Microarrays and Their Uses Brad Windle, Ph.D. 628-1956

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Presentation transcript:

Microarrays and Their Uses Brad Windle, Ph.D

Profile A set of data or characteristics pertaining to an item Profiles are sometimes referred to as Signatures or Fingerprints

Cellular Profiles Gene Expression Protein Expression Misc Data SNPs Methylation Cell State Drug Response Metabolitics Structural Genomic Protein States Disease Gene/Protein Sequence Protein Structure Drug Structure

Cellular Profiles Gene Expression Protein Expression Misc Data SNPs Methylation Drug Response Metabolitics Structural Genomic Protein States Gene/Protein Sequence Protein Structure Drug Structure

Profiles Have Two Sides A gene profile across samples and a sample profile across genes

Microarrays and Gene Expression Profiling

What do we want to know? The bigger picture Are cells or tissues related based on the genes they express? For an experimental cell model, are there conditions that are similar based on changes in gene expression? For certain experimental conditions, are there genes that show similar patterns of change (co-regulated)? The smaller picture What genes went up or down under an experimental condition?

How did we use to do this? Probe for 1 gene Analyze ~10 samples

Analyzing 1 gene for 10 samples Analyzing thousands of genes for 1 sample Now with Microarrays

Spotting Arrays

Affymetrix

cDNA synthesis labeling

cell or condition of interest control or reference cell hybridize to microarray Gene Expression Profiling

Applications of Gene Expression Profiling Tissue or Tumor Classification Gene Classification Drug Classification Drug Target Identification Drug Response Prediction

Ross et al Nature Genetics 24:227 (2000)

The Color Map Cells Genes a b c d e 1234adcd1234adcd

Ross et al Nature Genetics 24:227 (2000)

Alizadeh et al. Nature 403:503 (2000)

Gene Expression Array Genomic Content Array Methylation Array (Chromatin Array) Replication Array SNP Array

Comparative Genomic Hybridization (CGH) Structural Genomic Profiling cell with losses or gainsnormal cell hybridize to metaphase chromosomes

BAC or Oligo Array Normal Cancer Label DNA Genome Representation Profiling Using Arrays

Detection mainly for cancer and inherited deletions Tumor suppressor genes are deleted Oncogenes are amplified CGH

Methylation Profiling CCGG GGCC me CCGG GGCC me PCR linkers Hpa II / PCR Amplify/ Label PCR Amplify / Label CpG Island Array hybridize to array

Profile cells based on methylation state cell-type profiles Differences in the methylated state of cancers Compare methylation profiles to gene expression profiles

Synchronized cells in G1/S BrdU Labeling time Replication Profiling

Profiling Transcription Factor-Interactive DNA Immuno-precipitate w/ Ab to protein Chromatin IP or ChIP total genomic DNA

Combinatorial Elements Regulating Transcription

Single Nucleotide Polymorphism (SNPs) Profile Personal fingerprint Profile a small town Figure out who’s related to who What chromosomes came from whom What regions came from whom Figure out what meiotic exchanges occurred Pharmacogenomics - SNPs that affect disease and reaction to drugs

The ProfileData Sources SNPsDNA microarray, oligos, millions of SNP sites Protein expressionAb microarray, 2D gels, chromatographics Protein states2D gels, <1000 proteins resolved Drug responsebrute force, 70,000 compounds screened Metaboliticschromatographics DNA/protein sequenceSequencing, <20 people sequenced, brute force Drug structurein silico Protein structure3D crystallography, NMR, brute force Gene expressionDNA microarrays, oligo or PCR, 20-30,000 genes Structural genomicsDNA microarrays, BACs, ~one per 1Mb MethylationDNA microarrays, upstream sequences, CpG islands

Questions?