Reproducibility In Flow Cytometry Requires Correct Compensation

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Why do we have to compensate flow cytometry data?

Newcomers to flow cytometry are often confronted with one of the most confounding issues in flow cytometry. That is, trying to understand the whole idea of “compensation”. It can be explained theoretically, mathematically, by trial and error, or by “take my word for it”. Depending on the audience, a combination of these are used to get the point across.

Simply put, compensation is the mathematical process of correcting the spectral spillover of a fluorochrome into a secondary detector. It relates to the physics of fluorescence. To understand what this means, let’s start with the Jablonski diagram of fluorescence.

Figure 1: Jablonski diagram of fluorescence. Used user creative commons license. Original.

A fluorescent molecule starts at rest, with electrons in the ground state. When a photon of light hits this molecule, it is absorbed (purple line), promoting an electron to a higher energy state. There are a variety of ways that the energy release can happen — we are specifically interested in fluorescence. When a molecule fluoresces, it releases a photon of light of lower energy and higher wavelength than the photon, that excites the molecule.

That emitted photon is of lower energy, and therefore, a higher wavelength than the exciting photon. This process can be modeled, and using one of various spectral viewers available on the Internet, it is easy to see. Such a model is shown below for AlexaFluor™488 (AF488).

Figure 2: The excitation (dotted line) and emission (solid) for AlexaFluor™ 488.

In an ideal world, the fluorescence emission would be just a single wavelength, but this is not an ideal world. The maximal emission is about 520 nm, but the tail of the emission extends out past 600 nm.

This becomes a problem for flow cytometry when the emission spectrums of different fluorophores overlap into different detectors. Shown in Figure 3 superimposed on the AF488 emission spectra are two bandpass filters: a 530/30 nm (blue) and a 585/42 (green). Looking at the green filter, it is clear a percentage of the AF488 curve can be measured in that wavelength range.

Figure 3: AlexaFluor™ 488 emission profile showing overlap into a second filter range.

Compensation addresses this spectral spillover and is the focus of the next 3 blog posts. To start, let’s establish some ground rules, the “3 Rules of Compensation”, which first appeared here. Understanding these core concepts is essential to understand how to properly compensate and refute some of the myths of compensation.

1. The control should be at least as bright as the sample.

The first rule is that controls need to be at least as bright as any sample you will apply compensation to.

Mathematically, compensation is calculating the slope of the line between the single stained positive and the unstained negative. As shown in Figure 4, if dim particles are used to calculate the slope, the value is 23.58. When bright particles are used, the slope is 24.55. The difference is explained by the fact that there is a greater error in the dim particles than there is in the bright particles (note the size of the error bars). Since this is on a log-scale, it is further magnified as the error of the measurement varies with the square root of the absolute value of the measurement.

Figure 4: Demonstration of the first rule of compensation. Control must be at least as bright as the sample it is applied to.

Baked into the first rule are the following caveats: that is, that the signals need to be on scale and within the linear region of the PMT detector. When the signal violates either of these 2 conditions, accurate compensation is impossible. This is shown in Figure 5, where the signals in the yellow shaded regions violate these caveats, and will result in incorrect compensation.

Figure 5: Demonstration of the linear scale of a detector.

When this rule is violated, the consequences can be profound, as shown in the figure below. On the left, is the compensation control used to set compensation. This has been compensated. On the right, is the fully stained sample. As can be seen from the black line, representing the median for the compensation control, the positive sample is above this line, and based on the red line, undercompensated.

Figure 6. Consequences of violation of the 1st rule.

2. The background of the positive and negative carriers must be matched.

The second rule is that the background fluorescence should be the same for the positive and negative control population for any given parameter. Since the goal, as discussed above, is to calculate the slope of the line between the positive sample and the negative — for these to be comparable, the backgrounds must be the same.

In a future article, the advantages of cells or beads as a carrier will be discussed. For the purposes of the second rule, this means that if beads are used as a positive control, the correct comparison is an unstained bead. Likewise with cells. However, you cannot use cells as a negative control and beads as a positive. This is what the Universal Negative forces many users to do, and why it should be avoided.

Shown in Figure 7, are the autofluorescence of unstained beads and cells. Notice the difference in background between the two.

Figure 7: Autofluorescence of unstained cells (red) and beads (blue), along with positively stained beads (green).

The consequences of this can be seen in Figure 8. Here, the blue lines connect the ~medians of the unstained cells with the positive bead, and the red lines connect the ~medians of the unstained beads with the positive bead.

Figure 8: Consequences of violating the second rule.

In a compensation matrix, you can have some fluorochromes compensated with cells and others with beads, as long as there is the appropriate negative control for each sample. To put it another way, no matter if you are using cells or beads, just make sure you use the matched controls, i.e. matched positive and negative controls.

3. Compensation color must match experimental color exactly.

The third rule of compensation is that the compensation color must be matched to your experimental color. This means matched fluorochrome, matched sensitivity, and matched treatment. Shown in Figure 9 is the emission spectra of 3 different “green dyes”. All are typically read of blue excitation and a 530/30ish band pass filter. Is it possible to use one to compensate for the other?

Figure 9: Emission spectra of GFP, Brilliant Blue (BB)515 and Fluorescein (FITC). The emission max are indicated by the lines.

The answer is “no”! Each of these have different spectra, so you cannot substitute one for the other. This concept extends to tandem dyes. These manufactured dyes are comprised of 2 fluorochromes, a donor and an acceptor. The donor dye is excited, and rather than emit a photon, will excite the acceptor dye (with caveats), and the emission of the acceptor dye is what is measured by the flow cytometer. Some tandem dyes are easy to spot, as they have the names of 2 fluorochromes, such as: Cy5-PE, Cy7-APC, Cy5.5-PerCP. Others are not so easy to spot, including BV570, BV605, or BB700. Figure 10 shows 2 different lots of the same tandem dye.

Figure 10: Spectral overlap of 2 different lots of the same tandem dye (Cy7-PE).

If Lot 1 was used in the experiment and Lot 2 used for compensation, it is clear there would be compensation errors, resulting in incorrect data interpretation. So, make sure the dye lots are matched.

4. Collect enough events.

This is a bonus rule for everyone, an extension of the original rules. There is software that is present to collect some number of events for compensation. Don’t rely on that value.

For your flow cytometry data to be valid, you must collect enough events. The more events, the better. In the case of a bead carrier, at least 10,000 single beads. With cells, a minimum of 30,000 events — more, if the positive samples in question are rare. A good rule of thumb is a minimum of 5,000 positive events.

Figure 11: Effects of collecting more events on the compensation value.

Here, as the number of single events increases, the compensation value approaches the final value of 22.51. This is because with fewer events, the accuracy of the measure is decreased. Data storage is relatively inexpensive, as are beads, so don’t shirk from collecting enough events.

Understanding the 3 rules of compensation, and applying them to your everyday workflows is an essential step in good, consistent, and reproducible flow cytometry data. Making sure the controls are bright, and treated the same way is essential. Don’t bring unfixed controls when your samples are fixed, as the controls will not reflect the spectra from the fixed samples. Make sure not to rely on the “Universal Negative”, use a single sample to set background, and collect enough events to make sure an accurate measurement is made to further improve the quality of your control and therefore the data.

To learn more about Reproducibility In Flow Cytometry Requires Correct Compensation, 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

Tim Bushnell

I enjoy answering paradigm-shifting questions and trouble-shooting puzzling glitches. I also like finding new ways to enhance old procedures. I’m passionate about my professional relationships and strive to fill them with positive energy.

My other passions include grilling, wine tasting, and real food. To be honest, my biggest passion is flow cytometry, which is something that Carol and I share. My personal mission is to make flow cytometry education accessible, relevant, and fun. I’ve had a long history in the field starting all the way back in graduate school.
Tim Bushnell
This entry was posted in Compensation, Controls on by .

About Tim Bushnell

I enjoy answering paradigm-shifting questions and trouble-shooting puzzling glitches. I also like finding new ways to enhance old procedures. I’m passionate about my professional relationships and strive to fill them with positive energy. My other passions include grilling, wine tasting, and real food. To be honest, my biggest passion is flow cytometry, which is something that Carol and I share. My personal mission is to make flow cytometry education accessible, relevant, and fun. I’ve had a long history in the field starting all the way back in graduate school.

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