Question

Can you please check the grammar and also put them in proper paragraphs?

From the results above, we can confidently state that the results obtained are reliable and accurate. The absolute quantification of DNA using a calibration curve in QPCR is an accurate method. And the quantity in each DNA sample can be quantified successfully using the standard curve equation (y = -3.383x + 50.979). The quality of the data obtained and interpreted using standard/calibration curves depends on the quality of the curve; this is, in turn, governed by various statistical factors. For instance, the accuracy of the curve can be considered focusing on its R2 values (regression coefficient). In the given curve, the R2 is 0.997, which is regarded as a good fit since it is nearly approaching the ideal value of R2, i.e., 1.
Another way to tell the quality and accuracy of the QPCR data is by the cycle shift of the dilution, which is governed by the slope of the curve. Since the given curve has an ideal slope (cycle shift) of -3.383, it makes the obtained data reliable and superior. In QPCR, the efficiency of the experiment is governed by the slope of the standard curve obtained (in other terms, a cycle shift between each 1/10 dilution). Since in the given standard curve, the slope is -3.383, it lies between −3.6 and −3.3, and hence the efficiency is 90-100%. This indicates that the QPCR experiment in question is accurate, and thus can be used for quantification.
Furthermore, qPCR tests can be done either absolutely or relatively. Relative quantification is when the internal reference genes are used to determine the differences (in folds) in the expression of the target gene. The change in mRNA expression levels is governed and used for quantification. Thus, this is considered as an indirect method and hence is ‘relative.’ However, the technique used here is a ‘direct’ one and is based on the exact quantification of DNA about a set standard/calibration curve. This makes it more reliable and accurate. Thus, the QPCR method is accurate and can be used for quantification.

Answer 1

The quantification strategy is the principal marker in gene quantification. Generally two strategies can be performed in real-time RT-PCR. The levels of expressed genes may be measured by absolute or relative quantitative real-time RT-PCR. Absolute quantification relates the PCR signal to input copy number using a calibration curve, while relative quantification measures the relative change in mRNA expression levels. The reliability of an absolute real-time RT-PCR assay depends on the condition of ‘identical’ amplification efficiencies for both the native target and the calibration curve in RT reaction and in following kinetic PCR. Relative quantification is easier to perform than absolute quantification because a calibration curve is not necessary. It is based on the expression levels of a target gene versus a housekeeping gene (reference or control gene) and in theory is adequate for most purposes to investigate physiological changes in gene expression levels. The units used to express relative quantities are irrelevant, and the relative quantities can be compared across multiple real-time RT-PCR experiments.

Absolute quantification
link to speciefied absolute quantification sub-domain

Calibration curves are highly reproducible and allow the generation of highly specific, sensitive and reproducible data. However, the external calibration curve model has to be thoroughly validated as the accuracy of absolute quantification in real-time RT-PCR depends entirely on the accuracy of the standards. Standard design, production, determination of the exact standard concentration and stability over long storage time is not straightforward and can be problematic. The dynamic range of the performed calibration curve can be up to nine orders of magnitude from <101 to >1010 start molecules, depending on the applied standard material. The calibration curves used in absolute quantification can be based on known concentrations of DNA standard molecules, e.g. recombinant plasmid DNA (recDNA), genomic DNA, RT-PCR product, commercially synthesized big oligonucleotide. Sability and reproducibility in kinetic RT-PCR depends on the type of standard used and depends strongly on ‘good laboratory practice’. Cloned recDNA and genomic DNA are very stable and generate highly reproducible standard curves even after a long storage time, in comparison to freshly synthesized RNA. Furthermore, the longer templates derived from recDNA and genomic DNA mimic the average native mRNA length of about 2 kb better than shorter templates derived from RT-PCR product or oligonucleotides. They are more resistant against unspecific cleavage and proofreading activity of polymerase during reaction setup and in kinetic PCR (own unpublished results). One advantage of the shorter templates and commercially available templates is an accurate knowledge of its concentration and length. A second advantage is that their use avoids the very time consuming process of having to produce standard material: standard synthesis, purification, cloning, transformation, plasmid preparation, linearization, verification and exact determination of standard concentration.

A problem with DNA based calibration curves is that they are subject to the PCR step only, unlike the unknown mRNA samples that must first be reverse transcribed. This increases the potential for variability of the RT-PCR results and the amplification results may not be strictly comparable with the results from the unknown samples. However, the problem of the sensitivity of the RT-PCR to small variations in the reaction setup is always lurking in the background as a potential drawback to this simple procedure. Therefore, quantification with external standards requires careful optimization of its precision (replicates in the same kinetic PCR run – intra-assay variation) and reproducibility (replicates in separate kinetic PCR runs – inter-assay variation) in order to understand the limitations within the given application.

A recombinant RNA (recRNA) standard that was synthesized in vitro from a cloned RT-PCR fragment in plasmid DNA is one option. However, identical RT efficiency, as well as real-time PCR amplification efficiencies for calibration curve and target cDNA must be tested and confirmed if the recDNA is to provide a valid standard for mRNA quantification. This is because only the specific recRNA molecules are present during RT and the kinetics of cDNA synthesis are not like those in native RNA (the unknown sample) that also contain a high percentage of natural occurring sub-fractions, e.g. ribosomal RNA (rRNA, ~80%) and transfer RNA (tRNA, 10-15%). These missing RNA sub-fractions can influence the cDNA synthesis rate and in consequence RT efficiency rises and calibration curves are then overestimated in gene quantification. To compensate for background effects and mimic a natural RNA distribution like in native total RNA, total RNA isolated from bacterial or insect cell lines, can be used. Alternatively commercially available RNA sources can be used as RNA background, e.g. poly-A RNA or tRNA, but they do not represent a native RNA distribution over all RNA sub-species. Earlier results suggest, that a minimum of RNA background is generally needed and that it enhances RT synthesis efficiency rate. Low concentrations of recRNA used in calibration curves should always be buffered with background or carrier RNA, otherwise the low amounts can be degraded easily by RNAses. Very high background concentrations had a more significant suppression effect in RT synthesis rate and in later real-time PCR efficiency.

No matter how accurately the concentration of the standard material is known, the final result is always reported relatively compared to a defined unit of interest: e.g. copies per defined ng of total RNA, copies per genome (6.4 pg DNA), copies per cell, copies per gram of tissue, copies per ml blood, etc. If absolute changes in copy number are important then the denominator still must be shown to be absolute stable across the comparison. This accuracy may only be needed in screening experiments (amount of microorganism in food), to measure the percentage of GMO (genetic modified organism) in food, to measure the viral load or bacterial load in immunology and microbiology. The quality of your gene quantification data cannot be better than the quality of your denominator. Any variation in your denominator will obscure real changes, produce artificial changes and wrong quantification results. Careful use of controls is critical to demonstrate that your choice of denominator was a wise one. Under certain circumstances, absolute quantification models can also be normalized using suitable and unregulated references or housekeeping genes (see Normalization).