A window with a view: spying brain function at the two-photon microscope
description
Transcript of A window with a view: spying brain function at the two-photon microscope
A window with a view: spying brain function at the two-photon microscope
1) What is two photon microscopy?
2) Sensing of brain structure and function in vivo
3) Two photon spectroscopy in vivo: towards the quantitative measure of pH and [Cl].
Neuroscience for dummies: what is and where is the brain
Imaging in deep tissue: confocal microscopy
Z
t = 0.1 fs (1 10-16 s)
Two photons are more than one
≈ 3 108 m → 3 1014 µm/s 0.03 µm
2 photon vs. 1-photon excitation
0 2 4 6 8 101E-5
1E-4
1E-3
0.01
0.1
1
Tota
l flu
ores
cenc
e
Distance from FP
Dependency of total fluorescence as a function of z
Fluorescent spheres 0.2 mm
(m)
A window with a view
A trip into the brain
Spine motility in the juvenile cortex (SSctx, p25)
0 min30 min60 min
Watching the brain in operation
Functional imaging of the brain with single cell resolution
Watching a mouse brain that is watching TV
Ap2
p3
p1n1 n2 n3
n4n5
n6
n7
n9
NP
P3
P2
P1
A1
0 45 90 135 180 225 270 315 3600.00
0.05
0.10
0.15
0.20
0.25
Resp
onse
Grid orientation (deg)
Watching the brain in operation
pH and Clhoride imaging in vivo
+
+
++
--+
Excitation and inhibition in the brain
-85 mV +60 mV
The space and time resolved measure of Cl gradients is the key to understand inhibition in the brain
0 20 40 60 80-140
-120
-100
-80
-60
-40
-20
0Ne
rst P
oten
tial fo
r Chlo
ride
(mV)
Cl (mM)
Nerst potential for Chloride
Nerst potential for Chloride
0 20 40 60 80-140
-120
-100
-80
-60
-40
-20
0Ne
rst P
oten
tial fo
r Chlo
ride
(mV)
Cl (mM)
Nerst potential for Chloride
0 20 40 60 80-140
-120
-100
-80
-60
-40
-20
0Ne
rst P
oten
tial fo
r Chlo
ride
(mV)
Cl (mM)
ClopHensor
OH
Cl-
+Cl- Kd
Ka
+H+
O-
OH
λecc=543 nm λecc=488 nmλecc=458 nm λecc=543 nm
Static quenching
Arosio et al. Nature Meth. 2010.
Gradients of intracellular Chloride
042 043 048 049
050 053 054 058
A new hope: E2-mKate
A new sensor formed by the fusion of E2GFP with the Red protein mKate
400 450 500 550 600 650 700 750 8000.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1 emiss mKate2 exc mKate2
emiss
ion flu
ores
cenc
e int
ensit
y (a.
u.)
wavelength (nm)
Exciting properties of mKate excitation
400 450 500 550 600 650 700 750 8000.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1 emiss E2GFP emiss mKate2
emiss
ion flu
ores
cenc
e int
ensit
y (a.
u.)
wavelength (nm)
400 450 500 550 600 650 700 750 8000.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1 emiss E2GFP emiss mKate2
emiss
ion flu
ores
cenc
e int
ensit
y (a.
u.)
wavelength (nm)
How to evaluate the integrity of the bi-molecular sensor?
The correct measure of Cl concentration requires that the ratio between red and green fuorescent proteins is equal to 1.
If we can demonstrate that the protein remains in the correct conformation, with the green and red proteins attached, the stechiometry is ensured.
0 2
G/R @458nm exc
FCS
10 1000.0
0.2
0.4
0.6
0.8
1.0
1.2
N gre
en/N
red
pH 7 - exc 458 nm
[Cl-] (mM)
kCld = 5823 mM
0 2
G/R @458nm exc
FCS
10 1000.0
0.2
0.4
0.6
0.8
1.0
1.2
N gre
en/N
red
pH 7 - exc 458 nm
[Cl-] (mM) G
reen
/Red
nor
m. t
o [C
l- ]=
0
kCld = 5823 mM
kCld = 472 mM
Measuring the shuttling between nucleus and cytoplasm
pre-bleach 5 s 60 s 240 s
Measuring the shuttling between nucleus and cytoplasm
pre-bleach 5 s 60 s 240 s
Fun facts about N/C shuttling: proteins with MW<30kD freely diffuse between these two compartments.
Larger MW are associated to a very slow turnover
We can use the nuclear membrane as a molecular sieve to measure the size of the
fluorescent proteins!
pre-bleach 5 s 60 s 240 s
In vivo FRAP measure in cortical neurons
Pre bleach
0 1000 2000 3000 4000 5000 6000 70000,5
0,6
0,7
0,8
0,9
1,0
YFP H lineFrecovered= 97 % = 217 3 s
(I n(t)/
I all(t)
) / (I
npre /I all
pre )
time (s)
Recovery of fluorescence of YFP
Diffusion of CloPhensor is strongly limited
0 1000 2000 3000 4000 5000 6000 70000,5
0,6
0,7
0,8
0,9
1,0
YFP H E2GFP-lssmKate2
Frecovered= 97 % = 217 3 s
(I n(t)/
I all(t)
) / (I
npre /I all
pre )
time (s)
y = A exp(-t/) + FrecoveredFrecovered= 84 % = 856 20 s
800 850 900 950 1000
0,4
0,6
0,8
1,0
1,2
1,4
1,6
1,8
Inte
nsity
(ADU
)
Wavelength (nm)
0,0 0,2 0,4 0,6 0,8 1,00,0
0,2
0,4
0,6
0,8
1,0 No correction of R>G bleed through Corrected for R>G bleed through
pH 6.8
pH 6.4
pH 7.0
pH 7.2
Com
pone
nt p
rojec
ted
on p
H 8.
0
Component projected on pH 6.0
pH 7.6
Linear spectral composition for measuring cells pH
pH 6.0
pH 8.0
6,0 6,4 6,8 7,2 7,6 8,00
15
30
45
60
75
90 No R>G Bleed through R>G corrected
Angle
pH
Houston, we have a problem…
IMG_0823
Effects of excitation scattering on the spectra
750 800 850 900 950 10000
1000
2000
3000
4000
Fluo
resc
ence
(AU)
Wavelenght (nm)
770 800 830 860 890 920 950 980
0.20.40.60.81.0
Fluo
resc
ence
750 800 850 900 950 10000.5
1.5
2.5
Corre
ction
Wavelength (nm)
750 800 850 900 950 10000.2
0.4
0.6
0.8
1.0
Norm
alise
d flu
ores
cenc
e
Wavelength (nm)
0.50
0.75
1.00
1.25
1.50
1.75
2.00
inten
sity (
norm
. on
R910
) pH 6 pH 6.4 pH 6.8 pH 7 pH 7.2 pH 7.6 pH 8
800 820 840 860 880 900 920 940 960 980 10000.4
0.6
0.8
1.0
excitation wavelength (nm)
Unmixing the E2-mKate spectra
R(l) = Rrfp(l) + a Gsensor (l)G(l) = Gsensor + bRrfp (l)
P18
P4
In vivo mouse cortex
Road map to pH and Cl computation
Spectral unmixing of R and G channels
Use of the pH/Cl invariant R channel to compute excitation scattering
Correction of G channel for excitation scattering
Projection of the corrected G spectra on the reference spectra: pH computation
Looking at the red raw data
800 850 900 950 10000,25
0,50
0,75
1,00
1,25
1,50
1,75
Wavelength (nm)
Comparing the effects of spectra corrections
800 850 900 950 10000,25
0,50
0,75
1,00
1,25
1,50
1,75
Wavelength (nm)
pH 7.37R=3.63
Comparing the effects of spectra corrections
800 850 900 950 10000,25
0,50
0,75
1,00
1,25
1,50
1,75
pH 7.14R=0.82
Wavelength (nm)
pH 7.37R=3.63
Computing pH in vivo (p18)
0
2
4
6
8
10
12
14
MinimumResidue
IndividualRed
Corrrectionon mean Red
Unmix onlyRaw Data
Sum
of r
esidu
es
0
2
4
6
8
10
12
14
16
IndividualRed
Mean RedUnmixing
Resid
ue o
f pH
com
puta
tion
Raw Data
Sum of residues allows a statistical test of the data treatment
Computing pH in vivo (p18)
7,0
7,2
7,4
7,6
7,8
MinimumResidue
IndividualRed
Corrrectionon mean Red
Unmix onlyRaw Data
pH
What about extinction of the emitted light?
Cl measure depends on an equally efficient collection of the fluorescence emitted at the green and red channels.
Sadly, in a few seconds, I will provide evidences, that that is not the case
We can build a model for differential extinction to correct the data.
Or…
-50 0 50 100 150 200 250 3000,95
1,00
1,05
1,10
1,15
1,20
1,25
1,30re
d/gr
een
depth (um)
Equation y = A + B*xAdj. R-Square 0,97895
Value Standard ErrorMean Mean 0,97355 0,00139Mean Mean 8,17063E-4 7,57754E-6
Differential extinction of YFP fluorescence
Modeling extinction of emitted fluorescence
140 160 180 200 220 240 260 280-20
0
20
40
60
80
100
120 Clprime % (2)
Clpr
ime
Depth (micron)
Applying the extinction model to the in vivo data
Applying the extinction model to the in vivo data
140 160 180 200 220 240 260 280-20
0
20
40
60
80
100
120 Clprime ClGamma
Clpr
ime
Depth (micron)
The state of the art at the present day (1 wk ago)
Intracellular pH Intracellular Chloride
6,6 6,7 6,8 6,9 7,0 7,1 7,2 7,3 7,4 7,50,0
0,2
0,4
0,6
0,8
1,0
1 10 1000,0
0,2
0,4
0,6
0,8
1,0
P8P22
P18
P5
Perc
enta
ge
Intracellular pH
P4
P5A P8A P4A
P18 P18A P22aCl P32Ag P29Ag
Perc
enta
geChloride (nM)
rat’s lab
S. Sulis Sato, P. ArtoniL. Cancedda, J. Szczurkowska S. Luin, A. Idilli, D. Arosio
Telethon; FIRB Futuro ricerca; PRIN; Regione Toscana