ChIP-qPCR data needs to be normalized for sources of variability, including amount of chromatin, efficiency of immunoprecipitation, and DNA recovery.
Here we discuss two common methods used to normalize ChIP-qPCR data—the Percent Input Method and the Fold Enrichment Method. We prefer analyzing ChIP-qPCR data relative to input as this includes normalization for both background levels and input chromatin going into the ChIP. We recommend that ChIP experiments are run in replicate and that the results are presented together with the background signal and standard error when possible.
Percent Input Method
With this method, signals obtained from the ChIP are divided by signals obtained from an input sample. This input sample represents the amount of chromatin used in the ChIP. An example is illustrated below. Typically, 1% of starting chromatin is used as input.
To calculate percent input:
|Step 1||Step 2|
|*Adjusted input to 100%||Percent input|
|Raw Ct||(Ct Input – 6.644)||Triplicate average Ct||100*2^(Adjusted input – Ct (IP)|
|Input (1%)||32.7||26.1||Adjusted input||26.1|
* Note: For example, if the starting input fraction is 1%, then a dilution factor (DF) of 100 or 6.644 cycles (i.e., log2 of 100) is subtracted from the Ct value of diluted input.
Fold Enrichment Method
This normalization method is also called ‘signal over background’ or ‘relative to the no-antibody control’. With this method, the ChIP signals are divided by the no-antibody signals, representing the ChIP signal as the fold increase in signal relative to the background signal. The assumption of this method is that the level of background signal is reproducible between different primer sets, samples, and replicate experiments. Background signal levels do vary between primer sets, samples, and experiments. An example is shown below.
To calculate fold enrichment:
|Step 1||Step 2|
|Nonspecific adjustment||Fold enrichment|
|Raw Ct||(Ct IP) – (Ct mock)||(2-DDCt)|