What is flow cytometry an analysis of?

What is flow cytometry an analysis of?

Flow cytometry is a laser-based technique used to detect and analyze the chemical and physical characteristics of cells or particles. It is most commonly used to evaluate bone marrow, peripheral blood and other fluids in your body.

How do you detect microparticles?

Various methods of detection for microparticles include flow cytometry, enzyme-linked immunoassays, and functional assays. Flow cytometry has several advantages including its ability to quantitate and identify microparticles of different cellular origin.

What is the definition of cytometry?

[ sī-tŏm′ĭ-trē ] n. The counting of cells, especially blood cells, using a cytometer or hemocytometer.

Why is flow cytometry important?

The reason flow cytometry is so successful with heterogeneous cell populations is that it analyzes cells one at a time. It does this by using the properties of fluid dynamics. Flow cytometry data enables the user to understand each cell type and its properties on a deeper level.

How do you statistically analyze flow cytometry data?

5 Steps For Accurate Flow Cytometry Statistical Analysis Results

  1. Power the flow cytometry experiment properly.
  2. Establish the threshold (significance level) to your statistical test.
  3. Clearly state the hypothesis.
  4. Choose the correct statistical test.
  5. Know how to plot your data and do it first.

Who Discovered flow cytometry?

Len Herzenberg, an immunologist at Stanford University, was a pioneer of this method of sorting cells using the principles of flow cytometry. He coined the terms FACS – florescence activated cell sorter – which sorted cells as well as counting them. The original name for flow cytometry was pulse cytophotometry.

What are the principles of flow cytometry?

Flow cytometry uses three basic scientific principles—fluid dynamics, optics, and electronics—to detect, count, and do cell sorting.

Is flow cytometry data normally distributed?

With flow cytometry the data distribution is not normal. One way to overcome this is to log transform your raw data values which will give you a normal distribution.

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