Flow cytometry is a laboratory technique that analyzes individual cells or particles suspended in a fluid. It measures various characteristics of these cells as they pass through a laser beam. The primary output of this technique is a flow cytometry graph. This article aims to clarify how to interpret these graphs.
Graph Fundamentals
Flow cytometry graphs visually represent the data collected from thousands or millions of individual cells. These graphs feature X and Y axes, which represent different cellular parameters. For instance, the X-axis might display forward scatter (FSC), reflecting cell size, while the Y-axis shows side scatter (SSC), indicating the internal complexity or granularity of a cell. Each individual dot on the graph corresponds to a single cell or particle measured during the process.
There are two types of graphs used in flow cytometry: dot plots and histograms. Dot plots display the relationship between two parameters simultaneously, with each dot representing a cell’s measurements for both parameters. Histograms display the distribution of a single parameter, with the X-axis showing signal intensity and the Y-axis representing the number of events or cells at that intensity.
Identifying Cell Populations
Distinct groups or “populations” of cells often cluster together visually on a flow cytometry graph, reflecting their shared characteristics. Scatter parameters like Forward Scatter (FSC) and Side Scatter (SSC) are used to differentiate basic cell types. FSC measures the light scattered in the forward direction and correlates with cell size. SSC measures light scattered at a 90-degree angle and provides information about a cell’s internal complexity or granularity.
By plotting FSC against SSC, researchers can distinguish major cell types in a mixed sample, such as lymphocytes, monocytes, and granulocytes. For instance, lymphocytes show lower FSC and SSC, while granulocytes exhibit higher SSC due to their granular content. Beyond physical properties, fluorescence parameters are used to identify more specific cell populations. Cells are tagged with fluorescently labeled antibodies that bind to cell surface markers, allowing for their differentiation. For example, T cells can be identified by CD3, while B cells are characterized by CD19 or CD20 markers.
Understanding Gating
Gating is a fundamental process in flow cytometry analysis, allowing researchers to isolate specific cell populations for detailed examination. It involves drawing boundaries, or “gates,” around clusters of cells on a graph that share particular characteristics. The primary purpose of gating is to focus analysis on a particular group of cells while excluding others, such as debris, dead cells, or unwanted cell types.
Gating often begins with broader selections, like using FSC and SSC to identify viable, single cells and exclude cellular debris or clumps. Once an initial population is isolated, further gates can be applied using fluorescence parameters to identify increasingly specific cell subsets. For example, after identifying lymphocytes based on scatter, one might gate on cells expressing specific markers like CD3 to isolate T cells. This sequential process refines the population of interest, and subsequent graphs can then be generated from these gated populations, providing more detailed information about those selected cells.
Extracting Key Information
Once cell populations have been identified and isolated through gating, flow cytometry graphs yield quantitative data. The two main types of numerical information typically extracted are the percentage of cells within a specific population and the mean fluorescence intensity (MFI) for a given marker. The percentage indicates the relative abundance of a particular cell type within the analyzed sample or within a larger gated population. This provides a direct measure of how common a specific cell type is.
Mean fluorescence intensity (MFI) quantifies the average brightness of fluorescence emitted by cells in a population, which serves as a relative measure of the expression level of a particular antigen or protein. A higher MFI suggests a greater abundance of the target molecule on or within the cells. These numerical values provide quantifiable insights into the cellular composition and characteristics of a sample.