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There is an ongoing debate what is the best way to normalise qPCR data. Reference genes are the most common method, although single unverified reference genes invalidate the qPCR data generated. Total RNA, ribosomal RNA, and genomic DNA have been suggested as alternative methods.
Most common method. Frequently, a panel is used for normalization, e.g. [1] not just a single reference gene and including data on suitability as reference genes. Often housekeeping gene& is used here instead of reference gene but the term is poorly defined and can be misleading. It is to be noted that panels are often composed of genes that are supposed to be stable based on their function. However, more than 100 peer-reviewed articles report problems related to genes chosen from a panel, because they were not suitable for a particular context. A recent approach is to select a reference gene based on its stability across microarrays done within one''s condition of interest. There is a public tool called RefGenes that searches a microarray database of more than 50,000 arrays to identify genes that are stable across subsets of conditions. It is available at the Genevestigator website [2].
Total rRNA [3] [4], or total RNA. Drawback: rapidly dividing cells will have more rRNA and different rRNA/mRNA ratio which will c difference in cDNA synthesis not taken into account.
Genomic DNA or cell number. Drawbacks: RNA degrades faster than RNA which
sample cannot be DN efficiency of cDNA synthesis not taken into account.
Main article: Choosing reference genes for qPCR normalisation
Picking reference genes will make or break your quantification via qPCR (real time PCR). If you pick only one reference gene and your pick is not constant across different conditions or samples, your results will be skewed. Choose several reference genes and check whether they satisfy the criteria for a good reference gene. Some commonly used reference genes, like 18S and GAPDH, are known to be problematic but continue to be used.
Ajeffs 06:55, 21 April 2007 (EDT): Screen a handful of ref genes, select the most stable using genorm, bestkeeper etc, use at least 2 reference genes for subsequent reactions and normalisation. Inlcude your genorm M values when publishing qPCR data.
Main article: Choosing primers for qPCR
Choosing suitable primers is an early crucial step in your qPCR experiment. Reusing a tested primer pair from a repository or publication can save you some time. Otherwise primer selection from scratch is similar to that for a standard qualitative PCR experiment but the product size is typically much smaller (below 200nt) and the amplification characteristics of the primer have to be rigorously tested.
There are 3 common quantification methods. The standard curve method is the only one that gives you are absolute concentration. Both the Pfaffl method and the ΔΔCt method produce relative data with the Pfaffl method being superior.
requires template at known concentration (e.g. cDNA or TA cloned PCR product)
requires dilution series of known template for standard curve (more wells)
yields absolute concentrations by comparing unknown samples to known
requires that primer efficiency be known but needs to be determined only once with a standard curve or a different method
produces relative amount (e.g. treated is 2x untreated)
(name see Pfaffl 2001 PMID )
easiest, oldest, least reliable
assumes that primers for unknown and reference gene have very similar efficiency
or that v little correction is necessary (i.e. reference gene almost same level)
yields relative amounts
(Ct = point when fluorescence reading surpasses a set baseline)
A Ct difference of 1 between two samples has a different meaning depending on the efficiency of the primers used. If primers are 100% efficient, then ΔCt = 1 means one sample has twice the amount of template compared to the other. The simple ΔΔCt method, described above, often wrongly assumes perfect efficiency. It is better to experimentally verify the primer efficiency and use the Pfaffl method instead. The standard method takes the primer efficiency into account via the standard curve run with each sample. However, primer efficiencies in the standard curve dilutions and the actual samples are not necessarily the same.
Primer efficiencies can be calculated by making a dilution series, calculating a linear regression based on the data points, and inferring the efficiency from the slope of the line. For a base 10 logarithm the formulae is:
efficiency = 10^(-1/slope)
Slopes between -3.3 and -4 will thus give you estimated primer efficiencies between 100% and 78% respectively. It can happen that the calculated efficiencies are above 100% [5] [6] [7]. This may be due to incorrect template concentrations, too concentrated template, inhibition of the PCR reaction, unspecific PCR amplification, mistakes in the calculation [8], etc.
See a figure explaining the fitting process from the Hunts'' qPCR tutorial [9].
Efficiency (and Ct values) can also be calculated from the fluorescence data of a single PCR run or preferably replicates of the same PCR. The Miner algorithm (PMID ) is an example for this type of method and can be used online at [10].
Due to the small amount of liquid handled and the sensitivity of the technique, operator variability is high. Bustin reports that the same qPCR experiment repeated by 3 people using the same reagents lead to very different copy number estimations [Bustin 2002 PMID , figure 3]:
person A: 8?7 × 105
person B: 2?8 × 105 different by a factor of 3!
person C: 2?7 × 103 different by a factor of 300!!
Different lots of reagents can lead to different results. Experiment repeated by same operator 5 times, same RNA sample, values are copies/μg total RNA:
kit 1: 13±32 × 107
kit 2: 5.4±1.6 × 107 - different by a factor of 2.4
Similar experiment with old (9 months 4°C) and new probe (3 months 4°C), values are copies/μg total RNA:
old: (5.6 ± 1.3) x 103
new: (3.8 ± 0.6) x 108 - different by a factor of 100''000!!
both experiments above from [Bustin 2002 PMID , figure 4]
Partial transcript of a webcast discussing qPCR data quality
The most commonly used specialist reverse transcriptase enzyme for cDNA production is AMV reverse transcriptase. It has RNase H activity (so that RNA molecules are only transcribed once) and has a high temperature stability (to reduce RNA secondary structure and nonspecific primer annealing) [1].
Since RNA can degrade with repeated freeze-thaw steps, experimental variability is often seen during successive reverse transcription reactions of the same RNA sample [1].
Reverse transcriptase enzymes are notorious for their thermal instability. Repeated removals from the freezer can degrade the efficiency of the enzyme [1].
Producing total cDNA from total RNA can be advantageous because
cDNA is more stable than RNA so making total cDNA allows you to make multiple sequence-specific RNA measurements [1].
This approach could reduce experimental variability stemming from RNA degradation [1].
To make total cDNA
Use a polyT primer (most but not all eukaryotic mRNA) or random decamers (prokaryotic mRNA) [1].
Random decamers give longer cDNAs on average than random hexamer primers [1].
Use longer reverse transcription reaction times [1].
Ensure that the concentration of deoxynucleotides doesn''t run out [1].
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There is an ongoing debate what is the best way to normalise qPCR data. Reference genes are the most common method, although single unverified reference genes invalidate the qPCR data generated. Total RNA, ribosomal RNA, and genomic DNA have been suggested as alternative methods.
Most common method. Frequently, a panel is used for normalization, e.g. [1] not just a single reference gene and including data on suitability as reference genes. Often housekeeping gene
is used here instead of reference gene but the term is poorly defined and can be misleading. It is to be noted that panels are often composed of genes that are supposed to be stable based on their function. However, more than 100 peer-reviewed articles report problems related to genes chosen from a panel, because they were not suitable for a particular context. A recent approach is to select a reference gene based on its stability across microarrays done within one''s condition of interest. There is a public tool called RefGenes that searches a microarray database of more than 50,000 arrays to identify genes that are stable across subsets of conditions. It is available at the Genevestigator website [2].
Total rRNA [3] [4], or total RNA. Drawback: rapidly dividing cells will have more rRNA and different rRNA/mRNA ratio which will c difference in cDNA synthesis not taken into account.
Genomic DNA or cell number. Drawbacks: RNA degrades faster than RNA which
sample cannot be DN efficiency of cDNA synthesis not taken into account.
Main article: Choosing reference genes for qPCR normalisation Picking reference genes will make or break your quantification via qPCR (real time PCR). If you pick only one reference gene and your pick is not constant across different conditions or samples, your results will be skewed. Choose several reference genes and check whether they satisfy the criteria for a good reference gene. Some commonly used reference genes, like 18S and GAPDH, are known to be problematic but continue to be used.
Ajeffs 06:55, 21 April 2007 (EDT): Screen a handful of ref genes, select the most stable using genorm, bestkeeper etc, use at least 2 reference genes for subsequent reactions and normalisation. Inlcude your genorm M values when publishing qPCR data.
Main article: Choosing primers for qPCR Choosing suitable primers is an early crucial step in your qPCR experiment. Reusing a tested primer pair from a repository or publication can save you some time. Otherwise primer selection from scratch is similar to that for a standard qualitative PCR experiment but the product size is typically much smaller (below 200nt) and the amplification characteristics of the primer have to be rigorously tested.
There are 3 common quantification methods. The standard curve method is the only one that gives you are absolute concentration. Both the Pfaffl method and the ΔΔCt method produce relative data with the Pfaffl method being superior.
requires template at known concentration (e.g. cDNA or TA cloned PCR product)
requires dilution series of known template for standard curve (more wells)
yields absolute concentrations by comparing unknown samples to known
requires that primer efficiency be known but needs to be determined only once with a standard curve or a different method
produces relative amount (e.g. treated is 2x untreated) (name see Pfaffl 2001 PMID )
easiest, oldest, least reliable
assumes that primers for unknown and reference gene have very similar efficiency
or that v little correction is necessary (i.e. reference gene almost same level)
yields relative amounts (Ct = point when fluorescence reading surpasses a set baseline)
A Ct difference of 1 between two samples has a different meaning depending on the efficiency of the primers used. If primers are 100% efficient, then ΔCt = 1 means one sample has twice the amount of template compared to the other. The simple ΔΔCt method, described above, often wrongly assumes perfect efficiency. It is better to experimentally verify the primer efficiency and use the Pfaffl method instead. The standard method takes the primer efficiency into account via the standard curve run with each sample. However, primer efficiencies in the standard curve dilutions and the actual samples are not necessarily the same.
Primer efficiencies can be calculated by making a dilution series, calculating a linear regression based on the data points, and inferring the efficiency from the slope of the line. For a base 10 logarithm the formulae is:
efficiency = 10^(-1/slope) Slopes between -3.3 and -4 will thus give you estimated primer efficiencies between 100% and 78% respectively. It can happen that the calculated efficiencies are above 100% [5] [6] [7]. This may be due to incorrect template concentrations, too concentrated template, inhibition of the PCR reaction, unspecific PCR amplification, mistakes in the calculation [8], etc. See a figure explaining the fitting process from the Hunts'' qPCR tutorial [9].
Efficiency (and Ct values) can also be calculated from the fluorescence data of a single PCR run or preferably replicates of the same PCR. The Miner algorithm (PMID ) is an example for this type of method and can be used online at [10].
Due to the small amount of liquid handled and the sensitivity of the technique, operator variability is high. Bustin reports that the same qPCR experiment repeated by 3 people using the same reagents lead to very different copy number estimations [Bustin 2002 PMID , figure 3]:
person A: 8?7 × 105
person B: 2?8 × 105 different by a factor of 3!
person C: 2?7 × 103 different by a factor of 300!!
Different lots of reagents can lead to different results. Experiment repeated by same operator 5 times, same RNA sample, values are copies/μg total RNA:
kit 1: 13±32 × 107
kit 2: 5.4±1.6 × 107 - different by a factor of 2.4 Similar experiment with old (9 months 4°C) and new probe (3 months 4°C), values are copies/μg total RNA:
old: (5.6 ± 1.3) x 103
new: (3.8 ± 0.6) x 108 - different by a factor of 100''000!! both experiments above from [Bustin 2002 PMID , figure 4]
Partial transcript of a webcast discussing qPCR data quality
The most commonly used specialist reverse transcriptase enzyme for cDNA production is AMV reverse transcriptase. It has RNase H activity (so that RNA molecules are only transcribed once) and has a high temperature stability (to reduce RNA secondary structure and nonspecific primer annealing) [1].
Since RNA can degrade with repeated freeze-thaw steps, experimental variability is often seen during successive reverse transcription reactions of the same RNA sample [1].
Reverse transcriptase enzymes are notorious for their thermal instability. Repeated removals from the freezer can degrade the efficiency of the enzyme [1].
Producing total cDNA from total RNA can be advantageous because
cDNA is more stable than RNA so making total cDNA allows you to make multiple sequence-specific RNA measurements [1].
This approach could reduce experimental variability stemming from RNA degradation [1].
To make total cDNA
Use a polyT primer (most but not all eukaryotic mRNA) or random decamers (prokaryotic mRNA) [1].
Random decamers give longer cDNAs on average than random hexamer primers [1].
Use longer reverse transcription reaction times [1].
Ensure that the concentration of deoxynucleotides doesn''t run out [1].
相关实验方法
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Every PCR battle is the same: Too little amplification of your target DNA versus too much amplification of off-target DNA. But you can win the PCR battle and amaze your co-workers by mastering the use of PCR additives. PCR additives usually work one of two ways: 1) By reducing secondary DNA structures and thus increasing the amplification of your target DNA, or 2) by reducing non-specific priming and thus reducing the amplification of off-target DNA. Read below to learn which PCR additives do what, and what additives might be best for your circumstances.
Additives That Reduce Secondary Structures:
The following additives work to reduce complex secondary structure. By reducing secondary DNA structures these additives can increase the amplification of your target DNA and improve your yield of hard to amplify products, such as GC rich templates.
DMSO is thought to reduce secondary DNA structures, therefore the addition of DMSO is often recommended when amplifying GC rich templates. However, DMSO can also greatly reduce the activity of Taq polymerase. Therefore it is best to strike a balance between template accessibility and Taq activity. To find your best balance, test a variety of DMSO concentrations between 2-10%.
Non-ionic detergents
Non-ionic detergents such as 0.1-1% Triton X-100, Tween 20 or NP-40 are also thought to reduce secondary structures. The use of these additives may increase yield but may also increase non-specific amplification. Therefore while these additives may be helpful in clean low-yield PCRs they should be used cautiously in dirty PCR reactions. Another application of non-ionic detergents is to battle SDS contamination. SDS is a common carryover from the DNA extraction process and can greatly inhibit Taq polymerase. However, inclusion of 0.5% Tween-20 or -40 can neutralize the negative effect of SDS.
Betaine improves the amplification of DNA by reducing the formation of secondary structures and is often the mystery additive in commercial PCR kits. If you want to add Betaine to your PCR reaction use Betaine or Betaine mono-hydrate, NOT Betaine HCl, at a final concentration of 1.0-1.7M. Betaine may also enhance specificity by eliminating the base pair composition dependence of DNA melting.
Additives That Reduce Non-Specific Priming:
The following additives work to reduce non-specific priming. By reducing non-specific priming these additives can decrease the amplification of off-target DNA and clean up dirty PCR reactions.
Formamide is a widely used organic PCR additives. Formamide is thought to work by binding in the major and minor grooves of DNA, destabilizing the template double-helix and lower melting temperature. Formamide is usually used at 1-5%.
Tetramethyl ammonium chloride (TMAC) increases hybridization specificity and increases melting temperature. Thus TMAC can eliminate non-specific priming and potential DNA-RNA mismatch. Therefore, TMAC is often recommended in PCR conditions that use degenerate primers. It is usually used at a final concentration of 15-100mM.
Other Common Additives:
The following are common PCR additives that work in other ways to modulate PCR reactions.
Magnesium is a required cofactor for Taq polymerase. Without adequate free magnesium, Taq is inactive. However, excess magnesium can reduce Taq fidelity. The amount of free magnesium in a given PCR reactions is highly variable. Chelating agents (EDTA or citrate), dNTP concentration and proteins can all affect free magnesium. Therefore, if you are having PCR problems you should empirically test a variety of magnesium concentrations to find the ideal concentration for your particular reaction. To do this, test magnesium concentrations from 1.0–4.0mM Mg2+ in 0.5–1mM intervals.
Important Note: Multiple freeze-thaw cycles can cause magnesium chloride solutions to form concentration gradients. Therefore it is important to fully thaw and vortex your stock magnesium solution before each use.
Bovine serum albumin (BSA) is a common addition in several molecular biology applications, most notably restriction enzyme digestions and PCR. In PCR, BSA may be helpful in combating contaminants such as phenolic compounds. It is also reported to prevent reaction components from sticking to tube walls. Use up to 0.8 mg/ml of BSA in your PCR.
Boilerplate:
While the above PCR additives can improve PCR results, it is impossible to predict which additives at what concentrations are best for your PCR needs. Therefore, like always, you need to empirically test these additives yourself. I know… I know… not what you want to hear. Now stop whining and get back to the bench!
Good luck and happy PCRing!
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