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INNOVATIVE SOLTIONS FOR GENE FUNCTION ANALYSIS<br />

<strong>Latest</strong> <strong>soLaris</strong> <strong>technicaL</strong>/<strong>appLication</strong> <strong>notes</strong><br />

<strong>Including</strong> <strong>Detection</strong> <strong>of</strong> Related AKT Protein<br />

Kinase Family<br />

Back to Basics<br />

The Simple Secrets to Gaining<br />

Successful qPCR Data<br />

www.thermo.com/solaris<br />

ISSUE 24, 2010<br />

INsights | Issue 24<br />

Assays qPCR Gene Expression Assays qPCR Gene Expression Assays<br />

L issUe speciaL issUe speciaL issUe speciaL issUe speciaL issUe speciaL issUe speciaL issUe speciaL issUe specia<br />

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eciaL oFFer speciaL oFFer speciaL oFFer speciaL oFFer speciaL oFFer speci<br />

sion Assays qPCR Gene Expression Assays qPCR Gene Expression As<br />

iaL oFFer speciaL oFFer speciaL oFFer speciaL oFFer speciaL oFFer speciaL<br />

25% DiscoUnt<br />

on <strong>soLaris</strong> proDUcts<br />

Order before June 30, 2010 and receive<br />

a 25% discount on Solaris assays<br />

Please quote INSIGHTSSI in the “Enter your quote number here” box on the checkout page.<br />

Discounts will not appear on the web but your order value will be modified appropriately during order processing.<br />

INsights | Issue 24 www.thermo.com/gen


aL oFFer<br />

says<br />

oFFer<br />

Welcome<br />

Dear Reader,<br />

On launching Thermo Scientific Solaris qPCR Gene Expression Assays last year we felt the<br />

technology represented a clear first choice for many qPCR users. It has pleased us to hear<br />

other leading scientists supporting our view:<br />

Ease <strong>of</strong> use<br />

“One protocol for all assays for my<br />

genes <strong>of</strong> interest . . . this really made<br />

Solaris simple to establish in my lab.”<br />

Dagmar Lischke, Clinical Biochemistry,<br />

Hannover Medical School<br />

“We wanted to be able to buy small reaction<br />

numbers to test if a particular gene was<br />

altered in expression -- Solaris made this<br />

possible. All the assays we have tried<br />

had near 100% efficiency. That makes me<br />

very happy as the results are rolling in!”<br />

Denise O'Keefe, University <strong>of</strong> Pittsburgh<br />

Features<br />

Dna Detective 2<br />

Recent developments in qPCR detection chemistries<br />

solaris Q&a 6<br />

Most commonly asked questions about Solaris with answers from our technical support team<br />

customize your Lab 26<br />

Sheffield RnAi screening facility develops world's largest Drosophila functional<br />

genomic screening panel with Thermo Scientific ABgene pre-aliquoted plates<br />

Basics <strong>of</strong> qpcr technology 28<br />

The considerations required to gain successful real-time PCR data<br />

MiQe Guidelines 29<br />

The essential requirements for the publication <strong>of</strong> real-time qPCR data<br />

solaris technical Data<br />

application note 8<br />

<strong>Detection</strong> <strong>of</strong> Related AKT Protein Kinase Family Members in an RnAi-based Study<br />

<strong>of</strong> FOXO1 Regulation<br />

technical note 15<br />

Thermo Scientific Solaris Gene Expression Assays and Master Mix: Stability in the Laboratory<br />

technical note 18<br />

Demonstration <strong>of</strong> a ΔΔCq Calculation Method to Compute Relative Gene Expression<br />

from qPCR Data<br />

technical note 22<br />

High Performance RT-qPCR Using Thermo Scientific Solaris qPCR<br />

Gene Expression Reagents for Assessing Relative Gene Expression<br />

www.thermo.com/solaris<br />

Sequence Information Provided<br />

“Highly desirable and strongly encouraged<br />

. . .not all vendors <strong>of</strong> commercial predesigned<br />

assays provide this information."<br />

Stephen Bustin, Barts and the London<br />

School <strong>of</strong> Medicine and Dentistry<br />

This issue <strong>of</strong> Insights delivers further evidence<br />

<strong>of</strong> the robustness <strong>of</strong> Solaris assays, with<br />

the AKT tech note, in particular, demonstrating<br />

their high degree <strong>of</strong> specificity when<br />

changes in gene expression amongst a<br />

homologous gene family are assessed.<br />

Thank you for your continued interest.<br />

Toby HampsHire, ediTor<br />

Comments and suggestions are always<br />

welcome. To be included in the next<br />

edition <strong>of</strong> INsights, please contact us:<br />

insights@therm<strong>of</strong>isher.com<br />

Contact Information<br />

www.thermo.com/solaris<br />

US e-mail: techservice.genomics@therm<strong>of</strong>isher.com<br />

Europe e-mail: techservice.emea.genomics@<br />

therm<strong>of</strong>isher.com<br />

North America: 2650 Crescent Dr., Suite 100, Lafayette,<br />

CO 80026. Toll free: 800 235 9880, Tel: 303 604 9499,<br />

Fax: 303 604 3286<br />

Belgium: Tel: 0800 80543, Fax: 0800 80178, Technical<br />

support: +44 1372 840 410<br />

France: Tel: 08009 14294, Fax: 08009 11885, Technical<br />

support: 01 60 92 48 68<br />

Germany: Tel: 0800 1830746, Fax: 0800 1810366,<br />

Technical support: 040 23 51 36 79<br />

The Netherlands: Tel: 08000 223464, Fax: 08000 223291,<br />

Technical support: +44 1372 840 410<br />

United Kingdom: Tel: 08009 171501, Fax: 01372 840545,<br />

Technical support: 01372 840 410<br />

Switzerland: Tel: 08005 63505, Fax: 08008 38669,<br />

Technical support: +44 1372 840 410<br />

Other Countries: Technical support: +44 1372 840 410<br />

Editorial Team<br />

Editor: Toby Hampshire<br />

Contributors: Liz Bland, Christy Ogrean, Caroline<br />

Pritchard, Micky Glaser, Kirsty Maclean, Alexander<br />

Trampe, Steve Garside, Steve Brown<br />

Art & Design: Gudrun Jobst, Ginger Martin<br />

INsights | Issue 24


DnA<br />

Recent developments in qPCR<br />

detection chemistries<br />

SUmmARY OF<br />

ThE ARTICLE<br />

The development<br />

<strong>of</strong> qPCR and some <strong>of</strong><br />

the applications<br />

to which it is<br />

currenly applied<br />

Advantages<br />

<strong>of</strong> using probe<br />

technologies over<br />

SYBR Green I<br />

chemistries<br />

Superbases and<br />

MGB technology<br />

<strong>of</strong>fer maximal<br />

design flexibility<br />

Algorithm design<br />

<strong>of</strong> Solaris qPCR<br />

Gene Expression<br />

Assays<br />

INsights | Issue 24 | Article<br />

uantitative polymerase chain reaction<br />

(qPCR) methods were first described in the<br />

1980s1 , shortly after the discovery <strong>of</strong> PCR<br />

by Kary Mullis in 19832 . These earliest<br />

quantitative PCR methods relied on<br />

end-point analysis <strong>of</strong> the PCR product by<br />

staining, using a dye such as ethidium bromide, and visualizing<br />

by gel electrophoresis. The intensity <strong>of</strong> the amplified<br />

band would be compared to standards <strong>of</strong> a known concentration<br />

to give a semi-quantitative result. Although the<br />

value <strong>of</strong> a quantitative result was recognised, this technique<br />

was time consuming, lacked sensitivity and was not reliably<br />

quantitative. It wasn’t until some years later, in the 1990s,<br />

that real-time qPCR methods began to be described1 Tobias HampsHire pH.d | european pCr produCT<br />

containing less template require more<br />

cycles; the threshold being the point at<br />

markeTing manager | THermo FisHer sCienTiFiC<br />

which the fluorescence resulting from<br />

qPCR is a technique that gathered momentum during the 1990s amplified product is detectable above<br />

and has now become a standard method used by most molecular<br />

biology laboratories. Some more recent developments in probe<br />

background fluorescence. The number<br />

<strong>of</strong> PCR cycles that elapse before the<br />

threshold is reached (Cq) is a measure<br />

technology are discussed in relation to Thermo Scientific Solaris. <strong>of</strong> the input nucleic acid (Figure 1). By<br />

comparing the results <strong>of</strong> samples <strong>of</strong><br />

unknown concentration with a series<br />

<strong>of</strong> standards, the amount <strong>of</strong> template<br />

DNA in an ‘unknown’ reaction can be accurately determined,<br />

this approach being referred to as absolute quantification.<br />

The quantity <strong>of</strong> target nucleic acid can also<br />

be measured using relative quantification which<br />

measures the ratio between the target nucleic<br />

acid and one or more reference genes.<br />

As the method has developed, qPCR has become one <strong>of</strong><br />

the most powerful and sensitive gene analysis techniques<br />

available. It is used routinely for the quantification <strong>of</strong><br />

both DNA and RNA (following reverse transcription to<br />

cDNA) in a broad range <strong>of</strong> applications in industrial,<br />

academic and diagnostic laboratories and has become a<br />

routine method for measuring the expression <strong>of</strong> genes <strong>of</strong><br />

.<br />

interest, validating microarray experiments, monitoring<br />

In real-time qPCR, the target DNA sequence is amplified<br />

and simultaneously quantified throughout the ampli-<br />

biomarkers and measuring genetic variations (SNPs).<br />

fication reaction, during each PCR cycle. In a perfectly qpcr detection chemistries<br />

designed assay, when the amplification reaction is in the There are two main types <strong>of</strong> fluorescence-based detection<br />

log (linear) phase, the quantity <strong>of</strong> the PCR product is<br />

chemistries that are commonly used in qPCR: intercalating<br />

directly proportional to the amount <strong>of</strong> input nucleic acid. dyes and sequence-specific DNA probes. Changes in the<br />

Accumulation <strong>of</strong> the target DNA sequence in real-time fluorescent signal, resulting from the accumulation <strong>of</strong> target<br />

qPCR (from now simply referred to as qPCR) is detected amplicon, are measured using a fluorescence detection system<br />

and measured using a fluorescent reporter molecule. As the<br />

quantity <strong>of</strong> target amplicon increases, so does the intensity<br />

in the qPCR instrument.<br />

<strong>of</strong> fluorescence emitted from either a DNA-intercalating Intercalating dyes<br />

dye or from a probe that is specific to a sequence within Intercalating dyes, such as SYBR Green I, are non-specific<br />

the amplicon. Reactions that contain a higher concentra- fluorescent dyes that intercalate with double stranded (ds)<br />

tion <strong>of</strong> the target sequence take fewer cycles to accumulate DNA during PCR. Following primer-mediated replication<br />

a threshold concentration <strong>of</strong> PCR product, while those <strong>of</strong> the target sequence, the dye molecules bind to the dsDNA<br />

insights issue 23, 2009 | www.thermo.com/gen<br />

www.thermo.com/solaris


product and emit a greater fluorescent signal during excitation<br />

compared to the free dye in solution. As the quantity<br />

<strong>of</strong> target DNA (or cDNA) increases during PCR, so the<br />

intensity <strong>of</strong> fluorescence increases.<br />

There are various reasons why researchers may wish<br />

to focus on the use <strong>of</strong> intercalating dyes but there are<br />

also issues associated with their indiscriminate binding<br />

to dsDNA and quantification <strong>of</strong> non-specific amplification<br />

artefacts (such as primer-dimers) contributing to<br />

inaccurate quantification <strong>of</strong> target sequences. Intercalating<br />

dye technology is not the focus <strong>of</strong> this article.<br />

Sequence-specific DnA probes<br />

Sequence-specific DNA probes are oligonucleotides that<br />

are usually labelled with a fluorescent reporter molecule at<br />

one end and a quencher molecule at the other. The probe<br />

contains a sequence that is complementary to the target<br />

DNA sequence. Since their target sequence is designed to<br />

be within an amplicon, this increases the specificity <strong>of</strong> the<br />

fluorescent signal and the accuracy <strong>of</strong> the quantification, even<br />

in the presence <strong>of</strong> non-specific DNA amplification.<br />

There are several different types <strong>of</strong> fluorescent reporter<br />

DNA<br />

CDC20<br />

Efficiency = 95%<br />

r 2 = 0.999<br />

Dyn Range = 10<br />

LOD = 5 copies<br />

Figure 1: Ten 10-fold<br />

dilutions <strong>of</strong> cDNA<br />

synthesized from synRNA<br />

amplicon sequence or<br />

DNA amplicon sequence<br />

was amplified on an<br />

ABI 7900HT instrument<br />

using the Solaris qPCR<br />

Gene Expression Assay<br />

for CDC20. The log-scale<br />

amplification curves and<br />

standard curves are shown<br />

along with the performance<br />

<strong>of</strong> the assay including<br />

efficiency, r 2 value, dynamic<br />

range out <strong>of</strong> 10 log10<br />

dilutions and the lower<br />

limit <strong>of</strong> detection.<br />

www.thermo.com/solaris INsights | Issue 24 | Article


table 1. solaris design algorithm parameters<br />

1. When designing a functional assay, numerous design parameters are applied with<br />

high stringency including: overall GC content, optimal sequence length, melting<br />

temperature, stretches <strong>of</strong> homogenous nucleotides (eg GGGG).<br />

2. The algorithm adjusts the Tm and enables universal cycling conditions by incorporating<br />

the MGB moiety and by selective placement <strong>of</strong> Superbases.<br />

3. When there is more than one splice variant for a target gene, a consensus (or<br />

common) sequence is identified, representing a design space that produces an<br />

assay which can detect all known splice variants.<br />

4. BLAST analysis is a critical component <strong>of</strong> any comprehensive qPCR assay design<br />

protocol. The algorithm utilises genomic, transcript and pseudogene databases to<br />

identify and eliminate sequences that are more likely to lead to erroneous priming<br />

and detection (ie. <strong>of</strong>f-target effects).<br />

5. To mitigate the potential for genomic DNA amplification, the design algorithm,<br />

whenever possible, will place one <strong>of</strong> the assay components (probe or primer) or<br />

amplicon over an exon junction boundary.<br />

INsights | Issue 24 | Article<br />

probes. Some <strong>of</strong> the more complex probes contain a<br />

hairpin loop structure, such as Scorpion probes, where<br />

a complementary sequence (a PCR primer) is attached<br />

to one end <strong>of</strong> the stem, or molecular beacons, where the<br />

complementary sequence is contained within the loop.<br />

When a probe containing a hairpin loop is free in solution,<br />

the stem <strong>of</strong> the structure brings the fluorophore in close<br />

proximity to the quencher, resulting in quenching <strong>of</strong> the<br />

fluorescent signal. However, when the complementary<br />

sequence binds to the target DNA, the hairpin loop structure<br />

is disrupted and the fluorophore is separated from the<br />

quencher, resulting in a corresponding increase in fluorescence.<br />

Alternatively, probes can have a linear structure,<br />

such as TaqMan (Applied Biosystems) or a randomly<br />

coiled structure, such as Thermo Scientific Solaris.<br />

The fluorescent signal results from hydrolsis <strong>of</strong> the probe,<br />

following hybridization, or as a result <strong>of</strong> hybridization to the<br />

target sequence.<br />

• Fluorescence on hydrolysis (TaqMan)<br />

The fluorescent reporter and the quencher are<br />

maintained in close proximity while the probe is intact.<br />

The probe is designed to anneal to the target sequence<br />

between the forward and reverse primer sites. It is then<br />

hydrolyzed by the 5’-3’ exonuclease activity <strong>of</strong> Taq DNA<br />

polymerase, disrupting the proximity <strong>of</strong> the fluorophore<br />

and the quencher, and resulting in an increase in fluorescence.<br />

If no target PCR product is present, the probe<br />

is not degraded and the fluorescent reporter remains<br />

quenched.<br />

• Fluorescence on hybridization (Solaris)<br />

When the probe is free in solution the randomly coiled<br />

“<br />

algorithms, allowing<br />

viable<br />

structure brings the fluorescent reporter and the<br />

quencher together. Hybridization <strong>of</strong> the complementary<br />

sequence to target DNA during the annealing step<br />

increases the distance between the dyes, resulting in an<br />

increase in fluorescence that can be measured. During<br />

extension, the probe is released intact into the solution,<br />

where the fluorescent signal is once again quenched,<br />

allowing it to be used in subsequent cycles.<br />

Minor Groove Binder technology<br />

A recent development in the design <strong>of</strong> fluorescent reporter<br />

probes is the utilization <strong>of</strong> minor groove binder (MGB)<br />

technology. Examples <strong>of</strong> fluorogenic MGB probes include<br />

MGB-TaqMan (Applied Biosystems), MGB-Eclipse ®<br />

(Sigma-Aldrich) and Solaris (Thermo Fisher Scientific).<br />

MGBs are flat, crescent-shaped molecules that<br />

have a natural ability to fold back and fit snugly into<br />

the minor groove (the deep, narrow groove between<br />

the two phosphate-sugar backbones) <strong>of</strong> the dsDNA<br />

helix. This provides an extremely stable hybridization<br />

between the DNA probe and the target sequence. The<br />

stability <strong>of</strong> this hybridization increases the temperature<br />

needed to melt or dissociate the probe from its<br />

target (Tm), allowing shorter probe sequences to be<br />

used. Not only does this improve mismatch discrimination,<br />

but it also permits more efficient probe design3 .<br />

Prior to MGB technology, the size <strong>of</strong> designed probes<br />

had to be larger in order to produce melting temperatures<br />

consistent with efficient PCR. Such long probes reduce<br />

design flexibility, when restricted by small target regions,<br />

and are less sensitive to mismatch discrimination.<br />

superbase technology<br />

The strength <strong>of</strong> Superbase technology has been harnessed<br />

in the development <strong>of</strong> Solaris Probe Assays (Thermo Fisher<br />

Scientific). Often, the sequence <strong>of</strong> the target DNA region<br />

can affect the sensitivity and specificity <strong>of</strong> primer probe<br />

designs. For example, target sequences that are rich in A-T<br />

pairs <strong>of</strong>ten have lower melting temperatures and G-rich<br />

regions can be affected by guanine-guanine self association.<br />

www.thermo.com/solaris


Genomic DNA<br />

EXON 1 EXON 2 EXON 3 EXON 4 EXON 5 EXON 6 EXON 71<br />

1. VanGuilder HD,<br />

EXON 2<br />

Vrana, KE and<br />

Freeman, WM.<br />

(2008) Twenty-five<br />

EXON 3<br />

Splice Variants years <strong>of</strong> quantitative<br />

PRIMER<br />

PRIMER<br />

PCR for gene PRIMER<br />

PRIMER<br />

1 EXON 1 EXON 2 EXON 3 EXON 4 EXON 6 EXON 7<br />

1<br />

expression analysis.<br />

BioTechniques EXON 1 44(5): EXON 2<br />

619-626.<br />

Splice Variants<br />

Figure 2: Solaris qPCR<br />

Assays are designed to<br />

a consensus sequence<br />

PRIMER<br />

among all known Forward splice Primer<br />

variants so Probe one assay<br />

provides comprehensive<br />

PRIMER Reverse Primer<br />

results.<br />

2<br />

increase the flexibility <strong>of</strong> design<br />

otherwise difficult probe and<br />

genomic assays.“<br />

Superbases are modified derivatives <strong>of</strong> native nucleotides<br />

that, when substituted in primer and probe<br />

designs, can eliminate many <strong>of</strong> these problems and<br />

maximize the design space within a particular sequence.<br />

Super A and Super T improve the stability <strong>of</strong> traditionally<br />

weaker A-T bonds, enabling the Tm to be raised<br />

for more efficient hybridization and improving the<br />

performance <strong>of</strong> assays in A-T rich regions. Super G<br />

eliminates guanine-guanine self-association that interferes<br />

with proper hybridization in G-rich sequences.<br />

The use <strong>of</strong> Superbases can increase the flexibility<br />

<strong>of</strong> probe/primer design algorithms, allowing<br />

otherwise difficult probe and primer sequences<br />

to become viable genomic assays. They allow the<br />

algorithm to refine the Tm, reduce secondary structures<br />

and improve mismatch discrimination.<br />

The use <strong>of</strong> both MGB and Superbase technology<br />

in fluorogenic probes serves to adjust and standardize<br />

melting temperatures, allowing assays to be<br />

performed optimally under the same thermal cycling<br />

conditions. This is <strong>of</strong>ten referred to as ‘universal<br />

thermal cycling conditions’ and reduces the necessity<br />

for preliminary optimization <strong>of</strong> assay conditions.<br />

PRIMER<br />

PRIMER<br />

EXON 1 EXON 2 EXON 3 EXON 5 EXON 6<br />

3<br />

PRIMER<br />

PRIMER<br />

EXON 1 EXON 2 EXON 5 EXON 6<br />

Genomic DNA<br />

www.thermo.com/solaris INsights | Issue 24 | Article<br />

PRIMER<br />

PRIMER<br />

Forward Primer<br />

Probe<br />

Reverse Primer<br />

Design algorithms<br />

A robust primer/probe design algorithm is necessary to<br />

ensure optimal functionality, specificity and splice variant<br />

coverage (Figure 2). This is particularly important in gene<br />

expression assays where, previously, a significant amount <strong>of</strong><br />

time was spent in selecting and optimizing the primer/probe<br />

set. By incorporating important design rules (Table 1), it is<br />

possible to design a single high performance assay for specific<br />

gene expression experiments.<br />

Gold standard method<br />

Advancement and utilization <strong>of</strong> the technologies above have<br />

enabled qPCR to become the gold standard method for<br />

the validation <strong>of</strong> a wide-spanning number <strong>of</strong> scientific and<br />

medical approaches from the detection <strong>of</strong> pathogens in clinical<br />

specimens, the monitoring <strong>of</strong> genetic disease progression<br />

and therapeutic effect to the validation <strong>of</strong> data generated<br />

from microarray and RNAi based experiments in more<br />

fundamental research projects.<br />

For more info about<br />

Solaris go to:<br />

www.thermo.com/<br />

solaris<br />

toby.hampshire@<br />

therm<strong>of</strong>isher.com<br />

REFERENCES<br />

2. Saiki, RK, Scharf PRIMER<br />

PRIMER<br />

S, Faloona F, Mullis<br />

2 EXON 1<br />

KB, Horn GT, Erlich<br />

HA and Arnheim N.<br />

EXON 2<br />

(1985) Enzymatic<br />

amplification<br />

PRIMER<br />

<strong>of</strong> beta-globin 3 EXON 1 E<br />

genomic sequences<br />

and restriction<br />

site analysis for<br />

diagnosis <strong>of</strong> sickle<br />

cell anemia. Science<br />

230(4732): 1350–1354.<br />

3. Lukhtanov EA,<br />

Lokhov SG, Gorn VV,<br />

Podyminogin MA<br />

and Mahoney W.<br />

(2007) Novel DNA<br />

probes with low<br />

background and<br />

high hybridisationtriggered<br />

fluorescence.<br />

Nucleic Acid<br />

Research 35(5) e30.<br />

Once Gene. One<br />

qPCR Assay. Simple.<br />

E


Solaris<br />

Following the release <strong>of</strong><br />

Solaris qPCR assays we<br />

have been inundated with<br />

questions regarding the<br />

design and performance <strong>of</strong> the<br />

product. Here are the most<br />

commonly asked questions<br />

with answers from our<br />

technical support team.<br />

What are Supernucleotides and<br />

what function do they perform in<br />

my Solaris qPCR Gene Expression Assay?<br />

Supernucleotides (Superbases) are<br />

functionally equivalent chemically<br />

modified versions <strong>of</strong> native nucleotides.<br />

These specialized nucleotides have a<br />

higher melting temperature (T m<br />

) and<br />

are used to precisely adjust the overall<br />

Tm <strong>of</strong> the probe/primer sequence to<br />

which they are added. In this way they<br />

function to accurately maintain what<br />

is commonly referred to as "universal<br />

thermal cycling conditions" as well as<br />

to mitigate any unfavorable secondary<br />

structure within the sequence.<br />

How can I be sure that my<br />

Solaris Assay is target specific?<br />

The Solaris design algorithm performs<br />

a BLAST search on transcript, genomic<br />

and pseudogene databases to ensure all<br />

assays are specific.<br />

What is an MGB moiety and what<br />

function does it perform in my<br />

Solaris qPCR Gene Expression Assay?<br />

A minor groove binder (MGB) moiety,<br />

which is attached to the 5' end <strong>of</strong> a<br />

INsights | Special Issue | Solaris Q&A<br />

Solaris probe, increases its melting<br />

temperature, thereby allowing shorter<br />

probes to be designed. This increases<br />

design flexibility and gives improved<br />

assay performance. Furthermore, the<br />

MGB moiety, in conjunction with<br />

Superbases, gives the same Tm (60ºC)<br />

for every Solaris probe. This means the<br />

same thermal cycling conditions can be<br />

used for all Solaris Assays.<br />

What is meant by "universal<br />

thermal cycling conditions" when<br />

referring to my Solaris qPCR Gene<br />

Expression Assay?<br />

This refers to the use <strong>of</strong> identical<br />

thermal cycling conditions for all<br />

Solaris qPCR Gene Expression Assays,<br />

irrespective <strong>of</strong> the target gene.<br />

Can I use my Solaris qPCR<br />

Gene Expression Assay to detect<br />

specific splice variants <strong>of</strong> a gene target?<br />

The Solaris qPCR Gene Expression<br />

Assays have been designed to detect<br />

all known variants <strong>of</strong> a given gene<br />

target, thereby giving highly accurate<br />

quantification results. They cannot be<br />

used to detect a specific splice variant.<br />

Will my Solaris qPCR Gene<br />

Expression Assay detect genomic<br />

DNA (gDNA) if it is designed within a<br />

single exon?<br />

Solaris assays that are designed within<br />

a single exon may detect genomic<br />

DNA if it is present. This design<br />

information is provided for all Solaris<br />

Assays. Consequently, gDNA should be<br />

completely removed prior to the RT step.<br />

Are the sequences <strong>of</strong> the probe<br />

and primers for my Solaris qPCR<br />

Gene Expression Assay provided?<br />

Yes. The native nucleotide sequences<br />

<strong>of</strong> both primers and the probe <strong>of</strong> the<br />

assay are provided when the assay is<br />

purchased.<br />

Can I perform a faster protocol<br />

using Solaris Assays?<br />

Most Solaris assays perform very<br />

well using a faster protocol, provided<br />

they are used in conjunction with a<br />

master mix that promotes efficient<br />

amplification under these conditions.<br />

When used with the complimentary<br />

Solaris master mix, most Solaris assays<br />

perform very well using the faster<br />

protocol described below:<br />

Enzyme<br />

Activation<br />

Denaturation<br />

Annealing/<br />

Extension<br />

Temp. Time Cycles<br />

95 º<br />

95 º<br />

60 º<br />

10<br />

minutes<br />

1<br />

cycle<br />

5<br />

seconds 40<br />

20 cycles<br />

seconds<br />

What is the stability <strong>of</strong> the Solaris<br />

qPCR Gene Expression Assays?<br />

The shelf life is at least 2 years when<br />

stored at -20ºC.<br />

What is the difference between<br />

"Inventoried" and "Made<br />

to Order" Solaris qPCR Gene<br />

Expression Assays?<br />

Inventoried Assays are those commonly<br />

studied gene targets that have already<br />

been synthesized and placed into<br />

inventory. The delivery time for<br />

inventoried assays is 3-6 days after the<br />

order is received (direct countries only).<br />

Made to Order Assays are those less<br />

commonly studied gene targets that<br />

have already been designed but not yet<br />

synthesized. The delivery time for these<br />

assays is 5-12 days after the order is<br />

received (direct countries only).<br />

What real-time PCR instruments<br />

can I use with my Solaris qPCR<br />

Gene Expression Assay?<br />

Solaris qPCR Assays are compatible<br />

with qPCR instruments from<br />

major suppliers.<br />

www.thermo.com/solaris<br />

www.thermo.com/gen


Solaris<br />

8 akt application note<br />

www.thermo.com/solaris<br />

Thermo Scientific Solaris qPCR Gene Expression<br />

Assays: <strong>Detection</strong> <strong>of</strong> Related AKT Protein Kinase Family<br />

Members in an RNAi-based Study <strong>of</strong> FOXO1 Regulation<br />

Zaklina sTreZoska and kirsTeen maClean<br />

THermo FisHer sCienTiFiC, laFayeTTe, Co, usa<br />

15 stability technical note<br />

Thermo Scientific Solaris Gene Expression Assays<br />

and Master Mix: Stability in the Laboratory<br />

benjamin jayne, amanda Haas, melissa kelley<br />

THermo FisHer sCienTiFiC, laFayeTTe, Co, usa<br />

18 relative Quantification technical note<br />

Demonstration <strong>of</strong> a ΔΔCq Calculation Method to<br />

Compute Relative Gene Expression from qPCR Data<br />

josH Haimes, melissa kelley<br />

THermo FisHer sCienTiFiC, laFayeTTe, Co, usa<br />

22 high performance technical note<br />

High Performance RT-qPCR Using Thermo Scientific<br />

Solaris qPCR Gene Expression Reagents for Assessing<br />

Relative Gene Expression<br />

ben jaCkson, amanda Haas<br />

THermo FisHer sCienTiFiC, laFayeTTe, Co, usa<br />

INsights | Issue 24


Thermo Scientific Solaris qPCR Gene<br />

Expression Assays: <strong>Detection</strong> <strong>of</strong> Related<br />

AKT Protein Kinase Family Members in an<br />

RNAi-based Study <strong>of</strong> FOXO1 Regulation<br />

Zaklina Strezoska and Kirsteen H. MacLean, Thermo Fisher Scientific, Lafayette, CO, USA<br />

Abstract<br />

Quantitative real-time PCR<br />

(qPCR) is a routine laboratory<br />

technique for the measurement <strong>of</strong><br />

DNA or RNA that is both sensitive<br />

and specific with appropriate<br />

assay design. Thermo Scientific<br />

Solaris qPCR Gene Expression<br />

Assays are ideal for routine<br />

molecular applications because<br />

they combine the Minor Groove<br />

Binder technology (MGB; Epoch<br />

Biosciences Inc.) with a rigorous<br />

algorithm that permits detection <strong>of</strong><br />

all known splice variants <strong>of</strong> a gene<br />

target, while distinguishing among<br />

closely related family members,<br />

on a genome-wide scale. Here we<br />

describe the application <strong>of</strong> this<br />

new probe/primer qPCR detection<br />

technology in a study that deciphers<br />

the individual contributions <strong>of</strong><br />

the highly related gene family<br />

members, AKT1, AKT2 and AKT3,<br />

in regulating the activation and<br />

redistribution <strong>of</strong> the Forkhead/<br />

FOXO1 transcription factor.<br />

Introduction<br />

Protein kinase B (PKB) otherwise<br />

known as AKT is a serine/<br />

threonine protein kinase that acts<br />

as a central node in multiple cell<br />

signaling pathways (1). The AKT<br />

family <strong>of</strong> proteins is comprised<br />

<strong>of</strong> three highly homologous<br />

members sharing structural and<br />

sequence conservation: PKBα/<br />

AKT1, PKBβ/AKT2 and PKBγ/<br />

AKT3. The family members are<br />

induced following activation <strong>of</strong><br />

the phosphatidylinositol-3 kinase<br />

(PI3K) signaling pathway with<br />

growth factors, cytokines and other<br />

cellular stimuli. Phosphorylation <strong>of</strong><br />

AKT members triggers their activity<br />

where they in turn phosphorylate<br />

INsights | Issue 24 | Application Note<br />

a spectrum <strong>of</strong> substrates within the<br />

cell. One class <strong>of</strong> these substrates<br />

includes the FOXO transcription<br />

factors, which are members <strong>of</strong><br />

the Forkhead family (FKHR) <strong>of</strong><br />

transcription factors characterized<br />

by a conserved DNA-binding<br />

domain (2). FOXO transcription<br />

factors, comprised <strong>of</strong> four members<br />

in mammals (FOXO1, 3, 4 and 6),<br />

are at the nexus <strong>of</strong> critical cellular<br />

processes such as apoptosis, cellcycle<br />

progression, oxidative stress,<br />

glucose metabolism and energy<br />

homeostasis (3). Phosphorylation<br />

<strong>of</strong> FOXO by activated AKT leads<br />

to the retention <strong>of</strong> FOXO within<br />

the cytoplasm and therefore results<br />

in its inactivation (Figure 1A).<br />

Inhibition <strong>of</strong> the AKT activity<br />

within the cell (e.g., withdrawal <strong>of</strong><br />

survival factors) leads to FOXO<br />

dephosphorylation, nuclear<br />

translocation and activation <strong>of</strong> its<br />

target genes (Figure 1B) (4).<br />

The AKT protein family<br />

A cell membrane<br />

B<br />

PI3K<br />

PIP3<br />

cytosol<br />

AKT<br />

P<br />

P<br />

FOXO<br />

nucleus<br />

members are broadly expressed<br />

in most organs and tissues; AKT1<br />

is ubiquitously expressed at high<br />

levels; AKT2 is highly expressed<br />

in insulin-sensitive tissues such<br />

as the liver, skeletal muscle and<br />

adipose tissue and AKT3 is the<br />

predominant form expressed in the<br />

brain and testis (5). AKT kinases<br />

are involved in regulating critical<br />

cellular processes such as apoptosis,<br />

cell growth, differentiation and<br />

energy metabolism. Having such<br />

a diverse role in many normal<br />

processes, it is not surprising that<br />

AKT dysfunction is intimately<br />

involved in numerous disease states<br />

including diabetes and cancer (6, 7).<br />

Specifically, it has been shown that<br />

is<strong>of</strong>orm-specific functions <strong>of</strong> AKT<br />

family members can contribute to<br />

tumorigenesis on multiple levels;<br />

this is perhaps expected given the<br />

original discovery <strong>of</strong> AKT as the<br />

protein encoded by the cellular<br />

homolog <strong>of</strong> the viral oncogene v-akt<br />

PIP3<br />

X X<br />

PI3K<br />

cytosol<br />

cell membrane<br />

FOXO<br />

AKT<br />

Target gene<br />

nucleus<br />

FOXO maintained in cytosol FOXO localized in nuclei<br />

Target gene<br />

expression<br />

Figure 1: The AKT signaling pathway. Growth factor receptors recruit phosphatidylinositol-3<br />

kinase (PI3K) to the cell membrane and induce the production <strong>of</strong> second messengers<br />

phospatidylinositol-3,4,5-triphospahtate (PIP3) that convey signals from the cell surface to the<br />

cytoplasm. AKT binds these second messengers at the plasma membrane where it gets phosphorylated<br />

and activated by the kinase 3-phosphoinositide-dependent protein kinase. Proteins<br />

phosphorylated by activated AKT regulate diverse cellular functions. These include certain<br />

members <strong>of</strong> the FOXO family <strong>of</strong> transcription factors resulting in their retention in the cytosol<br />

(A). Inactivation (X) <strong>of</strong> the PI3K/AKT pathway results in FOXO dephosphorylation, nuclear<br />

translocation and transcriptional activation <strong>of</strong> its target genes (B).<br />

Application<br />

Note<br />

Key words<br />

• qPCR<br />

• Minor groove<br />

binder<br />

• RNA<br />

interference<br />

• AKT<br />

• FOXO<br />

• Transcription<br />

factor<br />

• Redistribution<br />

• High-content<br />

analysis<br />

www.thermo.com/solaris


(8). Numerous studies have since<br />

examined oncogenic amplifications<br />

<strong>of</strong> different AKT genes in primary<br />

human tumors and cancer cells. For<br />

example, AKT1 gene amplification<br />

and mutation occurs in gastric and<br />

colorectal cancer and amplifications<br />

<strong>of</strong> AKT2 are known to affect breast,<br />

ovarian and pancreatic cancer (9).<br />

Conservation <strong>of</strong> the AKT family<br />

members suggests potential for<br />

redundant functions <strong>of</strong> the AKT<br />

family members. For this reason,<br />

to ascertain their individual and<br />

combinatorial biological effects, it<br />

is crucial to have molecular tools<br />

capable <strong>of</strong> selectively modulating<br />

and detecting their specific roles.<br />

Such tools can enhance our<br />

understanding <strong>of</strong> the relative<br />

contribution <strong>of</strong> the AKT is<strong>of</strong>orms<br />

to different biological processes and<br />

might open a door to developing<br />

selective modes <strong>of</strong> therapy.<br />

Here we describe the use <strong>of</strong> RNA<br />

interference (RNAi) technology to<br />

down-regulate the expression <strong>of</strong><br />

each specific human AKT family<br />

member to examine their is<strong>of</strong>ormspecific<br />

roles on the regulation <strong>of</strong><br />

FOXO1 in a Thermo Scientific<br />

FKHR Redistribution cell line,<br />

engineered to assess FOXO1<br />

protein translocation by high<br />

content analysis. Specificity <strong>of</strong> the<br />

down-regulation <strong>of</strong> each family<br />

member was assessed using Solaris <br />

qPCR Gene Expression Assays.<br />

These probe-based qPCR assays<br />

represent probe/primer sets that are<br />

generated using a novel, tier-based<br />

algorithm to create reagents that<br />

detect all known splice variants<br />

<strong>of</strong> a given gene yet are capable <strong>of</strong><br />

distinguishing even closely related<br />

family members. The Solaris<br />

qPCR Gene Expression Assays<br />

incorporate Minor Groove Binder<br />

(MGB) (10), and Superbase (Epoch<br />

Biosciences Inc.) technologies for<br />

increased sequence design space and<br />

enhanced specificity. MGB probes<br />

hybridize more strongly to their<br />

complementary sequences than<br />

standard DNA probes and display<br />

an increased melting temperature<br />

allowing for the use <strong>of</strong> shorter but<br />

highly specific probes. In addition,<br />

the modified nucleic acid bases<br />

(Superbases) can be substituted in<br />

www.thermo.com/solaris<br />

primer and probe design to raise the<br />

melting temperature and eliminate<br />

many <strong>of</strong> the problems associated<br />

with AT- or GC-rich regions.<br />

Incorporation <strong>of</strong> these two chemical<br />

strategies with a fluorescent (FAM)<br />

reporter dye and corresponding<br />

Eclipse ® Dark Quencher<br />

fluorochrome results in high<br />

performance assays that consistently<br />

function under universal thermal<br />

cycling conditions.<br />

Results<br />

To study the roles <strong>of</strong> the closely<br />

related AKT family members on<br />

FOXO1 regulation, we used an<br />

RNAi approach to down-regulate<br />

the AKT family members in a<br />

U2OS FKHR Redistribution ® cell<br />

line (Figure 2). These recombinant<br />

cells stably express human FKHR/<br />

FOXO1 fused to the N-terminus<br />

<strong>of</strong> the enhanced green fluorescent<br />

protein (EGFP) for easy monitoring<br />

<strong>of</strong> the FOXO1 relocation. Using<br />

Thermo Scientific Cellomics high<br />

content analysis, one can track the<br />

consequences <strong>of</strong> inhibiting the AKT<br />

pathway. Cells plated in 96-well<br />

Plate FKHR/FOXO1<br />

Redistribution Cells<br />

24 hrs 24 hrs<br />

24 hrs 72 hrs<br />

Analyze the relative expression <strong>of</strong> AKT is<strong>of</strong>orms<br />

using Solaris qPCR Gene Expression Assays<br />

Transfect cells with<br />

ON-TARGETplus<br />

SMARTpool reagents<br />

targeting AKT is<strong>of</strong>orms<br />

Unstimulated cell:<br />

Majority <strong>of</strong> FKHR-EGFP<br />

localized in the cytoplasm<br />

Wortmannin<br />

(antagonist)<br />

Inhibited cell:<br />

FKHR-EGFP translocates<br />

to the nucleus<br />

ArrayScan VTI HCS Reader for<br />

FOXO1 Redistribution<br />

Figure 2: Experimental workflow. ON-TARGETplus ® SMARTpool ® siRNAs targeting either<br />

AKT1, AKT2 or AKT3 or a combination <strong>of</strong> them were transfected into the FKHR redistribution<br />

cell line, a recombinant U2OS cell line that stably expresses human FKHR/FOXO1 fused to the<br />

N-terminus <strong>of</strong> enhanced green fluorescent protein (EGFP). Duplicate micro-well plates were<br />

transfected to determine the relative AKT expression at 24 hours post-transfection by Solaris<br />

qPCR Gene Expression Assays and for high-content analysis for the FOXO1 redistribution at 72<br />

hours post-transfection on the Cellomics ArrayScan VTI HCS Reader by monitoring the translocation<br />

<strong>of</strong> a FKHR-EGFP fusion protein from the cytoplasm to the nucleus.<br />

micro-titer plates were transfected<br />

in triplicate with Thermo Scientific<br />

Dharmacon ON-TARGETplus<br />

siRNA SMARTpool reagents<br />

against individual or combinations<br />

<strong>of</strong> the AKT family members,<br />

including down-regulation <strong>of</strong><br />

all three kinases simultaneously.<br />

Replicate micro-titer plates were<br />

prepared to determine gene target<br />

expression levels as well as highcontent<br />

analysis. This approach<br />

allowed for quantification <strong>of</strong><br />

FOXO1 Redistribution from<br />

the cytoplasm to the nucleus on<br />

the Thermo Scientific Cellomics<br />

ArrayScan VTI HCS Reader as well<br />

as correlative detection <strong>of</strong> AKT gene<br />

expression.<br />

Alternatively spliced variants<br />

have been described for the AKT<br />

family <strong>of</strong> kinases: AKT1 has three<br />

splice variants that differ in the<br />

5’untranslated region (UTR) but<br />

encode the same protein and AKT3<br />

has two splice variants that differ<br />

in the 3’end-region, which includes<br />

a part <strong>of</strong> the coding sequence,<br />

resulting in two protein variants<br />

with a different C-terminus. AKT1,<br />

INsights | Issue 24 | Application Note


AKT2 and AKT3 mRNAs share<br />

a high degree <strong>of</strong> homology: 80 %<br />

sequence identity between AKT1<br />

and AKT2, 70 % sequence identity<br />

between AKT3 and AKT2 and<br />

71 % sequence identity between<br />

the AKT1 and AKT3 mRNA<br />

coding regions. Figure 3A depicts<br />

a schematic representation <strong>of</strong> the<br />

AKT1, AKT2 and AKT3 transcripts<br />

with all splice variants and the<br />

positions and size <strong>of</strong> the amplicons<br />

obtained using the Solaris qPCR<br />

Assays. Figure 3B shows the<br />

complementarities <strong>of</strong> the AKT2<br />

Solaris Assay with the sequences<br />

<strong>of</strong> all three AKT transcripts. Both<br />

primers and the probe have perfect<br />

complementarities only to the<br />

AKT2 but not the AKT1 or AKT3<br />

sequences. Since Solaris Assays are<br />

designed using a rigorous algorithm<br />

to specifically detect all known<br />

A<br />

AKT1 AKT1 v1 v1<br />

AKT1 AKT1 v2 v2<br />

AKT1 AKT1 v3 v3<br />

AKT2 AKT2<br />

AKT3 v1<br />

AKT3 AKT3 v2<br />

124 bp<br />

5’UTR 5'UTR CDS 3’UTR 3'UTR<br />

113 bp<br />

5’UTR 5'UTR CDS 3’UTR 3'UTR<br />

82 bp<br />

5’UTR 5'UTR CDS 3’UTR 3'UTR<br />

splice variants <strong>of</strong> a gene target, they<br />

represent an ideal tool to measure<br />

RNAi-mediated changes in the<br />

expression <strong>of</strong> each AKT family<br />

member. The design algorithm also<br />

incorporates preferences for assays<br />

to span exon-exon junctions where<br />

the primer, probe or amplicon<br />

crosses an exon-exon junction.<br />

Given the fact that all three kinases<br />

are highly homologous, primers<br />

and probe <strong>of</strong> the qPCR assay<br />

must be designed in regions where<br />

the sequence differences would<br />

allow for specific amplification<br />

<strong>of</strong> only that target which has<br />

perfect complementarities to the<br />

primer/probe set. The available<br />

is<strong>of</strong>orm-specific sequence that can<br />

be utilized for assay design may<br />

become limiting when designing<br />

assays to gene targets with multiple<br />

splice variants and family members.<br />

Solaris qPCR Assay<br />

0 1000 2000 3000 4000 5000 6000<br />

B Solaris qPCR Assay: AKT2<br />

AKT2<br />

3’-CATCGTCTTACGGTCGA-5’<br />

5’-ACGGTACTTCCTGCTGAAGAGCGACGGCTCCTTCATTGGG. . . GATCAGACTCTACCCCCCTTAAACAACTTCTCCGTAGCAGAATGCCAGCT<br />

3’-TGCCATGAAGGACGACTTCTCGCTGCCGAGGAAGTAACCC CTAGTCTGAGATGGGGGGAATTTGTTGAAGAGGCATCGTCTTACGGTCGA<br />

5’-ACGGTACTTCCTGCTGAAGA 5’-TAAACAACTTCTCCGTAG<br />

3’-CATCGTCTTACGGTCGA-5’<br />

x xx x<br />

5’-ACGCTACTTCCTCCTCAAGAATGATGGCACCTTCATTGGC. . . ACCAACGTGAGGCTCCCCTCAACAACTTCTCTGTGGCGCAGTGCCAGCT<br />

3’-TGCGATGAAGGAGGAGTTCTTACTACCGTGGAAGTAACCG TGGTTGCACTCCGAGGGGAGTTGTTGAAGAGACACCGCGTCACGGTCGA<br />

x x x<br />

x x x<br />

5’-ACGGTACTTCCTGCTGAAGA 5’-TAAACAACTTCTCCGTAG<br />

AKT1<br />

3’-CATCGTCTTACGGTCGA-5’<br />

x x x<br />

5’-AAGATACTTCCTTTTGAAGACAGATGGCTCATTCATAGGATAT. . . ATTTACCTTATCCCCTCAACAACTTTTCAGTGGCAAAATGCCAGTT<br />

3’-TTCTATGAAGGAAAACTTCTGTCTACCGAGTAAGTATCCTATA TAAATGGAATAGGGGAGTTGTTGAAAAGTCACCGTTTTACGGTCAA<br />

x x xx<br />

x x x x<br />

5’-ACGGTACTTCCTGCTGAAGA 5’-TAAACAACTTCTCCGTAG<br />

AKT3<br />

Figure 3: Schematic representation <strong>of</strong> the AKT1, AKT2 and AKT3 transcript variants and<br />

Solaris qPCR Assay positions with sequence alignment.<br />

A. AKT1 has three splice variants (v1, v2, v3), AKT2 has one mRNA and AKT3 has two splice<br />

variants (v1, v2). The different color bars correspond to different exons for each AKT gene.<br />

The amplicon positions and size produced by the Solaris qPCR Assays are represented by the<br />

black bars. All three Solaris Assays are exon spanning.<br />

B. Sequence complementarities <strong>of</strong> the AKT2 Solaris Gene Expression Assay to the AKT1, AKT2<br />

and AKT3 transcripts (primers shown in purple and green, probe shown in blue).<br />

INsights | Issue 24 | Application Note<br />

This restriction can be resolved by<br />

adjusting the annealing temperature<br />

through the incorporation <strong>of</strong> the<br />

MGB moiety and Superbases. This<br />

design strategy effectively increases<br />

the sequence design space and<br />

permits design <strong>of</strong> gene-specific<br />

Solaris Assays.<br />

We used the AKT1, AKT2<br />

and AKT3 Solaris qPCR Assays<br />

to measure the siRNA-mediated<br />

changes in expression <strong>of</strong> the<br />

endogenous levels for each AKT<br />

family member. Figure 4 illustrates<br />

that specific siRNAs designed<br />

against each <strong>of</strong> the AKT family<br />

members results in is<strong>of</strong>orm-specific<br />

mRNA knockdown (red bars)<br />

compared to associated control<br />

groups (blue bars). A SMARTpool<br />

siRNA reagent targeting AKT1 led<br />

to down-regulation <strong>of</strong> the target<br />

mRNA by > 90 % with no effect<br />

on the mRNA levels <strong>of</strong> AKT2 or<br />

AKT3 (Figure 4A). Similarly, the<br />

AKT2 SMARTpool siRNA regeant<br />

resulted in a knockdown <strong>of</strong> AKT2<br />

mRNA by ~ 95 % with no effect<br />

on the other family members<br />

(Figure 4B) while the siRNA pool<br />

against AKT3 led to a knockdown<br />

<strong>of</strong> the AKT3 mRNA by 90 % with<br />

no effect on AKT1 or AKT2 levels<br />

(Figure 4C). Furthermore this<br />

specific knockdown was maintained<br />

when combinations <strong>of</strong> siRNA pools<br />

against two <strong>of</strong> the three kinases<br />

was used for a double knockdown<br />

or all three for a triple knockdown<br />

<strong>of</strong> AKT family members. For<br />

example, when siRNA pools against<br />

AKT1 and AKT2 were combined,<br />

there was specific target mRNA<br />

knockdown <strong>of</strong> AKT1 and AKT2<br />

but not AKT3. Combinations <strong>of</strong> the<br />

siRNA pools targeted against all<br />

three AKT family members resulted<br />

in an efficient knockdown <strong>of</strong> all<br />

three family members. Figure 4D<br />

shows the log scale amplification<br />

curves obtained with Solaris qPCR<br />

Assays for AKT1, AKT2, AKT3 and<br />

a reference gene, GAPDH. Samples<br />

transfected with siRNA against the<br />

particular AKT is<strong>of</strong>orm assayed by<br />

the Solaris Assay show a shift in the<br />

amplification curves by greater than<br />

three Cq values. In contrast, the Cq<br />

values for GAPDH remained the<br />

same across all samples, indicating<br />

www.thermo.com/solaris


no change in the relative expression<br />

across distinct transfected samples.<br />

These values were used to normalize<br />

the expression <strong>of</strong> the AKT is<strong>of</strong>orms<br />

across the silencing experiments.<br />

The expression analysis clearly<br />

shows that mRNA knockdown was<br />

achieved by the is<strong>of</strong>orm-specific<br />

siRNAs and could be detected by<br />

the corresponding is<strong>of</strong>orm-specific<br />

Solaris Assays.<br />

Now with the appropriate<br />

tools in place to discern the<br />

gene- specific functions, the next<br />

step is assay selection. We chose<br />

the FKHR Redistribution Assay,<br />

which is ideally suited to screen<br />

for modulators <strong>of</strong> the FOXO1<br />

transcription factor by monitoring<br />

the cellular redistribution <strong>of</strong> the<br />

FOXO1-EGFP fusion protein<br />

(Figure 5). In dividing cells,<br />

FOXO1 is phosphorylated by the<br />

AKT kinases and is sequestered in<br />

the cytoplasm in its inactive form,<br />

as illustrated in Figure 5A. Upon<br />

inhibition <strong>of</strong> the AKT pathway<br />

the FOXO1 phosphorylation<br />

and cytoplasmic sequestration<br />

is hindered resulting in nuclear<br />

accumulation. Wortmannin, a<br />

common PI3K covalent inhibitor,<br />

inhibits the PI3K-AKT signaling<br />

pathway and results in a nuclear<br />

distribution <strong>of</strong> FOXO1 (Figure<br />

5B). We examined the effects <strong>of</strong><br />

down-regulation <strong>of</strong> individual<br />

or combinations <strong>of</strong> the AKT<br />

family members on the nuclear<br />

redistribution <strong>of</strong> FOXO1. In cells<br />

transfected with a non-targeting<br />

siRNA control (NTC) the FOXO1<br />

is localized primarily in the<br />

cytoplasm similar to the untreated<br />

cells (Figure 5C). Although robust<br />

silencing <strong>of</strong> the individual AKT<br />

family members was achieved, there<br />

were no significant changes in the<br />

redistribution <strong>of</strong> FOXO1 when each<br />

<strong>of</strong> the AKT kinases was individually<br />

down-regulated (Figure 5E). This<br />

supports previous observations<br />

that there is a level <strong>of</strong> redundancy<br />

inherent within the PI3 kinase/AKT<br />

signaling system (5). When AKT2<br />

and AKT3 family members were<br />

down-regulated together, there was<br />

an approximately six-fold induction<br />

<strong>of</strong> FOXO1 redistribution to the<br />

nucleus (Figure 5E). However,<br />

www.thermo.com/solaris<br />

A<br />

B<br />

C<br />

D<br />

AKT1<br />

Rel Expression<br />

AKT2<br />

Rel Expression<br />

AKT3<br />

Rel Expression<br />

120<br />

100<br />

80<br />

60<br />

40<br />

20<br />

0<br />

120<br />

100<br />

80<br />

60<br />

40<br />

20<br />

0<br />

120<br />

100<br />

80<br />

60<br />

40<br />

20<br />

0<br />

NTC<br />

AKT1<br />

AKT2<br />

AKT3<br />

NTC<br />

Figure 4: Analysis <strong>of</strong> the AKT knockdown by siRNA using Solaris qPCR Gene<br />

Expression Assays.<br />

A-C. Relative expression <strong>of</strong> AKT is<strong>of</strong>orms following siRNA transfection: qPCR was performed<br />

using Solaris qPCR Assays for detection <strong>of</strong> AKT1, AKT2, AKT3 and GAPDH on a Roche<br />

LightCycler480 (384-well) instrument. Knockdown <strong>of</strong> AKT1 (A), AKT2 (B) and AKT3 (C) was<br />

calculated using a ΔΔCq method (normalized to GAPDH reference gene and Non-Targeting<br />

Control (NTC) treated cells at the corresponding siRNA concentration). Treatment <strong>of</strong> the<br />

cells with siRNAs against each <strong>of</strong> the AKT is<strong>of</strong>orms results in the is<strong>of</strong>orm-specific mRNA<br />

knockdown (red bars) compared to associated control groups (blue bars). D. Log scale<br />

amplification curves obtained with Solaris qPCR Assays for AKT1, AKT2, AKT3 and GAPDH:<br />

samples transfected with siRNAs against the particular AKT is<strong>of</strong>orm being assayed by the<br />

Solaris qPCR Assay show a shift in the amplification curves by > 3 Cq values. GAPDH was<br />

used as a reference and shows no difference in Cq values among all samples.<br />

AKT1 + AKT2<br />

AKT2 + AKT3<br />

AKT1 + AKT3<br />

NTC<br />

20 nM 40 nM 60 nM<br />

AKT1 AKT2<br />

Control<br />

samples<br />

AKT1 siRNA<br />

transfected<br />

samples<br />

Control<br />

samples<br />

AKT3 GAPDH<br />

Control<br />

samples<br />

AKT3 siRNA<br />

transfected<br />

samples<br />

AKT1 + AKT2 + AKT3<br />

AKT2 siRNA<br />

transfected<br />

samples<br />

mock<br />

All<br />

samples<br />

INsights | Issue 24 | Application Note<br />

UT


more than a ten-fold increase in<br />

redistribution <strong>of</strong> FOXO1 from the<br />

cytoplasm to the nucleus is observed<br />

when siRNA pools were combined<br />

to simultaneously knockdown<br />

all three AKT family members<br />

(Figure 5D and E). This is about<br />

25 % <strong>of</strong> the response observed<br />

with the positive compound control<br />

wortmannin, which likely reflects<br />

the fact that reference compounds<br />

act differently than siRNAs at<br />

a biological level. For example,<br />

timing, potency and effects on<br />

the total enzyme pool are distinct<br />

for a small molecule compound<br />

as compared to siRNAs. Further,<br />

wortmannin has been shown to<br />

have a high degree <strong>of</strong> toxicity<br />

and non-specific effects on other<br />

kinase signaling pathways that may<br />

enhance the apparent phenotypic<br />

effect. Our data clearly indicate<br />

that FOXO1 translocation in the<br />

U2OS cell line is regulated by all<br />

three AKT family members, with<br />

AKT2 and AKT3 having some<br />

predominance in the regulation.<br />

Conclusion<br />

The three members <strong>of</strong> the AKT<br />

family <strong>of</strong> protein kinases have<br />

been implicated in a plethora <strong>of</strong><br />

cellular signaling processes with<br />

key functions in the control <strong>of</strong><br />

cellular metabolism, growth,<br />

proliferation and apoptosis. Defects<br />

in the AKT signaling underlie<br />

various human diseases including<br />

cancer and diabetes. Thorough<br />

insight into the is<strong>of</strong>orm-specific<br />

roles <strong>of</strong> AKT family is essential<br />

to fully understand the degree <strong>of</strong><br />

functional redundancy between the<br />

family members and their relative<br />

contributions to diverse biological<br />

processes and diseases. This could<br />

ultimately lead to the derivation <strong>of</strong><br />

is<strong>of</strong>orm-specific targeted therapies.<br />

Subsequent development <strong>of</strong><br />

is<strong>of</strong>orm-specific mouse knockout<br />

models and more recently, the use<br />

<strong>of</strong> is<strong>of</strong>orm-specific siRNA, have<br />

improved our understanding <strong>of</strong><br />

AKT regulation and the roles <strong>of</strong><br />

the different is<strong>of</strong>orms in distinct<br />

cellular processes (reviewed in (6)).<br />

For example, disruption <strong>of</strong> the<br />

Akt genes in the mouse germ line<br />

results in is<strong>of</strong>orm-deficient mice<br />

INsights | Issue 24 | Application Note<br />

A B<br />

UT wortmanin<br />

C D<br />

NTC<br />

E<br />

% effect (wortmanin =100%)<br />

30<br />

25<br />

20<br />

15<br />

10<br />

5<br />

0<br />

UT<br />

NTC<br />

AKT1<br />

AKT2<br />

Figure 5: FOXO1 redistribution from cytoplasm to nucleus upon AKT inhibition or downregulation<br />

by siRNAs.<br />

A-D. High-content microscopy analysis using the ArrayScan VTI HCS Reader was performed<br />

on FKHR Redistribution Cell line at 72 hours post transfection. Cell images are presented<br />

for untreated (UT) samples (A), cells treated with 150 μM wortmannin for 24 hours prior<br />

to analysis (B), cells transfected with the Non-targeting control (NTC) siRNA (C), or cells<br />

transfected with siRNA pools targeting all three AKT is<strong>of</strong>orms (D).<br />

E. Quantitative analysis <strong>of</strong> the FOXO1 redistribution for all samples was obtained by the<br />

Redistribution V3 BioApplication s<strong>of</strong>tware and is presented as percent <strong>of</strong> the redistribution<br />

effect obtained by the wortmannin control.<br />

displaying very specific phenotypes.<br />

Akt1 null mice show placental<br />

hypotrophy, with accompanied<br />

retardation <strong>of</strong> growth and reduction<br />

<strong>of</strong> body weight when compared<br />

to wild-type littermates. Akt2 null<br />

mice become hyperinsulinemic and<br />

hyperglycemic and Akt3 null mice<br />

exhibit reduced brain sizes. The<br />

fact that all three knockout mice<br />

strains are viable and demonstrate<br />

AKT3<br />

NTC<br />

AKT1 + AKT2 + AKT3<br />

AKT1 + AKT2<br />

AKT2 + AKT3<br />

AKT1 + AKT3<br />

NTC<br />

20 nM 40 nM 60 nM<br />

AKT1 + AKT2 + AKT3<br />

only subtle phenotypes suggests<br />

that all three is<strong>of</strong>orms compensate<br />

for each other. In support <strong>of</strong> this<br />

Akt1/Akt2 null mice show severe<br />

dwarfism, atrophy <strong>of</strong> multiple organ<br />

systems that include the skin and<br />

skeletal muscle resulting in an early<br />

neonatal lethality. Akt2/Akt3 double<br />

null mice, although viable, exhibit<br />

impaired glucose homeostasis and<br />

growth deficiencies (5). Support for<br />

www.thermo.com/solaris


the existence <strong>of</strong> both redundant and<br />

distinct functions <strong>of</strong> AKT is<strong>of</strong>orms<br />

has also emerged from the analysis<br />

<strong>of</strong> is<strong>of</strong>orm-specific siRNA-mediated<br />

AKT knockdowns in adipocytes.<br />

While knockdown <strong>of</strong> either AKT1<br />

or AKT2 led to a comparable defect<br />

in insulin-stimulated glycogen<br />

synthesis, similar to the phenotype<br />

displayed in knockout mice, AKT2<br />

knockdown had a more prominent<br />

effect on insulin-stimulated glucose<br />

uptake (11).<br />

Herein we used the AKT family<br />

as an example <strong>of</strong> investigating<br />

closely related members with both<br />

redundant and distinct functions by<br />

coupling RNAi tools to specifically<br />

modulate gene function with a novel<br />

detection technology to measure<br />

the consequences <strong>of</strong> is<strong>of</strong>ormspecific<br />

modulation. AKT family<br />

members share extensive sequence<br />

similarity: 80 % sequence identity<br />

across the coding region between<br />

AKT1 and AKT2 and 70 % or<br />

71 % sequence identity between<br />

AKT3 and AKT2, or AKT1,<br />

respectively. This represents a<br />

formidable challenge for the design<br />

<strong>of</strong> the appropriate siRNA and<br />

qPCR reagents. ON-TARGETplus<br />

siRNAs pools were used to downregulate<br />

the expression <strong>of</strong> the<br />

individual AKT family members<br />

and the consequences measured<br />

by monitoring the localization<br />

<strong>of</strong> FOXO1, a major downstream<br />

substrate. A novel primer/probebased<br />

qPCR methodology, Solaris<br />

qPCR Gene Expression Assays,<br />

was used to assess the downregulation<br />

<strong>of</strong> the expression <strong>of</strong><br />

the AKT is<strong>of</strong>orms. We found that<br />

AKT family members indeed<br />

exhibit redundant function in the<br />

regulation <strong>of</strong> the downstream<br />

substrate FOXO1 and that downregulation<br />

<strong>of</strong> all three is<strong>of</strong>orms<br />

is necessary to inhibit the AKT<br />

signaling to FOXO1. Now armed<br />

with these powerful molecular<br />

tools, researchers can extend their<br />

investigations to different biological<br />

systems to help decipher the role<br />

<strong>of</strong> highly related family members<br />

on the regulation <strong>of</strong> different<br />

downstream targets and better<br />

understand gene-specific regulation<br />

www.thermo.com/solaris<br />

and roles in normal and diseased<br />

human states.<br />

methods<br />

RNA Interference (RNAi)<br />

The FKHR Redistribution cell line<br />

(Thermo Scientific Cat # 008_01) is<br />

a recombinant U2OS cell line that<br />

stably expresses human FKHR/<br />

FOXO1 fused to the N-terminus <strong>of</strong><br />

enhanced green fluorescent protein<br />

(EGFP). Cells were cultured under<br />

recommended media conditions<br />

(http://www.thermo.com/com/cda/<br />

product/detail/1,,10143278,00.html)<br />

and plated in 96-well micro-well<br />

plates 24 hours prior to transfection<br />

at a density <strong>of</strong> 2,000 or 10,000 cells<br />

per well for high content imaging and<br />

for knockdown analysis, respectively.<br />

Cells were transfected with the ON-<br />

TARGETplus SMARTpool reagents<br />

(20-60 nM final concentration) using<br />

the Thermo Scientific DharmaFECT<br />

3 transfection reagent (Thermo<br />

Scientific Cat # T-2003-03) at the<br />

optimized final concentration for<br />

each cell density: (0.075 μL/well or<br />

0.3 μL/well). Parallel plates were<br />

transfected and analyzed by qPCR<br />

at 24 hours post-transfection or<br />

by high-content microscopy at 72<br />

hours post transfection. The siRNAs<br />

used for transfection included: ON-<br />

TARGETplus SMARTpool against<br />

AKT1 (Gene ID: 207), AKT2 (Gene<br />

ID: 208) and AKT3 (Gene ID: 10000)<br />

(Thermo Scientific Cat # L-003000-<br />

00; L-003001-00 and L-003002-00,<br />

respectively). When combinations<br />

<strong>of</strong> ON-TARGETplus SMARTpool<br />

reagents were used against AKT<br />

family members the final siRNA<br />

pool concentration against each<br />

AKT member was maintained at 20<br />

nM. Untreated, lipid only and cells<br />

transfected with the ON-TARGETplus<br />

Non-Targeting siRNA Pool<br />

(Thermo Scientific Cat # D-001810-<br />

10) (at 20 nM, 40 nM and 60 nM<br />

final concentrations) were used as<br />

negative controls.<br />

Quantitative Real-time PCR (qPCR)<br />

RNA was isolated using Promega<br />

SV 96 Total RNA Isolation System<br />

(Thermo Scientific Cat # Z3505).<br />

cDNA synthesis was performed<br />

using Thermo Scientific Verso cDNA<br />

synthesis kit (Thermo Scientific<br />

Cat # AB-1453/B). Gene expression<br />

analysis was performed using Solaris<br />

qPCR Gene Expression Assays<br />

(Thermo Scientific Cat # AX-003000-<br />

00-0100 (AKT1), AX-003001-00-<br />

0100 (AKT2), AX-003002-00-0100<br />

(AKT3) and AX-004253-00-0100<br />

(GAPDH)) and Master Mix<br />

(Thermo Scientific Cat # AB-4350/C),<br />

which includes an inert blue dye for<br />

visualization. qPCRs were carried out<br />

in 12.5 µL reaction volume with final<br />

oligonucleotide concentrations <strong>of</strong> 800<br />

nM each primer and 200 nM <strong>of</strong> MGBprobe.<br />

A standard qPCR thermal<br />

cycling protocol was employed<br />

(DNA polymerase activation at 95ºC,<br />

15 minutes, 1 cycle; denaturation<br />

at 95ºC, 15 seconds, annealing/<br />

extension at 60ºC, 60 seconds, 40<br />

cycles). Samples were run on a Roche<br />

LightCycler480 in 384-well white<br />

plates (Roche Cat # 04729749001).<br />

Expression data was normalized to<br />

a GAPDH reference gene using a<br />

∆∆Cq method. Expression levels were<br />

further normalized to Non-Targeting<br />

Control siRNAs and are reported as<br />

a percentage <strong>of</strong> the Non-Targeting<br />

Control expression level.<br />

High-content Analysis<br />

FKHR-U2OS cells transfected as<br />

described above and incubated<br />

for 72 hours were fixed with 4 %<br />

paraformaldehyde and stained with<br />

Hoechst 33258 (Molecular Probes,<br />

Cat # H1398) for subsequent highcontent<br />

analysis. Un-transfected<br />

wells were treated with 150 nM<br />

wortmannin (Calbiochem Cat #<br />

681675) for 1 hour prior to fixation,<br />

used here as a positive compound<br />

control that is known to induce robust<br />

nuclear redistribution <strong>of</strong> FOXO1 (12).<br />

The 96-well micro-well plates were<br />

imaged using a 10x objective (0.63X<br />

coupler), XF100 filter sets for Hoechst<br />

and FITC and the Thermo Scientific<br />

Redistribution V3 BioApplication<br />

s<strong>of</strong>tware. Three replicate wells for<br />

each siRNA transfection or treatment<br />

were analyzed. The data output<br />

used was the mean log <strong>of</strong> the ratio <strong>of</strong><br />

average fluorescence intensities <strong>of</strong> the<br />

nucleus and cytoplasm. Five fields in<br />

each well were measured (~300 cells/<br />

field). The averages and standard<br />

deviations were calculated for each<br />

treatment and numeric data evaluated<br />

INsights | Issue 24 | Application Note


with the vHCS Discovery Toolbox.<br />

The data was further normalized and<br />

presented as percent <strong>of</strong> the effect <strong>of</strong><br />

150 µM wortmannin. For cellular<br />

images, plates were re-scanned using a<br />

20x objective.<br />

References<br />

1. 1B. D. Manning, L. C. Cantley,<br />

Cell 129, 1261 (Jun 29, 2007).<br />

2. M. E. Carter, A. Brunet, Curr Biol<br />

17, R113 (Feb 20, 2007).<br />

3. V. Duronio, Biochem J 415, 333<br />

(Nov 1, 2008).<br />

4. A. Brunet et al., Cell 96, 857 (Mar<br />

19, 1999).<br />

5. T. F. Franke, Oncogene 27, 6473<br />

(Oct 27, 2008).<br />

6. B. Dummler, B. A. Hemmings,<br />

Biochem Soc Trans 35, 231 (Apr,<br />

2007).<br />

7. B. T. Hennessy, D. L. Smith, P. T.<br />

Ram, Y. Lu, G. B. Mills, Nat Rev<br />

Drug Discov 4, 988 (Dec, 2005).<br />

8. S. P. Staal, J. W. Hartley, W. P.<br />

Rowe, Proc Natl Acad Sci U S A<br />

74, 3065 (Jul, 1977).<br />

9. A. Carracedo, P. P. Pandolfi,<br />

Oncogene 27, 5527 (Sep 18,<br />

2008).<br />

10. E. A. Lukhtanov, S. G. Lokhov, V.<br />

V. Gorn, M. A. Podyminogin, W.<br />

Mahoney, Nucleic Acids Res. 35,<br />

e30 (March 12, 2007, 2007).<br />

11. Z. Y. Jiang et al., PNAS 100, 7569<br />

(June 24, 2003, 2003).<br />

12. B. M. Burgering, P. J. C<strong>of</strong>fer,<br />

Nature 376, 599 (Aug 17, 1995).<br />

Troubleshooting<br />

For technical information or<br />

troubleshooting contact Thermo<br />

Scientific Genomics Tech Support:<br />

In North America (US, Canada,<br />

Central/South America)<br />

Techservice.genomics@<br />

therm<strong>of</strong>isher.com<br />

+1 (800) 235-9880<br />

In Europe (EU, Middle East, Africa)<br />

Techservice.emea.genomics@<br />

therm<strong>of</strong>isher.com<br />

(+)44 1372 840410<br />

In Other Countries<br />

www.thermo.com/<br />

dharmacondistributors<br />

INsights | Issue 24 | Application Note<br />

Copyright © 2010 Thermo Fisher<br />

Scientific, Inc. All Rights Reserved.<br />

Literature Code: 00024809K01U<br />

The product(s) described herein<br />

(“Products”) are protected by<br />

patents, pending patents and other<br />

intellectual property owned or<br />

licensed by Dharmacon Inc., a<br />

wholly-owned subsidiary <strong>of</strong> Thermo<br />

Fisher Scientific Inc., as set forth in<br />

Dharmacon’s Terms and Conditions<br />

(found at www.thermo.com/<br />

dharmacon or included with the<br />

Products when sold). By using the<br />

Product(s), users accept the Terms<br />

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govern all use <strong>of</strong> the Product(s). The<br />

Product(s) are intended solely for<br />

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is a trademark <strong>of</strong> Eurogentec. All<br />

other trademarks are the property<br />

<strong>of</strong> Thermo Fisher Scientific Inc. and<br />

its subsidiaries.<br />

www.thermo.com/solaris


Prepared Solaris qPCR reagents<br />

are stable at standard laboratory<br />

conditions for up to 24 hours<br />

The next step was to examine<br />

the impact on performance <strong>of</strong> fully<br />

prepared Solaris qPCR reagents<br />

(i.e. complete reaction mixtures)<br />

after equilibrating to ambient<br />

conditions (up to 72 hours, 22°C).<br />

Reagent volumes adequate to<br />

perform multiple 384-well plates<br />

were prepared for Solaris qPCR<br />

Assays designed to detect ten gene<br />

targets. This reaction mix was then<br />

dispensed into 384-well plates.<br />

The 0 hour time point plate was<br />

Fluorescence (dRn)<br />

10<br />

1<br />

RPLP2 (0 hours)<br />

0.1<br />

0 5 10 15 20 25 30 35 40<br />

Fluorescence (dRn)<br />

10<br />

1<br />

Cycle<br />

RPLP2 (24 hours)<br />

0.1<br />

0 5 10 15 20 25 30 35 40<br />

Fluorescence (dRn)<br />

10<br />

1<br />

Fluorescence (dRn)<br />

Fluorescence (dRn)<br />

10<br />

1<br />

RPLP2 (8 hours)<br />

0.1<br />

0 5 10 15 20 25 30 35 40<br />

10<br />

1<br />

Cycle<br />

RPLP2 (48 hours)<br />

0.1<br />

0 5 10 15 20 25 30 35 40<br />

Cycle Cycle<br />

TMEM175 (0 hours)<br />

0.1<br />

0 5 10 15 20 25 30 35 40<br />

Fluorescence (dRn)<br />

10<br />

1<br />

Cycle<br />

TMEM175 (24 hours)<br />

0.1<br />

0 5 10 15 20 25 30 35 40<br />

Time (hours)<br />

0<br />

8<br />

16<br />

24<br />

48<br />

72<br />

Cycle<br />

Fluorescence (dRn)<br />

Fluorescence (dRn)<br />

Efficiency (%)<br />

96<br />

96<br />

100<br />

98<br />

93<br />

93<br />

INsights | Issue 24 | Technical Note<br />

10<br />

1<br />

0.1<br />

0 5 10 15 20 25 30 35 40<br />

10<br />

1<br />

TMEM175 (8 hours)<br />

Cycle<br />

TMEM175 (48 hours)<br />

0.1<br />

0 5 10 15 20 25 30 35 40<br />

Cycle<br />

placed into a thermocycler for<br />

amplification immediately while<br />

the remaining plates were stored on<br />

the bench top to various incubation<br />

times (2, 4, 8, 12, 16, 20, 24, 48 and<br />

72 hours) prior to the qPCR cycling<br />

protocol. Amplification results<br />

under the experimental conditions<br />

described were compared to the<br />

results from the 0 hour time point<br />

plate taking into account endpoint<br />

fluorescence, Cq value and assay<br />

performance measured by efficiency<br />

<strong>of</strong> the amplification, dynamic range<br />

and r 2 value. High performance<br />

assays exhibit amplification<br />

RPLP2 TMEM175<br />

2 r<br />

Efficiency (%)<br />

0.998<br />

1.000<br />

0.999<br />

0.998<br />

0.998<br />

0.998<br />

Fluorescence (dRn)<br />

10<br />

1<br />

RPLP2 (16 hours)<br />

0.1<br />

0 5 10 15 20 25 30 35 40<br />

Fluorescence (dRn)<br />

10<br />

1<br />

Cycle<br />

RPLP2 (72 hours)<br />

0.1<br />

0 5 10 15 20 25 30 35 40<br />

Fluorescence (dRn)<br />

10<br />

1<br />

Cycle<br />

TMEM175 (16 hours)<br />

0.1<br />

0 5 10 15 20 25 30 35 40<br />

Fluorescence (dRn)<br />

10<br />

1<br />

Cycle<br />

TMEM175 (72 hours)<br />

0.1<br />

0 5 10 15 20 25 30 35 40<br />

97<br />

98<br />

100<br />

102<br />

103<br />

97<br />

Cycle<br />

2 r<br />

0.999<br />

0.998<br />

0.998<br />

0.998<br />

0.996<br />

0.999<br />

efficiencies between 90-110 % and<br />

r 2 values <strong>of</strong> ≥ 0.995 over ≥ 5 log 10<br />

dilutions. For nine <strong>of</strong> the Solaris<br />

qPCR Assays tested, no effect was<br />

observed when complete reaction<br />

mixtures were exposed to ambient<br />

laboratory conditions for up to<br />

72 hours; Figure 2A illustrates the<br />

amplification plots for the RPLP2<br />

Solaris Assay (Thermo Scientific<br />

Cat # AX-004314) at the 0, 8, 16,<br />

24, 48, and 72 hour time points.<br />

The assay performance is shown<br />

in Table 1. Notably, in one assay,<br />

TMEM175 (Thermo Scientific Cat #<br />

AX-014856) a decrease in endpoint<br />

Figure 2. Solaris qPCR reagents in complete<br />

reaction mixtures (1X) are stable to 24 hours<br />

in ambient laboratory conditions<br />

Solaris qPCR reagents detecting RPLP2<br />

(A) and TMEM175 (B) were used to amplify<br />

five 10-fold dilutions <strong>of</strong> synthetic DNA (5<br />

million to 500 copies). Shown here are the<br />

amplification curves at 0, 8, 16, 24, 48, and 72<br />

hours (1X final concentration <strong>of</strong> Solaris qPCR<br />

Assay and Master Mix).<br />

Table 1. high performance <strong>of</strong> RPLP2 and<br />

TmEm175 Solaris qPCR Gene Expression<br />

Assays exposed to ambient conditions to<br />

72 hours<br />

Percent efficiency and r 2 values are shown<br />

for RPLP2 and TMEM175 Solaris Assays<br />

calculated from five 10-fold dilutions <strong>of</strong><br />

synthetic DNA template.<br />

www.thermo.com/solaris


fluorescence beginning at the 24<br />

hour time point was observed<br />

with the lowest concentration <strong>of</strong><br />

template (Figure 2b) although there<br />

was no negative impact on the<br />

assay performance as measured by<br />

efficiency, Cq and r 2 values (Table 1).<br />

Conclusions<br />

High throughput gene expression<br />

experiments utilizing qPCR<br />

detection may require preparation<br />

<strong>of</strong> multiple reactions simultaneously<br />

while only being able to analyze<br />

one or a small number <strong>of</strong> plates at a<br />

time. In the event <strong>of</strong> unanticipated<br />

delays in assay protocols,<br />

researchers may discard prepared<br />

plates as a precautionary measure<br />

fearing compromised integrity <strong>of</strong><br />

the reactions. In this Technical<br />

Note, we have demonstrated that<br />

Solaris qPCR Gene Expression<br />

Assays and Master Mixes are stable<br />

after sustained exposure to ambient<br />

laboratory conditions. Solaris<br />

qPCR Assays (20X concentration)<br />

are stable at room temperature<br />

and exposure to light for at least<br />

72 hours. Our data indicate no<br />

observable negative impact on<br />

complete Solaris qPCR reaction<br />

mixtures under these standard,<br />

benchtop laboratory conditions<br />

as determined by the Cq value,<br />

endpoint fluorescence and assay<br />

performance for at least 24 hours.<br />

At 24 hours, one <strong>of</strong> the ten assays<br />

did exhibit a decrease in endpoint<br />

fluorescence; however this effect<br />

was observed only at the lowest<br />

copy number concentration (500<br />

copies) and may be assay or target<br />

dependent.<br />

For long-term storage it is<br />

recommended that Solaris qPCR<br />

Assays and Master Mixes are stored<br />

at -20°C and assayed as soon as<br />

possible after complete reaction<br />

mixtures are prepared. However,<br />

in circumstances where exposure to<br />

ambient laboratory temperatures<br />

and light conditions cannot be<br />

avoided, the Solaris qPCR reagents<br />

prepared at 1X final concentrations<br />

are stable for at least 24 hours<br />

providing confidence to the<br />

researcher that high performance<br />

will be retained.<br />

www.thermo.com/solaris<br />

materials and methods<br />

For experiments testing the<br />

stability <strong>of</strong> the 20X Solaris Assays,<br />

100 ng <strong>of</strong> Stratagene ® qPCR<br />

Human Reference Total RNA<br />

(Thermo Scientific Ca t# 750500)<br />

was reverse transcribed using<br />

Thermo Scientific Verso cDNA<br />

Synthesis kit (Thermo Scientific<br />

Cat # AB4351) (20 µL reaction<br />

volume) and a 3:1 mixture<br />

<strong>of</strong> random hexamer and<br />

oligo(dT) primers as per the<br />

manufacturer’s instructions. One<br />

µL <strong>of</strong> cDNA template and two<br />

100-fold dilutions were used<br />

for amplification in technical<br />

triplicates. The 0 hr time point<br />

plate was placed in a thermal<br />

cycler immediately; at every<br />

subsequent time point each<br />

Solaris Assay was combined<br />

with a freshly prepared dilution<br />

series <strong>of</strong> cDNA template, Solaris<br />

Master Mix, and molecular grade<br />

water. A Stratagene Mx3000p<br />

qPCR instrument (96-well format)<br />

was used with the following<br />

thermal cycling parameters: 95°C<br />

for 15 minutes × 1 cycle, 95°C<br />

for 15 seconds denaturing and<br />

60°C for 60 seconds annealing<br />

and extension × 40 cycles. The<br />

amplification threshold was<br />

manually set to 0.845.<br />

For experiments testing the<br />

stability <strong>of</strong> the Solaris Assays and<br />

Master Mixes in final reaction<br />

concentrations (1X), synthetic<br />

DNA template representing<br />

the amplicon sequence for ten<br />

different Solaris Assays was mixed<br />

to 5 million copies and diluted 10<br />

fold to 500 copies. The 0 hour<br />

time point plate was immediately<br />

run from the same preparation<br />

<strong>of</strong> qPCR reagents as complete<br />

reactions that were exposed to<br />

the test condition. An Applied<br />

Biosystems Prism ® 7900HT qPCR<br />

instrument (384-well format)<br />

was used with the following<br />

thermal cycling parameters: 95°C<br />

for 15 minutes × 1 cycle, 95°C<br />

for 15 seconds denaturing and<br />

60°C for 60 seconds annealing<br />

and extension × 40 cycles. The<br />

amplification threshold was<br />

manually set to 0.100.<br />

Troubleshooting<br />

For technical information or<br />

troubleshooting contact Thermo<br />

Scientific Genomics Tech Support:<br />

In North America (US, Canada,<br />

Central/South America)<br />

Techservice.genomics@<br />

therm<strong>of</strong>isher.com<br />

+1 (800) 235-9880<br />

In Europe (EU, Middle East, Africa)<br />

Techservice.emea.genomics@<br />

therm<strong>of</strong>isher.com<br />

(+)44 1372 840410<br />

In Other Countries<br />

www.thermo.com/<br />

dharmacondistributors<br />

Copyright © 2010 Thermo Fisher<br />

Scientific, Inc. All Rights Reserved.<br />

Literature Code: 00024909K01U<br />

INsights | Issue 24 | Technical Note


Demonstration <strong>of</strong> a ΔΔCq Calculation<br />

Method to Compute Relative Gene<br />

Expression from qPCR Data<br />

Josh Haimes, Melissa Kelley, Thermo Fisher Scientific, Lafayette, CO, USA<br />

Abstract<br />

This Technical Note demonstrates<br />

the utility <strong>of</strong> a ∆∆Cq method for<br />

calculating relative gene expression<br />

and percent knockdown from<br />

quantification cycle (Cq) values<br />

obtained by quantitative real-time<br />

PCR (qPCR) analysis in an RNA<br />

interference (RNAi) experiment.<br />

In this study, the human aldolase<br />

A (ALDOA) message is silenced<br />

with the corresponding Thermo<br />

Scientific Dharmacon siGENOME<br />

SMARTpool siRNA. To determine<br />

relative gene expression, probebased<br />

qPCR is performed using<br />

Thermo Scientific Solaris qPCR<br />

Gene Expression Assays with<br />

cDNA synthesized from total RNA<br />

harvested from cell culture. Here, a<br />

∆∆Cq method is demonstrated as a<br />

normalized determination <strong>of</strong> gene<br />

knockdown, and the experimental<br />

controls it requires are described.<br />

Introduction<br />

RNAi-mediated gene silencing<br />

using small interfering RNAs<br />

(siRNAs) is an effective technique<br />

used for varied applications<br />

from primary academic research<br />

to therapeutic discovery. While<br />

phenotypic observations may<br />

elucidate the effect <strong>of</strong> targetspecific<br />

knockdown on biological<br />

systems, silencing efficacy should<br />

be confirmed to ensure confidence<br />

in phenotypic results. Efficacy is<br />

commonly reported as relative<br />

percent knockdown <strong>of</strong> mRNA levels<br />

compared to controls and can be<br />

determined in high throughput with<br />

easy-to-use, commercially available<br />

qPCR gene expression assays.<br />

The Solaris qPCR Gene<br />

Expression Assays are pre-designed,<br />

high performance gene-specific probe<br />

and primer pairs that utilize minor<br />

groove binder (MGB) and Superbase<br />

INsights | Issue 24 | Technical Note<br />

technologies (Epoch Biosciences Inc)<br />

to deliver reliable expression data.<br />

This Technical Note outlines a ∆∆Cq<br />

method for calculating experimental<br />

percent knockdown (%KD) from<br />

Cq values obtained by Solaris qPCR<br />

analysis in an RNAi experiment to<br />

knockdown the gene expression <strong>of</strong><br />

ALDOA in cell culture (Figure 1).<br />

Overall, ∆∆Cq yields a normalized,<br />

relative gene expression value. This<br />

is accomplished by normalization<br />

<strong>of</strong> a gene target with experimental<br />

treatment to an endogenous reference<br />

gene(s) whose expression should<br />

remain unchanged by the treatment.<br />

Subsequently, this value is normalized<br />

to the targeted gene’s expression<br />

detected in a separate control sample.<br />

Several variations on calculating<br />

relative gene expression from qPCR<br />

data exist; the method shown here<br />

is adapted for an experimental setup<br />

employing cells treated in biological<br />

replicates. For a method employing<br />

technical replicates, please see<br />

Bustin. 1<br />

materials and methods<br />

Cell Culture, siRNA Transfection and<br />

RNA Isolation<br />

HeLa cells (ATCC Cat # CCL-<br />

2) were plated at 10,000 cells/<br />

well in a 96-well format and<br />

incubated overnight at 37°C with<br />

5 % CO 2. Cells were treated with<br />

either siGENOME SMARTpool<br />

siRNA targeting ALDOA (Thermo<br />

Scientific Cat # M-010376-01) or<br />

siGENOME Non-Targeting siRNA<br />

Pool # 1 (Thermo Scientific Cat #<br />

D-001206-13) complexed with<br />

the DharmaFECT 1 transfection<br />

reagent (Thermo Scientific Cat #<br />

T-2001). siRNA complexes were<br />

transfected at 10, 1 and 0.1 nM final<br />

siRNA concentration in triplicate<br />

wells, creating biological replicates.<br />

Triplicates <strong>of</strong> mock transfected<br />

(MT; transfection reagent only) and<br />

untreated (UT) controls were also<br />

prepared.<br />

Total RNA was isolated from<br />

each well simultaneously with the<br />

vacuum manifold-based Promega<br />

SV 96 RNA Isolation System<br />

(Promega Cat # Z3500) 48 hours<br />

post-transfection. The RNA from<br />

each eluate was used in separate<br />

reverse transcription reactions. RNA<br />

concentrations were not quantified<br />

as the relative gene expressions data<br />

from each aliquot were normalized<br />

Fluorescence (483 - 533)<br />

Relative Gene Expression<br />

7<br />

6<br />

5<br />

4<br />

3<br />

2<br />

1<br />

siRNA Transfection<br />

• totRNAIsolation<br />

• cDNA Synthesis<br />

• qPCR <strong>Detection</strong><br />

0<br />

0 5 10 15 20<br />

Cycle<br />

25 30 35 40<br />

qPCR Amplification Plot<br />

• ∆∆Cq Calculation<br />

1.4<br />

1.2<br />

1<br />

0.8<br />

0.6<br />

0.4<br />

0.2<br />

0<br />

siRNA Dose Curve Controls<br />

Relative Gene Expression<br />

Figure 1. Experimental workflow<br />

siGENOME SMARTpool siRNA targeting<br />

ALDOA was transfected into HeLa cells and<br />

cDNA was synthesized from RNA isolated 48<br />

hours post-transfection. Solaris qPCR Gene<br />

Expression reagents were used to detect<br />

ALDOA and reference genes’ cDNA and<br />

relative gene expression was calculated from<br />

Cq values using a ΔΔCq method.<br />

Technical<br />

Note<br />

www.thermo.com/solaris


to an endogenous reference gene for<br />

each well.<br />

Reverse Transcription - Quantitative<br />

Polymerase Chain Reaction<br />

Total RNA from each sample<br />

(5 µL) was reverse transcribed<br />

with the Thermo Scientific Verso<br />

cDNA synthesis kit (Thermo<br />

Scientific Cat # AB-1453) using a<br />

3:1 (volume:volume) mixture <strong>of</strong><br />

random hexamers to anchored<br />

oligo-dT primers in a 20 µL reaction<br />

according to the manufacturer’s<br />

protocol. No reverse transcriptase<br />

controls were prepared from<br />

untreated cells’ total RNA and no<br />

template controls were prepared<br />

with water in place <strong>of</strong> total RNA<br />

A<br />

Fluorescence (483 - 533)<br />

7<br />

6<br />

5<br />

4<br />

3<br />

2<br />

1<br />

ALDOA (TAR) qPCR <strong>Detection</strong> Amplification Plot<br />

MT, UT, Non-targeting Controls<br />

0.1 nM siRNA<br />

1 nM siRNA<br />

10 nM siRNA<br />

0<br />

0 5 10 15 20<br />

Cycle<br />

25 30 35 40<br />

∆∆Cq Calculations<br />

In the RNAi experiment<br />

described here, expression <strong>of</strong><br />

the siRNA-treated ALDOA gene<br />

target (TAR) was normalized to<br />

non-targeted GAPDH or RSP18<br />

reference gene (REF) expression<br />

levels within the same sample to<br />

determine ∆Cq (Step 1, Box 1). This<br />

step serves to correct for nontreatment-related<br />

variation among<br />

wells such as potential differences in<br />

cell number. Cq values <strong>of</strong> technical<br />

replicates can be averaged at this<br />

step if they were included in the<br />

experimental design. For biological<br />

www.thermo.com/solaris<br />

to indicate potential genomic DNA<br />

contamination in isolated total RNA<br />

and contamination <strong>of</strong> reagents,<br />

respectively.<br />

Each biological replicate was<br />

assayed for the siRNA-targeted gene,<br />

ALDOA, as well as the endogenous<br />

reference genes GAPDH and RPS18<br />

using Solaris qPCR Gene Expression<br />

Assays (Thermo Scientific Cat #s<br />

AX-010376-00, AX-011890-00, AX-<br />

004253-00, respectively) and Solaris<br />

qPCR Master Mix plus ROX<br />

(Thermo Scientific Cat # AB-<br />

4350). No reverse transcriptase<br />

and no template controls were also<br />

assayed with qPCR detection for<br />

each target. Two µL <strong>of</strong> each three-<br />

replicates in this experiment,<br />

the ∆Cq for each replicate was<br />

exponentially transformed to the<br />

∆Cq Expression (Step 2) before<br />

averaging and determining the<br />

standard deviation (Step 3). The<br />

mean was then normalized to<br />

the expression <strong>of</strong> ALDOA (TAR)<br />

from a separate well treated with<br />

Non-Targeting siRNA to find<br />

∆∆Cq Expression (Step 4). This<br />

accounted for any effects associated<br />

with the experimental procedure<br />

and was expressed as the ratio <strong>of</strong><br />

the targeted ∆Cq Expression to the<br />

non-targeted ∆Cq Expression. In an<br />

Step 1. Normalize to REF: ∆Cq = Cq (TAR) – Cq (REF)<br />

Step 2. Exponential Expression Transform: ∆Cq Expression = 2 –∆Cq<br />

Step 3. Average replicates and calculate standard deviation<br />

Step 4. Normalize to treatment control<br />

Step 5. %KD = (1 – ∆∆C q )x100<br />

Given Values Step 1 Step 2 Step 4 Step 5<br />

Cq REF Cq TAR ∆Cq TAR-REF ∆C q Expression ∆∆Cq<br />

%KD<br />

Non-targeting Control 21.9 23.1 1.2 0.43 1.00 -<br />

TAR ALDOA 27.5 34.3 6.7 0.01 0.02 98<br />

B<br />

Fluorescence (483 - 533)<br />

2.5<br />

2<br />

1.5<br />

1<br />

0.5<br />

GAPDH (REF) qPCR <strong>Detection</strong> Amplification Plot<br />

All Samples<br />

0<br />

0 5 10 15 20<br />

Cycle<br />

25 30 35 40<br />

fold diluted cDNA reaction were<br />

used in 12 µL qPCR reactions and<br />

transferred into a white 384-well<br />

plate (Roche Cat # 04729749001)<br />

with the aid <strong>of</strong> a Thermo Scientific<br />

PlateMate 2x3 liquid handler. qPCR<br />

thermal cycling and fluorescent<br />

data acquisition were performed<br />

with a Roche LightCycler ® 480<br />

instrument and Cq values were<br />

called using the LightCycler 480<br />

s<strong>of</strong>tware’s ‘Fit Points’ algorithm<br />

yielding amplification plots (Figure<br />

2). A ∆∆Cq method was then used<br />

to process these data to calculate<br />

relative gene expression for the<br />

RNAi experiment.<br />

Figure 2. qPCR Amplification curves for ALDOA<br />

and GAPDh<br />

Amplification curves represent cDNA<br />

detected in samples treated for ALDOA siRNA<br />

knockdown (A) or control samples (B). The<br />

ALDOA amplification curves exhibit siRNA dosedependent<br />

message knockdown, with higher Cq<br />

values representing lower expression, or more<br />

effective silencing. For reference, a difference<br />

<strong>of</strong> one Cq value represents a 50 % change in<br />

expression, while differences <strong>of</strong> 3.3 and 6.6 Cq<br />

values represent approximately 90 % and 99 %<br />

changes, respectively. In contrast, amplification<br />

curves for all GAPDH reference samples yield<br />

similar Cq values, indicating GADPH expression<br />

was not significantly affected by the treatment.<br />

RNAi experiment, ∆Cq Expression<br />

is normalized to a corresponding<br />

Non-Targeting siRNA sample. In<br />

other experimental platforms – e.g.,<br />

small molecule treatment – it may<br />

be appropriate to normalize to<br />

untreated or vehicle only control<br />

samples. Percent knockdown<br />

was calculated by subtracting the<br />

normalized ∆∆Cq Expression from<br />

1 (defined by the level <strong>of</strong> expression<br />

for untreated sample) and<br />

multiplying by 100 (Step 5). Table 1<br />

illustrates a complete list <strong>of</strong> values<br />

showing how to carry multiple data<br />

points with biological replicates<br />

Box 1. ΔΔCq Overview<br />

INsights | Issue 24| Technical Note


1<br />

2<br />

3<br />

4<br />

5<br />

6<br />

7<br />

8<br />

9<br />

10<br />

11<br />

12<br />

13<br />

14<br />

15<br />

16<br />

17<br />

18<br />

19<br />

20<br />

21<br />

22<br />

23<br />

24<br />

25<br />

26<br />

27<br />

28<br />

A B C D E F G h I J K<br />

siRNA<br />

Treatment Conc.<br />

Cq<br />

GAPDH<br />

Table 1. Detailed ΔΔCq Example<br />

Cq<br />

ALDOA<br />

Column A: Treatment on the cell.<br />

ΔCq<br />

=(CqALDOA<br />

- CqGAPDH)<br />

ΔCq<br />

Expression<br />

=2 ^-ΔCq<br />

Column B: Final concentration (Conc) <strong>of</strong> siRNA [ALDOA or Non-Targeting Control (NTC)] or Not Applicable (N/A) for Mock and<br />

Untreated control samples.<br />

Column C: Cq value reported by s<strong>of</strong>tware for GAPDH (REF)<br />

Column D: Cq value reported by s<strong>of</strong>tware for ALDOA (TAR)<br />

Column E: Normalize Cq values for all TAR samples to the REF gene <strong>of</strong> its corresponding sample. This is expressed as the<br />

difference in Cq values for target (Column D) and reference (Column C) messages, ∆Cq.<br />

Column F: Exponentially transform ∆Cq to ∆Cq Expression for each biological replicate; 2 raised to the -∆Cq (Column E) yields ∆Cq<br />

Expression. Note the base <strong>of</strong> 2 assumes 100 % qPCR amplification efficiency for all reactions, or a doubling <strong>of</strong> amplicon<br />

with each subsequent qPCR cycle.<br />

Column G: Mean <strong>of</strong> ∆Cq Expression replicates (Column F).<br />

Mean ΔCq<br />

Expression<br />

Average<br />

Replicates<br />

Column H: Standard deviation <strong>of</strong> the mean for ∆Cq Expression replicates.<br />

ΔCq<br />

Expression<br />

StdDev<br />

StdDev<br />

Replicates<br />

ΔΔCq<br />

Expression<br />

ΔΔCq<br />

Expression<br />

StdDev<br />

Normalize to NTC<br />

Column I: Normalize the TAR Mean ∆Cq Expression to that <strong>of</strong> the Non-Targeting Control to obtain ∆∆Cq Expression. This is<br />

expressed as the ratio <strong>of</strong> the targeted Mean ∆Cq Expression to that <strong>of</strong> the non-targeted for samples <strong>of</strong> corresponding<br />

concentration. For MT and UT controls – that do not have association with concentration – normalize to the lowest<br />

concentration NTC Mean ∆Cq Expression.<br />

Column J: To find the standard deviation <strong>of</strong> ∆∆Cq Expression, divide the standard deviation <strong>of</strong> the targeted sample’s Mean ∆Cq<br />

Expression (Column H) by that <strong>of</strong> the Non-Targeting Control sample <strong>of</strong> corresponding concentration (Column G). The<br />

standard deviation <strong>of</strong> ∆∆Cq Expression for MT and UT controls is found by dividing their Mean ∆Cq Expression standard<br />

deviation’s (Column H) by the Mean ∆Cq Expression <strong>of</strong> the lowest concentration for the non-targeting group (Column G).<br />

Column K: Percent knockdown (%KD) is calculated by subtracting the normalized ∆∆Cq Expression from 1 (defined by the level <strong>of</strong><br />

expression for untreated sample) and multiplying by 100.<br />

%KD<br />

=(1-<br />

ΔΔCq)*100<br />

ALDOA 10 nM 20.6 27.6 7.01 0.008 0.009 0.002 0.027 0.004 97<br />

20.8 27.3 6.54 0.011 =G5/G14 =H5/G14<br />

20.9 27.6 6.69 0.010<br />

1 nM 20.7 25.6 4.89 0.034 0.034 0.003 0.111 0.010 89<br />

20.6 25.4 4.75 0.037 =G8/G17 =H8/G17<br />

20.6 25.6 5.00 0.031<br />

0.1 nM 20.7 23.5 2.82 0.142 0.123 0.016 0.394 0.052 61<br />

20.6 23.7 3.10 0.117 =G11/G20 =H11/G20<br />

20.4 23.6 3.17 0.111<br />

NTC 10 nM 20.5 22.2 1.72 0.304 0.349 0.051 1.000 0.145<br />

21.2 22.5 1.31 0.403 =G14/G14 =H14/G14<br />

21.0 22.5 1.56 0.339<br />

1 nM 21.2 22.5 1.37 0.387 0.306 0.073 1.000 0.239<br />

20.7 22.5 1.81 0.285 =G17/G17 =H17/G17<br />

20.6 22.6 2.03 0.245<br />

0.1 nM 21.9 23.4 1.48 0.358 0.312 0.045 1.000 0.145<br />

20.7 22.6 1.90 0.268 =G20/G20 =H20/G20<br />

20.5 22.2 1.69 0.310<br />

Mock<br />

Transfected<br />

N/A 20.8 22.1 1.29 0.409 0.364 0.077 1.168 0.247<br />

20.2 22.1 1.86 0.275 =G23/G20 =H23/G20<br />

20.9 22.2 1.29 0.409<br />

Untreated N/A 20.7 22.4 1.69 0.310 0.331 0.039 1.062 0.125<br />

21.0 22.4 1.41 0.376 =G26/G20 =H26/G20<br />

20.8 22.5 1.70 0.308<br />

INsights | Issue 24 | Technical Note<br />

www.thermo.com/solaris


and mock transfected and untreated<br />

controls through this ∆∆Cq method.<br />

Results and Conclusions<br />

Calculations using the ∆∆Cq<br />

method described here revealed<br />

dose-dependent silencing <strong>of</strong> ALDOA<br />

message with 61 %, 89 % and 97<br />

% knockdown when cells were<br />

treated with 0.1, 1 and 10 nM<br />

final concentration <strong>of</strong> the targeting<br />

SMARTpool siRNA, respectively<br />

[normalized to GAPDH REF (Figure<br />

3)]. Similar results were obtained<br />

with the REF gene, RPS18 (data<br />

not shown). Comparison <strong>of</strong> mock<br />

transfected and Non-Targeting<br />

siRNA samples to untreated<br />

samples indicated no significant<br />

impact <strong>of</strong> the transfection reagent<br />

or siRNA treatment, respectively,<br />

on expression <strong>of</strong> the REF genes<br />

detected in this experiment.<br />

In summary, the utility <strong>of</strong><br />

this ∆∆Cq method has been<br />

demonstrated in the context <strong>of</strong> an<br />

RNAi experiment for calculating<br />

relative gene expression from<br />

Cq values obtained from qPCR<br />

analysis. By normalizing changes<br />

between the target and reference<br />

genes within wells, and by<br />

normalizing this ∆Cq Expression<br />

to that <strong>of</strong> a control sample for the<br />

experimental treatment, the method<br />

described here yields relative gene<br />

expression values that account<br />

for both experimental and nonexperimental<br />

variation that may<br />

otherwise introduce bias in results.<br />

Relative Gene Expression<br />

1.4<br />

1.2<br />

1<br />

0.8<br />

0.6<br />

0.4<br />

0.2<br />

0<br />

97 %<br />

10 nM<br />

89 %<br />

1 nM<br />

61 %<br />

0.1 nM<br />

10 nM<br />

www.thermo.com/solaris<br />

1 nM<br />

0.1 nM<br />

Mock<br />

Transfected<br />

ALDOA Non-Targeting Controls Controls<br />

Untreated<br />

Reference<br />

1. Bustin, Stephen A., ed. A-Z <strong>of</strong><br />

Quantitative PCR. La Jolla, CA:<br />

International University Line,<br />

2004-2006. Print.<br />

Troubleshooting<br />

For technical information or<br />

troubleshooting contact Thermo<br />

Scientific Genomics Tech Support:<br />

In North America (US, Canada,<br />

Central/South America)<br />

Techservice.genomics@<br />

therm<strong>of</strong>isher.com<br />

+1 (800) 235-9880<br />

In Europe (EU, Middle East, Africa)<br />

Techservice.emea.genomics@<br />

therm<strong>of</strong>isher.com<br />

(+)44 1372 840410<br />

In Other Countries<br />

www.thermo.com/<br />

dharmacondistributors<br />

Copyright © 2010 Thermo Fisher<br />

Scientific, Inc. All Rights Reserved.<br />

Literature Code: 00025233B01U<br />

Figure 3. siRNA-mediated silencing <strong>of</strong><br />

ALDOA was assessed using a ∆∆Cq method<br />

to determine relative gene expression from<br />

qPCR data with GAPDH as an endogenous<br />

REF gene. The cells exhibited siRNA dosedependent<br />

knockdown <strong>of</strong> ALDOA message,<br />

with mRNA reduction by 61 %, 89 % and 97<br />

% when cells were treated with 0.1, 1 and<br />

10 nM final concentration <strong>of</strong> the targeting<br />

SMARTpool siRNA, respectively. Comparison<br />

<strong>of</strong> mock transfected and Non-Targeting<br />

siRNA to Untreated samples suggests that<br />

there is no significant effect <strong>of</strong> transfection<br />

reagent or transfection reagent plus siRNA,<br />

respectively, on the cell.<br />

INsights | Issue 24 | Technical Note


High Performance RT-qPCR Using Thermo<br />

Scientific Solaris qPCR Gene Expression Reagents<br />

for Assessing Relative Gene Expression<br />

Ben Jackson and Amanda Haas, Thermo Fisher Scientific, Lafayette, CO, USA<br />

Introduction<br />

Quantitative real-time PCR<br />

(qPCR) and reverse transcription<br />

qPCR (RT-qPCR) are powerful<br />

techniques for determining the<br />

abundance <strong>of</strong> cellular nucleic<br />

acids including DNA, messenger<br />

RNA and several forms <strong>of</strong> noncoding<br />

RNAs (e.g., microRNAs).<br />

While routinely used in a wide<br />

range <strong>of</strong> applications including<br />

gene expression analysis, RNA<br />

interference (RNAi) knockdown<br />

assessment, microarray validation<br />

and single nucleotide polymorphism<br />

(SNP) genotyping, the validity <strong>of</strong><br />

qPCR relies upon careful attention<br />

to experimental design, employment<br />

<strong>of</strong> appropriate controls and<br />

adoption <strong>of</strong> robust methods for<br />

detection <strong>of</strong> the amplified products.<br />

In this Technical Note we describe<br />

some <strong>of</strong> the challenges associated<br />

with qPCR technologies and<br />

illustrate how Thermo Scientific<br />

Solaris qPCR Gene Expression<br />

reagents can be used to accurately<br />

and consistently quantify changes in<br />

gene expression.<br />

In February <strong>of</strong> 2009, Bustin et<br />

al. highlighted the variability that<br />

can be introduced at different stages<br />

<strong>of</strong> qPCR protocols including RNA<br />

extraction, reverse transcription and<br />

amplification procedures. To ensure<br />

reproducibility and consistency<br />

across labs, the authors proposed<br />

a set <strong>of</strong> guidelines, the Minimal<br />

INsights | Issue 24 | Technical Note<br />

Information for Publication <strong>of</strong><br />

Quantitative Real-time PCR<br />

Experiments (MIQE) 1 , which<br />

provide a framework for<br />

performing and reporting future<br />

qPCR experiments. Specifically,<br />

the MIQE guidelines identify<br />

best practices for achieving a<br />

desired level <strong>of</strong> assay performance.<br />

Key attributes <strong>of</strong> optimal assay<br />

performance generally include<br />

attaining an amplification efficiency<br />

<strong>of</strong> ≥ 90 % and a dynamic range <strong>of</strong><br />

5 or 6 log 10 template concentrations<br />

with an r 2 value <strong>of</strong> ≥ 0.995 for<br />

amplification <strong>of</strong> gene targets with<br />

sufficiently high endogenous<br />

expression. To consistently attain<br />

these goals, it is essential that<br />

the qPCR reagents chosen for<br />

experiments provide adequate levels<br />

<strong>of</strong> efficiency, reproducibility and<br />

sensitivity.<br />

<strong>Detection</strong> <strong>of</strong> a qPCR product<br />

typically relies upon the use <strong>of</strong><br />

intercalating dyes or probe-based<br />

technologies (e.g., hydrolysis<br />

probes). The recently launched<br />

reagents <strong>of</strong>fer probes that integrate<br />

a minor groove binder (MGB)<br />

moiety and SuperBases, which<br />

increase probe annealing strength,<br />

minimize secondary structure<br />

and improve overall mismatch<br />

discrimination. Together with the<br />

probe’s FAM reporter and Eclipse ®<br />

Dark Quencher technologies,<br />

MGB and SuperBases lead to<br />

Table 1. Benefits <strong>of</strong> using Solaris Assays for gene expression analysis.<br />

the production <strong>of</strong> specific and<br />

consistent assay results with<br />

reduced overall background signal.<br />

An additional enhancement<br />

<strong>of</strong>fered by the Solaris technology<br />

involves the design algorithm used<br />

to identify gene-specific, primerprobe<br />

amplification sites. The<br />

algorithm has been developed<br />

to design qPCR assays based on<br />

optimal functionality, specificity<br />

and splice variant coverage. The<br />

result is a single recommended<br />

high performance assay for each<br />

target gene making assay selection<br />

rapid and simple. Because the<br />

algorithm incorporates the<br />

enhanced annealing properties <strong>of</strong><br />

MGB and Superbases, as well as<br />

thorough BLAST analysis, Solaris<br />

pre-designed primer-probe sets<br />

(together with the complementary<br />

Solaris Master Mix) provide highly<br />

specific qPCR under universal<br />

thermal cycling conditions. Each<br />

Solaris Assay is designed to detect a<br />

“consensus” region common to all<br />

known splice variants <strong>of</strong> any gene<br />

– thus only one assay is required<br />

per gene target. Moreover, the<br />

Solaris algorithm enables selection<br />

<strong>of</strong> primer-probe sets that can be<br />

run using a single universal thermal<br />

cycling protocol, requiring little to<br />

no optimization for each new assay.<br />

Importantly, the sequence <strong>of</strong> the<br />

probe and primer pair is provided<br />

with every assay, as specified in the<br />

Features Benefits<br />

Pre-designed qPCR assays Time savings<br />

Assay sequences provided Publish with sequence data<br />

>97% human and mouse genome coverage Complete pathway studies<br />

Complementary master mix Complete product system for optimal results<br />

Detect all known splice variants <strong>of</strong> target gene Comprehensive analysis with one assay<br />

Universal thermal cycling conditions No preliminary optimization required<br />

Technical<br />

Note<br />

www.thermo.com/solaris


MIQE guidelines, thereby increasing<br />

confidence in the data and<br />

facilitating journal publication. The<br />

Solaris Master Mix also contains<br />

an inert blue dye that enables<br />

researchers to easily visualize small<br />

volumes and thereby minimize<br />

pipetting errors. Together, the<br />

features associated with the Solaris<br />

technology facilitate ease-<strong>of</strong>-use and<br />

minimize the aspects <strong>of</strong> variability<br />

highlighted by Bustin and colleagues<br />

(Table 1).<br />

Two studies were conducted to<br />

demonstrate the high performance<br />

<strong>of</strong> Solaris qPCR reagents. In the<br />

first experiment, twenty different<br />

Solaris primer-probe sets were used<br />

to amplify transcripts from a cDNA<br />

library derived from human total<br />

RNA from ten different cell lines.<br />

Subsequent analysis compared the<br />

dynamic range, r 2 or error rate<br />

value and amplification efficiency <strong>of</strong><br />

Solaris technology with TaqMan ®<br />

(Applied Biosciences) technology.<br />

In the second experiment, Solaris<br />

Assays were used to detect RNAimediated<br />

mRNA knockdown.<br />

Briefly, seven expressed genes<br />

were individually targeted for<br />

silencing using highly specific<br />

small interfering RNA (siRNA)<br />

reagents and the overall levels <strong>of</strong><br />

transcript knockdown were assessed<br />

using Solaris and TaqMan qPCR<br />

technologies.<br />

materials and methods<br />

Experiment I. Performance <strong>of</strong><br />

Solaris and TaqMan qPCR Assays<br />

Generation <strong>of</strong> cDNA and qPCR<br />

amplification: Stratagene ® qPCR<br />

Human Reference Total RNA<br />

(Thermo Scientific Cat # 750500;<br />

100 ng) was reverse transcribed<br />

using the Thermo Scientific<br />

Verso cDNA Synthesis kit as per<br />

manufacturer’s instructions with<br />

a 3:1 mixture <strong>of</strong> random hexamer<br />

and oligo-dT primers<br />

(Thermo Scientific Cat # AB1453;<br />

20 μL reaction volume). For<br />

subsequent amplification<br />

experiments, one microliter <strong>of</strong><br />

the resulting cDNA was serially<br />

diluted (six 10-fold dilutions).<br />

All <strong>of</strong> the reported amplification<br />

studies were performed in technical<br />

triplicates. qPCR analysis was<br />

performed across the twenty gene<br />

set using either Solaris qPCR Gene<br />

Expression Assays and Solaris<br />

qPCR Master Mix, (Thermo<br />

Scientific Cat # AB4350) or<br />

TaqMan Gene Expression Assays<br />

(with Applied Biosystems Gene<br />

Expression Master Mix, Cat #<br />

4369514). All experiments were<br />

carried out in 12.5 μL reaction<br />

volumes. The final oligonucleotide<br />

concentrations for Solaris Assays<br />

were 800 nM each primer and 200<br />

nM <strong>of</strong> MGB-probe. The final oligo<br />

concentration for TaqMan assays<br />

was 900 nM for each primer and<br />

250 nM for the probe. For the<br />

Solaris Assays a standard (universal)<br />

qPCR thermal cycling protocol<br />

was employed: DNA polymerase<br />

activation at 95ºC, 15 minutes, 1<br />

Assay Dynamic<br />

Range<br />

cycle; denaturation at 95ºC, 15<br />

seconds, annealing/extension at<br />

60ºC, 60 seconds, 40 cycles. For the<br />

TaqMan assays the thermal cycling<br />

conditions were: 50°C, 5 minutes,<br />

1 cycle; 95°C, 10 minutes, 1 cycle;<br />

denaturation at 95ºC, 15 seconds,<br />

annealing/extension at 60ºC, 60<br />

seconds, 40 cycles. Samples were<br />

analyzed on an Applied Biosystems<br />

Prism ® 7900HT qPCR instrument<br />

(384-well format) or the Roche<br />

Lightcycler ® 480 (384-well format)<br />

in white (Thermo Scientific<br />

Cat # AB-1310/W, Roche<br />

Cat # 04 729 692 001) or clear<br />

plastic plates (Applied Biosystems<br />

Cat # 4309849).<br />

Solaris (ABI7900hT) Taqman (Roche LC480)<br />

r 2 %<br />

Efficiency<br />

Dynamic<br />

Range<br />

error %<br />

Efficiency<br />

ALDOA 6 0.999 101 6 0.046 97<br />

ALPL 4 0.987 115 4 0.185 108<br />

B2M 6 1.000 103 6 0.146 98<br />

BACH1 5 0.999 99 6 0.174 96<br />

CDC20 4 0.998 103 5 0.144 95<br />

CDC45L 5 1.000 100 5 0.169 103<br />

CDH1 4 0.996 102 4 0.179 93<br />

CFD 4 0.998 101 4 0.086 100<br />

GAPDH 5 0.999 98 6 0.067 100<br />

HPRT1 5 0.998 105 4 0.034 85<br />

MAPK8 4 0.998 98 4 0.120 84<br />

OCRL 4 0.996 112 4 0.086 99<br />

RINT1 4 0.998 99 4 0.256 99<br />

RPLP2 6 1.000 101 5 0.102 101<br />

RPS18 6 0.996 98 6 0.081 103<br />

RUNX2 4 1.000 110 4 0.166 113<br />

SLC25A6 6 0.998 104 5 0.054 95<br />

SPP1 4 1.000 97 5 0.110 94<br />

TMEM175 4 1.000 98 3 0.009 84<br />

ZEB1 4 1.000 110 4 0.077 103<br />

Mean 103% Mean 98%<br />

Standard<br />

Deviation<br />

5.3<br />

Standard<br />

Deviation<br />

7.4<br />

Table 2. Comparison <strong>of</strong> Solaris and TaqMan qPCR assay performance for expression<br />

analysis <strong>of</strong> twenty target genes. Dynamic range was determined using six 10-fold dilutions<br />

<strong>of</strong> cDNA template and high r 2 values (Applied Biosystems s<strong>of</strong>tware) or low error rate<br />

(Roche s<strong>of</strong>tware) and the amplification efficiencies calculated using the equation: %<br />

efficiency = 10 -1/slope -1.<br />

www.thermo.com/solaris INsights | Issue 24 | Technical Note


Experiment II. Using Solaris qPCR<br />

Reagents in mRNA Knockdown<br />

Studies<br />

Tissue Culture and mRNA<br />

knockdown: HeLa cells (ATCC<br />

No. CCL-2) were plated at a<br />

density <strong>of</strong> 10,000 cells per well<br />

(96-well format, see http://www.<br />

thermo.com/com/cda/ product/<br />

detail/1,10143278,00.html for<br />

culture conditions). After twentyfour<br />

hours, cells were transfected<br />

with pools <strong>of</strong> siRNAs targeting<br />

seven different genes (Thermo<br />

Scientific Dharmacon<br />

ON-TARGETplus SMARTpool<br />

reagents, 10 and 1 nM final<br />

concentrations) using the<br />

Thermo Scientific DharmaFECT<br />

1 transfection reagent (Thermo<br />

Scientific Cat # T-2001-03; 0.2 μL/<br />

well).<br />

Twenty-four hours posttransfection,<br />

the level <strong>of</strong> mRNA<br />

knockdown was assessed using<br />

both the Solaris and TaqMan<br />

technologies. Transfection controls<br />

used in these experiments included<br />

untreated and transfection<br />

reagent-treated cells (mock) as<br />

well as cells transfected with the<br />

ON-TARGETplus Non-Targeting<br />

Control siRNA Pool (Thermo<br />

Scientific Cat # D-001810-10; 10<br />

nM and 1 nM final concentrations).<br />

Target expression relative to controls<br />

1.2<br />

1.0<br />

0.8<br />

0.6<br />

0.4<br />

0.2<br />

0.0<br />

10 nM<br />

Solaris Assays<br />

TaqMan Assays<br />

1 nM<br />

10 nM<br />

1 nM<br />

INsights | Issue 24 | Technical Note<br />

10 nM<br />

Quantification <strong>of</strong> Gene Silencing<br />

Using Solaris Reagents: To assess<br />

the level <strong>of</strong> mRNA knockdown,<br />

RNA was first isolated from each<br />

well using the Promega SV 96<br />

Total RNA Isolation System<br />

(Thermo Scientific Cat # Z3505).<br />

cDNA synthesis followed (Verso<br />

cDNA Synthesis kit, 5 μL <strong>of</strong> the<br />

RNA eluate from each well into<br />

a 20 μL reaction volume) and<br />

gene expression analysis was<br />

subsequently performed on each<br />

<strong>of</strong> seven targeted genes using<br />

both the Solaris and TaqMan<br />

technologies (see above for reagents<br />

and conditions). All experiments<br />

were performed in biological<br />

triplicate and expression data was<br />

normalized, first to the GAPDH<br />

reference gene for well-to-well<br />

variation and then to the Non-<br />

Targeting Control siRNA-treated<br />

samples using a ∆∆Cq method 2 .<br />

Final data output is reported as a<br />

percentage <strong>of</strong> the expression level<br />

<strong>of</strong> each gene <strong>of</strong> interest in Non-<br />

Targeting Control samples matched<br />

to final concentration <strong>of</strong> siRNAs.<br />

Results<br />

Experiment I. Performance <strong>of</strong><br />

Solaris and TaqMan Assays<br />

The results generated from both<br />

Solaris and TaqMan qPCR gene<br />

1 nM<br />

10 nM<br />

1 nM<br />

10 nM<br />

1 nM<br />

10nM<br />

1nM<br />

expression assays were compared<br />

by focusing on three criteria: 1)<br />

the dynamic range <strong>of</strong> the assay, 2)<br />

the coefficient <strong>of</strong> determination<br />

(r 2 values or error rates values),<br />

and 3) the amplification efficiency<br />

(expressed as a percent value) across<br />

the twenty gene set investigated<br />

in the study. Both amplification<br />

technologies showed comparable<br />

dynamic range, determined by<br />

high r 2 values or low error rates<br />

depending on analysis s<strong>of</strong>tware,<br />

demonstrating similar sensitivity<br />

for the gene set (Table 2). The<br />

amplification efficiencies for Solaris<br />

Assays averaged 103 % over the<br />

collection <strong>of</strong> genes assessed with<br />

a standard deviation <strong>of</strong> 5.3 %.<br />

Together, these data demonstrate<br />

that over these three critical<br />

parameters <strong>of</strong> qPCR (dynamic<br />

range, r 2 value/error rate, and<br />

amplification efficiency), Solaris<br />

and TaqMan assays exhibit<br />

concordance.<br />

Experiment II. Using Solaris qPCR<br />

Reagents in mRNA Knockdown<br />

Studies<br />

An analysis <strong>of</strong> the level <strong>of</strong><br />

mRNA knockdown induced by<br />

ON-TARGETplus siRNA reagents<br />

further demonstrates the sensitivity<br />

<strong>of</strong> the Solaris qPCR technology.<br />

CTDSPL MIDN OCRL PGK1 PPIH ALDOA PPIB Non-Targeting<br />

Control<br />

10nM<br />

1nM<br />

10 nM<br />

1 nM<br />

mock<br />

untreated<br />

Controls<br />

Figure 1. Comparison <strong>of</strong> Solaris and TaqMan technologies for assessment <strong>of</strong> gene silencing<br />

ON-TARGETplus siRNAs were used to knockdown expression <strong>of</strong> seven genes <strong>of</strong> interest. Solaris qPCR Gene Expression Assays (yellow bars)<br />

and TaqMan assays (blue bars) were used to amplify and then calculate the relative gene expression following knockdown. Both detection<br />

technologies result in calculation <strong>of</strong> the same level <strong>of</strong> mRNA knockdown. Error bars are from biological triplicates.<br />

www.thermo.com/solaris


Across the seven gene set, Solaris<br />

and TaqMan qPCR assays report<br />

near-equivalent levels <strong>of</strong> gene<br />

silencing (Figure 1). Additionally,<br />

the levels <strong>of</strong> knockdown are<br />

comparable at both the 1 nM and<br />

10 nM concentrations <strong>of</strong> siRNA<br />

– demonstrating the effectiveness<br />

<strong>of</strong> ON-TARGETplus siRNA and<br />

detection <strong>of</strong> relative gene expression<br />

using Solaris reagents. Furthermore,<br />

knockdown was reproducible as<br />

judged by the error bars showing<br />

good consistency among biological<br />

replicates.<br />

Conclusions<br />

The MIQE guidelines proposed<br />

by Bustin et al. highlight multiple<br />

attributes that are necessary<br />

for qPCR experiments to be<br />

robust and comparable among<br />

experiments and sites. These include<br />

attention to experimental design<br />

(e.g., numbers <strong>of</strong> biological and<br />

technical replicates), optimization<br />

<strong>of</strong> steps associated with reverse<br />

transcription, mRNA quality<br />

control and primer/amplicon<br />

design. Preferably, assays have<br />

an amplification efficiency <strong>of</strong> ≥<br />

90 %, an r 2 value <strong>of</strong> ≥ 0.995 and<br />

a dynamic range <strong>of</strong> 5 or 6 log 10<br />

concentrations for amplification<br />

<strong>of</strong> gene targets with sufficiently<br />

high expression. As demonstrated<br />

in this Technical Note, Solaris<br />

qPCR reagents meet all <strong>of</strong> these<br />

standards. Across the twenty<br />

gene study reported above, Solaris<br />

reagents provided 103 ± 5.3 %<br />

efficiency in amplification, r 2 values<br />

<strong>of</strong> 0.996-1.000 and dynamic ranges<br />

<strong>of</strong> 4-6 log 10 , depending upon the<br />

endogenous expression levels <strong>of</strong> the<br />

gene target. Importantly, the Solaris<br />

technology has been effectively used<br />

to assess the levels <strong>of</strong> gene silencing<br />

in RNAi-mediated knockdown<br />

experiments. Together, these two<br />

applications demonstrate the<br />

successful design and integration <strong>of</strong><br />

MGB and SuperBase technologies<br />

into the Solaris qPCR platform<br />

and identify Solaris reagents as a<br />

validated tool for qPCR studies.<br />

High performance Solaris qPCR<br />

Gene Expression reagents are<br />

unique and <strong>of</strong>fer key advantages.<br />

These include rapid, straightforward<br />

assay selection, provision <strong>of</strong> primer<br />

and probe sequence information<br />

to the researcher and minimization<br />

<strong>of</strong> pipetting errors resulting in high<br />

performance qPCR gene expression<br />

assays for increased confidence in<br />

experimental data and results.<br />

References<br />

1. The MIQE Guidelines: Minimum<br />

Information for Publication<br />

<strong>of</strong> Quantitative Real-time<br />

PCR Experiments, Stephen A.<br />

Bustin et. al., Clinical Chemistry,<br />

55:4, 611 – 622 (2009)<br />

2. Demonstration <strong>of</strong> a ∆∆Cq Calculation<br />

Method to Compute<br />

Relative Gene Expression from<br />

qPCR Data, Josh Haimes and<br />

Melissa Kelley, Thermo Scientific<br />

Technical Note, http://www.<br />

dharmacon.com/uploadedFiles/<br />

Home/Resources/Product_Literature/delta_cq_solaris_tech_<br />

note.pdf . March 2010<br />

Website<br />

www.thermo.com/solaris<br />

Ancillary Reagents<br />

Assay Solaris<br />

Assay Cat #<br />

Troubleshooting<br />

For technical information or<br />

troubleshooting contact Thermo<br />

Scientific Genomics Tech Support:<br />

In North America (US, Canada,<br />

Central/South America)<br />

Techservice.genomics@<br />

therm<strong>of</strong>isher.com<br />

+1 (800) 235-9880<br />

In Europe (EU, Middle East, Africa)<br />

Techservice.emea.genomics@<br />

therm<strong>of</strong>isher.com<br />

(+)44 1372 840410<br />

In Other Countries<br />

www.thermo.com/<br />

dharmacondistributors<br />

Copyright © 2010 Thermo Fisher<br />

Scientific, Inc. All Rights Reserved.<br />

Literature Code:00025238C01U<br />

www.thermo.com/solaris INsights | Issue 24 | Technical Note<br />

Taqman<br />

Assay ID<br />

ON-TAR-<br />

GETplus<br />

SmARTpool<br />

siRNA<br />

Cat #<br />

ALDOA AX-010376-00 Hs00765620_m1 L-010376-00<br />

ALPL AX-008658-00 Hs01029144_m1<br />

B2M AX-004366-00 Hs99999907_m1<br />

BACH1 AX-007750-00 Hs00895421_m1<br />

CDC20 AX-003225-00 Hs00426680_mH<br />

CDC45L AX-003232-00 Hs00185895_m1<br />

CDH1 AX-003877-00 Hs01023895_m1<br />

CFD AX-005848-00 Hs00157263_m1<br />

CTDSPL AX-020003-00 Hs00195146_m1 L-020003-02<br />

GAPDH AX-004253-00 Hs99999905_m1<br />

HPRT1 AX-008735-00 Hs99999909_m1<br />

MAKP8 AX-003514-00 Hs01548508_m1<br />

MIDN AX-023894-00 Hs00997804_m1 L-023894-01<br />

OCRL AX-010026-00 Hs00914719_m1 L-010026-00<br />

PGK1 AX-006767-00 Hs99999906_m1 L-006767-00<br />

PPIB AX-004606-00 Hs00168719_m1 L-004606<br />

PPIH AX-008907-00 Hs00366740_m1 L-008907-01<br />

RINT1 AX-004976-00 Hs00222515_m1<br />

RPLP2 AX-004314-00 Hs01115130_g1<br />

RPS18 AX-011890-00 Hs02387368_g1<br />

RUNX2 AX-012665-00 Hs00231692_m1<br />

SLC25A6 AX-007487-00 Hs00745067_s1<br />

SPP1 AX-012558-00 Hs00959010_m1<br />

TMEM175 AX-014856-00 Hs00260494_m1<br />

ZEB1 AX-006564-00 Hs00611018_m1


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organism to investigate virulence mechanisms <strong>of</strong> a wide<br />

part <strong>of</strong> our custom PCR services, which <strong>of</strong>fer customized PCR or<br />

range <strong>of</strong> medically important pathogens, particularly<br />

qPCR (for plastics and reagent) solutions to enhance conven-<br />

due to the evolutionary conservation <strong>of</strong> innate mechaience<br />

and dramatically shorten processing times.<br />

nisms <strong>of</strong> host defence (Brennan & Anderson, 2004).<br />

Pre-aliquoted plates can significantly streamline processes<br />

The Sheffield based group made 29 copies <strong>of</strong> the<br />

and increase PCR productivity. All <strong>of</strong> our PCR or qPCR mixes are<br />

dsRNA library (containing >18,000 dsRNA probes)<br />

available pre-aliquoted into our high quality PCR plastics, includ-<br />

using a set <strong>of</strong> pre-designed primers provided by another<br />

ing individual tubes, strips, 96-well and 384-well PCR plates.<br />

leading Drosophila laboratory and arrayed these into<br />

Precise, state-<strong>of</strong>-the-art instruments in our PCR-focussed<br />

384-well plates. The facility uses these state-<strong>of</strong>-the<br />

manufacturing facility ensure reliable, quality controlled, pre-<br />

art, genome-wide, cell-based RNAi screens for the<br />

filled PCR plastics that will save time, money and effort for high<br />

study <strong>of</strong> functional genomics in Drosophila cells.<br />

throughput laboratories.<br />

Our custom PCR services also include custom barcoded PCR<br />

Why perform screens with<br />

plates. All Thermo Scientific ABgene fully skirted and semi-skirt-<br />

a Drosophila library?<br />

ed PCR plates are available with custom barcodes, which deliver<br />

Drosophila cells take up dsRNA fragments, with little<br />

reliable reading performance and industry-leading durability for<br />

systemic response, and demonstrate knock-down <strong>of</strong><br />

secure and efficient plate tracking. Further information about<br />

endogenous mRNA levels to


in silico<br />

design <strong>of</strong><br />

amplicons<br />

Boutros Lab The Sheffield RNAi Screening Facility<br />

First PCR<br />

Second<br />

PCR<br />

Figure1: A schematic diagram detailing an overview <strong>of</strong><br />

the process used to create the dsRNA probes.<br />

plates, which can be divided into functional categories.”<br />

“Two research groups have already used<br />

Large<br />

volume PCR<br />

our kinase and phosphatase sub-libraries<br />

for RNAi screens,” Steve continues.<br />

“Such screens can help to progress our understanding<br />

<strong>of</strong> human disease. One screen that we performed<br />

recently, for example, was used to identify genes that have<br />

a phenotype similar to the knock-down <strong>of</strong> Parkinson’srelated<br />

genes. This will help to identify genes that could<br />

potentially play a role in the Parkinson’s Disease pathway.”<br />

high throughput pcr<br />

The Facility used Thermo Scientific ABgene Pre-aliquoted<br />

PCR Plates for the high volume PCR amplification stage<br />

<strong>of</strong> the library development, even though they have a liquid<br />

handling robot at their disposal. The exact configuration<br />

ordered used Thermo Scientific ThermoPrime ReddyMix,<br />

pre-aliquoted in our 96-well skirted SuperPlates.<br />

The ability to obtain their enzyme mix pre-aliquoted<br />

into robust, high quality SuperPlates was welcomed<br />

by the Sheffield RNAi Screening Facility.<br />

“Preparing over 18,000 individual PCR reactions, all<br />

to the same degree <strong>of</strong> quality and quantity, proved to be a<br />

logistical challenge,” comments Steve. “To reduce waste<br />

and minimize pipetting errors, we chose to use ABgene<br />

Pre-aliquoted Plates. We found that they were competitively<br />

priced and gave us high quality amplicons.”<br />

“The overall benefit was great since we didn’t need to<br />

aliquot the enzyme mix ourselves. We calculated that we<br />

would lose 5% <strong>of</strong> the enzyme mix through pipetting if we<br />

had aliquoted the plates in-house, and so we saved both time<br />

and money. In addition, since the ReddyMix buffer already<br />

contained a loading dye, we didn’t need to waste pipette<br />

tips or further time in adding loading dye to the plates<br />

before we ran the products on our digitized e-gel systems”<br />

“I would recommend ABgene Pre-aliquoted Plates to<br />

anyone involved in high throughput work,” concludes<br />

Steve. “They are easy to work with, robust, and<br />

enable high throughput amplification <strong>of</strong> thousands<br />

<strong>of</strong> samples with greatly reduced handling time.”<br />

Run<br />

E-gel<br />

More about thermo scientific<br />

products used:<br />

For more info about<br />

these products go to:<br />

www.thermo.com/<br />

custompcr<br />

www.thermo.com/solaris INsights | Issue 24 | Article<br />

Run<br />

E-gel<br />

ThermoPrime Taq DnA Polymerase<br />

ThermoPrime Taq DNA Polymerase is an ultra-pure recombinant<br />

thermo-stable DNA polymerase obtained by high level expression<br />

<strong>of</strong> the Taq gene in Escherichia coli. The enhanced thermal<br />

stability <strong>of</strong> the ThermoPrime enzyme minimizes non-specific<br />

binding <strong>of</strong> template and primers. In addition, this recombinant<br />

enzyme is characterised by significantly higher purity than native<br />

Taq, resulting in superior lot-to-lot reproducibility.<br />

ReddyMix<br />

in vitro<br />

transcription<br />

Repeat<br />

Identify<br />

failed<br />

reactions<br />

Repeat<br />

ThermoPrime Taq DNA Polymerase is available with ReddyMix<br />

PCR Buffer. This unique buffer contains an inert red dye and a gel<br />

loading precipitant, which allows the PCR product to be loaded<br />

directly onto agarose gel. This saves valuable post-PCR analysis<br />

time by eliminating the need to add a loading dye following<br />

amplification. The ThermoPrime ReddyMix format has no adverse<br />

effect on the outcome <strong>of</strong> PCR, with equivalent yields to the<br />

standard ThermoPrime enzyme.<br />

ABgene SuperPlate<br />

Identify<br />

failed<br />

reactions<br />

The ABgene SuperPlate is four times more rigid than a standard<br />

96-well PCR plate, and has been specifically designed for<br />

compatability with automated/robotic systems and to ensure the<br />

integrity <strong>of</strong> PCR results. Its enhanced thermal stability<br />

minimizes plate warping (and consequent<br />

instrument failure) during thermal<br />

cycling and heat sealing, for reliable and<br />

consistent performance every time.<br />

The SuperPlate range includes<br />

plates to fit all major PCR<br />

thermal cyclers.<br />

Array<br />

screeening<br />

plates<br />

custompcr.genomics@<br />

therm<strong>of</strong>isher.com<br />

Store in<br />

freezer<br />

Building <strong>of</strong> the library<br />

CONTACT<br />

The Sheffield RNAi<br />

Screening Facility<br />

is a Wellcome Trust<br />

and University<br />

<strong>of</strong> Sheffield<br />

Department<br />

<strong>of</strong> Biomedical<br />

Sciences funded<br />

center. It is<br />

located in recently<br />

refurbished<br />

laboratories<br />

within the Alfred<br />

Denny Building at<br />

the University <strong>of</strong><br />

Sheffield and was<br />

<strong>of</strong>ficially launched<br />

at a special Opening<br />

RNAi Symposium on<br />

the 18th March 2010.<br />

Details <strong>of</strong> the facility<br />

can be obtained at<br />

www.shef.ac.uk/<br />

bms/research/rnai/<br />

index.html (www.<br />

rnai.group.sheffield.<br />

ac.uk) and groups<br />

wishing to use<br />

the facility should<br />

contact Steve:<br />

stephen.brown@<br />

sheffield.ac.uk<br />

to discuss future<br />

projects.


Basics <strong>of</strong> qPCR<br />

SUmmARY OF<br />

ThE ARTICLE<br />

Factors to<br />

consider when<br />

optimizing<br />

How to limit<br />

the likelihood <strong>of</strong><br />

contamination<br />

Normalization<br />

to an endogenous<br />

control set<br />

REFERENCES<br />

1. Nam DK, Lee S, et<br />

al. Proc Natl Acad<br />

Sci U S A. 99:6152-6<br />

(Apr 30, 2002)<br />

INsights | Issue 24 | Article<br />

kirsTeen maClean pH.d | Field appliCaTion<br />

sCienTisT | THermo sCienTiFiC genomiCs<br />

PCR is one the most widely used tools in molecular biology. Kirsteen<br />

Maclean discusses some <strong>of</strong> the considerations that are required on<br />

moving to real-time or quantitative PCR (qPCR) and gaining success.<br />

place importance<br />

on optimization<br />

The improvement <strong>of</strong> PCR techniques<br />

with real-time quantitative methods<br />

has taken PCR from what was once<br />

considered a preparatory method<br />

for molecular biology to a powerful<br />

diagnostic and analysis tool for clinical<br />

and research applications. While<br />

theoretically and indeed experimentally<br />

simplistic, there is unfortunately<br />

an enormous potential for variability.<br />

Common issues that have until<br />

recently lacked due diligence include:<br />

contamination issues, lack <strong>of</strong> appropriate<br />

controls, amplification bias, poor<br />

efficiency, repeatability and sensitivity.<br />

PCR is a doubling reaction, whereby<br />

under ideal circumstances, the quantity<br />

<strong>of</strong> the PCR product doubles with each<br />

successive PCR cycle (a PCR efficiency<br />

<strong>of</strong> 100%). To ensure a carefully<br />

optimized qPCR assay, running a<br />

standard curve using a serial-fold<br />

dilution <strong>of</strong> template DNA can help<br />

provide insight into the robustness <strong>of</strong><br />

the assay, providing important information<br />

such as the PCR efficiency (ideally<br />

90-105%), dynamic range (>6 orders <strong>of</strong><br />

magnitude), reproducibility (>0.992),<br />

sensitivity, and specificity <strong>of</strong> the assay.<br />

Factors contributing to poor PCR<br />

efficiency include overall primer design<br />

and cycling conditions, secondary structure<br />

within the amplicon (commonly<br />

attributed to G:G self annealing),<br />

amplicon length (ideally not exceeding<br />

150bp in length) and non-specific interactions<br />

such as primer-dimer formation.<br />

A re-design <strong>of</strong> primers together<br />

with a BLAST analysis is <strong>of</strong>ten the best<br />

and most straightforward<br />

strategy to improve PCR efficiency.<br />

Develop a standardized<br />

Laboratory approach<br />

Lack <strong>of</strong> qPCR reproducibility/repeatability<br />

are key issues within the scientific<br />

arena and unfortunately have lent<br />

themselves to erroneous data reporting,<br />

and in some cases retraction <strong>of</strong> top-tier<br />

publications. Poor reproducibility and<br />

repeatability can be caused by reasons<br />

including poor laboratory technique,<br />

particularly attributed to sample extraction,<br />

contamination and inaccurate pipetting.<br />

Due to the exquisite sensitivity <strong>of</strong><br />

PCR, contamination from non-template<br />

DNA present in the laboratory environment<br />

including bacteria, viruses, and<br />

human DNA represents a real problem.<br />

Precautions should therefore be taken to<br />

reduce the likelihood <strong>of</strong> contamination,<br />

the most successful including the use <strong>of</strong><br />

barrier (filter) tips, sample addition in<br />

a laminar flow cabinet, and if possible,<br />

the adoption <strong>of</strong> a special preparation<br />

area (or ‘clean-room’) for aliquoting<br />

PCR reagents prior to the addition <strong>of</strong><br />

sample material. Clean-rooms should be<br />

maintained under a positive air pressure.<br />

Some clinical laboratories additionally<br />

adopt a ‘clean’ side and a ‘DNA/<br />

RNA’ side to their laminar flow cabinet,<br />

as reducing false positives is critical to<br />

successful patient care.<br />

Another key consideration is assay<br />

linearity, in other words, the efficiency<br />

<strong>of</strong> the reverse transcriptase enzyme<br />

across a broad dynamic range. There<br />

are several priming options available:<br />

specific primers, which will only bind<br />

to the gene <strong>of</strong> interest; random primers,<br />

which bind indiscriminately and the use <strong>of</strong><br />

oligo d(T) which binds to the poly-A tail<br />

<strong>of</strong> the RNA and transcribes only RNA.<br />

Thermo Scientific Verso kits are provided<br />

with an ‘anchored’ oligo d(T) that ensures<br />

that priming occurs from the 3’ poly (A)<br />

and therefore diminishes the generation<br />

<strong>of</strong> truncated cDNAs caused by internal<br />

poly(A) priming1 . Common practices<br />

include the a combination <strong>of</strong> oligo d(T)<br />

primers and random primers which will<br />

give both high yields and long transcripts.<br />

Normalization <strong>of</strong> relative gene expression<br />

studies with the use <strong>of</strong> an appropriate<br />

endogenous control is crucial to achieving<br />

reliable data. Endogenous control<br />

genes should have a constant expression<br />

level in all the samples being compared.<br />

Unfortunately a universal control gene<br />

which is expressed at a constant level<br />

under all conditions and in all cells and<br />

tissues does not exist. Therefore, the most<br />

optimal approach for reference genes is<br />

to run a panel <strong>of</strong> potential genes on a<br />

number <strong>of</strong> representative test samples.<br />

Amplification <strong>of</strong> only the genuine<br />

target during a qPCR reaction ensures<br />

experimental specificity; however, this has<br />

been a long standing issue with the use <strong>of</strong><br />

SYBR Green I. SYBR is a DNA intercalating<br />

dye that binds indiscriminately to<br />

any dsDNA formed. To ensure adequate<br />

specificity in a SYBR assay the end user<br />

must extensively validate their primers and<br />

run a melt curve to check for primer-dimer<br />

formation. This can be avoided with the<br />

use <strong>of</strong> specific detection chemistries and<br />

well designed probes which can distinguish<br />

between nonspecific and specific targets.<br />

Different types <strong>of</strong> probes are available,<br />

and these include hydrolysis probes e.g.<br />

TaqMan (Applied Biosystems) probes, and<br />

hybridization probes e.g. Solaris probes.<br />

Considerations <strong>of</strong> all these issues and<br />

adequate optimization during each step<br />

within the qPCR workflow should ensure<br />

the end user has confidence in their results<br />

and data reporting.<br />

www.thermo.com/gen


Submit<br />

Style<br />

The MIQE Guidelines were<br />

first published early in 2009<br />

and are a recommended set <strong>of</strong><br />

guidelines, or minimal information<br />

requirement, for the submission<br />

<strong>of</strong> scientific papers containing<br />

qPCR data.<br />

In recent years there has been a lack<br />

<strong>of</strong> consensus within the scientific<br />

community with respect to experimental<br />

design, data reporting and analysis<br />

<strong>of</strong> qPCR experiments. Unfortunately,<br />

the absence <strong>of</strong> several experimental<br />

standards has raised concerns within<br />

the scientific research community over<br />

the reliability <strong>of</strong> qPCR data interpretation.<br />

In an effort to provide such<br />

standardization when reporting qPCR<br />

results, key opinion leaders in the<br />

qPCR community recently published a<br />

set <strong>of</strong> guidelines known as "The MIQE<br />

Guidelines: Minimum Information for<br />

Publication <strong>of</strong> Quantitative Real-Time<br />

PCR Experiments". The aim <strong>of</strong> this<br />

publication is to provide a benchmark<br />

for the quality assessment <strong>of</strong> a qPCR<br />

assay reported in a given publication.<br />

the essential selection<br />

The MIQE guidelines now define<br />

the minimum information required<br />

for evaluation <strong>of</strong> qPCR results, and<br />

include a checklist to be included in<br />

the initial submission <strong>of</strong> a manuscript<br />

to a publisher. That checklist includes<br />

85 specifications <strong>of</strong> which 57 are<br />

deemed essential, in that they must be<br />

reported on to ensure the relevance,<br />

accuracy, correct interpretation, and<br />

repeatability <strong>of</strong> a qPCR experiment.<br />

Briefly, the central tenets within the<br />

MIQE publication involve adoption <strong>of</strong><br />

standardized nomenclature (e.g. the use<br />

<strong>of</strong> the quantification cycle (Cq), rather<br />

than threshold cycle (Ct), crossing point<br />

(Cp), or take-<strong>of</strong>f point (TOP)) and<br />

considerations <strong>of</strong> several other issues<br />

including:<br />

• Reporting on the analytical<br />

sensitivity or minimum number<br />

<strong>of</strong> copies that can be accurately<br />

measured for a given assay;<br />

• The analytical specificity, whereby<br />

only the appropriate target is<br />

amplified and avoiding other<br />

non-specific targets or gDNA that<br />

may be present;<br />

• The overall accuracy referring<br />

to the difference between experimentally<br />

measured and actual<br />

concentrations;<br />

• Intra-assay repeatability and<br />

• Inter-assay reproducibility reflecting<br />

the precision and robustness <strong>of</strong><br />

the assay.<br />

Now, both experienced and<br />

new users to the qPCR arena can<br />

benefit from adopting these best<br />

practices to enhance the quality<br />

and consistency <strong>of</strong> data reported in<br />

the literature, and longer term, it is<br />

likely that journals will make MIQE<br />

a basic submission requirement.<br />

COMING SOON TO AN<br />

EXHIBITION NEAR YOU<br />

www.thermo.com<br />

Global events pages<br />

Did you know that we keep an up to date list <strong>of</strong> all the<br />

global scientific events that we are attending on our web<br />

site so that you know when and where to find us?<br />

In addition to detailing where and when the event<br />

will be taking place, there is <strong>of</strong>ten further information<br />

that details:<br />

• Booth location<br />

• Registration details<br />

• Details <strong>of</strong> new and featured products<br />

Stay in touch with our whereabouts and win prizes when<br />

you visit us at selected events.<br />

We are on Lab tube tV!<br />

See the latest interviews with Jeremy Gillespie<br />

(Group Products Manager):<br />

At RNAi Europe<br />

www.labtube.tv/avc-interest.aspx?i=5&c=1&v=302<br />

At Analytica<br />

www.edgefactory.com/thermo/innovation/2010/munichgene-expression-essays.html<br />

www.thermo.com/solaris<br />

INsights | Issue 24 INsights | MIQE Guidelines | Issue 24


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Scorpion is a trademark <strong>of</strong> Sigma-Aldrich. MGB-Eclipse is a trademark <strong>of</strong> Sigma-Aldrich. Stratagene is a trademark <strong>of</strong> Agilent Technologies.<br />

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INsights | Issue 24 www.thermo.com/gen

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