bioline/RT-qPCR Extraction Control Orange/500 Reactions/MDX029

Product Highlights

• Simple – streamlined protocol for straightforward validation of RNA extraction and determination of RT-qPCR assay inhibition
• Sensitive – control assay identifies even small effects on RNA extraction and inhibition of amplification
• Optimized - control RNA has a sequence with no known homology to any organism thereby avoiding detection of sample RNA
• Specific – probe-based assay designed specifically for the REC control sequence
• Flexible – ideal for use with a wide range of sample types, including inhibitor-rich samples like blood, urine and sputum samples

Product Description

A common practice in qPCR is to add a known amount of spiked control RNA after RNA extraction, this monitors PCR inhibition but has no value as an extraction control. The ideal situation is to have the test sample and internal control undergo the same processing prior to qPCR. Meridian has developed a RT-qPCR Extraction Control, which more closely mimics the test sample, as compared to spike controls. Genetic material from the test sample and the RT-qPCR Extraction Control is simultaneously extracted by common extraction methods, with the extraction control being as sensitive to inhibition and extraction failure as the test sample.

Artificial RT-qPCR Extraction Control cells are of a known concentration, containing the Internal Control RNA sequence. This sequence contains no known homology to any organism and, importantly, has minimal interference with detection of sample RNA. The RT-qPCR Extraction Control cells are spiked into the lysis buffer with the target sample, prior to RNA extraction. Control Mix, which primers and probe, is then added to the reaction mix before amplification. Signal derived from the Internal Control RNA confirms the success of the extraction step. RT-qPCR Extraction Control also monitors co-purification of PCR inhibitors that may cause biased or false amplification patterns.

Applications

• Pathogen detection
• Cancer risk assessment
• Gene expression analysis
• Drug therapy efficacy
• Biomarker validation
• Copy number variation (CNV) analysis
• Genotyping
• Viral loading