Biomedical Engineering Reference
In-Depth Information
determined by careful consideration of the quality of the RNA template [13-15], the choice
of cDNA priming strategy [16] and reverse transcriptase enzyme [17, 18], the character-
istics of the PCR primers [19, 20] and the validity of the normalization method [21-29].
Not surprisingly, the pervasive penetration of this technology has led to the development of
numerous, distinct and frequently divergent experimental protocols that often generate dis-
cordant results [30-32]. Fortunately, the resulting uncertainty has increased the awareness
of a need for common guidelines, in particular those relating to quality assessment of every
component of the RT-qPCR assay and appropriate data analysis [11]. This clear requirement
for improved consistency of gene expression measurements is particularly relevant in rela-
tion to human clinical diagnostic assays [7, 33]. Certainly, it is apparent that while qPCR is
driving PCR-based innovations, effective guidelines regulating its use are essential for the
future of molecular diagnostics within biomedical sciences [34, 35].
Detailed discussions of the many considerations that are essential for obtaining biologi-
cally relevant data can be found elsewhere [36] and online (http://www.gene-quantification.
info/).
6.2 Methods and approaches
A major reason for the popularity of RT-qPCR is its capacity to characterize and
quantitate RNA templates that are present in very low copy numbers in minute amounts
of sample. Although there are alternative technologies, both PCR-based (e.g. competitive
methods such as StaRT-PCR [37]) and non-PCR-based methods [38], the ostensible
simplicity and sheer ubiquity of real-time assays has ensconced it firmly as the method
of choice in most areas of life, medical and agricultural sciences. Although undoubtedly a
straightforward technology, many of the problems generally associated with quantification
using conventional RT-PCR [39] remain, and a successful RT-qPCR assay is characterized
by a sequential series of steps that must be executed carefully in order to complete a
meaningful quantification experiment (see Figure 6.1).
6.2.1 Sample selection
Optimal sample quality is a prerequisite for generating valid quantitative data. Hence, sam-
ple selection and collection, as well as RNA quality control, are critical parameters in test
performance and must be optimized [40, 41]. In general, extraction of RNA from tissue
culture, blood and serum is relatively straightforward, while there are significant problems
associated with the extraction of RNA from solid tissue, feces, semen, plants and soil sam-
ples. A critical consideration is the quantification of RNA from complex tissue samples,
as these usually contain several different cell types that may express the target RNA(s) at
different levels of abundance. This inevitably results in the averaging of the expression of
the transcript from different cell types, and the expression profile of a specific cell type may
be masked, lost or ascribed to and dismissed as illegitimate transcription. This is particularly
relevant when comparing gene expression profiles between normal and cancer tissues, since
normal cells adjacent to a tumor may be phenotypically normal, but genotypically abnormal
or exhibit altered gene expression profiles due to their proximity to the tumor. This problem
can be addressed by microdissection, in particular laser capture microdissection (LCM) [42],
and significant differences have been detected in the gene expression profiles of microdis-
sected and bulk tissue samples [43, 44]. A further refinement combines RT-qPCR with
in situ
hybridization on adjacent tissue sections and so allows simultaneous spatiotemporal
