Real-Time qRT-PCR

1
Real-Time qRT-PCR

• Sample Preparation, Quality Control,
  Troubleshooting, and PCR Arrays

2
Real-Time qRT-PCR Applications

• Gene expression
• Biallelic discrimination
• Pathogen detection
• Viral quantitation
• miRNA quantitation
• Methylation detection
• Copy number analysis

3
ADVANTAGES -measurement taken in real time (log
phase), NOT endpoint -Highly sensitive
method -uses very little sample -increased
specificity (dual-labeled probe
assays) -increased sensitivity
4
Real-Time Quantitative PCRMeasurement in log
phase vs. endpoint
endpoint
Threshold
Sample A
Sample B
Sample A Ct25.2 Sample B Ct26.5
5
Steps of qRT-PCR
Experimental Design Primer/Probe Design
Cells Tissue
RNA Extraction RNA
Quantification Spectrophotometer/Fluorimete
r RNA Assessment cDNA
reaction PCR rxn Raw
data analysis Export analyzed data
into excel
DNase 1 treatment
Agarose gel/Bioanalyzer/ Experion
100ng to 2ug total RNA
Reverse transcription primer
Primers (and probe)
Set baseline and threshold
Apply Std curve or Comp. Ct method
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Ten Most Common Pitfalls in Real-Time qRT-PCR

• Poor primer and probe design
• Quality of RNA
• Master mixes
• Introducing cross contamination
• Not using an (-)RT
• Using an inappropriate normalizer (endogenous
  ctrl)
• Not performing Melting curve with SYBR green
• Not setting baseline and threshold properly
• Efficiency of reaction is poor
• Using inappropriate range for standard curve

Ambion Technotes www.ambion.com/techlib/tn/102/17
.html
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Experimental Design Critical

• Consultation with Computational Biologist and
  Microarray staff
• number of samples (statistical relevant)
• Biological replicates
• Experimental replicates
• Technical replicates
• Pooling of samples
• Proper controls are implemented

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Assay Type Considerations

• Sample type (source, sample size)
• Development time
• Multiplex/Duplex
• Turnaround time, speed
• Specificity
• Cost
• Canned Assays
• SYBR Green or Dual-labeled probe

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Primers and Probe Design/ Choices

• Many companies sell predesigned assays (assays
  off-the-shelf), i.e., ABI, Qiagen,
• RT2 Profiler PCR Array( SuperArray), Roche
  Universal Probe Library
• -custom design

-Software packages commonly used for primer and
probe design -Freeware -Primer 3
http//frodo.wi.mit.edu/cgi-bin/primer3/primer3_ww
w.cgi -Vector NTI Invitrogen website
(academics only, free trial commercial) -RealT
imeDesign www.biosearchtech.com/products/probe_de
sign.asp -Sigma-Genosys http//orders.sigma-ge
nosys.eu.com/probedesign/ -Other software for
design -Primer Express (ABI) -Beacon
Designer (Premier Biosoft) -Oligo
(MBI) -SciTools (IDT) -Oligo Analyzer
www.idtdna.com/analyzer/Applications/OligoAnalyzer
/
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qRT-PCR Probe and Primer Database
http//web.ncifcrf.gov/rtp/gel/primerdb
This resource is a collaborative effort of the
NCI-Frederick Gene Expression Lab and the CGAP
Genetic Annotation Initative. Over 3,000 sets
for Human and Mouse http//medgen.ugent.be/
rtprimerdb Provided by Ghent University in
Belgium, Currently 3,600 sets for 2,211 genes.
Many Species
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Got Genomic DNA?
Qiagen QuantiTect PCR Handbook, October 2004
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Primer and Probe Design can impact the qRT-PCR in
terms of

• PCR efficiencies
• Specific PCR products
• No co-amplification of genomic DNA
• No amplification of pseudogenes
• Most sensitive results

Eurogentec www.bioscience-events.com/leipzig/Spa
n-Eurogentec-qPCR-Leipzig.pdf
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Different Sample Types or applications may
require different handling procedures
-Cell Culture -Whole blood
-Tissue -ensure only tissue desired is
present -Flash Freeze immediately -Into
RNAlater or RNA Ice -FFPE for LCM -Picture
before and after capturing
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Effect of Tissue Handling on Gene Expression
Analysis cont
A. Almeida, et. Al., Analytical Biochemistry 328
101-108, 2004
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Effect on mRNA levels (expression) Detected for
six Targets
A. Almeida, et. Al., Analytical Biochemistry 328
101-108, 2004
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RNA

• Extraction Recommendations
• Quantitative and Qualitative Considerations

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RNA Extraction
-Trizol works well for tissue, followed by column
purification -Higher recovery for tissue, lower
purity (?) -Lose up to 50 in column, but
increased purity -DNase I treatment on
column -Have utilized novel abrasives for
difficult tissues -Columns work well for cell
culture or Whole Blood -silica-gel based, i.e.,
Qiagen RNeasy -Columns (micro) have worked well
for LCM -miRNA Trizol or miRNA specific kits,
i.e., miRVANA, miRACLE,
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RNA Quantification prior to cDNA
Reaction Quality Control Checks

• MUST
• -start with same concentration of RNA/sample (100
  ng to 2 ug)
• -260/280 ratios between 1.8-2.1
• -260/230 ratios above 1.5
• ADVANTAGES
  - Requires small volume of
  sample (1-2ul)
• - Direct measurement of sample (no dilutions)
• No cuvettes
• Dynamic range of 2ng/ul to 3.7 ug/ul
• Identify contaminants absorbing at other
• wavelengths that can cause PCR inhibition
• LCM
• -Quantitate on 2100 Bioanalyzer/Experion
• -ribogreen quantitation
• 
  

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RNA Integrity Agarose Gel
28S rRNA
18S rRNA
5S/ 5.8s rRNA
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RNA Assessment Tools
Agilent Bioanalyzer 2100
Bio-Rad Experion
Used for RNA Assessment
RNA NanoChip
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Characteristics of Intact Eukaryotic Total RNA
No small, well defined peaks
between ribosomal peaks
Distinct 28S Ribosomal Subunit
(usually 2X 18S)
Distinct 18S Ribosomal Subunit
Flat Baseline throughout
electropherogram
(5s Subunit) Prep Dependant
There maybe a small peak present at 24 seconds
that represents 5s, 5.8s and tRNA. This is
especially noted with phenol or Trizol
extraction, and is eliminated when total RNA is
prepped using Qiagen columns which remove the
small RNAs. (Substitute 16S and 23S for
prokaryotic samples)
Agilent, Bioanalyzer Show
22
Partially Digested total RNA
18S ribosomal subunit
28S ribosomal subunit
In general, the 28S peak begins to degrade first.
Intensities of the smaller degraded RNA increases
The peaks begin to shift toward the left of the
electropherogram
Intensities of the peaks decrease.
Baseline between and to the left of the
ribosomal peaks becomes jagged.
Samples that result in electropherograms like the
above are borderline for inclusion in an assay
and should be under serious consideration of
re-extraction.
Agilent, Bioanalyzer Show
23
Impact of RNA Integrity on Expression
Levels10-fold difference
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DNase treatment alone is not enough!
DNase Treatment If needed
Recommend Double the enzyme units and
incubation Can impact small target recoveries
Must prove that gDNA has been removed -run RNA
on gel Look for gDNA -try to amplify off
total RNA from sample, visualize on gel -use
same amount of RNA equivalents as represented
in cDNA amplified reaction -generate (-)RT for
each sample, perform amplifcation step
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Checking for gDNA contamination with a (-)RT
sample
RT
-RT
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Checking for gDNA contamination with a (-)RT
sampleBiological significance?
Ct range19.8-39.2 Fold difference
range11,000,000
RT
-RT
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Checking for gDNA contamination with a (-)RT
sampleBiological significance?
Ct range 4 -7 Fold Difference range16-128
RT
-RT
Repeat DNase I treament again and check RNA
before proceeding to cDNA step
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Reverse Transcription RXN Choices
-One-step versus two-step qRT-PCR -How much RNA
to use -100 to 2 ug of RNA -What primer to
use -random primers -hexamers, octamers,
nonamers, decamers, penta-decamers -oligo
d(T) -oligo d(T) and random hexamer mix -target
specific primer -What RT enzyme to use? -MMLV,
AMV,( Superscript III/MMLV)
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Reverse Transcription Reaction Best Priming
Strategies?
-2006 ABRF NARG (Nucleic Acid Research Group)
study -Comparison of Five Different RNA Priming
Strategies Using Two Genes expressed at Different
Levels-Human GUS (?-Glucuronidase) and TBP
(TATAA Binding Protein) genes were selected as
genes with different transcript levels -GUS
Medium-Expressed Transcript -TBP Low-Expressed
Transcript -Data was generated from SYBR Green
and TaqMan style assays
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Experimental design
Random Hexamers
Gene-Specific primer
OligodT
RHdT
No primer
RNA
RT
3 RTs X 5 primer types
Taqman
SYBR
PCR
And/Or
Results
ABRF NARG 2006 Study
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Method of Analysis

• Examine the differences among each priming
  strategy
• Express the differences as the ?Ct between an
  individual strategy, i, and no primer (NP)
• ?Ct(I) Ct (NP) - Ct(I)

ABRF NARG 2006 Study
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Ranking of Priming Strategies

• Use the calculated ?Cts to rank each priming
  reagent in each laboratorys data set
• Assign value 1 to the strategy with the lowest Ct
  Assign a value of 4 to the strategy with the
  highest Ct
• Calculate a call percentage of all rankings for
  each priming strategy.

ABRF NARG 2006 Study
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Priming Efficacy for Gus
ABRF NARG 2006 Study
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Priming Efficacy for TBP
ABRF NARG 2006 Study
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Conclusion on RT Priming Strategies

• Optimal priming strategy may be target-dependent.
• Overall, priming with an gene-specific primer
  resulted in the lowest Ct
• Oligo(d)T was the second best primer for GUS and
  third for TBP, RH-(d)T the second favored for
  TBP, but third for GUS
• The gene-specific primer was overwhelmingly the
  most effective priming strategy for TBP (88),
  but it was only slightly better than Oligo(d)T
  for GUS (63)
• In this study random hexamers appears to be a
  poor choice for priming.

ABRF NARG 2006 Study
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Choice What Reverse Transcriptase?
STUDY Clinical Chemistry 50 1678-1680,
2004 Comparison of Reverse Transcriptases in
Gene Expression Analysis Anders
Ståhlberg, Mikael Kubista, and Michael Pfaffl
-Examined Eight
Reverse Transcriptases Moloney murine leukemia
virus RNase H- (MMLVHPromega) MMLV (Promega)
avian myeloblastosis virus (AMV Promega),
Improm-II (Promega) OmniScript (Qiagen) cloned
AMV (cAMV Invitrogen) ThermoScript RNase H-
(Invitrogen) and SupeScript III RNase H- -RT
enzyme with or without RNase acitivity?
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Analysis of RT enzymes Ct values reflecting the
amounts of cDNA produced by a variety of reverse
transcriptases
Key AMMLV BMMLV H CAMV DImprom II
EOmniScript FcAMV GThermoScript
H HSuperScript III
A B C D E F G H
A B C D E F G H
A. Stahlbergs, et. Al., Clinical Chemistry 50
1678-1680, 2004 10.1373/clinchem.2004.035469
38
RT Enzyme Choice Conclusions

• For the low expressors, HTR1a, HTR1b, HTR2b, the
  reverse transcription yields for the eight RT
  enzymes were similar
• HTR2a, B-Actin, and GAPDH showed substantial
  variation between the eight RT Enzymes
• May be a result of mRNA folding. Variation would
  be expected with targets with tight structures
  because of inability for primer to bind
  efficiently. Data indicates this may be the case
  for HTR2, B-actin, and GAPDH.
• RT Enzyme that performed best with these targets
  was SuperScript III.
• No advantage was noted in using an RT enzyme
  without RNase activitiy(SuperScript III, MMLVH,
  and ThermoScript)

A. Stahlbergs, et. Al., Clinical Chemistry 50
1678-1680, 2004 10.1373/clinchem.2004.035469
39
Normalization Strategies
Goal To compensate for differences in starting,
RT/PCR efficiency, differences in samples
(contaminants), and pipetting

• Normalize starting amount of RNA
• Choose endogenous control that does not change
  due to treatment or exposure. No one internal
  reference gene is suitable for all experimental
  conditions and each must be tested
• Geometric averaging of multiple internal control
  genes (GeNorm). J Vandesomple, et.al., Genome
  Biol. 2002 Jun 183(7)
• Normalization to quantified cDNA
• J. Libus and H Storchova, BioTechniques 41
  156-164, August 2006

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Normalization Strategies cont.

• Choose an endogenous or housekeeping gene that is
  abundant and constantly expressed in samples
• Most of the common ones used, such as GAPDH, are
  the least reliable.
• Always a good idea to test the stability of the
  housekeeping gene with the sample types (i.e.,
  treated and untreated)
• More than one can be applied

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Number of Normalizing Genes Used
70 0f Respondents Evaluate Only 1 Normalizing
Gene
NARG Survey 2007
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Battery of HKGS Determine Stable HKG

• Human
• 18s, HPRT, B2M, B-Act
• Mouse
• 18s, HPRT, B2M, GAPDH
• Rat
• 18s, more to add?

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Housekeeping Gene Parameters used in choosing a
stable HKG

• Our Core QC lt1 Ct differential between control
  vs. experimental

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Endogenous (Housekeeping) Control One Size Does
Not Fit All
Good Choice
Bad Choice
Treated
Untreated
Usually normalize to one housekeeping gene
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HPRT gt 1 Ct differential
18s rRNA gt 1 Ct differential
?-Actin lt1 Ct differential, is stable and
chosen as endogenous control
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Quantitation is Important in Identifying a Stable
Housekeeping Gene
18s rRNA B-2 Micro. HPRT.
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PCR Arrays

• Operational Policies

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PCR Arrays Discount Pricing through the
Facility64 to date

• Extra Cellular Matrix and Adhesion Molecules
• Neuroscience
• Signal Transduction
• Stem Cell and Development
• Toxicology and Drug Metabolism

• Apoptosis
• Biomarkers
• Cancer
• Cell cycle
• Common diseases
• Cytokine and Inflammatory Response

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Apoptosis Array Content
TNF Ligand Family Fasl (Tnfsf6), Tnf, Tnfsf10,
Tnfsf12, Tnfsf5, Tnfsf7. TNF Receptor Family
Fas (Tnfrsf6), Ltbr, Tnfrsf10b, Tnfrsf11b,
Tnfrsf1a, Tnfrsf5. Bcl-2 Family Bad (Bbc2),
Bag1, Bag3, Bak1, Bax, Bcl2, Bcl2l1, Bcl2l2,
Bcl2l10, Bid, Bnip2, Bnip3, Bnip3l, Bok, Mcl1.
Caspase Family Casp1, Casp2, Casp3, Casp4,
Casp6, Casp7, Casp8, Casp9, Casp12, Casp14. IAP
Family Birc1a, Birc1b, Birc2, Birc3, Birc4,
Birc5. TRAF Family Traf1, Traf2, Traf3. CARD
Family Apaf1, Bcl10, Birc3, Birc4, Card4, Card6,
Card10, Casp1, Casp2, Casp4, Casp9, Cradd, Nol3,
Pycard (Asc), Ripk1. Death Domain Family Cradd,
Dapk1, Fadd, Fas (Tnfrsf6), Ripk1, Tnfrsf10b
(TRAIL-R), Tnfrsf11b, Tnfrsf1a. Death Effector
Domain Family Casp8, Cflar (Cash), Fadd. CIDE
Domain Family Cidea, Cideb, Dffa, Dffb. p53 and
DNA Damage-Induced Apoptosis Akt1, Apaf1, Bad
(Bbc2), Bax, Bcl2, Bcl2l1, Bid, Casp3, Casp6,
Casp7, Casp9, Trp53 (p53), Trp53bp2, Trp53inp1,
Trp63, Trp73. Anti-Apoptosis Akt1, Api5, Atf5,
Bag1, Bag3, Bcl2, Bcl2l1, Bcl2l10, Bcl2l2,
Birc1a, Birc1b, Birc2, Birc3, Birc4, Birc5,
Bnip2, Bnip3, Casp2, Cflar, Dad1, Dsip1, Fas
(Tnfrsf6), Hells, Il10, Lhx4, Mcl1, Nfkb1, Nme5,
Pak7 (Arc), Pim2, Polb, Prdx2, Rnf7, Sphk2, Tnf,
Tnfsf5 (CD40L), Zc3hc1 (Nipa).
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Sensitivity Testing Different Concentrations of
Same Sample
Distribution of Ct Values
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Reproducibility Testing
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Quality Control Checks

• High and Low Expressing Housekeeping Genes
• RT and PCR efficiency
• - ?Ct AVG CtRTC AVG CtPPC should be lt5
  
• - AVG CtPPC should be 20 2
• gDNA contamination
• ?Ct CtGDC Ct AVG HKG
• gt4 (human, mouse), gt10 (rat) indicates less
• than 1 gDNA contamination

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Operational Policies

• Investigator purchase Plates
• Users of facility get discounted pricing, see
  staff
• Make sure to order A designation
• Submit RNA
• Must be tested for integrity
• Must perform DNase treatment
• STRONGLY Recommend gDNA contamination test
• Facility can provide designated primer sets for
  gDNA test
• Facility performs cDNA and PCR reactions
• Data uploaded to biodesktop
• In PCR Array excel worksheet (train users on
  analysis)

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Acknowledgements
VCC DNA Analysis Facility
UVM Microarray facility
MaryLou Shane Romaica Omaruddin Meghan Brown
Scott Tighe