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My 3-Step Panel Validation Pocket Guide

panel validation guide cover photo

Written By: Tim Bushnell, PhD

Experimental validation refers to the process of evaluating a panel and ensuring it’s robust. Sometimes, you’re just handed a panel that you have to follow, and it might not be the best panel for your question or even your instrument. There’s a proper way to go about experimental validation, including protocols to follow. Here are 3 parts of high-quality experimental validation.

1. Optimize your reagents.

The first step is to optimize your reagents. You’ll order the antibodies and titrate them to make sure of 2 things: 

  • The antibodies work
  • You know the optimal concentration to use for your experiment. 

Once this is done, you’ll proceed to what we call “voltration” or a voltage walk, starting at the recommended voltage for your instrument. You’ll then move the voltage up and down to see if you can improve the separation index —the separation between the positive and negatives. If you find a better value, keep that new value. To help track that, use a bead – I like the peak 6 bead from Spherotech (RCP-30-5A-6), and I set target values based on that. You can use any bead that you want, though, as long as you use it consistently.

Next, you should run the voltages and try to hit those target values off your bead – this will help set your voltages consistently and repeatedly. You’ll also want to check any other reagents you might be using. For example, if you’re going to perform intracellular staining, you’ll need to make sure that the fixation and permeabilization methods you’re using are optimized and will work for your system.

Panel validation QC template

Figure 1:  Using the QC template to set voltages.

2. Optimize your assay.

The optimization process is designed to help you figure out the best conditions for your assay. You might already have protocols in place that will work sufficiently, but you have to test that. You’ll need to know whether it works on the samples you’ll be using. This will allow you to find and identify your populations of interest.

FMO controls to set positivity

Figure 2:  Using FMO control to set positivity.

Start by running all the controls that you might need to properly interpret your data. This includes FMO controls, biological controls, reference controls, and the like. Identify a set of controls that are necessary for you to find your population of interest.  Don’t forget to establish things like how long you can wait to analyze your samples, methods for testing a master mix and the like.

3. Perform validation.

Once you have optimized, it’s time to validate. During this step, you will run the panel under the established conditions using different methods, in different ways, to make sure that the panel is working consistently and reproducibility. Establishing the expected parameters of the assay is critical and will help to troubleshoot if a sample is out of the expected range.  At a minimum, it is ideal to run the same sample on multiple days, to establish the inter-assay CV.  Running the same samples stained by different people can help establish the intra-assay CV.  Finally, running the same sample on multiple instruments can help determine intra-instrument CV. 

Make sure you document this procedure in its entirety, from reagent optimization all the way to the validation step. When you put your panel into production, if you have that documentation, you will have a record explaining all the choices you’ve made throughout the procedure and will be able to address any concerns that might be raised when you go to report your results.

To learn more about important control measures for your flow cytometry lab, and to get access to all of our advanced materials including 20 training videos, presentations, workbooks, and private group membership, get on the Flow Cytometry Mastery Class wait list.

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Tim Bushnell, PhD

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Advanced 4-10 Color Compensation, Learn strategies for designing advanced antibody compensation panels and how to use your compensation matrix to analyze your experimental data.

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