Rapid Quantification of Gene Expression by Competitive RT-PCR and Ion-Pair Reversed-Phase HPLC. (February 1996)

• Record Type: Journal Article
• Authors: Hayward-Lester, Amanda; Oefner, Peter J.; Doris, Peter A.
• Is Part Of: Biotechniques. Volume 20:Number 2(1996)
• Page Start: 250
• Page End: 257
• Publication Date: 1996-02
• Subjects: Biology, Experimental -- Periodicals; Molecular biology -- Periodicals; Medical technology -- Periodicals; Biology, Experimental; Medical technology; Molecular biology; Clinical Laboratory Techniques -- Periodicals; Research -- Periodicals; Medical Laboratory Science -- Periodicals; Periodicals; Electronic journals; 570
• DOI: 10.2144/96202rr02
• Languages: English
• ISSNs: 0736-6205

Abstract

Competitive reverse-transcription PCR (RT-PCR) techniques for quantification of gene expression employ titrations in which the products of multiple PCRs must be separated, analyzed and quantified to compute gene expression in a single sample. We have employed a novel, ion-pair reversed-phase HPLC (IP-RP-HPLC) system to analyze and quantify RT-PCRs performed with mutant RNA internal standards. PCR products could be separated and quantified in 6 minutes per reaction using the absorbance signal from an on-line UV detector. Crude PCR products can be analyzed without further processing and without the addition of radioactive or fluorescent markers to reactions. Analysis of titration regression and slope values approached mathematical ideals indicating that amplification of native and competitor RNA occurred with equal efficiency. Further, serial dilution of input RNA over three orders of magnitude did not affect the calculated level of gene expression or the slope of the titration. IPRP-HPLC appears to offer important advantages to quantitative measurements of gene expression. These include rapid sample analysis and column re-equilibration, reduced sample handling and opportunity for introduction of quantification error, avoidance of fluorescent or radioactive tracers, high detector sensitivity and linearity and excellent quantitative reliability.