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Plotting the path from RNA to microarray: the importance of experimental planning and methods

Plotting the path from RNA to microarray: the importance of experimental planning and methods

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Plotting the path from RNA to microarray: the importance of experimental planning and methods

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  1. Plotting the path from RNA to microarray: the importance of experimental planning and methods Glenn Short Microarray Core Facility/Lipid Metabolism Unit Massachusetts General Hospital

  2. Talk Outline • Why perform a microarray experiment? • Choosing a microarray platform • Sources of variability that lend to experimental considerations • Overcoming experimental variability

  3. Why perform a microarray experiment? • Genomic vantage point • Detect gene expression • Compare gene expression levels • Over time • Over treatment course • Map genes to phenotypes • Map deleted or duplicated regions • Identify genes that modulate other genes • Binary decision-making ????

  4. When not to perform a Microarray Experiment • Interested in a small number of specific genes QRT-PCR, Northern blots • Desire quantitative results • Low tolerance of variability • Cannot afford to perform experiment with adequate replication

  5. Asking a Specific Question • The most fundamental; the MOST IMPORTANT • Simplifies experimental design • Empowers interpretation of data Simplicity, simplicity, simplicity! I say let your affairs be as one, two, three and to a hundred or a thousand… We are happy in proportion to the things we can do without.--Henry David Thoreau

  6. Considerations of Microarray Experimental Design • Which microarray platform will be used? • What is the end goal of the experiment? • What is the specific question being asked? • What are the most pertinent comparisons? • What controls will be applied to the experiments? • Which statistical methods will be used during data analysis? • What methods will be used to verify results from the microarrays?

  7. Choosing a Microarray Platform • Are genes of interest included on the array? • Are genes replicated? • Tiling of genes that undergo splicing • Controls on array • Quantity of RNA needed for testing • Are the arrays adequately QC’d? • Cost

  8. Affymetrix Platform

  9. Affymetrix Platform

  10. Affymetrix Platform • Pro’s • standardized production • gene replication • probe tiling across gene • Reproducible • Affymetrix custom database user-friendly • Con’s • Expensive • Annotation differences • single sample per chip

  11. cDNA Platform cDNA clones (probes) • Pro’s • Genome sequence independent • High stringency hybridization • Little need for signal amplification • Con’s • Clone handling • Clone authentication • cDNA resources difficult to access and often cross- contaminated 1. PCR product amplification 2. Purification 3. Printing PCR products used as probes

  12. Spotted oligonucleotide Platform Synthesized oligonucleotides in 384 well plates • Pro’s • Complete control over oligo sequences • Absence of contamination • Additional probes may be added when needed • Flexibility of design, probe replication, and tiling • Inexpensive, enabling experimental replication • Con’s • Sequence data required for probe design • No consensus set of probe design algorithms • Must have arraying instrumentation • Purification • QC • Printing Oligonucleotides used as probes

  13. Probe design and synthesis probe set Spotted Oligonucleotide vs Affymetrix Arrays Oligonulceotide Affymetrix

  14. ParaBioSys Platform • Long Oligonucleotides, 70mer • Designed and synthesized in-house • 5’-amine modified • Extensively QC’d • Probes designed to the 5’-orf • Set is updated as known orf list grows • Currently 20,000 probes

  15. ParaBioSys probe design and synthesis • Probe design using OligoPicker • based on gen-pept database • Tm’s of selected oligos approx. the same • improved specificity

  16. Oligonucleotide Quality Control pass • Use of mass spectral analysis • Identifies relative abundance • Ensures probe is of the expected mass based upon sequence fail • Capillary Electrophoresis • Identifies relative abundance of full-length product

  17. Array Quality Control • Spotted probes are 3’-labeled with dCTP-Cy3 using terminal deoxynucleotidyl transferase • First and last array of the print-run are QC’d

  18. Understanding sources of variability in microarray experiments ? ? ?

  19. Sources of Variation • Differences in identical treatments • Intrinsic biological variation • Technical variation in extraction and labeling of RNA samples • Technical variation in hybridization • Spot size variation • Measurement error in scanning

  20. When graphing expression data, use log 0 5 10 15 20 -4 -2 0 2 4 ratio (T/C) log2 ratio (T/C)

  21. Plotting expression data log2 C M A log2 T M= log ratio vs A=log geometric mean

  22. Expression data-cont Genes expressed up relative to reference by a factor of 32. log2(Ti /Ci) Genes expressed down relative to reference by a factor of 1/32. Low expressed Highly expressed

  23. Differences Due to Treatment • RNA isolation protocol differences • Cell-culture media changes • Expression differences over time • Cell cycle genes (synchronization) • Variables need to be minimized!

  24. Biological Variability • Self-self hybridizations of four independent biological replicates • Biological variability of inhibitory PAS domain protein

  25. Technical Variability • Self-self hybridization (Cerebellar vs cerebellar) • Sample 1 and 2 labeled together and hybridized on separate slides • Sample 3 labeled separately • Arises from differences in labeling, efficiency in RT, hybridization, arrays, etc. Sample 2 Sample 3 Sample 1 Sample 1

  26. Dye Effects • Variation in quantum yield of fluorophores • Variation in the incorporation efficiency • Differential dye effects on hybridization Environmental Health Perspectives • VOLUME 112 | NUMBER 4 | March 2004

  27. Hybridization Variability

  28. Printing Variability

  29. Differences in Probe Performance Academic_1 Academic_2 ParaBioSys Vendor Probe design algorithms will cause changes in the expression pattern Once a platform is chosen all future comparisons should be performed on the same platform Cross-platform comparisons as a means of validation

  30. Differences Across Commercial Platforms P<0.001 Nucleic Acids Research, 2003, Vol. 31, No. 19, 5676-5684

  31. Controlling Variability Experimental Plan

  32. Increased Quality Control • Probe QC • Array QC • Total RNA QC • denaturing agarose gel • Agilent Bioanalyzer • Labeling QC

  33. Controlling biological and technical variability with replication Integrin alpha 2b Pro-platelet basic protein Average across replicates Essential to the estimation of variance Critical for valid statistical analysis

  34. Controlling Dye Effects Dye-Swap T C T C

  35. Controlling Variability through Experimental Design • Replication • Spot • Multiple arrays per sample comparison (technical) • Dyeswap • Multiple samples per treatment group (biological) • Increased precision and quality control • Estimate measurement error • Estimate biological variation • Pooling • Reduce biological variation

  36. Controlling Variability through Experimental Design –cont. Normalize data to correct for systematic differences (spot intensity, location on array, hybridization,dye,scanner, scanner parameters…) on the same slide or between slides, which is not a result of biological variation between mRNA samples Minimize printing differences by using a contiguous series of slides from the same print run If wanting to do historical comparisons, use the same platform

  37. Planning your experiment • Experimental Aim • Specific questions and priorities among them • How will the experiments answer the questions posed? • Experimental logistics • Types of total RNA samples • Reference, control, cell line, tissue sample, treatment A…. • How will the samples be compared? • Number of arrays needed • Other Considerations • Plan of experimental process prior to hybridization: • Sample isolation, RNA extraction, amplification, pooling, labeling • Limitations: number of arrays, amount of material • Extensibility (linking)

  38. Planning your Experiment- cont • Other Considerations-cont • Controls: positive, negative, in-spike controls • Methods of verification: • QRT-PCR, Northern, in situ hybridization,… • Performing the experiment • Reagents (arrays-from same print run), equipment (scanners), order of hybridizations

  39. Controls • Positive Controls • used to ensure that target DNAs are labeled to an acceptable specific activity • single pool of all probe elements on array • Negative Controls • used to assess the degree of non-specific cross- hybridization • probes derived from organisms with no known homologs/paralogs to the organism of study • derived in silico (alien sequences) • In-spike controls • Known amounts of polyadenylated mRNAs added to each labeling reaction • Should not cross-hybridize with with any probe sequences • Alien sequences • Spot-report (Stratagene) • Lucidea ScoreCard (Amersham Biosciences) • Can be used to assess dynamic range of the system

  40. Validation If you have failed to validate your array data, you have NOT completed your analysis ParaBioSys has developed Primer Bank for QRT-PCR primer sequences

  41. Many thanks for your attention Glenn Short Microarray Core Massachusetts General Hospital