How to validate a new adapted protocol after adjusting the gate on NC-200/NC-250

Background

When we use NC-200 or NC-250 to count different cell types or cells in different conditions, we can encounter situations where the core cell population falls out of the default gate and therefore, a counting gate needs to be adapted in the Plot Manager and saved in the file. Afterwards we can create a new protocol accordingly using ‘Protocol Adaptation Wizard’ and save it. Once a new adapted protocol is created, we might need to validate that the new gate is now correct.

How to validate a new adapted protocol after adjusting the gate on NC-200/NC-250

In the following section, some general points to validate a new adapted protocol after adjusting the gate are recommended:

  • First, check the cells on the images and go back and forth between the scatter plots and the image. It gives you the possibility to make a decision on what is a cell (which you would like to count) and a debris.

Use triplicates at each measurement and raise some general points with the validation of a cell line: 

  • Do not compare individual measurements during validation experiments. Always do at least triplicate measurements for any sample and make inferences based on the variation levels (coefficient of variation) to be in compared. A single stand-alone value does not provide the predictive assumption (statistics) necessary for comparison of measurements. 
  • Use referenced values (from ‘expected results’) in order to infer on instrument performance: 

a. Dilution curves (expected dilution values): Dilute a cell sample in culture media in order to generate a series of dilution ratios. Check for correlation between expected and obtained values. 

b. Viability dilution curves (expected viability values): chemically kill a cell population, say, with 80% ethanol over 24h, and then mix healthy cells with increasing ratios of the dead cell sample in order to create a gradient of increasing expected viability. Check for correlation between expected and obtained values.

  • Eliminate undesired sources of variation in comparisons: 

a. Always normalize cell density when comparing independent samples. Fluctuating cell densities add an extra source of variation and render the samples less comparable 

b. Sample handling (dilution steps, incubations etc) should be equal throughout all samples to be compared.

Last updated byAnonymous