Conversion of cDNA differential display results (DDRT-PCR) into quantitative transcription profiles

TL;DR

This paper presents a method to convert gel-based DDRT-PCR gene expression data into quantitative profiles using DNA sequencer analysis, enabling broader transcription studies especially in non-model organisms.

Contribution

The authors developed a novel data processing procedure that transforms DDRT-PCR results into quantitative transcription profiles, expanding the utility of gel-based gene expression analysis.

Findings

Successfully converted DDRT-PCR data into quantitative profiles

Demonstrated method on oilseed rape and fungus interaction

Provides an open-end alternative to microarray analysis

Abstract

Background: Gene expression studies on non-model organisms require open-end strategies for transcription profiling. Gel-based analysis of cDNA fragments allows to detect alterations in gene expression for genes which have neither been sequenced yet nor are available in cDNA libraries. Commonly used protocols are cDNA Differential Display (DDRT-PCR) and cDNA-AFLP. Both methods have been used merely as qualitative gene discovery tools so far. Results: We developed procedures for the conversion of DDRT-PCR data into quantitative transcription profiles. Amplified cDNA fragments are separated on a DNA sequencer. Data processing consists of four steps: (i) cDNA bands in lanes corresponding to samples treated with the same primer combination are matched in order to identify fragments originating from the same transcript, (ii) intensity of bands is determined by densitometry, (iii) densitometric values are normalized, and (iv) intensity ratio is calculated for each pair of corresponding bands. Transcription profiles are represented by sets of intensity ratios (control vs. treatment) for cDNA fragments defined by primer combination and DNA mobility. We demonstrated the procedure by analyzing DDRT-PCR data on the effect of secondary metabolites of oilseed rape Brassica napus on the transcriptome of the pathogenic fungus Leptosphaeria maculans. Conclusion: We developed a data processing procedure for quantitative analysis of amplified cDNA fragments. The system utilizes common software and provides an open-end alternative to microarray analysis. The processing is expected to work equally well with DDRT-PCR and cDNA-AFLP data and be useful in research on organisms for which microarray analysis is not available or economical.