7 Advanced Flow Cytometry Data Analysis Tips For Multi-Color Experiments
There was a time when two- and three-color experiments were considered very complex flow cytometry experiments.
A limited number of dyes and light sources meant a limited number of options overall.
In fact, the first experiments sorting cells with fluorescently-labeled antibodies were performed by Len Herzenberg in 1972 and could only detect fluorescence from an Argon laser source above 530 nm. These early experiments were performed using rhodamine- and fluorescein-tagged antibodies.
Times have changed. Now, we have spatially separated lasers and a seemingly unlimited number of dyes.
From 2-Colors To 10-Colors
Not long after Herzenberg’s work, Howard Shapiro began designing a series of multiparameter instruments. While instrumentation was evolving, there was parallel development in fluorescent colors to allow for detection of more antigens.
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Why Understanding The Jablonski Diagram Will Help You Publish Your Flow Cytometry Data
We are all used to interruptions during our working day, from the ping of an email notification to the knock of a fellow researcher who wants to troubleshoot their experiment.
Fortunately, most of these interruptions only last a few minutes. Some past researchers were not so lucky.
Imagine your work being interrupted by a war. Imagine it being interrupted by two wars that you had to fight in.
Alexander Jablonski often had his studies interrupted, not by emails or colleagues, but by war. Jablonski’s work was held up for years due to military service in two wars. First, he served in the war for Polish independence in 1916, then he served again in the Polish-Bolshevik war in 1920.
In 1930, after the wars were over, Jablonski earned his doctorate. His dissertation, entitled “On the influence of the change of wavelengths of excitation light on the fluorescence spectra” laid the foundation for the rest of his career in physics.
A few years later, in 1935, he created what we flow cytometrists call the Jablonski Diagram.
When To Use (And Not Use) Flow Cytometry Isotype Controls
Antibodies can bind to cells in a specific manner – where the FAB portion of the antibody binds to a high-affinity specific target or the FC portion of the antibody binds to the FcR on the surface of some cells.
They can also bind to cells in a nonspecific manner, where the FAB portion binds to a low affinity, non-specific target. Further, as cells die, and the membrane integrity is compromised, antibodies can non-specifically bind to intracellular targets.
The question has always been how to identify and control for the nonspecific antibody binding observed.
This led to many research groups using a control called the Isotype control.
The concept of this control is that an antibody targeting a protein not on the surface of the target cells, has the same isotype (both heavy and light chain) as the antibody of interest. When used to label cells, those that showed binding to the isotype, would be excluded as they represented the non-specific binding of the cells.
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6 Keys To Running A Proper Clinical Flow Cytometry Experiment
Clinical use of flow cytometry has paralleled the development of instrumentation and reagents.
One early application for flow cytometry is the measurement of DNA content.
Malignancies and neoplasms often have abnormal amounts of DNA, and this can be assessed with a variety of protocols and dyes.
Comparing DNA Index (DI) of a known 2N control to a sample can yield useful information, but the clinical application of this information was limited, as it was not known how it turned these data into meaningful biological and clinical data insights.
The Rise Of Clinical Flow Cytometry
How To Perform A SPICE Analysis With FlowJo
Flow cytometry data analysis is getting more complex.
Gone is the rule of 2-3 color experiments. Even beginners are starting with 5+ color assays, and the adoption of mass cytometry has the potential to increase our headaches even more.
Current data analysis methods are good for single tubes or small cohort studies. What do you do when you have a large dataset, with multiple sampling conditions, and multiple outcome measurements?
With data complexity of this nature, one can export the numerical data to a third party analysis package, but even then the analysis can be difficult to perform.
To overcome this limitation, and to allow for better discovery science, Mario Roederer and his colleagues have developed a solution. SPICE was developed in order to make sense of the increasingly complex data sets that modern flow cytometric methods can produce. You can read the paper about the design and math behind SPICE here.
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