4 Critical Components In Cellular Proliferation Measurement

Cellular proliferation is a critical component in biological systems.

While normal cell proliferation keeps the body functioning, abnormal proliferation (such as in cancer) can be a target for therapy.  

Measuring how cells proliferate in response to a stimulus is a time-honored assay in science. This can be as simple as a cell count between untreated and treated cells. More sophisticated assays can include the use of ³H-thymidine or colorimetric assays (MTT assays).

While these all can measure proliferation, they lack the finesse that flow cytometry can bring to the assay – which allows the phenotypic identification of which cells are actually dividing, as well as allowing for calculations of values such as the precursor frequency, the percentage of cells that have divided, a proliferation index, and more.

Proliferation calls for cells to make a trip around the cell cycle, and there are many ways to measure cell division.

The focus here is on the long-term measure of cell division—the ‘temporal’ dimension for measuring such biological processes as:

• Proliferation of immune cells in response to stimulation
• Self-renewal of stem cells
• Biological homeostasis
• Tumor cell proliferation

To this end, there are several critical components in developing, validating and optimizing an assay to make these measures using flow cytometry. These 4 components are…

1. Pick the right cellular proliferation dye.

Determine which dye you want to use for proliferation. The qualities of a good dye for proliferation include:

• It is taken up by live cells
• It stains brightly
• It is well retained by the cells
• It segregates equally between daughter cells

There are two major classes of these dyes, based on where the dye is retained by the cells.  

The first class is the intracellular dyes that enter the cell, are acted upon by cellular esterases which cleave the compound into a fluorescent form that can also interact with intracellular molecules, thus binding inside the cell.

The most common of these dyes is carboxyfluorescein diacetate succinimidyl ester (or CFDA-SE).  When this enters the cell, it is cleaved to the active form CFSE, which is amine reactive, binding to intracellular proteins, and has been used extensively for cell tracking and proliferation.

The second class of dyes for proliferation are lipophilic dyes that bind to the cell membrane.  

These dyes are typically not fluorescent until they are incorporated into the cellular membrane, and over time distribute over the whole cell. There are a host of these dyes, one of the most popular is PKH26. Table 1 below lists some of the most common cell proliferation dyes available.

There are many more dyes available, and a quick search of your favorite vendor’s catalog will reveal one that will work for your needs. The Molecular Probes Handbook is especially useful in selecting a dye for your experiment needs and instrument capabilities.

Table 1:  Some Common Cell Proliferation Dyes

2.  Validate your cellular proliferation dye—make sure your cells like it!

After you have selected your dye, it is critical to optimize the labeling reaction for the dye.

This will include optimizing the labeling solution (typically between 0.1 to 10 μM), the cell concentration (between 1-20 million cells per ml), the incubation time and temperature, and the quenching step.

In the case of succinimidyl dyes, this would involve adding protein (BSA) and letting the cells rest for 5-10 minutes before washing. As part of this assay development, it is important to make sure that the dye does not kill the cells.

After labeling, a viability check is critical.  

Shown below are data from Dr. Andy Filby (head of flow cytometry at Newcastle University): cells were labeled with increasing amounts of CellTrace Violet (CTV), CFSE and eFluor670 (EPD) and the viability measure.  At 1 μM, the cells have about the same viability, but this rapidly changes, especially for CFSE, where increasing amounts of the dye increased cell death.

More dye is better, in so much as the brighter the signal, the more generations can be measured in the experiment, but not at the expense of increased dead cells.

Dead cells tell no tales.

3.  Optimize your flow cytometry instrument.

In an ideal world, as the cells divide, the fluorescence signal would decrease precisely by ½ and calculating the proliferation metrics would be easy.

As shown in this figure, modified from a lecture Dr. Andy Filby presented for Expert Cytometry (and available to Mastery Class members here), the reality is not that pretty.

Several factors influence the spread of the data, including:

• The true biological variance inherent in the cell systems
• The intrinsic spread because of fluorochrome labeling and speed of the cells through the system
• The extrinsic spread because of the instrument (such as optics, laser power, alignment)

The consequences of these factors can be reduced in several ways:

• Careful planning of the experiments
• Run the cells at low differential pressure
• Monitor the laser alignment
• Keep the instrument (flow cell) clean

Before running samples, run a standard bead set to validate the instrument. Checking linearity, sensitivity, and alignment before the actual samples are run is a good way to help minimize the causes that can be controlled.

4.  Analyze your proliferation data correctly.

In designing a proliferation experiment, as with any flow cytometry experiment, it’s important to develop a data analysis plan before beginning the experiments.

There are three informative parameters that can be measured from a properly constructed experiment. These values are:

• The Percent Divided – the measure of the percent of the input cells that entered division.
• The Division Index – a measure of the average number of divisions which includes the undivided cells.
• The Proliferation Index – the average number of divisions that exclude the undivided cells.

There is a clear temptation to manually gate on the populations and calculate these values, because of the overlap between peaks. It is impossible to easily set the gates to avoid the overlap. This is where modeling of the data comes into play.

Take, for example, the below data (courtesy of Dr. Andy Filby)…

On the left, the populations have been manually gated, while on the right, they have been fitted to a model using the FlowJo™ proliferation package.

Manually fitting the data gives a percent divided at 78%, while the model fitted data shows a percent divided of only 59%.

It’s clear that the above difference has a major consequence on the interpretation of the data.  

It’s also important to remember dyes like CFSE have a 24-48 hour proliferation-independent loss of signal that must be taken into account before measuring proliferation.

Another thing to remember is that as the cells divide, it will become over-represented in the data by 2^{division round}.  To determine the true percentage of dividing cells, you have to do some math. This starts by correcting the frequency of each generation by dividing by 2^{division round}.

These values are then added together, and the data normalized by that value to determine the real frequency in each population. Furthermore, by subtracting the undivided fraction from 100, it is possible to get the precursor frequency.

Making sure that you know the data needed to answer the biological questions, and the power (and limitations) of the analysis of the data is critical.

Define the question, define the statistics needed and then, and only then, foray into the lab and begin the experiments.

Proliferation assays are a powerful tool for understanding and monitoring this important cell process. Understanding this process, how it can be dysregulated and what ways it can be controlled (chemically), is a critical process. The development of new treatments for cancer, for example, target the proliferation of the cancer cells. Failure to properly design and analyze the data will result in missed opportunities and false leads. Knowing the steps to optimize these assays and properly interpret the results, as discussed here, will help ensure the best data and best opportunities are pursued.

To learn more about getting your flow cytometry data published 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.