qPCR Analysis: The Key to Unlocking Disease Diagnosis and Treatment

Real-time quantitative PCR (qPCR) assays are gold standard tests for accurate, sensitive, and rapid analysis. Whether  gene expression analysis or PK samples in clinical trials, qPCR assays are increasingly becoming mainstream. Besides, due to the options of ddPCR assays, drug developers are increasingly relying on qPCR analysis to understand disease diagnosis and treatment. 

Efficient experimental design, high-quality assay and reagent, quality controls, and proper quantification models are crucial for successful qPCR analysis. The current article discusses critical qPCR analysis parameters for understanding and unlocking the diagnosis and treatment of disease and medical conditions. 

qPCR analysis for diagnosing and treating disease

Researchers often conduct three pilot experiments before beginning qPCR analysis. The first study involves the validity of newly designed qPCRs. A second pilot study is needed to identify the best reference reagents while working on a new sample type.. The final pilot experiment assesses the magnitude and variability observed in the study results, enabling an adequate approach for reaching statistical significance. 

High-quality controls are crucial for obtaining accurate and precise results. Once new assays are developed, a pilot experiment is conducted to test the newly developed assay. These experiments require three types of quality controls: melt curve analysis, product size analysis, and dilution series. Notably, commercially developed assays should also undergo similar empirical quality control. 

The quantification approach for qPCR assays is based on raw quantification cycle values (Cq). This value is determined using two different algorithms. The initial baseline threshold method identifies the baseline cycle range and then uses different standard deviations of the noise levels to establish a threshold line. Other methods include identifying unique points on the sigmoidal amplification curve. 

Data quality controls are critical for qPCR experiments. They are vital in both preanalytical steps and during data analysis. Quality controls include inspecting melt curves for all data points to confirm similar specificity determined during pilot experiments. Additional types of data quality control evaluate intermediate results and Cq values. 

Researchers can include different controls in an experiment. The most commonly used control is the no template control. Each qPCR assay and new assay run requires no template control. Ideally, no template control does not result in amplification and hence has no Cq value. Moreover, adding PCR replicates can improve accuracy. Three or more replicates can help detect outliers and exclude them from further calculations. These replicates are adequate for assessing the overall quality of the data points and the identification of outliers. 

Furthermore, reporting can be efficiently achieved through tools such as the MIQE checklist that can help researchers evaluate and report qPCR experiments  accurately and relevantly. Conforming to such guidelines can help researchers validate the results and help them avoid pitfalls during qPCR analysis. 

qPCR analysis is most commonly applied for studying gene expression. Gene expression studies are critical for several biomedical and clinical research. In most gene expression assessments, scientists determine and compare the relative gene expression levels in control samples versus study subjects. 

In Conclusion

qPCR analysis has several applications in drug development and discovery. Besides, adequate technological advances can help qPCR become the key to unlocking disease diagnosis and treatment.