PNNL-13501 - Pacific Northwest National Laboratory

identify novel proteins of importance in signal
transduction and to more fully characterize interactions
among proteins expressed at low levels and, therefore,
most likely to be rate-limiting and critical to the disease
process. Ultimately these analytical approaches will be
coupled with sophisticated mathematical modeling
techniques to both guide and refine the developing picture
of intracellular signaling.
Results and Accomplishments
cDNA Arrays
The ability to simultaneously measure the expression
level of thousands of genes allows us to identify
differentially expressed genes in a relative short time. In
the past year, we have established the capability to
fabricate cDNA microarrays and to analyze data
generated from these cDNA microarrays. To establish
this capability, we acquired a robotic arrayer from
Cartesian Technologies for fabricating cDNA
microarrays, and a dual laser scanner to acquire and
analyze the expression signals. Furthermore, software,
Imagene 3.0 (Figure 1) and OmniViz, is being
evaluated for data mining and clustering analysis.
To establish the process of fabricating mouse cDNA
microarray, we acquired 23,000 unique mouse cDNA
clones from Research Genetics and National Institute
of Aging. High-throughput polymerase chain reaction
(PCR) amplification of cDNA clones, robotic arraying,
cross-linking of PCR products on chemically treated
slides, labeling of target RNA, hybridization, and signal
analysis have been optimized (Figure 2). Applications of
these cDNA microarrays will be developed in the coming
years. We have also used a few commercial cDNA
microarrays to establish the protocols and to identify
differentially expressed genes in ovarian cancer. From
the cDNA microarray experiment, we were able to
identify 47 genes that are over expressed in ovarian
cancer (Wong et al. 2000). A patent has been filed for
detection of ovarian cancer using the identified genes.
Characterization of Proteins by Mass Spectrometry
We have developed matrix assisted laser desorption
ionization mass spectrometry (MALDI-MS) techniques
for identifying unknown proteins and for determining
structural modifications to these proteins. Using these
approaches, we have demonstrated the ability to identify
both known and unknown proteins in partially purified
protein fractions. Using antibodies to immunoprecipitate
tyrosine phosphorylated proteins coupled with separation
on 1D-PAGE gels, we have identified several
phosphorylated proteins associated with malignant
transformation in an ovarian cancer model (Figure 3).
While we have shown that current procedures are
adequate for identifying proteins, these methods typically
provide less than 50% coverage of the peptide fragments
contained in a single protein. Identification of protein
variations associated with covalent modifications such as
phosphorylation, mRNA splicing, or proteolysis requires
Figure 1. Data mining of differentially expressed genes in ovarian cancer using software Imagene 3.0. 1a. Differentially expressed
gene analysis on three pooled normal HOSE cultures (HOSE17, HOSE636, and HOSE642) and three pooled ovarian cancer cell
lines (OVCA420, OVCA433, and SKOV3) using a commercially available cDNA microarray containing 2400 human cDNA.
Probes derived from cancer cell lines and normal HOSE cells were fluorescently labeled with cy3 (green) and cy5 (red)
respectively. 1b. Scatter plot analysis of a single microarray experiment using Imagene software. The control is the relative
expression level of genes in HOSE cells, and the non-control is the relative gene expression level of genes in ovarian cancer cell
lines. When a dot is highlighted, the name of the gene, the flourescent intensity, and actual spot image will be shown in the right
side panel and directly linked to Medline and GenBank databases.
Biosciences and Biotechnology 49