Lecture 7. Functional Genomics: Gene Expression Profiling using
DNA microarrays Slide 2 Functional Genomics: Development and
Application of Genome- Wide Experimental Approaches to Assess Gene
Function by making use of the information and reagents provided by
Structural Genomics Slide 3 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. Slide
4 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 Slide 5 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 Slide 6 A. Oligonucleotide chip Multiple
Oligonucleotides synthesized in situ on glass slides using light
directed combinatorial chemistry Slide 7 Oligonucleotide chip:
10-20 25mer oligos represent each mRNA Control: same oligos with a
one bp mismatch @ center Slide 8 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
25- mer 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. Slide 9 RNA from yeast cells: In
rich media starved Slide 10 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 Slide 11
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
Slide 12 Ex. MLI cells (green) vs. irradiated ML1 cells (red) Slide
13 Alternative to PCR Amplified sequences: Long (70-80)
Oligonucleotides Spotted in the Same Manner Slide 14 Slide 15
Detection Limit can be INCREASED depending on how RNA is labeled
Slide 16 Slide 17 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 self- organizing 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 DATA ANALYSIS Slide 18 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. Slide 19
4X3 SOM of the HL60 data Node Related nodes are closer together in
the SOM Slide 20 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 Slide 21
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.
Slide 22 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. Slide 23 Fibroblast Growth
Factor RNA After growth factor stimulation Example: Growth factor
stimulation Of Fibroblast Proliferation cDNA (Fluorescent label)
vs. cDNA from unstimulated cells Green=increased expression
Red=decreased expression http://rana.lbl.gov/EisenSoftware.htm
Eisen MB, Spellman PT, Brown PO, Botstein D. 1998. PNAS
95:14863-14868. Slide 24 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 Slide 25
