Molecular diffusion in living cells using Fluorescence ... · Fluorescence Recovery After Photobleaching - FRAP Principle of FRAP measurements Diﬀerent FRAP modes (Spot FRAP, Confocal

Inserm - Délégation RégionalePaca et Corse - BP 17213276 Marseille Cedex 09

Contact : Marie-Laure OLIVETél. : 04 91 82 70 10Fax : 04 91 82 70 [email protected]

Didier MARGUETIM2V platform scientific managerMathieu FALLETSébastien MAILFERTVincent ROUGERArnauld SERGETomasz TROMBIK

Molecular diffusion in living cellsusing Fluorescence Microscopy

Comparison of FRAP, FCS and SPT

October 19th - 22th 2010

Centre d’Immunologie de Marseille Luminy

Edition: October 15th 2010

Contents

I Objective & Program 3

II Course Part 9

1 General introduction of molecular diffusion measurement aspects 11

2 Fluorescence Recovery After Photobleaching 33

3 Advances in Fluorescence Correlation Spectroscopy 43

4 Dynamic Multiple-Target Tracing @ cell membranes 59

5 Labeling Strategies 79

III Practical Part 99

1 Confocal & Gaussian FRAP 1011.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1021.2 Experimental Part . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1031.3 Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

2 Spot FRAP 1072.1 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1082.2 Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1102.3 Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

3 svFCS 1153.1 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1163.2 Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1183.3 Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

4 SPT using Qdots 1234.1 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1244.2 Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1254.3 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

5 SPT using organic dyes 1295.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1305.2 Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1305.3 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

IV Name of organizers and participants 133

V Bibliography 137

VI Notes 1431

2

Part I

Objective & Program

3

4

The observations will be conducted on the optical setups available within the platform ’Molec-ular Interactions in Living Environment - IM2V’ at the CIML. Three methodological approacheswill be presented: Fluorescence Recovery After Photobleaching (FRAP), Fluorescence CorrelationSpectroscopy (FCS) & Single Particle Tracking (SPT).

� Fluorescence Recovery After Photobleaching - FRAPPrinciple of FRAP measurementsDifferent FRAP modes (Spot FRAP, Confocal FRAP / FLIP, Gaussian FRAP, Spot Vari-ation FRAP)Advantages and disadvantages of FRAP compared to other methods

� Fluorescence Correlation Spectroscopy - FCSPrinciple of FCS measurementsTheoretical modelsDescription of optical setupsComparison between with ’commercial’ and ’non-commercial’ FCS microscopesAdvantages and disadvantages of FCS compared to other methods

� Single Particle Tracking - SPTThe concept and interest of SPTDescription of experimental setupExperimental limitations during measurements (cell types, molecules types)Analysis 1: detection, estimation and reconnectionAnalysis 2: molecular interactions, Brownian motions and confinementAdvantages and disadvantages of SPT compared to other methods

� Labeling strategiesGeneral problems of molecular diffusion measurements linked to molecule detectionPhotophysical properties of fluorophoresIndirect labeling (monoclonal antibodies coupled with fluorophores)Direct labeling (fluorescent proteins or tags)Relationship between the labeling method and the microscopy technique

5

Tues

day

Wed

nes

day

Thurs

day

Fri

day

8h30

-9h

00T

heo

ryof

FR

AP

Theo

ryof

Lab

elin

gR

esult

san

alysi

sby

all

grou

ps

Com

par

ison

bet

wee

nte

chniq

ues

Con

clusi

on

9h00

-9h

309h

30-

10h00

Intr

oduct

ion

and

gener

alit

ies

Pau

sePau

se10

h00

-10

h30

SP

TD

yes

(G1)

SP

TQ

Dot

s(G

2)G

auss

ian

FR

AP

(G3)

Spot

FR

AP

(G1)

svFC

S(G

2)SP

TD

yes

(G3)

10h30

-11

h00

11h00

-11

h30

11h30

-12

h00

12h00

-12

h30

Lunch

Lunch

Lunch

Dep

artu

re12

h30

-13

h00

13h00

-13

h30

13h30

-14

h00

Theo

ryof

FR

AP

Theo

ryof

svFC

SG

auss

ian

FR

AP

(G1)

Spot

FR

AP

(G2)

svFC

S(G

3)

14h00

-14

h30

14h30

-15

h00

SP

TQ

Dot

s(G

1)G

auss

ian

FR

AP

(G2)

Spot

FR

AP

(G3)

svFC

S(G

1)SP

TD

yes

(G2)

SP

TQ

Dot

s(G

3)15

h00

-15

h30

15h30

-16

h00

Pau

se16

h00

-16

h30

Pau

sePau

seG

auss

ian

FR

AP

(G1)

Spot

FR

AP

(G2)

svFC

S(G

3)

16h30

-17

h00

SP

TQ

Dot

s(G

1)G

auss

ian

FR

AP

(G2)

Spot

FR

AP

(G3)

svFC

S(G

1)SP

TD

yes

(G2)

SP

TQ

Dot

s(G

3)17

h00

-17

h30

17h30

-18

h00

18h00

-18

h30

Sam

ple

Pre

par

atio

nSam

ple

Pre

par

atio

nR

esult

sco

mpilat

ion

18h30

-19

h00

6

Exper

imen

tG

roup

1G

roup

2G

roup

3

SP

TD

yes

Acq

uis

itio

nby

vid

eo-m

icro

scop

yusi

ng

diff

eren

tdye

sA

cquis

itio

nby

vid

eo-m

icro

scop

yusi

ng

diff

eren

tco

nce

ntr

atio

ns

Acq

uis

itio

nby

vid

eo-m

icro

scop

yusi

ng

diff

eren

tla

ser

inte

nsi

ties

SP

TQ

-Dot

sSP

Ton

EG

FR

and

Thy1

Impac

tof

label

den

sity

Impac

tof

spee

dac

quis

itio

ns

FR

AP

Spot

Wai

st3

Eva

luat

ion

ofpow

ereff

ect

for

Thy1-

GFP

Wai

st2

and

4diff

eren

cebet

wee

nE

GFR

-GFP

and

Thy1-

GFP

Wai

st1

onT

hy1-

GFP

and

Diff

er-

ence

bet

wee

nG

FP

and

Ale

xa

onE

GFR

Con

foca

l&

Gau

ssia

nFR

AP

Com

par

ison

bet

wee

nE

GFR

-GFP

and

KR

asG

12V

-GFP

Eva

luat

ion

ofth

eex

chan

geti

me

for

Kra

s-G

12V

-GFP

Eva

luat

ion

ofth

eex

chan

geti

me

for

EG

FR

-GFP

svFC

SW

aist

1on

Thy1-

GFP

and

vis

cos-

ity

effec

ton

Rh6G

Wai

st3

Diff

eren

ceof

pow

ereff

ect

onT

hy1-

GFP

Wai

st2

and

4on

Thy1-

GFP

and

Kra

s-SA

AX

-GFP

7

8

Part II

Course Part

9

10

Chapter 1

General introduction of moleculardiffusion measurement aspects

11

12

1

IM2V platform

“Interactions Moléculaires en Milieu Vivant”

Biophotonics – interaction of light and matterThe conception, development and implementation of innovative optical and photonic technologies applied to the field of life sciences

Interdisciplinary research programs at IM2Va multidisciplinary approach for functional imaging at molecular levelmethodological innovation and technological development in the

specificity of the IM2V platform

methodological innovation and technological development in the biophysical approach

development of accurate analytical methodsproof of principle demonstrated for each new methodology

Relevance of IM2V platform for CIML teamsto provide experimental setups at the state of the art to provide expertise in quantitative microscopyto train collaborators at both the experimental and analytical levels

a need for quantification of new observables

2

location- at the Devenson level

5 optical benches one small lab for biological sample preparationone office & computer room

platform support and funding

IM2V organization

platform support and funding- since 2003, supported on specific grants from

platform staffSÉBASTIEN MAILFERT (CIML) supports users by setting up procedures for:- instrument quality control over the time sequence of a project- standardized analytical and statistical methods- standardized archival and access methods for data storage

ARNAUD SERGÉ (SPT) CYRILLE BILLAUDEAU (Raman & simulation)

IM2V organization

ARNAUD SERGÉ (SPT), CYRILLE BILLAUDEAU (Raman & simulation)and participations of other members of H&M team and imaging facility

available equipmentsdata acquisition (1) FCS & FRAP (2) FCCS (3) HOT and imaging

(4) STORM nanoscopy (5) Raman microscopy(6) fluorimeter

data analysis (1) computers with dedicated software

SPT at high density (MTT)

single moleculeimaging

nanoscopy

ONGOING FLUORESCENCE TECHNIQUESMOLECULAR DYNAMICS IN LIVE CELL

FCS, PCH, FCCS& FRAP

polarimetric analysis

u

y

x

IXIYAPD

APD

EX

Y

Polarizationbeamsplitters

Raman & CARS microscopy

CONTRASTING METHODS

wave front technology

μ-stereolithography

ACCESSORY TOOLS

HOT & dual color imaging

3

Molecular diffusion in living cellsusing fluorescence microscopy

Centre d’Immunologie de Marseille-Luminy

Didier MARGUETInteractions moléculaires en milieu vivantIM2V

Théodore Géricault – Derby d’Epson

Eadweard Muybridge – Study of a Horse at Full Gallop

4

B i ti d d lkBrownian motion and random walks

diffusion arise from motion due to thermal energy

dye dispersing in water

cold hotmedium

average kinetic energy mv2/2 = kT / 2root mean square velocity v2 ½= (kT / m)½

m lysozyme = 2.3 10-20 g v2 ½ = 1.3 103 cm/sec

d t

http://www.inventioneeringco.com/commentary-files/brownian_motion.swf

5

The journey of a thousand miles begins with a single step.

Lao Tsufrom Tao Te Ching


d t

1

probability of finding particles

d t

d = 2Dt4

16

6


d

d t

d t

d = 2Dt

Brownian motion and diffusionD = 1μm2s-1

a Maxwell’s demon experiment

Brownianagitation

from Shinbrot & Muzzio (2001) Noise to order. Nature 410:251

order

"other than homogeneity can result from Brownian agitation"

7

diffusion and domain formationspreading of a heterogeneity by diffusion

domain formation due to differential interaction

d lk i ll bi lrandom walk in cell biology

membrane [lipid] fluidity

8

GY S1

S2

T2

I C

Vibrational energy levelsRotational energy levelsElectronic energy levels

Singlet States Triplet States

fluorescence imaging

ENER

G

S0

T1

ABS FL I.C.

ABS - Absorbance S 0.1.2 - Singlet Electronic Energy LevelsFL - Fluorescence T 1,2 - Corresponding Triplet StatesI.C.- Nonradiative Internal Conversion IsC - Intersystem Crossing

PH - Phosphorescence

IsC

IsCPH

[Vibrational sublevels]

fast slow (phosphorescence)much longer wavelength

Triplet state

single moleculeimaging

ONGOING FLUORESCENCE TECHNIQUESMOLECULAR DYNAMICS IN LIVE CELLS

FCS, PCH, FCCS& FRAP

u

y

x

IXIYAPD

APD

EX

Y

Polarizationbeamsplitters

probing molecular organization in living cells

SPT at high density (MTT) nanoscopy polarimetric analysis

Raman & CARS microscopy

CONTRASTING METHODS

wave front technology

μ-stereolithography

ACCESSORY TOOLS

HOT & dual color imaging

FRAP (fluorescence recovery after photobleaching)- provides averaged information on motion of a population of molecule- high fluorescence signal, bleaching, separate weakly multiple components…

FCS (fluorescence correlation spectroscopy)

a comparative summary of the current methods

gold particleØ 40nm SPT SDT (single particle tracking & single dye tracing)

- resolves modes of motion of individual molecules- size of particle, multivalence, hydrodynamic interactions…

FCS (fluorescence correlation spectroscopy)- concentration and aggregation measurements- diffusion analysis (random, active transport, subdiffusion…)- molecular interactions (autocorrelation & cross-correlation)

9

fl ft h t bl hifluorescence recovery after photobleachingprinciple

mobile fraction% of fluorescence recovery M = [F( ) - F(0)]/[F(t<0) - F(0)]

half-time for recovery t1/2 = F(0) + [F( ) - F(0)] /2

diffusion coefficientD = 2 / 4t1/2

10

Bleach areas

Fluorescencemeasurement areas

fluorescence loss in photobleaching - FLIP

0 20 40 60 80 1000

20

40

60

80

100

120NT

MZ

RFI

(%)

RFI (%)

11

Gaussian FRAP

fl l ti tfluorescence correlation spectroscopyprinciple

12

120002001341231021111311251110233133322111224221226122142345241141311423100100421123123201111000111*211001320000010011000100023221002110000201001*3331220002312210240111101*1222112231000110331110210110010103011312121010121111211*100032210123020121213211101100233122421100012030101002217344101010021122114444212114401321233143130112221233

basic concept

10121111222412231113322132110000410432012120011322231200*253212033233111100210022013011321131200101314322112211223234422230321421532200202142123232043112312003314223452134110412322220221

Svedberg & Inouye (1911) Zeitschr. F. physik. Chemie

Modern FCS

FCS in living cells

confocal volume

cellcover glass

13

high temporal resolution (microseconds to seconds range)non invasive laser light (<10kW/cm2)low probe concentration (nM range)

time

< I >

number of moleculesdiffusion parametersmolecular aggregation / multimerizationhigh temporal resolution and statistical accuracy

temporal fluctuations

1.5

2

G(

)

d

amplitude fluctuations

requ

ency

)

10-2

10-1

< I >

10 1 10 100

G

1/M

number of photons

log(

fr

10-3

0 1 2 3 4 5 6 7 8

Molecular brightness

photon counting histogram (PCH)

multimerization

autocorrelation function (ACF)

d corresponds to the average time molecules stay within the spot of illumination

14

sensitivity / molecular concentration

biological applications

direct read out :

diffusion timemolecular number

triplet timetriplet fraction

- molecular interactions- DNA / protein- lipid /protein - receptor / ligand

- cellular measurements

chemicalreactions

rotationaltriplet fractionbound/free ratio

ce u a easu e e ts- uptake & cellular transport- mobility of molecules in cell

- membranes measurements- lateral diffusion - receptor distribution- submicroscopic clusters

diffusion

translationaldiffusion

flow

oligomerization& binding

FCS in living cells

0.6

0.8in solutioncytosolmembrane (lipid)membrane (IgE receptor)

1.6

1.8

D = 0.03 m2/s

1E-3 0.01 0.1 1 10 100 1000 100000.0

0.2

0.4

D = 300 m2/s

G()

[ms]

1.0

1.2

1.4

g(2)

RkTD 6

Dxy

D 4

2

15

from Yechiel & Edidin (1987) J. Cell Biol. 105:755

increased focal spot size

spot variation FCS

spot area

longer diffusion time

effd D

t4

2

0

Lenne, Wawreziniek et al. EMBO J. (2006) 25:3245

meshwork

diffu

sion

tim

e

t0 > 0

dynamic partition(self-assembling)

accessible size

optical diffraction limit

free diffusion

spot area0

t0 < 02

0 41 wD

teff

d

Wawrezinieck et al. Biophys. J. (2005) 89:4029

16

FCS through nanoholes

Wenger et al. Biophys. J. (2007) 92:913

aluminium 100 to 500 nm

i l l l i isingle molecule imaging

Imaging a single molecule

-10 -5 0 5 10

inte

nsity

k a sin( )-10 -5 0 5 10

inte

nsity

k a sin( )

17

Airy patterns & the limit of resolution

single molecule localization

18

Dietrich et al., Biophys J 2002

19

the Multi Target Tracing algorithmallow to resolve the motion of thousands of molecule

to achieve an exhaustive detection of particlesby using deflation loop

to reconnect accurately trajectoriesby taking into account past information

to translate the data into a map of local molecular dynamicsby identifying confinement events

the source code of the MTT software is freely available online: http://www.ciml.univ-mrs.fr/labs/he-marguet.htm

dynamic map of the confinement areas

the source code of the MTT software is freely available online: http://www.ciml.univ-mrs.fr/labs/he-marguet.htm

32

Chapter 2

Fluorescence Recovery AfterPhotobleaching

33

34

FRAPFluorescence Recovery After Photobleaching

Mathieu Fallet

Mathieu Fallet CIML septembre 2010

Plan :Plan :

1) Principe du FRAP 1) Principe du FRAP 2) La diffusion effective, la r2) La diffusion effective, la rééaction en 2Daction en 2D3) FRAP en mode spot (mod3) FRAP en mode spot (modèèle dle d’’AxelrodAxelrod))4) Le fit et le nombre de param4) Le fit et le nombre de paramèètrestres5) FRAP en mode 5) FRAP en mode confocalconfocal (mod(modèèle de le de SoumpasisSoumpasis))6) FRAP en mode LINE6) FRAP en mode LINE7) FLIP 7) FLIP 8) FRAP gaussien 8) FRAP gaussien 9) FRAP 9) FRAP àà deux deux waistswaists10) Strat10) Stratéégies dgies d’’acquisitionacquisition11) Conclusion11) Conclusion

1) Principe du FRAP 1) Principe du FRAP ::

R fraction mobile :

2) Les mod2) Les modèèles : Diffusion libre, les : Diffusion libre, ReactionReaction pure pure

Diffusion libre :Réaction:

Fraction libre non-bleachable

Les modLes modèèles : Diffusion effective les : Diffusion effective

3) Mod3) Modèèle dle d ’’AxelrodAxelrod : analyse d: analyse d’’un spot gaussien un spot gaussien

ω=1.1μm

90% de bleach

Intensité

Rayon

Le diamètre effectif = 2.2 μm

Kprofondeurde

bleach

La solution exacte :

Une approximation valide pour des faibles photo-blanchiment(<80%):

ModModèèle dle d ’’AxelrodAxelrod::

On détermine (intensité moyenne avant le bleach) :

Equation d’Axelrod (8 termes):

On tronque la série à 8 termes. On ajuste K, td, R et F(0).

F(0)/Fi=[1-e(-K)]/K

Equation de Kwon (2 termes):

On ajuste F(0), R et t1/2.

On calcule Beta à partir d’une table de conversion fonction du pourcentage de bleach.

4) Le Fit et le nombre de param4) Le Fit et le nombre de paramèètres :tres :

Avec 4 paramètres on peut fitter/ajuster un éléphant. Avec 5 paramètres, on peut lui faire bouger sa trompe !

Lorsque l’on a trop de paramètres, ceux-ci sont corrélées les uns aux autres, il est alors impossible de les déterminer (par exemple : a+b=2 !)

Exemple : Simulation dExemple : Simulation d’’une expune expéérience de FRAP avec un profil rience de FRAP avec un profil gaussiengaussien

Paramètres : Moyenné sur 30 valeurs, 500k molécules, Boite : 15um*15um, waist=0.5um, Intensité de frap=8000, D=1um²s-1, 16s (200 avant), toutes les 1msRésultat :D=1.03 um²s-1 ( stdev=0.35, moyenne sur 30 valeurs )

*Après moyennage temporel log à base de 2 : D=1.02 um²s-1 (stdev=0.18)

=>Le moyennage temporel (binning log) améliore l’erreur global du fit sans modifier la moyenne. Il donne autant de poids au début de la courbe qu’à la fin de la courbe.

*L’ajustement sur 2 secondes au lieu de 16s (sans binning) donne : D=1.06 (stdev=0.41)

Avec un td =0.065, 2s représente 30x le temps de demi-recouvrement ce qui est suffisant.

Comparaison Soumpasis : D=1.03 um²s-1 (stdev=0.07) sur 2s Meilleur car moins de paramètres de fit, les paramètres sont corrélées !

ExempleExemple : FRAP : FRAP dansdans lele noyaunoyau

t1/2=0.23t1/2=0.11t1/2=0.22t1/2=0.18t1/2=0.88

Fm=0.9Fm=0.83Fm=0.87Fm=0.8fm=0.35

GFP libreMutant-nucpmutant-nucWt-nucWt-sp

D=1um²/s, w=1um => td=0.25 s (valeur théorique)

Fit avec équation de Soumpasis :

Temps de recouvrement =1s Temps de recouvrement=10std=0.25 s ! td=0.2sOn ne peut pas conclure sur 1 courbe =>faire de la stat

Sur un recouvrement de 2s et w=0.5um, on trouve : D=1.03 um²s-1 (stdev=0.07, moyenne sur 30 valeurs )

5) Mod5) Modèèle dele de SoumpasisSoumpasis : analyse d: analyse d’’un profil carrun profil carréé

6) FRAP en mode 6) FRAP en mode confocalconfocal ::

Facile à mettre en œuvre mais difficile à quantifier•FRAP puis mesure de la cellule entière

•FLIP pour la détermination connexion entre compartiments

•Line FRAP (« bleach » de bande, diffusion 1D)

Différences avec le mode spot:

(i)Temps de bleach plus long car la surface est plus grande

Possibilité de recouvrement pendant le « bleach »

(ii) Temps de recouvrement plus long

Possibilité de mouvement cellulaire pendant la mesure

(iii)Temps de mesure plus long

Processus d’échange membrane/cytoplasme

6) FRAP en mode 6) FRAP en mode confocalconfocal : Line FRAP: Line FRAP

FRAP avec GFP-L236P dans la membrane du R-E (d’après Lippincott-Schwartz)

7) FLIP en mode 7) FLIP en mode confocalconfocal ::

FRAP répétitifs sur deux molécules exprimées dans le R-E révèlent différents mécanisme de rétention (résidus de fluorescence).(d’après Lippincott-Schwartz)

8) FRAP pour 8) FRAP pour éétudiertudier les interactions entre les interactions entre protprotééinesines ::

--FRAPFRAP beambeam--size:size:Utilisation de deux « »waists » de laser (objectifs 40x et 63x)

« Pure lateral diffusion » : τ =τd proportionnel à w²/4D (rapport =2.56)

« Dynamic exchange » : τ ne dépend pas de la taille du faisceau (rapport=1)

--FRAPFRAP GaussienGaussien :: Analyse du profile dAnalyse du profile d’’intensitintensitéé ::

I(x,t)=Io.[1- Bo.Wo/Sigma.exp(-x²/Sigma²).exp(-t/tau)]

Bo = profondeur de bleachWo = largeur de la Gaussienne à t=0 Io= Intensité moyenne avant le bleach

Sigma²(t)=4.D.t+W0²

FRAPFRAP GaussienGaussien ::

Fit de D : analyse de la pente de sigma Fit de tau : analyse de la hauteur de la gaussienneaprès avoir injecté sigma trouvé précédemment :Fit en exp(-t/tau)

Bo

Wo

StratStratéégiesgies dd’’acquisitionacquisition ::

•Faire un « timelapse » pour déterminer la puissance laser qui ne provoque pas de « bleaching « conséquent (autours de 0.1% à 1% du laser).

•Optimiser la vitesse d’acquisition, l’ouverture du pinhole, la puissance laser et la taille du pixel pour obtenir un signal/bruit satisfaisant.

•Vérifier que le temps de « bleach » est très inférieur au temps de recouvrement de la moléculeTemps de bleach= 100ms pour une itération du laser sur un confocal ZeissD= 0.01 um²/s pour une protéine membranaire

•Vérifier que le temps d’enregistrement est égal à environ >20 fois le temps de demi-recouvrement de la molécule.

Conclusion : Conclusion :

FRAP/FLIP confocal:

Trafic intracellulaireMesure de la fraction mobile Diffusion macroscopique (moyenne entre diffusions microscopiques, réactions chimiques et échange entre compartiments)Forte concentration de marqueurs

Attention à l’interprétation du coefficient de diffusion D mesuré qui peut être de 10 à 100x supérieur sur un montage confocal commercial (temps d’acquisition, de bleach,..)

42

Chapter 3

Advances in Fluorescence CorrelationSpectroscopy

43

44

What is FCS ?Homemade or Commercial ?

FCS DerivatesImage Correlation techniques

Advances in Fluorescence Correlation Spectroscopy

Sébastien MAILFERT

October, 2010

Atelier INSERM, October 2010



General PurposeConfocal based microscopyAuto-correlation analysisApplicationsLimitations

Summary

1 What is FCS ?General PurposeConfocal based microscopyAuto-correlation analysisApplicationsLimitations

2 Homemade or Commercial ?Optical SchemeHow to build your own FCS setup ?Market availability

3 FCS DerivatessvFCSFCCSScanning FCS

4 Image Correlation techniquesICSRICSSTICS





General Purpose 3 / 24

• Single Molecule detection technique : recordind and computing fluorescence correlation withina small volume

• High spatio-temporal accuracy


Notes

Notes

Notes





Click here






Confocal measurement

� Fluorescence fluctuations analysis� Confocal spot : ωxy from 200 to

400 nm� Two main parameters :

1 Mean number of molecules :N

2 Mean diffusion time : τd

Advantages

� Low excitation power (few kW/s2)� Low numbers of molecules (from 1 to 100)� Physiological conditions @ 37◦C� Living cells� High spatio-temporal resolution (μs to s, 200 to 400 nm)� Photophysical aspects : triplet state, free diffusion (2D,

3D), active transport velocity, rotational motion, etc.





Confocal based microscopy 5 / 24

� Excitation laser (green beamer here) focused with a high Numerical aperture (NA) microscopeobjective

� Excitation and fluorescence separated by a dichroïc mirror� Fluorescence at the focus plane selected by a pinhole (few μm typ.)� One point detector (Avalanche photodiode or photomultiplicator tube)

Excitation volume : 3D Gaussian, few femtoliter

V = π3/2ωxyωz

Click here !


Notes

Notes

Notes




Auto-correlation analysis 6 / 24

Self-similarity analysis : signal is compared to itself after a lag time τ

The normalized autocorrelation function is defined as :

G(τ) =〈δF(t)δF(t + τ)〉

〈F(t)〉2

G(0) =1N

=1

Veff〈C〉Relative fluctuations become smaller with increasing numbers of fluorescent particles

≈ 10−10M to ≈ 10−6M






Different equations for different samples

Mobility (1 species, 3D diffusion) :

G(τ) = 1 +1N

1(1 +

τ

τd

) √1 + s2 τ

τd

Mobility (1 species, 3D diffusion) & Triplet blinking :

G(τ) = 1 +

⎛⎜⎜⎜⎜⎜⎜⎜⎜⎝1 − T + Te− ττT

⎞⎟⎟⎟⎟⎟⎟⎟⎟⎠1N

1(1 +

τ

τd

) √1 + s2 τ

τd

Mobility (2 species, 2D/3D diffusion) :

G(τ) = 1 +1N

⎛⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎜⎝A(

1 +τ

τd1

) +1 − A(

1 +τ

τd2

) √1 + s2 τ

τd2

⎞⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎟⎠






Example : 2 species, 3D (τd1 = 200μs) + 2D (τd2 = 200ms)

2.0

1.8

1.6

1.4

1.2

1.0

Aut

ocor

rela

tion

Func

tion:

G(

)

10-5 10-4 10-3 10-2 10-1 100 101

Lag Time (s)


Notes

Notes

Notes




Applications 7 / 24

� Dyes : fluorescent proteins, inorganic dyes, etc.� Samples : on living cells, every cellular compartment could be analysed� Optical setup : easy to implement on a confocal microscope

� Concentrations : G(0) analysis highly precise� Mobility studies

� Size : hydrodynamic radius calculated from D =kT

6πηV Rhwith Rh = 3

√3m

NA 4πρ� Reaction kinetics� Membrane organization : see svFCS� Interactions : see FCCS





Limitations 8 / 24

• Higher sensitivity to low probe concentration• Higher sensitivity to fast events (μs to ms)• Diffraction limit• Diffusion coefficients available : 0.1 to 10 μm2/s• Difficult to discriminate 2 populations with similar diffusion time




Optical SchemeHow to build your own FCS setup ?Market availability

Summary






Notes

Notes

Notes




Optical setup 10 / 24





How to build your own FCS setup ? 11 / 24

Excitation : 1PE (i.e. argon or HeNe laser, fewmW are needed) or 2PE (i.e. IR pulsed laser)






Objective : high NA (typ. 40X, C-Apochromat,NA=1.2, Water immersion, Zeiss), hightransmission, avoid achromatism & asphericalaberrations


Notes

Notes

Notes





Optics : achromatic (i.e. Newport optics)






Filters and dichroïc mirrors : highly selective,flat & thick mirrors to avoid beam distortions (i.e.Chroma filters)






Pinhole : typ. 50μm, could be a simple multimodeoptical fiber


Notes

Notes

Notes





Detectors : single photon sensitivity, low darknoise (i.e. APDs or PMT)






Correlator : hardware (i.e. ALV or Correlator.com)or software (homemade with fast acquisitionboard)






XYZ scanner : 2D imaging and spot positioning,nm accuracy (i.e. Physik Instrumente or Mad CityLabs)


Notes

Notes

Notes





Microscope : motorized





Comparison of 3 different vendors 12 / 24

Key fea-ture

Zeiss LSM 780 Leica TCS SMD Series Alba

Techniques FCS, FCCS, RICS,FRET, FRAP, FLIM

FCS, FLIM, FRET, FRAP,FLCS

PCS, Polarization, Par-ticle tracking, Scanning-FCS, FCCS, PCH

Excitation 1PE, 2PE 1PE, 2PE 1PE, 2PEOpticalsplitting

TwinGate beamsplitting Acousto-Optic BeamSplitter

Classic

Detectors 32 channels GaAsP de-tectors + 2 PMTs

APDs (Perkin Elmer) orCMOS APDs (MPD)

APDs or PMTs




svFCSFCCSScanning FCS

Summary






Notes

Notes

Notes




Diffusion law concept 14 / 24

“FCS diffusion law”

diff

usio

n ti

me

d

spot area

diff

usio

n ti

me

d

spot area

increasing focal spot sizeincreasing focal spot size

longer diffusion timelonger diffusion time





Diffusion Laws : experimental results & computer simulation 15 / 24

Free diffusion

Dynamic partitionIn isolated domains

(like Thy1)t0 > 0

Accessible spot size

t0 = 0

Trapping in meshwork(like TfR)

Observation volume

Lipid nanodomain Fluorescent molecule(non excited/excited)

t0 < 0

Actin cytoskeleton

HO

HO

HOHO

HOHO

HOHO HOHOHOHO

L. Wawrezinieck et al.

Biophys. J., 49 :4029-4042, 2005.

P.-F. Lenne et al.

EMBO J., 25 :3245-3256, 2006.





Nanoholes : Beyond the diffraction limit 16 / 24

J. Wenger et al.

Biophys. J., 92 :913-919, 2007.


Notes

Notes

Notes




STED-FCS : Beyond the diffraction limit 17 / 24

The transit times determined forphosphoethanolamine (opensquares) decrease linearly with thearea, confirming free diffusion(solid line). Two distinct modalitiesof molecular transits (grey arrows)demonstrating hindered diffusionand transient trapping ofsphingomyelin (SM)

C. Eggeling et al.

Nature, 457 :1159-63, 2009.





FCCS : Enzyme kinetics, molecular interactions 18 / 24

GG(τ) = 1 +< δIG(t)δIG(t + τ) >

< IG >2

GR(τ) = 1 +< δIR(t)δIR(t + τ) >

< IR >2

GGR(τ) = 1 +< δIG(t)δIR(t + τ) >

< IG >< IG >

where

GGR(0) − 1 =1N

=NGR

(NG + NR)(NR + NGR)





FCCS : Enzyme kinetics, molecular interactions 18 / 24

U. Kettling et al.

PNAS, 95 :1416-20, 2007.


Notes

Notes

Notes




sFCS 19 / 24

Moving detection volume instead of static volume

Access to different locations : more informations (parallel measurements)

Reduction of photobleaching effects

Could replace the volume calibration if the scan path is well known

A membrane motion could be removed into raw data

Z. Petrasek et al.

Methods in Enzymology, 472 :317-343, 2010.





sFCS 19 / 24

Scan type Main benefit/application ReferencesNo scanning Fast diffusion, simple implementa-

tion, inhomogeneous sampleRigler and Elson (2001)

Single-line, large-circle Slow motion, photobleaching, ro-bustness, spatiotemporal correla-tion

Petrasek et al. (2008b) andRies et al. (2009a)

Small-circle Robustness, precision, small area Skinner et al. (2005) and Pe-trasek and Schwille (2008b)

Double-line Robustness, precision, mem-brane motion

Ries and Schwille (2006)

Raster Slow motion, spatiotemporal cor-relation

Digman et al. (2005)

Perpendicular to mem-brane

Membrane motion Ries and Schwille (2006)

Z. Petrasek et al.






sFCS 19 / 24

Z. Petrasek et al.



Notes

Notes

Notes




sFCS 19 / 24

Z. Petrasek et al.






sFCS 19 / 24

Z. Petrasek et al.





ICSRICSSTICS

Summary






Notes

Notes

Notes



ICSRICSSTICS

ICS : Image Correlation Spectroscopy 21 / 24

Direct calculation of the correlation function of a 2D image

g(ξ, η) =

(1

NM

)∑Nk=1

∑Ml=1 i(k , l)i(k + ξ, l + η)

((1

NM

)∑Nk=1

∑Ml=1 i(k , l)

)2− 1

Fourier transform based technique

G(ξ, η) = F −1(F (i(x, y)) ∗ F [(] i(x, y)))




ICSRICSSTICS

ICS : Image Correlation Spectroscopy 21 / 24

ξ η

Precise calibration of the spot diameter (0.27μm) from the 2D correlation function

Number of beads =Image Area

g(0, 0)πω20

=(512 × 0.04μm)2

25.7π(0.27μm)2= 71

N.O. Petersen et al.

Biophysical journal, 65 :1135-1146, 1993.




ICSRICSSTICS

RICS : Raster Image Correlation Spectroscopy 22 / 24

Combination of FCS & ICS : correlation between pixels on the same image

Spatial resolution depends on the diffusion coefficient ((higher for slower D)Correlation function (not known !) expressed in terms of :

Pixel size δr : 0.05 - 2 μmPixel resident time τp : 2-100 μsLine repetition time τl : 3 - 100 ms

M.A. Digman et al.

Biophysical journal, 89 :1317-1327, 2005.


Notes

Notes

Notes



ICSRICSSTICS

STICS : Spatiotemporal Image Correlation Spectroscopy 23 / 24

Combination of FCS & ICS : correlation between images in a stack

E. Gratton et al.

WIREs Systems Biology and Medecine, 1 :273-282, 2009.




ICSRICSSTICS

That’s all folks ! 24 / 24

Thanks for your attention


Notes

Notes

Notes

Chapter 4

Dynamic Multiple-Target Tracing @ cellmembranes

59

60

Arnauld SergArnauld Sergéé

Dynamic MultipleDynamic Multiple--Target Tracing Target Tracing @ cell membranes@ cell membranes

Dynamic MultipleDynamic Multiple--Target Tracing Target Tracing @ cell membranes@ cell membranes

Introduction: membrane dynamics

Method: labeling & setup

MTT analysis: detect, estimate & reconnect

Dynamic confinements

MTT in 3DOutlooks, nanoscopy

Lippincott-Schwartz et al. Nat Rev Mol Cell Biol 2001

On which spatio-temporal scale these heterogeneities take place?

What are the major determinants dictating their organization?

What are the functional implications?

Lateral diffusion in the plasma membrane Lateral diffusion in the plasma membrane

mesoscopic assembly of aggregated molecules (~1012 molecules)

build up by weak interactions to produce a cooperative phenomena

non-random & non-uniform lateral organization

Why expecting local Why expecting local heterogeneities?heterogeneities?

IntersectionIntersection in Hanoi Vietnamin Hanoi Vietnam

Stochastic motions? Interactions?

Toward a cartography of Toward a cartography of membrane dynamicsmembrane dynamics

100 nm

confinementSPT -> global measure with local accuracy-> overall spatiotemporal dynamics-> cartography of heterogeneities

Jacobson et al. Science 1995

in the nanometer range, heterogeneities are weakly characterized

Small & dynamic structures

experimentally difficult to access

In 1827, the botanist Robert Brown observed the erratic motionof pollen particles on water. This was not strictly diffusion, sincethe particle is macroscopic, but this random walk, hence namedBrownian motion, will be used as a model system for diffusion.

In 1855, Adolph Fick propose empirical laws, in analogy with Fourier forheat and Ohm laws for electricity.

The flux of diffusion is proportional to the gradient of concentration, hence,in one dimension:

Diffusion & Brownian motion historyDiffusion & Brownian motion history –– 11

c: concentrationt: timeD: diffusion coefficientx: distance

Iterative equation : xn+1 = xn + d.randomd : mean stepxn : ne position (idem for y, z)

Cf. Wall street fluctuations (1D), drunk walker (2D), fly flight (3D)…

= Dx²²c

tc

Albert Einstein demonstrates Fick’s laws and theirmolecular origin in 1905 with his work on stochasticity.

In 1908, Jean Perrin, funder of the CNRS and Nobel price in physics,achieved the first measure of trajectories of particles undergoingBrownian motion, hence confirming Einstein theory.

Louis Bachelier, in his thesis in 1900, demonstrated that it is not themean of the displacements <r> which characterizes the motion, butthemean square (in dimension n) :

<r²> = 2nDt

R: ideal gas constantT: temperatureNA: Avogadro number: viscosity

r: radius

t

<r²>

D =6 NA r

RT

Diffusion & Brownian motion historyDiffusion & Brownian motion history –– 22

Structure & dynamicsStructure & dynamicsof membranesof membranes

Major modifications brought to the fluid mosaic model.Size, composition, dynamics, physiological relevance of those structures ?

Dynamic map ?

SubSub--membranemembrane actinactin cortexcortex

Thomishige et al. 1998

50 - 700 nm ?

Lipid raftsLipid rafts

NanoNano--domains probed by FCSdomains probed by FCS

FCS has revealed the presence of dynamic confinement,related to • lipid nanodomains• actin meshworkWith confinement duration of a few tens of ms.

Wawrezinieck et al. Biophys J. 2005Lenne*, Wawrezinieck* et al. EMBO J. 2006 Wenger et al. Biophys J. 2006 Lasserre*, Guo*, Conchonaud* et al. Nature Chem .Biol. 2008

Single molecule on live cellsSingle molecule on live cellsDMPE-Cy5 and DOPE-Cy3in muscle cells

Schütz et al. 2000

eYFP-Ca2+ channelsHarms et al. 2001

GlycineR-qdotsDahan et al. 2003

EGFREGFR signallingsignalling

4. Cell response (gene expression, cell division...)

1. Ligand binding dimerisation

2. Trans-phosphorylation3. Signalling cascade

Yarden & Sliwkoski Nat. Rev. Mol. Cell. Biol. 2001

FCS SPT HOT

MultiMulti--scale measuresscale measures

Snapshot of membrane dynamicsSnapshot of membrane dynamics

Space-time requirements:Dense labellingExhaustive detectionFast acquisitions

Multiple-Target tracing

Sergé et al. 2008

Michalet et al. Science 2005

Labelling strategies for SPT/SDTLabelling strategies for SPT/SDT

Organic Dyes

bleaching issue

Latex/gold colloids

size issue

Coupling intermediates

size issue

Quantum dots

compromise

Quantum dots advantagesQuantum dots advantages

• Efficient excitation in UV

• Narrow & symmetric emission, tunable according to Qdot size (2-10 nm)

compromise stability / valence

High photo-stability

Wu et al. Nat. Biotech. 2003

Tunable emission605 nm

0

500

1000

1500

0 2 4 6 8time (s)

Inte

nsity

(cnt

)

1 m

Blinking: single molecule signatureBlinking: single molecule signature

Real time ( t = 36 ms)

Results obtained by Adrien Fauré

DualDual--View View optical setupoptical setup

Excitation450/50 Dichroic 1

480

Objective alpha Plan-Fluar

100x 1.45 NA

Dichroic 2565

‘green’emission 525/40

‘red’emission 605/40

ModuleDual-View(Chroma)

camera

Cube Zeiss #37

Qd525 Qd605

Qd525Qd525 Qd605Qd605

Main steps of MTTMain steps of MTT

Microscope Biological

system

Raw

imagesAlgorithm Maps

Optimized MTT protocolOptimized MTT protocol

Experiment on cells• Setup, • Cell physiology, • Efficient labelling…

Data analysis• Find fluorescent peaks• Connect into trajectories• Further analyses (mode of motion…)

See Sergé et al. Nature Protocols Network 2008

EMEM--CCDCCD cameracamera

High sensibility for low signals

2. Frame transfer

3. Electron multiplication (or not)

1. CCD (back illuminated)

Source: micro.magnet.fsu.edu

2 – Labeling of cells

Tagging membrane components Tagging membrane components with quantum dotswith quantum dots

Anti-EGFR-biotin

100x excess biotin

1 – Pre-incubation of Qdots and antibody

EGFR

Qdot-streptavidin

MTT AlgorithmMTT Algorithm

Filtered image

Stack acquisition

Sub-pixel localization

Fit with Gaussian

Tracing

xy

t

Detected peaks

past info.

Further analyses of the trajectories…

Conf. analysis

Sergé et al. Nat. Methods 2008

Peak fittingPeak fitting

Detection of fluorescence peaks in each image, by test between hypotheses,then local adjustment with a Gaussian, leading to• peak intensity (-> stoichiometry)• position (-> trajectories & diffusion) with sub-pixel precision.

width intensity

offset+/- noise

Position x, y

mr

m

H0 hypothesis: no target

H1 hypothesis: presence of a centered Gaussian

?

?

Detection improvement by deflationDetection improvement by deflationRaw image

1st deflated image

Detected peaks

2nd deflated image

last deflated image

Deflated Gaussiansinitial datafirst fit

deflated datasecond fitsubtracted peak

Deflation allows to detect “hidden” peaks.

Detection becomes ~ exhaustive, reconnection is thus facilitated.

PIC

Evaluation of detectionEvaluation of detection

vs. fluo SNR vs. particles density

SNR density

Simulateddata

Evaluation of estimationEvaluation of estimation

MTT computation on MTT computation on experimental data experimental data

3rmax

3rmaxtblink1/2

P(x1,y1,I1,b1 | x2…)

Reconnecting peaksReconnecting peaks

Trajectory

Intensity

xn-1yn-1<r> = rlocal

x

y

Past information

<Ion>Ion

blink state

t

I fullon

fastblink

fulloff

<Ion>

Statistical laws

r

Pdiff

rmax

rlocal

2 Gaussians, for local & max. diffusion

t

Poff

off 3 off

Off: expo. decayBlink: equi-proba. On: Gaussian law

I

Pint

<Ion>

Reconnection test

xn-1

yn-1

3rlocal

3rmax

x

ySearch area

Use of past information for reconnectionUse of past information for reconnection

Connecting peaksConnecting peaks

ntrc > npk (blink @ t+1)

t

t+1

ntrc = npk (no blink)

t

t+1

ntrc = npk (multi-blink)

tblink

t

t+1

ntrc < npk (blink before t)

tblink

t

t+1

Diffusion Evaluation Tracing efficiency

Evaluation of connectionEvaluation of connection

Mean Square Displacement computationMean Square Displacement computation

MSD(3 t) = <MnMn+32>


MSD( t) = <MnMn+12>

1

2

3

4

5

67


...

…

MSD = <r2> Averaged over every possible step of the trajectory

t

MSD

1 2 3 4 …

MSD allows to discriminate between several MSD allows to discriminate between several characteristic movementscharacteristic movements

• Pure Brownian diffusionFick’s 2nd law gives<r2> = 4Dt

• Diffusive and linear movement

<r2> = v2t2+4Dt

• Confined movement

<r2> = Kusumi et al. Biophys. J. 1993

• Anomalous diffusion (with obstacles)

<r2> = 4Dt , < 1

• Rotative (or confined) and lateral diffusion

<r2> = 2R2{1-exp(-Drott)}+4DlattSergé et al. J. Neuro. 2002

R

R

t

MSD

odd,1n2

22

44

22

)R4Dtnexp(n

1R1283R4

Confinement Confinement detectiondetection

=> Succession of free& confined events

Detailed inspection of dynamics fluctuations in time, within each trace.

Confinement cartographyConfinement cartography

freeconfined

conf. level Lconf

10 μm 10 μm

MTT at increased acquisition rate MTT at increased acquisition rate

10 μm

5 μm

2 ms

High speed Cascade 128

EM-CCD

MTT extended in 3DMTT extended in 3D

<PSFexp>

rx > ry

rx < ry

Huang et al. Science 2007

Elliptic PSF <=

Documents

Molecular diffusion in living cells using Fluorescence ... · Fluorescence Recovery After Photobleaching - FRAP Principle of FRAP measurements Diﬀerent FRAP modes (Spot FRAP, Confocal