Quantitative PCR (qPCR)

Quantitative PCR (qPCR) and reverse transcription qPCR (RT-qPCR) are utilized for targeted quantification of DNA or mRNA, microRNA and lncRNA.

It is well known that qPCR-based approaches for gene expression studies have excellent sensitivity, specificity, linear dynamic range of quantification, as well as a high level of flexibility.

In our laboratories, we have processed tens of thousands of clinical samples, including various types of liquid biopsies and FFPE material using qPCR for quantification of both protein coding messenger RNAs (mRNAs) and non-coding microRNAs and long non-coding RNAs (lncRNAs).

Our team has proficiencies with the following key platforms: cobas (Roche), Rotor-Gene Q (Qiagen), Idylla (Biocartis), QuantStudio (ThermoFisher), and Promega.

Our team can support any qPCR-based application, using either off-the-shelf qPCR assay or expertly designed and validated custom primers.

• >1000 gene expression panels for specific pathways and disease states available
• Proprietary data processing software: qbase+

Various qPCR projects performed both in research and clinical trial settings:

• Quantification of microRNA expression in liquid biopsies, including a PCR based pre-amplification step to maximize sensitivity
• Optimization of diagnostic RT-qPCR based tests on sorted cells
• Measuring multi-gene signatures in whole blood and FFPE samples
• Quantification of gene expression in cell lysates, including a gene-specific PCR based pre-amplification step
• SNP genotyping
• Customized qPCR-based applications such as detection of RCR/RCL in PBMCs

Our team is internationally recognized for its qPCR expertise, setting standards in gene expression analysis with landmark papers and contribution to guidelines:

• A comprehensive assessment of RNA-seq accuracy, reproducibility and information content by the Sequencing Quality Control Consortium (SEQC/MAQC-III Consortium, Nature Biotechnology, 2014)
• The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments (Bustin SA et al., Clinical Chemistry, 2009)
• Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes (Vandesompele J et al., Genome Biology, 2002)

Digital PCR