The Polymerase Chain Reaction Pcr
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The polymerase chain reaction was first developed in 1983 by Kary Mullis. This reaction is commonly used in molecular biology to amplify and generate thousands to millions of copies of specific DNA sequences across several orders of magnitude (4-1). It relies on thermal cycling, consisting of cycles of denaturation, primer (short DNA fragment) annealing and primer extension (4-7). PCR can also be used for the analysis of RNA sequences and to qualitatively detect RNA expression levels through creation of complementary DNA (cDNA) transcripts from RNA by use of reverse transcriptase. This technique is called reverse transcription-PCR (RT-PCR) (5-2). Although PCR and RT-PCR have revolutionized many areas of biomedical science, they are not suitable for the quantitative analysis of analysis of samples. Hence, real-time or quantitative PCR (qPCR) techniques need to be employed (5-8, 5-9).
RT- qPCR distinguishes itself from other methods available for gene expression, such as northern-blot analysis, ribonuclease (RNase) protection assay and competitive RT-PCR, in term of accuracy, sensitivity and fast results (2,6). RT-qPCR does not required post-amplification manipulation and it can produce quantitative data with wide dynamic range of detection (7 to 8 logs). In addition, RT-qPCR assay is 10,000 to 100,000-fold more sensitive than RNase protection assays and 1000-fold more sensitive than dot blot hybridization (3).
RT- qPCR also can even detect a single copy of a specific transcript and can reliably detect gene expression differences as small as 23% between samples (3-6, 3-7). Furthermore, it has lower coefficients variation (cv; TaqMan at 24%; SYBR Green at 14.2%) than end point assays such as probe hybridization and band densitometry (45.1%; 44.9% respectively) (3-8). RT- qPCR can differentiate between messenger RNAs (mRNAs) with almost identical sequences and requires much less RNA template than other methods of gene expression analysis. Because of this, RT- qPCR has established itself as the gold standard for the detection and quantification of RNA targets (1-2,2). Furthermore, it is firmly established as a mainstream research technology (1-3). However, the major disadvantage of RT-qPCR is that required expensive equipment and reagents (3). The principle of RT-qPCR is straight forward: following the reverse transcription of RNA in to cDNA, it needs an appropriate detection chemistry to detect the presence of PCR products, an instrument to monitor the amplification in real-time and compatible software for quantitative analysis. RT- qPCR is characterized by the point in time during cycling when a PCR product amplification is first detected (Figure 1, 1). A direct relationship between the starting copy number of the nucleic acid target and the time required to observe fluorosence increasing. Nowadays, there are four fluorescent DNA probes available for RT-qPCR detection of PCR products: TaqMan, SYBR Green, Molecular Beacons, and Scorpions. All of them generate a florescent signal to allow the detection of PCR products. While the TaqMan probes, SYBR Green, Molecular Beacons, and Scorpions generation of fluorescence depend on Forster Resonance Energy Transfer (FRET) coupling of the dye molecule and a quencher moiety to the oligonucleotide substrates, the SYBR Green dye simply emits its fluorescent signal by binding to the double-strand DNA in solution (5-34).
As RT-qPCR has extremely high sensitivity and reproducibility, in depth understanding of normalization techniques is imperative for accurate conclusions (6). Normalization of gene expression data is an essential component of a reliable RT-qPCR assay and it is used to control for error between samples (7,3). This error could be introduced at one or more stages throughout the experimental protocol; (input sample, RNA extraction, etc.) however, there are many strategies to control this error ( please, read the discussion section strategies for more details). Currently, internal control genes, which are often referred to as housekeeping genes, are most frequently used to normalize the messenger RNA (mRNA) fraction. This housekeeping gene should remain constant in the tissues on cells under investigation, or in response to experimental treatment (8, 8-69, 8-70, 6,7). In addition, the ideal housekeeping genes should by stably expressed, and their abundances should show strong correlation with mRNA total amounts present in the samples (8). Consequently, normalization against a single housekeeping gene in not acceptable and can falsely bias results unless the researchers present clear evidence for the reviewers that confirms its invariant expressions conditions described (3,8-71,8-73). In this study we carried out an evaluation the gene expression of three commonly used housekeeping genes (GAPDH, Î²-action, ALAS1) in three different cell lines which are derived from T-cell leukemia, B-cell lymphoma and myeloid leukemia, using RT-qPCR as an analytical tool.
Our goal was to recognize a housekeeping gene with minimal variability under different experimental conditions.
Materials and Methods:
Samples, RNA handling and isolation:
Cell line pellets (5-10X106 cells) which have been frozen in 0.5 ml TRIsure Reagent (Bioline code BIO-38033). Cell lines are: Jurkat, CEM-C7 and MOLT-4 (all T-cell leukemia-derived); SKW 6.4, BJAB and JeKo-1 (all B-cell lymphoma-derived); and HL-60, NB4 and K562 (all derived from myeloid leukemia).
RNA was isolated from cell pellets using Trizol procedure. However, isolating and handling RNA ask for special precautions because naked RNA is highly susceptible to degradation by ribonucleases (RNases) (1). RNases are found everywhere and they are very stable and active enzymes that do not require metal ion co-factors to function and can maintain activity even after prolonged autoclaving or boiling. So that, all equipment and reagents should be treated to inactive RNases prior to use. Wearing gloves while handling reagents and RNA samples, changing gloves frequently, keeping tubes closed whenever possible and keeping isolated RNA on ice when aliquots are pipetted for downstream applications could prevent RNase contamination. We are used sterile, disposable plastic ware and they were RNase-free and do not require pretreatment to inactive RNAses (8-46,8-47). The quantity of the isolated RNA was determined by nanodrop spectrophotometry (absorbance at 260 nm of a 40Âµg/ml solution of RNA is 1.0) using nuclease-free water as a blank.
A sample was reserved for quality assurance (see below) and the remainder was stored at -80 degree centigrade for one week.
Gel electrophoresis (quality assurance of RNA) :
The quality of the isolated RNA was verified by agarose gel electrophoresis. The gel was run at 100V for 30 minutes and photograph under UV transillumination.
A DNase digestion step was performed as a precaution using RQ1 RNase-free DNase kit although the TRIzol method generally results in RNA which is essentially free from genomic DNA (2). A sample was reserved for reverse transcription (see below) and the remainder was stored at -80 degree centigrade.
11 ÂµL of DNase-treated sample was reverse transcripted, using Superscript II reverse transcriptase, to complementary (cDNA) by random hexamer priming.
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