Team:MIT/HowtoreadaCytoflowgraph

• ABOUT US
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• BACKGROUND
  Alternative Splicing CRISPR RNA Binding Proteins REST
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• LAB WORK
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• MODELLING
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Interpreting CytoFlow Results

A flow cytometer is a machine that measures properties of individual cells, including size, shape and fluorescent output. Cells are individually flowed passed a series of lasers, and by measuring the scatter patterns of the lasers, we are able to gather information about every cell.

From the raw cytometry data we want to make claims about our cellular population. In order to accurately compare across all the cells, we need to gate our cells using multiple different factors.

First, we choose only the live cells in our sample. Next, we only want to analyze cells that received plasmid. We do this by transfecting all of our cells with a constitutively expressed fluorescent protein. We look at the amount of fluorescence the cells outputted and select only the cells that are above a fluorescent threshold.

The x-axis is the amount of fluorescence produced by the cells. The y-axis is the number of cells that express that level of fluorescence. The dark blue line represents the fluorescence threshold, which is around 102 arbitrary units (AU).

Once we have determined which cells received plasmids, we want to determine how much of each plasmid the cells received. Binning is a method of standardizing your output across different cell plasmid concentrations. If a cell receives more plasmid, we would expect it to have a higher fluorescence output across all colors. Using our constitutive color as a marker, we divided our population into 40 transfection bins. The software assigns each of these bins a color that can be used in our later analysis.

The population is divided by the amount of plasmid uptake of every cell.

Later in our experiment, we use the transfection bins to separate out our population. For example, if we are analyzing red output of a cell, we would expect that cells in lower transfection bins will have less red, but should still exhibit the same overall trend as the rest of our population.

An example graph of using transfection bins to distinguish between red output.

Note: Sometimes you have very few cells in the highest and lowest transfection bins, often leading to a disruption in the overall trend of your graph. These disruptions do not generally affect the overall conclusions of your experiments.