3 – Data analysis

Cell proliferation can readily be measured by flow cytometry.  Depending on the research question, there are several different techniques that can be used. Cell counting experiments This relatively straightforward experiment where the investigator uses one of several counting techniques to see if there is an increase in the populations. This can be done using a microscope and a hemacytometer, a flow cytometer with counting beads, image based tools (like the T-20, the Countess, or the Cellometer), or coulter counter type instruments (including the Coulter Counter the Scepter and the Casy counter) Cell cycle experiments One of the earliest techniques developed…

A variation on biexponential scaling similar to logicle scaling. The biexonential scale is a combination of linear and log scaling on a single axis using an arcsine function as its backbone. Biexponential scales are more generally referred to as hybrid scales and include other variations like lin/log or log with negative. More information on Hyperlog scaling can be found here: Bagwell, CB. (2005). Hyperlog-a flexible log-like transform for negative, zero, and positive valued data. Cytometry. 64: 34-42.

Cell cycle analysis by flow cytometry uses a DNA binding dye, such as propidium iodide (PI), 7- aminoactinomycin D (7-AAD) or 4’,6-diamidino-2phenylindole (DAPI), to determine the cell cycle state of a cell population. The Gap1 (G1) phase of an eukaryotic cell is defined as having 2C DNA. The synthesis (or S) phase is where the DNA is synthesized going from 2C->4C. Cells then spend some time in the Gape 2 (G2) phase before completing mitosis and the whole cycle starts over again. Since the cells in G0/G1 and G2/M have defined amounts of DNA, with the S phase having an…

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:…

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…

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…

Understanding statistics and fow cytometry statistical analysis is critical to understanding flow cytometry data. One of the powers of flow cytometry is the fact that we generate large amounts of data that are amenable to statistical analysis of our populations of interest.  Using the standard set of statistical analysis tools allows for hypothesis testing and ultimately determining if there is statistical significance in the datasets. There are two basic classes of questions that are typically asked in flow cytometry.  The first class relate to changes in the number or percent of a specific population upon treatment or disease state.  A…

Flow cytometry is the science of measure the physical and biochemical processes on cells and cell-like particles. This analysis is performed in an instrument called the flow cytometer.  FACS Analysis is the short-hand expression for this type of cell analysis The term FACS stands for Fluorescent Activated Cell Sorting, a term first coined by Len Herzenberg in the 1970’s, and later trademarked by Becton Dickinson. Since that time, FACS has come to be used as a generic term for all of flow cytometry, even though it is a specific trademarked term.