[Abstract]  [Full Text]  [PDF]

Jack M. Gallup^1* and Mark R. Ackermann¹

¹ Department of Veterinary Pathology, College of Veterinary Medicine, Iowa State University. Ames, Iowa 50011-1250. USA.

* To whom correspondence should be addressed: Jack M. Gallup, Department of Veterinary Pathology, College of Veterinary Medicine, Iowa State University. Ames, Iowa 50011-1250. USA. Phone: 1-515-294-5423. Fax: 1-515-294-5844. Email: eag@iastate.edu

Biol. Proced. Online 2006;8:87-152. doi:10.1251/bpo122
Submitted: May 02, 2006; Accepted: July 10, 2006; Published: September 15, 2006.

Indexing terms: Reverse Transcription; DNA, Complementary.

The purpose of this manuscript is to discuss fluorogenic real-time quantitative polymerase chain reaction (qPCR) inhibition and to introduce/define a novel Microsoft Excel-based file system which provides a way to detect and avoid inhibition, and enables investigators to consistently design dynamically-sound, truly LOG-linear qPCR reactions very quickly. The qPCR problems this invention solves are universal to all qPCR reactions, and it performs all necessary qPCR set-up calculations in about 52 seconds (using a pentium 4 processor) for up to seven qPCR targets and seventy-two samples at a time – calculations that commonly take capable investigators days to finish. We have named this custom Excel-based file system "FocusField2-6GallupqPCRSet-upTool-001" (FF2-6-001 qPCR set-up tool), and are in the process of transforming it into professional qPCR set-up software to be made available in 2007. The current prototype is already fully functional.

Bearing in mind that it is not possible to state with absolute certainty the exact causes of qPCR inhibitory phenomena, and since more than one kind of inhibition may be present at the same time, we begin this communication by creating a list of the top five most likely sources of such inhibition – two of which (inhibition Types 2 and 3) are inherently a function of one another. We propose that all five affect either the activity of reverse transcriptase enzymes, Taq DNA polymerases, or both. In order to avoid using sample RNA (or cDNA) at dilutions permissive of or conducive to real-time qPCR inhibitory phenomena (regardless of the type of inhibition), we have created the FF2-6-001 qPCR set-up tool which is used to analyze preliminary qPCR Test Plate data generated by up to seven qPCR targets from serial progressive dilutions of representative (Stock I) RNA or cDNA mixtures all used in fluorogenic hydrolysis probe-based qPCR. Once Test Plate threshold cycle (C_T) values are obtained for each target on any given Test Plate, they are entered into the TestPlateResultsAnalysis2006.xls portions of the FF2-6-001 qPCR set-up tool which the user interacts with in order to quickly and precisely identify the useful RNA dilution ranges for each qPCR target – within these ranges which each target can be expected to amplify without inhibition, with LOG-linearity and with high efficiency. The FF2-6-001 qPCR set-up tool then applies these ranges to final qPCR reaction designs allowing the investigator to formulate high-fidelity qPCR reactions every time since the FF2-6-001 file system ensures that each real-time qPCR reaction is carried out under the most dynamically sound conditions possible for each different genomic or transcriptomic target of interest. As a result, investigators are able to consistently attain credible real-time qPCR target and housekeeper C_T values. The FF2-6-001 qPCR set-up tool is also universally adaptable to any master mix and qPCR reagent-use selection (e.g. SYBR Green, one-step and two-step, beacon, scorpion and hydrolysis probe methods) for both relative and absolute quantitative qPCR approaches. Since real-time qPCR is lauded by many as the most powerful tool in all of molecular biology for quantitative analysis of gene expression, and since it is still considered the tool of choice for validating micro-array data, any new ideas, methods or approaches that improve its precision in common practice represent important constructive advances furthering the responsible evolution of an already broadly-accepted scientific technique.

A variety of problematic inhibitory phenomena have been reported that plague qPCR assays (1). Inhibition of the enzymatic reactions involved in generating real-time qPCR signals from specific cDNA templates using specific primers, fluorogenic probes, or combinations of primers and fluorogenic probes can severely impact the precision of absolute and relative gene expression quantitative analysis. Any factor, experimental, user-introduced, environmental or otherwise, that has an impact on the activity of RT (reverse transcriptase) enzyme and/or Taq polymerase used in any one-step real-time qPCR reaction will invariably affect the results generated. In worst-case scenarios, these deficiencies go unnoticed and remain unaddressed. Recently, others have suggested that many as-yet unidentified sample-specific substances (or impurities) are often carried over as a result of different RNA isolation methods (preceding real-time qPCR of any variety) which cause RT enzyme or Taq DNA polymerase-based qPCR inhibition (1, 2). Exogenous contaminants such as glove powder and phenolic compounds from the extraction process and plastic-ware (pipette tips, tubes and plates) can also have an inhibiting effect. With regard to tissue-specific inhibition of DNA amplification, tissue type was found to be the largest source of variance of inhibitory phenomena while primer sequences appeared to have the least affect. In other words, tissue type from which total RNA was extracted had the most significant effect on PCR kinetics, thus on final threshold cycle (C_T) values (1, 4). This is thought to be caused by different kinds and amounts of cellular debris present in samples after RNA extraction (2, 3). Endogenous contaminants such as blood or fat are thought to play an important role in affecting both the PCR as well as the preceding reverse transcription reaction. Other inhibitory contaminants are thought to be hemoglobin, heme, porphyrin, heparin (from peritoneal mast cells), glycogen, polysaccharides and proteins, cell constituents, Ca²⁺, DNA or RNA concentration, and DNA (and possibly RNA) binding proteins (5-12). MicroRNA (miRNA) is not thought to be a contributing factor to qPCR inhibition since high thermocylcing temperatures (94-95°C) most likely prevent the formation of stable RNA-binding complexes which might otherwise associate with template RNA (Ambion technical support information).

Types of qPCR inhibition

Because of the severe impact inhibition can have on results, we feel it is important to address it and attempt to identify the possible form(s) that may be present or active throughout real-time pPCR procedures (37). Toward this end, based on experimental observations of the dynamics of numerous real-time qPCR reactions, we have organized qPCR inhibitory phenomena into five semi-distinct categories; Types 1 through 5 (Figs. 1-6). We describe them as: inhibition of reverse transcriptase (RT) enzyme(s) and/or Taq DNA polymerase(s) by excessive rRNA and possibly tRNA in concentrated RNA samples (sample concentration-related template inhibition; Type 1 inhibition); inhibition from method of RNA isolation due to the carryover of inhibitory biological components or molecules (RNA isolation method-related inhibition; Type 2 inhibition); inhibition arising from the type of tissue or cell that sample RNA has been isolated from (sample-specific inhibition; Type 3 inhibition); inhibition arising as a result of the interaction of a specific qPCR target template with sub-optimal concentrations, designs or any other thermodynamic factors concerning its specific probe and/or primer(s) (target-specific kinetic inhibition; Type 4 inhibition); and inhibition caused by compounds such as EDTA, GIT, TRIS, glycogen (sometimes used as a carrier agent during RNA isolation; inhibition of RT enzyme has been observed when glycogen is present in excess of 4 mg/ml during reverse transcription), (13, 14), or other user-introduced reagents (chemical inhibition; Type 5 inhibition). Although the reality of Type 6 inhibition (connoting all other as-yet unknown causes of qPCR inhibition) looms large, for the purposes of this paper, only proposed inhibition Types 1 through 5 are addressed.

Type 1 inhibition of reverse transcriptase (and possibly Taq DNA Polymerase) due to rRNA and tRNA is yet poorly understood, but it has been acknowledged and referred to in product literature as being of serious concern (15). Understandably, inhibition Types 2 and 3 will invariably be a function of one another since method of RNA isolation and tissue or cell type from which RNA is isolated will always affect one another distinctly, while all types of qPCR inhibition are diminished (and eventually eliminated) by sheer dilution of the RNA samples. Indeed, diluting RNA out too far can obviously result in the generation of weak or absent qPCR signals from lower abundance mRNAs in any transcriptome. Inhibition types 4 and 5 are more generally understood as they have been familiar concerns in the conventional PCR world since its inception in 1983. Since the qPCR studies used as examples in this paper involve the sole use of the TaqMan^® (hydrolysis) probe method (which includes the use of sequence-specific forward and reverse primers), we discuss here only observations gathered by this approach using total tissue or cellular RNA in single-plex fluorogenic one-step real-time qPCR (Fig. 7). All reactions were run in an Applied Biosystems Incorporated (ABI) GeneAmp 5700^® Sequence Detection System unless otherwise stated (in one case, a Stratagene Mx3005P real-time qPCR machine was employed – using ABI TaqMan^® One-Step RT-PCR Master Mix Reagents Kit). Any experimental results shown in this paper are meant to illustrate the unique prowess of the FF2-6-001 qPCR set-up tool and to aid in discussing the concepts of qPCR inhibition and optimal qPCR target dynamic range; they are not intended to represent a complete scientific study per se.

Inhibition encountered in experimental assays

By examining the results from numerous one-step real-time qPCR studies using total RNA isolated from mammalian tissue or mammalian cell cultures either by Trizol^® (14), or a column purification method (Rapid Total RNA Purification System, Cat. No. 11502-050, Marligen), we found that a direct relationship existed between the severity of qPCR inhibition and the method used to isolate sample total RNA. This was a clear example to us that qPCR inhibition Types 2 and 3 were interrelated. Most Trizol^®-isolated total RNA, when used in one-step real-time qPCR, showed inhibition until a final post-DNase, in-well (See Appendix 1) RNA dilution of ~1:150. At 1:200 final (post-DNase, in-well) RNA dilutions and beyond, most targets (i.e. SBD-1, ovTTF-1, ovSP-A, ovSP-D, ovICAM-1, SMAP29, bRSV and ovRPS15; see Appendix 14) showed lack of inhibition and began to behave as classic real-time qPCR templates. The only exception to this was hRIBO18S RNA, which did not exhibit normal real-time qPCR template behavior until a dilution of ~1:4,000 and higher (Fig. 2). Significantly less qPCR inhibition was observed with RNA samples that were isolated using the Marligen column-based method (Clark-Sponseller equine studies, 2005-2006 unpublished). Inhibition for all samples disappeared at final (post-DNase, in-well) RNA dilutions of 1:50 and higher for equine targets IL-10, IL-12p35, IL-12p40 and GA3PDH (Figs. 3, 4 and Appendix 14). Equine RIBO18S RNA was not studied, so the effect of Marligen column isolation on this target is unknown. Final, in-well RNA concentrations were never greater than 0.5 ng/μl in any of these qPCR studies (~0.3 ng/μl seemed to work the best), so inhibition of RT enzyme and/or Taq DNA polymerase by excess RNA in the reaction wells (Type 1 inhibition) was reasonably eliminated as a source of any of the inhibition phenomena witnessed (since by the time most samples reached this final in-well concentration, they had already incurred dilutions of 1:3,000 or greater – certainly outside the range where most forms of inhibition would be reasonably expected, with the possible exception of inhibition Type 4) (See Appendix 2).

We further make the assumption that our one-step qPCR reactions are safely outside the realm where Type 1 inhibition might be expected. This is based on product literature and guidelines from ABI and others that 10 picograms to 100,000 picograms of total RNA per each 50 μl one-step real-time qPCR reaction is generally considered to be the normal range within which one-step qPCR amplifications can be expected to exhibit favorable LOG-linear kinetics (2, 28, 29, 31, 39). Routinely, we design our final 25 μl qPCR reactions to contain no less than 0.005 pg of total RNA per 25 μl reaction (e.g. for the last point of typical standard curves for the hyper-abundant housekeeper, 18S ribosomal RNA) and no more than 12,500 pg of total RNA per 25 μl reaction mixture (i.e. for often rarely-expressed targets such as SBD-1, IL-10, IL-8 and TNF-α). Above 12,500 pg total RNA per 25 μl reaction, we begin to observe problematic qPCR inhibitory phenomena (with Trizol^®-isolated tissue total RNA) of Type 1, Type 2, Type 3 (and presumably Type 4) varieties. Interestingly, at first, the qPCR inhibition we observed seemed to be either a byproduct of Turbo-DNase (Ambion) treatment (Type 5 inhibition), or rRNA and tRNA inhibition of the RT enzyme during reverse transcription (Type 1 inhibition). But, then it became apparent that this inhibition was more likely due to the method of total RNA isolation (our final Turbo-DNase treated RNA samples never comprised more than 26% of each final one-step real-time qPCR reaction volume; an amount that is safely within Ambion product literature guidelines regarding the proper use of Turbo DNase-treated RNA in qPCR reactions). In our studies, Trizol^® RNA isolation (which we used for 15 different sheep tissues, 14 different chicken tissues, JS7 ovine lung cell and H441 human adenocarcinoma cell cultures) and Marligen column-based RNA isolation procedures (used for equine dendritic and macrophage cell cultures (Clark-Sponseller, 2005-2006, Iowa State University)) were both followed by identical Turbo-DNase treatments. But, Trizol^®-isolated RNA always showed a greater degree of qPCR inhibitory characteristics than Marligen column-isolated RNA samples. Since all conditions were identical for these samples except method of RNA isolation, this indicated to us that qPCR inhibition Types 2 and Type 3 were a function of one another. Further, in our studies, the possibility that Type 4 inhibition (target-specific kinetic inhibition) is a source of RT enzyme and/or qPCR (e.g. Taq DNA polymerase) inhibition seemed to be most probable only with the hyper-abundant 18S ribosomal RNA target, whereas inhibition of RT enzyme by rRNA (and possibly tRNA) and chemical inhibition seem to mainly affect those targets which are only able to elicit ample qPCR signals when using more concentrated RNA during qPCR. In our previous work, Type 5 inhibition was clearly demonstrated with LCM RNA samples that received EDTA during DNase-treatment preceding fluorogenic one-step real-time qPCR; the ABI one-step master mix used was especially prone to even very small exogenously-introduced amounts of EDTA (which of course forms a chelate with divalent metal ions such as Mg²⁺ – keeping them from participating as crucial co-factors in enzymatic reactions such as reverse transcription and PCR) (16).

All 5 proposed types of inhibition present themselves during two-step qPCR as well (using cDNA synthesized separately, prior to subsequent qPCR procedures), but to a much smaller degree than is seen during one-step qPCR for the identical target. The differences here can be largely ascribed to the amount of template present and available for qPCR since cDNAs synthesized prior to qPCR are often 20 ng/μl or less and have already incurred enough dilution in most cases (since template RNA isolation) to have minimized or eliminated the chances that any of the five currently-proposed causes of qPCR inhibition would be present. Corresponding RNA samples in the same regard are often 200-1,000 ng/μl before use. Quite logically, the more concentrated one must use RNA samples during one-step qPCR in effort to find “quieter” target signals of interest, the higher the risk there is of allowing qPCR inhibitory phenomena of any variety to manifest itself. Since our studies have expanded to the use of total RNA isolated from ovine lung, nasal turbinate, trachea, rumen, abomasum, jejunum, ileum, spiral colon, rectum, liver, gall bladder, urinary bladder, kidney, uterus (adult) and placenta (fetus) tissue, and chicken bone marrow, jejunum, crop, testes, oviduct, lung, skin, spleen, liver, kidney, bursa, trachea, conjunctiva, tongue, ovine and human lung cell cultures, and equine macrophage and dendritic cell cultures (courtesy of Dr. Brett Sponseller and Sandra K. Clark), we have witnessed and have successfully dealt with numerous different qPCR inhibitory profiles (using the FF2-6-001 qPCR set-up tool). Others have acknowledged the importance of this battle as well (1, 3-12, 39). With regard to Trizol^® versus Marligen column-based RNA isolation, it is clear that inhibitory artifacts of RNA isolation can be augmented or diminished according to the method of RNA isolation employed, and by the extent of dilution RNA samples undergo prior to their use in qPCR.

On account of the inability of investigators to find an RNA isolation method which will not introduce one-step real-time qPCR inhibition at some point, of some kind to some degree, we found it an absolute necessity to create a tool (FF2-6-001) that could quickly reveal the dilution ranges within which each real-time qPCR target of interest amplified without inhibition. Our approach emphasizes (as do methodologies offered of most companies that provide the world with qPCR technology) the importance of performing preliminary qPCR RNA template dilution studies for all targets every time RNA samples are isolated for the purpose of gene expression analysis. What ABI describes as a "validation" plate, we call a "Test Plate" (Figs. 18, 22 and 28).

Fluorogenic real-time qPCR; one-step versus two-step

Fluorogenic one-step (for final relative quantitative target analyses) and two-step real-time qPCR (for initial target primer-probe optimizations; primers and probes designed using ABI Prism Primer Express^™ version 2.0) were carried out as described previously (16-24). The fluorogenic 5' nuclease assay (TaqMan^® hydrolysis probe method) is a convenient, self-contained process which uses a fluorogenic probe consisting of an oligonucleotide to which a reporter dye and a quencher dye are attached. During PCR, the probe anneals to the target of interest between the forward and reverse primer sites. During extension, the probe is cleaved by the 5' nuclease activity of the DNA polymerase. This separates the reporter dye from the quencher dye, generating an increase in the reporter dye's fluorescence intensity. Once separated from the quencher, the reporter dye emits its characteristic fluorescence (Figs. 7 and 8). The threshold cycle, or C_T value, is the cycle at which a significant increase in normalized reporter fluorescence, ΔR_n, is first detected (See Appendix 3); where ΔR_n is calculated from R_n⁺ and R_n^-, where R_n⁺ is the R_n value of a reaction containing all components, and R_n^- is the R_n value of an un-reacted sample (the baseline value or the value detected in the no-template control, NTC). ΔR_n is thus the difference between R_n⁺ and R_n^- and it is an indicator of the magnitude of the signal generated only by the fluorogenic PCR (25). For fluorogenic hydrolysis probe designs, we use 'C-probes' instead of 'G-probes' whenever possible since empirical data from ABI has shown that use of TAMRA-quenched probes containing more Cs than Gs improves the overall magnitude of fluorescent signal generated (i.e. greater overall ΔR_n is observed). Primer-probe sets were also designed to span genomic introns whenever feasible; especially probe sequences. However, when deciding whether to use the sense or anti-sense probe sequence in each case, we were careful to avoid using C-probes which contained a G on the 5’ end (immediately adjacent to the reporter dye) – a feature that should be strictly avoided since Guanine is a potent inhibitor of reporter dye fluorescence. It is important to note here, however, that the "C-probe versus G-probe" rationale does not apply to minor groove-binding non-fluorescent quencher (MBGNFQ)-based probes. The ABI GeneAmp^® 5700 Sequence Detection System measures the increase in the reporter dye’s fluorescence during the thermal cycling of the PCR, and this data is then used by the sequence detection software to generate C_T values for each target which we finish processing and interpreting using custom Excel files. We feel strongly that being able to process one’s own C_T values into final quantitative results is paramount since qPCR machines of all varieties cannot discern between erroneous (either user- or machine-introduced) signals and legitimate signals 100% of the time. Additionally, processing one’s own data (rather than allowing qPCR machine processing) not only acquaints one directly with the interesting mathematical terrain associated with qPCR, it also exposes one first-hand to some of the fascinating intricacies and nuances associated with qPCR that are often not readily apparent to the user – all things which allow one to garner additional stratagems to apply to future troubleshooting and qPCR assay optimization endeavors.

One-step real-time qPCR

Fluorogenic one-step real-time qPCR differs from fluorogenic two-step real-time qPCR in three major regards: 1.) in a one-step approach, RNA is added directly as the nucleic acid template in qPCR reactions instead of cDNA, 2.) reverse primer concentrations have to be increased for use in one-step analyses due to first-strand synthesis requirements and, 3.) a different master mix is employed for one-step as opposed to two-step qPCR. One-step reactions typically contain both reverse transcriptase and Taq DNA polymerase enzymes and are subjected to thermocycler programs which address both enzymes in turn. For one-step real-time qPCR, we use ABI Cat. No. 4309169, TaqMan^® One-Step RT-PCR Master Mix Reagents Kit. In this kit, 250 μl of Multiscribe^™ (MuLV) RT enzyme (10 U/μl) arrives already pre-mixed with RNase inhibitor (40 U/μl) as a 40X solution. The one-step RT-PCR master mix in the kit (containing AmpliTaq Gold^® hot-start DNA Polymerase, undisclosed amounts of MgCl₂, A, C and G dNTPs and dUTP, 300 nM ROX passive internal reference molecule, other ABI-proprietary buffer components, but no AmpErase^® UNG enzyme) arrives as a separate 2X solution (5 ml total). Each of our final 25 μl one-step real-time qPCR reactions contains: 12.5 μl one-step master mix, 0.25 U/μl Multiscribe^™ RT enzyme, 0.4 U/μl RNase inhibitor, optimal forward primer and fluorogenic probe concentrations (as previously established for each target by two-step real-time qPCR according to classic ABI protocol, (25)), reverse primer concentrations adjusted for one-step use (See Appendix 4), nuclease-free water, and 6.5 μl of each RNA sample/template. Before use, all solutions are gently vortexed and spun down, then allowed to undergo fluorogenic one-step qPCR reactions using the following thermocycler conditions: 35 minutes at 48°C (for reverse transcription; normally 30 minutes; ABI), 10 minutes at 95°C (for AmpliTaq Gold^® DNA polymerase hot-start activation), and 50 cycles of: 15 seconds at 95°C (for duplex melting), 1 minute at 58°C (for annealing and extension; normally 60°C; ABI). For pipetting accuracy purposes, we always prepare enough of each reaction mixture to accommodate 30 μl reaction sizes but, in the end, use only 25 μl of each in the final reaction wells in 96-well qPCR reaction plates.

Two-step real-time qPCR

Our use of fluorogenic two-step real-time qPCR is now limited only to performing preliminary optimization and validation plates for brand-new target primers and probes since it is generally less expensive than the corresponding one-step procedure. Toward this end, for two-step qPCR, we use ABI Cat. No. 4304437 TaqMan^® Universal PCR Master Mix 2X which contains AmpliTaq Gold^® (hot-start) DNA Polymerase, undisclosed amounts of MgCl₂, A, C and G dNTPs and dUTP (in order for the AmpErase^® UNG system to work), AmpErase^® UNG Enzyme, 300 nM ROX passive internal reference molecule, a PCR product carryover correction component and other proprietary buffer components. Primer optimization plates are run in a GeneAmp^® 5700 real-time PCR machine (GeneAmp^® 5700 Sequence Detection System, ABI) using the following thermocycler conditions (a specific thermocylcer program created and optimized by ABI to be used specifically with the TaqMan^® Universal PCR Master Mix 2X, and two or three other related ABI 2X Master Mix reagents): Hold for 2 minutes @ 50°C to activate the AmpErase^® UNG enzyme (See Appendix 5), Hold for 10 minutes @ 95°C (to "hot-start" activate the AmpliTaq Gold^® DNA polymerase) and then 50 cycles of 15 seconds @ 95°C (for duplex melting) followed by 1 minute @ 60°C (to accomplish the annealing and extension phases of the PCR). Each 50-cycle run lasts 2 hours and 14 minutes, after which the GeneAmp^® 5700 sequence detection system software and custom Microsoft Excel files are used in conjunction with one another to analyze and interpret the resultant fluorogenic qPCR R_n or C_T values. For all optimization trials, each sample is analyzed in either triplicate or quadruplicate. On the primer-optimization plate for each target, primer amounts that, upon analysis, provide the highest R_n value with the lowest primer concentration(s) are identified as the optimal concentrations for each primer pair for each of the respective qPCR targets of interest. To test each probe for optimal efficacy, a second plate is designed for each target to enable the testing of various concentrations of each probe ranging from 25 nM to 225 nM in the presence of optimal primer concentrations (as already established by the primer-optimization plate in each case). For each probe, in each well, each 25 μl PCR reaction contains the [two-step]-identified optimal concentrations of each primer for each target, 2.5 μl of 1:5 or 1:10-diluted Stock I cDNA (See Appendix 6), 12.5 μl of the ABI commercial master mix (mentioned above) and nuclease-free water. For the purpose of providing real-life examples for this paper, we address several targets of interest to us including: sheep beta-defensin-1 (SBD-1), ovine thyroid transcription factor-1 (ovTTF-1), ovine surfactant protein A (ovSP-A), ovine surfactant protein D (ovSP-D), and housekeepers ovine ribosomal protein S15 (ovRPS15) and human 18S ribosomal RNA (hRIBO18S) (Figs. 22, 23, 27, 28 and Appendix 14). For these targets, we found optimal primer [two-step] concentrations in each case to be 300 nM and 900 nM for SBD-1, 1 μM and 1 μM for ovTTF-1, 300 nM and 300 nM for ovSP-A, 300 nM and 300 nM for ovSP-D, 1 μM and 1 μM for ovRPS15, and 50 nM and 50 nM for hRIBO18S forward and reverse primer concentrations, respectively. For one-step analyses, (for reasons already discussed above regarding the partial use of reverse primers due to first-strand syntheses), these same primer sets were used at 500 nM and 1 μM for SBD-1, 1 μM and 1 μM for ovTTF-1, 500 nM and 500 nM for ovSP-A, 500 nM and 500 nM for ovSP-D, 1 μM and 1 μM for ovRPS15, and 50 nM and 50 nM for hRIBO18S RNA forward and reverse primer concentrations, again respectively. Each reaction mixture on each optimization plate for each target was run in triplicate or quadruplicate in order to bolster the statistical significance of sample assessments. In all cases, replicate sample well C_T values never deviated more than 0.5% from one another, lending high credence to the technique's consistency, stability and reproducibility (Figs. 9 and 10). Probe-optimization plates were also run in the GeneAmp^® 5700 sequence detection system using the same thermocycler program as used for the primer-optimization plates. For analysis of the data from probe-optimization plates, the combination of reactants that yielded the lowest C_T values with the lowest probe concentrations were chosen as the optimal fluorogenic probe concentration in each case (which we found to be 150 nM, 150 nM, 50 nM, 100 nM, 150 nM and 200 nM for SBD-1, ovTTF-1, ovSP-A, ovSP-D, ovRPS15 and hRIBO18S RNA probes, respectively – and we used these same probe concentrations for one-step qPCR as well). Next, as a validation test that target and endogenous reference (housekeeper) cDNA amplification reactions were all proceeding at acceptable efficiencies across a spectrum of Stock I cDNA concentrations, a third plate (the validation Test Plate) was designed to enable the testing of various concentrations of cDNA ranging from full-strength Stock I cDNA to a 1:15,625 (e.g. the seventh in a series of progressive 1:5 dilutions) dilution of Stock I cDNA. In each well, constant (optimal) concentrations of forward and reverse primers and constant (optimal) concentrations of probe were used along with 12.5 μl of ABI (Cat No. 4304437) master mix, 2.5 μl of sequentially-diluted Stock I cDNA and nuclease-free water. Also included on this plate, were wells identical to the ones just described, but instead of ovine target primers and probe, they contained either the endogenous reference/housekeeper (hRIBO18S RNA) forward and reverse primers and probe at their optimal real-time concentrations (50 nM primers and 200 nM probe; as established by ABI for this target) or ovRPS15 forward and reverse primers and probe at their optimal concentrations. Validation plates included all samples in triplicate and were run in the GeneAmp^® 5700 sequence detection system using the same universal thermocycler protocol as used for the primer-probe optimization plates, and resulting C_T values were subsequently analyzed using custom Excel files (16, 19).

RNA isolation and cDNA synthesis

RNA isolation from whole tissue samples

Briefly, entire tissue samples (1-2 grams of each in cryovials stored at -80°C immediately post-necropsy) are carefully weighed, placed immediately into 3 ml of Trizol^® reagent inside nuclease-free 50 ml conical centrifuge tubes (Greiner-USA Scientific) and homogenized for 30 seconds using a TH OMNI Homogenizer (OMNI International, Inc.) to obtain Trizol^®-tissue pre-homogenates. Measured amounts of Trizol^® are then added to calculated portions of each pre-homogenate to obtain 0.091 mg tissue per ml. This makes each tissue homogenate as experimentally similar as possible and ensures that the RNA extraction capabilities of Trizol^® itself are not exceeded (as per manufacturer’s guidelines). After brief vortexing, 1.1 ml of each final Trizol^®-adjusted homogenate is transferred to a nuclease-free 1.5 ml vial (USA-Scientific) and allowed to sit for 5 minutes at room temperature. 200 μl nuclease-free chloroform (Fisher Scientific) is added to each and tubes are shaken vigorously for 15 seconds. Samples are allowed to sit for 3 minutes at room temperature then microfuged at 12,000 × g for 10 minutes at 4°C. Top aqueous layers are carefully removed and transferred into new nuclease-free 1.5 ml vials, and 500 μl nuclease-free 2-propanol (Fisher Scientific) is added to each. Samples are briefly vortexed, allowed to stand at room temperature for 10 minutes, then microfuged at 12,000 × g for 10 minutes at 4°C. Large white pellets are visible at the bottom of each sample tube at this point and the 2-propanol is subsequently dumped from each tube followed by three washes with pre-cooled (-20°C) 75% nuclease-free ethanol prepared with nuclease-free water (Sigma-Aldrich, Ambion). The first two of these washes are carefully dumped off, while the third wash is vortexed until each pellet is swirling in solution to more fully wash any lingering guanidine isothiocyante (GIT) or other salts out from underneath each pellet – salts which might otherwise inhibit subsequent procedures. Next, all samples are microfuged at 15,300 × g for 5 minutes at 4°C, the final 75% ethanol supernatant is carefully dumped off, and samples are air-dried for approximately 35 minutes under a fume hood. 170 μl of nuclease-free 0.1 mM EDTA (Sigma) prepared in HPLC-grade water (Fisher) and adjusted to pH 6.75 is added to each pellet (See Appendix 7), each sample is vortexed briefly, heated to 65°C for 5 minutes (to aid in RNA pellet resolubilization), vortexed briefly again, then stored at 4°C. RNA isolates are then assessed at 1:50 dilution for quantity and purity by spectrophotometry at 260_nm and 280_nm followed immediately by DNase treatment with TURBO DNase (TURBO DNA-free kit, Ambion). Each DNase treatment reaction consists of 60 to 70 μl RNA isolate, 8 to 18 μl nuclease-free water, 10 μl 10X TURBO DNase Buffer and 12 μl TURBO DNase enzyme. Reaction mixtures (100 μl each) are placed into an Applied Biosystems Incorporated GeneAmp^® 2400 thermocycler (Perkin Elmer/ABI) for 30 minutes at 37°C. 1 μl DNase Inactivation Reagent per 10 μl solution is added to each tube. The tubes are incubated for 2 minutes at room temperature with intermittent vortexing every 10 to 15 seconds, and then centrifuged at 10,000 × g for 3 minutes to pellet the Inactivation Reagent. Next, if RNA is to be used directly in one-step qPCR applications, 80 μl is carefully recovered from each DNase-treatment reaction; the upper transparent layer containing the RNA is transferred to a new tube (care is taken to avoid ~15-25% of the solution on the bottom of each tube – which is the pelleted Ambion DNase Inactivation Reagent polymer complex that can inhibit PCR reactions) and diluted 1:10 with nuclease-free water (Ambion) resulting in 800 μl of each RNA isolate to use for [FF2-6-001-calibrated] real-time qPCR analyses. However, for one-step qPCR analyses, it is important to note two things at this point: 1.) even at 1:10 dilution post DNase treatment, the RNA samples are still too concentrated to generate uninhibited qPCR target signals, and 2.) we never freeze the RNA samples from this point on before their use in qPCR; they are stored at 4°C in nuclease-free 1.5 ml vials. Age-matched samples and Stock I solution-derived standards are run on the final qPCR plates. Prior to isolating total RNA from cultured cells, we collect cells from culture flasks by standard methods, pre-homogenize them in 1 or 2 ml of Trizol^® by hand-pipetting, then store the resulting cell pellet-Trizol^® pre-homogenates at -80°C until they are needed for total RNA isolation.

To freeze or not to freeze RNA samples

A controversial maneuver we perform is to never freeze our RNA isolates before use. One can freeze RNA isolates and use them later – but, we prefer to use them immediately to avoid any potential issues that might arise from freeze-thawing RNA. In order to minimize the potential effects of RNA degradation on qPCR results, we use only 'age-matched' RNA samples (RNAs isolated, DNase-treated and stored at 4°C on the same day) and corresponding standards (prepared from age-matched Stock I solutions) during final one-step qPCR analyses. In the event that Stock I solutions are out of date with newer sample unknowns, previously-generated age-matched standard curves are used for quantitative analysis. A major reason we currently avoid freezing RNA is based on our observations of shifts in target C_T values after using freeze-thawed total RNA Trizol^®-isolated from whole sheep lung in qPCR applications. These shifts, curiously, are often to lower C_T values – indicating either improved reverse transcription efficiency (presumably due to less, or different secondary structures on shorter transcripts) (See Appendix 8) or possibly due to less reactants being used up during first-strand synthesis (during reverse transcription) on account of there being shorter freeze-fractured/truncated transcripts to work with; leaving more reactants available during the fluorogenic PCR phase, thereby improving the 'voracity' of the PCR. But, no matter the reason, this was troubling enough that we have since avoided freezing tissue and cell culture RNA isolates entirely. However, we have indeed observed that rarer targets (i.e. IL-10) in Stock I solutions tend to exhibit steadily weaker qPCR signals over a three month period, but it is not clear yet if this indicates degradation of RNA stored at 4°C, or if it is the result of using primers and probes that have been repetitively freeze-thawed. One of the features of a closed system is that it eventually breaks down; so we advise investigators to use their RNA samples and Stock I preparations as quickly as possible (when using real-time one-step qPCR). Two-step real-time qPCR has the added advantage that cDNA is more stable, but, even with one-step real-time qPCR; transcriptomic profiles are skewed to some degree always in direct accordance with the method of reverse transcription used.

Laser capture microdissection (LCM)-derived RNA sample isolates

We have developed a different line of reasoning altogether to handle RNA obtained by laser capture microdissection (LCM). Because there is precious little RNA in most LCM-acquired RNA isolates, we have not studied the behavior of LCM RNA samples under as many different conditions as we would like to. In addition, the fact that LCM-derived RNA samples are often tiny to begin with (e.g. 25 cells worth of RNA-containing total cell isolate) also means that it cannot withstand some of the immense dilutions spoken of elsewhere in this paper. But, we have used unfrozen and once-frozen LCM-derived total cell extracts directly in real-time one-step qPCR without noticeable differences in final results as long as samples were isolated from sections less than 8 days old in each case (16). In addition, given the different methodologies involved, there is no reason to think that the same rules would apply to LCM-derived RNA as apply to the relatively abundant RNA we get from tissues and cell cultures; extraction methods are different, carryover of potentially inhibitory biological material during RNA isolation is minimal, and sample component composition during qPCR is different (See Appendix 9). Truly, one of the great features of real-time qPCR is that it relies on very small sequence regions for successful amplification (~150 bases or less typically). The law of averages would seem to favor the notion that the very small real-time qPCR regions of amplification will be left intact after multiple sample freeze-thaws and even outright RNA degradation – which is the very reason that real-time qPCR still yields spectacular results on highly-abused nucleic acid samples. In fact, we have demonstrated that extensively-freeze-thawed, five-year-old whole lung tissue Trizol^®-isolated total RNA used in one-step real-time fluorogenic qPCR generated nearly identical C_T values for several targets as it did on the first day of its isolation (RNA sample from ewe 265, Caverly-Grubor-Gallup-Ackermann, 2002 unpublished). Because of this, we believe real-time qPCR will remain one of the most important, reliable tools for genetically analyzing very old and degraded RNA and DNA samples given its extreme sensitivity and modest requirement that only very small stretches of nucleic acid sequences within samples need remain intact.

cDNA synthesis using SuperScript^™ III and a custom reverse transcription buffer

When two-step qPCR is to be run, instead of diluting RNA isolates 1:10 post-DNase treatment, they are each diluted to 59.4 ng/μl and used as templates for complementary deoxyribonucleic acid (cDNA) synthesis (for use as samples or Stock I cDNAs in two-step qPCR); we use SuperScript^™ III RT enzyme (Invitrogen) for reverse transcription. We prepare and use our own 10X reverse transcription buffer formulation (300 mM TRIS:HCl, 625 mM KCl, pH 8.3) in order that the ionic strength of our resulting cDNA solutions is similar to the ionic strength of the two-step master mix we use (TaqMan^® Universal PCR Master Mix 2X, ABI). Briefly, reverse transcription master mix containing 3.38% nuclease-free water, 31.17 mM TRIS, 64.94 mM KCl, 5.71 mM MgCl₂, 2.08 mM dNTP mix, 2.6 μM random hexamers and 0.0222 μg/μl TURBO DNase-treated RNA is heated for 5 minutes at 65°C then snap-cooled on ice for at least 1 minute. We pre-dilute our TURBO DNase-treated RNA samples (to 59.4 ng/μl so that adding 36 μl of each RNA to each final 100 μl reverse transcription reaction results in all reactions containing 2.1389 μg total RNA. Two to four such 100 μl reactions are created from the same original reverse transcription master mix for all samples. Samples are spun down, and RNAse inhibitor (20 U/μl, ABI) and SuperScript^™ III RT enzyme (200 U/μl, Invitrogen) are finally added to each cooled sample reverse transcription mixture (now 200 to 400 μl each). The final concentrations attained of each reverse transcription component are: 3.25% nuclease-free water, 30 mM TRIS, 62.5 mM KCl, 5.5 mM MgCl₂, 2 mM dNTPs (0.5 mM each of dATP, dCTP, dTTP and dGTP), 2.5 μM random hexamers, 3.5 U/μl SuperScript^™ III RT enzyme, 0.4 U/μl RNAse inhibitor and 0.021389 μg/μl TURBO DNase-treated RNA. These reagents are vortexed gently, split into 100 μl amounts into nuclease-free 0.2 ml tubes (Midwest Scientific), and the tubes are placed into the GeneAmp^® 2400 thermocycler (which only accepts samples of 100 μl or less) for reverse transcription using thermocycler conditions of: 5 minutes at 25°C, 45 minutes at 53°C, 15 minutes at 70°C, followed by a safety hold at 4°C.

Concerns over the use of cDNA in two-step fluorogenic real-time qPCR

For those who prefer to make their own cDNAs beforehand in pursuit of two-step real-time qPCR as the relative quantitative tool of choice, it is interesting to note that cDNAs, when reverse transcribed from Trizol^®-isolated RNAs showing original sample o.d._260nm readings (at 1:50 dilution) of 0.011 to 0.022 and higher are (by the time they are synthesized and diluted i.e. 1:10 before use in qPCR) already safely outside the dilution range where most qPCR inhibition would exist. For column-isolated RNAs, the lowest acceptable original o.d._260nm value at 1:50 dilution for each RNA isolate can be calculated to be about 0.00275 to 0.0055 in the same regard. These observations apply to fairly standard reverse transcription reactions wherein 2 μg of RNA is used per each 100 μl reverse transcription reaction for cDNA synthesis (according to standard ABI practice) whereas 1 μg of RNA is used per each 100 μl reverse transcription reaction during Invitrogen SuperScript^™ II reverse transcription reactions. Additionally, to improve the overall yield of cDNA synthesis reactions, it has been recently noted that priming reverse transcription reactions with random pentadecamers (as opposed to random hexamers or other primers) boosts cDNA yields by 2-fold while increasing the number of detectable transcripts by 11-fold (26) (See Appendix 10). In our experience, qPCR inhibition is still evident with the most concentrated cDNA standards or samples examined for the presence of the frequently-used housekeeping gene, 18S ribosomal RNA, so care should be taken to dilute all similarly destined cDNAs at least 500 to 1,000-fold further before trustworthy C_T values can be generated from such robustly-abundant target transcripts (See Appendix 11). An additional caveat to note regarding two-step real-time qPCR is that rare targets are often not amplified as efficiently by two-step as they are by one-step real-time qPCR. This, we have concluded, is the very result of cDNA templates already having suffered considerably more dilution along the way from RNA isolation, through reverse transcription reactions and any additional dilutions before qPCR takes place. We have found our strongest qPCR signals from rare targets using one-step as opposed to two-step real-time qPCR. Further, by setting up one-step real-time qPCR plates in strict accordance with what the FF2-6-001 qPCR set-up tool (see Figs. 11 through 39 for depictions and descriptions of the different portions of the FF2-6-001 file system) reveals to us about the proper dynamic range of each target, we avoid diluting our RNA samples too much or too little and are therefore able to preserve maximal qPCR signal strength from each target amplification of interest while at the same time avoiding all qPCR inhibitory phenomena. Real-time signal (either target or housekeeper-derived) contributions generated from genomic DNA-contaminated samples during qPCR can be mathematically addressed by custom files as well (Fig. 42).

Housekeeping gene considerations

Another area of concern has been choosing appropriate housekeepers for qPCR, and most recently it appears that ubiquitin, transcription and elongation factors, transferrin receptor and several ribosomal protein mRNAs are among the most stable housekeeping signals, whereas GA3PDH, β-actin, β-tubulin and even 18S ribosomal RNA have been given progressively more negative and mixed reviews in this regard. Since 18S rRNA is a more globular structural form of RNA (and therefore likely subject to different degradative stresses or processes than mRNA), it is being increasingly thought of as a poor representative of linear transcripts in general (comment by Jim Wicks, Ph.D., PrimerDesign Ltd). It is also possible that the same housekeeper's usefulness may vary from tissue to tissue, but Ubiquitin still seems to be quite stable in this regard (1, 2). However, in vivo (endogenous) housekeepers may become a thing of the past as more investigators explore the use of in vitro synthetic constructs or transcripts from highly disparate species (which exhibit no homology with the genome of the species being studied) – e.g. a jellyfish photoprotein (aequorin; GenBank accession number L29571) cRNA was successfully used as a ‘reference gene’ (as an externally-introduced 'housekeeping' gene) in recent murine studies at the University of Bonn, in Bonn, Germany. The foreign reference jellyfish cRNA was found to be just as reliable as three other endogenous murine housekeeping genes (β-actin, GA3PDH and HPRT1) in that study (29). Normalization of gene expression using expressed Alu repeat elements is also currently being proposed (40), which will be highly useful for primate RNA samples. In addition, RNA samples taken from other mammalian genomes (for the purposes of qPCR) which house similarly unique (species-specific) repetitive genetic elements (many of which, like Alu sequences, are found within the untranslated regions of numerous mRNAs throughout the transcriptome), (41), might also take advantage of this approach, while RNA samples from animals with indigenously fewer unique repeats, such as birds, may benefit little from it (42).

Review of basic real-time qPCR math

Crucial to the proper interpretation of any real-time qPCR data is a clear understanding of the mathematical principles underlying generation of the data. Though it is not the intent of this manuscript to promulgate the entire possible range of the math involved, it is nonetheless important to touch on the most relevant equations and concepts; some of which are likely generally familiar and accepted, and one or two of which may be unique. In brief, the ideal slope (m) of the dilution curve for any real-time qPCR target is invariably -1/LOG₁₀(2) or the value "-3.3219..." etc. Such a slope indicates a reaction Efficiency or 'Efficacy' (E) of 1 (or 100% Efficiency), which correlates to an Exponential Amplification (E_AMP) value of 2 (indicating a perfect doubling of template every cycle). When efficacy of template doubling per cycle is sub-optimal, E <1 and E_AMP <2, e.g. if E = 0.83, then E_AMP = 1.83 since the expressions for E and E_AMP are [10^(-1/m)– 1] and 10^(-1/m), respectively. When E is not known, the expression, "2^ΔΔ" (or "2^-ΔΔCt") can sometimes be used to compute the approximate fold change in gene expression between control and treated samples (or normal vs. abnormal, diseased vs. non-diseased, or any sample vs. an appropriately-selected "calibrator" sample, etc.), but this expression is simplistic in that it entirely ignores the impact that E has on the target and housekeeper reactions in each case (36). When E is known for targets and housekeepers, the 2^-ΔΔCt expression can be expanded into: Fold increase = (2 x E)^ΔΔCt where the term "ΔΔC_T" =[(C_{Ttarget,control}-C_{Thousekeeper,control})-(C_{Ttarget,treated}-C_{Thousekeeper,treated})] (27). This ("expanded 2^ΔΔCt") equation generates similar (though not identical) results to the "Pfaffl Equation" (28) and the "ISU Equation" (see below) only when the term "(2 x E)" is replaced by the more precise term "(1 + E)" to yield the corrected "expanded 2^ΔΔCt" equation: Fold increase = (1 + E)^ΔΔCt. Use of the term "(1 + E)" here is more appropriate since it is in direct keeping with the original universal expression for all PCR amplifications: "X_n= X_o(2)ⁿ," which, for less-than-100%-efficient reactions, by necessity becomes: "X_n= X_o(1+E)ⁿ." In this equation, "X_o" represents the initial number of target copies, "n" represents the number of cycles elapsed, "X_n" represents the number of target amplicons generated after "n" cycles, and "E" = Efficiency ([10^(-1/m) – 1]), (36). When the term "(2 x E)" is used (27), the "expanded 2^ΔΔCt equation" is prone to underestimating fold differences between samples, whereas using 2^ΔΔCt, by itself, consistently overestimates fold differences since it is inherently erroneous in that it assumes all qPCR reactions to be 100% efficient. 2^ΔΔCt is nonetheless a very helpful and informative approximation when efficiencies are unknown.

Efficiency of reaction versus exponential amplification

It is always important for one to differentiate between "E" and "E_AMP" since the Pfaffl Equation uses "E_AMP" instead of "E" when solving for relative quantitative and absolute quantitative gene expression results. These two terms are often confused in the literature and are mistakenly represented as interchangeable, which they are not. As a result, investigators can be thrown off course during the computation of their qPCR results. The Pfaffl Equation can be written as follows:

Ratio (or fold change) = (E_target)^{ΔCttarget(control - treated)} / (E_housekeeper)^{ΔCthousekeeper(control - treated)}

where R = "ratio" or calculated fold change in a specific target gene's presence or expression level when comparing RNA isolated from a treated (or infected) plant or animal tissue or cell type to RNA from the corresponding normal, control or calibrator RNA samples (28). The value often written as "E" in the Pfaffl Equation is indeed "E_AMP"; 10^(-1/m), and should not be confused with the symbol "E" which connotes amplification reaction efficiency; [10^(-1/m) – 1]. In addition to the Pfaffl Equation, there is another important partial equation that can be repetitively incorporated into a mathematical expression to form an equation (the ISU Equation) which generates values identical to that of the Pfaffl Equation. However, the ISU Equation makes room for the investigator to plug in the values of "m" and "b" from target and housekeeper standard curves and is derived from a partial equation indirectly alluded to in ABI User Bulletin #2 (31), namely: "Qty = 10^((Ct-b)/m)," where "Qty" = the relative calculated quantity for any target, C_T = the observed C_T generated for the particular target or housekeeper being evaluated, and "m" and "b" are the slope and y-intercept, respectively, of that target or housekeeper's standard curve (which are plots of LOG₁₀ of the Stock I or sample dilution factors or "LOG₁₀ input," versus C_T). The expression "Qty = 10^((Ct-b)/m)" can be directly assembled into the ISU Equation in the following way:

Ratio (or fold change) = [(Qty_{treated,[target]})/(Qty_{treated,[housekeeper]})] divided by: [(Qty_{control,[target]})/(Qty_{control,[housekeeper]})]

Notice that, implicit in the ISU Equation, are the Efficiency (E) values for housekeeper and target gene amplifications by virtue of the equation's direct inclusion of "m" and "b" values from the corresponding target and housekeeper standard curves. Although one finds that the Pfaffl and ISU equations both generate identical results, be aware that these values are not yet amenable to sound statistical analysis – the resulting values must be logarithmically transformed, using any logarithmic base (Figs. 40 and 41). We have chosen to transform to LOG base 2 values (LOG₂) in accordance with Gilsbach et al. (29). In this form, qPCR (and PCR) data of any kind appropriately lends itself to correct parametric, t-test and/or box-plot statistical analyses (29, 30, 43 and Dr. Marcia de Macedo, 2004 unpublished). We LOG transform our qPCR data as soon as we have calculated our relative quantity values (either before or after division by housekeeper values). Subsequently, treatment group averages, standard deviations within treatment groups and standard error of the mean (s.e.m.) error bar ranges are all calculated from the LOG-transformed quantity values. Once this first stage of data processing is complete, control group averages are then directly subtracted from themselves and all related treatment group averages (so all control groups appear at "0" level expression), a maneuver which is supported by the law of logarithms wherein LOG A/B = (LOG A – LOG B), and finally, new standard error bars are recalculated using the equation:

which can be derived through statistical variance equations. LOG transformation of PCR data of any kind is a necessity – it is the nature of PCR to show higher variability with lower mean quantity values due to the ever increasing Monte-Carlo effect with decreasing presence of target template in nucleic acid samples (2, 37). This reality is exposed only after LOG transforming quantitative qPCR data. If PCR data is not LOG transformed, one tends to see an opposite, counterintuitive trend: i.e. increasing variance with higher relative quantity means – which is definitely not true as many qPCR investigators can certainly attest to (2, 29, 30, 43). The trend most commonly observed in qPCR shows that final quantitative results generated from consistently lower C_T values are generally more stable from replicate to replicate, from sample to sample (low variance), but once target or housekeeper C_T values rise above 40, quantitative data begins to exhibit greater and greater degrees of statistically unacceptable variance due to the Monte-Carlo effect in addition to the background "noise" of the assay itself (resulting from the increasing accumulation of, and fluorescent signals from cleaved or displaced probe fluorogens and/or fluorescence-capable quenchers as the PCR proceeds) (32, 33). In our work, it has often been informative to additionally categorize qPCR targets according to the C_T range within which we usually expect them to appear during qPCR. Since C_T values are directly indicative of original target template abundance, we have created four categories into which most qPCR targets seem to fit, namely: 1.) rare transcripts; (C_T range of 38-47), 2.) intermediate-abundant transcripts; (C_T 26-37), 3.) abundant transcripts (housekeepers such as GA3PDH, β-actin, β-tubulin and RPS15); (C_T 20-25), and 4.) hyper-abundant transcripts (i.e. 18S ribosomal RNA); (C_T 12-19). It is within the latter portion of the "rare transcripts" C_T range noted above that investigators can also expect to experience the Monte-Carlo and assay "noise" effects to some degree. This "rare transcript" status can result from target mRNA being either endogenously rare by nature, made rare by experimental treatment or disease, by sheer sample degradation, or by over-diluting template-containing RNA or cDNA samples during qPCR set ups.

qPCR sample dilution and C_T relationships

To continue, the final important mathematical relationships which are essential to one’s understanding of the 'mathematical terrain' associated with qPCR include the following interesting expressions:

A.) 2^λ= f, where "λ" = the ideal expected frequency of appearance of C_T values for any dilution series between or among samples and "f" = the known dilution factor between or among samples. Expression A can be rearranged to give expression B:

B.) λLOG₁₀(2) = LOG₁₀(f), which can be rearranged further into expression C:

C.) λ = LOG₁₀(f)/LOG₁₀(2), which can be rearranged to result in an interesting expression for Efficiency (not Exponential amplification) using the same variables:

D.) E = f^(1/λ) - 1, or: E = f^(1/Δλ) - 1 (when "f" is known), and E = Δf^(1/λ) - 1 (when "λ" is known)

The utility of expressions A and C above become immediately obvious when one realizes, for instance, that a serial 1:2 progression of diluted standards should ideally generate curves crossing threshold (generating C_T values) at a frequency of "LOG₁₀(f)/LOG₁₀(2)" or 1 cycle apart; since f = 2 in this case, the final expression here becomes "LOG₁₀(2)/LOG₁₀(2) = 1." If the serial progression were 1:7, C_T values obtained from the corresponding amplification curves would be expected to be spaced "LOG₁₀(7)/LOG₁₀(2)" or 2.80973 cycles apart. When serial progressive dilutions of samples are 1:10, C_T values from the amplification curves would be expected to be spaced "LOG₁₀(10)/LOG₁₀(2)" or 3.3219 cycles apart, and so on. On the other hand, when solving for "λ," if for instance the observed C_T values of a progressive target dilution series were observed to be about 2.3219 cycles apart, one can calculate that the progressive dilution series factor is "2^λ" or 2^2.3219 or "5" (indicating that the underlying dilution pattern was based on serial progressive 1:5 dilutions of the qPCR sample RNA, cDNA, Stock I, viral RNA or DNA, etc.). This 5-fold difference in initial target template amounts between samples reveals the utility of the expression, "2^ΔCt", or "2^λ", in that 2^λ_(target) divided by 2^λ_{(housekeeper)} approximates the Pfaffl equation, and E_AMP^λ_(target) divided by E_AMP^λ_{(housekeeper)} is the Pfaffl equation. By additionally dividing the resulting value of the above expression, [2^λ_(target) / 2 ^λ_{(housekeeper)}], by the 2^λ value of a calibrator sample, one achieves efficiency-uncorrected "2^ΔΔCt" (or "2^-ΔΔCt") analysis of qPCR data (36). On the other hand, dividing the resulting value of the expression, [E_AMP^λ_(target) / E_AMPλ_{(housekeeper)}], by the E_AMP^λ value of a calibrator sample, one achieves efficiency-corrected "E_AMP^ΔΔCt" (or "E_AMP^-ΔΔCt") analysis of qPCR data. Values generated by this latter equation are identical to results obtained from both Pfaffl and ISU equations. When fold change in gene expression is not calculated in comparison to a calibrator sample's target expression levels, the equations [2^λ_(target) / 2^λ_{(housekeeper)}] or [E_AMP^λ_(target) / E_AMP^λ_{(housekeeper)}] suffice to reveal fold differences in target gene expression between samples. But, in order to statistically assess the data generated by any of these equations correctly, LOG-transformation of quantity values is required beforehand (29, 30, 36). Any departures from expected C_T frequencies (λ) of course indicate departures from ideal amplification reaction efficiencies, and for dealing with non-ideal situations (which predominate in practice), we have developed the equation, -LOG₁₀(f) x (1/LOG₁₀(2) – ((1/LOG₁₀(((10^{(1/((ΔCt)/LOG₁₀(f)))})))))), to predict C_T appearances for any dilution factor between or among samples at any amplification reaction efficiency (E). The FF2-6-001 qPCR set-up tool is based on such equations.

Efficiency of target amplification concerns

It is important to note that the efficiencies of qPCR amplification reactions are initially only as good as primer and/or probe designs allow. But, equally important are the nucleic acid template dilutions used on a per-target basis. Greater than 100% efficiency (indicative of Type 1 inhibition in our experience) may be observed, and different primer-probe designs (even for the same target) will exhibit varying degrees of susceptibility (or be differentially prone) to each type of inhibition. These potentially confounding phenomena indeed present ongoing challenges to enzymologists and other scientists to further elucidate the molecular mechanisms underlying each particular form of qPCR-related inhibition. In general, the slope of a qPCR target standard curve is the best indicator of whether or not there are problems with one’s qPCR primer-probe designs or template dilutions. Further, after optimizing primers and probes and determining optimal template dilutions for each qPCR target, by running standard curves for all targets on each qPCR plate, one can logistically side-step two common qPCR pit-falls: 1.) since all qPCR reactions for each target on a plate can be assumed to be governed by the same target-specific reaction efficiency (or indeed, inefficiency), including standard curves on each plate (for each target) essentially controls for plate-specific variations for each qPCR target since all same-qPCR-target samples on any given qPCR plate will be judged on the same ‘kinetic playing-field’ as their standard curves (i.e. all sample targets and their corresponding standards on each plate can be thought to have experienced the same environment together throughout a qPCR amplification) and, 2.) ordering and testing multiple primer-probe sets for the same target is cost-prohibitive for many labs (unless one is willing to sacrifice time and target specificity by using SYBR Green-based real-time assays during optimization). Preparing standard curves for each different target on each plate provides a reliable way for investigators to get valid qPCR information even when using sub-optimal primer-probe designs as long as the Monte-Carlo effect is not present as an additional, confounding factor (2, 37). It is important to emphasize that these ideas appear to be most rigorous in the aftermath of running a proper Test Plate for all targets beforehand. It has been our experience almost 100% of the time, that when we design our real-time primer-probe qPCR sets using Primer Express v. 2.0, our resulting observed reaction efficiencies are consistently in the 90-110% range – but, again, only after we have responsibly performed the appropriate Test Plate(s) and analyzed the data using the FF2-6-001 qPCR set-up tool.

The high importance of running a Test Plate

On occasion, one may observe larger departures from ideal efficiencies among series of plates using the same standard curve template source, but, even then, efficiencies as low as 60% and as high as 140% can still be used to acquire credible data if the standards and samples on a single plate are weighed against one another for that plate alone and not cross-compared to results from other plates which have exhibited significantly different efficiencies for the same targets. However, when identical standards and/or inter-plate calibrators generate nearly identical C_T values for the same target(s) from plate to plate, investigators can directly compare results among plates with confidence. Still, far and above any other single issue regarding one-step qPCR optimization, we are solidly convinced that the most powerful thing one can do to attain ideal (or near-ideal) efficiencies from any qPCR target amplification is run a Test Plate to physically determine which specific RNA dilution ranges work best for each different qPCR target. For a good example of this see Figures 5 and 6. The three (never-before-tried) primer and probe sets used in this particular study/example (Brockus-Harmon-Gallup-Ackermann, 2006 unpublished) were designed using Primer Express v.2.0, never (two-step)-optimized, and used directly at 'saturating concentrations' in each case (i.e. primers at 1 μM, and probes at 150 nM). After running a Test Plate for all three targets to identify the optimal RNA dilution range for each, we were able to obtain virtually 100% efficiency from each target amplification in the final qPCR study. Since we have repeated this approach successfully numerous times with other genes, we are confident that it is template dilution that affects the efficiency of real-time qPCR reactions to the greatest degree – barring any obvious thermodynamic flaws in real-time qPCR primer/probe designs or reaction formulations. Again, any qPCR RNA sample’s ability to inhibit qPCR reactions can be diminished and eventually eliminated entirely the further one dilutes RNA samples in effort to attain the useful ranges for each target (as dictated by what one discovers by appropriate Test Plate analyses). There is indeed a "happy sample dilution range" for each qPCR target.

The effect(s) of sheer sample dilution

In most cases, useful RNA dilution ranges are so dilute (with respect to the originally-isolated RNA samples themselves) that most qPCR inhibitory phenomena has already been eliminated by the time the reactions are run. Factors known and unknown (which would normally bring about real-time qPCR inhibition when more concentrated sample RNAs are used) present no threat to qPCR reaction kinetics whatsoever after ample RNA sample dilution has occurred. Once ideal qPCR template dilution ranges are established for each different target, intended real-time qPCR reactions are allowed to proceed undaunted by inhibition of any kind. In this way, one can use the sensitivity of the real-time qPCR technique itself in its own favor since most targets can be detected – even when RNA is diluted extensively (e.g. most housekeepers still give robust qPCR signals even when diluted out beyond 1:1,000,000!) (See Appendices 11 and 12). Template dilutions thus serve a two-fold purpose: 1.) they achieve optimal template concentrations for each qPCR target of interest, and, 2.) they aid in greatly reducing and eliminating all potential forms of qPCR inhibition. Real-time qPCR inhibition can indeed be muted and even eliminated merely by RNA sample dilution in most cases, therefore leaving all nucleic acid target templates within each RNA sample genuinely available to participate in highly efficient (and-therefore-quantitatively-accurate) qPCR amplifications. Once rendered non-existent by dilution, it simply doesn't matter what type of sample-related inhibition could have had the potential to manifest itself during qPCR using more concentrated RNA; once it is carefully and premeditatedly eliminated by dilution, it is no longer of any consequence. Clearly, preliminary qPCR Test Plates serve to verify where each target reaction's optimal, non-inhibitory RNA dilution range is by examining an RNA sample (or mixture of RNA samples, i.e. Stock I) that is truly representative of all RNA samples examined in each particular qPCR study. In the event that the optimal dilution of an RNA sample for a rare-but-present qPCR target impinges on that target's ability to amplify, further purification of sample RNA may be necessary to minimize its inhibitory characteristics (2). Toward addressing and attaining each of these important objectives, the FF2-6-001 qPCR set-up tool is well suited since it enables investigators to entirely avoid over- or under-diluting RNA and DNA samples.

Finding optimal dilution ranges for each qPCR target by running a Test Plate

Before a qPCR Test Plate procedure can be correctly performed, it is often helpful that all qPCR primers and probes have already been optimized on cDNA template (by two-step qPCR) weeks or months prior to using them in hydrolysis probe-based fluorogenic one-step qPCR reactions. Or, if optimization is not affordable cost or time-wise, one may simply use saturating concentrations of primers (1 μM) and probes (150 nM) for all targets. Additionally, if two-step optimizations have already been carried out, reverse primer concentrations should all be increased at least 200 nM in each case (for use in one-step qPCR) unless saturating concentrations are already being used. Reverse primers are more highly exhausted by one-step than two-step qPCR on account of their being incorporated into the amplicons during first-strand synthesis (16). By running a preliminary, universally-useful qPCR template dilution Test Plate (Figs. 18 and 22) for all targets in a one-step real-time qPCR assay over a (post-DNase, in-well) dilution range spanning from 1:38.46 to 1:5,000,000) of a representative RNA sample or RNA mixture (See Appendix 13), we quickly identify the useful dilution ranges for each particular qPCR target by using FF2-6-001 custom Excel files TestPlateResultsAnalysis2006.xls and TestPlateResultsAnalysis2006b.xls (Figs. 22-29 and 34). In all cases, immediately after Turbo-DNase treating our RNAs, we dilute them 1:10 with nuclease-free water and place the RNAs at 4°C for safe-keeping (never -80°C); we never freeze our isolated RNAs before use. The C_T values obtained from the Test Plate are entered into the TestPlateResultsAnalysis2006.xls file, the user then selects points for each target dilution study which give the best efficiency for each target and activates the appropriate pre-programmed macros which function to accept the user's modifications and serve to help identify the optimal/useful RNA sample dilution ranges for each qPCR target. After the investigator has selected the dilution points for each qPCR target which demonstrate lack of qPCR inhibition, high reaction efficiency (generally accepted to be efficiencies between 80 and 110%) and LOG-linear behavior, the FF2-6-001 qPCR set-up tool quickly calculates the appropriate progressive dilution series for each target's optimal sample dilution, optimal standard curve range and standard dilution series, all serial dilutions required of the Stock I solution and RNA samples, and all master mix/primer and probe amounts to complete the entire qPCR set-up. As a general rule, it is always wise for the investigator to include standard curves for each different target on every plate since this (in theory) allows qPCR studies to tolerate lower amplification efficiencies without unacceptably compromising relative target expression C_T analyses. Since all samples on a plate are subjected to the same environment, lower efficiency target reactions analyzed on plates including the corresponding standard curves (at least 3-point standard curves indicating efficiencies no lower than 60%) still yield results that are truly reflective of relative target expression. The FF2-6-001 qPCR set-up tool-determined dilution ranges additionally represent the useful standard curve dilution ranges for each target – within which sample unknowns and calibrators are specifically diluted so they will most likely appear between the first two standards of each Test-Plate-data-determined useful dilution curve for each different qPCR target (based on Stock I Test Plate analysis). A "sample aiming device" feature additionally allows the user to globally adjust this latter parameter as well (Fig. 39). The FF2-6-001 qPCR set-up tool’s TestPlateResultsAnalysis2006.xls and TestPlateResultsAnalysis2006b.xls files are also used to make sure that real-time qPCR signals from each target remain sufficiently strong at the outer (most dilute) region of each assay by revealing the limit of 'signal exhaustibility' for each target. This information is used to ensure that each sample RNA’s highest-but-useable dilution retains enough qPCR signal strength to allow them to remain useful as qPCR samples (e.g. as the most dilute target or housekeeper standard, etc.). The FF2-6-001 qPCR set-up tool saves time by automatically performing numerous and necessary calculations and it is a goal of ours to transform it into user-friendly software that can either be obtained on CD, or downloaded by visiting the link: http://www.dna.iastate.edu/frame_qpcr_res.html in the near future.

MasterEntrySheet.xls user interface portion of the FF2-6-001 qPCR set-up tool

The MasterEntrySheet.xls portion of the FF2-6-001 qPCR set-up tool is the main user interface wherein the investigator can enter RNA sample o.d._260nm and o.d._260nm/280nm readings and the dilution factor at which o.d._260nm and o.d._260nm/280nm readings were taken. This interface is also used to tell the FF2-6-001 qPCR set-up tool the method of RNA isolation, DNase treatment conditions, which samples will be included in the Stock I mixture, how much of each sample and standard are desired to complete the entire study, all Test Plate, Sample Plate and NRC (no reverse transcription control) Plate requirements, sample use, inter-plate calibrator and NTC usages (Figs. 11-17 and 20). The Stock I dilution profile for the Test Plate can also be adjusted to more fully interrogate the signal dynamics of any RNA or cDNA dilution range of interest, but, in order to save on precious RNA or cDNA samples, we suggest using the default file settings for Test Plate runs in the vast majority of all real-time qPCR situations. Instructions on how to use the FF2-6-001 qPCR set-up tool will be included either within the tool files themselves, or in an accompanying manual. The MasterEntrySheet.xls user interface file is connected (by equations and Visual Basic macros) to 22 other Excel files which make up the entire FF2-6-001 device. Each file is a unique tool in and of itself which has been carefully designed to perform numerous specific calculations within thousands of active cells. The results of one file are used by multiple other files in sequence to carry out extensively layered algorithms. Appropriate "error" messages and other reminders also appear within the files to inform the investigator when mathematically impossible demands have been entered into the system. The FF2-6-001 qPCR set-up tool also automatically identifies samples with problematically low RNA or cDNA concentrations and tells the user which samples will be expected to exhibit qPCR inhibition. After one global macro has been activated and allowed to run, Sheet 2 within the MasterEntrySheet.xls file provides all the printable information investigators will need to set-up everything for each entire qPCR study (Figs. 19, 22, 31-35).

Given the meticulous nature of real-time qPCR, it is clear to many who perform this technique that any time-saving device during design and set-up is highly valuable (Figs. 43 and 44). Our own use of the FF2-6-001 tool has saved incredible amounts of time during qPCR set-ups. The development of such a tool became an absolute necessity for us after frequently dealing with as many as sixty samples and seven qPCR targets at a time – for which well-rendered preliminary targets tests and set-up calculations often took several days. With the advent of liquid-handling robot technology (Fig. 45), a marriage between such machines and the FF2-6-001 qPCR set-up tool would be most helpful and indeed welcome in the qPCR world. Addressing qPCR inhibition is of utmost importance toward attaining accurate quantitative data, and it is necessary for all qPCR investigators to be able to clearly demonstrate that they are using their qPCR samples at non-inhibitory concentrations (no matter what the cause of inhibition is). Manuscripts lacking such proof yet espousing accurate qPCR results should be read with caution since qPCR inhibition, in particular, is perhaps the most problematic feature of the assay, and we feel it has not yet been addressed as much as it really should be. We hope that this manuscript serves a role in that regard. As a case in point, what if RNA sample-related one-step real-time qPCR inhibition were to remain uncorrected or unaddressed during routine analysis for H5N1 in infected duck tissues for example (Fig. 46) and, what if studies using Northern analyses to assess gene expression or viral presence produced data which indicated a much higher fold gene expression or viral presence than correlate real-time qPCR studies showed? Such scenarios could be explained on the troubling basis that sample RNA (or cDNA) was not responsibly ascertained to be non-inhibitory to the qPCR assay itself beforehand. This is why it is so important to run a Test Plate using a mixture of some or all of the DNase-treated RNAs (or cDNAs) in an experiment as a Stock I RNA (or cDNA) solution – and test this Stock I for each target (up to 7 targets in our designs) in singlet (to save on master mix) along a dilution profile typically ranging from 1:10-diluted Stock I, on out to 1:1,300,000-diluted Stock I (which translates into a range of 1:38.46 to 1:5,000,000 final in-well sample dilutions). Keeping track of the approximate nanograms of RNA or cDNA per μl for each sample throughout all procedures (by taking into account all dilutions incurred by each sample since original o.d._260nm readings were performed for each individual RNA) becomes important when trying to identify possible causes of observed inhibitory phenomena based on final in-well sample RNA or cDNA concentrations. In cases where one is using extremely small amounts of total RNA in-well (e.g. as with many housekeeping targets), one has already eliminated inhibition Types 1, 2, 3 and 5 by dilution alone, but, one has not eliminated the possibility that sequence-specific interactions with specific primers and probe for each qPCR target could still be causal agents of inhibition (38). This is complex and has been discussed recently as being tissue-specific as well (1, 2). As yet unknown or unidentified causes of qPCR inhibition (i.e. "Type 6") may indeed abound.

In closing, it is important to note that we have crafted the FF2-6-001 qPCR set-up tool to address as many features of qPCR as possible in order that users are able to swiftly attain precise qPCR set-ups specifically tailored to each experiment's unique dynamics. Investigators can speed things along toward meaningful real-time qPCR results in other crucial ways as well by: 1.) using primers and probes that have already been optimized on cDNA and have been shown to be able to generate reliable, high efficiency dilution/calibration/standard curves days or weeks in advance (or one may use 1 μM primers and 150 nM probe in all cases where primer-probe optimizations have not yet been performed), 2.) identifying the best, sample-specific RNA isolation technique which yields RNA isolates that exhibit the least amount of inhibitory phenomena during qPCR; and sticking with that method while bearing in mind that Trizol^® isolation is still the cheapest way to go, 3.) running a Test Plate for all targets of interest using a representative sample or mixture of samples (e.g. Stock I), 4.) analyzing Test Plate results to identify the target-specific dilution ranges within which each different target generates a near-ideal slope (e.g. -3.3219) while exhibiting LOG-linear behavior, then, diluting all samples into these multiple ideal ranges so they can be used for each different target within each target’s confirmed useful dilution range. This not only improves confidence in targets being able to amplify within the proven useful range of their respective standard curves, but also ensures that inhibition of any variety is absent and that high fidelity reactions can be consistently expected – all things which the FF2-6-001 qPCR set-up tool quickly and faithfully calculates, 5.) identifying, validating and using at least two reliable sample-specific housekeepers in each qPCR study, 6.) making sure all nucleic acid samples are treated identically before qPCR (i.e. DNase-treatment conditions, etc.) – including the RNA or DNA used to generate absolute or relative standard curves, and 7.) running standard curves on each and every plate for each target tested – never forget that it is always correct to run standard curves on every plate – for this way, samples and standards both suffer the same degree of reaction efficiency or inefficiency – whichever the case may be, thus allowing one to obtain more precise relative data. For fluorogenic real-time qPCR, our own bias is toward using the hydrolysis probe method in a one-step approach exclusively in all situations since it has the added advantage of being able to use forward and reverse primers to further reinforce the specificity of the fluorogenic probe in target amplifications by acting as 'rooks [primers] guarding the fidelity of the [probe] king’ – making doubly sure that only a highly-specific reaction takes place – especially in cases where one, two, or all three of these players can be designed to span a genomic intron – or introns – or other strategically advantageous regions. MGBNFQ probes allow even more possibilities. And finally, fortunately, it is the nature of PCR to amplify extremely small amounts of starting nucleic acid template material, and our studies have all benefited from this classic feature in that all tissue RNAs isolated for all studies so far (and subsequently diluted appropriately on a target-by-target basis to each of their optimal non-inhibitory, LOG-linear ranges) have exhibited solid target signals. We contend that all Stock I solutions will be useful if they are comprised of the experimental samples involved in each qPCR study. Stock I solutions in and of themselves (by virtue of them being composed of either portions of all the samples in a study, or portions of those samples most expected to contain all qPCR targets of interest) represent self-mitigating/self-attenuating tools already tailored to the specific confines (known and unknown) of each particular qPCR study. A "closed system" is formed this way – a system that by default is allowed to establish its own characteristic dynamic(s) since, by design, it uses the very stuff it is made of in order to study itself.

Appendix 1

Final, in-well dilution of sample RNA refers specifically to the dilution that each RNA sample incurs post DNase-treatment by the time it exists in the PCR plate reaction wells. In our lab, for Turbo DNase-treating RNA, we typically use 60 to 80 μl of RNA sample in each 100 μl DNase-treatment reaction, which, after inactivation reagent is added, each become 110 μl – from which 80 μl of each final DNase-treated RNA sample is recovered. The DNase-treated RNAs are then immediately diluted 1:10 with nuclease-free water (Ambion) and stored at 4°C (not -80°C as commonly suggested). After optimal dilutions for each target (as established by FF-2-6-001-based Test Plate analyses) have been carried out, 7.8 μl of each optimally-diluted RNA sample is subsequently used in each final 30 μl qPCR reaction mixture (of which 25 μl is added to the final 96-well qPCR reaction plates). The in-well dilution of each RNA sample is thus 0.26 in each case. So the most concentrated RNA sample possible (post DNase-treatment, in-well) in our one-step real-time qPCR studies is a 1:38.46-diluted RNA sample (e.g. 0.1 _{[immediately post-DNase dilution factor]} x 0.26 _{[in-well dilution factor]}). But, this dilution is rarely useful as it consistently demonstrates severe qPCR inhibition; no amplification of target signals. Consequently, it is only useful as the sample on Test Plates which most clearly exhibits the stark reality of real-time qPCR inhibitory phenomena (possibly all five types). We routinely use all of our DNase-treated sample RNAs within 6 months and have found most of them to be stable (stored at 4°C) for over three years as real-time qPCR templates (which is highly contrary to the plethora of technical literature which warns against this). We have designed the FF2-6-001 qPCR set-up tool to keep track of all RNA sample dilutions at every step along the way throughout each entire qPCR procedure; from initial RNA spectrophotometer 260_nm and 280_nm readings on. This feature makes it possible for us to know the ng/μl in-well RNA concentrations for each final target qPCR reaction (see Figures 37 and 38).

Appendix 2

Type 4 inhibition is especially difficult to assess or identify when using commercial master mixes since such reagents are restrictive in that they are very rarely altered by investigators before use in qPCR. Mg²⁺ and dNTP concentrations, along with other corporately undisclosed proprietary components (including passive reference molecules such as "ROX") are assumed to be "optimal" as is – as purchased from the manufacturer. But, as most conventional PCR users know, adjusting Mg²⁺ concentrations, in particular, has profound effects on the efficacy of PCR reactions in general, and such adjustments to commercial master mixes would undoubtedly influence the kinetics of primer-probe interactions with qPCR target templates. Adjusting, for instance, ABI master mix Mg²⁺ concentration from 7.5 mM to 5.5 mM has been demonstrated be more appropriate for qPCR amplification of the ubiquitously-abundant 18S ribosomal RNA [housekeeping] transcript (34), but, in practice, most investigators find such adjustments too nebulous or laborious to pursue. The "high-throughput" philosophy of real-time qPCR relies heavily on the existence of immediately-available, ready-to-use, pre-optimized (yet notoriously expensive) reagents, and as a result, the impetus for investigators to manipulate [pre-made] master mix components in effort to study the finer thermodynamic details of primer-probe interactions with real-time qPCR target templates under different conditions (e.g. to more fully explore the nature of Type 4 qPCR inhibition) is diminished in many cases. The reality that many important proprietary components in key reagents can never be openly discussed (for fear of violating copyrights or company privacy policies) ultimately limits the rate at which such questions might be answered.

Appendix 3

R_n, or normalized reporter fluorescence, is the level of fluorescence detected during PCR. R_n is calculated by dividing the reporter signal by the passive internal reference dye, rhodamine-5-carboxy-X (ROX). During PCR, R_n increases as target nucleic acid is amplified until the reaction approaches a plateau. ROX is a proprietary passive internal reference dye that does not participate in the PCR reaction where the "X" in each form of ROX differs from company to company (which is why ROX from different sources does not work the same). ROX emits a constant background fluorescent signal throughout the reaction. If there is a change in the delivered volumes of PCR Master Mix due to pipetting errors or sample evaporation, there will be a change in the intensity of fluorescence produced from the ROX molecule. e.g. if a sample has slightly evaporated – the components in the mixture are more highly concentrated and thus would give off more fluorescent signal per unit volume; this is true not only for ROX, but your target and endogenous reference reactions as well. Most quantitative PCR machines automatically correct for these changes in sample volume by normalizing all signals to ROX. The emission intensity of the target fluorescence signal (the target gene, sequence or message, or endogenous reference signal you are looking for) is divided by the emission intensity of ROX. Theoretically, a perfect plate would contain equal amounts of ROX in every well – but in practice, even well-rendered/executed triplicates can show unacceptable variations in signal intensity, and if this problem is due to master mix addition discrepancies, ROX is used to correct for that (as well as non-PCR-related well-to-well fluctuations