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Uses of Microarrays in Research

Uses of Microarrays in Research. Anne Rosenwald Georgetown University. Schena, Shalon, Davis, and Brown (1995) Science 270, 467 Differential expression of 45 Arabidopsis genes!. Microarrays in Research: A Survey of PubMed. Recent Microarray Papers: I. New Techniques/Applications.

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Uses of Microarrays in Research

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  1. Uses of Microarrays in Research Anne Rosenwald Georgetown University

  2. Schena, Shalon, Davis, and Brown (1995) Science 270, 467 Differential expression of 45 Arabidopsis genes! Microarrays in Research:A Survey of PubMed

  3. Recent Microarray Papers: I. New Techniques/Applications • Transcriptional regulatory networks in Saccharomyces cerevisiae • Lee et al. (2002) Science 298 799-804 • “ChIP-on-chip”

  4. Recent Microarray Papers: I. New Techniques/Applications • 5,000 RNAi experiments on a chip • Lehner and Fraser (2004) Nat Methods 1, 103 • RNA living-cell microarrays for loss-of-function screens in Drosophila melanogaster cells • Wheeler et al. (2004) Nat Methods 1, 127 • Spots on chip contain dsRNA • Chip incubated with Drosophila cells • Cells induced to “take-up” RNA • Are cells alive or dead? • Do cells have phosphorylated Akt? • Do cells have altered actin fibrils?

  5. Recent Microarray Papers: II. Improved Methods for Analysis/Access • Reproducibility and statistical rigor • outbred organisms (i.e. humans) • do different platforms give the same answers? • Tools for analysis • MAGIC Tool! • (many others….)

  6. Recent Microarray Papers: II. Improved Methods for Analysis/Access • Tools for access and annotation • GeneCruiser • http://www.broad.mit.edu/cancer/genecruiser • Liefeld et al. Bioinformatics (2005) 21, 3681 • Uses Affymetrix-generated data • incorporates GO terms and links info with SwissProt, RefSeq, LocusLink, etc. • Primarily for mouse and human data • GEO (Gene Expression Omnibus) • http://www.ncbi.nlm.nih.gov/geo/ • Barrett et al. (2007) NAR 35, D760 • Also heavily weighted to mouse and human data

  7. Recent Microarray Papers: II. Improved Methods for Analysis/Access • Tools for access and annotation (cont). • Stanford Microarray Database • http://genome-www5.stanford.edu/ • Links to Caryoscope – a way to look at gene expression data in a whole genome context • http://dahlia.stanford.edu:8080/caryoscope/index.html

  8. Recent Microarray Papers: III. Scientific Endeavors • Mutational change: compare “wild type” to mutant • Tissue-specific gene expression • Environmental change: compare same organism in two different environments • Development: compare different stages along a particular lineage • Therapeutics: compare in cells/tissues treated with and without the drug of interest • Investigate changes in gene copy number • Investigate differences in methylation: Epigenetics • Disease: compare affected with normal • 2006 papers with term “microarray” = 19226 • Of those, also with term “cancer” = 5561 (29%)

  9. Recent Microarray Papers: III. New Scientific Endeavors • Transgenic C. elegans as a model in Alzheimer's research • Curr Alzheimer Res. 2005 Jan;2(1):37-45. • Compared wild type worms with worms expressing human Ab • Behavior and the limits of genomic plasticity: power and replicability in microarray analysis of honeybee brains • Genes Brain Behav. 2005 Jun;4(4):267-71 • Compared bees with long-standing behavioral differences (nursers v. foragers) • Compared recently hatched bees beginning to express behavioral differences (nursers v. foragers v. gravetenders)

  10. Beyond Microarrays Ivakhno (2007) FEBS J. 10, 2439

  11. Some basic yeast biology • Yeast come in two mating types • MATa • MATa • Can live either as haploids or as diploids • diploids referred to as MATa/a • haploids are either MATa or MATa mating sporulation/meiosis MATa/a + MATa MATa

  12. Yeast resources • General website for Saccharomyces (SGD) • http://www.yeastgenome.org/ • Materials available • ~5500 genes cloned with tags for purification • TAP-tagged fusion collections • GFP-tagged fusion collections • Insertional mutant collections • Knockout collections • Most of these available from OpenBiosystems • www.openbiosystems.com

  13. The yeast knockout collection • Yeast knockout resources • MATa/a heterozygous diploids (entire genome) • MATa haploids (non-essentials) • MATa haploids (non-essentials) • MATa/ahomozygous diploids (non-essentials)* • Yeast knockout website • http://www-sequence.stanford.edu/group/yeast_deletion_project/deletions3.html *I have this collection, so if there’s a mutant you want, let me know.

  14. The yeast knockout collection http://www-sequence.stanford.edu/group/yeast_deletion_project/deletions3.html

  15. Using the knockouts for microarrays • A Robust Toolkit for Functional Profiling of the Yeast Genome • Pan et al. (2004) Mol Cell 16, 487 • Takes advantage of the MATa/a heterozygous diploid collection • identifies synthetic lethal interactions viadiploid-based synthetic lethality analysis by microarrays (“dSLAM”) • Uses dSLAM to identify those strains that upon knockout of a query gene, show growth defects • synthetic lethal (the new double mutant = dead) • synthetic fitness (the new double mutant = slow growth)

  16. Step 1: Creating the haploid convertible heterozygotes Important point: This HIS3 gene is only expressed in MATa haploids, not in MATa haploids or MATa/a diploids So in other words, can select against MATa/a diploidsto ensure you’re looking at only haploids later on.

  17. Step 2: Inserting the query mutation Knockout one copy of your gene of interest (“Your Favorite Gene”) with URA3

  18. Step 3: Make new haploids and select for strains of interest Sporulate to get new haploids Select on –his medium to ensure only haploids survive (no diploids) selects against query mutation so genotype is xxxD::KanMX YFG1 selects for query mutation so genotype is xxxD::KanMX yfg1::URA3

  19. Reminder about YKO construction

  20. Step 4: Prepare genomic DNA and do PCR with common TAG sequences U1 D1 U2 D2 Using common oligos U1 and U2 (or D1 and D2) amplifies the UPTAG (or DNTAG) sequence unique to each of the KOs

  21. Step 4: Prepare genomic DNA and do PCR with common TAG sequences The two different conditions are labeled with two different colors** The labeled DNA is then incubated with a TAG microarray **The PCR reactions create a mixture of TAGs (representing all the strains in the pool), since each KO has a unique set of identifier tags (UPTAG and DNTAG) bounded by common oligonucleotides

  22. Evidence this really works – part I On average, the intensity is the same before and after 1 copy of the CAN1 gene is knocked out Strains x-axis y-axis XXX/xxxD::KanMX CAN1/CAN1 XXX/xxxD::KanMX CAN1/can1D::MFA1pr-HIS3

  23. Evidence this really works – part II Red spots illustrate that fraction of the strains with KOs in essential genes, so when haploid, not present in pool Strains x-axis y-axis DIPLOIDS XXX/xxxD::KanMX CAN1/can1D::MFA1pr-HIS3 HAPLOIDS XXX orxxxD::KanMX can1D::MFA1pr-HIS3

  24. Another variation: Drug sensitivity

  25. Another variation: Drug sensitivity

  26. Summary • If you can compare two different conditions and you have a way to stick things to slides, some sort of microarray is possible!

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