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Posted on Thu, Jan 16, 2020

Flow Cytometry Interpretation

Flow cytometry, or fluorescent activated cell sorting (FACS) has become a fundamental method for analyzing and collecting cell populations. Flow cytometry tells you the percentage of cells in a particular population that have the characteristics that you are interested in. These characteristics are defined by the array of surface proteins on each cell. The principle of flow cytometry involves labeling cell surface proteins with fluorophores and using lasers to record the fluorescent profile of a population of cells; these cells can also be sorted and isolated into enriched populations during the FACS process. The results of flow cytometry are read by the technicians and scientists performing the assay, and are typically displayed as two-dimensional dot plots with color density information included for greater detail and dynamic range. 

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Interpretation of flow cytometry data plots

The plots generated by flow cytometry include a data point for each individual cell. The axes represent the intensity of a fluorophore, which can be customized based on your experiment, typically represented in a logrithmic or bi-exponential “logicle” scale. The data is analyzed by mapping the cells into the plot according to two flurophores at a time. The cell populations generally appear as positive and negative populations in each fluorophore. The population of cells with the desired characteristics are then “gated” by the software and moved to a second plot with another set of fluorophores. This analysis is repeated until all of the flurophores have been interpreted and the user is presented with detailed information on the cell population of interest. This process can also be used during the sorting process to obtain an enriched population of desired cells from a mixed starting solution. 

Compensation and Logrithmic vs logicle plots

Compensation is a term used to describe the process of teaching the FACS instrument what flurophores to identify as positive or negative. This process reduces the overlap of fluroescence between flurophores with similar emission spectra, and also reduces background noise by clearly identifying which intensities are positive and which are negative. The compensation process is performed by the FACS user and requires single color controls, which are cells labeled with only one fluorophore at a time. These cells are run through the instrument and the fluroescence intensity is recorded. The user then defines the cutoff between positive and negative intensity. This requires intimate knowledge of the labeling strategy and flurophores used. Another strategy is to create fluorescence minus one (FMO) controls that contain all fluorophores minus the one being compensated for. Proper compensation leads to clean FACS data. However, when logrithmic plots are used for compensation it can lead to error and confustion because logrithmic plots can be misleading. The problem with logrithmic plots is that negative cells are plotted very close to the axes and are often difficult to see in comparison to the positive cells. It is hard to see that a population of negative cells is similar in size to a population of positive cells if they are all bunched up on the axis lines. The logicle plot was developed to fix this problem. The logicle plot is a bi-exponential plot that serves to collect these negative cell data points and plot them clearly around zero. The zero point in a logicle plot is clearly visible and the axes continue to values below zero. This brings this negative population away from the axis lines and into the body of the plot, which allows these negative cells to be clearly displayed as a population. The user is then able to get an accurate impression of the size of the negative population in comparison to the size of the positive population and can properly “gate” the data. When a user gates the data they tell the software which cell population they are interested in based on the fluorophore intensity

The compensation and gating of flow cytometry data is a fluid process if the goal is simply the analysis of cell characteristics. In this case the fluroescence intensity data is first collected by the instrument and then loaded into a software analysis program. The compensation and gating can be applied within the software as required. This process requires a thorough understanding of how flow cytometry works, and when these plots are published it is important that publishers and readers carefully consider how the data was interpreted and presented by the authors. Likewise, it is important to consider compensation and gating when cell populations are enriched via flow cytometry. The collection of cells is entirely dependent on accurate compensation and gating strategy. Flow cytometry interpretation is key to understanding the significance and validity of published scientific work.

Citation: 

Herzenberg, Tung, Moore, Herzenberg, and Parks. Nature Immunology, 7:7 2006.


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