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Gene Expression Profiling: Understanding Cellular Profiles

Explore the world of gene expression profiling and discover how it can help us understand cellular profiles and uncover patterns in gene expression. Learn about the different applications and techniques used in gene expression profiling.

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Gene Expression Profiling: Understanding Cellular Profiles

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  1. Gene Expression Profiling Brad Windle, Ph.D. 628-1956 bwindle@hsc.vcu.edu http://www.people.vcu.edu/~bwindle/Courses/BIOC605/WindleGEPlecture.ppt

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

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

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

  5. 20,000 - 30,000 human genes

  6. Factors in Gene Expression 1. Presence or absence of the genes, and the number of genes Differences within the human population and big differences that occur during oncogenesis 2. Epigenetics, chromatin state Cell to cell and host to host variability unknown 3. Homeostasis and environmental factors Cell to cell and host to host variability unknown but environmental factors is variable of interest

  7. 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)? What do we want to know? The smaller picture What genes went up or down under an experimental condition? What are the differences in gene expression between two cell types?

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

  9. Microarrays and Gene Expression Profiling

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

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

  12. Spotting Arrays

  13. Affymetrix

  14. cDNA synthesis labeling

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

  16. Issues of Multiplicity 10,000 genes analyzed p=0.001 Therefore, there should be ~10 genes found even when there is no significant difference

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

  18. Gene Expression Array Genomic Content Array Methylation Array (Chromatin Array) SNP Array

  19. Structural Genomic Profiling Comparative Genomic Hybridization (CGH) cell with losses or gains normal cell hybridize to metaphase chromosomes

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

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

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

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

  24. Combinatorial Elements Regulating Transcription

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

  26. Analyzing Gene Expression Data and Profiles Cluster Analysis

  27. Cluster Analysis We start with no hypothesis or a very general hypothesis We want the data to reveal what is relatively significant This approach is a hypothesis generator

  28. We can’t observe the patterns unaided? The patterns are too complex or abstract. There’s too much data. There’s too much noise.

  29. Pearson

  30. Two-way Clustering

  31. Gene A Gene X

  32. Clustering Methods Divisive Agglomerative (Aggregative)

  33. Cluster Linkage Methods Nearest Neighbor or Single Linkage Furthest Neighbor or Complete Linkage Average Neighbors or Average Linkage

  34. The Color Map Cells 1 2 3 4 5 a b c d e 1 2 3 4 a d c d Genes

  35. The Profile Data Sources Gene expression DNA microarrays, oligo or PCR, 20-30,000 genes Structural genomics DNA microarrays, BACs, ~one per 1Mb Methylation DNA microarrays, upstream sequences, CpG islands SNPs DNA microarray, oligos, millions of SNP sites Protein expression Ab microarray, 2D gels, Mass Spectrometry Protein states 2D gels, <1000 proteins resolved Drug response brute force, 70,000 compounds screened Metabolitics GC-MS DNA/protein sequence Sequencing, <20 people sequenced, brute force Drug structure in silico Protein structure 3D crystallography, NMR, brute force

  36. Questions?

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