1. Lecture 7. Functional Genomics: Gene Expression Profiling using
DNA microarrays

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

3. Goals of Functional Genomics:
1)DNA
2)RNA
3) Protein
4) Whole organism
5) Society
Lander, E The New Genomics: Global Views of Biology. Science 274:

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

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

6. A. Oligonucleotide chip
Multiple Oligonucleotides synthesized in situ on glass slides using light directed combinatorial chemistry

7. Oligonucleotide chip:
mer oligos represent each mRNA
Control: same oligos with a one bp center

8. 2) 6200 yeast gene each represented by 20 different oligonucleotides
Example: Simultaneous monitoring of gene expression in yeast grown in rich media or after starvation.
Wodicka, Dong, Mittmann, Ho, and Lockhart Genome-wide expression monitoring in S. cerevisiae. Nature Biotech. 15:
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.

9. RNA from yeast cells:
In rich media
starved

10. B. cDNA microarrays
a) PCR amplify individual cDNA clones ( 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

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

12. Ex. MLI cells (green) vs. irradiated ML1 cells (red)

13. Alternative to PCR Amplified sequences:
Long (70-80) Oligonucleotides Spotted in the
Same Manner

15. Detection Limit can be INCREASED depending on
how RNA is labeled

17. SELF ORGANIZING MAPS (SOMS)
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 Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. PNAS 96:
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

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.

19. 4X3 SOM of the
HL60 data
Node
Related nodes are
closer together in the
SOM

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

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.

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.

23. Example: Growth factor stimulation Of Fibroblast Proliferation
After growth
factor stimulation
Example: Growth factor stimulation
Of Fibroblast Proliferation
Eisen MB, Spellman PT, Brown PO, Botstein D PNAS 95:
Growth Factor
Green=increased expression
Fibroblast
RNA
cDNA
(Fluorescent label)
vs.
cDNA from
unstimulated cells
Red=decreased expression

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