lecture 5 fcs, autocorrelation, pch, cross-correlation enrico gratton
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Lecture 5FCS, 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
Collected data by counting (by visual inspection) the number of particles in the observation volume as a function of time using a “ultra microscope”
1200020013241231021111311251110233133322111224221226122142345241141311423100100421123123201111000111_211001320000010011000100023221002110000201001_333122000231221024011102_1222112231000110331110210110010103011312121010121111211_10003221012302012121321110110023312242110001203010100221734410101002112211444421211440132123314313011222123310121111222412231113322132110000410432012120011322231200_253212033233111100210022013011321113120010131432211221122323442230321421532200202142123232043112312003314223452134110412322220221
Experimental data on colloidal gold particles:
1
2
3
4
56
10
2
3
4
56
100
Freq
uenc
y
86420
Number of Particles
7
6
5
4
3
2
1
0
Part
icle
Nu
mb
er
5004003002001000
time (s)
*Histogram of particle
counts
*Poisson behavior*Autocorrelation not
available in the original paper. It can be easily calculated today.
Particle Correlation
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
Sample Space
Observation Volume
1. The Rate of Motion
2. The Concentration of Particles
3. Changes in the Particle Fluorescence while under Observation, for example conformational transitions
What is Observed?
Defining Our Observation Volume:One- & Two-Photon Excitation.
1 - Photon 2 - Photon
Approximately 1 um3
Defined by the wavelength and numerical aperture of the
objective
Defined by the pinhole size, wavelength, magnification and
numerical aperture of the objective
Brad Amos MRC, Cambridge, UK
1-photon
2-photonNeed a pinhole to define a small volume
Data Treatment & Analysis
0
10
20
30
40
50
0 20 40 60 80 100
Time
Co
un
ts
Time Histogram
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.01 0.10 1.00 10.00 100.00
Time (ms)
Au
to C
orr
ela
tio
n Fit
Data
Autocorrelation
1
10
100
1000
10000
100000
1000000
0 5 10 15
Counts per Bin
Nu
mb
er
of
Oc
cu
ran
ce
s
Photon Counting Histogram (PCH)
Autocorrelation Parameters: G(0) & kaction
PCH Parameters: <N> &
Autocorrelation Function
)()()( tFtFtF
),()()( tCWdQtF rrr
G() F(t)F(t )
F(t)2
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 observation volume
C(r,t) is a function of the fluorophore concentration over time. This is the term that contains the “physics” of the diffusion processes
Factors influencing the fluorescence signal:
t1
t2
t3
t4
t5
0 5 10 15 20 25 30 3518.8
19.0
19.2
19.4
19.6
19.8
Time (s)
De
tect
ed
In
ten
sity
(kc
ps)
The Autocorrelation Function
G() F(t)F(t )
F(t)2
G(0) 1/NAs time (tau) approaches 0
Diffusion
2)(
)()()(
tF
tdFtdFG
Calculating the Autocorrelation Function
Photon Counts
26x103
24
22
20
18
16
14
12
Flu
orescence
35302520151050Time
t
t +
time
Average Fluorescence
The Effects of Particle Concentration on theAutocorrelation Curve
<N> = 4
<N> = 2
0.5
0.4
0.3
0.2
0.1
0.0
G(t
)
10-7
10-6
10-5
10-4
10-3
Time (s)
Why Is G(0) Proportional to 1/Particle Number?
NN
VarianceG
1)0( 2
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:
VarianceNumberParticle _
2
2
2
2
)(
)()(
)(
)()0(
tF
tFtF
tF
tFG
2)(
)()()(
tF
tFtFG
G(0), Particle Brightness and Poisson Statistics
1 0 0 0 0 0 0 0 0 2 0 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0
Average = 0.275 Variance = 0.256
Variance = 4.09
4 0 0 0 0 0 0 0 0 8 0 4 4 4 0 0 0 0 0 0 4 0 0 0 0 0 0 0 4 0 4 0 0 0 4 0 0 4 0 0
Average = 1.1 0.296
296.0256.0275.0 2
2 VarianceAverageN
Lets increase the particle brightness by 4x:
Time
N
What about the excitation (or observation) volume shape?
Effect of Shape onthe (Two-Photon) Autocorrelation Functions:
For a 2-dimensional Gaussian excitation volume:
G() N
1 8Dw
2 DG
2
1
For a 3-dimensional Gaussian excitation volume:
G() N
1 8Dw
3 DG
2
1
1 8Dz
3 DG
2
12
1-photon equation contains a 4, instead of 8
Additional Equations:
G() 1 1N
1
D
1
1 S 2
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.
3D Gaussian Confocor analysis:
Triplet state term:
)1
1( TeT
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 Device Size(μm) MoleculesTimemilliliter cuvette 10000 6x1014 104
microliter plate well 1000 6x1011 102
nanoliter microfabrication 100 6x108 1picoliter typical cell 10 6x105 10-2
femtoliter confocal volume 1 6x102 10-4
attoliter nanofabrication 0.1 6x10-1 10-
6
The Effects of Particle Size on theAutocorrelation Curve
300 um2/s90 um2/s71 um2/s
Diffusion Constants
Fast Diffusion
Slow Diffusion
0.25
0.20
0.15
0.10
0.05
0.00
G(t
)
10-7
10-6
10-5
10-4
10-3
Time (s)
D k T
6 r
Stokes-Einstein Equation:
3rVolumeMW
and
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
Qiao Qiao Ruan, Y. Chen, M. Glaser & W. Mantulin Dept. Biochem & Dept Physics- LFD Univ Il, USA
Examples of different Hela cells transfected with AK1-EGFP
Examples of different Hela cells transfected with AK1 -EGFP
Flu
orescence In
tensity
Time (s)
G(
)
EGFPsolution
EGFPcell
EGFP-AK in the cytosol
EGFP-AK in the cytosol
Normalized autocorrelation curve of EGFP in solution (•), EGFP in the cell (• ), AK1-EGFP in the cell(•), AK1-EGFP in the cytoplasm of the cell(•).
Autocorrelation of EGFP & Adenylate Kinase -EGFP
Autocorrelation of Adenylate Kinase –EGFPon the Membrane
A mixture of AK1b-EGFP in the cytoplasm and membrane of the cell.
Clearly more than one diffusion time
Diffusion constants (um2/s) of AK EGFP-AK 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.
10 & 0.1816.69.619.68
10.137.1
11.589.549.12
Plasma MembraneCytosol
Autocorrelation Adenylate Kinase -EGFP
13/0.127.97.98.88.2
11.414.4
1212.311.2
D D
Multiple Species
Case 1: Species vary by a difference in diffusion constant, D.
Autocorrelation function can be used:
(2D-Gaussian Shape)G()sample fi
2 G(0) i 1 8Dw
2DG
2
1
i 1
M
G(0)sample fi
2 G(0)i
G(0)sample is no longer /N !
!
Antibody - Hapten Interactions
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.
Binding siteBinding site
carb2
120
100
80
60
40
20
0
Fra
cti
on
Lig
an
d B
ou
nd
10-10
10-9
10-8
10-7
10-6
[Antibody]free (M)
Digoxin-Fl•IgG(99% bound)
Digoxin-Fl
Digoxin-Fl•IgG (50% Bound)
Autocorrelation curves:
Anti-Digoxin Antibody (IgG)Binding to Digoxin-Fluorescein
Binding titration from the autocorrelation analyses:
triplet state
Fb m S
free
Kd
Sfree
c
Kd=12 nM
S. Tetin, K. Swift, & , E, Matayoshi , 2003
Two Binding Site Model
1.20
1.15
1.10
1.05
1.00
0.95G
(0)
0.001 0.01 0.1 1 10 100 1000Binding sites
IgG + 2 Ligand-Fl IgG•Ligand-Fl + Ligand-Fl IgG•2Ligand-Fl
[Ligand]=1, G(0)=1/N, Kd=1.0
IgG•2Ligand
IgG•Ligand
No quenching
50% quenching
1.0
0.8
0.6
0.4
0.2
0.0
Fra
cti
on
Bo
un
d
0.001 0.01 0.1 1 10 100 1000Binding sites
Kd
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
The autocorrelation function is not suitable for analysis of this kind of data without additional information.
We need a different type of analysis
21 DD assuming that
The quantity Go becomes the only parameter to distinguish species, but we know that:
G(0)sample fi
2 G(0)i
Photon Counting Histogram (PCH)
Aim: To resolve species from differences in their
molecular brightness
Single Species: p(k) PCH(, N )
Sources of Non-Poissonian Noise
Detector NoiseDiffusing Particles in an Inhomogeneous
Excitation Beam*Particle Number Fluctuations*Multiple Species*
Poisson Distribution: p(N ) N N e N
N!
Where p(k) is the probability of observing k photon counts
Photon Counts
freq
uenc
y
PCH Example: Differences in Brightness
n=1.0) n=2.2) n=3.7)
Increasing Brightness
Single Species PCH: Concentration
5.5 nM Fluorescein
Fit: = 16,000 cpsmN = 0.3
550 nM Fluorescein
Fit: = 16,000 cpsmN = 33
As particle concentration increases the PCH approaches a Poisson distribution
Photon Counting Histogram: Multispecies
Snapshots of the excitation volume
Time
Inte
nsit
y
Binary Mixture: p(k) PCH(1 , N1 ) PCH(2 , N2 )
Molecular Brightness
Concentration
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.
Sanchez, S. A., Y. Chen, J. D. Mueller, E. Gratton, T. L. Hazlett. (2001) Biochemistry, 40, 6903-6911.
Examination of a Protein Dimer with FCS: Secreted Phospholipase A2
sPLA2 Membrane Binding
sPLA2 Self-Association
Interfacial sPLA2Self-Association
membrane
sPLA2 Interfacial Binding
CH3 N
CH3
CH3
CH2
CH2
O
P O
O
O
CH2
CH2
CH2
CH2
CH2
CH2
CH2
CH2
CH2
CH2
CH2
CH2
CH2
Dodecylphosphocholine (DPC)Micellar Lipid Analog (CMC = 1.1 mM)
Choline Group
12 Carbon Tail
Lipid Interfaces
Multibilayers (MLVs)
Vesicles(SUVs, LUVs
& GUVs)
Micelles
In Solution: a Tight Dimer
Fluorescein-sPLA2
0.4
0.3
0.2
0.1
0.0
An
iso
tro
py
10-9
10-8
10-7
10-6
[Fl-sPLA2]
1E-10 1E-9 1E-8 1E-7 1E-6
4
5
6
7
8
b
a
Nu
mb
er
of
pa
rtic
le x
Dilu
tion
Fa
cto
r[PLA2] M
[Fl-sPLA2]
Steady-State Anisotropy Fluorescence Correlation Spectroscopy
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?
sPLA2
G(0)=0.021D = 72 um2/s
sPLA2 + 3M Urea G(0)=0.009D = 95 um2/s
In Solution: Fluorescein-sPLA2 +/- Urea
sPLA2
= 0.6 N = 3.29
sPLA2 + 3M Urea = 0.6 N = 8.48
Change in number of particles, little change in brightness!
1. Autocorrelation
2. PCH analysis
Adjusted for viscosity differences
Increasing Particles
Increasing Particles
The Critical Question: Is sPLA2 a Dimer in the Presence of Interfacial Lipid?
Monomer Lipid Micellar Lipid
C.atrox sPLA2
(Poor Substrate) (Preferred Substrate)
Observing Fluorescein-labeled sPLA2
Ddimer
N particles
What Could We Expect to Find in the FCS Data?
Upon dissociation, the mass could increase due to lipid binding. Better count the number of particles!
sPLA2
G0 = 0.0137
D = 75 um2/s
sPLA2 + 20 mM DPC
G0 = 0.0069
D = 55 um2/s
FCS on Fluorescein - sPLA2 in Buffer (RED) and with DPC Micelles ( BLUE )
1. Autocorrelation Analysis
sPLA2
= 0.41
N = 6.5
sPLA2 + 20 mM DPC
= 0.45
N = 12.2
2. PCH Analysis
+DPC = increase in particles
+DPC = increase in particles
•The PLA2 dimer dissociates in the presence of micelles.•Active enzyme form in a micellar system is monomeric.
Fluorescein-sPLA2 Interaction with DPC
0.01 0.1 1 10
[DPC] (mM)
EDTA Ca2+
6
8
10
12
D = 73 um2/s
D = 55-60 um2/s (Dmicelle=57 um2/s)
(Ddimer= 75 um2/s)
N
N
++
+
+
+
+
+
+++
+
+
+
Schematic of sPLA2 - Dodecylphosphocholine Interactions
+
+
+
+
++
+
+
+
++
+
+
+
+
+
++
+
+
+
+ +
+
+
+
+
+
+
+
++
+
+
+
+
+
++
+
+
+
+ +
++
++
Co-Micelle sPLA2-MicelleMonomer-LipidAssociation
sPLA2
++
+
+
+
+
+
++
+
+
+
+
Two Channel Detection:Cross-correlation
Each detector observes the same particles
Sample Excitation Volume
Detector 1 Detector 2
Beam Splitter1. Increases signal to noise
by isolating correlated signals.
2. Corrects for PMT noise
Removal of Detector Noise by Cross-correlation
11.5 nM Fluorescein
Detector 1
Detector 2
Cross-correlation
Detector after-pulsing
)()(
)()()(
tFtF
tdFtdFG
ji
ji
ij
Detector 1: Fi
Detector 2: Fj
26x103
24
22
20
18
16
14
12
Flu
orescence
35302520151050Time
26x103
24
22
20
18
16
14
12
Flu
orescence
35302520151050Time
t
Calculating the Cross-correlation Function
t +
time
time
Thus, for a 3-dimensional Gaussian excitation volume one uses:
21
212
1
212
1212
81
81)(
z
D
w
D
NG
Cross-correlation Calculations
One uses the same fitting functions you would use for the standard autocorrelation curves.
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
Each detector observesparticles with a particular color
The cross-correlation ONLY if particles are observed in both channels
The cross-correlation signal:
Sample
Red filter Green filter
Only the green-red molecules are observed!!
Two-color Cross-correlation
G ij() dF
i(t) dF
j(t )
Fi(t) F
j(t)
)(12 G
Equations are similar to those for the cross
correlation using a simple beam splitter:
Information Content Signal
Correlated signal from particles having both colors.
Autocorrelation from channel 1 on the green particles.
Autocorrelation from channel 2 on the red particles.
)(1 G
)(2 G
Experimental Concerns: Excitation Focusing & Emission Collection
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)
We assume exact match of the observation volumes in our calculations which is difficult to obtain experimentally.
Uncorrelated
Correlated
Two-Color Fluctuation Correlation Spectroscopy
1)()(
)()()(
tFtF
tFtFG
ji
jiij
Interconverting
2211111 )( NfNftF 2221122 )( NfNftF
Ch.2 Ch.1
450 500 550 600 650 7000
20
40
60
80
100
Wavelength (nm)
%T
For two uncorrelated species, the amplitude of the cross-correlation is proportional to:
2
222212112212211
2
11211
222211121112
)()0(
NffNNffffNff
NffNffG
Does SSTR1 exist as a monomer after ligand binding whileSSTR5 exists as a dimer/oligomer?
Somatostatin
Fluorescein Isothiocyanate (FITC)
Somatostatin
Texas Red (TR)
Cell Membrane
R1
R5 R5
R1
Three Different CHO-K1 cell lines: wt R1, HA-R5, and wt R1/HA-R5Hypothesis: R1- monomer ; R5 - dimer/oligomer; R1R5 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 VictoriaHospital, Montreal, QC, Canada H3A 1A1; †Department of Chemistry and Physics, Clarkson University, Potsdam, NY 13699
Green Ch. Red Ch.
SSTR1 CHO-K1 cells with SST-fitc + SST-tr
• Very little labeled SST inside cell nucleus• Non-homogeneous distribution of SST• Impossible to distinguish co-localization from molecular interaction
Minimum
MonomerA
Maximum
DimerB
= 0.22G12(0)
G1(0)
= 0.71G12(0)
G1(0)
Experimentally derived auto- and cross-correlation curves from live R1 and R5/R1 expressing CHO-K1 cells using dual-color two-photon FCS.
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.
R1 R1/R5
Patel, R.C., et al., Ligand binding to somatostatin receptors induces receptor-specific oligomer formation in live cells. PNAS, 2002. 99(5): p. 3294-3299
- 4 Ca2+ + 4 Ca2+
CFP YFP
calmodulin M13
CFP
“High” FRET
“Low” FRET
trypsin
CFP YFP+
NO FRET
(a)
(b)
(c)
Molecular Dynamics
What if the distance/orientation is not constant?
• Fluorescence fluctuation can result from FRET or Quenching
• FCS can determine the rate at which this occurs
• This will yield hard to get information (in the s to ms range) on the internal motion of biomolecules
450 500 550 600 6500
10000
20000
30000
40000
50000
60000
Fluo
resc
ence
Int
ensi
ty (
cps)
Wavelength (nm)
trypsin-cleaved cameleon
CFP cameleon
Ca2+-depleted cameleon
Ca2+-saturated YFP
A)
10-3 10-2 10-1 100 101
0
40
80
120
160
200
(s)
G( )
B)
. 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.
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)
Ca2+ Saturated
Are the fast kinetics (~20 s) due to conformational changes or to fluorophore blinking?
In vitro Cameleon Data
Diffusion constants (um2/s) of AK EGFP-AK 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.
10 & 0.1816.69.619.68
10.137.1
11.589.549.12
Plasma MembraneCytosol
Autocorrelation Adenylate Kinase -EGFP
13/0.127.97.98.88.2
11.414.4
1212.311.2
D D
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0
20
40
60
80
100
120
FRET Efficiency
FRET Efficiency Distribution in Low Mg++
Low-FRET Conformation
High-FRETConformation
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
• Explore spatial-temporal correlation.
• Detection and characterization of membrane rafts.
GUV made from integral membrane fragment
Visible raft-like domains
300nm
“Invisible” raft
Scenarios for Scanning FCS application
Macro scale Nano scale
How to see the invisible rafts?
Probe must be selective for rafts
Probe concentration must be large to properly paint the rafts
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
Method developed by Angelova et al
Chamber Design
BBM LIPID EXTRACT GUVs BLM LIPID EXTRACT GUVs
min maxINTENSITY
20 m
BBM and BLM Lipid Extract GUVs exhibit formation of lipid domains.
GUVs composed of BBM and BLM natural lipid extracts. Micron-sized non-fluorescent 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.
Lipid Extract GUV Morphology
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.
min maxINTENSITY
20 m
Integral BBM GUV
Integral Membrane GUV Morphology
Integral BLM GUV
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
Tim
eMeasure association/dissociation equilibrium in protein
systems (Berland et al, Biophys. J. 1996)
Fitting Model for Scanning FCS
2)(
)()()(1
tF
tFtFG
Calculation of the Autocorrelation Function:
Fitting Model:
S() exp 4A2 (1 cos())
(1 8D
w02
)w02
Gs() S() G()
D=5m2/s
1E-4 1E-3 0.01 0.1 1
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
G()
Seconds
Conventional FCS (vs) Scanning FCS in solution
Autocorrelation curve of fluorescein labeled beads in suspension.
FCS of fluorescent antibody (labeled with Alexa 488) in solution
1E-5 1E-4 1E-3 0.01 0.1
0.00
0.01
0.02
0.03
0.04
G( )
D=21.41.3m2/sec
Free fluorophore
1E-3 0.01 0.1
0.000
0.002
0.004
0.006
0.008
0.010
0.012
0.014
0.016
G()
D=20.61.1m2/sec
Point FCS Scanning FCS
Scanning FCS on GUV
Laurdan labeled GUV, surface
POPC
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)
FCS at one point
Scanning FCS
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.
Protein interactions on GUVs can be detected by Scanning FCS
Hyperspace: Vertical axis is time, horizontal axis is location along the orbitAutocorrelation at different positions
Tim
e One line is 1 m
sA
utoc
orre
latio
n tim
e (lo
g ax
is)
1E-3 0.01 0.1 1
0.000
0.002
0.004
0.006
0.008
0.010
0.012
G()
Time (Seconds)
1E-3 0.01 0.1 1
0.000
0.005
0.010
0.015
0.020
0.025
G()
Time (Seconds)
D=20 m2/secD1=24 m2/sec
D2=0.11 m2/sec
1E-3 0.01 0.1 1
-0.020
-0.015
-0.010
-0.005
0.000
0.005
0.010
0.015
G()
Time (Seconds)
Diffusion coefficient of the antibody obtained from scanning FCS
In solutionOn GUVs
Inside GUVs
Mid section of GUV
FCS-hyperspace
Autocorrelation curve
Tim
e
Membrane Undulation Detected by Scanning FCS: Example of spatial correlation
Membrane labeled with Laurdan
a
c
time
b
G()
time
Intensity Autocorrelation
Protein Interactions on GUVs Detected by Scanning FCS
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
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