Real-Time qRT-PCR
Sample Preparation, Quality Control,
Troubleshooting, and PCR Arrays
Real-Time qRT-PCR Applications
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Gene expression
Biallelic discrimination
Pathogen detection
Viral quantitation
miRNA quantitation
Methylation detection
Copy number analysis
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
Real-Time Quantitative PCR
Measurement in log phase vs. endpoint
endpoint
Threshold
Sample A
Sample B
Sample A Ct=25.2
Sample B Ct=26.5
Steps of qRT-PCR:
Experimental Design
Primer/Probe Design
Cells
Tissue
RNA Extraction
DNase 1 treatment
RNA Quantification
RNA Assessment
cDNA reaction
Spectrophotometer/Fluorimeter
Agarose gel/Bioanalyzer/ Experion
100ng to 2ug total RNA
Reverse transcription primer
PCR rxn
Raw data analysis
Export analyzed data into excel
Primers (and probe)
Set baseline and threshold
Apply Std curve or Comp. Ct method
Ten Most Common Pitfalls in Real-Time
qRT-PCR
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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
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
Assay Type: Considerations
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Sample type (source, sample size)
Development time
Multiplex/Duplex
Turnaround time, speed
Specificity
Cost
“Canned Assays”
SYBR Green or Dual-labeled probe
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_www.cgi
-Vector NTI: Invitrogen website (academics only, free trial commercial)
-RealTimeDesign: www.biosearchtech.com/products/probe_design.asp
-Sigma-Genosys: http://orders.sigma-genosys.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/
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
Got Genomic DNA?
Qiagen: QuantiTect PCR Handbook, October 2004
Primer and Probe Design can impact
the qRT-PCR in terms of:
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PCR efficiencies
Specific PCR products
No co-amplification of genomic DNA
No amplification of pseudogenes
Most sensitive results
Eurogentec: www.bioscience-events.com/leipzig/Span-Eurogentec-qPCR-Leipzig.pdf
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
Effect of Tissue Handling on Gene
Expression Analysis cont:
A. Almeida, et. Al., Analytical Biochemistry 328: 101-108, 2004
Effect on mRNA
levels
(expression)
Detected for six
Targets
A. Almeida, et. Al., Analytical
Biochemistry 328: 101-108, 2004
RNA
Extraction Recommendations
Quantitative and Qualitative
Considerations
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,
RNA Quantification prior to cDNA Reaction:
Quality Control Checks
MUST:
-start with same concentration of
RNA/sample (100 ng to 2 ug)
-260/280 ratio’s between 1.8-2.1
-260/230 ratio’s 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
RNA Integrity: Agarose Gel
28S rRNA
18S rRNA
5S/ 5.8s rRNA
RNA Assessment Tools:
Agilent Bioanalyzer 2100
Bio-Rad Experion
Used for RNA Assessment
RNA
NanoChip
Characteristics of Intact Eukaryotic Total RNA
120
110
No small, well defined peaks
between ribosomal peaks
90
Distinct 28S Ribosomal Subunit
(usually ~2X 18S)
80
70
60
Distinct 18S Ribosomal Subunit
Flat Baseline throughout
electropherogram
50
40
(5s Subunit)
Prep Dependant
30
20
0
19
24
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34
2 8 S
10
1 8 S
F lu o r e s c e n c e
100
39
T im e ( s e c o n d s )
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49
54
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
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Partially Digested total RNA
2 .2 5
2 .0 0
F lu o r e s c e n c e
1 .7 5
18S ribosomal
subunit
1 .5 0
28S ribosomal subunit
In general, the 28S peak
begins to degrade first.
1 .2 5
Intensities of the smaller
degraded RNA
increases
1 .0 0
0 .7 5
The peaks begin to shift toward
the left of the electropherogram
0 .5 0
0 .0 0
19
24
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T im e ( s e c o n d s )
Baseline between and to the left of the
ribosomal peaks becomes jagged.
2 8 S
1 8 S
0 .2 5
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49
54
59
Intensities of the peaks decrease.
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
F lu o r e s c e n c e
250
200
150
Impact of
RNA
Integrity on
Expression
Levels:
100
0
19
24
29
34
39
2 8 S
1 8 S
50
44
49
54
59
64
69
T im e (s e c o n d s )
50
lu o r e s c e n c e
45
40
35
30
25
F
20
15
10
1 8 S
5
0
19
24
29
34
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T im e (s e c o n d s )
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~10-fold
difference
DNase Treatment: If needed
Recommend: Double the enzyme units and incubation
Can impact small target recoveries
DNase treatment alone is not enough!
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
Checking for gDNA contamination with a (-)RT sample
+RT
-RT
Checking for gDNA contamination with a (-)RT sample:
Biological significance?
Ct range=19.8-39.2
Fold difference range=~1:1,000,000
+RT
-RT
Checking for gDNA contamination with a (-)RT sample:
Biological significance?
Ct range= 4 -7
Fold Difference range=~16-128
+RT
-RT
Repeat DNase I treament again and check RNA before proceeding to cDNA step
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)
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
Experimental design
Random
Gene-Specific
Hexamers OligodT RH+dT
No primer
primer
RNA
3 RTs X 5 primer types
Taqman®
And/Or
SYBR
RT
PCR
Results
ABRF NARG 2006 Study
Method of Analysis
• Examine the differences among each priming strategy
• Express the differences as the ΔCt between an individual
strategy, i, and no primer (NP)
ΔCt(I)= Ct (NP) - Ct(I)
ABRF NARG 2006 Study
Ranking of Priming Strategies
• Use the calculated ΔCts to rank each priming
reagent in each laboratory’s 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
Priming Efficacy for Gus
100.0
90.0
Percentage of Calls
80.0
70.0
60.0
Most favored
50.0
2nd favored
40.0
3rd favored
Least favored
30.0
20.0
10.0
0.0
Spec
dT
RH/dT
RH
ABRF NARG 2006 Study
Priming Efficacy for TBP
Percentage of Calls
100.0
80.0
Most favored
60.0
2nd favored
40.0
3rd favored
Least favored
20.0
0.0
Spec
dT
RH/dT
RH
ABRF NARG 2006 Study
Conclusion on RT Priming Strategies:
• Optimal priming strategy may be target-dependent.
• Overall, priming with an gene-specific primer resulted in the
lowest Ct
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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
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- (MMLVH;Promega); 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?
Analysis of RT enzymes: Ct values reflecting the amounts of
cDNA produced by a variety of reverse transcriptases
Key:
A B C D E F G H
A B C D E F G H
A=MMLV
B=MMLV H
C=AMV
D=Improm II
E=OmniScript
F=cAMV
G=ThermoScript H
H=SuperScript III
A. Stahlbergs, et. Al., Clinical Chemistry 50: 1678-1680, 2004; 10.1373/clinchem.2004.035469
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
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 18;3(7):
• Normalization to quantified cDNA
J. Libus and H Storchova, BioTechniques 41: 156164, August 2006
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
Number of Normalizing Genes Used:
4 (3)
5 or more (1)
3 (16)
2 (17)
1 (79)
~70% 0f Respondents Evaluate
Only 1 Normalizing Gene
NARG Survey 2007
Battery of HKG’S: Determine Stable HKG
• Human
– 18s, HPRT, B2M, B-Act
• Mouse
– 18s, HPRT, B2M, GAPDH
• Rat
– 18s, more to add?
Housekeeping Gene: Parameters
used in choosing a stable HKG:
Our Core QC: <1 Ct differential
between control vs. experimental
Endogenous (Housekeeping) Control:
One Size Does Not Fit All
Good Choice
Bad Choice
Treated
Untreated
Usually normalize to one housekeeping gene
HPRT: > 1 Ct differential
18s rRNA: > 1 Ct differential
-Actin: <1 Ct
differential,
is stable and chosen
as endogenous control
Quantitation is Important in Identifying
a Stable Housekeeping Gene:
18s rRNA
B-2 Micro.
HPRT.
PCR Arrays:
Operational Policies
PCR Arrays: Discount Pricing through the Facility
64 to date
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Apoptosis
Biomarkers
Cancer
Cell cycle
Common diseases
Cytokine and
Inflammatory
Response
• Extra Cellular Matrix
and Adhesion
Molecules
• Neuroscience
• Signal Transduction
• Stem Cell and
Development
• Toxicology and Drug
Metabolism
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).
Sensitivity Testing: Different Concentrations of
Same Sample
Distribution of Ct Values
Ct Values
500 ng
100 ng
25 ng
5 ng
<25
67
61
42
22
25-30
14
19
31
42
30-35
10
11
8
21
Absent
Calls
4
4
7
9
Reproducibility Testing:
45
40
Ct Values
35
30
25
Exp1
Exp2
20
15
10
5
0
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77
Transcript
Quality Control Checks:
• High and Low Expressing Housekeeping Genes
• RT and PCR efficiency
- ΔCt = AVG CtRTC – AVG CtPPC should be <5
- AVG CtPPC should be 20 + 2
• gDNA contamination
- ΔCt = CtGDC – Ct AVG HKG
>4 (human, mouse), >10 (rat) indicates less
than 1% gDNA contamination
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)
Acknowledgements:
VCC DNA Analysis Facility
UVM Microarray facility
MaryLou Shane
Romaica Omaruddin
Meghan Brown
Scott Tighe