Flow cytometry is a technology used to analyze cells one by one as they pass through a laser beam. Each cell scatters light and emits fluorescence, allowing researchers to gather multiple data points about its physical properties and molecular makeup. This process generates complex data that is typically displayed in plots and graphs, which must be systematically interpreted to draw meaningful biological conclusions. Understanding how to read these visual representations is the first step in translating light signals into information about cell function and identity.
Understanding Scatter Plots
The initial step in analyzing flow cytometry data involves examining the scatter plots, which display the physical characteristics of the cells. These plots typically use two parameters: Forward Scatter (FSC) and Side Scatter (SSC), which are measurements of light deflected by the cell as it passes the laser. The intensity of the light scattered in the forward direction, or FSC, is generally proportional to the cell’s size, meaning larger cells produce a higher FSC signal.
Side Scatter, or SSC, measures the light scattered at a 90-degree angle and provides information about the internal complexity or granularity of the cell. Cells with a high number of internal structures, such as the granules found in neutrophils, exhibit a higher SSC signal compared to cells with smoother interiors, like lymphocytes. Plotting FSC against SSC allows for the preliminary separation of major cell lineages based solely on their physical properties. This scatter plot is often used to exclude unwanted events like cellular debris, which typically have very low FSC and SSC values, before proceeding to molecular analysis.
Decoding Fluorescence Signals
While scatter plots reveal physical properties, fluorescence signals provide the molecular identity of the cells. This is achieved by labeling cells with fluorescent dyes, known as fluorochromes, which are typically attached to antibodies that recognize specific cellular markers. When the laser excites these fluorochromes, they emit light at a particular wavelength, and the intensity of this emitted light is measured to determine the expression level of the target marker.
Data showing fluorescence intensity is displayed either in a one-dimensional histogram or a two-dimensional dot plot. A histogram plots the signal intensity of a single marker against the cell count, showing the distribution of expression within the sample. In a two-dimensional plot, two different fluorescence markers are compared simultaneously, providing a more detailed view of co-expression. Populations that exhibit high fluorescence intensity are considered “positive,” while those with intensity close to the background are considered “negative.” Analyzing these plots translates light intensity into the presence or absence of specific proteins.
The Technique of Gating Populations
Gating is the core interpretive methodology in flow cytometry, involving drawing boundaries around specific cell clusters in the plots to isolate them for further analysis. This technique is typically performed hierarchically, starting with physical properties before moving to molecular signals. For example, a researcher might first draw a gate around the lymphocyte cluster in the FSC vs. SSC plot to analyze only that subset of cells.
Sequential gating ensures that the subsequent analysis focuses only on the cells of interest, excluding noise from debris, dead cells, or cell aggregates called doublets. Doublets, where two cells are counted as one, can distort data and are typically excluded by comparing different aspects of the FSC signal, such as area versus width, to ensure only single cells are analyzed. Setting the appropriate boundaries for these gates relies heavily on experimental controls, which help distinguish a true positive signal from the background fluorescence or non-specific antibody binding.
Controls such as Fluorescence Minus One (FMO) or isotype controls are used to establish the precise threshold between positive and negative populations. The FMO control is useful in multi-color experiments, as it helps determine how the fluorescence from other markers might bleed into a specific channel, influencing where the gate should be set. Improper gate placement risks either excluding true positive cells or including cells that are merely exhibiting background noise, both of which lead to erroneous quantification and biological conclusions.
Identifying Specific Cell Types
The final stage involves applying scatter analysis, fluorescence decoding, and gating to identify specific cell types, a process known as immunophenotyping. This uses a panel of fluorescence-tagged antibodies to characterize cells based on the unique combination of markers they express. Immune cells are classified using Cluster of Differentiation (CD) markers; a cell expressing CD3 is identified as a T-cell, while a cell expressing CD19 is identified as a B-cell.
Identifying subsets relies on the co-expression of multiple markers within a single gated population. After isolating lymphocytes using the scatter plot, a subsequent plot of CD3 versus CD4 can be created to identify T-helper cells (CD3+ and CD4+). This refinement allows researchers to convert abstract visual data into a concrete biological conclusion, such as determining the percentage of cytotoxic T-cells (CD3+ and CD8+) in a patient sample. Quantifying these distinct subsets provides the meaningful result, translating the cluster location into a precise measure of a cell population.