Presentation Possibilities of FACS-Data

Flow cytometry (Fluorescence-Activated Cell Sorting, FACS) is a method for the analysis and preparation of particles in mixtures of substances based on scattered light and fluorescence properties. The high analysis speed and sensitivity as well as the objective quantification and multiparametric correlations (relationship of at least two variables) open up an almost unlimited field of applications for flow cytometry in research and diagnostics. The focus of interest is always the individual cell. In contrast to traditional biochemical and cytochemical methods, no average values of a cell preparation are obtained, but the correlation of the result with the individual cell is maintained. Analysis techniques rely on representations using one-dimensional (e.g. histograms), two-dimensional (e.g. dot-plot) figures, and even higher-order graphs (plots) (3D-plots, SPADE trees, etc.).


The most common and well-known evaluation representation of flow cytometry is the histogram. It represents a frequency distribution of the measured signals of a parameter. Typically, figures with data from different conditions are shown in one diagram. The horizontal axis represents the intensity of the individual measurements and the vertical axis represents the number of cells. In this way, the Gaussian distribution of a parameter is obtained, which is called the population. When cells are stained with fluorescently labeled antibodies, the fluorescence intensity is directly proportional to the number of binding sites (antigens) present, i.e., the more binding sites there are, the brighter the cell glows. Flow cytometry thus shows the distribution of different fluorescence intensities using a relative scale. The user must specify which areas should be considered “positive” or “negative” to properly evaluate a given population. These ranges are defined with control. Histograms are useful for cell cycle and proliferation analyses but are less useful for plotting data for several reasons. First, relationships between different markers will not be detected, i.e., double-positive cells cannot be identified. Second, small populations are lost in larger distributions, thus rare events are not noticed.


If two different parameters are recorded during one measurement, a histogram is not sufficient and a two-dimensional representation is used. It allows to show the correlation distribution, i.e. the relationship between two different characteristics, and thus to identify more complex phenotypes. Thus, the populations in demand can be isolated using gating. The original two-dimensional plot is also known as a “dot plot,” a graph that showed the relationship between two traits but lacked detail in terms of the intensity of the number of events in a given region. Therefore, two-dimensional plots have some utility in showing how populations of interest are identified.

Scatter graphs

Another form of representation is a scatter graph, which can show information about dependency structures of two defined characteristics. The data are shown as scatter graphs, in which distributed characteristics can overlap if the same values are present several times. From scatter graphs, various focal points can be shown, such as the number of experiments performed to generate the data, or the mean, dispersion, and significance of the data.


In flow cytometry, frequency distributions (populations) of cells are defined by their scattered light parameters – forward scattered light (FSC) and side scattered light (SSC) – and by their fluorescence. Since this is a relative measurement, controls must be used to define what is considered “positive” or “negative”. In the subsequent analysis, first, a pre-selection of the raw data is made (FSC versus SSC gate) and then the boundary between a negative and a positive population is defined, often based on a negative control (“threshold method”). A gate (a defined region) can of course also be set on the fluorescence parameters, for example, to select a specific lymphocyte population. All gates can be linked together. Thus, the gates act like filters connected in series. 


Compensation is the proportional subtraction of a noticeable neighboring fluorescence in overlapping fluorescence spectra. In compensation, a relative amount is subtracted from a fluorescence signal by calculation and the difference in light quantity is referred to as the compensated signal. A disadvantage of compensation is that the positive population, due to the logarithmic amplification, is pulled apart in the direction of compensation, i.e. it is scattered more widely.


Gating is the process of defining a group of cells and gating them into another plot. However, since more than one cell population or property is studied in the research, gating can be very time-consuming. It is also a common criticism of flow cytometry data in general, as it represents a subjective evaluation.

With the proliferation of new automated analysis techniques, this problem is also being addressed while assuring that the data extracted for downstream statistical analysis comes from a robust, peer-reviewed process.


About the author
Elif Karakurt
medical content creator
Elif is a medical student and works for Cytolytics in the branches of content creation and marketing alongside her studies. She is the head of the Cytolytics blog and could already gather experience in writing medical articles for various magazines. Her interests are recent health issues and news about medicine, health technologies, and digital health.

Flow Cytometry& Cytolytics

Flow cytometry is a recent laboratory technique of great importance in contemporary medicine and cancer research. It is used to examine blood components, bone marrow and other substances. This method provides important information about immune status, diagnosis and progression of blood cancer and other immunodeficiency diseases. It is also used in the examination of plants, small organisms (viruses, bacteria, spores) and food samples. Flow cytometry is a powerful method because it quickly, accurately and easily collects data on many parameters from a heterogeneous mixture of fluids. Depending on the software, the duration and quality of the evaluation of the sought parameters differ. Very few adequate software are capable of performing more complex analyses without gating in the shortest time with high reliability.

What is flow cytometry (FC)?

Flow cytometry (FC) is widely used in the research field because it enables accurate phenotyping of cells as well as rapid detection of large cell populations in a liquid medium (e.g. blood sample). At the same time, immune cell subtypes are easy to identify, separate and label due to size and morphology.

Where does the name flow cytometry (FACS) come from?

In flow cytometry, particulate structures (events), e.g. immune defense cells, are transported one after the other through a narrow measuring chamber like a string of pearls. Lasers detect these events laterally as they flow through, hence the name flow cytometry. Depending on the device and software, these lasers detect different properties of the events via their scattered light. The device with which flow cytometry is performed is also called a FACS device. FACS stands for fluorescence activated cell sorting.

What does scattered light mean?

When an event interacts with laser light, scattered light is produced. Depending on the intensity and strength of the scattered light, properties such as cell size, cell membrane structure and intracellular components are detected by detectors. A distinction is made between forward scatter (FSC) and side scatter (SSC), which provide information about size and granularity.

What is the purpose of fluorescence?

In FACS analysis events can be loaded with fluochrome to more accurately characterize the properties and differences between them. The fluochrome absorbs the energy of the laser and light emissions typical of the dye are released. The resulting signals are detected by appropriate fluorescence detectors and later analyzed. The more fluochromes are bound to the events, the more intense the signals are.

How does FACS analysis work?

At the end of the FACS analysis, by labeling the events with the fluorochrome, passing the lasers and measuring the emitted light by detectors, the data is finally analyzed. The signals, which are detected by the detectors, are sorted graphically according to properties and presented for the research question.

How does the signal processing work?

The signals and values are processed and evaluated by using software. The data can be mapped linearly or logarithmically. Depending on the configuration and device, up to 60 parameters can be measured simultaneously at individual events. The representation of the parameters are listed depending on the problem in different variants such as the one, two parameter representation, (overlapping) populations and compensation.

What is gating?

Gating is the so-called selecting of events that really interest you. Simply said, someone defines a group of cells and gates them into another diagram. However, since more than one cell population or trait is studied in research, gating can be very time consuming. It typically takes up to several hours. New software, like Cytolytics allows reliable, flexible and above all, fast evaluation without gating.

What does Cytolytics offer?

Cytolytics is a medical technology company specializing in the analysis of medical data using Artificial Intelligence methods. Through cutting-edge artificial intelligence technology, it offers fully automated, fast and reliable flow cytometry analysis. Hours of gating are eliminated by Cytolytics’ fully automated software, which analyzes FACS data up to 60 times faster and provides comprehensible reliable analysis for the research. Cytolytics enables concrete and understandable settings and steps with a high degree of individualization.

What does Artificial Intelligence (AI) mean?

Cytolytics works with the innovation of unsupervised machine learning, which sorts and classifies complex relationships, patterns and similarities using certain criteria and features. Artificial intelligence is built like an artificial neural network that discovers data sets with similar content from a large amount of data in a fully automated way according to predefined rules. Classifies and evolves at the same time. AI can be found in the areas of research, business and game development, as it is ideal for scientific questions, pattern recognition and image processing.

What innovation does Cytolytics offer?

In addition to sequential gating for detailed analyses, the graphical user interface for different computers and a plausibly explained and understandable software language, Cytolytics offers above all a complete automation of the evaluation. With the help of the new innovation of full automation, not only large data sets are compared, but also abnormalities, anomalies and outliers are detected on the basis of the standardized setting. The automated analysis provides a standardized evaluation option, so that introduction with the Cytolytics software is effortless. The automated result documentation can be exported to various applications such as PPT, Word and Excel, and a result presentation is possible directly and quickly without reprocessing. By means of full automation, not only fast evaluations but also comparable, valid, repeatable and meaningful results are delivered. With today’s technology, FACS can identify which tumor and which cells are affected in the case of a cancer diagnosis. However due to the time-consuming nature of gating, therapy planning and corresponding therapy control examinations often take place under great time pressure. Time is a life-saving factor and can in sufficient quantities, enable precise, targeted therapy planning, for example in the case of leukemia. Cytolytics provides an innovative and intelligent solution for this. With time-saving analysis without gating, more time is available for planning therapies for life-threatening diseases and for preparing as well as publishing research.

About the author
Elif Karakurt
medical content creator
Elif is a medical student and works for Cytolytics in the branches of content creation and marketing alongside her studies. She is the head of the Cytolytics blog and could already gather experience in writing medical articles for various magazines. Her interests are recent health issues and news about medicine, health technologies, and digital health.