Before running an ELISA, consider the following best practices to get accurate and consistent data:
1. Run samples in replicate.
To help evaluate the extent of error, each standard and sample should be tested in replicate (duplicate or triplicate, depending on the number of samples and room on the plate). Afterwards, the average, standard deviation (SD), and coefficient of variation (CV) can be calculated to provide confidence in pipetting precision. As a best practice, the replicates should have a CV of less than 20%. If the CV is higher than 20%, consider the following:
Reasons the CV may be high:
2. Run a standard curve on every plate.
Every ELISA runs slightly differently depending on the operator, pipetting, incubations, and temperature. Taking these variables into account, it is a best practice to run a standard curve on each plate.
3. Run a positive control sample.
Running a control sample with a known concentration on each plate will indicate whether the ELISA was successfully executed. If the control sample represents the correct concentration, you can be confident in the results of the other unknown samples.
4. Run blank samples.
Blank samples are composed of the buffer or water with no protein sample included. These samples allow the subtraction of background absorbance from the rest of the data points to ensure the most accurate OD readings.
5. Dilute samples so they fall within the linear range of the standard curve.
To get the most accurate results, dilute the samples so they fall within the linear range of the standard curve. Values that fall toward the top or bottom of the curve tend to have a higher amount of error because of the assay’s limits. Many operators test samples at multiple dilutions to ensure that at least one of them falls within the linear range.
After running the assay and getting an output, the data must be analyzed. Whether using software that works with a spectrophotometer or the Microsoft™ Excel™ program on the exported absorbance reading, similar analysis must be done.
During analysis, consider the following best practices:
1. Use a 4-parameter algorithm to generate the standard curve.
Preferably, use curve-fitting software to generate the standard curve. A 4-parameter algorithm provides the best standard curve fit.
2. Subtract background absorbance from all data points.
Remember to use the blank samples to subtract any background from the readings. If the blank samples are reading higher than usual, this may indicate that there was an error in the assay.
3. Take into account dilution factors.
Once the unknown sample’s absorbance is known, remember to apply the dilution factors, if used. For example, if all samples were diluted 5-fold, multiply the absorbance by 5 and use the standard curve to get the final concentration.
4. Calculate average, standard deviation, and CV when running replicates.
After taking blanks and dilution factors into account, the concentrations of unknown samples can be compared to the standard curve. Then, if replicates were run, analyze the data for the average, standard deviation, and CV for the final results.
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