Transcript Introduction to Flow Cytometry

What is Flow Cytometry?
Introduction to Flow Cytometry
IGC Workshop
Flow Cytometry
Rui Gardner (IGC)
Telma Lopes (IGC)
Carlos Tadokoro (IGC)
uic
Fundamentals of Flow Cytometry (cont.)
IGC – December 15, 2009
The Instrument
2
Filters
Blocked
FilteredFilteredBlocked
BP
LP::Band
Long Pass
Pass Filter
Filter
530> /500
60
4
Filters
5
Optical Layout
6
Detection
PMT
Photo Multiplier Tube
PMT’s collect photons that are then converted into voltage signals
8
Pulse
Flowing Stream
Voltage pulse
Laser
9
Pulse Parameters
W
H
H:
A
A:
W:
10
Height vs Area
H
A
H
A
For non spherical cells, Height (FL-H) is not an adequate
parameter to analyze
Area (FL-A) is the most adequate. However, we still need to
remove doublets from the analysis...
11
Doublet Discrimination
W
2W
W
2H
H
2A
doublets
2A
FL-H
A
FL-W
H
Single cells
FL-A
FL-A
12
Threshold
Forward Scatter Threshold
H
W
Threshold
Time
Small Cells
and debris
Cells of Interest
13
Analysis Software
Flowjo
VenturiOne
CellQuest
Summit
FCSExpress
Kaluza
FACSDiva
15
Gating
Common Gate Shapes
Logical Gating
AND, OR, NOT
16
Gating
Positive or Negative?
A “positive” cell or event is that which falls outside the “negative” gate.
Neg
Pos
17
Back Gating
Back gating a positive population can enrich the population of interest and
help identify it correctly
CD4 FITC
18
Acquisition
How many cells should I acquire?
Counting cells follows Poisson statistics:
cv % =
1
Precision
cv % =
sd
x 100
mean
x 100
N
N is the number of cells counted
Example:
Population of interest is 1% of total population and want 5% precision
1002
10,000
N=
=
= 400
(cv %)2
25
40,000
Number of cells to be counted
in the region of interest
Number of total cells
to be counted
19
Dot vs Countour Plots
Dot Plots
Contour Plots
20
Logarithmic or Linear?
Anti-CD4-labeled antibody
Signals vary >100-fold
Use Log scale
Linear
Log
DNA-labeling dye
Signals vary 2- to 10-fold
Use Linear scale
Linear
Log
21
Logicle (Bioexponential) Transformation
Taken from Herzenberg, et. al (2006) “Interpreting flow cytometry data: a guide for the perplexed”, Nat Immunol, 7:681-685
22
Logicle Transformation (compensation)
Taken from Herzenberg, et. al (2006) “Interpreting flow cytometry data: a guide for the perplexed”, Nat Immunol, 7:681-685
23
What is Flow Cytometry?
Introduction to Flow Cytometry
IGC Workshop
Flow Cytometry
Rui Gardner (IGC)
Telma Lopes (IGC)
Carlos Tadokoro (IGC)
uic
Fundamentals of Flow Cytometry (end)
IGC – November 9, 2009