Transcript Uses of Microarrays in Research - Davidson

Uses of Microarrays in
Research
Anne Rosenwald
Biology Department
Georgetown University
Microarrays in Research:
A Survey of PubMed
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Number of PubMed Citations
3500
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Schena, Shalon, Davis, and
Brown (1995) Science 270, 467
Differential expression of 45
Arabidopsis genes!
Microarray
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Protein Array
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Year
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2004
Recent Microarray Papers:
I. New Techniques/Applications
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5,000 RNAi experiments on a chip
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Lehner and Fraser (2004) Nat Methods 1, 103
RNA living-cell microarrays for loss-offunction screens in Drosophila
melanogaster cells
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Wheeler et al. (2004) Nat Methods 1, 127
 Spots on chip contain dsRNA
 Chip incubated with Drosophila cells
 Cells induced to “take-up” RNA
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Are cells alive or dead?
Do cells have phosphorylated Akt?
Do cells have altered actin fibrils?
Recent Microarray Papers:
I. New Techniques/Applications
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Transcriptional regulatory networks
in Saccharomyces cerevisiae
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Lee et al. (2002) Science 298 799-804
“ChIP-on-chip”
Recent Microarray Papers:
II. Improved Methods for Analysis/Access
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Reproducibility and statistical rigor
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outbred organisms (i.e. humans)
do different platforms give the same answers?
Tools for analysis
Tools for access and annotation
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an example based on Affymetrix chips
GeneCruiser: a web service for the annotation
of microarray data Liefeld et al. Bioinformatics
(2005) Jul 19 [epub]
can incorporate GO terms and link info with
SwissProt, RefSeq, LocusLink, etc.
Primarily for mouse and human data
Recent Microarray Papers:
III. Scientific Endeavors
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Mutational change: compare “wild type” to mutant
Tissue-specific gene expression
Environmental change: compare same organism in two
different environments
Development: compare different stages along a
particular lineage
Therapeutics: compare in cells/tissues treated with
and without the drug of interest
Investigate changes in gene copy number
Cancer: compare tumor with normal surrounding
tissue
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2005 papers with term “microarray” = 2450
Of those, also with term “cancer” = 624 (25%)
Recent Microarray Papers:
III. New Scientific Endeavors
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Transgenic C. elegans as a model in Alzheimer's
research
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Curr Alzheimer Res. 2005 Jan;2(1):37-45.
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Compared wild type worms with worms expressing
human Ab
Behavior and the limits of genomic plasticity:
power and replicability in microarray analysis of
honeybee brains
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Genes Brain Behav. 2005 Jun;4(4):267-71
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Compared bees with long-standing behavioral
differences (nursers v. foragers)
Compared recently hatched bees beginning to
express behavioral differences (nursers v. foragers
v. gravetenders)
Some basic yeast biology
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Yeast come in two mating types
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Can live either as haploids or as diploids
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MATa
MATa
diploids referred to as MATa/a
Haploids of opposite mating type can
mate to form new diploids
Diploids can be induced to undergo
meiosis (“sporulation”) to make new
haploids
Yeast resources
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General website for Saccharomyces (SGD)
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Materials available
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http://www.yeastgenome.org/
~5500 genes cloned with tags for purification
TAP-tagged fusion collections
HA-tagged fusion collections
GFP-tagged fusion collections
Insertional mutant collections
Knockout collections
Most of these available from OpenBiosystems
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www.openbiosystems.com
The yeast knockout collection
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Yeast knockout resources
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MATa/a heterozygous diploids (entire genome)
MATa haploids (non-essentials)
MATa haploids (non-essentials)
MATa/a homozygous diploids (non-essentials)*
Yeast knockout website
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http://wwwsequence.stanford.edu/group/yeast_deletion_project
/deletions3.html
*I have this collection, so if there’s a mutant you want, let me know.
The yeast knockout collection
http://www-sequence.stanford.edu/group/yeast_deletion_project/deletions3.html
Using the knockouts for microarrays
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A Robust Toolkit for Functional Profiling of
the Yeast Genome
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Takes advantage of the MATa/a
heterozygous diploid collection
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Pan et al. (2004) Mol Cell 16, 487
identifies synthetic lethal interactions via diploidbased synthetic lethality analysis by microarrays
(“dSLAM”)
Uses dSLAM to identify those strains that
upon knockout of a query gene, show
growth defects
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synthetic lethal (the new double mutant = dead)
synthetic fitness (the new double mutant = slow
growth)
Step 1: Creating the haploid
convertible heterozygotes
Important point:
This HIS3 gene is
only expressed in
MATa haploids, not in
MATa haploids or
MATa/a diploids
So in other words,
can select against
MATa/a diploids to
ensure you’re looking
at only haploids later
on.
Step 2: Inserting the query mutation
Knockout one copy
of your gene of
interest (“Your
Favorite Gene”)
with URA3
Step 3: Make new haploids and
select for strains of interest
Sporulate to get new
haploids
Select on –his medium
to ensure only
haploids survive (no
diploids)
selects against query
mutation so genotype
is xxxD::KanMX YFG1
selects for query mutation
so genotype is
xxxD::KanMX yfg1::URA3
Reminder about YKO construction
Step 4: Prepare genomic DNA and do
PCR with common TAG sequences
U1
D1
U2
D2
Using common oligos U1 and U2 (or D1 and D2) amplifies
the UPTAG (or DNTAG) sequence unique to each of the KOs
Step 4: Prepare genomic DNA and do
PCR with common TAG sequences
The two different
conditions are labeled
with two different
colors**
The labeled DNA is then
incubated with a TAG
microarray
**The PCR reactions create a mixture of TAGs (representing all the
strains in the pool), since each KO has a unique set of identifier tags
(UPTAG and DNTAG) bounded by common oligonucleotides
Evidence this really works – part I
On average, the
intensity is the same
before and after 1
copy of the CAN1
gene is knocked out
Strains
x-axis
XXX/xxxD::KanMX
CAN1/CAN1
y-axis
XXX/xxxD::KanMX
CAN1/can1D::MFA1pr-HIS3
Evidence this really works – part II
Red spots illustrate
that fraction of the
strains with KOs in
essential genes, so
when haploid, not
present in pool
Strains
x-axis
y-axis
DIPLOIDS
XXX/xxxD::KanMX
CAN1/can1D::MFA1pr-HIS3
HAPLOIDS
XXX or xxxD::KanMX
can1D::MFA1pr-HIS3
Another variation: Drug sensitivity
Another variation: Drug sensitivity
Summary
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If you can compare two different
conditions and you have a way to
stick things to slides, some sort of
microarray is possible!