Relative Quantification - CRN2M

Relative Quantification
Applied Biosystems 7300 Real-Time PCR System
Applied Biosystems 7500 & 7500 Fast Real-Time
PCR System
Relative Quantification
No standard curve if slopes are equal and close to 3.32
Calculation of results by comparison of Ct values
– „Comparative Ct method“
Definition of:
– Endogenous Control
– Calibrator
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Validation Experiment using Comparative Ct Method
35
30 Target gene: s = - 3.35
Ct
25
20
15
EC: s = - 3.39
10
0 2 4 6 8 10
Log (amount of cDNA)
• Ct remains constant!
• Optimal slope is – 3.3 to –3.4
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What if the Slopes are not Equal
Ct
35
30
25
20
15
Endogenous
control
s= -3.31
Target gene
s = -4.03
Change your target assay !
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0 2 4 6 8 10
Log (amount of cDNA)
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Impact of Efficiency on Results
Fact: the more similar the efficiency of two assays is, and
the closer to 1, the more accurate results will be
The scope of your study determines what difference you
can/want to accept (screening vs. small fold change
detection)
Do not forget about the other factors which may influence
your result!
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Selection of Quantification Method
Standard curve method
– Must be used if difference in (properly measured!!!) PCR
efficiencies can not be accepted (e.g. long amplicons)
– Will introduce an error; the better the standard curve is
done, the smaller the error will be
– Efficiency on standards must be identical to samples
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Selection of Quantification Method
Comparative Ct method
– Easy to use, even for large gene numbers
– Good design process will result in very high assay
efficiencies
– Added amount of sample must not cause inhibition
• The goal must be to optimize extraction, RT and PCR
conditions to avoid inhibition
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Endogenous Control (EC)
The perfect EC reflects the amount of cDNA per well
EC has a constant expression level in all samples
which are used in that study
The EC normalizes for
– RNA input measurement errors
– RT efficiency variations
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Endogenous Control
Often ”house-keeping genes” are used but they don’t
necessarily need to be suitable EC’s
There is no universal EC
Validate RT before looking into EC’s
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Endogenous Control Selection
Approach I:
– Selection of candidates (TaqMan® assays)
– Select ”study-representative” samples
– Run RT with equal amounts of RNA in parallel, load
equal amounts of cDNA
– Choose gene with little / no variation
• Variation seen with this gene determines the limit
of fold change detection, e.g. 2 Cts variation equal
a factor of 4 range/error
• Tip: Try other candidates if no gene delivers
acceptable results
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TaqMan® Human Endogenous Control Plate Results
YES
YES
NO
NO
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∆C T (cycles)
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TaqMan® Human Endogenous Control Plate
Results
2.00
1.00
0.00
-1.00
-2.00
-3.00
-4.00
-5.00
0.12
0.06
-0.19
IPC
18S
huPO
huβA
huCYC
-0.57
-0.39
-0.18
-2.58
-2.89
-1.09
-1.75
-2.82
-0.77
-2.77
-2.94
-0.04
0.97
0.75
-0.98
-2.44
-1.35
-2.03
-2.35
-2.14
-1.35
huGAPDH
huPGK
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huβ2m
huGUS
huHPRT
huTBP
-2.65
-4.05
-1.64
-2.16
-1.21
-0.03
-4.67
-1.94
-0.66
0.81
0.08
-0.79
huTfR
Choose endogenous control with minimal variation

Endogenous Control Selection
Approach IIa
Run several candidates throughout the entire study and
choose one for normalisation at the end:
• Often done e.g. on TaqMan® arrays
Approach IIb
Run several endogenous controls throughout the study and
average results at the end:
• Has been published (Qbase, Normfinder, Real Time
StatMiner sw)
• May introduce errors
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Replicates and Controls
Number of PCR replicates required is given by the
precision you demand
– No replicates no statistics possible
– no useful results
– Required to detect ”low”-fold differences
– Required to get low expression correctly quantified
Run no template controls (NTC‘s) for each assay
used on the plate
RT minus controls for the detection of genomic
DNA contamination
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Biological Variation
What factors may have an impact on the situation found in
the sample
– Gender
– Age of the individual
– Whether sample was taken during the day or in the night
– Single cell type or mixture of cells (whole tissue)
– ...
– Biological variation within a „normal“ or
affected/disease/treated group
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During the Study
Load equal amount of RNA (converted to cDNA) into all
wells
– Endogenous control should be almost equally expressed in
all samples
– All samples will have almost identical Ct values for the
endogenous control = quality control
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Repetitions of an Experiment
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Experimental precision
– Identical replicates: mix sample with TaqMan®
Universal PCR Master Mix and load 50µl mix/well =
monitor precision of instrument
– Experimental replicates: load 45µl TaqMan® Universal
Master Mix and add 5µl sample = monitor true
experimental precision
Consider: replicates of other steps ”from cell
to Ct”
– E.g. two samplings from the same sample
– Parallel extractions
– Reveal variation of entire process
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TRIPLICATES
Triplicates are mandatory for any statistics
Minimum for the Grubbs algorithm
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Calibrator Concept: an Example Using four
Samples
The calibrator is one of the sample of the study.
The calibrator is usually the normal physiological state of
the study : i.e
– Untreated cells
– Normal glycemia
– Non tumoral tissue
If working on animals, multiple samples can be
considered as calibrator
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Calibrator Concept: an Example Using four Samples
Calibrator
t=0 t=12 t=24 t=48
time
total RNA
total RNA
total RNA
total RNA
cDNA
cDNA
cDNA
cDNA
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Comparison of Target Gene and Endogenous
Control
∆Rn
∆Ct = 24 – 14 = 10
Ct =14 Ct = 24
Cycles
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Endogenous control
Target gene
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What if we Added the Double Amount of cDNA
∆Rn
∆Ct = 23 – 13 = 10
Ct =14 Ct = 24
Ct = 13 Ct = 23
Cycles
24
Endogenous control
Target gene
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Comparative Ct Method: an Example Using the Four
Samples
∆Rn
t=0
∆Rn
t=12 h
Ct=15
Ct=35
Cycles
Ct=15
Ct=30
Cycles
∆Rn
t=24 h ∆Rn
t=48 h
Ct=9 Ct=24 Cycles
Ct=14
Endogenous control
Target gene
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Ct=34
Cycles
Comparative Ct Method Calculation Steps
step 1: Normalization to endogenous control
Ct Target gene – Ct Endogenous control = ∆Ct
Do both for calibrator and samples
step 2: Normalization to calibrator sample
∆Ct Sample – ∆Ct Calibrator = ∆∆Ct
step 3: use the formula
2
-∆∆Ct
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Relative Quantification Result of the Four Samples
x-fold Expression
1
32 32
1
"!&%('*)
"!+'-,.)
"!/,¢0$)
"!$#
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Calibrator
t=0
Samples
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Exercise 1
Brain
Liver
18S rRNA Ct 14
IL-10 Ct 32
18S rRNA Ct 16
IL-10 Ct 28
Which cDNA is higher concentrated,
and how many times higher
Brain, 4 times higher (2 2 )
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Exercise 2
Brain
(Calibrator)
Liver
18S rRNA Ct 14
IL-10 Ct 32
18S rRNA Ct 16
IL-10 Ct 28
How does the expression of IL-10 differ
in Liver and Brain
∆Ct Brain = 18
∆Ct Liver = 12 12 -18 = -6 2 6 = 64
IL-10 is 64-times higher expressed in Liver!
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Exercises: Relative Quantification
ABI PRISM ® 7300/7500 SDS
System

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