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

• Glenn Short
• Microarray Core Facility/Lipid Metabolism Unit
• Massachusetts General Hospital

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Talk Outline

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

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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

• ????

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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

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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

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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?

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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 QCd?
• Cost

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Affymetrix Platform
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Affymetrix Platform
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Affymetrix Platform

• Pros
• standardized production
• gene replication
• probe tiling across gene
• Reproducible
• Affymetrix custom database user-friendly
• Cons
• Expensive
• Annotation differences
• single sample per chip

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cDNA Platform
cDNA clones (probes)

• Pros
• Genome sequence independent
• High stringency hybridization
• Little need for signal amplification
• Cons
• 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
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Spotted oligonucleotide Platform
Synthesized oligonucleotides in 384 well plates

• Pros
• 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
• Cons
• Sequence data required for probe design
• No consensus set of probe design algorithms
• Must have arraying instrumentation

1. Purification
2. QC
3. Printing

Oligonucleotides used as probes
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Spotted Oligonucleotide vs Affymetrix Arrays
Oligonulceotide Affymetrix
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ParaBioSys Platform

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

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ParaBioSys probe design and synthesis

• Probe design using OligoPicker
• based on gen-pept database
• Tms of selected oligos approx. the same
• improved specificity

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Oligonucleotide Quality Control
pass
fail

• Use of mass spectral analysis
• Identifies relative abundance
• Ensures probe is of the expected mass based upon
  sequence

• Capillary Electrophoresis
• Identifies relative abundance of full-length
  product

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Array Quality Control

• Spotted probes are 3-labeled with dCTP-Cy3 using
  terminal deoxynucleotidyl transferase
• First and last array of the print-run are QCd

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Understanding sources of variability in
microarray experiments
?
?
?
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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

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When graphing expression data, use log
0 5 10 15 20
-4 -2 0 2 4
ratio (T/C) log2
ratio (T/C)
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Plotting expression data
log2 C
M
A
log2 T
M log ratio vs Alog geometric
mean
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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
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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!

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Biological Variability

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

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Technical Variability
Sample 2
Sample 3
Sample 1
Sample 1

• 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.

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Dye Effects
Environmental Health Perspectives VOLUME 112
NUMBER 4 March 2004

• Variation in quantum yield of fluorophores
• Variation in the incorporation efficiency
• Differential dye effects on hybridization

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Hybridization Variability
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Printing Variability
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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

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Differences Across Commercial Platforms
Plt0.001
Nucleic Acids Research, 2003, Vol. 31, No. 19,
5676-5684
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Controlling Variability
Experimental Plan
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Increased Quality Control

• Probe QC
• Array QC
• Total RNA QC
• denaturing agarose gel
• Agilent Bioanalyzer
• Labeling QC

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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

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Controlling Dye Effects

• Dye-Swap

T
C
T
C
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Controlling Variability through Experimental
Design

• Replication
• Spot
• Multiple arrays per sample comparison (technical)
• Dye swap
• Multiple samples per treatment group (biological)
• Increased precision and quality control
• Estimate measurement error
• Estimate biological variation
• Pooling
• Reduce biological variation

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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

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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)

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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

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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

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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
• http//pga.mgh.harvard.edu/primerbank/

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Many thanks for your attention
https//dnacore.mgh.harvard.edu http//pga.mgh.ha
rvard.edu
Glenn Short Microarray Core Massachusetts
General Hospital