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Transcript
DNA arrays for early disease detection
Chem 395
Bioanalytical chemistry
Instructor
Prof.James F.Rusling
Presented by
Vigneshwaran Mani
Outline
What is a Microarray?
 Types of Microarray
 Steps involved in Microarray fabrication
 What happens to the Genes in disease
state?
 Application of Microarray
 Summary

What is a Microarray?
A microarray is a spatially ordered,
minituarized arrangement of
multitude of immobilized reagents
Why Microarrays?????




Small volume- miniaturization
High throughput analysis
Large information generated
Less time required to analyze
Types of Microarrays



cDNA and Oligonucleotide
Microarrays
Probe: ssDNA,Oligonucleotide
Target: ssDNA,ssRNA
Principle: Hybridization
Protein Microarrays
Probe: Antibody
Target: Antigen
Principle: peptide chemistry
Others
Tissue arrays
Gene expression
Nucleus
RNA POLYMERASE
RIBOSOMES
Cytoplasm
cDNA ARRAYS
Marketed By: Agilent technologies
Steps Involved in Microarrays

Microarray
printing

Hybridization

Detection
DNA-Microarray printing






100-10000 spots
Glass slide used as substrate
DNA is attached covalently
to glass slide
96-384 well microtitre plates
used
Spot Volume 0.25-1nl.
Spot size 100-150µm in
diameter
•
•
•
•
•
•
1,2,3-X,Y,Z
direction
4-print head
5-glass slide
6-Microtitre plate
7-Distilled water
8- Drying
Hybridization
mRNA Control
A
C
U
mRNA Diseased
G
A
Reverse transcriptase
T
G
A
C
G
A
U
G
Reverse transcriptase
cDNA
T
G
C
A
Fluorescent labeling
Fluorescent labeling
Cy3
T
C
Cy5
C
T
G
A
Hybridize target to
microarray
C
Hybridization
Detection:
The slide is scanned twice
-Once to measure red
intensity
-Once to measure green
intensity
The images are overlayed
to produce one image
Scanning
M = logR/G = logR – logG



M<0: gene is over-expressed
in green-labeled sample
compared to red-labeled
sample.
M=0: gene is equally expressed
in both samples.
M>0: gene is over-expressed
in red-labeled sample
compared to green-labeled
sample.
What happens to Genes in (Cancer)
Disease State
Certain genes undergo overexpression.
 No. of copies of particular genes may
increase.
 Gene mutation.

Changes in gene expression levels

Gene is overexpressed in a certain disease state,

More cDNA(target) will hybridizes to probe, as
compared to control cDNA,

In turn, the spot will fluorescence red with greater
intensity than it will be with green.

Expression patterns of various genes is characterized
involved in many diseases,

Compare expression pattern of the gene from the
individual with the expression pattern of a known
disease.
Genomic gains and losses



Number of copies of a particular target gene has
increased.
Large amount of (Disease) sample DNA will hybridize
to those spots on the microarray compared to (normal)
control DNA hybridizing to those same spots.
Those spots containing the sample DNA will fluoresce
red with greater intensity than they will fluoresce
green, indicating that the number of copies of the
gene involved in the disease has gone up.
Gene Expression profile analysis in Human
hepatocellular carcinoma by cDNA microarray

Eun Jung Chung,Young Kwan Sung,MohammadFarooq,Younghee
Kim, Sanguk Im,Won Young Tak,Yoon Jin Hwang, Yang Il Kim,
Hyung Soo Han, Jung-Chul Kim, and Moon Kyu
Kim.,Mol.Cells,Vol.14,pp382-387,2002
HCC (Hepatocellular carcinoma)

Primary liver cancer (HCC)
Somatic mutations and activation of certain
oncogenes.
These events Lead to expression changes in genes.

cDNA arrays are used to analyze expression patterns


cDNA arrays
Computer analysis
3063 human cDNA
8 different samples of HCC
Up-regulated genes in HCC
 Galectin-3
 Serine/threonine kinase
 Fibroblast growth
factor receptor
 Ribosomal protein L35A
Down-regulated genes in
HCC
 mRNAs of Nip3
 Decorin
 Insulin-like growth
factor binding protein-3
Gene expression patterns of 8 hepatocellular carcinomas.
The genes were primarily classified into three groups,
based on their clustering pattern.
Application of Microarrays
Application
cDNA array.
Tumor classification, risk assessment,
and prognosis prediction.
Expression
analysis.
Drug development, drug response, and
therapy development
Summary



Data can be generated in a high throughput,
parallel fashion.
Less time required for analysis.
If gene expression data is already known for
a certain disease. Then we can compare the
gene expression data of the individual with
the known, and predict the disease
References




David J. Duggan, Michael Bittner, Yidong Chen, Paul
Meltzer & Jeffrey M. Trent, Nature genetics
supplement, volume 21, january 1999.
Sunil R. Lakhani, Michael J.O’hare & Alan ashworth,
Nature medicine,volume 7,number 4,april 2001.
Vivian G. Cheung, Michael Morley, Francisco Aguilar,
Aldo Massimi,Raju Kucherlapati & Geoffrey Childs,
Nature genetics supplement,volume 21,january
1999.
http://www.ncbi.nlm.nih.gov/
Acknowledgement


Prof. James F. Rusling
Chem 395 students