Cytometry and Antibody Technology

The Right and Wrong Way to Set Up Automated Compensation Tools: How to Achieve Accurate Compensation

by | Jun 24, 2020 | Compensation, Learn: Basics of Flow, Learn: Cytometer Setup, Learn: Spectral Cytometry | 11 comments

Edit October 21, 2021: It should be noted that the tips for gate placement described in this post can be applied to spectral unmixing as well, just replace the term “compensation” with “unmixing”. For more tips on spectral unmixing specifically, see this video.

When calculating compensation, automated tools are the gold standard. However, people often struggle to get good results from the automated compensation tools and will turn to manual compensation to fix any errors. Why is it difficult to get accurate results from automated tools? The only explanation that I’ve heard from the flow cytometry experts is that suboptimal results are caused by poor quality compensation controls. In this post I’d like to demonstrate that poor controls are not always the cause. The part that is often overlooked is that automated compensation tools are not fully automated – they require users to subjectively set gates on positive and negative cells within each control. Yes, some of the tools do have automated gate placement, but that feature often fails to generate appropriate gates. Gate placement is a critical component in determining the outcome of an automated compensation tool.

Setting Up the Automated Compensation Tool

Before I discuss gate placement I want to quickly go over how autocompensation tools work. Compensation determined to be correct when the median fluorescence intensity (MFI) of the positive population is in line with the MFI of the negative population (Figure 1). The flaw of automated compensation tools is that the user usually determines where to set the MFI for each population by placing gates on the positive and negative populations. Thus, the accuracy of the compensation is dependent on a subjective gate placement.

To demonstrate the effect of gate placement I’m using FlowJo’s automated compensation tool, though this should be applicable to any comparable software. I tested four different gate positions (Figure 2):

  1. The most positive (brightest) of the positive cells, which cuts through a high-expressing population and would never be used for analysis
  2. The whole bright/high-expressing population of the positive cells, assuming this control has a bright population and a dim population
  3. The entire positive population of cells, including dim and bright cells
  4. The whole dim/low-expressing population of cells, avoiding the bright/high-expressing cells

 

Results of Automated Compensation

To check the accuracy of the compensation determined by the automated compensation tool, I applied the compensation matrix to the same file and checked 5 plots. Again, the compensation is correct when the MFI of the positive and negative populations match. Here are my results:

The compensation using the first gate – on the most positive of the positive cells – gave the most accurate results. The second gate was nearly perfect, though two of the 5 plots are very slightly undercompensated. The compensation from the third and fourth gates produced suboptimal results with significant compensation errors. These data highlight that setting gates for autocompensation is very different from setting gates for analysis. The first gate would never be used to analyze data, yet it provides the best compensation results. The other gate options could be used for data analysis, but clearly do not work as well for calculating compensation.

Why does the first gate work so well to calculate compensation? If you remember the rules for compensation controls, one of the rules is that the control must be as bright or brighter than the sample. Compensation can be calculated for cells that are the same intensity or dimmer than the control, but calculating compensation for cells brighter than the control does not work well. Since compensation is calculated by MFI, that rule should be translated to state that the MFI of the control must be as bright or brighter than the sample. The first gate works the best for calculating compensation because the MFI (shown as the dashed vertical line in FlowJo in Figure 2) is the highest. In all the other gates, there are cells that are brighter than the MFI used to calculate the compensation, so those gates have essentially created a control that breaks the rules of a “good quality compensation control”.

In practice, my recommendation is to use an automated compensation tool, but know its limitations and always check the accuracy of the compensation prior to analyzing data. For best results, use high quality compensation controls and set the gates on the brightest cells. When setting the gates on the controls, remember that you are not analyzing the data, it is perfectly acceptable to cut a through a population with the goal of calculating a high MFI.

Looking for more flow cytometry resources?

Compensation

Learn how to choose between compensating on the cytometer or in an analysis software, tips for troubleshooting compensation errors, etc.

Spectral Unmixing

Learn tips and tricks for performing spectral unmixing in SpectroFlo, how the spectral unmixing algorithm works, etc.

11 Comments

  1. Well written article and clear explanation. Thanks for sharing Laura

    Reply
  2. Excellent article. Thank you.

    Reply
  3. I had been wondering about optimal gate placement for marking the positives for single color compensation controls. This article answered all my questions, and explained why. Thank you!

    Reply
  4. This is great! I love the images. How about placement of the negative gate when you have both populations in one tube? I’ve seen some folks draw gates differently depending on whether they’re using log or biex (and how appropriately the biex is scaled). I haven’t systematically tested it, but it looks like you have a good dataset for it!

    Reply
    • Hi Rachael,

      Good point! I used the universal negative for this dataset, and I haven’t played around with the negative gates much either. In general, I tend to put the gate on the most negative of the negative. I’ll try to play around with the negative gates and see if it makes any impact.

      Laura

      Reply
  5. I think this also shows the importance of titrating antibodies on beads to get the brightest signal. I saw so many people using the exact same concentration on beads as what they have on cells.

    Reply
  6. hi i just want to clarify that it IS ok to cut through a pop for the compensation but you just wouldn’t do it on your sample or FMOs right?

    Reply
    • Hi Ashley,

      The best gate placement for setting up a compensation wizard is one that captures the brightest intensity, even if that cuts through a population. The best gate placement for analyzing a fully stained sample (assuming you’re interested in separating positive and negative cells) is to draw it based on an FMO control. You could gate through the middle of a “population” if the FMO justifies it (like when there is a continuous signal between negative and positive cells instead of two distinct populations). It is very likely that the gates needed for the compensation wizard are completely different than gates needed for sample analysis.

      Reply
  7. Once you understand the mathematics underlying compensation, you realize that setting the gate with the highest possible MFI of the positives is your best bet. Note – on FlowJo, you have to take at least 2% of the events in the histogram.

    Reply
    • Mathematically, the data is correctly compensated when the MFI of the positives and the MFI of the negatives in the secondary detector(s) are equivalent, not just look like a straight line.

      Reply
  8. The blog and videos are well-explained. Learned a lot. Thank you, Laura.

    Reply

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