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