DNA cell cycle analysis is a very powerful technique in flow cytometry. It is deceptively easy, but there are several critical things to remember to ensure successful analysis. Collect enough events. Cell cycle analysis involves fitting of the data using one of several mathematical models that describe the behavior of the data. These models make different assumptions about the S phase as well as the G1 and G2/M phases. To have enough data, one should collect 100 events for each channel between the beginning of the G1 peak and the end of the G2/M peak. Thus, if the G1 peak…
With the ability to capture expression data at the single cell level through many thousands of cells in a short time, flow cytometry data is very numbers rich. The importance of those numbers and how to use them in hypothesis testing is critical to ensure the robustness of the analysis. After establishing the null hypothesis for the experiment, the type of statistical test, and the numbers necessary will become obvious. For example, if the null hypothesis states that the ‘treatment of B cells with thiotimoline does not change the expression of CD221B in normal patients.’ Based on this null hypothesis:…
An implementation of biexponential scaling published by the Herzenberg lab at Stanford. The biexonential scale is a combination of linear and log scaling on a single axis using an arcsine function as its backbone. The “logicle” implementation of biexponential was implemented in many popular software packages like FACSDiva and FlowJo. Other types of biexponential scaling exist, including Hyperlog. Biexponential scales are more generally referred to as hybrid scales and include other variations like lin/log or log with negative. More information on logicle sclaing can be found here: Parks DR, Roederer M, Moore WA. (2006). A new “Logicle” display method avoids…
After completing the perfect staining and cytometry run, the hard work begins – data analysis. To properly identify the cells of interest, it is critical to pull together knowledge of the biology with the controls run in the experiment to properly place the regions of interest that will be dictate the final results. Gating is an all-or-nothing data reduction process. Cells inside the gate move to the next checkpoint, while cells outside the gate – even by a pixel, are excluded. 1. Before beginning, know as much as you can about the populations of interest. While it may sound flip,…
Depending on the experimental design, many researchers will be doing complex assays that will require statistical analysis to determine if the hypothesis being tested is statistically significant or not. Unfortunately, many researchers go about this analysis the wrong way, resulting in spurious conclusions. The following points are guides to help think about the steps necessary in flow cytometry data analysis. 1. Before you start Define your hypothesis. This may sound simplistic, but understanding the purpose of the experiments is the first step in performing good statistical analysis. Stating the hypothesis will allow the researcher to choose the correct statistical test…
A laser type in a flow cytometer with a wavelength of about 560nm. The green and yellow laser are more effective at exciting PE and its tandems than the traditional blue laser. The yellow laser is also often used to excite the “fruit” dyes like mCherry. For more information, please review this journal article: Telford W, Murga M, Hawley T, et.al. (2005). DPSS yellow-green 561-nm lasers for improved fluorochrome detection by flow cytometry. Cytometry. 68A: 36-44
After completing the perfect staining and cytometry run, the hard work begins – data analysis. To properly identify the cells of interest, it is critical to pull together knowledge of the biology with the controls run in the experiment to properly place the regions of interest that will be dictate the final results. Gating is an all-or-nothing data reduction process. Cells inside the gate move to the next checkpoint, while cells outside the gate – even by a pixel, are excluded. 1. Before beginning, know the populations of interest. While it may sound flip, knowing what cells are the target…
The laser type in flow cytometers with a wavelength of around 530nm. Standard “green” lasers are about 532nm, but vary between 530nm and 535nm usually. The green and yellow laser are more effective at exciting PE and its tandems than the traditional blue laser.
A laser with a wavelength in the UV range. Typically in flow cytometers, the UV laser has a wavelength of 350nm or 355nm. Some have a wavelength of 375nm.
Another very common laser after the “blue” and “red” laser in flow cytometers. A “violet” laser in flow cytometry typically is referred to as the 405 because most flow cytometers use a violet laser with a wavelength of 405nm. Pacific Blue and Pacific Orange are the most common fluorophores used with this laser, but Brilliant Violet fluors are gaining popularity.
The second most common laser in a flow cytometer after the “blue” laser. The “red” laser typically has a wavelength of 633nm, but new flow cytometers are starting to use a “red” laser with a wavelength of 640nm. The most common fluorophores excited and detected off this laser are APC, Alexa Fluor 660, Alexa Fluor 700, and APC-tandems.
The most common laser type in a flow cytometer. Typically, this laser has a wavelength of 488nm in flow cytometers. In fact, the term “Blue” laser is often interchanged with “488” laser. Frequently used fluorophores excited and detected by this laser are FITC, Alexa Fluor 488, PE, PerCP, and their tandems.