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Explore the impact of smoking on genes by observing lung tissue from smokers and non-smokers using DNA chips (microarrays). This lab experiment involves printing the DNA chips, washing, hybridization with target DNA, washing again, and sending to a scanning facility for analysis with bioinformatics tools. Learn about the process of making DNA chips, terminology related to probe and target DNA, and considerations of microarray technology including cost, equipment, and skilled personnel required.
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DNA Chips (Microarrays - see Animation) Lab Experiment What does Smoking do to my Genes? Observe lung tissue from smokers and non-smokers
LAB Overview 1. Print DNA Chip 1.5. (Teacher) Bake (20-80 min ~ 80°C) 2. Wash (4 x 2 min in SDS and water) 3. Hybridize with Target DNA (room temp ~ 20 min) 4. Wash (4 x 2 min in SDS and salt solutions) 5. Send to Scanning Facility 6. Analyze - bioinformatics tools
Make a DNA Chip Lab Step 1 - DNA Chip Printing
Probe DNA 1 microliter spots of DNA in solution Each grid: 11 genes in duplicate
DNA Chip Terminology Probe DNA - short pieces of single stranded DNA attached to glass Target DNA - cDNA from cells grown under different conditions Floating in solution on top of probe DNA example: cDNA from seedlings grown in light vs. dark
Probe DNA - attachment to Glass Slide Treated slide From Telechem International
Hybridization Probe (Chip) DNA + Target cDNA (Simulated) ~20 minutes room temp
Microarray Technology Considerations • Costly • One Array ~ $400. Not including Tissue Preparation • One array can cost >$1000. • Scanning equipment >$50-200,000. • Complicated- each step requires controls for validation and replicates for reliability • Harvesting Tissue • Preparing Chip and Tissue • Hybridizing • Analyzing • Software is complicated and expensive • Huge data sets • Requires sophisticated statistical analysis
WE NEED HIGHLY SKILLED PEOPLE! • Physicists • Develop instrumentation • Chemists • develop chip printing, target labeling and Hybridization • Biologists • Tissue growth and harvesting; interpretation of results • Computer scientists and statisticians • software development and validation