCACGGCTGGAAAGCCCGGACAAACTTCCCACCCGCCGATCTTTCT 510 ************************************************************
tolerant TCGCCGCCGCCGCCGTCGCAGCCGCTCTGCTTCTTGCTCAACGACAACGGCCTCATCACA 600 susceptible TCGCCGCCGCCGCCGCCGCAGCCGCTCTGCTTCTTGCTCAACGACAACGGCCTCATCACA 570 *************** ********************************************
tolerant ATCGGAGAAGCGCCGACCGACGACGCCGCGTCGACGTCGACGTCGACGACGGAGGCGTCC 660 susceptible ATCGGAGAAGCGCCGACCGACGACGCCGCGTCGACGTCGACGTCGACGACGGAGGCGTCC 630 ************************************************************
tolerant GGCGACGCGCGCATACAACTGGAGTGCTGCTCGGACGACGTGATGGACAGCCTCCTCGCC 720 susceptible GGCGACGCGCGCATACAGCTGGAGTGCTGCTCGGACGACGTGATGGACAGCCTCCTCGCC 690 ***************** ******************************************
tolerant GGCTACGACGTGGCCAGCGGCGACGACATATGGACATGGACATCTGGAGCCTCCTCCACC 780 susceptible GGCTACGACGTGGCCAGCGGCGACGACATATGGACATGGACATCTGGAGCCTCCTCCACC 750 ************************************************************
tolerant TCTGTTAACCAAGAGATCAAGACCCCATCGATCCACCAAAACATATCATATGCAGGTGCC 840 susceptible TCTGTTAACCAAGAGATCAAGACCCCATCGATCCACCAAAACATATCATATGCAGGTGCC 810 ************************************************************
tolerant CGCCCCATGACTTGTCACTTTAAGAATCATAAAAACACTTTTGTACAAATGGAGTGCTCA 900 susceptible CGCCCCATGACTTGTCACTTTAAGAATCATAAAAACACTTTTGTACAAATGGAGTGCTCA 870 ************************************************************
tolerant ACCATGCTAAACTTACTCAAAGGCCACAAACAATAA 936 susceptible ACCATGCTAAACTTACTCAAAGGCCACAAACAATAA 906 ************************************
CCGTCGC = tolerant (S186)
CCGCCGC = intolerant (P186)
ACAACTG = tolerant
ACAGCTG = intolerant
SNP
SNP detection using Microarray
Heuer, 2006
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