proliferation tutorial
TRANSCRIPT
FlowJo Data Analysis Software for Flow Cytometry
PC User Documentation
Proliferation Tutorial
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FlowJo was written by Adam Treister and Mario Roederer beginning in 1996,
based on concepts developed at the Herzenberg laboratory at Stanford. We are
indebted to our active and enthusiastic users worldwide for their ideas, discussions
and tireless testing of new versions.
FlowJo, its tutorials, documentation and web site are Copyright © Tree Star, Inc.
1997-2011. All Rights Reserved.
• FlowJo Advanced Tutorial •
• © MMX•
Revision Date: 1 January 2011
Version 7.6.2
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FlowJo Proliferation Tutorial
FlowJo is a software application designed to be a comprehensive tool for analyzing
flow cytometric data. Because proliferation is common task in cytometric analysis,
FlowJo includes a platform for applying mathematical models to cell cytometric
data fully integrated into the software.
This tutorial is designed to instruct you on how to use the FlowJo proliferation
platform to analyze proliferation data. A brief overview on the biology of cell
proliferation is provided mainly for the purpose of defining the terms, but this
manual does not include a comprehensive discussion. This tutorial is also written
with the assumption that the user is familiar with the most basic operations in
FlowJo, such as loading data, making simple gates, dragging analysis nodes to
other samples groups, or the layout and table editors. If you are not familiar with
these operations, a general tutorial of FlowJo is available at www.Flowjo.com as
well.
This tutorial is designed to introduce you to the proliferation platform. Reading
through it, you will learn how to operate FlowJo. Run the program as you perform
the steps in the tutorial so that you can get the best feel for how the program
works! Included with the demo data are a series of completed workspaces that will
enable you to jump into the tutorial at any point and follow along.
As a note, we are pleased to be able to frequently update FlowJo to provide new
features & analysis capabilities. Therefore, it is possible that the graphics shown in
this tutorial may not exactly match the windows that you see when you run the
most recent version of FlowJo. You can always download the most recent version
of FlowJo from http://www.flowjo.com/home/windows.html.
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Table of Contents
Introduction ..................................................................................5
Proliferation Basics......................................................................5
The Experimental Data ................................................................5
Lesson 1: The Proliferation Platform .......................................6
Creating a New Model .................................................................6
Proliferation Statistics in FlowJo.................................................7
An Example Summary Statistics Calculation ..............................8
Lesson 2: Applying Constraints................................................9
Fixing the Undivided Mean .........................................................9
Fixing the Ratio............................................................................10
Fixing the CV...............................................................................10
Fixing the Background.................................................................10
Creating Gates..............................................................................11
Lesson 3: Creating Proliferation Outputs ................................12
Applying the Model to other Samples .........................................12
Creating a Table of Proliferation Statistics..................................12
Creating a Layout of Proliferation Graphics................................13
Appendix ..................................................................................14
Resources ..................................................................................15
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Introduction
This tutorial will guide you through the process of applying and modifying
mathematical models to proliferation data in FlowJo, from adding data to
producing tables of statistics and publication quality graphics.
The tutorial is divided up into 5 sections so you can perform it piecewise if desired.
The tutorial is written for the user to perform all operations, but the completed
workspaces represent the outcome of the operations performed in each chapter so
that you can jump to any stage of the tutorial. If you would like to perform the
tutorial starting with a lesson other than 1, just open the workspace from the
preceding lesson and you will have all of the work completed through that lesson.
Proliferation Basics
The Proliferation assays serve as powerful tools to understand the functionality of
different cell types. While phenotypic characteristics provide the first level of
analysis, it is only through the functional assays that one can fully understand the
capacity of these different phenotypes of cells to respond or activate to different
conditions.
There are a number of dyes used in cell proliferation assays, with CFSE being the
most common. Several companies have introduced new dyes in recent years.
However, all of the dyes bind to lipids or proteins on the cell membrane, and so the
mechanism is the same. When a cell divides, each daughter cell gets
approximately half of the initial bound dye, and when measured with flow
cytometry, the daughter progeny have half the fluorescence intensity compared to
the parent.
The Experimental Data
The data for this tutorial is a set of seven files of peripheral blood mononuclear
cells; one unstimulated control and six files at subsequent time points labeled
Sample 1 through 6. Each file is stained with a panel of Foxp3 GFP, CD25 PE, 7-
AAD, CD4 PECy7, and a protein binding dye from eBiosciences called eFluor®
670. This dye is excited at 647nm and emits at around 660nm.
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Lesson 1: The Proliferation Platform
This lesson will guide you through the basic use of the proliferation platform.
Creating a New Model
1) Begin the lesson by opening the initial workspace, Prolif_Lesson_1.wsp. The
primary gating has already been done.
2) In sample 1, click on the ‘new live’ gate and right click.
3) Select Proliferation from the pop up menu.
A proliferation node will be
created under the new live
gate and the proliferation
interface will open. FlowJo
will attempt to determine
which parameter is the
proliferation marker. If the
correct parameter is not
displayed by default, click
the drop down parameter list
below the x-axis and select
the correct parameter.
Set the parameter to Comp-
Red_600_20_APC. Once
you have done so, your
platform will look like the
graphic to the right.
4) The default number of peaks is set to eight. This is arbitrary, so the next step is
to set the number of peaks to the appropriate number based on the data. To do so:
• Open the Options disclosure triangle at the bottom of the proliferation
platform interface.
• Count the number of peaks to get a starting estimate. By “eyeball metrics”
there are five peaks in this data file.
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Note: Inputting the “correct” number of peaks is not critical. If you overestimate,
FlowJo will still attempt to fit peaks in at roughly half the intensity of the
preceding peak, and will include sparsely populated additional populations. If you
underestimate, FlowJo will not have enough peaks to fit the data which will
produce a poor model. As a rule of thumb is to count the number of peaks you can
see and then add one. So begin this tutorial with 6 peaks.
Proliferation Statistics in FlowJo
FlowJo keeps track of descriptive statistics regarding the model and a series of
summary statistics that can be used to summarize the
proliferation of the cells from the time of staining to the time of
collection. The statistics that FlowJo uses are shown in the
figure to the right.
The descriptive statistics are the number of peaks, the CV, or
width, of the peaks, and the location of the undivided peak mean.
By identifying the number of generations and the count in each
generation, FlowJo uses the model as a basis to calculate a series
of statistics that allow for the end result to be summarized in a
number or two. The summary statistics are the percent of cells
that divided at least once, the division index, the proliferation
index, and the mean root mean square error.
• Percent Divided is how many cells divided at least once.
• Proliferation Index is the average number of divisions of just the responding
cells (cells that underwent at least one division).
• The Division Index is the average number of divisions for all of the cells in the
original starting population.
• mRMS is an acronym for mean root mean square error. The distance of the
composite model line from the histogram of the data is calculated, squared, and
then the square root is taken. The mean is taken over all of the peaks. We use
this process so that portions of the model above the actual data (producing a
positive distance) do not cancel out the error on portions of the model below
the actual data (producing a negative distance), resulting in a low RMS and a
terrible model. Since RMS is a measure of the distance from the model to the
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data, a smaller RMS indicates a better fit. RMS is not appropriate to compare
between experiments, as the experimental condition will play an important role
in determining what a good RMS is, making between experiment comparisons
a case of comparing apples to oranges. RMS can be used to determine which
model fit the data tighter, and whether a constraint improved the fit or not.
An Example Summary Statistics Calculation
Consider a culture of 1 million cells where upon stimulation, 50% of the cells each
divide twice. You will now have a culture with 2.5 million cells (0.5 million never
divided, 0.5 million divided twice resulting in 2 million cells). CFSE analysis
would show 2 peaks: an undivided, no cells in the first generation, and cells in the
second generation. The CFSE platform would return the following statistics:
Percent Divided: 50% 50% of the original cells divided
Proliferation Index: 2 Responding cells divided twice on average
Division Index: 1 Half the cells divided twice, half never divided for
an average of 1.
Note: the following is always true: (DI) * (%Div) = PI
Thus, of these three values, there are only two independent measurements.
The ends lesson 1. The work to this point is saved as Prolif_Lesson_2.wsp.
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Lesson 2: Applying Constraints
The models within FlowJo will not fit all data files properly without additional
input by an expert user. There are many reasons for this, but a common cause is
that the models are initialized to fit the “standard” shape that we’ve seen so far,
and many experiments perturb the cell cycle and cause the data to deviate from the
standard form. This lesson will teach you to use constraints to limit how the
model can fit the data to an appropriate solution. By imparting some of your
expert knowledge of the biological system onto the model, you can improve the fit.
To begin the tutorial from this point, open Prolif_Lesson_2.wsp.
Fixing the Undivided Mean
You can determine from the undivided sample that Generation 0 is at 37,600
fluorescence units. In the workspace right click on the ‘new live’ gate under the
undivided sample, select the Proliferation model from the Tools menu and change
the peak count to 1. You will get a model like the one pictured below to the left.
Return to Sample 1 and check the box for ‘Fix Peak 0’. The data entry box will
enable. Enter 37,600. The model will now look like the figure above to the right.
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The RMS can often be lowered by constraining the model. In this case the RMS
dropped to 1.8, indicating that this was a good constraint to apply.
Fixing the Ratio
Checking the Fixed Ratio box and entering a value sets the fluorescence ratio
between peaks to the number you entered. The standard starting point is 0.5,
indicating perfect conservation of the proliferation dye with 50% of the dye going
to each daughter cell. For example, if the MFI of Generation 0 is 200, than the
MFI of Generation 1 will be 100 fluorescence intensity units. The ratio will
usually be less than 0.5, as cells typically loose some dye during the division
process. In the example we are using, the ratio was calculated at about 0.56,
indicating the background fluorescence wasn’t properly modeled (which can
happen sometimes). A ratio of greater than 0.5 is biologically impossible. It
implies that both daughter cells received more than half of the dye.
Click the Fixed Ratio box and enter 0.5 in the tutorial. Notice that the RMS
decreases to roughly 2.9, indicating that this is an improvement to the model, and
biologically possible compared to the 0.56 that the model initially generated. You
can try varying the input number to see if another ratio produces a better fit.
Fixing the CV
Fixing the CV sets the coefficient of variation in the distribution, which is the
width of each population. Typically, the CV of the undivided peak is a good place
to start. The unstimulated sample had a CV of 2.7. Fix the CV to match the
unstimulated as well. You will notice that the RMS now drops to 1.95, again an
improvement.
Fixing the Background
Fixing the Background sets the amount of fluorescence to be subtracted as
background from every cell. The model assumes that fluorescence of each
generation is equal to the fluorescence of the previous generation times the ratio,
adjusted for background noise. Expressed mathematically the fluorescence is:
F(n) = [ F(n-1) - B] * r + B.
F(i) = fluorescence of the ith
generation; B = background, r = ratio.
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The background was set
properly by the model so
for this experiment, we
do not need to adjust it.
Try constraining it and
observe that a constraint
negatively impacts the
model, resulting in a
higher RMS.
The final model will look
like the figure to the
right.
Creating Gates
Press the Create Gates
button at the top of the
workspace, and accept
the “Generation” prefix.
It is important to note
that when you press the
Create Gates button
FlowJo places hard line
gates on the model at the
points where it becomes less probable that a cell belongs to one population and
more probable that it belongs to another. Cells that are in the gate are treated as
having a probability of membership of 100% for the “winning” phase and 0% for
all other. This is a different manner of calculating the frequencies and thus the
statistics that display in the workspace next to created gates will usually be slightly
different then the statistics displayed in the model. If you have the opportunity to
use the model statistics, do so. The stochastic approach models the data better.
However, if you take the proliferation model created subpopulations, continue the
analysis, and in the end need to come up with fractions that add up to 100%, you
will need to use the gate frequencies.
Note: These data were chosen because the samples need modification to fit
properly. Many data sets do not need any modification (other than the peak
setting) and will fit perfectly.
The ends lesson 2. The work to this point is saved as Prolif_Lesson_3.wsp.
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Creating Proliferation Outputs
When satisfied with the proliferation model you can easily take advantage of all
the other basic tools within FlowJo for expediting analyses. In this chapter we will
cover applying a proliferation model to other samples, creating tables of
proliferation model statistics, and creating layouts of proliferation graphs.
To begin the tutorial from this point, open workspace Prolif_Lesson_3.wsp.
Applying the Model to other Samples
The cell cycle model or the gates created using the model, like any other analysis
node in FlowJo, can be dragged and dropped to other levels of the hierarchy, to
other samples, or to the group. Drag the proliferation node up to the group under
the ‘new live’ gate. This will apply the proliferation model to every sample.
Of course, you will have to go back and adjust the model for the unstimulated
sample to have 1 peak, but scroll through the other samples and notice that they
look pretty good!
Creating a Table of Proliferation Statistics
Drag the proliferation node into the table editor to place all the statistics in a table.
The resulting table editor will look like the figure below.
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Feel free to delete any of the statistics that are not necessary. If you batch, you’ll
get those statistics for all samples.
Creating a Layout of Proliferation Graphics
Drag the proliferation node into the layout editor to place all the model graphics
into a layout. The resulting layout will look like the figure below.
The workspaces completed to this point are saved as Prolif_Lesson_4.wsp.
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This ends the tutorial. There is more documentation available in the reference web
pages, which you’ll reach from any of the help menu items within the program, or
by looking at: http://www.flowjo.com/
If you have any questions, or ideas for improvements, please contact us at:
…
FlowJo Proliferation Tutorial and Web Site are Copyright © Tree Star, Inc. 1997-
2011.
Revision Date: January 1, 2011 Version 7.6.2
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Appendix
Equation used:
Sum (i * Ni / 2^i) / Sum (Ni / 2^i); where Ni = number of events in peak i.
For example:
The Division Index is the number of number of divisions that took place during
culture divided by the number of cells at start of culture.
The Proliferation Index is the number of number of divisions that took place
divided by the number of cells of the original population that went into division.
The number of cells that you had at start of culture is:
G0 + G1/2 + G2/4 + G3/8 + ... + Gn/(2^n)
The number of cells that went into division is the number of cells that you had at
start of culture minus G0.
The total number of divisions is:
G1/2 * 1 + G2/4*2 + G3/8*3 + G4/16*4 + ... + Gn/(2^n)*n
G0 = 15888
G1 = 32922
G2 = 13647
G3 = 897
The number of cells at start of culture:
15888 + (32922/2) + (13647/4) + (897/8) = 35872.87
The total number of divisions:
(32922/2)*1 + (13647/4)*2 + (897/8)*3 = 23620.87
The number of cells that went into division:
35872.875 - 15888 = 19984.875
Division Index: 23620.875 / 35872.875 = 0.66
Proliferation Index: 23620.875 / 19984.875 = 1.18
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Resources
http://www.flowjo.com/v9/html/proliferation.html
http://www.flowjo.com/v76/en/proliferation.html
http://www.flowjo.com/v76/en/prolifmodeladjust.html
http://flowjo.typepad.com/the_daily_dongle/2007/05/dongleoids_inde.html