Flow cytometry is a sophisticated technology used to analyze large numbers of individual cells suspended in a fluid. As cells flow one by one through a precisely focused laser beam, the instrument gathers various data points about each cell, such as its relative size and internal complexity. It also quantifies the expression levels of specific proteins on the cell surface or within the cytoplasm, which are tagged with fluorescent dyes. This high-throughput process generates an immense amount of data, detailing multiple parameters for thousands to millions of individual cells within a sample.
Consider a researcher studying immune responses within a patient’s blood sample, which contains a diverse mix of cells. To understand a particular type of immune cell, such as a specific subset of T-lymphocytes, they need to isolate information exclusively pertaining to those cells. The challenge then arises in deciphering this immense and often noisy dataset, distinguishing the precise cell population of interest from all other cellular components, debris, and unwanted particles. The sheer volume and inherent complexity of the raw data necessitate a systematic approach to extract meaningful biological insights.
The Purpose of Gating in Flow Cytometry
Gating is the fundamental process of selecting specific cell populations from the complex, multi-parameter dataset generated during a flow cytometry experiment. This procedure involves defining analytical boundaries around groups of data points that represent cells sharing particular measured characteristics, such as light scatter properties or fluorescent marker expression. The goal of gating is to isolate a pure population of interest for more focused and accurate downstream analysis, ensuring that subsequent investigations are based on relevant cellular subsets.
This process allows researchers to exclude unwanted events from their analysis, including cellular debris, aggregates of multiple cells (doublets), or dead and dying cells. Removing these irrelevant or compromised components ensures that subsequent data analysis reflects only the viable, single cells that are truly part of the experiment’s specific focus. This significantly enhances the reliability and precision of scientific findings, making it a foundational step in interpreting flow cytometry results.
Visualizing Cell Populations for Analysis
Flow cytometry data is presented on two-dimensional scatter plots, which serve as the visual framework for identifying and isolating distinct cell populations. The initial step often involves plotting Forward Scatter (FSC) against Side Scatter (SSC). FSC measures light diffracted in the forward direction, indicating a cell’s relative size; larger cells have higher FSC values.
Side scatter measures light refracted at a 90-degree angle, reflecting a cell’s internal complexity or granularity. Cells with more internal structures scatter more light to the side, leading to higher SSC values. On an FSC versus SSC plot, different cell types within a heterogeneous sample, such as human peripheral blood mononuclear cells (PBMCs), form distinct “clouds” of data points. For instance, lymphocytes cluster in one region, monocytes in another, and granulocytes in a third. Cellular debris, with very low FSC and SSC values, is found in a separate, lower left region.
Beyond physical properties, flow cytometry utilizes fluorescent markers to identify cells based on their molecular composition. Antibodies conjugated with fluorochromes bind to unique proteins on the cell surface or within the cytoplasm. Data from these markers are displayed on fluorescence plots, showing the intensity of one or more fluorescent signals. For example, a plot might show CD4 and CD8 intensity, allowing identification of helper T-cells (CD4+) or cytotoxic T-cells (CD8+). These plots provide a detailed molecular fingerprint of each cell, revealing its identity and functional state.
The Process of Drawing Gates
Creating gates involves drawing a boundary around a specific cluster of data points representing the cell population of interest on scatter plots. A gate functions as a numerical or graphical boundary, defining which cellular events are included or excluded for subsequent analysis. This process filters the raw dataset, ensuring only selected cells are carried forward for examination.
Common shapes are used for these analytical boundaries, chosen based on the cell population’s distribution. Rectangular or square gates are used for clearly separated populations. For irregular or elongated distributions, polygon gates offer flexibility, allowing researchers to draw custom boundaries that precisely enclose desired cells while excluding noise.
For plots displaying two fluorescent markers simultaneously, quadrant gates are frequently utilized. These gates divide the plot into four sections, identifying cells positive for one marker, the other, both (double positive), or neither (double negative). The chosen gate defines the precise subset of cells included in all subsequent analyses, isolating them from the broader sample for targeted quantification.
Sequential Gating for Identifying Subpopulations
Gating in flow cytometry is a hierarchical and sequential process, where the output of one gate serves as the input for the next analytical step. This systematic approach allows for the progressive refinement of the cell population, transitioning from a broad initial selection to a highly specific subset. This multi-step strategy is effective for identifying and quantifying even rare cell populations within complex biological mixtures.
A typical sequential gating strategy begins by displaying all collected events on an FSC versus SSC plot, providing an overview of cell size and granularity. An initial gate encompasses the main viable cell population, excluding cellular debris and aggregated clumps. From these selected cells, a subsequent plot, often comparing FSC-Area to FSC-Width, is used to create a “singlet gate.” This step removes clumps of two or more cells, ensuring each data point corresponds to a single cell.
Following singlet selection, a viability dye marker establishes a “live/dead gate.” Cells stained with dyes like propidium iodide exhibit high fluorescence if their membranes are compromised, indicating they are dead. This allows researchers to exclude non-viable cells and focus analysis on the healthy, living population. From the live singlets, a fluorescence plot displaying a general lineage marker, such as CD3, isolates the primary cell type of interest, like T-lymphocytes.
Finally, within the isolated T-lymphocyte population, another fluorescence plot comparing CD4 and CD8 surface proteins can further separate them into distinct functional subsets. This identifies helper T-cells (CD4-positive, CD8-negative) and cytotoxic T-cells (CD4-negative, CD8-positive), which play different immune roles. This nested approach ensures precise identification and quantification of specific cellular subsets, enabling detailed analysis of their characteristics.