These could include contaminating a control with a second fluorophore, running the same control twice under different file names, poor panel design, poor instrument settings, and extremely high autofluorescence. If you’re really struggling to get the single stain controls to look correctly compensated there are probably several issues contributing to the compensation errors. I don’t often see this mistake being the sole reason for major compensation errors, but it’s something to be aware of. This is easy to overlook if the wizard uses a universal negative feature by default and there are multiple types of single stained controls (unstained cells and single stained compensation beads or different tissues for different controls). You should never assume that automated compensation wizards do everything correctly!Īnother mistake in setting up automated compensation wizards happens when the autofluorescence of the positive particles is not appropriately matched to the autofluorescence of the negative particles. A note about automated compensation wizards – sometimes they don’t match up the correct controls, so make sure the PE single stain control is assigned to PE, the APC single stain control is assigned to APC, etc. It’s likely that you didn’t set up the gates in your automated compensation wizard correctly (see more on that here), or you tried to do manual compensation and made a mistake. The easiest explanation for this situation is that you just didn’t calculate compensation correctly, so you need to go back and fix it. Step 3a: Solutions for errors in both controls and full stains The compensation error only exists in the fully stained tube, but the single stained tubes are correctly compensated.The exact same compensation error pattern exists in both the fully stained tube and single stained tube.In order to determine the best approach for addressing the error(s), it’s important to get a full picture of which tubes contain the error. Step 2: Determine which tubes contain the compensation error. The easiest way to identify those is to look for events below zero, especially populations that are significantly skewed into the negative region as opposed to being symmetrically centered around zero. I talked about this in the first post of my bad flow cytometry data blog series (find that here) but as a reminder you should always be on the lookout for compensation errors. Step 1: Determine if compensation errors exist. However, I hope it will help you get started in identifying the most common causes and how to fix them. This workflow also works for spectral unmixing – just replace “compensation” with “unmixing” as you read the post! The key to getting good at this is really time and experience, and there is no way this post will be able to cover how to solve every possible error. In this post I’m going to walk you through my workflow for identifying flow cytometry compensation errors and determining the appropriate approach for fixing them. But to me compensation is an opportunity to solve a complicated puzzle – and I really love a good puzzle. Usually when I bring up compensation I’m met with a chorus of frustrated noises. I have an unpopular opinion: I love compensation.
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