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Chris J. Sullivan, Ph.D. Department of Biological Sciences

Microarrays: Gene Expression Data to Biological Insight. Chris J. Sullivan, Ph.D. Department of Biological Sciences. The Scientific Method. Observation. Before a testable hypothesis and experiments comes?. Microarrays - Global Gene Expression Hypothesis Generation.

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Chris J. Sullivan, Ph.D. Department of Biological Sciences

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  1. Microarrays: Gene Expression Data to Biological Insight Chris J. Sullivan, Ph.D. Department of Biological Sciences

  2. The Scientific Method Observation Before a testable hypothesis and experiments comes? Microarrays - Global Gene Expression Hypothesis Generation

  3. Microarrays: tools for gene expression A microarray is a solid support (such as a membrane or glass microscope slide) on which DNA of known sequence is deposited in a grid-like array. RNA is isolated from matched samples of interest. The RNA is typically converted to cDNA, labeled with fluorescence (or radioactivity), then hybridized to microarrays in order to measure the expression levels of thousands of genes.

  4. Advantages of microarray experiments Fast Data on 20-50,000 genes in days Comprehensive Entire genome represented on 1-2 chip(s) Flexible • Countless organisms available • Custom arrays can be made to represent genes of interest Easy You can submit RNA samples to a core facility for analysis Cheap? Chip set representing 47,000 genes for $350 Robotic spotter/scanner cost $100,000 In-house much cheaper, time consuming

  5. Observation Microarrays - Global Gene Expression Hypothesis Generation Generate hypotheses about the mechanisms underlying observed phenotypes (disease) Ability to uncover unanticipated connections

  6. What can you do with information about the expression of 10,000’s of genes? • Examples? • Breast cancer samples that appear the same in tissue appearance but why different survival of patients? • Genes involved in biological processes • Genes involved in disease pathogenesis • Pathways for drug targets; Pathways targeted by drugs!

  7. Disadvantages of microarray experiments Cost Many researchers can’t afford to do appropriate controls, replicates RNA Do mRNA levels reflect Protein expression? significance Quality Cross hybridization control* Imperfections on arrays leading to error Difficulty of data analysis: statistics to evaluate In-house; repeatability by others? *this is less of an issue as the technology matures and becomes more common place: use of commercial arrays

  8. A microarray is a tool to rapidly evaluate gene expression (mRNA level) for tens of thousands of genes in a sample GeneChip is a brand microarray made by Affymetrix 1.3cm x 1.3cm Rat GeneChip RAE 230A has over 15,000 genes and transcripts represented on the array

  9. Low High Control Sample #1 Diabetic Sample #1

  10. Stage 1: Experimental design [1] Biological samples: technical vs biological replicates (technical- repetition of same samples; biological- use multiple biological sources) [2] RNA extraction, conversion, labeling, hybridization [3] Microarray platform (dual color or single color) X Pooling of samples and mRNA

  11. Dual color (two samples on one microarray) Sample acquisition RNA: purify, label Data acquisition Microarray: hybridize, wash, image Data analysis Data confirmation (validation) Biological insight

  12. Dual color: two samples one microarray

  13. Single color (one sample on one microarray) Sample acquisition RNA: purify, label Data acquisition Microarray: hybridize, wash, image Data analysis Data confirmation (validation) Biological insight

  14. Stage 2: RNA and sample preparation For Affymetrix chips, need total RNA (about 2-10 ug) Confirm purity by running agarose gel Measure a260/a280 to confirm purity, quantity “Garbage in = Garbage out” RNA quality is key!

  15. Gel image 28s 18s Baseline is relatively flat

  16. Most Intact 1 Cells 1 2 3 4 5 2 Tissue 5 Most Degraded

  17. Stage 3: hybridization to DNA arrays The array consists of cDNA or oligonucleotides Oligonucleotides can be deposited by photolithography The sample is converted to cRNA or cDNA ------------------- Hybridization for hours or overnight… sample bind to complimentary sequences on microarray

  18. Steps for Microarray Experiment Total RNA RNA Single color (one sample per microarray) RNA RNA RNA RNA RNA RNA RNA RNA cRNA cRNA cRNA cRNA cRNA cRNA cRNA Processing, amplification and labeling of RNA samples cRNA cRNA cRNA cRNA cRNA

  19. Stage 4: Image analysis mRNA expression levels are quantitated Fluorescence intensity is measured with a scanner, or radioactivity with a phosphorimager

  20. Low High Control Sample #1 Diabetic Sample #1

  21. Stage 5: Data analysis • What genes were expressed (Present call) • Differential gene expression? (ANOVA analysis) • What are the relative differences in expression (Ratio Analysis) • What are the criteria for statistical significance? • Are there meaningful patterns in the data • (such as groups)?

  22. Microarray data analysis preprocessing global normalization local normalization scatter plots inferential statistics exploratory statistics t-tests ANOVA Ratio clustering

  23. Rattus norvegicus Ceruloplasmin (ferroxidase) (Cp), mRNA. ANOVA analysis, P = 0.00000566 RATIO ANALYSIS, fold change 4.3 upregulated in Diabetic Group Average Expression Intensity (n=5, biological replicates)

  24. Quantified Gene Expression Differentially Expressed Genes (Based on p-value and fold change) Biological Interpretation (List of 529 “significant” genes) Gene Ontology Pathways (KEGG) Literature Mining (Pubmatrix) Clustering grouping BLAST ESTs

  25. Clustering: Unique Expression Profiles Molecular Phenotyping Unsupervised hierarchical clustering using expression values for ALL of the ~22,000 transcripts on the HG-U133A_2 GeneChip.

  26. Identifying Genes Selectively Expressed in a group Two-dimensional hierarchical clustering using complete link and Pearson correlation using only those genes with comparison p-value  0.01 between at least two groups.

  27. Matrix of genes versus samples Metric (define distance) principal components analysis clustering Trees (hierarchical, k-means) supervised, unsupervised analyses self- organizing maps

  28. Stage 6: Confirmation and Validation The differential up- or down-regulation of specific genes can be measured using independent assays such as -- Northern blots (does anybody do these???) -- Polymerase chain reaction (Realtime RT-PCR) -- In situ hybridization --Western blot --Immunohistochemistry

  29. Stage 7: Microarray databases There are two main repositories: Gene expression omnibus (GEO) at NCBI ArrayExpress at the European Bioinformatics Institute (EBI)

  30. http://www.dnachip.org

  31. Microarray Analysis of Diabetes-Induced Erectile Dysfunction in the Rat

  32. STZ Experimental Design Control Group (n=5) Physiology to confirm ED 12 weeks of diabetes Diabetic Group (n=5) Tissue Harvest for Gene Expression (Microarrays) Single injection of streptozotocin causes loss of insulin producing Beta cells in pancreas

  33. Steps for Microarray Experiment Total RNA RNA Single color (one sample per microarray) RNA RNA RNA RNA RNA RNA RNA RNA cRNA cRNA cRNA cRNA cRNA cRNA cRNA Processing, amplification and labeling of RNA samples cRNA cRNA cRNA cRNA cRNA

  34. continued Labeled RNA sample Into GeneChip (microarray) Scanning and Imaging the GeneChip Hybridization Quantification of Gene Expression for each Chip

  35. Making Meaning of Array Data Data filtered using p0.01 and at least 1.5 fold changein expression 622 genes differentially expressed Control vs. Diabetic

  36. Quantified Gene Expression Differentially Expressed Genes (Based on p-value and fold change) Biological Interpretation (List of 529 “significant” genes) Gene Ontology Pathways (KEGG) Literature Mining (Pubmatrix) BLAST ESTs

  37. Literature Mining with PubMatrix 529 differentially expressed genes Control vs. Diabetic Gene names or symbols Various search terms of interest Automated online search tool to query 100 search terms by 10 modifier terms in the PubMed database (National Library of Medicine) http://pubmatrix.grc.nia.nih.gov

  38. Rattus norvegicus Ceruloplasmin (ferroxidase) (Cp), mRNA. ANOVA analysis, P = 0.00000566 RATIO ANALYSIS, fold change 4.3 upregulated in Diabetic Group Average Expression Intensity (n=5, biological replicates)

  39. Ceruloplasmin splice variants upregulated in diabetes Array: 4.3 fold 1.9 fold PCR: 16 fold 2.4 fold ABI systems real time PCR

  40. What about humans with diabetes? Is Cp upregulated? Cp expression based on PCR using human erectile tissue diabetic patients versus healthy brain dead organ donors 2 fold upregulation

  41. Hypothesis: Ceruloplasmin contributes to the pathogenesis of diabetic ED: vascular dysfunction Wildtype mice Ceruloplasmin knockout mice Cp -/- Cp +/+ Give mice diabetes Prediction: Lack of ceruloplasmin will be protective (reduced or no diabetic ED in knockout mice)

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