A colleague related the following conversation/mini-rant that happened in their facility just the other day. The names have been changed to protect the innocent.
New User #1: “Hi, I want to sort and run a 10-color panel on preparation of dissociated rat prostate tumor cells. Can you tell me what I need to order so we can do this next week?”
Wise Flow Director: Mental facepalm. “Can you be a bit more specific?”
New User #1: “I need to look at all the different cells in this prostate tumor and sort them for RNAseq. What colors do I need?”
Sadly, this scenario is repeated more frequently than not around the world. Let’s see how this might have played out with a more informed user.
New User #2: “Hi, I want to sort and run a 10-color panel on preparation of dissociated rat prostate tumor cells. I know what antibodies I need, but I don’t know the instrument configuration. I would like to do the sort next week.”
Wise Flow Director: “Great, here is our sorter configuration. I’m happy to help you design your panel, but next week might be a bit early to plan for the experiment.”
New User #2: “But, we have an <insert excuse here> due next week.”
Notice, the user has started the process of experimental design by knowing what antibodies are needed for the assay.
When designing an experiment, the first step is to know what the experimental question is, and which antibodies are needed to answer the question.
The second step is to know the instrument characteristics. There are 3 different components of the instrument configuration that the user needs to know.
1. Excitation Sources
The excitation sources dictate which fluorochromes can be excited.
Another feature that is important to know is if the lasers are collinear (sharing the same light path), or spatially offset. This difference will have an impact on what fluorochrome combinations are feasible.
Notice the spectra Cy5.5-PE and Alexa 700 below (Figure 1). Cy5.5-PE is excited by the 488 nm laser, and Alexa 700 by the 633 nm laser. However, both have similar emission profiles, so there is overlap between them.
These 2 fluorochromes would be poor choices for a collinear arrangement, but work quite well on a spatially offset layout, where they are not excited at the same time.
Figure 1: Laser arrangement impacts fluorochrome choice.
2. Detector Arrangement
This is the information of what detectors are paired with each excitation source, and the specific wavelength of light each detector is measuring.
Photomultiplier tubes (PMTs) can convert any photon that hits the photocathode to a photocurrent, so flow cytometers use filters to control the specific wavelengths a given PMT encounters. An example instrument configuration is shown below (Figure 2).
Figure 2: An example of an instrument configuration showing excitation sources (left), emission filter ranges (middle), and common fluorochromes (right).
3. Spillover Issues
This information is less readily available, but is extremely useful when developing a polychromatic panel. This analysis identifies which fluorochromes contribute high levels of spectral spillover into other detectors.
Spillover reduces sensitivity of measurements in that specific detector.
One such analysis is based on the paper by Nguyen and co-workers (2013). This analysis outputs values representative of the relative amount of spillover for each detector/fluorochrome pair, but an easier way to look at this is using a heat-map type analysis, as shown below (Figure 3). The more red the color, the greater the contribution of error for that fluorochrome into the detector.
Figure 3: Spectral Spillover Matrix to help design panels by avoiding the red regions of this chart.
The resulting chart looks a bit intimidating, so for convenience, if one sums across the chart, a number representing the amount of spectral spillover a given detector receives. Summing down the columns provides a measure of the amount of spillover a given fluorochrome contributes to a panel. That is much simpler to understand, as shown in Figure 4, where these values were calculated and arranged highest to lowest.
Figure 4: Summary of data from Figure 3.
Now that the information about the fluorochromes and antibodies is known, the next step of panel design is to pair fluorochromes with antibodies such that the brightest fluorochromes are paired with poorly expressed antibodies, and dimmer fluorochromes with more highly expressed antibodies.
Of course, don’t forget a viability dye, and please consider using a dump channel.
Once all that is worked out and the reagents are ordered, there is still more work to do. One can’t just throw the antibodies together and stain the cells. For the best success, some optimization must occur. That’s why our new user cannot reasonably expect to do the experiment next week. Of course, there are several steps in optimization.
1. Sample Prep
The first user in our SRL drama planned to make single cell suspensions from a prostate tumor.
It is critical to optimize the sample preparation procedure even before ordering antibodies.
Successful flow cytometry relies on a good single cell suspension. For many samples (water, blood, bone marrow, and spleen), this is straightforward. Solid tissues and adherent cells require more optimization.
Once the sample preparation looks good under the microscope, it’s time to give the SRL team a call and get some time on the instrument of choice — sorter or analyzer — so that the quality of the preparation can be assessed.
One recommendation when doing this is to co-stain the cells with a cell-permeant nuclear dye, like DRAQ5, and cell-impermeant nuclear dye, such as DAPI.
This combination will help assess the cell preparation in 2 ways. First, it will show how many dead cells are in the sample. Some preparation procedures are very harsh on the membrane and the cell-impermeant dye will show cells that have been damaged.
Figure 5. The use of a cell-impermeable viability dye, such as DAPI, in conjunction with a cell-permeant dye, like DyeCycle Ruby, helps clarify where live undamaged cells are in a tissue prep. Optimize dissociation conditions to maximize this population.
The cell-permeant dye will help assess the total cells and how many may be doublets.
One other thing that this test analysis can assist with is validating the instrument nozzle size. It is recommended that the nozzle be 4-5 times the size of the cells. If the cells are too large for the nozzle, the result will be stream fanning, and poor sort yields and recoveries.
Running a test sort on this optimization sample is a good way to make efficient use of user material.
High concentrations of antibody cause a loss of sensitivity by increasing non-specific background binding.
It is always good to perform a titration experiment to identify the best concentration for the cell type and assay. A typical titration curve is shown below (Figure 6).
The blue line represents the vendor recommended concentration, and as seen from these curves, only Ab#3 is recommended to be used at the vendor concentration, while Ab#1 and #2 can be used at approximately ¼ the recommended concentration.
Figure 6: Titration results from 3 different antibodies. The Staining Index, as calculated based on the Telford method, compares the separation between positive and negative. The best SI is chosen for staining.
3. Voltage optimization
To summarize, single-stained cells (at the titrated recommended concentration) are run across a voltage range and the SI calculated. The optimal voltage is where the following criteria are met.
- The positive signal is on-scale
- The positive signal is in the linear range of the PMT
- The best SI is obtained
That is a lot of work to get to a point where one can begin to perform the experiment.
Hopefully, it is now clear why just walking into the flow cytometry facility and announcing that a sort must be done next week is a bit fatuous.
Don’t forget to estimate how many cells will be needed to perform your downstream analysis. A quick back-of-the-envelope calculation can give you an estimate of how many cells you need to bring to the instrument.
For example, Table 1 shows some numbers assuming 100,000 cells are needed for a downstream application. The frequency of the population, the recovery from the sorter, and recovery from sample processing all factor into how many cells an investigator needs at the start of the experiment.
The time to sort is an estimate of how long it would take to sort the cells at different nozzle sizes (which relates to how fast the sorter can run, and thus the droplet generation frequency).
Table 1: Relationship between cells needed for downstream application, frequency of target population, and expected recoveries from sample processing and sorting, and the time to sort. This does not include setup time on the instrument.
So, as can be seen, there is a lot of preparatory work that needs to be done before the first experiment can be attempted. Each step builds upon the last step, and extends where the assay is going. This doesn’t include testing the final panel, identifying the best controls for gating, and monitoring the performance of the experiment. Be prepared for some trial and error in this process, and don’t expect perfect results the first time around.
In conclusion, an educated user is a good user, and makes the SRL staff’s job that much easier. The partnership between investigator and SRL staff is a rewarding one, when both parties work together to achieve the ultimate goal of generating excellent data and sort results that help answer the biological question being tested. A little knowledge, some planning, and careful validation goes a long way to this end.
Overheard in the sort lab:
New User #3: “My PI sent me over here with this tube, and he wants the red cells.”
Wise Flow Director (to self): “Here we go again…”
Note: These are actual conversations, the names have been changed to protect the innocent (or guilty).
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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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