Sample and Experimental Considerations

1. Integrated DNA Technologies
Technical Tips for qPCR
Sample and Experimental Considerations
Aurita Menezes, PhD
qPCR Product Manager

2. 1
Session Outcomes
We will discuss:
 Intercalating Dyes (SYBR® Green) vs 5′ nuclease assays
 Steps to a Successful qPCR Experiment
 Assay design criteria
 Experimental design considerations
 Sample isolation
 Sample quantification
 cDNA synthesis
 Dye and instrument compatibility
 Experimental layout
 Multiplexing
 Experimental plate layout
 Methods of quantification

3. 2
5′ Nuclease Assays vs Intercalating Dyes (SYBR® Green)
For use with intercalating dyes such as
SYBR® Green
Primers and probe for 5′ nuclease assays

4. 3
Intercalating Dyes (e.g., SYBR® Green)
 cheap
 nonspecific PCR products and primer-dimers will also contribute to the fluorescent signal
 longer amplicons create a stronger signal
 requires melting point curve determination
 Cannot multiplex or genotype
5′ Nuclease assays
 3rd sequence in assay (the probe) adds specificity
 Splice form specific amplification
 Rare transcript detection
 Pathogen detection
 No need for post run analysis such as melt curves
 Multiple dye ratio options for multiplexing
 Can perform SNP genotyping
 Can be slightly more expensive (IDT solution is the PrimeTime® Mini)
5′ Nuclease Assays vs Intercalating Dyes (SYBR® Green)

5. 4
Steps to a Successful qPCR Experiment
Assay design
RNA
cDNA
Reverse Transcription
qPCR reaction set upAnalysis of data
Experimental set-up
RNA, DNA—
isolate, purify, quantify

6. 5
Assay Design

7. 6
Assay Design: Steps to Designing a Good Assay
 Know your gene
 How many transcripts are associated with that gene?
 Which exons are common or specific between the transcripts?
 Obtain a Refseq accession number
 Use NCBI databases to identify exon junctions, splice variants, SNP locations
 Align related sequences
 For splice specific designs
 Identify unique regions within which to design primers and probe
 Blast primer and probe sequences
 ensure no cross reactivity with other genes within the species

8. 7
Primer and Probe Design Criteria
 Primer
 Primer Tm values should be similar +/- 2oC
 For 5′ nuclease qPCR assay, this is normally around 60–62oC
 Aim for 18–30 bases
 Do not contain runs of 4 or more Gs
 GC content range of 35–65% ( ideal 50%)
 Probe
 Tm value 4–10oC higher than primers
 No runs of consecutive Gs, G+C content 30–80%
 No G at the 5′ end
 Probe length no longer than 30 bases (IDTs ZEN Double Quenched Probes are an
exception)
 Probe can be designed on either the sense or antisense strand
 Amplicon
 Size is between 70–200 bp
 If using SYBR® Green then amplicon length is designed to be slightly bigger to
enable differentiation from primer dimers on a melt curve
-> Always BLAST potential primer sequences and
redesign if primer sequence cross reacts

9. 8
April 2008
15M SNPs
Sept 2010
30M SNPs
June 2012
53M SNPs
Designed to Avoid SNPs
 Next Generation Sequencing has significantly increased the number of
SNPs and splice variants identified
 Having up-to-date sequence information is critical to qPCR assay
performance

10. 9
The shift due to a SNP at the
3′ end of a primer varies
from 0 to >10 Cq’s.
This shift misrepresents a
gene expression fold change
of as much as 1000 fold!
Primers on SNPs Can Lead to Erroneous Gene Expression Data
Effect of SNPs within primer locations on Tm

11. 10
PrimeTime® Predesigned qPCR Assays for Human, Mouse, and Rat
1. Designed to avoid SNPS
2. We share primer and probe sequences upon purchase
3. Cross reactivity check to eliminate non-specific amplification
4. Reduce impact from secondary structure formation

12. 11
Experimental Design Considerations

13. 12
Experimental Design Considerations
 Number of reactions
 Number of replicates
 Number of samples
 Number of controls
 Number of reference genes
 Sample maximization versus gene maximization

14. 13
Experimental Setup
24h 48h 72h 24h 48h 72h
qPCR for
1) gene of interest and
2) Multiple reference sequences tested for stable expression across
experimental conditions
Normal Mutant
Multiple “Biological Replicates”
2 “RT Preps” for each sample +
1 “No RT Control”
3 “Technical Replicates” for each sample
3 “No Template Control” for each qPCR assay tested

15. 14
RNA Sample Isolation
Guanidinium thiocyanate/phenol:chloroform
Pros:
 Higher yield
 Works with larger amounts of cells
 Works better with troublesome tissues (e.g., adipose tissue, bone, cartilage, etc.)
Cons:
 Higher DNA contamination
 Separate DNase I digestion with additional purification needed
 Residual phenol inhibits PCR
Spin columns are available that have on column DNase digestion yielding
 Loading capacity maybe limited and small RNA is lost

16. 15
Sample Quantification
Many quantification methods are available
 Spectrophotometry (UV spec or Nanodrop [>2 ng])
 Easy to use, high amount of starting material (photometer), not specific for DNA or RNA,
highly variable, don’t trust absorptions <0.1
 Microfluidic analytics
 Agilent Bioanalyzer [>50 pg/μL], BioRad’s Experion
 These methods provide a quantitative assessment of the general state of the RNA
sample (RIN number)
 Fluorescent dye detection
 RNA dyes such as RiboGreen® Dye
 Very sensitive (0.5 ng–1 μg), expensive
 Specific for RNA (RiboGreen Dye), dsDNA (PicoGreen® Dye)

17. 16
Sample Quantification
 Always use the same method of quantification
 Comparison of data obtained using RNA isolated by different
methods is not advisable
 Comparison of data obtained using different RT priming strategies is
not recommended
 Accurate quantification is crucial for true estimation by qPCR

18. 17
Reverse Transcription
Reverse transcription can be a major source of error in qRT-PCR
RT is a non-linear process: Standardize your input amount
 Use same amount of RNA (or same number of cells) for all samples
 RT reagents are inhibitory to PCR, so dilute the reaction

19. 18
Priming Strategy Can Make a Difference
Oligo(dT) < Hexamer < Oligo(dT) + Hexamer < Gene Specific Primer
 Random primers and oligo (dT) primers will produce random cDNA, while gene-specific primers will
produce cDNA only for a specific target
 Random primers
 Bind to RNA at a variety of complementary sites, resulting in short, partial-length cDNAs
 Can be used when the template has extensive secondary structure
 Will produce the greatest yield, but the majority of the cDNA will be copies of ribosomal RNA, unless it is
depleted prior to RT-PCR
 Advantage: Transcriptome is preserved so that any remaining cDNA can be used in other qPCR assays
 Disadvantage: Low abundance messages may be under-represented due to consumption of reagents during
cDNA synthesis of the more prevalent RNAs
 Oligo(dT) primers
 will ensure that mRNA containing poly(A) tails are reverse transcribed
 These primers are more commonly used when trying to limit the amount of ribosomal RNA being copied, or
when the qPCR assays are designed to target the 3′ end of the RNA
 If the mRNA is long, the 5′ end of the message may be under-represented
 Gene-specific oligonucleotide primers, which selectively prime the mRNA of interest
 Yields the least complex cDNA mixture and avoids reagent depletion
 Gene specific primers can yield earlier Cqs, however only one gene can be tested per cDNA sample
 Disadvantage: cDNA produced cannot be used for assaying other genes

20. 19
Two -Step Protocol One-Step Protocol
Primers used in RT
•Oligo(dT) primers
•Random hexamers
•Gene-specific primers
•A mix of these
•Gene-specific primers
Advantages
•Choice of primers
•Optimize reactions for maximum yield
•Modulate amount of RT that goes into PCR—controlling
for target abundance
•Perform multiple PCR reactions on the same cDNA
sample
•Experiment with different RT and Taq enzymes
•Quick setup and limited handling
•Easy processing of multiple samples for repetitive tests,
or high-throughput screening
•Fewer pipetting steps, reducing potential errors
•Eliminates possibility of contamination between the RT
and qPCR steps
Considerations
•Requires more setup, hands-on, and machine time
•Additional pipetting increases the chances for
experimental errors and contamination
•Uses more reagents
•Must “start over,” or save RNA aliquot and perform new
RT to analyze new target(s) or repeat amplifications
•Reaction conditions are not optimal—efficiency and thus
quantification are affected
Best for:
•Amplifying multiple targets from a single RNA source
•When you plan to reuse cDNA for additional
amplifications
•Working with multiple RNA samples to amplify only a few
targets
•Assays performed repeatedly
Choosing Between One-Step and Two-Step RT-qPCR

21. 20
Controls
 Negative Controls
 No Template Control (detects contamination)
 Minus RT (examines genomic DNA presence)
 Biological Control sample wherein the GOI is not expressed
 Positive control
 Sample in which gene is expressed
 Synthetic template such as gBlocks® Gene Fragments, Ultramer ® Oligonucleotides
 Normal control
 Untreated sample
 Healthy individual (normal)

22. 21
Multiplexing

23. 22
Why Multiplex?
 Sample amount, cost, and time
 With limited sample amounts, one of the best ways to minimize
consumption is to run qPCR assays in multiplex format
 Most of the qPCR instruments on the market can simultaneously measure
2–5 different dyes in a single well
 Expression levels of several genes of interest can be determined
quickly and with minimal sample size
 Best practices in qPCR usually require multiple gene normalizers,
all of which can be run at the same time

24. 23
Features of a Successful Multiplex Experiment
 Little to no change in the cycle
position (Cq) at which the
signal first appears, as
compared to the singleplex
reaction
 Similar amplification
efficiencies
 No loss in the Limit of
Detection (LOD)
CSK-FAM
PDK2-Cy5
Singleplex
Fourplex
Singleplex
Fourplex

25. 24
Effect of Suboptimal Master Mix
 qPCR triplex run using a “fast”
master mix with the manufacturer’s
recommended conditions
 Target input consisted of 20, 2, 0.2,
and 0.02 ng cDNA using same
company’s cDNA kit
 Assay results are shown for the
HPRT gene
 Note the absence of signal at 0.02 ng
cDNA
EDIT NAME
EDIT JOB TITLE
Same qPCR triplex run using
a master mix formulated for multiplexing.
LOD has been increased 10 fold.
Rn HPRT PrimeTime® qPCR Assay
Singleplex
Triplex
Rn HPRT PrimeTime® qPCR Assay
20 ng
2 ng
0.2 ng
0.02 ng
0.02 ng
20 ng
Using a commercial mix not formulated for multiplexing, can result in a poor LOD
(Limit of Detection)

26. 25
Effect of Target Abundance
 Fourplex was set up with varying target concentrations
 Tag lower abundance target with FAM Dye
 No Cq difference should be observed between singleplex and multiplex
Target Copy Number
LIMK1 TEX615 2.00E+05
CDK7 CY5 2.00E+04
ACVR2B HEX 2.00E+03
ACVR1B FAM 2.00E+02
ACVR1B FAMACVR2B HEX
LIMK1 TEX615 CDK7 CY5

27. 26
gBlocks® Gene Fragments: For Generation of Standard Curves
 Double-stranded DNA Fragments
 125–1000 bp in length
 Sequence-verified
 200 ng DNA provided, dry

28. 27
Generating Standard Curves: gBlocks® Gene Fragments
0.0
10.0
20.0
30.0
40.0
2.00E+06 2.00E+04 2.00E+02
CqValues
Copies
gBlocks Standards
Hs LIMK1
Hs CDK7
Hs ACVR1B
Hs ACVR2B
A single DNA source for 4 different standard curves ACVR2B-LIMK1-ACVR1B-CDK7
wt
TCATACCTGCATGAGGATGTGCCCTG
GTGCCGTGGCGAGGGCCACAAGCCGT
CTATTGCCCACAGGGACTTTAAAAGT
AAGAATGTATTGCTGAAGAGCGACCT
CACAGCCGTGCTGGCTGACTTTGGCT
TGGtttttGAACATCATCCACCGAGA
CCTCAACTCCCACAACTGCCTGGTCC
GCGAGAACAAGAATGTGGTGGTGGCT
GACTTCGGGCTGGCGCGTCTCATGGT
GGACGAGAAGACTtttttGTATGTGA
TCAGAAGCTGCGTCCCAACATCCCCA
ACTGGTGGCAGAGTTATGAGGCACTG
CGGGTGATGGGGAAGATGATGCGAGA
GTGTTGGTATGtttttgGATGTATGG
TGTAGGTGTGGACATGTGGGCTGTTG
GCTGTATATTAGCAGAGTTACTTCTA
AGGGTTCCTTTTTTGCCAGGAGATTC
AGACCTTGATCAGCTAACAgcggccg
c
Separate sequences by a series
of T bases, and, if cloning, add in
a restriction site for linearization
of plasmid if necessary.

29. 28
Range of Dilution
Range of dilution while generating standard curves
 A standard curve across multiple log10 units is needed
 The concentrations should span a minimum of 4 log10 of magnitude, but
preferably 5−6 log10
 The concentrations of the test unknowns should fall within the range of
concentrations used within the standard curve without the need to extrapolate
 The PCR efficiency is close to 100% when the slope of the amplification curve is
close to −3.32

30. 29
Summary: Establishing Robust Multiplex Assays
 Use master mix formulated for multiplexing.
 Regular master mixes may need to be supplemented with additional
dNTPs, Mg+2, polymerase.
 Follow the recommended cycling conditions.
 Dye choices are made based on separation of excitation/emission
wavelengths and filter combinations available on a particular platform.
 Always test assay efficiency. Run each assay first in singleplex reaction
before conducting multiplex qPCR.

31. 30
Real Time PCR Instrument
 Choose your fluorescent dyes dependent on instrument dye filter set
 Beware of potential cross talk between selected dyes
 Take time to calibrate your instrument if testing a new dye

32. 31
Dye and Instrument Compatibility Table

33. 32
Instrument Dye 1 Dye 2 Dye 3 Dye 4 Dye 5
ABI 7000 FAM HEX™ or JOE TAMRA
ABI 7300 FAM HEX™ or JOE
ABI 7500 FAM HEX™ or JOE Cy3 or Tye™ 563 Texas Red® Cy5 or Tye™ 665
ABI 7900 FAM TET or JOE
ABI StepOne™ FAM HEX™ or JOE
ABI StepOnePlus™ FAM HEX™ TAMRA
Bio-Rad CFX 384 FAM HEX™ Cy3 or Tye™ 563 Texas Red® Cy5 or Tye™ 665
Bio-Rad CFX96 FAM HEX™ Cy3 or Tye™ 563 Texas Red® Cy5 or Tye™ 665
Bio-Rad iCycler FAM HEX™ Cy3 or Tye™ 563 Texas Red® Cy5 or Tye™ 665
Bio-Rad MiniOpticon™ FAM HEX™
Bio-Rad Myl Q2 FAM HEX™
Bio-Rad MylQ5 FAM HEX™ TAMRA Texas Red® Cy5 or Tye™ 665
Roche LightCycler®480 FAM HEX™ or JOE LCRed 610 LC640
Agilent Mx3000P FAM HEX™ or JOE Cy3 or Tye™ 563 Texas Red®
Agilent Mx3005P FAM HEX™ or JOE Cy3 or Tye™ 563 Texas Red® Cy5 or Tye™ 665
Dyes Available from IDT and Instrument Compatibility for Multiplexing

34. 33
PrimeTime® 5′ Nuclease qPCR Assays Utilize ZEN Double-Quenched Probes
Multiplexing….Use probes with the lowest background
ZEN probes have the lowest background and highest sensitivity when
compared to all other quenchers tested

35. 34
Zen vs BHQ
Dark Blue: IDT FAM-ZEN™-Iowa Black® Fq
Light Blue: Same probe with FAM-BHQ-1® (dual purification)
Red: Same probe with FAM-BHQ-1® ( single purification)
Comparison Study of ZEN™ Quencher vs BHQ
Delta Rn vs Cycle Rn vs Cycle
Synthetic templates for this study were generated using gBlocks® Gene Fragments

36. 35
Experimental Plate Layout

37. 36
Plate Layout—Maximize Samples or Genes?
 Sample maximization
 No increase in variation due to absence of inter-run variation
 Suitable for retrospective studies and controlled experiments
 Gene maximization
 Introduces inter-run variation
 Applicable for larger studies in which the number of samples do not fit
 Inter-run calibration
 Identical sample measured for the same gene in different runs

38. 37
Sample Maximization vs Gene Maximization
 Experiment with 11 samples (S1–11), 1 negative control (W) and 6 genes (3
genes of interest (GOI) and 3 reference genes (REF), all measured in
duplicate
 In the gene maximization strategy,
 it is recommended that a few samples are repeated in both runs (so-called inter-run
calibrator samples) in order to detect and remove inter-run variation (Hellemans et
al., Genome Biology, 2007).
 In general, the sample maximization strategy is preferred
 absence of sample related inter-run variation
 easier to set up, fewer reactions
Sample Maximization Gene Maximization

39. 38
Methods of Quantification

40. 39
Quantification Strategies in Real Time qRT-PCR
Reference: Quantification Strategies in Real-Time PCR
Michael W. Pfaffl (2004)
Chapter 3, pp 87–112.
In: A-Z of Quantitative PCR (SA Bustin, Editor)
International University Line (IUL)

41. 40
Absolute Quantification
 Absolute quantification
 Created by diluting a nucleic acid sample (typically a plasmid, oligonucleotide, or
purified PCR product). The unknown “test sample” amount can then be
interpolated from the standard curve calculation
 Amplification efficiency of the standards must be equivalent to that of the test
samples
 Standards are assayed simultaneously with the test samples.
 The reliability of this method is dependent on:
 Identical amplification efficiencies of the known and test samples
 The accuracy with which the standard samples are quantified

42. 41
For reliable quantification unknown should fall within the range of standard
curve dilutions
Panel A. ERBB3 Sample and Known
Standard Amplification Plots.
Panel B. CTNNB1 Sample and Known
Standard Amplification Plots.
Panel C. ERBB3 Cq Falls Outside
Standard Curve Range.
Panel D. CTNNB1 Cq Falls Within
Standard Curve Range.
Target
Target

43. 42
Relative Quantification
 Relative quantification
 Expressed as the fold difference in gene expression between test and control samples for a given gene
 Generated by serially diluting, e.g., cDNA prepared from a total RNA sample for which the concentration
of the different genes is not known
 Relative quantification cannot be easily used to compare expression levels between genes (due to the
assay-dependent relationship between Cq value and input amount)
 You can amplify the target and endogenous control in the same tube, increasing throughput and
reducing pipetting errors
 When RNA is the template, performing amplification in the same tube
provides some normalization against variables such as RNA integrity and
reverse transcription efficiencies
Source:
http://www.eppendorf.de/int/index.php?l=131&action=pr
oducts&contentid=101&sitemap=2.5.1

44. 43
RT-qPCR Data Normalization Using Reference Genes
A measured difference in RNA expression level between 2 samples is
the result of both true biological as well as experimentally induced
(technical) variation
 Variables that contribute to technical variation need to be minimized
e.g., the amount and quality of starting material, enzymatic efficiencies, and
overall transcriptional activity
 The remaining technical variation should be further reduced by using a proper
normalization approach, focusing the data on true biological variation
 Use multiple stable reference genes
Source:Vandesompele et al. (2002) Genome Biology;
Bustin et al. (2009) Clinical Chemistry.

45. 44
Genes Used for Normalization

46. 45
Efficiency Calculations
 The ΔΔ-Cq method was developed by Livak
and Schmittgen
 Assumes perfect amplification efficiency by setting
the base of the exponential function to 2
 Uses only one reference gene for normalization
 Pfaffl et al. consider PCR efficiency for both
the gene of interest and a reference gene
 Still uses only 1 reference gene which may not be
sufficient to obtain reliable results
 Hellemans et al. proposed a method that
considers
 Gene-specific amplification efficiencies
 Allows normalization of Cq values with multiple
reference genes based on the method proposed by
Vandesompele et al.
Sources:
Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time
quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods, 25:402–408.
Pfaffl MW. (2001) A new mathematical model for relative quantification in real-time RT-PCR.
Nucleic Acids Res, 29:E45.
Vandesompele J, De P, Pattyn F, Poppe B, Van R, De P, Speleman F: (2002) Accurate
normalization of real-time quantitative RT-PCR data by geometric averaging of multiple
internal control genes. Genome Biol , 3:RESEARCH0034.

47. 46
Highlights of the MIQE Guidelines:
 Experimental design—Number within each group
 Sample—Storage, isolation method, frozen or fixed tissue
 Nucleic acid—Procedure, instrumentation, DNase RNase treatment?,
Quantification, RIN, purity A260/A280
 Reverse transcription—Priming method, amount of RNA used, RTase conc, Cqs
+/-Rtase
 qPCR target information—Accession number, location of primers and amplicon,
amplicon length
 qPCR primer and probe—Sequences, Location and identity of any modification
 qPCR protocol—Primer probe, dNTP and Mg2+ concentration, reaction volume,
amount of cDNA
 Data Analysis
Minimum Information for Publication of Quantitative Real-Time PCR Experiments

48. 47
Thank you
Questions?

49. 48
Single or Multiple Thresholds
Multiple thresholds are the exception rather than the
rule for the vast majority of runs that target medium-
level mRNAs.
One example of when to use multiple thresholds is
when there are clear signs of amplification in a negative
control, and application of the default baseline and/or
threshold would result in a negative Ct. Altering the
threshold, or the baseline if a wandering baseline is the
problem, usually corrects this technical inconsistency
and allows the operator to record a positive Ct.
Source:
J Biomol Tech. Sep 2004; 15(3): 155–166.
Pitfalls of Quantitative Real-Time Reverse-Transcription
Polymerase Chain Reaction
Stephen A Bustina and Tania Nolanb