The cycle threshold, or Ct, marks the point at which fluorescence from amplified product becomes measurable. Because fluorescence is monitored during cycling, the Ct provides information about the amount of starting template: samples reaching the threshold earlier contain more initial target than samples requiring additional cycles. This relationship enables quantitative comparisons between biological samples.
Primers determine which DNA sequence is amplified by providing sequence-specific starting points. Fluorescent dyes or probes generate a signal as amplification proceeds, allowing product accumulation to be followed during the reaction rather than only after cycling ends. Together, these components provide both target selectivity and a measurable signal for nucleic-acid quantification.
PCR amplification targets DNA, so RNA-based assays first use reverse transcription to convert RNA into complementary DNA. That complementary DNA then serves as the template for repeated amplification and fluorescence monitoring. This two-stage design allows qPCR to examine RNA-associated measurements, including gene expression, while retaining the quantitative cycle-threshold readout.
The principal difference is the initial template-processing step. DNA assays can proceed directly to amplification, whereas RNA assays require reverse transcription before the PCR cycles begin. This distinction matters when interpreting results because the measured signal in an RNA workflow reflects the conversion of RNA into complementary DNA followed by amplification of the selected sequence.
A typical workflow begins with the nucleic-acid sample and selection of primers targeting the sequence of interest. For RNA measurements, reverse transcription precedes amplification. The reaction is then cycled while fluorescence is recorded as product accumulates, and the resulting cycle-threshold value is used to assess the starting amount of the target sequence.
Researchers apply qPCR when they need quantitative information about a selected nucleic-acid sequence. In biology, uses include measuring gene expression, detecting pathogens, supporting genetic testing, and validating results from biological experiments. The method can also help examine disease mechanisms or changes in cellular responses under different experimental conditions.