Question 10 of 20 Fundamentals Concept
What does a Cq value represent in qPCR, and why does a lower Cq mean more template?
Short answer
The Cq, or quantification cycle, is the fractional cycle at which a reaction's fluorescence crosses a set threshold. Product roughly doubles each cycle at full efficiency, so a sample with twice the starting template crosses about one cycle earlier.
Why
During the exponential phase each cycle multiplies the product by a near-constant factor, the amplification efficiency. The more template a well starts with, the fewer cycles it needs to reach the threshold. That is why Cq falls as input rises, by about 3.32 cycles per tenfold more template when efficiency is 100 percent.
The number is relative, not absolute. It depends on where the threshold and baseline are set, on the instrument and on the reaction's real efficiency. For example, wells at Cq 22 and Cq 25 differ about eightfold at 100 percent efficiency, but closer to sevenfold at 90 percent efficiency.
MIQE 2.0 calls Cq values intermediate data. It recommends converting them into efficiency-corrected target quantities before any statistics. It also points out that averaging Cq values gives the geometric mean of the quantities, not the arithmetic mean.
The simple rule fails late in the run. Near the plateau, fluorescence no longer tracks product. At very low copy numbers, chance in how many molecules land in a well dominates the Cq.
A strong answer covers
- Cq is the fractional cycle where fluorescence crosses a threshold
- About 3.32 cycles per tenfold change at 100 percent efficiency
- Cq depends on threshold, baseline and real efficiency
- Convert Cq to efficiency-corrected quantities before statistics
- Relationship breaks at the plateau and at very low copy numbers
Common mistakes
- Comparing Cq values from runs analysed with different thresholds as if they were on one scale
- Assuming every one-cycle difference is exactly twofold when efficiency is below 100 percent
- Running t-tests directly on raw Cq values without efficiency correction or normalization
The interviewer might ask next
How would you convert a Cq difference into a fold change if the assay efficiency were 92 percent?