fitting model for diffusion in 3d fit by 3d gaussian model: aragon & pecora j. chem. phys. 64,...
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B
z 1 1
0,0,0g 0,0,r
d22p
22p
d
1111
Fitting Model for Diffusion in 3DFitting Model for Diffusion in 3D
d
22p
exp τ4
ω D
-12-8exp scm 10 x 2.4 D
-12-8theory scm 10 x 2.3 D
Fit by 3D GaussianModel:
Aragon & Pecora J. Chem. Phys. 64, 1791-1803 (1976)ICS: D 10-8-10-12 cm2s-1
Diffusing Population: 2D SystemDiffusing Population: 2D System
Simulation: <N>=10
<N>spatial = <g(0,0)n>-1 = 10.18 ± 0.03
<N>temporal = g(0,0,0)-1 = 10.18 ± 0.01
Autocorrelation Function Amplitude: Number Density
Simulation: D = 0.01 m2s-1; <N>=10
Diffusing Population: 2D SystemDiffusing Population: 2D System
(s)
0 10 20 30 40
r 11(
0,0
,)
0.000
0.025
0.050
0.075
0.100
D2D = 0.0101 ± 0.0005 m2s-1
B τ
τ 1 (0,0,0)g τ0,0,r
-1
d1111
Best Fit with 2D
Diffusion Model
Simulation: D = 0.01 m2s-1; <N>=10
Flowing Population & Diffusing Population 2DFlowing Population & Diffusing Population 2D
Simulation: <N>diff =5 <N>flow =5
<N>spatial = <g(0,0)n>-1 = 10.01 ± 0.03
<N>temporal : <N>diff =4.9 ± 0.3 <N>flow =5.1 ± 0.3
Autocorrelation Function Amplitude: Number Density
Simulation: D = 0.01 m2s-1; <N>diff =5 |v| = 0.071 m s-1; <N>flow =5
(s)
0 10 20 30 40
r 11(
0,0
,)
0.000
0.025
0.050
0.075
0.100
Flowing Population & Diffusing Population 2DFlowing Population & Diffusing Population 2D
D2D = 0.010 ± 0.001 m2s-1
|v| = 0.071 ± 0.001 m s-1
Best Fit with 2D Two populationDiffusion & Flow Model
B τ
τ-exp(0,0,0)g
τ
τ 1 (0,0,0)g τ0,0,r
2
ff11
-1
dd1111
Simulation: D = 0.01 m2s-1; <N>diff =5 |v| = 0.071 m s-1; <N>flow =5
2-Photon ICS on Living Cells2-Photon ICS on Living Cells
10 m
CHO Cell: Actinin/EGFP Plated on Fibronectin
Two-photon fluorescence microscopy 7.5 min 37 °C
QuantifyDynamics &Clustering…
2-Photon ICS on Living Cells2-Photon ICS on Living Cells
10 m
(s)
0 50 100 150
r 11(0
,0,
)
0.00
0.05
0.10
0.15
0.20
0.25
Region 1 64x64 pixels
Diffusion & Flow & Immobile3 Populations
D2D = 0.0031 ± 0.0003 m2s-1
|v| = 1.12 ± 0.07 m min-1
10% Immobile
<N>Diff = 2.6 ± 0.2 m-2
<N>flow = 0.33 ± 0.04 m-2
<N>Immobile = 0.34 ± 0.03 m-2
(s)
0 50 100 150
r 11(0
,0,
)
0.000
0.025
0.050
0.075
0.100
0.125
2-Photon ICS on Living Cells2-Photon ICS on Living Cells
10 m
Region 2 128x128 pixels
Diffusion & Immobile2 Populations
D2D = 4.7± 0.3 x10-4 m2s-1
21% Immobile
<N>Diff = 7.6 ± 0.3 m-2
<N>Immobile = 2.0 ± 0.2 m-2
Transport Map for Transport Map for -actinin in a Living Cell-actinin in a Living Cell
10 m Fraction ImmobileFraction DiffusingFraction Flowing
10 min Diffusion Dist.
10 min Flow Dist.
Wiseman et al. Journal of Cell Science 117, 5521-5534, 2004Highlighted in Nature Reviews Mol. Cell Biol. Vol 5, 953, 2004
2P-Image Cross-Correlation Spectroscopy (ICCS)2P-Image Cross-Correlation Spectroscopy (ICCS)
t=0
t=1
t=2
t=n
BiologicalSample
Auto1 Auto2 Cross
t bta
baab i i
) t ,y (x,i t)y, (x,i ,0 ,0r
Temporal Correlation
tbta
bajab i i
t),y ,(xi t)y, (x,i 0 , ,r
SpatialCorrelation
Wiseman et al., J. Microscopy 200, 14-25 (2000)
Temporal Two-photon ICCSTemporal Two-photon ICCS
Actinin - CFP 5 Integrin-YFP
10 m
2-Photon Imaging: 10 min, t = 5s, 3 hr after platingCHO-K1 Cells on 10 g/mL FN Ex. 880 nm Em. 485 & 560 nm
B τ
τ-exp (0,0,0)g
τ
τ 1 (0,0,0)g τ0,0,r
2
ffab
-1
ddabab
2D Diffusion 2D Flow (s)
0 50 100 150
r ab(0
,0,
)
0.0
0.2
0.4
0.6Crosstalk Corrected
Spatio-Temporal Image Correlation SpectroscopySpatio-Temporal Image Correlation Spectroscopy
t t
11 i i
) t ,y ,i(x t)y, i(x, , ,r
Calculate r11(,,) Central Peak is r11(0,0,)
t=0
t=1
t=2
t=3
r11(,,)
(s)
0 10 20 30 40
r 11(0
,0,
)
0.000
0.025
0.050
0.075
0.100
r 11(
0,0
,)
Hebert et al. Biophys. J. 88-3601 (2005)
Full Space Time Correlation on Living CellsFull Space Time Correlation on Living Cells
Directed Flow of -actinin at the basal membrane
Hebert et al. Biophys. J. 88-3601(2005)
Raw Data
ImmobileFiltered
r(0,0,)
Correlation PeakTracking
0 50 100 150 200-1.5
-1.0
-0.5
0.0
0.5
Gau
ssia
n po
sitio
n (
m)
(s)
x position y position
0 200 4000.10
0.15
0.20
r(0,
0,)
Tem
pora
l Cor
rela
tion
Fun
ctio
n
(s)
Temporal Correlation Function Fit (2 pop flow/diffusion)
i)
ii)
Δt=15s 45s 75s 105s 135sA)
B) C)
1 μm
r()
B
τ
τ-expA
τ
τ 1 A τ0,0,r
2
ff
-1
dd11
Vector Maps of Vector Maps of -actinin MEF Cell-actinin MEF Cell
TIRF Microscopy Time 100 s with Images sampled at 0.1 HzDr. Claire Brown and Ben Hebert
Vector Maps of Vector Maps of -actinin-actinin
0 μm/min
9 μm/min
5 μm
0.0 0.2 0.4 0.6 0.8 1.0 1.20.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Re
tro
gra
de
flo
w (
m/m
in)
Protrusion rate (m/min)0 s 2.8 s1.5 s
r()
r()Ff
Inverse relationship b/w retrograde flow & protrusion speed(similar to actin)
Quantum Dots…NanoparticlesQuantum Dots…Nanoparticles
Photostable…Different sizes…Different Colours
Colored Marker Tags for Molecules
CdSe CoreZnS CapSurfaceFunctionalized
Quantum Dot: Semiconductor Materials
10 nm
Silicon &Germanium Based Quantum Dots
Particle Tracking of QD Labeled AMPA ReceptorsParticle Tracking of QD Labeled AMPA Receptors
Richard Naud (Wiseman Group) withProf. Paul DeKoninck Laval University
20 m
Rat Purkinje neuron
Dendritic Spine and Synapse
‘Off’ state
‘On’ state
(CdSe)ZnS – Streptavidin (QD605) (CdSe)ZnS – Streptavidin (QD605) TIRF TIRF Illumination CCD Illumination CCD Detection Detection 50ms Integration Time 50ms Integration Time 2000 Frames 2000 Frames
Nirmal et al. Nature(London) (1996)
But…Quantum Dots Blink!But…Quantum Dots Blink!
See Bachir et al. JAP 99 (2006)Affects ICS measurements
Single Dot i(t) trace
Some New Things: kICSSome New Things: kICS
Point sourcefluorescence
emitters
Imageseries
2D Fouriertransformof images
k-space timecorrelation
function
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Is This Just another Acronym?
No! k-space Correlation has distinct advantages…
For Photobleaching and Blinking of FluorophoresSpecial Thanks to Prof. David Ronis
See Kolin et al. Biophysical Journal 91 3061-3075 (2006)
0 2 4 6 8-15
-10
-5
0
5
10
0 2 4 6 8-101
Residuals
InterceptPhotophysics
SlopeTransport Properties
Some New Things: kICSSome New Things: kICS
Transport CoefficientsIndependent ofPhotophysics
Blinking orPhotobleaching!
Determine the slopes for each value of τ, plot them as a function of τ:
2 4 6 8
-0.12
-0.11
-0.1
-0.09
-0.08
-0.07
-0.06
-0.05
2 4 6 8-0.01
0
0.01
Residuals
Intercept
Slope
Some New Things: kICSSome New Things: kICS
D independentof o
No non-linearCurve fitting
Slope
kICS Live cell measurement kICS Live cell measurement 5 integrin5 integrin
For a given :
ConclusionsConclusions
Fluctuations Contain Information about Molecules
Fluctuation Size…Concentrations/Oligomerization
Fluctuation Time…Dynamics/Kinetics
FCS…Temporal Analysis of Fluctuations
Image Correlation…Space & Time Analysis
Quantum Dots…Promising…but not perfect!
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