1
Super-sensitive Ras activation in dendrites and spines revealed by 2-photon
fluorescence lifetime imaging
Ryohei Yasuda1,2, Christopher Harvey2, 3, Haining Zhong2, Aleksander Sobczyk2,4, Linda van Aelst3 and
Karel Svoboda2,3
1. Present address and address for correspondence: Neurobiology Department, Duke University Medical
Center, Durham, North Carolina 27710
2. HHMI, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724
3.Watson School of Biological Science, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York
11724
4. Department of Physics, State University of New York at Stony Brook, Stony Brook, New York 11794
Abstract
To understand the biochemical signals regulated by neural activity it is necessary to measure proteinprotein interactions and enzymatic activity in neuronal microcompartments, such as axons, dendrites and
their spines. We have combined 2-photon excitation laser scanning with fluorescence lifetime imaging
to measure fluorescence resonance energy transfer (FRET) at high resolutions in brain slices. We have
also developed sensitive GFP-based sensors for the activation of the small GTPase protein Ras with
slow (FRas) and fast (FRas-F) kinetics. Using FRas-F we found in CA1 hippocampal neurons that
trains of back-propagating action potentials rapidly and reversibly activate Ras in dendrites and spines.
The relationship between firing rate and Ras activation was highly non-linear (Hill coefficient ~ 5). This
steep dependence was caused by a highly cooperative interaction between Ca2+ and Ras activators. The
Ras pathway therefore functions as a super-sensitive threshold detector for neural activity and Ca2+.
2
Introduction
In the central nervous system, most excitatory synapses occur on dendritic spines, tiny (volume 0.1 0.01 femtoliters) mushroom-shaped protrusions emanating from the dendritic surface {Nimchinsky,
2002 #96}. Dendritic spines contain hundreds of species of signaling molecules associated with the
postsynaptic density and the actin cytoskeleton {Kennedy, 2005 #122}. These molecules are part of
complex signal transduction pathways, many of which are activated or modulated by Ca2+. Ca2+ imaging
methods have been used to dissect the life-cycle of Ca2+, from influx to extrusion {Sabatini, 2002 #95}
and to measure quantitatively the dynamics of Ca2+ channels and synaptic receptors at the level of
individual channels {Sabatini, 2000 #98; Yasuda, 2004 #87}. Imaging methods with similar sensitivity
and resolution, which could reveal the dynamics of signaling downstream of Ca2+, have not been
demonstrated.
The small GTPase protein Ras relays calcium elevations in neurons to various forms of neuronal
plasticity {Rosen, 1994 #13; Brambilla, 1997 #199; Zhu, 2002 #56; Wu, 2001 #16; Thomas, 2004 #58;
Kennedy, 2005 #122}. Ras activity is controlled by its activators, guanine exchange factors (GEFs), and
inactivators, GTPase activating proteins (GAPs). Several calcium-dependent GEFs and GAPs have been
identified {Farnsworth, 1995 #110; Davis, 1996 #194; Ebinu, 1998 #202; Walker, 2004 #192; Kennedy,
2005 #122; Liu, 2005 #193}. Ras activates the extracellular signal-related kinase (ERK) {Iida, 2001
#14; Zhu, 2002 #56; Wu, 2001 #16; Thomas, 2004 #58; Kennedy, 2005 #122}, and depending on the
biological situations, this can lead to diverse cellular responses {Thomas, 2004 #58; Kennedy, 2005
#122}. ERK activation is critical for AMPA receptor insertion during LTP in hippocampal neurons
{Zhu, 2002 #56}, some forms of metabotropic glutamate receptor-dependent LTD {Gallagher, 2004
#103} and spine formation {Wu, 2001 #16}. ERK is also important for dendritic protein synthesis
3
{Klann, 2004 #191} and gene transcription important for maintenance of synaptic change {West, 2002
#134}.
The Ras-ERK pathway is thus involved in signaling events spanning different compartments,
including spines, dendrites and the nucleus. This indicates that the spatial and temporal dynamics of
Ras-ERK signaling is likely important in determining the downstream effects. However, most of our
knowledge of the biochemistry of Ras-ERK signaling and other signal transduction pathways comes
from classical biochemical experiments that lack spatial resolution and have limited temporal resolution.
A deeper understanding of the coupling between synaptic activity and intracellular signal transduction
dynamics will require measurements of biochemistry at the level of dendrites and even individual
dendritic spines {Yasuda, 2003 #88; Kennedy, 2005 #122}.
Fluorescence resonance energy transfer (FRET) can be used to image protein-protein interactions
in cells. FRET refers to the process of energy transfer from an excited donor fluorophore to an acceptor
fluorophore, mediated by dipole-dipole interactions {Lakowicz, 1999 #93}. FRET decreases the donor
fluorescence and increases the acceptor fluorescence. The efficiency of FRET, the fraction of absorbed
photons transferred to the acceptor (YFRET), is a steep function of the distance between the fluorophores,
r:
Y FRET 
1
(Eq. 1)
1  r 6 / R06
where Ro is the Förster distance, given by

Ro  8.79  1017  n  4 QD 2 J

1/ 6
(nm),
(Eq. 2)
in which n is the refractive index of the medium (~ 1.33-1.35), QD is the quantum yield of the donor, 2
is the orientation factor (ranging from 0 to 4), J is the overlap integral between the donor emission
spectrum and the acceptor absorption spectrum {Lakowicz, 1999 #93}. Typical values for the Förster
4
distance are in the range of 2-6 nm for good FRET pairs, including some fluorescent proteins {Patterson,
2000 #73}. Because of the strong dependence of FRET on r, and the molecular length of the Förster
distance, FRET can be used as a readout of protein-protein interactions for proteins fused to
fluorophores {Lakowicz, 1999 #93; Miyawaki, 2003 #190}.
With the introduction of green fluorescent protein and its variants {Tsien, 1998 #67}, numerous
protein-protein interactions have been probed with FRET microscopy {Zacharias, 2002 #53; Erickson,
2001 #10; Miyawaki, 2003 #190}. In addition, sensors have been developed that can detect Ca2+
dynamics and the activity of enzymes {Miyawaki, 2003 #190} including Ras {Mochizuki, 2001 #4;
Jiang, 2002 #117; Rocks, 2005 #32}.
Although FRET measurements are conceptually simple, quantifying FRET remains challenging.
Interpretation of intensity measurements is complicated by the fact that donor and acceptor absorption
and emission spectra overlap (spectral bleed-through) and fluorescence intensities at any wavelength
depend on the concentrations (expression levels) of donor and acceptor. Intensity measurements are
further ambiguous in molecular terms because they fail to distinguish between a small fraction of
interacting molecules with high FRET efficiency and a large fraction of interacting molecules with low
FRET efficiency. Additional technical challenges come into play when imaging microcompartments
such as single dendritic spines in intact tissue. Changes in fluorescence emission upon FRET may
involve only a few copies of each protein embedded in a light-scattering environment. Therefore
sensitive experimental techniques and molecular probes in combination with 2-photon excitation are
required {Denk, 1997 #81}.
To measure FRET in individual dendritic spines in intact tissue, we have combined 2-photon
excitation laser scanning microscopy with fluorescence lifetime imaging (2pFLIM) {Becker, 2001 #69;
Gratton, 2003 #47; Jakobs, 2000 #59; Peter, 2005 #71}. To study Ras signaling in microcompartments
5
of intact neurons, we have developed FRas and FRas-F, sensitive Ras sensors based on fluorescence
resonance energy transfer (FRET) between fluorescent proteins {Mochizuki, 2001 #4; Jiang, 2002 #117;
Rocks, 2005 #32}. We have used 2pFLIM to measure Ras activation in dendrites and spines of neurons
transfected with FRas-F. Our measurements show that Ras activation is a highly non-linear function of
neural activity and dendritic [Ca2+].
Results
Measurement of protein binding using 2-photon fluorescence lifetime imaging microscopy
As a quantitative readout of FRET, we measure the fluorescence lifetime of the donor defined as the
average time elapsed between fluorophore excitation and photon emission {Lakowicz, 1999 #93}. After
excitation by a short pulse of light from a mode-locked Ti:sapphire laser, the donor excited state
typically decays with a single-exponential time-course with time-constant D. Donors bound to acceptors
experience FRET, and the usual decay processes collaborate with FRET to dissipate the excited state
and thus shorten the fluorescence lifetime. The FRET efficiency can be expressed in terms of
measurable quantities as
YFRET  1   AD /  D ,
(Eq. 3)
where AD is the fluorescence lifetime of the donor in the presence of the acceptor. In typical
experimental situations free donors coexist with donors bound to acceptors and the fluorescence decay
curve contains two exponentials {Lakowicz, 1999 #93}:
F (t )  F0   PAD exp(t /  AD )  (1  PAD )exp(t /  D ) ,
(Eq. 4)
where Fo is the fluorescence intensity at the time fluorophore is excited (t = 0). PAD is the fraction of
donor bound to acceptor (binding fraction); thus it is possible to derive this biologically important
parameter from the fluorescence decay curve (also see Methods). Since only the donor fluorescence is
6
involved, fluorescence lifetime measurements of FRET are independent of fluorophore concentrations,
and are insensitive to spectral bleed-through and wavelength-dependent light scattering.
To image the fluorescence lifetime we used time correlated single photon counting (TCSPC),
which directly measures the time elapsed between a laser pulse and a fluorescence photon {Lakowicz,
1999 #9; Becker, 2001 #0}. In combination with 2-photon excitation laser scanning microscopy this
technique is ideal for imaging fluorescence lifetime of relatively slow (> 30 seconds) dynamic processes
in small compartments in scattering brain tissue (Supplementary Note 1) {Becker, 2001 #69; Gratton,
2003 #47; Jakobs, 2000 #59; Peter, 2005 #71}.We combined a commercial TCSPC board (Becker-Hickl,
Berlin, German) with our custom-made two-photon microscope {Pologruto, 2003 #54} (Fig. 1a). By
measuring fluorescence lifetimes pixel-by-pixel, a high spatial resolution image of fluorescence lifetime
can be generated. In the TCSPC system, fluorescence decay curves (Eq. 4) are convolved with the
instrument response, the uncertainty in photon arrival times imposed by our instrumentation (Fig. 1b,c).
To calculate the FRET efficiency (YFRET) and binding fraction (PAD) the instrument response has to be
measured and taken into account (see Methods).
Shot noise limits the signal-to-noise ratio (SNR) in fluorescence lifetime measurements. The
SNR is related to the number of detected photons (n), YFRET, and the change in the binding fraction PA
(PAD) in response to a stimulus (Supplementary Note 2). The number of photons required to measure
PAD with SNR =1 is given by
2
nSNR 1
2
 (1  PAD ) 2  PAD
(1  YFRET ) 3  (1  PAD ) PAD (1  YFRET )( 2  YFRET ) 

 .


P
Y
(
1

Y
)
AD FRET
FRET


(Eq. 5).
(Supplementary Note 2). The required number of photons decreases as ~YFRET-2 for moderate FRET
efficiencies (YFRET < 0.3; Supplementary Figure 1). However, the SNR is a non-monotonic function of
YFRET. For large values of YFRET (>0.5) the required number of photons for a given signal to noise ratio
7
increases with YFRET, because the short lifetime component is difficult to detect against the background
of the long lifetime component. The optimal range is YFRET ~ 0.3-0.7 (Supplementary Figure 1).
Development of high sensitivity FRET sensors for Ras activation
To monitor Ras activation we have developed FRas and FRas-F, FRET sensors for Ras activation
{Mochizuki, 2001 #4; Jiang, 2002 #117; Rocks, 2005 #32} optimized for brightness and FRET
efficiency.
Fluorescent proteins. To achieve large signals it is important to choose a bright donor fluorophore and
to maximize the Förster distance (Eq. 2). The most commonly used FRET pair is enhanced cyan
fluorescent protein (ECFP) as the donor and enhanced yellow fluorescent protein (EYFP) as the acceptor
{Miyawaki, 2003 #190}. However, ECFP and its brighter variant Cerulean {Rizzo, 2004 #74} have
problems for high-sensitivity 2pFLIM imaging: i) The fluorophores are relatively dim compared with
EGFP, ii) the optimal excitation wavelength (~ 800 nm) can produce background fluorescence in brain
tissue, and iii) the fluorescence decay curves are multi-exponential, complicating quantitative analysis
{Tramier, 2002 #126} (Supplementary Figure 2).
The development of bright, monomeric forms of red fluorescent proteins (RFPs) {Campbell,
2002 #51; Shaner, 2004 #52} has made the use of enhanced green fluorescent protein (EGFP) or yellow
fluorescent protein (EYFP) as donors possible {Erickson, 2003 #12; Peter, 2005 #71}. EGFP and Venus
(an improved version of EYFP {Nagai, 2002 #50}) have fluorescence lifetime curves that decay with a
single exponential (Supplementary Figure 2). In our hands, EGFP is brighter under 2-photon excitation
than EYFP or Venus. Therefore, we used EGFP as the donor. To avoid dimerization of EGFP we used
monomeric EGFP (mEGFP, EGFP A206K) {Zacharias, 2002 #53}. We tested three RFP acceptors,
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Tdimer2, mRFP {Campbell, 2002 #51} and mCherry {Shaner, 2004 #52} in combination with mEGFP.
Tdimer2 was rejected because it emits significant green fluorescence interfering with mEGFP
fluorescence. Both mRFP {Peter, 2005 #71} and mCherry were found to be excellent acceptors with
negligible spectral bleed-through (< 0.5 % under our conditions, data not shown).
For any given fluorescent protein, populations with and without a functional chromophore
coexist in a cell {Nagai, 2002 #50}. Since acceptors without a functional chromophore do not contribute
to FRET, their presence will reduce the signal level and also lead to an underestimate of the binding
fraction in FRET experiments. To quantify the fraction of mRFP and mCherry without a functional
chromophore, we expressed the fusions mEGFP-mRFP, mRFP-mEGFP or mCherry-mEGFP in
HEK293 cells and measured the fluorescence decay curves of mEGFP (Supplementary Figure 2). As
expected, the apparent binding fraction was less than one (mEGFP-mRFP, 0.49 ± 0.04; mRFP-mEGFP,
0.57 ± 0.03; mCherry-mEGFP, 0.55 ± 0.03), implying that about 50% of mRFP or mCherry do not
function as acceptors in cells. This implies that the mEGFP / mRFP (or mCherry) FRET pair has a
maximum effective binding fraction of ~ 0.50. Quantitative measurements of binding fraction need to
take this factor into account.
Optimization of FRET sensors for Ras activation in HEK293 cells. Ras (H-ras) was tagged with
mEGFP on its N terminus and the Ras binding domain of Raf (RBD) {Lowy, 1993 #186} with mRFP on
either C or N terminus or both termini (Fig. 2a). FRas variants were tested in HEK293 cells, which
show robust Ras activation in response to epidermal growth factor (EGF) receptor stimulation {Takai,
2001 #168} (Fig. 2b,c). Activation of mEGFP-Ras increased the affinity between RBD-mRFP and
mEGFP-Ras, decreasing the fluorescence lifetime of mEGFP-Ras. Since FRET depends on the distance
and the angle between donor and acceptor (Eq. 2) we optimized the linkers between the fluorescent
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proteins and Ras / RBD for signal-to-noise ratio by expressing in HEK293 cells a constitutively active
Ras mutant (G12V), which has a high affinity for RBD {Lowy, 1993 #186}. Interestingly, with our best
linkers (Methods) mRFP fusions to either the C- or N-terminus of RBD displayed excellent FRET
efficiency (Fig. 2d). The apparent binding fractions under these conditions were ~ 0.5 (0.49 ± 0.04 for
C-terminus, 0.46 ± 0.1 for N-terminus) (Fig. 2e). This provides independent confirmation that only half
of mRFP molecules form functional chromophores (Supplementary Figure 2). We tested if a pair of
acceptors, one at each terminus, would enhance the FRET efficiency. With two ideal acceptors, the
fluorescence decay is given by a single exponential with shorter lifetime than with one acceptor. The
total FRET efficiency can be expressed in terms of the FRET efficiencies for each acceptor (Y1,FRET and
Y2,FRET) as
1
YFRET

Y1,FRET
Y2,FRET 
 1  1 

 .
 1  Y1,FRET 1  Y2,FRET 
(Eq. 6)
(Supplementary Notes 2 for derivation). However, the experimental situation is more complex because
only about 1/2 of acceptors mature (i.e. mRFP, mCherry) (Supplementary Figure 2). We expect that
1/4 of the fusion protein has two functional acceptors, 1/2 only one functional acceptor, and 1/4 two
non-functional acceptors. In the case of FRas, RBD was fused to mRFP at both the C- and N-termini
(Fig. 2). The apparent FRET efficiency of the binding population was larger (55 ± 1 %) compared to Cand N-terminus fusions alone (consistent with Eq. 11, Methods; Fig. 2d). The measured binding fraction
was also larger with two functional acceptors (0.54 ± 0.04) than with one acceptor (Fig. 2e). Consistent
with these results, the change in the binding fraction in response to EGF was enhanced by tagging RBD
with mRFP at both termini, as opposed to only N or C terminus alone (Fig. 2f).
To test if FRas selectively reports Ras activation, we made sensors with constitutively inactive
(S17N) mutations in Ras {Feig, 1988 #187}. As expected, FRas (S17N) showed low binding fraction
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compared to FRas (G12V) (Fig. 2f). Neither S17N nor G12V showed a fluorescence lifetime change
after activation of the Ras pathway by EGF (Fig. 2f). These experiments suggest that FRas selectively
reports Ras activation in cells.
Tuning FRas kinetics in neurons. We further tested FRas in CA1 pyramidal neurons in cultured
hippocampal brain slices. A sparse subset of neurons was transfected with FRas using ballistic gene
transfer {McAllister, 2000 #104}. 2pFLIM measurements were performed on thick apical dendrites,
before and after application of KCl (62.5 mM, 10 min.); these depolarizations open voltage sensitive
calcium channels (VSCCs) and activate Ras {Rosen, 1994 #13; Dolmetsch, 2001 #15; Wu, 2001 #16;
Iida, 2001 #14} (Fig. 3a,b). To determine if changes in the FRas fluorescence lifetime reflect the
dynamics of endogenous Ras, we performed parallel experiments measuring endogenous Ras activation
using standard Ras pull-down assays {de Rooij, 1997 #2} (Fig. 3c,d). Comparing the time-course of
activation of FRas (Fig. 3b, green line) and endogenous Ras (Fig. 3d, blue line) revealed that the
activation rates were similar, but the FRas signal decayed much more slowly after washout of KCl than
activation of endogenous Ras.
Why is the recovery of FRas slow? Ras inactivators (GAPs) cannot access activated Ras that is
bound to RBD {Herrmann, 1995 #1}. Ras activation and inactivation in the presence of RBD is thus
given by the following scheme:
k GEF
Ras GDP




k GAP
Ras GTP 
 Ras GTP - RBD .
(Eq. 7)
KD
where RasGTP is activated Ras, KD is the dissociation constant of RBD and RasGTP (10 – 100 nM)
{Herrmann, 1995 #1; Jaitner, 1997 #3}, and kGEF and kGAP are the Ras activation and inactivation rate
11
constants determined by the activity of GEFs and GAPs respectively. The dissociation rate of RasGTP
and RBD is on the order of 10 s-1 {Sydor, 1998 #171}, much faster than the deactivation time of Ras (~
10 minutes) 52. Thus, overexpression of RBD decreases free [RasGTP] and the rate of RasGTP inactivation
by a factor of

[Ras GTP ] Free
KD
.

GTP
[Ras ]Total [RBD ]  K D
(Eq. 8)
Since RBD is overexpressed to ~ 1 M or more {Erickson, 2003 #12}, the GAP activity could be
inhibited by up to 100 fold compared to the native case. To minimize the effects of overexpression of
RBD on the rate of Ras inactivation we introduced a point mutation (R59A) in RBD, increasing KD to
several M {Jaitner, 1997 #3} . The resulting sensor, FRas-F, reversed rapidly after application of KCl
(Fig. 3b). In addition, FRas-F showed improved signal-to-noise ratio compared to FRas in neurons (Fig.
3b) and HEK 293 cells (Fig. 2f), mainly because the baseline binding fraction was decreased. Blocking
L-type VSCCs (20 M Nimodipine) partially inhibited Ras activation as reported by FRas-F as well as
by the pull-down assay (cf. Fig. 3b,d). We conclude that FRas-F accurately reports endogenous Ras
activation in neurons. FRas-F was used for all subsequent experiments.
Activity-dependent Ras activation in dendrites and spines
Action potentials (APs) propagate into proximal dendrites and spines, opening VSCCs to produce
relatively global calcium accumulations lasting ~ 0.1 - 1 sec {Callaway, 1995 #85; Maravall, 2000 #86;
Sabatini, 2000 #98; Yasuda, 2003 #88; Pologruto, 2004 #116}. We measured Ras activation in proximal
dendrites (40-80 m from somata) evoked by trains of APs (Fig. 4). Action potentials were evoked with
current injections in the perforated patch clamp mode {Bi, 1998 #102}, avoiding washout of the Ras
sensor and other signaling factors. High-frequency AP trains (83 Hz x 40 pulses, 4 times with 5 s
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intervals) evoked rapid (rise time < 2 min) and robust Ras activation in dendrites and spines (Fig. 4a,b).
Ras decayed much more slowly than calcium (decay time constant decay = 4.7 ± 0.6 min at 83 Hz; 4.5 ±
0.7 min at 50 Hz; 3.7 ± 0.5 min at 33 Hz, N = 7, obtained by dividing the area by the peak amplitude of
the transient; Fig. 4b). Because of undersampling (1 minute per image) we underestimated the peak
amplitudes by ~ 1-exp(-0.5 min / decay) ~ 10-13 %. Ras activation could be triggered repeatedly in
recordings lasting hours (Fig. 4c). In distal dendrites (140-190 m from somata), Ras activation was
significantly smaller (10 ± 7% of the signal in proximal dendrites; N=3), presumably due to the
attenuation of AP propagation at high frequencies in distal dendrites {Callaway, 1995 #85; Spruston,
1995 #165}.
Ras activation increased monotonically with the number of high-frequency trains (83 Hz ×
40 pulses per stimulus, 5 s intervals) during a stimulus (Fig. 4d) with Michaelis-Menten type saturation
behavior (Fig. 4e). Half-saturation was achieved with eight trains. To avoid saturation of the Ras sensor,
we therefore set the number of AP trains per stimulus to four for the rest of experiments (variable
frequency, 40 pulses, 4 times with 5 s intervals).
We explored the dependency of Ras activation on the action potential frequency within
individual AP trains. Surprisingly, trains with frequencies below 30 Hz caused little Ras activation,
while Ras activation seemed to saturate at 50-60 Hz (Fig. 4f). Thus Ras activation is a highly nonlinear
function of AP frequency. All recorded neurons showed similar steep saturation behaviors with a large
Hill coefficient (4.9 ± 0.9; N = 7; fitting to each cell; Fig. 4g). The frequency corresponding to 50%
saturation (f50) was 44 ± 12 Hz. Note that the non-linearity of the response is not due to FRas-F, because
the FRas-F response was linear as a function of the number of AP trains (Fig. 4e, Hill coefficient ~ 1).
Also, binding between Ras and RBD is known to follow a simple Michaelis-Menten scheme {Herrmann,
1995 #1; Jaitner, 1997 #3} (Eq. 13, Methods). We conclude that the Ras pathway is a super-sensitive
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threshold detector of firing rate in CA1 dendrites within a physiological firing rate {Czurko, 1999
#178}.
Ca2+-dependent Ras activation in dendrites and spines
Ca2+ is known to couple to Ras activation in neurons. To investigate the relationship between Ras
activation and dendritic [Ca2+], we performed [Ca2+] measurements using low affinity calcium indicators
(167 M Fluo-4FF) {Yasuda, 2004 #87}. Trains of APs produced large [Ca2+] transients with a plateau
phase (Fig. 5a). The amplitude of the plateau [Ca2+] was proportional to AP frequency up to 83 Hz (Fig.
5b), consistent with previous measurements {Maravall, 2000 #86}. The deviation from the straight line
at 100 Hz is likely due to AP propagation failures {Callaway, 1995 #85}. The [Ca2+] dynamics were
indistinguishable in transfected and untransfected neurons, implying that transfection with FRas-F
affects neither the shape of action potentials nor the densities or properties of VSCCs in the dendritic
membrane. Other cell parameters such as resting potential (transfected, -57.1 ± 1.1 mV; untransfected, 56.7 ± 0.9 mV) and input resistance (transfected, 144 ± 9 M untransfected, 141 ± 4 M) were also
not changed by transfection. The linear relationship between AP frequency and dendritic [Ca2+] implies
that the nonlinearity in Ras activation is downstream or independent of Ca2+.
Using the data in Fig. 5b as a ‘ruler’, we derived a relationship between [Ca2+] and Ras
activation. Ras activation averaged over cells was a highly non-linear function of [Ca2+] (Fig. 5c, blue
circles). For the averaged data the effective Hill coefficient was somewhat lower (~ 3.1) than for
individual neurons, mainly because the f50 varied from cell to cell (Fig. 4g). [Ca2+] ~ 0.9 M (~ 1 s)
activated Ras to 50%. Therefore, taking into account the resting [Ca2+] of the cells (0.05 - 0.1 M)
{Maravall, 2000 #86; Sabatini, 2002 #95}, Ras is activated half-maximally by [Ca2+] ~1 M, elevated
for ~1 second.
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To test if [Ca2+] alone determines Ras activation, we manipulated dendritic [Ca2+] and measured
its effect on Ras activation. The [Ca2+] transient amplitude is proportional to [Ca]ex in the external
solution {Sabatini, 2000 #98}. While exciting cells with high-frequency AP trains (83 Hz; Fig. 4a,b) we
thus varied [Ca2+] transient amplitudes by changing [Ca]ex (1 – 4 mM; Fig. 5d). The relationship
between the amplitude of [Ca2+] transients and Ras activation was again non-linear (Fig. 5c, red circles),
and agreed with the relationship obtained from varying the frequency modulation (Fig. 5c, blue circles).
These results imply that [Ca2+] determines Ras activation.
Previous experiments have suggested that the Ras-ERK pathway is activated in Ca2+
microdomains. In particular, Ca2+ entering through L-type channels may be privileged to activate the
Ras-ERK pathway, even though other channels could dominate Ca2+ accumulations in the cytoplasm
{Mermelstein, 2000 #195; Dolmetsch, 2001 #15; Wu, 2001 #16}. This would imply that the bulk [Ca2+]
measured in our imaging experiments might not necessarily predict Ras activation. To test this
possibility we blocked subsets of VSCCs and measured dendritic [Ca2+] and Ras activation in parallel
experiments. A nonspecific VSCC blocker (300 M CdCl2) reduced [Ca2+] transient amplitudes by
> 90%, showing that AP-evoked [Ca2+] enters the cell almost exclusively through VSCCs (Fig. 5e). We
next used specific blockers (L-type channel blocker Nimodipine, 20M; R-type channel blocker, 250
M NiCl2; N,P/Q-type channel blocker, a cocktail of 10 M Conotoxin MVIIC and Conotoxin
GVIA; Fig. 5e). These experiments revealed that proximal dendrites of CA1 neurons contain a rich
collection of VSCCs: ~10 % L-type, ~30% R-type, ~30% N/P/Q-type VSCCs. These results differ from
[Ca2+] transients in spines and higher order dendrites where R-type channels dominate {Sabatini, 2000
#98; Yasuda, 2003 #88}.
Next we measured Ras activation in response to AP trains in the presence of Nimodipine or
NiCl2 (Fig. 5f,g). Consistent with the small contribution of L-type channels to dendritic [Ca2+],
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Nimodipine had little effect either at 50Hz or 83Hz. In contrast, in the presence of NiCl2, the amount of
block of Ras activation was dependent on the AP frequency (Fig. 5f,g), as expected from the nonlinear
relationship between [Ca2+] and Ras activation (Fig. 5c). We again obtained the relationship between
[Ca2+] transient amplitudes and Ras activation normalized to the activation at 83Hz under the control
condition. The amplitude of [Ca2+] transients was obtained from the frequency vs. [Ca2+] relation (Fig.
5b) corrected for fraction block after drug applications (Fig. 5e). The results (cyan and green circles in
Fig. 5c) are indistinguishable from those derived by changing AP frequency (blue circles in Fig. 5c) or
[Ca2+]ex (red circles in Fig. 5c). These results suggest that Ras activation is independent of the source of
dendritic Ca2+ and global [Ca2+] predicts Ras activation.
Cortical neurons contain Ca2+ dependent modulators of Ras. RasGRF is regulated by calcium
bound calmodulin {Farnsworth, 1995 #110} and SynGAP is modified by phosphorylation by
calmodulin dependent kinases, including CaMKII {Chen, 1998 #115; Kim, 1998 #114}. To test if
RasGRF or SynGAP shape Ras activation in response to back-propagating APs, we blocked binding of
free calmodulin to its target proteins using calmidazolium (Fig. 5g). Calmidazolium blocked Ras
activation by calcium entering the cytoplasm through NMDA receptors (Unpublished data).
Interestingly, calmidazolium had no effect on Ras activation by action potential trains, suggesting that
RasGRF and synGAP are not involved (Fig. 5g).
Discussion
2pFLIM as a tool for synaptic physiology
We have shown that 2pFLIM is a powerful tool to measure protein-protein interactions at the level of
single synapses in highly light-scattering tissue. Fluorescence lifetime measurements have key
advantages over standard intensity based measurements, where the FRET dependent quenching of donor
16
fluorescence and enhancement of acceptor fluorescence are quantified. For example, a simple measure
of FRET (often used for dynamic measurements, i.e. before and after a stimulus) is the ratio of donor
and acceptor fluorescence. Interpretation of this ratio is complicated by the fact that donor and acceptor
absorption and emission spectra overlap (spectral bleed-through) and fluorescence intensities at any
wavelength depend on the concentrations (expression levels) of donor and acceptor. Various methods
have been devised to deal with spectral bleed-through and fluorophore concentrations {Wallrabe, 2005
#72}. Donor fluorescence can be measured in the presence of the acceptor and after removal of the
acceptor (‘dequenching’) such as by photobleaching {Bastiaens, 1996 #125; Zacharias, 2002 #53} .
However, this destructive procedure precludes dynamic measurements. Another approach involves the
use of multiple (three or more) excitation wavelengths {Gordon, 1998 #170; Erickson, 2001 #10;
Wallrabe, 2005 #72}. Because of the requirement for multiple excitation wavelengths these techniques
are difficult to combine with 2-photon microscopy, which is required for high-resolution imaging in
scattering tissue. Other problems associated with intensity measurements are more difficult to address.
Intensity measurements are ambiguous in molecular terms because they fail to distinguish between a
small fraction of interacting molecules with high FRET efficiency and a large fraction of interacting
molecules with low FRET efficiency. Furthermore, when imaging in tissue, intensity measurements are
distorted by wavelength-dependent light scattering. Finally, intensity based techniques are sensitive to
artifacts, especially for measurements in small compartments. For example, only ~10% of Ras is
activated under physiological conditions in neurons (Figs. 4,5) corresponding to intensity changes of
~5 %. Such small changes are easily polluted by movement of the tissue, photobleaching, changes in
fluorophore concentration, morphology and light scattering, and other effects. Additional artifacts can
arise when interactions are probed that involve translocation, for example between membrane and
soluble protein pools. Movement to the membrane can dramatically alter the intensity ratio between
17
donor and acceptor, making bleed-through compensation difficult. Fluorescence lifetime measurements
are robust to all of these effects. Furthermore, lifetime measurements allow the calculation of the
fraction of donor bound to acceptor directly, even with fairly small numbers of signal photons.
Optimization of signaling sensors for fluorescence lifetime measurements has to obey different
design criteria than for intensity based methods. First, it is important that the isolated donor have singleexponential fluorescence decays to allow straightforward calculation of binding fractions (Eq. 4).
Second, for fluorescence lifetime measurements, the acceptor quantum efficiency is not important since
only the donor fluorescence is used. Acceptors with small quantum efficiencies may even be preferable
because this will result in reduced spectral bleed-through. Third, the signal-to-noise ratio (SNR) is a
non-monotonic function of FRET efficiency (Eq. 5; Supplementary Figure 1). Optimization of the
SNR requires sensors with YFRET ~ 0.5. In contrast, for the intensity based method SNR increases
monotonically with YFRET.
2pFLIM with fluorescent proteins has additional limitations. Since only a fraction of acceptors
have functional chromophores, the measured binding fraction will be underestimated. We measured the
fraction of functional acceptors as ~50% for mRFP or mCherry. For FRas and FRas-F the maximum
apparent binding fraction is ~60 % as shown in Fig. 2e. Thus, the binding fraction underreports the true
binding fraction by ~40%. Quantitative measurements of the binding fraction need to take this factor
into account.
Ras sensor
Several Ras sensors based on fluorescent proteins have been reported. The prototypical Ras sensor
Raichu consists of a fusion of ECFP, Ras binding domain of Raf (RBD), Ras (H-Ras without the CAAX
tail), and EYFP, with ECFP anchored in the plasma membrane using K-Ras CAAX tail {Mochizuki,
18
2001 #4}. When Ras is activated, the affinity between Ras and RBD increases, leading to binding and
increased FRET between ECFP and EYFP. However, Raichu was not suitable for 2pFLIM
measurements in small compartments because of the small signal upon Ras activation and dim ECFP
fluorescence. Also, the targeting of the sensor may not resemble endogenous Ras {Choy, 1999 #118}.
Translocation of RBD tagged with EGFP has also been used to measure endogenous Ras activation
{Bivona, 2003 #119; Chiu, 2002 #120}. However, this approach is not suitable for small neuronal
compartments where the membrane structure cannot be resolved. Similar to FRas, FRET between Ras
and RBD tagged with fluorescent proteins has also been used as a readout of Ras activation {Jiang, 2002
#117; Rocks, 2005 #32}. However, these non-mutated sensors would not be able to follow rapid Ras
signals observed in neurons (Eqs. 7, 8, Fig. 3c). By optimizing the signal and the kinetics of our sensor,
we have developed a reversible, high-sensitivity Ras sensor (FRas-F) optimized for 2pFLIM
measurements in small neuronal compartments. By tagging RBD with two mRFPs, we obtained ~20%
additional signal (Fig. 2e,f) and thus require ~ 40% fewer photons to obtain a significant signal-to-noise
ratio (Eq. 5). By reducing the affinity between Ras and RBD, the sensor became reversible with fast
decay kinetics (Fig. 3c).
For quantitative measurements of Ras dynamics, the affinity and free concentration of mRFPRBD-mRFP are important factors. This is analogous to quantitative calcium imaging with calcium
buffers {Neher, 1992 #198; Yasuda, 2004 #87} . High-affinity calcium indicators are sensitive to small
calcium changes. However, because the indicator reduces free [Ca2+], extrusion of calcium is inhibited,
slowing [Ca2+] dynamics. Similarly, overexpressing of high affinity mRFP-RBD-mRFP slows the decay
of Ras activation by reducing free [RasGTP] (Eqs. 7, 8). FRas and FRas-F are thus analogous to highaffinity and low-affinity [Ca2+] indicators, respectively. However, unlike the calcium indicator case,
19
overexpression of high affinity mRFP-RBD-mRFP does not reduce Ras activation, because the Ras
sensor measures the binding ratio of mEGFP-Ras directly.
Supersensitive Ras response in neurons
Calcium accumulation in dendrites and spines can activate diverse signaling pathways important for
synaptic plasticity such as long-term potentiation and depression {Kennedy, 2005 #122} and spine
morphogenesis {Wu, 2001 #16}. How can one second messenger be involved in diverse, often opposite
outcomes? Mechanisms such as the super-sensitive Ras activation may be one explanation. Since the
Ras signaling pathway shows little activation below a threshold of [Ca2+] ~ 1 M, there would be
minimal cross talk between Ras signaling and other Ca2+-dependent signaling activated at a lower [Ca2+].
Super-sensitive signaling can also produce sharp spatial boundaries between active and inactive
neuronal domains. Distal dendrites (more than ~150 m from somata), where most synapses are located,
showed little Ras activation with AP trains, presumably due to decremental AP back-propagation and
the super-sensitivity of the Ras pathway. This mechanism might be used to suppress the activation of
LTP pathways nonspecifically by suppressing activation of synaptic Ras.
How is the super-sensitivity achieved? In a Goldbeter-Koshland type switch the system can
become bistable when activators (GEFs) and deactivators (GAPs) regulating the activity of a protein
(Ras) saturate {Goldbeter, 1981 #107}. However, this scheme is not easily reconciled with non-zero
basal activity. Another possibility is cooperative calcium binding to Ras regulators. Nonlinear increases
in GEF activity or decreases in GAP activity could cause nonlinear Ras responses. Ras regulation by a
CaM-dependent GEF or GAP is one possibility {Krueger, 1998 #173; Pologruto, 2004 #116}. However,
our results suggest that CaM association is unlikely the mechanisms because blocking CaM association
with calmidazolium did not block Ras activation (Fig. 5g}. Thus, GEFs and GAPs dependent on CaM
20
association, such as RasGRF and synGAP, likely do not contribute to Ras activation evoked by APs.
However, it is possible that CaM is preassociated with a Ras regulator to control its activity {Erickson,
2001 #10}. Alternatively, CalDAG-GEFs, a family of GEFs regulated by directly binding Ca2+ through
a pair of EF hands{Ebinu, 1998 #202}, could contribute to non-linear Ras activation. Other GAPs
including p120GAP {Davis, 1996 #194}, RASAL {Walker, 2004 #192} and CAPRI {Liu, 2005 #193}
are translocated to the plasma membrane in a calcium-dependent manner to inactivate Ras. They could
suppress Ras responses selectively at low [Ca2+] to cause the apparent cooperativity in the [Ca2+] vs. Ras
activation relationship.
In our experiments with trains of back-propagating action potentials, an antagonists of L-type
calcium channels (Nimodipine) does not block [Ca2+] transients nor Ras activation (Fig. 5e,g). These
results are in conflict with previous studies that have shown selective coupling between L-type channels
and ERK activation {Mermelstein, 2000 #195; Dolmetsch, 2001 #15; Wu, 2001 #16; Dudek, 2002
#188}. There are several possible reasons for this inconsistency. First, Nimodipine may not block Ltype channels opened by action potentials, due to their state-dependent blocking kinetics {Helton, 2005
#201}. However, the trains of action potentials we used in our experiments (160 pulses total) should
block L-type calcium channels significantly {Helton, 2005 #201}. Second, FRas may not be targeted as
endogenous Ras, and may be insensitive to L-type calcium channel microdomains. This is unlikely
because L-type channels contributed to similar degrees to the activation of FRas and endogenous Ras
when the stimulus was membrane depolarization by KCl application (Fig. 3b,d). Third, most of the
studies showing L-type channel/ERK coupling use KCl application, while we use back-propagating APs.
The long depolarizations provided by KCl and postsynaptic potentials favors opening of L-type channels
{Mermelstein, 2000 #195}. Still, our results are inconsistent with a study showing L-type VSCCdependent ERK activation by AP trains {Dudek, 2002 #188}. This might be caused by differences in
21
stimulation pattern (theta burst versus regular train) or in the preparation (adult acute slices versus
cultured slices from young animals). Fourth, Ras activation may be not sufficient for ERK activation.
For example, opening of L-type channels may be important for blocking the activation of phosphatases
that inactivate ERK. Future experiments combining 2pFLIM with electrophysiological assays and [Ca2+]
imaging will help in dissecting the coupling between Ras-ERK activation and Ca2+ sources activated by
a variety of physiologically meaningful stimuli. Further experiments are required to dissect the coupling
between L-type channels and the Ras/ERK pathway under physiological conditions.
Methods
Constructs. mRFP {Campbell, 2002 #51}, Cerulean {Rizzo, 2004 #74}, EGFP-Ras {Ohba, 2000 #167}
and EGFP-RBD {Bondeva, 2002 #166} were gifted by Drs. R. Tsien (UCSD), D.W.Piston (Vanderbilt
Univ.), M. Matsuda (Osaka Univ.) and T. Bhalla (NIH), respectively. Monomeric EGFP (A206K)
{Zacharias, 2002 #53}, mCherry {Shaner, 2004 #52} were generated by mutagenesis from EGFP and
mRFP respectively. mEGFP-Ras and RBD-mRFP were constructed in a pCI expression vector
(Promega, Madison, WI). FRas consists of mEGFP-Ras (same as Ohba, 2000 except for A206K in
EGFP) and mRFP-RBD-mRFP. The linker between RBD and mRFP is YRSTMN for the N- terminus
and GILQSTVPRARNP for the C-terminus. mRFP-RBD-mRFP had a slight tendency to accumulate in
nucleus.
Preparations. HEK293 cells were cultured in DMEM/high glucose supplemented with L-Glutamine
(Invitrogen) in the presence of 10% fetal bovine serum (FBS), and transfected with mEGFP-Ras and
RBD-mRFP using Lipofectamine 2000 (Invitrogen, Carlsbad, CA). The molar ratio of mEGFP-Ras and
RBD-mRFP plasmids was 1:3 to 1:4. Our results showing slow reversal of FRas (Fig. 3b) suggest that
22
the acceptor (RBD-mRFP) concentrations were higher than the donor (mEGFP-Ras) concentrations.
After 1-2 days of transfection, cells were serum-starved in 0.2 % FBS overnight. Imaging was
performed in a solution containing 25 mM HEPS (pH 7.4), 114 mM NaCl, 2.2 mM KCl, 2 mM CaCl2, 2
mM MgCl2, 22 mM NaHCO3, 1.1 mM NaH2PO4 and 22 mM glucose at 24-25 °C. To activate Ras, 100
ng/ml epidermal growth factor (EMD Bioscience, San Diego, CA) was added to the solution.
Hippocampal slices were prepared from P6 or P7 rats as described {Shi, 1999 #172; Stoppini,
1991 #124}. After 1-2 weeks in culture, cells were transfected with ballistic gene transfer (Helios,
BioRad) {McAllister, 2000 #104}. Gold beads (diameter, 1.6 m; 6 mg) were coated with mEGFP-Ras
(8 g) and mRFP-RBD-mRFP (32 g). To minimize the effects of Ras overexpression on synaptic
efficacy, 8 mM MgCl2 was added (final concentration 10.8 mM) to the culture medium {Zhu, 2002 #56}.
After 3-4 days of expression, 2pFLIM imaging was performed in a solution containing 127 mM NaCl,
2.5 mM KCl, 4 mM CaCl2, 4 mM MgCl2, 25 mM NaHCO3, 1.25 mM NaH2PO4 and 25 mM glucose
aerated with 95% O2/5% CO2 at 24-25 °C. To activate Ras with KCl the extracellular solution was
exchanged to 62.5 mM KCl, 67 mM NaCl, 4 mM CaCl2, 4 mM MgCl2, 25 mM NaHCO3, 1.25 mM
NaH2PO4 and 25 mM glucose. To block synaptic receptors, NBQX 5 M and D-CPP 5 M were
included.
Instrument response and curve-fitting. In time-domain lifetime measurements, the fluorescence decay
curves are convoluted with the pulse response function of the microscope (PRF), also called the
instrument response. Assuming a Gaussian PRF (Fig. 1b) and an exponential fluorescence decay curve,
the measured function can be expressed as:
FSingle(t )  P  G (t , t0 , D , G )  F0
 2
1
t  t0    G2   D (t  t0 ) 
,
 erf 
exp  G 

2
2


2


D 
 D
D G


(Eq. 9)
23
where G is the convolution of the Gaussian PRF with standard deviation G and an exponential decay
with constant D. t0 is the time offset. The width of the PRF is usually dominated by the transit time
spread (TTS) of the photo-detector. The TTS of typical photomultiplier tubes (PMT) used for
fluorescence microscopy are in the nanosecond range, comparable to the fluorescence decay time. These
broad PRFs degrade the resolution of the lifetime measurement and it is thus important to choose a PMT
with small TTS (< 0.5 ns). Multi-channel plate PMTs have very short TTS (~ 30 ps), but they are
expensive and easily damaged by moderate light levels (~107 photons /s). As a compromise we use a
cooled GaAsP PMT (Hamamatsu H7422-40) with an acceptable TTS (~0.3 ns FWHM), which also has
other favorable properties (e.g. quantum efficiency ~ 40%; gain > 106).
To determine the fluorescence decay one needs to determine the PRF (Eq. 9). This can be
achieved by directing the pulsed laser directly into the PMT (Fig. 1b). Note that because of the low
sensitivity of the PMT in the Ti:sapphire laser wavelength regime (700-1000 nm) uncomfortably high
excitation light is required . We therefore did not use this method routinely. Instead, we measured a
fluorescence decay curve for GFP, which decays with a single exponential time-course {Pepperkok,
1999 #35}. We fit the fluorescence decay with Eq. 9 to obtain the width of the Gaussian (G) as 0.12 –
0.16 ns (depends on applied voltage etc.) as well as the fluorescence lifetime of mEGFP as 2.59 ± 0.01
ns. Because the noise is proportional to the square root of the number of photons, our fitting routines
minimized dt·F2)/F, where F is F subtracted by the theoretical curve.
Quantifying protein binding in living cells. One of the key advantages of fluorescence lifetime
measurements is that one can quantify the fraction of donor bound to acceptor (binding fraction, PDA)
independent of YFRET. The fluorescence decay needs to be fit with two exponentials with time constants
24
AD and D, corresponding to donor bound to acceptor and free donor. In addition the PRF (width G)
needs to be taken into account:
FDouble(t, t0 , D , G , YFRET , P1 , P2 )  F 0 PD  G(t, t0 , D , G )  PAD  G(t, t0 , AD , G )
(Eq. 10).
In practice it is useful to constrain the fit by determining D, G, AD independently under favorable
conditions. We measured these parameters in HEK293 cells transfected with FRET sensors. To improve
the stability of the fits we determined D and G separately in the absence of acceptor using Eq. 9. The
remaining parameters, t0, F0, PDA (=1-PD), and AD were obtained from measurements in the presence of
acceptor. The fitting is still unstable when PDA is small. Therefore, when we measured these parameters
for Ras sensors, we used constitutively active mutants (G12V) to maximize PDA. The offset arrival time
t0 is highly sensitive to the position of the sample (3 mm = 0.01 ns) and thus should be determined by
fitting using the fluorescence decay averaged over all pixels in the image.
Although fluorescence lifetimes measure the fraction of donor bound to acceptor independent of
protein concentrations, the binding itself is a function of the concentration of donor and acceptor as well
as their affinity. In our experiments we took care to express an excess of acceptor. As a membrane
targeted protein the donor (Ras-mEGFP) had a tendency to be expressed at lower concentrations than
the cytoplasmic acceptor RBD-mRFP. In fact, when we used FRas (i.e. wild type RBD-mRFP), the
sensor was only slowly reversible, indicating that the majority of Ras was bound to RBD. Furthermore,
for the quantitative measurements, we used the linear regime where the Ras sensor is not saturated (Fig.
4e).
Generation of color coded images Since curve fitting is time consuming it is useful to develop
alternative estimates of the fluorescence lifetime to generate color-coded images. The fluorescence
25
lifetime < averaged over multiple populations is derived form the mean photon arrival time <t> as
follows
 dt tF (t )  t
 dt  F (t )
   t  t o 
o
.
(Eq. 11)
The theoretical value for the average of multiple populations (with decay time constant i and fraction Pi
for ith population) is given by
 dt t  P  exp( t /  )   P
  ~
 dt  P  exp( t /  )  P
i
i
i
i
2
i i
i
i
i
i
i i
.
(Eq. 12)
The mean lifetime was used to generate fluorescence lifetime images (Figs. 2b, 3a, 4a).
GTP-Ras Pull-down Assay. Individual hippocampal slices were incubated at room temperature in
ACSF for 30 minutes followed by stimulation with 62.5 mM KCl in ACSF containing 10M NBQX,
5M CPP, and 1M TTX. Slices were homogenized by brief sonication in cold buffer containing 25
mM HEPES (pH 7.5), 150 mM NaCl, 1% NP-40, 0.25% Na deoxycholate, 10% glycerol, 10 mM
MgCl2, 1mM EDTA, 10g/mL leupeptin, 10g/mL aprotinin, 0.2 mM PMSF. GTP-bound Ras was
precipitated from the lysate by incubation at 4C for 1 hour with purified GST-RBD protein coupled to
glutathione-sepharose beads {de Rooij, 1997 #2} . Aliquots of whole cell lysate and precipitates of
GTP-bound Ras were analyzed by immunoblotting with anti-Ras antibody (1:1000; BD Biosciences)
and goat anti-mouse secondary antibody conjugated with Alexa Fluor 680 (1:2000; Molecular Probes).
Blots were imaged using the Odyssey Infrared Imaging System (Licor), and fluorescence intensities
were obtained using the associated software. Precipitated GTP-bound Ras immunoreactivity was
normalized to total Ras immunoreactivity from the whole cell lysate.
26
Electrophysiology. Perforated patch clamp was performed as described {Bi, 1998 #102} with a patch
pipette including K gluconate 136.5 mM, KCl 17.5 mM, NaCl 9 mM, MgCl2 1 mM, HEPES 10 mM (pH
7.2), EGTA 0.2 mM and amphotericin B 0.1-0.3 mg/ml. Resulting series resistance was typically 40-200
M. Action potentials were produced by injecting current (2-6 nA, 4 ms). The relation between Ras
activation (Ras) and number of AP train (NAP), frequency (f) or [Ca2+] was fitted by Michaelis-Menten
curve,
Ras  Constant
1
1  ( X / X 50 ) H
(X = NAP, f or [Ca2+]).
(Eq. 13)
where H is Hill coefficient and X50 is X at which Ras is half saturated.
Calcium imaging. Calcium imaging was performed as described {Yasuda, 2004 #87} using 167 M
Fluo-4FF (green) and 10 M Alexa 594 (red). The ratio between the change of green fluorescence (G)
and red fluorescence (R) was used to quantify [Ca2+] transient ([Ca2+]) as
[Ca 2 ] 
G/R
K D ,Fluo4FF
(G/R) max
(Eq. 14)
where the (G/R)max is the ratio at saturated calcium me assured in a pipette including 10 mM CaCl2, and
KD,Fluo4FF is dissociation constant of Fluo4-FF (10 M) {Yasuda, 2004 #87}.
Acknowledgements
We thank A. Karpova for mutagenesis of mEGFP, X. Zhang for preparation of cultured slices, R. Tsien
(UCSD), D.W.Piston (Vanderbilt Univ.), M. Matsuda (Osaka Univ.) and T. Bhalla (NIH) for plasmids,
A. Karpova and V. Iyer for critical reading of the manuscript. The research is supported by HHMI, NIH,
Nystar, BWF (RY), NARSAD (HZ), and a David and Fanny Luke Fellowship (CH).
27
Figure legends
Figure 1. 2-photon fluorescence lifetime imaging microscopy (2pFLIM) system.
(a) Wiring diagram of our 2pFLIM system. We have developed 2pFLIM based on ScanImage
{Pologruto, 2003 #54} (http://svobodalab.cshl.edu/software_main.html) and a commercial time
correlated single photon counting (TCSPC) imaging board (SPC-730, Becker & Hickl, Berlin).
ScanImage uses a data acquisition board (PCI-6110, National Instruments) to control scan-mirrors and
to acquire fluorescence signals from photomultiplier tubes (PMTs; R3896, Hamamatsu, Bridgewater,
NJ). An additional board (PCI-6713, National Instruments), running on the same clock as the PCI-6110
board, was used to generate frame and line clocks to synchronize ScanImage and the SPC-730 board.
The lifetime decay curve was measured comparing the time of laser pulses (Ti:sapphire laser, 80 MHz,
MaiTai, Newport, Irvin, CA) detected by a photodiode (FDS010, Thorlabs, North Newton, NJ) and
photon pulses from a fast PMT (H7422-40, Hamamatsu). To selectively image mEGFP, we used a
bandpass filter (HQ510/70, Chroma) in front of the PMT. The epi-fluorescence PMT was used for
2pFLIM, and the trans-fluorescence PMT for ScanImage {Mainen, 1999 #200}.
(b) Instrument response function (open circle) and an overlaid Gaussian fit (grey; G = 0.126 ns).
(c) Fluorescence lifetime curve of mEGFP. The measured fluorescence curve (open circles) is the
convolution of the fluorescence lifetime curve (red; D ~ 2.59 ns) and the instrument response function
(grey; see b; G ~ 0.146 ns). The fluorescence curve was fitted using Eq. 9 (Methods, black curve).
Figure 2. FRET sensors for Ras activation (FRas) in HEK293 Cells.
(a) Schematic of FRas. mEGFP was tagged to Ras at the N terminus and two mRFPs were attached to
RBD at N and C termini.
28
(b) Fluorescence lifetime images in HEK293 cells transfected with FRas, before and after application of
EGF (color coding according to Eq. 11, Methods).
(c) Fluorescence lifetime decay curves corresponding to (b).
(d-e) The FRET efficiency (d) and binding fraction (PAD) (e) between constitutively active Ras mutants
(V12G) fused with GFP co expressed with: left, RBD fused with mRFP on the C-terminus only; middle,
N-terminus only; right, N&C terminus. FRET efficiency and binding fractions were calculated from Eq.
10 (Methods).
(f) Fraction of donor bound to acceptor before and after application of EGF. Error bars indicate the
standard errors over 4-10 fields from 1-2 dishes. Donor: mEGFP was tagged to wild type Ras (WT),
constitutively active Ras (G12V), or constitutively inactive Ras (S17V). Acceptor: mRFP was fused to
N-terminus of RBD (N), C-terminus of RBD (C) or both (N&C). Wild type RBD (WT) or RBD mutants
with low affinity to Ras (R59A) were used.
Figure 3. Ras activation in the apical dendrites of CA1 pyramidal neurons in response to depolarization.
(a) Fluorescence lifetime images before and after application of KCl.
(b) Time-course of Ras activation measured as the fraction of mEGFP-Ras bound to RBD-mRFP,
(binding fraction; 7-8 cells each) (Nimo, 20 M Nimodipine).
(c) Activity of endogenous Ras. Western blot of Ras pulled down with GST-RBD beads (PD) and in the
lysate (Lys). The numbers indicate the time after KCl application (minutes). KCl was removed at 10 min.
(d) Time course of endogenous Ras activity (5-8 slices for each time-point).
Figure 4. Action potential-evoked Ras activation in proximal apical dendrites. The stimuli (arrows)
consisted of four sets of AP trains, 40pulses each, repeated at 5 sec intervals.
29
(a) Fluorescence lifetime image of Ras activation in a dendrite transfected with FRas-F.
(b) Time course of Ras activation in spines (colored code as in (a)) and dendrites (black).
(c) Ras in dendrites can be activated repeatedly with AP trains (4 sets of AP trains with 83Hz, 40 pulses).
(d) Time course of Ras activation in response to different number of AP trains (83 Hz 40 pulses).
(e) Ras activation as a function of number of trains (normalized to Ras activation with 4 AP trains).
Error bars are SEM, 5-8 neurons. The fit is a Michaelis-Menten curve (Eq. 13, Methods) with Hill
coefficient H = 1. The number of AP trains for half-saturation was calculated from the fit as ~ 8. The
vertical axis was re-normalized to the saturation value obtained from the fit.
(f) Time course of Ras activation in response to trains of action potentials with different frequencies
(number of APs was fixed per stimulus).
(g) Relationship between frequency and Ras activation (normalized to Ras activation at 83 Hz). Thin
colored lines are from different cells. Each curve was fit to Eq. 13 (Methods), obtaining the half
saturation frequency as f50 = 44 ± 12 Hz and the Hill coefficient as H = 4.9 ± 0.9 (N=7). The thick line
shows the best fit with f50 and H averaged across cells.
Figure 5. Coupling between Ras activation and [Ca2+].
(a) [Ca2+] transient (top) produced by a train of action potentials (bottom) measured using the low
affinity indicator Fluo4-FF. The shutter was closed during the train (dashed line) to reduce photodamage.
The amplitude of the [Ca2+] transient was measured at the end of the AP train.
(b) Amplitude of [Ca2+] transients as a function of AP frequency. Linear regression lines (transfected,
slope 0.0197 M/Hz; untransfected, 0.0206 M/Hz) are also shown.
(c) Ras activation (normalized to Ras activation at 83 Hz) as a function of the amplitude of [Ca2+]
transients. [Ca2+] transients were changed by changing the AP frequency (Freq; blue circles),
30
extracellular [Ca2+] (Ca; red circles), Nimodipine (Nimo; green circles) or Nickel (Ni; cyan circles). The
fit to the frequency modulation data gave [Ca2+]50 = 0.9 M and H = 3.1 (Eq. 13).
(d) Time course of Ras activation in response to trains of action potentials (APs) in different
concentration of extracellular [Ca2+]. Each stimulus (arrows) consisted of 40 APs at 83Hz, repeated 4
times with 5 second inter-stimulus intervals.
(e) Amplitude of [Ca2+] transients (relative [Ca2+]) in the presence of 20 M Nimodipine (Nimo), 250
M NiCl2 (Ni), cocktail of 10 M -Conotoxin MVIIC and -Conotoxin GVIA (Ctx) or 300 M
CdCl2 (Cd). The conotoxin cocktail was applied by pressure injections into the brain slice through
pipettes placed close to patched cells. Each stimulus (arrows) was similar as in (d), except that the
frequency was varied as indicated.
(f) Time course of Ras activation in response to AP trains before and after a NiCl 2 application (gray bar).
(g) Ras activation in the presence of calcium channel blockers (Nimo, Ni), or calmodulin blocker, 50
M calmidazolium (CMZ) relative to control condition.
31
References