Biomedical Engineering Reference
In-Depth Information
in the experimental RNA samples. The
C
q
value is inversely proportional to the log of
the initial copy number. Therefore, a standard curve is generated by plotting the
C
q
values
against the logarithm of the initial copy numbers. The copy numbers of experimental RNAs
can be calculated after real-time amplification from the linear regression of that standard
curve, with the y-intercept giving a measure of the sensitivity and the slope a measure of the
amplification efficiency. Standard curves can be constructed from PCR fragments,
in vitro
RNA polymerase-transcribed sense RNA transcripts, artificial, synthesized single-stranded
sense-strand oligodeoxyribonucleotides, or from commercially available universal reference
RNAs [83].
Absolute quantification is most obviously used for determination of RNA copy numbers
as surrogates for quantifying tumor cells, or infectious particles like viruses or bacteria
in body fluids, but it is also usefully applied to quantify changes in mRNA levels. The
accuracy of absolute quantification depends entirely on the accuracy of the standards. In
general, standard curves are highly reproducible and allow the generation of specific and
reproducible results. Nevertheless, it is difficult to calibrate these standards so that they per-
mit universal, absolute quantification, and results may not be comparable to those obtained
using different probe/primer sets for the same markers, and will be different from results
obtained using different techniques. Furthermore, external standards cannot detect or com-
pensate for inhibitors that may be present in the samples. For this it is necessary to spike
the sample with an internal control (e.g. a synthetic amplicon) or perform a prior screen to
detect inhibitors using the SPUD assay (see Protocol 6.2).
Relative quantification is the most widely used quantification method, although its use is
associated with a number of complications [75]. Perhaps the most obvious one concerns the
choice of reference gene to use for expressing the relative quantity of any target gene(s).
The 'gold standard' for relative quantification normalizes the
C
q
values from target RNAs
to the geometric means of approximately three internal reference genes found to show the
least variability [21] and results are expressed as relative fold over- or under-expression
compared with the reference sample. This produces a corrected relative value for the
target-specific RNA product that can be compared between samples and allows an
estimate of the relative expression of target mRNA in those samples. It is crucial that the
amplification efficiencies of target and reference are similar, since this directly affects the
accuracy of any calculated expression result. Several models have been published that use
different algorithms to correct for efficiency and claim to allow a more reliable estimation
of the real expression ratio (see above). However, since the expression of most reference
genes is regulated and their levels usually vary significantly with treatment or between
individuals, relative quantification can be misleading [84]. Furthermore, if the relative
levels of the reference and target genes vary by orders of magnitude, then the former may
have entered its plateau phase by the time a
C
q
for the target becomes apparent. This is
likely to interfere with the accurate quantification of the target mRNA.
6.2.8 RT-qPCR standardization
Any publication involving the quantification of mRNA using an RT-qPCR assay should
include the following information [85]:
•
RNA quality data (quantity, integrity, absence of inhibitors);
•
sequence database accession number of the target gene;
