Transcript Introduction to Fluorescence Correlation Spectroscopy (FCS

Lecture 5
FCS, Autocorrelation, PCH,
Cross-correlation
Enrico Gratton
Principles of Fluorescence Techniques
Laboratory for Fluorescence Dynamics
Fluorescence Parameters & Methods
1. Excitation & Emission Spectra
• Local environment polarity, fluorophore concentration
2. Anisotropy & Polarization
• Rotational diffusion
3. Quenching
• Solvent accessibility
• Character of the local environment
4. Fluorescence Lifetime
• Dynamic processes (nanosecond timescale)
5. Resonance Energy Transfer
• Probe-to-probe distance measurements
6. Fluorescence microscopy
• localization
7. Fluorescence Correlation Spectroscopy
• Translational & rotational diffusion
• Concentration
• Dynamics
First Application of Correlation Spectroscopy
(Svedberg & Inouye, 1911) Occupancy Fluctuation
Experimental data on colloidal gold particles:
120002001324123102111131125111023313332211122422122612214
2345241141311423100100421123123201111000111_2110013200000
10011000100023221002110000201001_333122000231221024011102_
1222112231000110331110210110010103011312121010121111211_10
003221012302012121321110110023312242110001203010100221734
410101002112211444421211440132123314313011222123310121111
222412231113322132110000410432012120011322231200_253212033
233111100210022013011321113120010131432211221122323442230
321421532200202142123232043112312003314223452134110412322
220221
Collected data by counting (by visual inspection) the number of particles
in the observation volume as a function of time using a “ultra microscope”
Particle Correlation
7
6
Particle Number
5
4
3
2
1
0
0
100
200
300
400
500
time (s)
*Histogram of particle
counts
*Poisson behavior
*Autocorrelation not
available in the original
paper. It can be easily
100
6
5
4
Frequency
3
2
10
6
5
4
3
2
1
0
2
4
6
8
calculated today.
Number of Particles
They estimated the particle size to be 6 nm. Comments to this paper conclude that
scattering will not be suitable to observe single molecules, but fluorescence could
In FCS
Fluctuations are in the Fluorescence Signal
Diffusion
Enzymatic Activity
Phase Fluctuations
Conformational Dynamics
Rotational Motion
Protein Folding
Example of processes that could generate fluctuations
Generating Fluctuations By Motion
What is Observed?
1. The Rate of Motion
2. The Concentration
of Particles
Observation
Volume
Sample Space
3. Changes in the Particle
Fluorescence while under
Observation, for example
conformational transitions
Defining Our Observation Volume:
One- & Two-Photon Excitation.
2 - Photon
1 - Photon
Defined by the pinhole size,
wavelength, magnification and
numerical aperture of the
objective
Approximately 1 um3
Defined by the wavelength
and numerical aperture of the
objective
1-photon
Need a pinhole to
define a small volume
2-photon
Brad Amos
MRC, Cambridge, UK
Data Treatment & Analysis
Time Histogram
50
0.04
0.035
Auto Correlation
40
Counts
Autocorrelation
30
20
10
0.03
Fit
0.025
Data
0.02
0.015
0.01
0.005
0
0
20
40
60
80
100
Time
0
0.01
0.10
1.00
10.00
Time (ms)
Photon Counting Histogram (PCH)
Autocorrelation Parameters:
G(0) & kaction
Number of Occurances
1000000
100000
10000
1000
PCH Parameters: <N> & e
100
10
1
0
5
10
Counts per Bin
15
100.00
Autocorrelation Function
G( ) 
F(t)F(t   )
F(t)
2
F (t )  F (t )  F (t )
Factors influencing the fluorescence signal:
F (t )  Q  dr W (r )C (r, t )
Q = quantum yield and detector
sensitivity (how bright is our
probe). This term could contain
the fluctuation of the
fluorescence intensity due to
internal processes
W(r) describes our
C(r,t) is a function of the
fluorophore concentration
over time. This is the term
that contains the “physics”
observation volume
of the diffusion processes
The Autocorrelation Function
t3
Detected Intensity (kcps)
t5
t4
t2
t1
19.8
19.6
19.4
19.2
19.0
18.8
0
5
10
15
20
25
30
35
Time (s)
G(0)  1/N
0.4
As time (tau) approaches 0
0.3
G()
Diffusion
0.2
G( ) 
0.1
0.0
10
-9
10
-7
10
-5
Time(s)
10
-3
10
-1
F(t)F(t   )
F(t)
2
Calculating the Autocorrelation Function
26x10
3
Fluorescence
24
22
20
Photon Counts
18
16
time
14
12
0
5
10

t
15
Time
20
25
30
35
Average Fluorescence
t+
G ( ) 
dF (t )  dF (t   )
F (t )
2
The Effects of Particle Concentration on the
Autocorrelation Curve
0.5
0.4
<N> = 2
G(t)
0.3
0.2
0.1
<N> = 4
0.0
10
-7
10
-6
10
-5
10
Time (s)
-4
10
-3
Why Is G(0) Proportional to 1/Particle Number?
A Poisson distribution describes the statistics of particle occupancy fluctuations.
In a Poissonian system the variance is proportional to the average number of
fluctuating species:
Particle_ Number  Variance
G( ) 
0.4
F (t )F (t   )
F (t )
G()
0.3
0.2
G (0) 
F (t )
F (t )
0.1
2
2

2
F (t ) 
F (t )
F (t )
0.0
10
-9
10
-7
10
-5
Time(s)
10
-3
10
-1
G(0) 
Variance
N
2

1
N
2

2
G(0), Particle Brightness and Poisson Statistics
1000000002011100000010000000101000100100
Time
Average = 0.275
Variance = 0.256
2
0
.
275
N  Average2 Variance
 0.296
0.256
Lets increase the particle brightness by 4x:
4000000008044400000040000000404000400400
Average = 1.1 Variance = 4.09
N 
0.296
What about the excitation (or observation) volume shape?
Effect of Shape on
the (Two-Photon) Autocorrelation Functions:
For a 2-dimensional Gaussian excitation volume:
1
 
8D   
G( )   1  2  
N   w 2 DG  
1-photon equation contains a 4, instead of 8
For a 3-dimensional Gaussian excitation volume:
1
   8D   
G( )   1  2  
N   w3 DG  
1
  8D  
 1  2  
  z3 DG  
2
Additional Equations:
3D Gaussian Confocor analysis:
1
1   
G( )  1   1   
N   D  


 
2
  1  S   
D  

1
2
... where N is the average particle number, D is the diffusion time (related
to D, D=w2/8D, for two photon and D=w2/4D for 1-photon excitation),
and S is a shape parameter, equivalent to w/z in the previous equations.
Triplet state term:

T T
(1 
e )
1 T
..where T is the triplet state amplitude and T is the triplet lifetime.
Orders of magnitude (for 1 μM solution, small molecule, water)
Volume
milliliter
microliter
nanoliter
picoliter
femtoliter
attoliter
Device
Size(μm)
Molecules
cuvette
10000
6x1014
plate well
1000
6x1011
microfabrication
100
6x108
typical cell
10
6x105
confocal volume
1
6x102
nanofabrication
0.1
6x10-1
Time
104
102
1
10-2
10-4
10-6
The Effects of Particle Size on the
Autocorrelation Curve
Diffusion Constants
0.25
300 um2/s
90 um2/s
71 um2/s
0.20
Slow Diffusion
0.15
G(t)
Fast Diffusion
0.10
Stokes-Einstein Equation:
k T
D
6    r
0.05
0.00
10
and
MW  Volume r 3
-7
10
-6
-5
10
10
Time (s)
-4
10
-3
Monomer --> Dimer
Only a change in D by a factor of 21/3, or 1.26
Autocorrelation Adenylate Kinase -EGFP
Chimeric Protein in HeLa Cells
Examples of different Hela cells transfected with AK1b -EGFP
Qiao Qiao Ruan, Y. Chen, M. Glaser & W. Mantulin Dept. Biochem & Dept Physics- LFD Univ Il, USA
Fluorescence Intensity
Examples of different Hela cells transfected with AK1-EGFP
Autocorrelation of EGFP & Adenylate Kinase -EGFP
EGFP-AK in the cytosol
EGFP-AKb in the cytosol
EGFPsolution
EGFPcell
Time (s)
Normalized autocorrelation curve of EGFP in solution (•), EGFP in the cell (• ),
AK1-EGFP in the cell(•), AK1b-EGFP in the cytoplasm of the cell(•).
Autocorrelation of Adenylate Kinase –EGFP
on the Membrane
Clearly more than one diffusion time
A mixture of AK1b-EGFP in the cytoplasm and membrane of the cell.
Autocorrelation Adenylate Kinaseb -EGFP
Cytosol
D
10 & 0. 18
16. 6
9.61
9.68
10. 13
7.1
11. 58
9.54
9.12
Plasma Membrane
D
13/0.12
7.9
7.9
8.8
8.2
11.4
14.4
12
12.3
11.2
Diffusion constants (um2/s) of AK EGFP-AKb in the cytosol -EGFP in the cell
(HeLa). At the membrane, a dual diffusion rate is calculated from FCS
data. Away from the plasma membrane, single diffusion constants are
found.
Multiple Species
Case 1: Species vary by a difference in diffusion constant, D.
Autocorrelation function can be used:
G( )sample
1
 8D 
2
  fi  G(0) i  1  2 
i 1
 w2DG 
M
(2D-Gaussian Shape)
!
G(0)sample   fi G(0)i
2
G(0)sample is no longer /N !
Antibody - Hapten Interactions
Binding site
Binding site
carb2
Mouse IgG: The two heavy chains are shown
in yellow and light blue. The two light chains
are shown in green and dark blue..J.Harris,
S.B.Larson, K.W.Hasel, A.McPherson, "Refined
structure of an intact IgG2a monoclonal
antibody", Biochemistry 36: 1581, (1997).
Digoxin: a cardiac glycoside used to treat
congestive heart failure. Digoxin competes
with potassium for a binding site on an
enzyme, referred to as potassium-ATPase.
Digoxin inhibits the Na-K ATPase pump in
the myocardial cell membrane.
Anti-Digoxin Antibody (IgG)
Binding to Digoxin-Fluorescein
triplet state
Digoxin-Fl•IgG
(99% bound)
Autocorrelation curves:
Digoxin-Fl•IgG
(50% Bound)
Digoxin-Fl
120
autocorrelation analyses:
m  Sfree
Fb 
c
K d  S free
100
Fraction Ligand Bound
Binding titration from the
80
Kd=12 nM
60
40
20
0
10
S. Tetin, K. Swift, & , E, Matayoshi , 2003
-10
10
-9
10
-8
[Antibody]
10
fr ee
(M)
-7
10
-6
Two Binding Site Model
IgG•2Ligand-Fl
IgG•Ligand-Fl + Ligand-Fl
IgG + 2 Ligand-Fl
1.0
1.20
50% quenching
0.8
Kd
0.6
IgG•Ligand
0.4
G(0)
Fraction Bound
1.15
1.10
1.05
0.2
IgG•2Ligand
No quenching
1.00
0.95
0.0
0.001
0.01
0.1
1
Binding sites
10
100
1000
0.001
0.01
[Ligand]=1, G(0)=1/N, Kd=1.0
0.1
1
Binding sites
10
100
1000
Digoxin-FL Binding to IgG: G(0) Profile
Y. Chen , Ph.D. Dissertation; Chen et. al., Biophys. J (2000) 79: 1074
Case 2: Species vary by a difference in brightness
assuming that D1  D2
The quantity Go becomes the only parameter to distinguish species,
but we know that:
G(0)sample   fi G(0)i
2
The autocorrelation function is not suitable
for analysis of this kind of data without additional information.
We need a different type of analysis
Photon Counting Histogram (PCH)
Aim: To resolve species from differences in their
molecular brightness
Poisson Distribution:
p(N ) 
N
 e N
N!
N
Sources of Non-Poissonian Noise
Detector Noise
Diffusing Particles in an Inhomogeneous
Excitation Beam*
Particle Number Fluctuations*
Multiple Species*
Single Species:
p(k)  PCH(e, N )
Where p(k) is the probability of observing k photon counts
frequency
PCH Example: Differences in Brightness
en=1.0)
en=2.2)
en=3.7)
Increasing Brightness
Photon Counts
Single Species PCH: Concentration
5.5 nM Fluorescein
Fit:
e = 16,000 cpsm
N = 0.3
550 nM Fluorescein
Fit:
e = 16,000 cpsm
N = 33
As particle concentration increases the PCH approaches a Poisson distribution
Photon Counting Histogram: Multispecies
Binary Mixture: p(k)  PCH(e1 , N1 )  PCH(e2 , N2 )
Molecular
Brightness
Concentration
Intensity
Snapshots of the excitation volume
Time
Photon Counting Histogram: Multispecies
Sample 2: many but dim (23 nM fluorescein at pH 6.3)
Sample 1: fewer but brighter fluors
(10 nM Rhodamine)
Sample 3: The mixture
The occupancy fluctuations for each specie in the mixture becomes a convolution
of the individual specie histograms. The resulting histogram is then broader than
expected for a single species.
Examination of a Protein Dimer with FCS:
Secreted Phospholipase A2
Sanchez, S. A., Y. Chen, J. D. Mueller, E. Gratton, T. L. Hazlett. (2001) Biochemistry, 40, 6903-6911.
sPLA2 Interfacial Binding
sPLA2 Self-Association
sPLA2 Membrane Binding
Interfacial sPLA2Self-Association
membrane
Lipid Interfaces
C H3
C H3
N
C H3
C H2
Multibilayers
(MLVs)
C H2
Choline Group
O
O
P
O
O
C H2
C H2
C H2
C H2
Vesicles
(SUVs, LUVs
& GUVs)
C H2
C H2
C H2
C H2
12 Carbon Tail
C H2
C H2
C H2
C H2
Micelles
C H2
Dodecylphosphocholine (DPC)
Micellar Lipid Analog (CMC = 1.1 mM)
In Solution: a Tight Dimer
Fluorescein-sPLA2
Fluorescence Correlation Spectroscopy
Steady-State Anisotropy
Number of particle x Dilution Factor
0.4
Anisotropy
0.3
0.2
0.1
8
7
a
6
b
5
4
0.0
10
-9
10
-8
10
-7
10
-6
1E-10
1E-9
1E-8
1E-7
1E-6
[PLA2] M
[Fl-sPLA2]
[Fl-sPLA2]
Time-Resolved Anisotropy:
Rotational correlation time 1 = 12.8 ns (0.43)
Rotational correlation time 2 = 0.50 ns (0.57)
measured
predicted (by sedimentation)
Why this large discrepancy?
In Solution: Fluorescein-sPLA2 +/- Urea
1. Autocorrelation
sPLA2
G(0)=0.021
D = 72 um2/s
Increasing Particles
sPLA2 + 3M Urea
G(0)=0.009
D = 95 um2/s
2. PCH analysis
sPLA2
e = 0.6
N = 3.29
Increasing Particles
sPLA2 + 3M Urea
e = 0.6
N = 8.48
Adjusted for viscosity differences
Change in number of particles, little change in brightness!
The Critical Question:
Is sPLA2 a Dimer in the Presence of Interfacial Lipid?
What Could We Expect to Find in the FCS Data?
Monomer Lipid
Micellar Lipid
(Poor Substrate)
(Preferred Substrate)
C.atrox sPLA2
Ddimer
N particles
Observing Fluorescein-labeled sPLA2
Upon dissociation, the mass could
increase due to lipid binding. Better
count the number of particles!
FCS on Fluorescein - sPLA2 in Buffer (RED)
and with DPC Micelles ( BLUE )
1. Autocorrelation Analysis
+DPC = increase in particles
sPLA2
G0 = 0.0137
D = 75 um2/s
sPLA2 + 20 mM DPC
G0 = 0.0069
D = 55 um2/s
2. PCH Analysis
sPLA2
e = 0.41
N = 6.5
sPLA2 + 20 mM DPC
e = 0.45
N = 12.2
+DPC = increase in particles
Fluorescein-sPLA2 Interaction with DPC
EDTA
N
Ca2+
D = 55-60 um2/s
12
10
N
8
6
(Dmicelle=57 um2/s)
(Ddimer= 75 um2/s)
D = 73 um2/s
•The PLA2 dimer dissociates
in the presence of micelles.
•Active enzyme form in a
micellar system is monomeric.
Schematic of sPLA2 - Dodecylphosphocholine Interactions
sPLA2
Monomer-Lipid
Association
Co-Micelle
sPLA2-Micelle
Two Channel Detection:
Cross-correlation
Sample Excitation
Volume
1.
2.
Beam Splitter
Increases signal to noise
by isolating correlated
signals.
Corrects for PMT noise
Detector 1
Detector 2
Each detector observes
the same particles
Removal of Detector Noise by Cross-correlation
Detector 1
11.5 nM Fluorescein
Detector 2
Detector after-pulsing
Cross-correlation
Calculating the Cross-correlation Function
26x10
3
Fluorescence
24
Detector 1: Fi
22
20
18
16
time
14
12
0
5
10
15
Time
20

t+
t
26x10
Gij ( ) 
25
30
35
dFi (t )  dFj (t   )
Fi (t )  F j (t )
3
Fluorescence
24
Detector 2: Fj
22
20
18
16
time
14
12
0
5
10
15
Time
20
25
30
35
Cross-correlation Calculations
One uses the same fitting functions you would
use for the standard autocorrelation curves.
Thus, for a 3-dimensional Gaussian excitation volume one uses:
  8D12 
G12 ( ) 
1

N12 
w2 
1
 8D12 
1 

2


z


1
2
G12 is commonly used to denote the cross-correlation and G1 and
G2 for the autocorrelation of the individual detectors. Sometimes
you will see Gx(0) or C(0) used for the cross-correlation.
Two-Color Cross-correlation
The cross-correlation
Sample
ONLY if particles are observed in both channels
Red filter
Each detector observes
particles with a particular color
The cross-correlation signal:
Only the green-red molecules are observed!!
Green filter
Two-color Cross-correlation
Equations are similar to those for the cross
correlation using a simple beam splitter:
Information Content
Correlated signal from
particles having both colors.
Autocorrelation from channel 1
on the green particles.
Autocorrelation from channel 2
on the red particles.
G ij ( ) 
dFi (t) dFj (t  )
Fi (t)  Fj (t)
Signal
G12 ( )
G1 ( )
G2 ( )
Experimental Concerns: Excitation Focusing &
Emission Collection
We assume exact match of the observation volumes in our calculations
which is difficult to obtain experimentally.
Excitation side:
(1) Laser alignment
(2) Chromatic aberration
(3) Spherical aberration
Emission side:
(1) Chromatic aberrations
(2) Spherical aberrations
(3) Improper alignment of detectors or pinhole
(cropping of the beam and focal point position)
Two-Color Fluctuation Correlation Spectroscopy
Uncorrelated
Gij ( ) 
 Fi (t ) F j (t   ) 
 Fi (t )  F j (t ) 
1
100
F2 (t )  f12 N1  f 22 N2
Ch.1
80
F1 (t )  f11 N1  f 21 N2
60
%T
Correlated
Ch.2
40
20
0
450
500
550
600
650
700
Wavelength (nm)
Interconverting
For two uncorrelated species, the amplitude of the
cross-correlation is proportional to:

f11 f12 N1  f 21 f 22 N 2
G12 (0)  
2
 f11 f12 N1  ( f11 f 22  f 21 f12 ) N1 N 2  f 21 f 22 N 2


2

Does SSTR1 exist as a monomer after ligand binding while
SSTR5 exists as a dimer/oligomer?
Collaboration with Ramesh Patel*† and Ujendra Kumar*
*Fraser Laboratories, Departments of Medicine, Pharmacology, and Therapeutics and Neurology and Neurosurgery, McGill University, and Royal Victoria
Hospital, Montreal, QC, Canada H3A 1A1; †Department of Chemistry and Physics, Clarkson University, Potsdam, NY 13699
Fluorescein Isothiocyanate (FITC)
Texas Red (TR)
Somatostatin
Somatostatin
Cell Membrane
R1
R1
Three Different CHO-K1 cell lines: wt R1, HA-R5, and wt R1/HA-R5
Hypothesis: R1- monomer ; R5 - dimer/oligomer; R1R5 dimer/oligomer
SSTR1 CHO-K1 cells with SST-fitc + SST-tr
Green Ch.
Red Ch.
• Very little labeled SST inside cell nucleus
• Non-homogeneous distribution of SST
• Impossible to distinguish co-localization from molecular interaction
A
Monomer
10
G1
G2
G 12
8
6
G()
G12(0)
= 0.22
G1(0)
4
Minimum
2
0
-2
10
10
-4
10
-3
10
-2
10
-1
(s)
Dimer
8
G1
G2
G 12
6
G12(0)
= 0.71
G1(0)
Maximum
G()
B
-5
4
2
0
10
-5
10
-4
10
-3
(s)
10
-2
10
-1
Experimentally derived auto- and cross-correlation curves from live R1 and
R5/R1 expressing CHO-K1 cells using dual-color two-photon FCS.
R1
G1
G2
G1 2
0.04
G1
G2
G1 2
0.12
G( )
0.06
G()
R1/R5
0.16
0.02
0.08
0.04
0.00
0.00
-0.04
10
-4
10
(s)
-2
10
0
10
-5
10
-4
10
-3
10
(s)
-2
10
-1
The R5/R1 expressing cells have a greater cross-correlation relative to the
simulated boundaries than the R1 expressing cells, indicating a higher level
of dimer/oligomer formation.
Patel, R.C., et al., Ligand binding to somatostatin receptors induces receptorspecific oligomer formation in live cells. PNAS, 2002. 99(5): p. 3294-3299
Molecular Dynamics
“High” FRET
(a)
P
YF
What if the distance/orientation
is not constant?
• Fluorescence fluctuation can
result from FRET or
Quenching
CFP
- 4 Ca2+
+ 4 Ca2+
(b)
calmodulin
M13
CFP
• FCS can determine the rate
at which this occurs
• This will yield hard to get
information (in the ms to ms (c)
range) on the internal motion
of biomolecules
YFP
“Low” FRET
trypsin
CFP
+
NO FRET
YFP
Fluorescence Intensity (cps)
60000
trypsin-cleaved
cameleon
CFP
cameleon
2+
Ca -depleted
cameleon
2+
Ca -saturated
YFP
50000
40000
30000
20000
10000
0
450
500
550
Wavelength (nm)
600
650
A)
B)200
160
G()
120
80
40
0
-3
10
-2
10
-1
10
 (s)
. A) Cameleon fusion protein consisting of ECFP, calmodulin, and EYFP.
[Truong, 2001 #1293] Calmodulin undergoes a conformational change that allows the
ECFP/EYFP FRET pair to get cl ose enough for efficient energy transfer. Fluctuations
between the folded and unfolded states will yield a measurable kinetic component for the
cross-correlation. B) Simulation of how such a fluctuation would show up in the
autocorrelation and cross-correlation. Red dashed line indicates pure diffusion.
0
10
1
10
In vitro Cameleon Data
Ca2+ Saturated
0.10
Donor Ch. Autocorrelation
Acceptor Ch. Autocorrelation
Cross-correlation
0.08
G()
0.06
0.04
0.02
0.00
-0.02
10
-5
10
-4
-3
10
10
-2
-1
10
 (s)
Crystallization And Preliminary X-Ray
Analysis Of Two New Crystal Forms Of
Calmodulin, B.Rupp, D.Marshak and
S.Parkin, Acta Crystallogr. D 52, 411
(1996)
Are the fast kinetics (~20 ms) due to
conformational changes or to fluorophore
blinking?
10
0
Autocorrelation Adenylate Kinaseb -EGFP
Cytosol
D
10 & 0. 18
16. 6
9.61
9.68
10. 13
7.1
11. 58
9.54
9.12
Plasma Membrane
D
13/0.12
7.9
7.9
8.8
8.2
11.4
14.4
12
12.3
11.2
Diffusion constants (um2/s) of AK EGFP-AKb in the cytosol -EGFP in the cell
(HeLa). At the membrane, a dual diffusion rate is calculated from FCS
data. Away from the plasma membrane, single diffusion costants are
found.
FRET Efficiency Distribution in Low Mg++
120
Low-FRET
Conformation
100
80
High-FRET
Conformation
60
40
20
0
0.1
0.2
0.3
0.4
0.5
0.6
FRET Efficiency
0.7
0.8
0.9
What is scanning FCS?
How is it implemented?
What kind of information scanning FCS provides that
cannot be obtained with single-point FCS?
Definition:
“Simultaneous” FCS measurements at multiple sample positions
“Simultaneous” = multiplexing
Principle:
Return to the same location before the particle leaves the volume of
observation
Scenarios for Scanning FCS application
•
Explore spatial-temporal correlation.
•
Detection and characterization of membrane rafts.
Visible raft-like domains
“Invisible” raft
GUV made from integral
membrane fragment
How to see the invisible rafts?
Probe must be selective for
rafts
Probe concentration must be
large to properly paint the rafts
300nm
Macro scale
Nano scale
If the rafts are stationary,
measurement at only one
position will not provide the
number of rafts in a pixel
If we know the number, we can
determine the average size
Chamber Design
Method developed by
Angelova et al
Lipid Extract GUV Morphology
BBM LIPID EXTRACT GUVs
BLM LIPID EXTRACT GUVs
BBM and BLM Lipid Extract GUVs exhibit formation of lipid domains.
GUVs composed of BBM and BLM natural lipid extracts. Micron-sized nonfluorescent circular domains appear on the surface of the GUVs. Domain
formation occurs at 43 °C for the BBM and at 38°C for the BLM.
20 mm
min
INTENSITY
max
Integral Membrane GUV Morphology
Integral BBM GUV
Integral BLM GUV
Integral BBM and BLM GUVs exhibit domain formation.
GUVs composed of integral membrane fragments. On the surface of the GUVs
appear micron-sized non-fluorescent domains which are irregularly shaped.
Domain formation occurs at 43 °C for the BBM and at 41.5°C for the BLM.
20 mm
min
INTENSITY
max
Measure the molecular weight of DNA (Weissman, Proc. Natl. Acad, Sci,
USA 73: 2776 (1976))
Measure the Diffusion of Fluorescent Beads and DNA (Koppel and others.
Biophysical J. Vol 66: 502, 1994)
Advantages:
• Improved signal to noise ratio
Data analysis
Time
Measure association/dissociation equilibrium in protein
systems (Berland et al, Biophys. J. 1996)
Fitting Model for Scanning FCS
Calculation of the Autocorrelation Function:
G1 ( ) 
F (t )F (t   )
F (t ) 2
Fitting Model:
G ( )  S( ) G( )
s




4A 2 (1  cos()) 
S( )  exp

8D 2
 (1 

)w
2
0


w


0
D=5mm2/s
Conventional FCS (vs) Scanning FCS in solution
0.40
0.35
0.30
G()
0.25
0.20
0.15
0.10
0.05
0.00
1E-4
1E-3
0.01
0.1
1
Seconds
Autocorrelation curve of fluorescein labeled beads in suspension.
0.04
Free fluorophore
0.016
0.014
0.03
D=21.41.3mm2/sec
0.012
D=20.61.1mm2/sec
0.010
0.02
G()
G()
FCS of fluorescent antibody (labeled with Alexa 488) in solution
0.01
0.008
0.006
0.004
0.002
0.00
0.000
1E-5
1E-4
Point FCS
1E-3
0.01
0.1
1E-3
0.01
Scanning FCS
0.1
Scanning FCS on GUV
POPC
Scanning FCS
Laurdan labeled GUV, surface
FCS at one point
Scanning frequency is 1 ms per orbit
We have a bandwidth sufficient to
observe diffusion of a small molecule
such as Laurdan or prodan in a fluid
membrane (POPOC)
Advantage of scanning FCS for
membrane and cellular studies
•
•
•
•
•
•
Less photo damage
Easy to locate membrane border
Multiple points simultaneously
Distinguish moving from immobile fraction
Spatial cross correlation…
Velocity direction and gradients
Protein interactions on GUVs can not be detected by imaging
background
Addition of fluorescent
antibody to GUV
The GUVs were made from rat kidney brush-border membrane extract containing all
the integral membrane proteins. The antibody against one specific membrane
protein (NaPi II) was labeled with Alexa 488, no enhanced fluorescence was
observed on the GUV membrane bilayers. Performing FCS measurements on the
bilayers was difficult due to the lack of contrast.
Autocorrelation at different positions
Time One line is 1 ms
Autocorrelation time (log axis)
Protein interactions on GUVs can be detected by Scanning FCS
Hyperspace: Vertical axis is time, horizontal
axis is location along the orbit
Diffusion coefficient of the antibody obtained from scanning FCS
D1=24 mm2/sec
D=20 mm2/sec
0.012
0.025
0.010
0.020
0.008
In solution
G()
0.015
0.006
On GUVs
0.010
0.004
0.005
0.002
0.000
0.000
1E-3
0.01
0.1
1
1E-3
0.01
0.1
Time (Seconds)
Time (Seconds)
0.015
0.010
0.005
0.000
G()
G()
D2=0.11 mm2/sec
-0.005
Inside GUVs
-0.010
-0.015
-0.020
1E-3
0.01
0.1
Time (Seconds)
1
1
Membrane Undulation Detected by Scanning FCS: Example
of spatial correlation
Membrane labeled with Laurdan
Autocorrelation curve
Time
Mid section of GUV
FCS-hyperspace
Protein Interactions on GUVs Detected by Scanning FCS
a
Intensity
Autocorrelation
G()
c
time
time
b
The GUV is made out of
whole membrane
extract from the
basolateral membrane
(BLM) of OKP cells.
The antibody labeled
with Alexa488 was
against NaPi II
cotransporter. This
protein was suppose to
be present in the BLM.
This experiment proved
that our GUV
generation method also
incorporated the
membrane proteins. It
is relatively close to the
natural composition of
the plasma membrane.
Conclusions
Scanning FCS is feasible both in solution, in model membranes and in cells
When the characteristic times for diffusion (or reactions) are about 1 ms or longer,
it could be convenient to perform scanning FCS
Scanning FCS provides autocorrelation functions at many positions in the sample
in a multiplexing way
Scanning FCS offers the possibility to distinguish processes such as true diffusion
and flow from local movements
Scanning FCS allows to clearly distinguish the location (and movements) of
membranes proteins and membrane domain while performing FCS measurements
Scanning FCS can be used to determine with nanometer precision the positions of
particles (molecules) and membranes