Transcript Transcriptional Expression Analysis

Lecture 26
GWAS
Based on chapter 9
Functional and
Comparative Genomics
Copyright © 2010 Pearson Education Inc.
1 - RNA Expression Analysis – Determining
Genomewide RNA Expression Levels
•
Genomewide RNA expression analysis
• Types of microarrays
• Making microarrays
• Hybridization to microarrays
7 - Genomic Expression Analysis Methods
1. Microarrays
a. Hybridization based
2. SAGE – Serial analysis of gene expression
3. MPSS – Massively parallel signature
sequencing
12 - Microarray Hybridization
1. Usually comparative
a. Ratio between two
samples
2. Examples
a. Tumor vs. normal
tissue
b. Drug treatment vs.
no treatment
c. Embryo vs. adult
samples
mRNA
cDNA
DNA
microarray
25 - Labels
1. Cy3 and Cy5
a. Fluoresce at different wavelengths
b. Used for competitive hybridization
2. Biotin
a. Binds to fluorescently labeled avidin
b. Used with Affymetrix GeneChips
28 - Analysis of Hybridization
1. Results given as
ratios
2. Images use colors:
Cy3 = Green
Cy5 = red
Yellow
3. Yellow is equal
intensity or no
change in expression
29 - Example of Spotted Microarray
1. RNA from irradiated
cells (red)
2. Compare with
untreated cells (green)
3. Most genes have little
change (yellow)
4. Gene CDKN1A: red =
increase in expression
5. Gene Myc: green =
decrease in
expression
CDKNIA
MYC
2 – Yeast Cell Cycle Experimental
3 - Analysis of cell-cycle regulation
1. Yeast cells stopped at
different stages of cell
cycle
 G1, S, G2, and M
2. RNA extracted from
each stage
3. Control RNA from
unsynchronized
culture
4 - Results of cell-cycle analysis
1.
800 genes identified whose expression changes
during cell cycle
2. Grouped by peak expression
a. M/G1, G1, S, G2, and M
3. Four different treatments used to synchronize cells
a. All gave similar results
4. Results from Spellman et al., 1998; Cho et al., 1998
5 - Cell-cycle regulated genes
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Each gene is a line on
the longitudinal axis
Treatments in different
panels
Cell-cycle stages are
color coded at top
Vertical axis groups
genes by stage in
which expression
peaks
Alpha
cdc15
cdc28
Elu
M/G1
G1
S
G2
M
Brown and Botstein, 1999
7 - Profiling tumors
1. Image portrays gene
expression profiles
showing differences
between different
tumors
2. Tumors:
a. MD
(medulloblastoma)
b. Mglio (malignant
glioma)
c. Rhab (rhabdoid)
d. PNET (primitive
neuroectodermal
tumor)
3. Ncer: normal cerebella
1. Gene expression
differences for
medulloblastoma
correlated with
response to
chemotherapy
2. Those who failed to
respond had a
different profile from
survivors
3. Can use this approach
to determine treatment
60 different samples
8 - Cancer Diagnosis by Microarray
9 - Analysis of microarray results
1. Inherent variability: need for repetition
a. Biological and technical replicates
2. Analysis algorithms
a. Based on statistical models
3. Means of generating hypotheses that
need to be tested
10 – Serial Analysis of Gene Expression
(SAGE)
1. Serial analysis of gene expression
2. Concept: sequence a small piece of each
cDNA in a library
a. Gives measure of abundance of each
RNA species
3. Method
a. Cut off “tag” from each cDNA
b. Ligate tags together into a concatemer
c. Sequence the concatemer
13 - SAGE IV
1. Sequence the concatemers
2. Identify tag borders
a. Size of tag and restriction-enzyme sites
3. Compare tag sequences to database
4. Abundance of tag is measure of abundance
of that RNA species
14 - MPSS I
1. Massively parallel
signature sequencing
2. Means of determining
abundance of RNA
species
3. Unique tags added
to cDNAs
4. Tags hybridized to
oligonucleotides on
microbeads
Slide 15 – MPSS I
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Sequencing performed
in glass chamber
Initiated by restriction
enzyme revealing fourbase overhang
Hybridization of fourbase adapters used to
read sequence
Number of times a
particular sequence is
found is measure of
RNA abundance