A method for quantitative analysis of standard and high-throughput qPCR expression data based on input sample quantity.

Mateusz G Adamski,Patryk Gumann,Alison E Baird,John R Battista

doi:10.1371/journal.pone.0103917

Mateusz G Adamski, Patryk Gumann + Show 2 more

Open Access

https://doi.org/10.1371/journal.pone.0103917

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Abstract

Over the past decade rapid advances have occurred in the understanding of RNA expression and its regulation. Quantitative polymerase chain reactions (qPCR) have become the gold standard for quantifying gene expression. Microfluidic next generation, high throughput qPCR now permits the detection of transcript copy number in thousands of reactions simultaneously, dramatically increasing the sensitivity over standard qPCR. Here we present a gene expression analysis method applicable to both standard polymerase chain reactions (qPCR) and high throughput qPCR. This technique is adjusted to the input sample quantity (e.g., the number of cells) and is independent of control gene expression. It is efficiency-corrected and with the use of a universal reference sample (commercial complementary DNA (cDNA)) permits the normalization of results between different batches and between different instruments – regardless of potential differences in transcript amplification efficiency. Modifications of the input quantity method include (1) the achievement of absolute quantification and (2) a non-efficiency corrected analysis. When compared to other commonly used algorithms the input quantity method proved to be valid. This method is of particular value for clinical studies of whole blood and circulating leukocytes where cell counts are readily available.

Highlights

Over the past decade a rapid increase has occurred in the understanding of RNA expression and its regulation
Accurate analysis of Quantitative polymerase chain reaction(s) (qPCR) data is crucial for optimal results and a number of welldefined methods are in use to calculate gene expression
Several gene expression analysis methods are in common use, but the input quantity approach presented here offers two major advantages

Summary

Introduction

Over the past decade a rapid increase has occurred in the understanding of RNA expression and its regulation. Accurate analysis of qPCR data is crucial for optimal results and a number of welldefined methods are in use to calculate gene expression. These include the comparative CT method [1], the efficiency corrected method [2] and sigmoidal curve fitting methods [3], all of which provide relative quantitative information. In the efficiency corrected method by Pfaffl [2] the relative expression ratio is calculated only from the real-time PCR efficiencies and the crossing point deviation of an unknown sample versus a control This model needs no calibration curve and gives improved quantification but is complex to use and requires determination of the amplification efficiency

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