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Lecture 7. Functional Genomics:
Gene Expression Profiling using
DNA microarrays
Functional Genomics:
Development and Application of GenomeWide Experimental Approaches to Assess
Gene Function by making use of the
information and reagents provided by
Structural Genomics
Goals of Functional
Genomics:
1)DNA
2)RNA
3) Protein
4) Whole organism
5) Society
Lander, E. 1996. The New Genomics: Global
Views of Biology. Science 274: 536-539.
2. RNA
Simultaneous monitoring of the expression of
all genes
EG: What do gene expression patterns look
like in tumor vs. normal cells? What about
following chemotherapy?
Will reveal Regulatory Networks
Gene Expression Profiling
Experimental Strategies for Analyzing Gene Expression
Patterns:
1) Sequencing of cDNA
Either complete cDNAs or partial cDNAs
(Serial Analysis of Gene Expression {SAGE} technique)
2) DNA Microarrays-"Chip" technologies
a) oligonucleotides synthesized in situ on glass
slides using light directed combinatorial chemistry
b) "printing" of cDNA onto glass slides
Two things to consider:
-the chip
-how to label the RNA for hybridization to the chip
A. Oligonucleotide chip
Multiple Oligonucleotides synthesized in situ on glass slides
using light directed combinatorial chemistry
Oligonucleotide chip:
10-20 25mer oligos represent each mRNA
Control: same oligos with a one bp mismatch @ center
Example: Simultaneous monitoring of gene expression in
yeast grown in rich media or after starvation.
Wodicka, Dong, Mittmann, Ho, and Lockhart. 1997 Genome-wide expression
monitoring in S. cerevisiae. Nature Biotech. 15: 1359-1367.
1) 1.28 X 1.28 cm chip contains 65,000 independent 25mer oligonucleotides. ~70,000 copies of each individual
oligo per slot on the chip
2) 6200 yeast gene each represented by 20 different
oligonucleotides
6200 X 20= 128,000 oligos
3) Control- For each oligo, also synthesize a second oligo
having one base mismatch (center base in the 25 mer)
Total of 260,000 oligos on 4 chips: total is about 1 sq. in.
RNA from yeast cells:
In rich media
starved
B. cDNA microarrays
a) PCR amplify individual cDNA clones (500-1000 bp) displayed in 96 well dishes;
b) stamp each well on a glass slide (robot); ~1 ul of DNA per spot. Denature and bake the
DNA onto the glass slide
c) label test and control RNA with different fluorescent markers
d) hybridize to the chip; detect hybridization by color of fluorescence
Density of microarray is not as great; however cDNA is longer and
hybridization more efficient than oligonucleotide hybridization
(remember: hybridization is COOPERATIVE).
EM of cDNA Microarray
Ex. MLI cells (green) vs. irradiated ML1 cells (red)
Alternative to PCR Amplified sequences:
Long (70-80) Oligonucleotides Spotted in the
Same Manner
Making the RNA probe:
1) directly label RNA with tag or isotope
2) make cDNA with fluorescent tag
3) make ds cDNA and produce cRNA
(amplification of signal).
mRNA
AAAAAAA
TTTT-T7
RT
promoter
Detection Limit can be INCREASED depending on
how RNA is labeled
DATA ANALYSIS
In a typical experiment, several hundred-or even several
thousand- genes might change expression pattern when two
conditions are compared!
How do you make sense of this massive amount of data?
Cluster Analysis
Tamayko et al. 1999. Interpreting patterns of gene expression with selforganizing maps: methods and application to hematopoietic differentiation.
PNAS 96:2907-2912
SELF ORGANIZING MAPS (SOMS)
Mathematical technique for identifying underlying
patterns in complex data arrays. Essentially clusters
data points in multidimensional space. SOMS impose
structure on a data set, clustering like data in “nodes”.
GENECLUSTER: program developed to produce SOMS
from microarray data:and available from these authors
Example: An oligonucleotide microarray containing 6416
genes (5223 known, 1193 unknown ESTs) was used to
monitor gene in HL60 cell line induced to differentiate
into macrophage-like cells by phorbol ester (PMA)
treatment:
Cells were treated for 0, 0.5, 6 or 24 hrs with PMA, RNA
extracted for each treatment, and used to make cRNA.
The analysis indicated that the expression of 567 genes
varied by more than 4-fold.
4X3 SOM of the
HL60 data
Node
Related nodes are
closer together in the
SOM
More complicated example:
differentiation of four cell
lines was studied by
microarray analysis: two that
differentiate into
macrophages (HL60 &U937);
one into neutrophils (NB4),
and one into activated T-cells
(Jurkat)
1,036 genes varied in the
analysis
6X4 SOM representing the
data
HIERARCHICAL CLUSTERING
Relationships among objects (genes) are
represented by a tree whose branch lengths
reflect the degree of similarity between the
objects, as assessed by a pairwise similarity
function.
In sequence comparison, these methods are
used to infer the evolutionary history of
sequences being compared.
For analysis of gene profiling data such methods
are useful in their ability to represent varying
degrees of similarity and more distant
relationships among groups of closely related
genes, as well as in requiring few assumptions
about the nature of the data.
The computed trees can be used to order genes in
the original data table, so that genes or groups of
genes with similar expression patterns are
adjacent. The ordered table can then be displayed
graphically, with a representation of the tree to
indicate the relationships among genes.
Example: Growth factor stimulation
Of Fibroblast Proliferation Eisen MB, Spellman
PT, Brown PO,
Botstein D. 1998.
PNAS 95:14863-14868.
Growth Factor
http://rana.lbl.gov/EisenSoftware.htm
Fibroblast
Green=increased expression
RNA
cDNA
(Fluorescent label)
vs.
cDNA from
unstimulated cells
Red=decreased expression
After growth
factor stimulation
Hierarcical Cluster Analysis of 3800 Yeast Genes
whose expression changed one or more times
under
365 different experimental treatments
(including genetic manipulation, drugs, growth conditions)
Known genes
all involved
in yeast mating
Hypothesis: Unknown Genes
are involved in yeast mating