1

Lecture 7FCS, Autocorrelation, PCH,

Cross-correlationJoachim Mueller

Principles of Fluorescence Techniques Laboratory for Fluorescence Dynamics Figure and slide acknowledgements:

Enrico Gratton

Historic Experiment: 1st Application of Correlation Spectroscopy(Svedberg & Inouye, 1911) Occupancy Fluctuation

1200020013241231021111311251110233133322111224221226122142345241141311423100100421123123201111000111_211001320000010011000100023221002110000201001_333122000231221024011102_1222112231000110331110210110010103011312121010121111211_10003221012302012121321110110023312242110001203010100221734410101002112211444421211440132123314313011222123310121111222412231113322132110000410432012120011322231200_253212033233111100210022013011321131200101314322112211223234422230321421532200202142123232043112312003314223452134110412322220221

Gold particles

time

Svedberg and Inouye, Zeitschr. F. physik. Chemie 1911, 77:145

Statistical analysis of raw data required

Collected data by counting (by visual inspection) the number of particles in the observation volume as a function of time using a “ultra microscope”

7

6

5

4

3

2

1

0

Part

icle

Num

ber

5004003002001000

time (s)

• Autocorrelation not available in the original paper. It can be easily calculated today.

Particle Correlation

0 2 4 6 810 0

10 1

10 2

experim ent p red ic ted P o isson

frequ

ency

n um ber o f pa rtic les

*Histogram of particle counts *Autocorrelation

• Poisson statistics

1.55N =

( )1 1.560

NG

= =

Graph of

Raw Data:

2

What we learn from the correlation function? 2

Expectedµm70

6 sBk TD

Rπη= ≈

(Stokes-Einstein)

Svedberg claimed: Gold colloids with radius R = 3 nm

2 2 Dx Dτ∆ ≈

Slit

2µm

0 5 10 150.0

0.2

0.4

0.6

G(τ

)

τ (sec)

characteristic diffusion time

1.5sDτ ≈

Experimental facts:2µm1

sExpD⇒ ≈

200nmR⇒ ≈

Conclusion: Bad sample preparation

The ultramicroscope was invented in 1903 (Siedentopf and Zsigmondy). They already concluded that scattering will not be suitable to observe single molecules, but fluorescence could.

In FCS Fluctuations are in the Fluorescence Signal

DiffusionEnzymatic ActivityPhase Fluctuations

Conformational DynamicsRotational Motion

Protein Folding

Example of processes that could generate fluctuations

Fluorescence Correlation Spectroscopy (FCS)

objective

Coverslip

Generating Fluctuations By Motion

Sample

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?

3

Observation Volume in 1- & 2-Photon Excitation.

1-Photon: 2-Photon:

Approximately 1 µm3

Observation Volume: Defined by the confocal pinhole size, wavelength, magnification and numerical aperture of the objective

1-photon excitation beam

Fluorescence excited everywhere along the beam

2-photon excitation beam

Fluorescence only excited at focal volume

Observation Volume: Defined by wavelength, and numerical aperture of the objective

http://www.andor.com/image_lib/lores/introduction

1-photon

2-photon

pinhole needed to define a small volume

Brad Amos MRC, Cambridge, UK

Two-Photon FCS

0.4 µm

Fluorescence signal at detectorTwo-photon effect

F

time

fluctuation

0 100 200 300 4002000

2500

3000

3500

4000

4500

5000

5500

Inte

nsity

(cps

)

time (sec)

Example:Fluorescently labeled viral particles

Data Treatment & AnalysisTime Histogram Autocorrelation

Photon Counting Histogram (PCH)

Autocorrelation Parameters: G(0) & krate

PCH Parameters: <N> & ε

0 100 200 300 4002000

2500

3000

3500

4000

4500

5000

5500

Inte

nsity

(cps

)

t im e (sec)10-4 10 -3 10 -2 10 -1 100 101

0

5

10

15

20

25

da ta fit

g(τ)

τ (sec)

prob

abili

ty

Counts per bin

4

Autocorrelation Function

)()()( tFtFtF −=δ

( ) ( ) ( ),F t Q d W C tκ= ∫ r r r

G(τ) =δF(t )δF(t + τ)

F(t ) 2

κQ = 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:

Average fluorescence signal: ( )F t

Fluorescence fluctuation:

10-9 10-7 10-5 10-3 10-1

0.0

0.1

0.2

0.3

0.4

Lag time τ (s)

G(τ

)

10-9 10-7 10-5 10-3 10-1

0.0

0.1

0.2

0.3

0.4

Lag time τ (s)

G(τ

)

Calculating the Autocorrelation Function

F(t)in photon counts

t

τ

t + τ

Fluorescence Fluctuation

( ) ( )dF t F t F= −

t + τ

τtime

Average Fluorescence

F

2

( ) ( )( )

F t F tG

F

δ δ ττ

⋅ +=

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)

Det

ecte

d In

tens

ity (k

cps)

The Autocorrelation Function

10-9 10-7 10-5 10-3 10-1

0.0

0.1

0.2

0.3

0.4

Time(s)

G(τ

)

G(τ) =δF(t )δF(t + τ)

F(t ) 2

G(0) ∝ 1/NAs time (tau) approaches 0

Diffusion

5

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?

NNVarianceG 1)0( 2 ==10-9 10-7 10-5 10-3 10-1

0.0

0.1

0.2

0.3

0.4

Time(s)

G(τ

)

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

−==

δ

2)(

)()()(

tF

tFtFG

τδδτ

+=

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

6

Effect of Shape on the (2-Photon) Autocorrelation Functions:

(for simple diffusion)

For a 2-dimensional Gaussian excitation volume:

1

20

8( ) 1 DGN wγ ττ

−⎛ ⎞

= +⎜ ⎟⎝ ⎠

For a 3-dimensional Gaussian excitation volume:

11 2

2 20 0

8 8( ) 1 1D DGN w zγ τ ττ

−−⎛ ⎞ ⎛ ⎞

= + +⎜ ⎟ ⎜ ⎟⎝ ⎠ ⎝ ⎠

1-photon equation contains a 4, instead of 8

Model of observation volume

γ: shape factor (0.354 for 3DG, 0.5 for 2DG)N: average number of particles inside volumeD: Diffusion coefficientwo: radial beam waist of two-photon laser spotzo: axial beam waist of two-photon laser spot

Additional Equations:

... where N is the average particle number, τD is the diffusion time (related to D, τD=w2/8D, for two photon and τD=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 ττ−

−+

..where T is the triplet state amplitude and τT is the triplet lifetime.

G(τ) = 1 +1N

1 +ττ

D

⎛

⎝ ⎜

⎞

⎠ ⎟

−1

⋅ 1 + S 2 ⋅τ

τD

⎛

⎝ ⎜

⎞

⎠ ⎟

− 12

2

( ) ( )( )

F t F tG

F

ττ

+ ⋅=Note: The offset of one is caused by a different definition of G(τ) :

Orders of magnitude (for 10nM solution, small molecule, water)

DiffusionVolume Device Size(µm) Molecules Time (s)milliliter cuvette 10000 6x1012 104

microliter plate well 1000 6x109 102

nanoliter microfabrication 100 6x106 1picoliter typical cell 10 6x103 10-2

femtoliter confocal volume 1 6x100 10-4

attoliter nanofabrication 0.1 6x10-3 10-6

7

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 ⋅π ⋅ η ⋅ r

Stokes-Einstein Equation:

3rVolumeMW ∝∝

and

Monomer --> Dimer Only a change in D by a factor of 21/3, or 1.26

FCS inside living cells

1E-5 1E-4 1E-3 0.01 0.10.0

0.2

0.4

0.6

0.8

1.0

in solution

inside nucleus

g(τ)

τ (sec)

Dsolution

Dnucleus= 3.3

Correlation Analysis

Measure the diffusion coefficient of Green Fluorescent Protein (GFP) in aqueous solution in inside the nucleus of a cell.

objectiveCoverslip

Two-PhotonSpot

Autocorrelation Adenylate Kinase -EGFPChimeric 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 AK1β -EGFP

Fluorescence Intensity

8

Time (s)

G(τ

)

EGFPsolution

EGFPcell

EGFP-AKβ in the cytosol

EGFP-AK in the cytosol

Normalized autocorrelation curve of EGFP in solution (•), EGFP in the cell (• ), AK1-EGFP in the cell(•), AK1β-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-AKβ in the cytosol -EGFP in the cell (HeLa). At the membrane, a dual diffusion rate is calculated from FCSdata. 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 Kinaseβ -EGFP

13/0.127.97.98.88.2

11.414.4

1212.311.2

D D

9

Multiple SpeciesCase 1: Species differ in their diffusion constant D

Autocorrelation function can be used:

(2D-Gaussian Shape)G(τ)sample = fi

2 ⋅ G(0) i ⋅ 1 +8Dτw2DG

2

⎛

⎝ ⎜

⎞

⎠ ⎟

−1

i=1

M

∑

G(0)sample = fi2 ⋅G(0)i∑

G(0)sample is no longer γ/N !

! fi is the fractional fluorescence intensity of species i.

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

Frac

tion

Liga

nd B

ound

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 ⋅ Sfree

Kd + Sfree

+ c

Kd=12 nM

S. Tetin, K. Swift, & , E, Matayoshi , 2003

10

Two Binding Site Model

1.20

1.15

1.10

1.05

1.00

0.95

G(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

Frac

tion

Bou

nd

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 G(0) becomes the only parameter to distinguish species, but we know that:

G(0)sample = fi

2 ⋅G(0)i∑

Multiple Species

11

Photon Counting Histogram (PCH)

Aim: To resolve species from differences in their molecular brightness

Single Species:

p(k) = PCH(ε, N )

Sources of Non-Poissonian Noise

Detector NoiseDiffusing Particles in an Inhomogeneous

Excitation Beam*Particle Number Fluctuations*Multiple Species*

Poisson Distributionfor particle number:

p(N ) =N N ⋅ e− N

N!

p(k) is the probability of observing k photon counts

But distribution of photon counts is Non-Poissonian:

Photon Counting Histogram (PCH)

Aim: To resolve species from differences in their molecular brightness

Single Species:

p(k) = PCH(ε, N )

Sources of Non-Poissonian Noise• Detector Noise• Diffusing Particles in an Inhomogeneous Excitation Beam*• Particle Number Fluctuations*• Multiple Species*

where p(k) is the probability of observing k photon counts

Molecular brightness ε : The average photon count rate of a single fluorophore

PCH: probability distribution function p(k)

16000cpsmN 0.3

ε =

=

Note: PCH is Non-Poissonian!

Photon Counts

freq

uenc

y

PCH Example: Differences in Brightness

(εn=1.0) (εn=2.2) (εn=3.7)

Increasing Brightness

12

Single Species PCH: Concentration

5.5 nM Fluorescein

Fit:ε = 16,000 cpsmN = 0.3

550 nM Fluorescein

Fit:ε = 16,000 cpsmN = 33

As particle concentration increases the PCH approaches a Poisson distribution

Photon Counting Histogram: Multiple Species

Snapshots of the excitation volume

Time

Inte

nsity

Binary Mixture: p(k) = PCH (ε1 , N1 ) ⊗ PCH(ε2 , N2 )

Brightness

Concentration

Photon Counting Histogram: Multiple Species

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.

p(k) = PCH(ε1 , N1 ) ⊗ PCH(ε2 , N2 )

13

-10-8-6-4-20

log(

PCH

)

0 5 10 15k

-2

0

2

resi

dual

s/σ

-8-6

-4

-20

log(

PCH

)

0 5 10 15k

-2

0

2

resi

dual

s/σ

Singly labeled proteins Mixture of singly ordoubly labeled proteins

Resolve a protein mixture with a brightness ratio of two

Alcohol dehydrogenase labeling experiments

+

c 1ε 1N 2ε 2N

Sample A 0.190.1826.2+

− 0.0040.0040.540+

− ----- -----

Sample B 0.61.225.1+

− 007.0002.0155.0 +

− 101056+

− 008.0003.0006.0 +

−

Both species havesame• color

• fluorescence lifetime• diffusion coefficient

• polarization

kcpsm kcpsm

0

1x104

2x104

3x104

EGFPsolution

EGFPcytoplasm

EGFPnucleus

autofluorescencenucleus

autofluorescencecytoplasm

Mol

ecul

ar B

right

ness

(cps

m)

The molecular brightness of EGFP is a factor ten higher than that of the autofluorescence in HeLa cells

Excitation=895nm

PCH in cells: Brightness of EGFP

Chen Y, Mueller JD, Ruan Q, Gratton E (2002) Biophysical Journal, 82, 133 .

Brightness Encodes Stoichiometry

EGFP

Protein

F

time

ε

Sample Fluorescence Brightness

N1

F

time

2ε

N2

+ F

time

2ε

N2N1

ε

14

100 10000

2500

5000

7500

10000

EGFP Brightness EGFP

ε app (

cpsm

)

Concentration [nM]

104 105 106

Brightness of EGFP2 is twice the brightness of EGFP

Brightness and Stoichiometry

Intensity (cps)

EGFP

100 10000

2500

5000

7500

100002 x EGFP Brightness

EGFP Brightness EGFP EGFP2

ε app (

cpsm

)

Concentration [nM]

104 105 106

EGFP2

Chen Y, Wei LN, Mueller JD, PNAS (2003) 100, 15492-15497

Distinguish Homo- and Hetero-interactions in living cells

Apparent Brightness

BA

A B

A B A B A B+ A A

ε ε

ε 0

2ε

ε

ε

ε 2ε

2ε2 905nmγλ =

2 965nmγλ =

ECFP: EYFP:

• single detection channel experiment• distinguish between CFP and YFP by excitation (not by emission)!• brightness of CFP and YFP is identical at 905nm (with the appropriate filters)• you can choose conditions so that the brightness is not changed by FRET between CFP and YFP• determine the expressed protein concentrations of each cell!

0 4000 8000 120000.0

0.5

1.0

1.5

2.0

2.5

3.0

- RXR agonist + RXR agonist

ε app/ε

mon

omer

total protein concentration [nM]0 1000 2000 3000 4000

0.0

0.5

1.0

1.5

2.0

2.5

3.0

- RXR agonist + RXR agonist

ε app/ε

mon

omer

RXRLBD-YFP [nM]

PCH analysis of a heterodimer in living cells

We expect: RARRXR

2 965nmγλ =2 905nmγλ =

The nuclear receptors RAR and RXR form a tight heterodimer in vitro. We investigate their stoichiometry in the nucleus of COS cells.

Chen Y, Li-Na Wei, Mueller JD, Biophys. J., (2005) 88, 4366-4377

15

Two Channel Detection:Cross-correlation

Each detector observesthe same particles

Sample Excitation Volume

Detector 1 Detector 2

Beam Splitter1. Isolate 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

jiij ⋅

+⋅=

ττ

Detector 1: Fi

Detector 2: Fj

t

τ

Calculating the Cross-correlation Function

t + τ

time

time

16

Thus, for a 3-dimensional Gaussian excitation volume one uses:

21

212

1

212

1212

8181)(

−−

⎟⎟⎠

⎞⎜⎜⎝

⎛+⎟

⎟⎠

⎞⎜⎜⎝

⎛+=

zD

wD

NG ττγτ

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!!

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.

17

Two-color Cross-correlation

Gij(τ) =dFi (t) ⋅dFj (t + τ)

Fi(t) ⋅ Fj (t)

)(12 τG

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 τG

)(2 τG

G12(τ)

log τ

A + B uncorrelated

A + B uncorrelated, but crosstalk

A + B correlated

Spectral CrosstalkUncorrelated

Correlated

Uncorrelated+Crosstalk Ch.2 Ch.1

450 500 550 600 650 7000

20

40

60

80

100

Wavelength (nm)

%T

Uncorrelated

Two-Color FCS: Correct for Spectral Overlap

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:

11 12 1 21 22 212 2 2

11 12 1 11 22 21 12 1 2 21 22 2

(0)( )

uncorrelated f f N f f NG

f f N f f f f N N f f N

⎡ ⎤+∝ ⎢ ⎥

+ + +⎢ ⎥⎣ ⎦

fij: fractional intensity of species i in channel j

G12uncorrelated(0)

log τ

A + B uncorrelated, but crosstalk

A + B correlatedG12

correlated(0)

∆G12

18

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

10-5

10-4

10-3

10-2

10-1

-2

0

2

4

6

8

10

τ(s)

G(τ

)

G1 G2 G12

Minimum

MonomerA

10-5

10-4

10-3

10-2

10-1

0

2

4

6

8

τ(s)

G(τ

)

G1 G2 G12Maximum

DimerB

= 0.22G12(0)G1(0)

= 0.71G12(0)G1(0)

19

10-4 10-2 100

0.00

0.02

0.04

0.06

τ(s)

G(τ

)

G1 G2 G1 2

10-5

10-4

10-3

10-2

10-1

-0.04

0.00

0.04

0.08

0.12

0.16

τ(s)

G(τ

)

G1 G2 G1 2

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

YFP“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 µs to ms range) on the internal motion of biomolecules

450 500 550 600 6500

10000

20000

30000

40000

50000

60000

Fluo

resc

ence

Inte

nsity

(cps

)

Wavelength (nm)

trypsin-cleaved cameleon

CFP cameleon

Ca2+-depleted cameleon

Ca2+-saturated YFP

20

A)

10-3 10-2 10-1 100 101

0

40

80

120

160

200

τ (s)

G( τ)

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.

10-5 10-4 10-3 10-2 10-1 100-0.02

0.00

0.02

0.04

0.06

0.08

0.10

G(τ

)

τ (s)

Donor Ch. Autocorrelation Acceptor Ch. Autocorrelation Cross-correlation

Crystallization And Preliminary X-Ray Analysis Of Two New Crystal Forms Of Calmodulin, B.Rupp, D.Marshakand S.Parkin, Acta Crystallogr. D 52, 411 (1996)

Ca2+ Saturated

Are the fast kinetics (~20 µs) due to conformational changes or to fluorophore blinking?

In vitro Cameleon Data

Cross-CorrelationDual-Color PCH

Fluo

resc

ence

F(t)

<FA>δFA

Time t

<FB>δFB

Time t

Fluo

resc

ence

F(t)

Dual-color PCH analysis (1)

21

Tsample

Sign

al A

Tsample

Sign

al B

Brightness in each channel: εA, εB

Average number of molecules: N

Tsample

Sign

al A

Tsample

Sign

al B

Brightness in each channel: εA, εBAverage number of molecules: N

Dual-color PCH analysis (2)

Single Species: ( , ) ( , , )A B A Bp k k PCH Nε ε=

Dual-Color PCHfluctuations

2 channels

2 species model χ2 = 1.01

2000 3000 4000 5000 6000 7000 8000 9000100002000

3000

4000

5000

6000

7000

8000

9000

10000

EYFP alone ECFP alone species 1 from fit species 2 from fit

ε A (c

psm

)

εB (cpsm)

1 species modelχ2 = 17.93

ECFP & EYFP mixture resolved with single histogram.

Resolve Mixture of ECFP and EYFP in vitro

Note: Cross-correlation analysis cannot resolve a mixture of ECFP & EYFP with a single measurement!

Chen Y, Tekmen M, Hillesheim L, Skinner J, Wu B, Mueller JD, Biophys. J. (2005), 88 2177-2192