Microarrays for Gene Expression Analysis

1. Microarrays for Gene Expression Analysis Questions: What genes are expressed in this tissue under these conditions? What genes are expressed in my treated cells versus the control? What genes are expressed during the phases of the cell cycle? What genes are expressed in diseased tissue versus normal tissue?

2. Microarrays – other uses Questions: What point mutations exist and what bases are located at the substitution positions? What bases are substituted where there are multiple mutations very close together? Which allele of this gene do we have? Is this the mutant or wildtype?

3. Goals Finding Co-Regulated Genes Understanding Gene Regulatory Networks

4. Expressed Genes = mRNA DNA messenger RNA protein

5. Expressed Genes = Currently Transcribed Extract RNA Isolate mRNA’s mRNA mRNA mRNA mRNA mRNA

6. Affymetrix Oriented • Fluorescently tagged cRNA • One chip per sample • One for control • One for each experiment • Other methods include two dyes/one chip • Red dye • Green dye • Control and experiment on same chip

7. Reverse Transcriptase mRNA cDNA Creating Targets PCR Amplification of DNA In Vitro transcription to create cRNA

8. RNA-DNA Hybridization Targets RNA probe sets DNA (25 base oligonucleotides of known sequence)

9. Non-Hybridized Targets are Washed Away Targets (fluorescently tagged) “probe sets” (oligo’s) Non-bound ones are washed away

10. Picture of Gene Chip

12. Handling Chip

13. 570nm Argon laser 488nm Scanner based on epifluorescence confocal microscopy

14. Custom Chips vs Affy Chips • Affy chips contains thousands of gene probes • Genes selected from sources such as GenBank • Custom chips can be designed for individual • investigators • Few genes, but more copies of each • Done on microscope slide

15. Example Affy Chips • Rat Toxicology Chip - >850 genes • CYP450’s, Heat Shock proteins • Drug transporters • Stress-activated kinases • Rat Neurobiology chip - > 1,200 genes • Synuclein 1, prion protein, Huntington’s disease • Syntaxin, Neurexin, neurotransmitters

16. Example Affy Chips • Arabidopsis Genome Chip • Murine Genome Chip - >36,000 genes • E. coli Genome Chip - >4,200 ORF’s • Drosophila Genome Chip - >13,500 sequences • Yeast Genome Chip - >6,400 ORF’s • Human Genome Chip - >>60,000 human genes

17. Definitions Probe – a single-stranded DNA oligonucleotide complementary to a specific sequence. Each probe cell consists of millions of probe molecules. Probe Array – a collection of probes sets. Probe Set – a set of probes designed to detect one transcript. 16-20 probe pairs. A 20 probe pair set is made up of 20 PM and 20 MM for a total of 40 probe cells. Probe Pair – Two probe cells, a PM and its corresponding MM. Perfect Match(PM) – probes that are designed to be complementary to the reference sequence. MisMatch(PM) – probes that are designed to be complementary to the reference sequence except for 1 base. Target – sequence from your sample.

18. GeneChip Hierarchy • Probe Array = Chip • Probe Set – 16-20 probe pairs(to detect particular gene) • Probe Pair • Probe Cell (MisMatch) 20 • Probe Cell (Perfect Match) 20 • Probes <= 25 bases (millions of copies) • Pixels 24 sq. um

19. Probe Set 1 Probe Set 2 Probe Set 3 probe cell probe pair Probe Array (chip)

20. Probe Set PM MM PM MM MM Probe Cell Probe Pair Each pair represents a different subsequence of the gene

21. Probe – a single-stranded DNA oligonucleotide complementary to a specific sequence. Each probe cell consists of millions of the same probe molecules. The intensity of each cell is an average of each of its scanned pixels. Pixel 3 – 24 um Probe Cell 20 - 50 micrometers

22. Affymetrix Tiling Strategies • Standard • Alternative • Block • Expression

23. Affymetrix Standard Tiling • Purpose:detection of mutations and polymorphisms and determination of which base is at a certain position. • Probes are arranged in sets of 4. • Each probe in a set of 4 has one of 4 bases at the substitution position. • Compares the four target-to-probe hybrid intensities in each set to identify the base in the substitution position.

24. C-T-C-C-A-A-A-A-A-A-A-T-T-T-C-A-T-T-C-T C-T-C-C-A-A-A-A-A-A-C-T-T-T-C-A-T-T-C-T C-T-C-C-A-A-A-A-A-A-G-T-T-T-C-A-T-T-C-T C-T-C-C-A-A-A-A-A-A-T-T-T-T-C-A-T-T-C-T Substitution position Affymetrix Standard Tiling

25. Affymetrix AlternativeTiling • Purpose:determination of base where multiple mutations are close together as opposed to a single point mutation. • Probes are arranged in sets of 5. • Includes a single base deletion at substitution point. • Compares the four target-to-probe hybrid intensities in each set to identify the base in the substitution position.

26. C-T-C-C-A-A-A-A-A-A-A-T-T-T-C-A-T-T-C-T C-T-C-C-A-A-A-A-A-A-C-T-T-T-C-A-T-T-C-T C-T-C-C-A-A-A-A-A-A-G-T-T-T-C-A-T-T-C-T C-T-C-C-A-A-A-A-A-A-T-T-T-T-C-A-T-T-C-T Substitution position C-T-C-C-A-A-A-A-A-A--T-T-T-C-A-T-T-C-T Affymetrix Alternative Tiling

27. Affymetrix Block Tiling • Purpose:determination of genotype – wildtype or mutant. Determines which allele is present. • Probes are arranged in sets of 5. • Includes a single base deletion at substitution point. • Compares the four target-to-probe hybrid intensities in each set to identify the base in the substitution position.

28. C-T-C-C-A-A-A-A-A-A-A-A-C-A-G-A-T-T-C-T C-T-C-C-A-A-A-A-A-A-A-A-G-A-G-A-T-T-C-T C-T-C-C-A-A-A-A-A-A-C-A-C-A-G-A-T-T-C-T C-T-C-C-A-A-A-A-A-A-C-A-G-A-G-A-T-T-C-T C-T-C-C-A-A-A-A-A-A-G-A-C-A-G-A-T-T-C-T C-T-C-C-A-A-A-A-A-A-G-A-G-A-G-A-T-T-C-T C-T-C-C-A-A-A-A-A-A-T-A-C-A-G-A-T-T-C-T C-T-C-C-A-A-A-A-A-A-T-A-G-A-G-A-T-T-C-T Substitution position Affymetrix Block Tiling M S -2 -1 +1 +2

29. Affymetrix Expression Tiling • Purpose: measure the relative abundance of various mRNA’s. • A set of probe pairs for each mRNA • PM – perfect match • MM – mismatch by one base • Software compares the hybridization intensities of the PM to those of the MM to determine the absolute or difference call for each probe set.

30. Affymetrix Expression Tiling TARGET ACGGATG PM ACGGATG MM ACAGATG

31. Data Analysis for Gene Expression *.cel file (pixel readings) *.dat file (average intensities, etc. are calculated) *.chp file (parameters are calculated) data mining, statistical analysis

32. Probe Cell Raw Data to Cooked Data # of pixels Intensity Probe cell Avg Intensity

33. Raw Data to Cooked Data • Calculate the Average Intensity of every probe cell • Calculate the background • Subtract the background • Calculate the Noise (pixel-to-pixel variation within a probe cell) • Determine numbers of Positive and Negative probe pairsfor every probe set. • Positive Probe Pair = PM intensity > MM intensity • Negative Probe Pair = MM intensity > PM intensity • Calculate Positive Fraction • Calculate Pos/Neg Ratio • Calculate Log Average Ratio & Avg Difference

34. Quality control informational Absolute Analysis Parameters • Probe Set Name • Positive - number of pairs scored positive • Negative – number of pairs scored negative • Pairs – number of probe pairs for a probe set • Pairs Used – those not masked for some reason • PairsInAvg – excludes those with extremely intense or weak scores

35. Quality control Absolute Analysis Parameters • PM Excess – have exceeded limit for intensity • MM Excess – have exceeded limit for intensity • Avg Diff – average difference of fluorescence intensity between the PM and MM cells. • Log Avg Ratio – a measure of the hybridization performance • Higher = better • Log Avg = 0 indicates random cross hybridization • Pos/Neg – ratio of positive probe pairs to negative probe pairs • Positive Fraction – positive probe pairs/probe pairs • Abs Call – Present, Absent or Marginal. Is this gene present in this sample?

36. Raw Data to Cooked Data Positive Fraction Pos/Neg Ratio Log Avg Ratio Decision Matrix Absolute Call (Present, Absent, Marginal)

37. Data Analysis Absolute Analysis – used to determine whether transcripts represented on the probe array are detected or not within one sample(uses data from one probe array experiment). Comparison Analysis – used to determine the relative change in abundance for each transcript between a baseline and an experimental sample(uses data from two probe array experiments). Intensities for each experiment are compared to a baseline/control.

38. Approaches • What genes are Present/Absent in my tissue? • What genes are Present/Absent in the experiment vs control? • Which genes have increased/decreased expression in experiment vs control? • Which genes have biological significance based on my knowledge of the biological system under investigation?

39. Approaches to Data Analysis Database Queries Graphical Analysis Statistical Analysis Biological Knowledge

40. Set Filter Parameters Adjust filter parameters Query Pivot Scatter/Fold Graph Select Points Add probe sets to filter Bar Graph Identify interesting relationships

41. Data Analysis Absolute Analysis – used to determine whether transcripts represented on the probe array are detected or not within one sample(uses data from one probe array experiment). Comparison Analysis – used to determine the relative change in abundance for each transcript between a baseline and an experimental sample(uses data from two probe array experiments). Intensities for each experiment are compared to a baseline/control.

42. Comparison Analysis Parameters • Inc – number of probe pairs that increased • Dec – number of probe pairs that decreased • Inc Ratio • Dec Ratio • Max Inc & Dec Ratio • Pos Change • Neg Change • Inc/Dec • DPos-DNeg Ratio • Log Avg Ratio Change • Diff Call – did this gene increase or decrease? • Increase, Marginal Increase, Decrease, Marginal Decrease, • No Change

43. Comparison Analysis Parameters(continued) • Avg Diff Change – how much did the difference between PM and MM change from the control to the treated?(Avg Dif Exp – Avg Dif Control) • B=A – was this gene present in the control? • Fold Change – how many times more expression did the treated have compared to the control? (positive or negative) • Sort Score – a ranking based fold change and avg diff change

44. Data Analysis Filter/Query: Select those oligo’s which have shown a real,significant change.

45. Filter & Sort to Find Real Changes Avg. Difference Change >= 200 and FoldChange > 3 and INC >= 70% and DEC = 0%

46. Query Results

47. Query Results Probe1 Probe2 Probe3 Exp1 1 4 9 Exp2 3 6 8

48. Pivot the Query Results • Experiments = columns • Genes = Rows • Shows how genes change across experiments

49. Pivot Results Exp1 Exp2 Probe1 1 3 Probe2 4 6 Probe3 9 8

50. Pivoted Data Can be sorted by any parameter. Sort in descending order to show greatest differences.