3 Questions You Should Be Asking About Flow Cytometry Controls For Your Experiments
What are the controls that you should be considering when planning and designing your flow cytometry experiments?
Are you using isotype controls? Should you be?
Isotype controls are one of the controls used in flow cytometry today and is very controversial. In fact, I encourage people to not use it.
Are you following the rules of compensation and including the appropriate controls?
Compensation is such an important thing to get correct when running your experiments, especially if you’re doing polychromatic flow cytometry, which many of us are doing nowadays.
What types of quality controls should you have in place? Quality control is an important part of the whole experimental process any yet many people don’t worry about it or even think about it. But if left unchecked, poor quality control can cause major problems.
For example, a colleague of mine was searching for a biomarker for a disease that they were studying. They had done several years worth of analysis and over time acquired many samples. When they went to analyze the data they found what they thought was a potential biomarker.
So they went back to try and prove it. Well, it turns out that they couldn’t prove that the biomarker was actually valuable. When they dug deeper they found out that halfway through their experimental runs, the laser on the system had been replaced – and no one had told them.
There was no procedure in place, so nobody knew the laser had been replaced other than the service engineer. Ultimately that impacted all of their data and wasted years worth of time, money and effort.
Having the right controls in place would have prevented this very frustrating situation.
Here we dive into 3 questions you should be asking yourself to ensure that you are producing reliable and reproducible data…
1. Should you be using isotype controls?
The theory for using isotype controls is that the isotype control is an antibody with the same isotype as your target antibody but which binds to a non-target. This is supposed to be able to tell you what the non-specific binding is of your target antibody.
But, does it really tell you this? Are Isotype controls really acting as a control in your experiments?
Well, the first question you have to ask is are there primary targets for the isotype control on your cells of interest, and how do you know this?
For example, MOPC-173 is a common isotype mouse IgG2a kappa, it was first published in the 1970s has not been shown to bind any specific target. In fact, the production sheet from one vendor specifically states that this antibody was chosen as an isotype control after screening on a variety of resting, activated, live, and fixed mouse, rat, and human tissues.
Now, how much characterization was done as we continue to subset our populations of interest, we always find new and novel things. Look at the fact that murine reagent B220, thought to be on B cells of mouses has recently been shown to be present on a subset of human B cells as well.
So, looking at broad strokes, usually, because the isotype doesn’t bind, it’s not necessarily encouraging.
Second, do you know that the affinity of the variable region of your isotype control has the same affinity with the variable region of your target control? Now, how do you figure that out?
Third, is, of course, the fluorochrome to protein ratio.
Now, if you’re using PE, yes, the one-to-one is probably reasonable, but in many other cases, especially using things like Alexa Fluor or FITC, we don’t necessarily know what the F to P ratio is, unless you get it directly from the vendor, or you try to figure it out. While historically, isotype controls have been used to determine positivity, they really only help you show if blocking was effective.
I really discourage the use of isotype controls.
But you might be worried about what the reviewers of your publications will say.
This happened to a colleague who recently submitted a paper for publication, and one of the reviewers criticized the paper for the lack of isotype controls.
We put together a two-page document explaining why the isotype controls were not valid in this case and why they are not useful. We gave it to the investigator to send back to the reviewers. The paper ultimately got published.
If you are not convinced, read this paper by Anderson and co-workers from 2016.
In this paper, they do a really good job of showing you the value — or in this case the lack of value — of isotype controls.
2. Do you have a quality control procedure in place?
Quality control is something that a lot of core facilities do.
Running the instrument, checking how things are behaving, using some metrics.
You might think it’s okay to leave this in the hands of the core facility, but it’s really important that you, the end user, ask about those metrics, look at them, and try and understand what that information is telling you.
As a core director and core operator, you need to look at that data and you can’t just run it and walk away. You have to monitor it over time.
Because if you don’t monitor it over time, you don’t know and won’t notice trends.
Figure 1: User-driven quality control measurement of instrument performnace.
If you’re using OEM protocols, you have to understand the power and limitations of those protocols. So that you can decide yourself when and where you might want to intervene in quality control.
Secondly, quality control improves the rigor of our experiments.
Rigor is a huge component of the whole reproducibility initiative. So knowing that your machine is behaving the same way on a day to day, week to week, month to month basis because of quality control it is essential to have good data for your publications. Now don’t just rely on the core facility’s personal to do quality control. You, the end user can build in your own quality control metrics as well.
It’s very simple to get a standard bead that you run every single time you run your experiment. The peak 6 beads from Spherotech for example. When you start your experimental protocols, you run that bead and you establish some target values. And then every time you run an experiment on that machine before you run your samples, you put that bead on and you check those target values.
Make sure you’re getting the same target values. If you have to adjust the voltage monitor how much you have to change the voltage and make a note of that. If it’s too large, more than 10%, you really wanna talk to the core facilities.
Find out what might’ve happened. Maybe there was an instrument replacement? A laser replacement? A PMT replacement? Maybe there’s something wrong with the machine?
But before you put your samples on, you’ve done that quality control.
3. Are you following the 3 cardinal rules of compensation?
You need to be following the 3 rules of compensation whenever you run a flow cytometry experiment. The three rules of compensation are as follows.
- The control fluorescence must be at least as bright as the experimental fluorescence if not brighter.
- The background fluorescence of the carriers of a positive and a negative sample must be matched.
- You need to have identical characteristics between the control and the experimental sample.
The first rule, that we need to have the control sample at least as bright as the experimental sample, exists because when we calculate compensation we want to get an accurate measurement of the slope of the line between the negative and the positive. The more accurate that measurement the better.
Figure 2: Compensation Rule 1.
So at a higher fluorescence with more photons being collected we get a better signal. Implicit in the first rule is the fact that the signal is on scale. So we need to be in the linear dynamic range of the PMT detector as well as in the on scale of the detector.
If your signal is off scale you need to think about reevaluating how you set up compensation. Especially when you develop either polychromatic panel, I recommend that you make sure your compensation is accurate. And make sure your compensation signals are on scale.
Figure 3: Inspecting signal to ensure data is on-scale and in the linear region of the PMT.
The second rule, that the background carrier, has to be matched, is important because compensation is a property of the fluorochrome, not the antibody or the carrier. Since the compensation will be based on the background signal, it is imperative that the backgrounds are matched.
Figure 4: Impact of the second rule of compensation.
You can use beads or cells. You can have beads and cells in your sample compensation matrix, but you need to have a positive and a negative for both. Because you need to have the same background fluorescence on the positive sample that is on the negative control sample. The third rule, which is most important, is that you need to have the same fluorochrome and the same sensitivity.
This means you can’t use FITC to compensate GFP. Even though they both are green and they both are typically measured in the same detector, they are very different spectrally.
More in importantly this is critical for tandem dyes. Which are manmade and will have different variations lot to lot. So you really want to make sure that you use the same tandem dye. Which is why beads are useful.
Figure 5: Different lots of tandem dyes cannot be used to compensate each other.
The second thing is that you have to have the same sensitivity. So that means you need to use the same voltage. You do not want to be reusing your compensation metrics day in and day out because your voltages may change over time. Even a small five-volt change can impact your compensation.
Controls are an incredibly important part of your flow cytometry experiments. If not done correctly, poor controls will waste time and money. But with proper care, high-quality controls will result in high-quality data. Just be sure to ask yourself these key questions, should you be using isotype controls, do you have a quality control procedure in place, and are you following the 3 cardinal rules of compensation.
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