Transient state imaging of live cells using single plane illumination and arbitrary duty cycle excitation pulse trains.

J. Biophotonics 1–9 (2014) / DOI 10.1002/jbio.201400015

Journal of

BIOPHOTONICS FULL ARTICLE

Transient state imaging of live cells using single plane illumination and arbitrary duty cycle excitation pulse trains Jonas Mu¨cksch**, Thiemo Spielmann**, Evangelos Sisamakis, and Jerker Widengren* Experimental Biomolecular Physics, Department of Applied Physics, Royal Institute of Technology, Albanova University Center, 106 91 Stockholm, Sweden Received 6 February 2014, revised 14 March 2014, accepted 19 March 2014 Published online 7 April 2014

Key words: fluorescence, single plane illumination microscopy, transient state imaging, triplet state, redox state, live cell imaging

Supporting information for this article is available free of charge under http://dx.doi.org/10.1002/jbio.201400015 We demonstrate the applicability of Single Plane Illumination Microscopy to Transient State Imaging (TRAST), offering sensitive microenvironmental information together with optical sectioning and reduced overall excitation light exposure of the specimen. The concept is verified by showing that transition rates can be determined accurately for free dye in solution and that fluorophore transition rates can be resolved pixel-wise in live cells. Furthermore, we derive a new theoretical framework for analyzing TRAST data acquired with arbitrary duty cycle pulse trains. By this analysis it is possible to reduce the overall measurement time and thereby enhance the frame rates in TRAST imaging. Transient state imaging with single plane illumination allows fluorophore transition rates related to dark states to be spatially resolved in the plane of excitation.

1. Introduction In fluorescence spectroscopy and imaging, longlived, photo-induced, dark states, such as triplet, redox and isomerized states of the fluorophores, are often considered undesirable. They reduce the fluorescence signal [1, 2] and may act as precursor states for photobleaching [3, 4]. However, in recent years

these transient states have been found to be useful as a basis for high-resolution microscopy [5–7] and they hold great potential for environmental sensing purposes [8]. The fluorescence lifetime of a fluorophore is in the range of ~109 s, whereas the transient dark states typically decay within ~106 103 s. Consequently, dyes in such states have orders of magnitude longer time to interact with their environ-

* Corresponding author: e-mail: [email protected], Phone: +46855378030 ** The authors contributed equally.

# 2014 by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

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ment, compared to dyes in the excited singlet state. Still, the use of long-lived transient states as readouts in biomolecular research has to date been relatively limited. Different methodologies to quantify transient states and their kinetics exist. Transient state absorption techniques allow detailed investigations of a broad range of photoinduced dark states [9–12]. However, these techniques usually require advanced equipment, micromolar sample concentrations, suffer from overlap of spectra from different dark states, and are typically restricted to cuvette measurements. Triplet states can be observed via phosphorescence intensity and lifetime measurements [13], albeit the achievable signal intensity is usually considerably lower than in fluorescencebased studies. In addition, triplet states are often subject to quenching, especially by oxygen. Consequently, deoxygenation of the samples is usually required [14], or engineered macromolecules with limited quenchability need to be used [15]. Fluorescence Correlation Spectroscopy (FCS) is a versatile tool to investigate dark state dynamics, combining the high detection sensitivity of the fluorescence readout with the environmental sensitivity of long-lived states [16–19]. However, FCS experiments are limited to nanomolar concentrations, and to relatively bright and photostable dyes. Moreover, since triplet state investigations require high temporal resolution and quantum yield of detection, FCS measurements are mainly restricted to single point detectors. To circumvent these limitations, we recently developed Transient State (TRAST) imaging, which also measures the dynamics of photoinduced dark states by using fluorescence as a read-out parameter [20]. In TRAST, the fluorescent sample is subject to a modulated excitation. Depending on the excitation pulse train characteristics (e.g. pulse duration, separation and height) long-lived photo-induced states (e.g. triplet, photo-isomerized and photo-ionized states) of the fluorophores in the sample are populated to different extents. Upon transient state population buildup in the sample, the fluorescence intensity decreases. By systematically varying the excitation pulse train characteristics, and registering how the time-averaged fluorescence intensity changes, the population kinetics of the transient states can be retrieved. In contrast to FCS, TRAST does not rely on fluorescence detection with high sensitivity and time resolution, or on a certain dye concentration in the sample. TRAST is thus widely applicable and enables imaging of triplet and other dark state kinetics also in a massively parallel fashion, using a CCD with low temporal resolution for detection. So far, TRAST imaging has been performed in regular and laser scanning confocal [20, 21], widefield [22, 23] and total internal reflection [24] mode. TRAST measurements require that a significant population difference in the monitored transient states


J. Mu¨cksch et al.: TRAST imaging of live cells by SPIM

is generated upon variation of the excitation pulse train characteristics. For standard rhodamine dyes in air-saturated aqueous environments, significant triplet state population requires excitation irradiances in the range of 100 kW/cm2 [21, 22]. Doses following from such high irradiation levels can easily exceed hundreds of J/cm2 and are likely to be toxic to cells [25]. In this work, we use dyes with high triplet state yields so that the irradiances necessary can be reduced by several orders of magnitude. Second, for both wide-field and confocal setups, the excitation beams are not confined: all heights of the sample are illuminated, although fluorescence collection only takes place in certain positions. The exposure can be reduced significantly by illuminating only the plane, from which fluorescence is actually detected, using Single Plane Illumination Microscopy (SPIM) [26]. In this approach, an asymmetric excitation profile with sheet-like character is created [27, 28]. Within the last decade, the interest in SPIM and other sheet-like illumination techniques has increased considerably. Major applications concern three-dimensional and high-resolution fluorescence imaging [27, 29–32], but also the combination of SPIM and FCS [33–35]. In this work, we introduce SPIM with timemodulated excitation for TRAST imaging, thereby providing an additional means to reduce the excitation light exposure of the sample during transient state measurements. Moreover, we derive the theoretical framework for analyzing SPIM-TRAST data efficiently and extend the theory to arbitrary duty cycles of the excitation scheme. We demonstrate the applicability of SPIM to TRAST imaging, first by solution measurements, then by imaging of live cells, thereby further extending the range of fluorescence-based methods for quantitative biomolecular imaging.

2. TRAST model The model used comprises a singlet ground state jS0 i, an excited singlet state jS1 i, a triplet state jTi and a redox state jRi (Figure 1, top, middle graph). In general, several different redox states are conceivable, but in many cases, a single redox state properly describes the observations [24, 36]. The probability of each state to be populated at a time t reads S0(t), S1(t), T(t) and R(t), respectively. The excitation and de-excitation rates between the ground and the first excited singlet state are denoted by k01 and k10, kisc and kt represent the rates of intersystem crossing and triplet decay back into jS0 i. The redox state jRi is populated and depopulated with the rates kox and kred. This notation suggests that jRi always represents an oxidized intermediate. How-

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Figure 1 Setup and measurement principle for SPIM-TRAST imaging. A pulse train (pulse width: tp duty cycle: h) is created by an Acousto-Optic Modulator (AOM) from the emission of a CW laser. The use of a cylindrical lens results in a sheet-like excitation profile in the sample. The fluorophores in the sample are described by a simplified, electronic four-state model, comprising two singlet, one triplet and one redox state. The detection takes place perpendicularly to the excitation sheet and the image is detected on an EMCCD chip. For each pulse train, with a certain width of the pulses, tp, one image, representing the total detected fluorescence, is acquired. When plotting this signal as a function of tp, as used in the excitation schemes, a TRAST curve (bottom left) is obtained in each and every pixel (L1, L2, L3 lenses, DM: Dichroic Mirror).

ever, it may also be a reduced product (see e.g. [37]), then with different names of the rate variables. The measurements in this work were performed with rectangular pulse trains, and mostly with low excitation duty cycles (h 1%). This ensures that almost all fluorophores are relaxed back to their ground state at the onset of each pulse in the pulse train. Simulations (see Supporting information) and experimental data (see Results and Discussions) further validated this approach. For a fluorophore described by the electronic four-state model, S01TR, depicted in Figure 1, the average fluorescence intensity in response to N excitation pulses of low duty cycle reads [24]: ! Ð p P jTþt k01 k10 1 N1 hFS iNtp ðtp Þ ¼ FF S1 ðtÞ dt k01 þ k10 Ntp j¼0 jT ¼

k0 F0 1 1 el1 tp þ isc ð1 el2 tp Þ t p l1 l22 ! kt k0ox kt kred l3 tp ð1 e Þ þ tp l2 l3 l2 l23


ð1Þ

A detailed derivation of hFS iNtp is included in Eqs. (S1–S17) in the supporting material. Here, tP denotes the pulse width of the excitation pulses, and the eigenvalues of the transition matrix between states have been approximated by: l1 ¼ ðk01 þ k10 Þ, l2 ¼ ðk0isc þ kt Þ and l3 ¼ ðk0ox þ kred Þ, with the equivalent intersystem crossing and oxidation rak01 ð rÞ tes given by: k0isc ¼ kisc and ðk01 ð rÞ þ k10 Þ 0 k k0ox ¼ kox 0 isc . The fluorescence emitted by a ðkisc þ kt Þ single fluorophore in the sample in the absence of k10 k01 any dark state population reads: F0 ¼ FF , k10 þ k01 where FF represents the fluorescence quantum yield. For dyes with moderate to low triplet yields, the two singlet states can be merged to one effective, instantly equilibrated state (STR model, Figure S1). This model results in the same expression as in Eq. (1), but without the term related to l1 Eq. (S16). Equation (1) relies on the assumption that the fluorophore is relaxed back to its ground state at the onset of each pulse of a pulse train. However, in some


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measurements it may be beneficial to decrease the acquisition times by increasing the duty cycle. Therefore, we derived an expression for the mean emitted fluorescence upon excitation by pulse trains of arbitrary duty cycles. The detailed derivation, based on propagation matrices, is presented in the supporting material.

using the optical setup described in the supporting material. Each of the curves presented in Figure 2A corresponds to the mean over an area of 40 18 mm2 around the center of illumination. Figure 2 C and D show the intersystem crossing rates, kisc, and the triplet decay rates, kt, obtained by separately fitting the experimental TRAST curves recorded in each pixel at different excitations I0 using Eq. (S16) (equivalent to Eq. (1), except that it is based on the simpler STR model). Both kisc and kt are constant within the standard deviations, indicating that the fitting model and the assumed excitation rate distribution are suitable. Consistent results within the fitting area are important to be able to resolve local differences in kt within cells in in vivo measurements. Under isotropic conditions, the obtained distributions of kisc and kt values

3. Results and discussion 3.1 Free dye in solution We recorded TRAST curves from Rhodamine 110 (Rh110) at different peak excitation irradiances, I0,

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Figure 2 TRAST measurement data from free Rh110 in solution, recorded at different excitation irradiances, with peak irradiances I0, and with an excitation modulation duty cycle of 1%. (A): Representative TRAST curves () showing the normalized average fluorescence intensity over a rectangle of 40 18 pixels, each 1 1 mm2 large, and the variation of this intensity with the excitation pulse width. For every I0, the TRAST curve obtained for each pixel is fitted according to Eqs. (S15) and (S19–S20) and the obtained average fitting curves () approximate well the measurement data as the residuals appear to be randomly distributed and do not reveal any systematic deviations. (C, D, E, F): From every fitted TRAST measurement, a set of four rates is extracted. The means and standard deviations of the rates within the mentioned region are shown. The measurements were for several powers, with the peak irradience I0 in the center of excitation ranging from I0 ¼ 11 kW cm2 taken 2 to I0 ¼ 33 kW cm (for clarity not all of these measurements were shown as TRAST curves in (A)). (B) All standard deviations for kisc and kt over the 720 pixels are around 10% of their means, or lower. A representative histogram for a measurement at I0 ¼ 24 kW cm2 is provided, showing distinct peaks in the distributions of kisc and kt.




have standard deviations around 10% of their means, which we expected to be sufficient to resolve local differences of kisc and kt of a corresponding size in cellular measurements. The power dependence of the standard deviations is attributed to the higher fluorescence brightness and the higher contrast of the TRAST curves for larger irradiences. For the transitions into and out of the redox state, an irradiance dependence is discernible, in particular in Figure 2E. This may be caused by higher excited states, which are more likely to be populated at increasing photon densities, and which also show a stronger yield of redox state formation [19, 38]. Those transitions are not accounted for within the applied model and the fitting procedure may therefore yield increasing kox with higher I0. However, apart from Rh110, no increase in oxidation rate could be seen for the other dyes tested in this study (Figure S5D, I, N). These dyes were excited at considerably lower irradiances and therefore the population of higher excited states was less probable. The exact determination of redox rates for freely diffusing fluorophores would require consideration of diffusion through the detection volume, as both dynamics take place on similar time scales. Absolute values of kox and kred are therefore not accessible within the applied fitting model. Nevertheless, as previously shown [24], our model allows relative changes in redox parameters to be followed. We conducted similar experiments on several organic fluorophores (Figure S5). The obtained transition rates are provided in Tables 1 and S2. All outcomes for SPIM-TRAST imaging were reproduced by confocal TRAST measurements and were in good agreement with literature values, when available. Taken together, the results from the solution measurements on several dyes and at many different I0, indicate that the presented approach using SPIMTRAST can properly access the transition rates between the transient dark states of fluorophores.

3.2 Acquisition with arbitrary duty cycle pulse trains Next, we evaluated under what conditions in the TRAST measurements the assumption is valid, that all molecules are in their ground state at the onset of an excitation pulse. First, simulations were performed following an STR model (Eqs. S23–S24), assuming commonly found parameters for Rh110. The simulations show that the total dark state population at the beginning of each pulse indeed is small for a duty cycle of 1% (Figure S3). This was then experimentally verified by SPIM-TRAST measurements on Rh110 in aqueous solution, acquired with differ-


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Table 1 Triplet transition rates for Rh110, Fluorescein, Atto 495 and Eosin Y in PBS (pH 7.4). The measurements were performed in SPIM-TRAST (720 pixels) and confocal (Conf) TRAST mode (12 measurements). All error bars correspond to the standard deviations. If found, reference values are provided. The cited values for Fluorescein were measured in Tris buffer (pH 8.2), whereas the TRAST measured have been performed in PBS at pH 7.4. However, for both solvents, the majority of Fluorescein molecules is expected to be deprotonated. kisc [ms1 ]

kt [ms1 ]

Rh110

SPIM-TRAST Conf TRAST Reference

1.05 0.11 1.18 0.06 1.3 0.1 [39] 1.0 0.1 [39] 1.2 0.2 [24]

0.37 0.04 0.33 0.02 0.49 0.03 [39] 0.45 0.04 [39] 0.37 0.02 [24]

Fluorescein


9.29 0.55 10.30 0.60 13.5 1.0 [39] 11.1 1.0 [39]

0.58 0.03 0.42 0.03 0.66 0.04 [39] 0.45 0.04 [39]

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33.64 4.06 36.43 2.39

1.12 0.13 0.95 0.05

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746 34 0.48 0.01 757 79 0.42 0.02 420 50 [40] 840 80 [41]


ent duty cycles h (Figure 3). The experimental TRAST curves were analyzed based on the electronic state model in Figure S1, now using the solution to the system of differential equations derived where no constraints on h are applied Eqs. (S23–S24). The obtained kisc and kt were in good agreement with the results shown in Figure 2. As the computational effort to process the data is considerably higher compared to the previous section, a smaller area of 19.5 7.5 mm2 and larger pixel sizes (1.5 1.5 mm2 ) were analyzed. For Rh110 the mean rates and standard deviations over this area were determined to kisc = (1.03 0.07) ms1 and kt = (0.35 0.01) ms1. From the rate parameters in Figure 3, no trend is visible between the obtained kisc and kt values and the applied duty cycle. These results validate the newly derived model for fitting TRAST data, acquired at arbitrary duty cycles. Second, this new model allows to decrease measurement times significantly without loss of precision. The gain in measurement times comes however at the cost of longer analysis times. Moreover, our findings show that the previously used assumption of complete fluorophore relaxation to the ground state at the onset of each pulse does not affect the obtained values of kisc and kt for measurements with low duty cycles. The rates related to the redox state reveal however a dependence on the duty cycle. For this, different causes are conceivable. In particular, the contrast


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Figure 3 Data from TRAST measurements on free Rh110 in solution, acquired with different duty cycles, h, of the excita tion pulse trains and at an excitation irradiance of I0 ¼ 33 kW cm2. All values depicted in the TRAST curves of (A) correspond to the mean fluorescence averaged over a 65 pixels large area, each of them 1.5 1.5 mm2 big. The TRAST curves were fitted pixel-wise according to the newly derived model (Eqs. S23–S24). The residuals indicate that the model accurately represents the measurement data. (B, C, D, E): The mean and standard deviations of the determined rate parameters within the mentioned region are shown for each of the TRAST curves in (A), recorded at different h of the excitation pulse trains.

in the TRAST curves decreases with increasing duty cycle, causing less certainty in the outcome. Moreover, as the duty cycle increases, the pause between two pulses decreases. Therefore the diffusionmediated recovery depends on the duty cycle. This effect is not taken into account within the presented theory. Nevertheless, as discussed in the previous section, we can, with the model used in this work, expect to resolve relative spatial differences in the redox rates.

3.3 Live cell SPIM-TRAST imaging SPIM-TRAST imaging was applied to MCF-7 cells, homogeneously labeled with CEDA-SE or CFDA-SE (see the methods and materials section in the supporting material for details). These measurements were analyzed in a different manner from the solution data. First, the excitation rate was determined by a global fit, as described in the supporting material Eqs. (S18–S20). Then, the TRAST measurements on cells were fitted in a pixel-wise manner using the STR model Eq. (S15) for CFDA-SE. For CEDA-SE an S01TR model was applied (Eq. (1)), because this dye already shows triplet build-up before both singlet


states are equilibrated. As in [22], the amount of free parameters was decreased to obtain a robust fit at the decreased signal-to-noise ratio (SNR) experienced in live cell imaging. For the Eosin derivate CEDA-SE, we assumed kisc = 120 ms1 and kred = 0.01 ms1, and kept only kt and kox as free parameters. The chosen kisc value showed the best overall agreement between experimental data and the fits. Moreover, epi-fluorescence wide-field TRAST measurements on CEDASE stained MCF-7 cells supported this assumption [23]. The whole decay of the normalized mean fluorescence due to build-up of the R state could not be followed in the TRAST curves within the maximally applicable pulse widths, because of pronounced photobleaching. Therefore, kred was fixed to a low boundary to increase the stability of the fitting procedure. The lower kisc of CEDA-SE when bound to proteins, as compared to Eosin Y in solution (Table 1), may be caused by O2 shielding effects and viscosity changes of the protein environment. Fluorescence lifetime imaging microscopy (FLIM) also yields faster fluorescence decay rates (1 tf ¼ k10 þ kisc ) for Eosin Y, as compared to protein bound CEDA-SE (Table S1). This further supports the view that kisc is indeed higher for Eosin Y in solution. Although the assumed rates may introduce a bias in the fitted kt and kox values, the capability of imaging relative changes be-



tween different measurement conditions or local differences within a cell is maintained. The same arguments hold for CFDA-SE, for which we assumed kisc = 10 ms1 and kred = 0.05 ms1. A representative MCF-7 cell stained with CEDA-SE is depicted in Figure 4. An almost homogeneous labeling was achieved (Figure 4A). The TRAST curves acquired in each of the pixels representing the cell were fitted separately. The residuals of the fit were slightly larger than in the solution measurements, which could be due to small cell movements, or the fixed rates possibly deviating from the real values. Nevertheless, the TRAST curve approaches the measurement data properly, and allows for the spatially resolved determination of kt as shown in Figure 4B. The average and standard deviation of kt over the pixels of the cell in Figure 4B read kt ¼ ð91:0 10:2Þ ms1 . The kt image (Figure 4B) shows gradients, with faster rates being measured in the cell center, a feature found for practically all measured cells. No obvious spatial relation between the fluorescence image and the obtained ktimage can be noticed, indicating that the kt-image does not seem to be biased by the local fluorescence

Figure 4 (A–C): Representative SPIM-TRAST measurement on a CEDA-SE stained MCF-7 cell. The excitation was performed with a peak irradiance I0 ¼ 0:8 kW cm2 and all fits are based on an S01TR model (Eq. (1)). (A) shows the fluorescence image of the cell, (B) the kt-image and (C) the kox-image. All scale bars correspond to 8 mm. (D): The acquired average TRAST curve (blue) over the whole cell is adequately fitted by the proposed model (red). (E): The 2D-histogram of the kt and kox values from (B) and (C) indicates a correlation between both rates.


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intensity levels. Instead, the kt rates can be expected to be proportional to the local concentration of molecular oxygen, which is a predominant triplet state quencher [17]. Although a direct translation of measured kt into absolute local concentrations of oxygen is difficult, recent TRAST data have also shown that for cultured cells grown under different partial oxygen pressures the average kt rates in the cells depend linearly on this partial oxygen pressure [23]. In contrast, the kox-images (Figure 4C) revealed patterns much more influenced by noise. This is partly a result of the maximum pulse width being shorter than the full redox decay in the TRAST curves. Moreover, due to the large triplet yield, CEDA-SE mainly populates its triplet state upon excitation, and thus has its strengths primarily for the investigation of triplet states. The mean rate and the standard deviation over the cell in the kox image in Figure 4C were determined to kox ¼ ð0:4 0:2Þ ms1. To distinguish between reasonably fitted and noise governed outcomes, the starting value of kox in the fitting procedure of the experimental data was set to values below the detectable limit (1 s1 ). After fitting, a distinct peak in the distribution of kox could be separated from the lower threshold, and all determined oxidation rates, which were equal or lower than this starting value, were disregarded (see Figure S6 A for discussion). We further analyzed each cell using kox –kt 2Dhistograms. The obtained kt distribution in Figure 4E is broader than in the case of solution measurements (Figure 2) where a comparable amount of pixels is considered. As a clear spatial pattern is obtained in the kt images, not related to the local fluorescence intensity levels, the main reason for the broadening of the kt distribution is likely the heterogeneous intracellular environment and oxygen depletion. Moreover, the kox –kt 2D-histogram reveals an inclination, indicating a correlation between the fitted kt and kox values. To further investigate these findings, we compiled a corresponding 2D-histogram from 88 measurements on 21 different cells, comprising a total of 40590 pixels (Figure S6B). This histogram includes data from measurements on different days, and indicated small day-to-day variation compared to the intrinsic spread. The mean rates and their standard deviations were found to be: kt ¼ ð92:5 19:7Þ ms1 and kox ¼ ð0:5 0:3Þ ms1 . Concerning the kox-distribution, both the intracellular distributions in reducing and oxidating agents, and the limited observable tp range of the redox decay in the TRAST curves can be possible reasons for the large spread of kox-values. Since the determined kox values are also influenced by the diffusion properties, local variations and restrictions of the dye mobility can also contribute to this spread. Nevertheless, the distribution reveals a distinct peak and thus a relative, mean oxidation rate. While a


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quantitative determination of these rates is challenging, SPIM-TRAST measurements can thus provide a relative measure for the detection of the redox state of investigated cells, which may be used e.g. to screen potentially toxic substances and their influence on cellular redox levels. The inclination of the 2D-histograms for CEDASE in Figure 4E is seen also in the histogram presented in Figure S6B, representing the compiled results for many cells. It is likely, that the local oxygen concentrations cause a correlation between kox and kt, because oxygen enhances both triplet state decay rates and oxidation rates of fluorophores [24]. The tilted ellipse in the 2D-histogram may thus be a manifestation of different local oxygen concentrations in the cells. Nevertheless, the inclination could at least partly originate from the determination of both rates from one and the same fit. Similarly, we also performed measurements on MCF-7 cells, using the Fluorescein derivative CFDASE (Figure 5A–D). The previous discussion for CEDA-SE is also valid for CFDA-SE. However, the applied irradiance, the determined rates and hence the dark state build-up are different. The following mean and standard deviations were obtained from the pixel values of the cell presented in Figure 5: kt ¼ ð179:5 14:9Þ ms1 and kox ¼ ð5:0 0:9Þ ms1 .


Compared to CEDA-SE, CFDA-SE is better suited for imaging kox. Although its high bleaching rate does not allow using illumination schemes with pulse widths over 200 ms, a larger part of the redox process is still visible in the TRAST curves, since the rates are significantly faster than for CEDA-SE. Therefore only very few pixels had to be omitted for reasons of non-convergence to a reasonable solution during the fit (as described above for CEDA-SE). Similar to the results on CEDA-SE, the 2D-histogram indicates a slight correlation between the kt and kox rates (Figure 5E). The same holds, when the results from 27 measurements on 13 different MCF-7 cells are compiled (Figure S6 C). Both the triplet decay and the oxidation rates appear to be distributed around central, unique peaks, with the following means and standard deviations: kt ¼ ð180:9 47:3Þ ms1 and kox ¼ ð5:3 3:0Þ ms1 . The anticipated reason for the broadness of the distributions is also in this case the heterogeneous intracellular environment. Moreover, Fluorescein is highly prone to photobleaching and is known to have a strongly pH-dependent brightness [38, 39] and the corresponding protonation-deprotonation reactions may therefore also partly contribute to the broadening.

4. Conclusion We have introduced a novel combination of Transient State (TRAST) imaging and Single Plane Illumination Microscopy (SPIM). By this concept, the overall excitation light exposure of the specimen is reduced considerably. Transition rates of known fluorophores were determined and in good agreement with previously reported results. SPIM-TRAST imaging on homogeneously labeled live cells was demonstrated, resolving local variations of the fluorophore transition rates. Furthermore, we derived a new theoretical framework for TRAST imaging with arbitrary duty cycles. This approach enables frame rates of TRAST imaging to be increased by more than a factor of 10 compared to previous experiments. Taken together, our developments reduce the constraints further and extend the applicability of TRAST imaging for live cell studies.

Figure 5(A–C): Representative fluorescence image (A), the kt-image (B) and kox-image (C) recorded from a CFDA-SE stained MCF-7 cell at I0 ¼ 8:9 kW cm2 and fitted pixel-wise with an STR model (scale bars 8 mm). (D): Acquired mean TRAST (blue) and fitted (red) curve averaged over the whole cell. (E): The 2D-histogram of the kt and kox values presented in (B) and (C).


Acknowledgements This work was supported by means from EU FP7 (FLUODIAMON 201 837), the Swedish Research Council (VRNT 2012–3045), and the Knut and Alice Wallenberg Foundation (KAW 2011.0218). T. S. was supported by the National Research Fund, Luxembourg. The authors thank Thorsten Wohland, Natl Univ Singapore, for advice regarding the SPIM configuration. Author biographies online.

Please see Supporting Information



References [1] G. Donnert, J. Keller, R. Medda, M. A. Andrei, S. O. Rizzoli, R. Lu¨hrmann, R. Jahn, C. Eggeling, S. W. Hell, and R. Lu, PNAS 103, 11440 (2006). [2] G. Donnert, C. Eggeling, and S. W. Hell, Nature Methods 4, 81 (2007). [3] J. Widengren and R. Rigler, Bioimaging 4, 149 (1996). [4] C. Eggeling, J. Widengren, R. Rigler, and C. A. M. Seidel, Analytical Chemistry 70, 2651 (1998). [5] S. W. Hell and M. Kroug, Applied Physics B 60, 495 (1995). [6] E. Betzig, G. H. Patterson, R. Sougrat, O. W. Lindwasser, S. Olenych, J. S. Bonifacino, M. W. Davidson, J. Lippincott-Schwartz, and H. F. Hess, Science (New York, N.Y.) 313, 1642 (2006). [7] M. J. Rust, M. Bates, and X. Zhuang, Nature Methods 3, 793 (2006). [8] J. Widengren, Journal of the Royal Society Interface 7, 1135 (2010). [9] V. E. Korobov and A. K. Chibisov, Journal of Photochemistry 9, 411 (1978). [10] E. Chen and M. R. Chance, Methods in Enzymology 226, 119 (1993). [11] E. J. Peterman, F. M. Dukker, R. van Grondelle, and H. van Amerongen, Biophysical Journal 69, 2670 (1995). [12] H. van Amerongen and R. van Grondelle, Methods in Enzymology 246, 201 (1995). [13] J. M. Vanderkooi, G. Maniara, T. J. Green, and D. F. Wilson, The Journal of Biological Chemistry 262, 5476 (1987). [14] G. Marriott, R. M. Clegg, D. J. Arndt-jovin, and T. M. Jovin, Biophysical Journal 60, 1374 (1991). [15] I. Dunphy, S. A. Vinogradov, and D. F. Wilson, Analytical Biochemistry 310, 191 (2002). [16] J. Widengren, R. Rigler, and . Mets, Journal of Fluorescence 4, 255 (1994). [17] J. Widengren, . Mets, and R. Rigler, The Journal of Physical Chemistry 99, 13368 (1995). [18] J. Widengren and P. Schwille, The Journal of Physical Chemistry A 104, 6416 (2000). [19] J. Widengren, A. Chmyrov, C. Eggeling, P.-A. Lo¨fdahl, and C. A. M. Seidel, The Journal of Physical Chemistry A 111, 429 (2007). [20] T. Sandeń, G. Persson, P. Thyberg, H. Blom, and J. Widengren, Analytical Chemistry 79, 3330 (2007). [21] T. Sandeń, G. Persson, and J. Widengren, Analytical Chemistry 80, 9589 (2008). [22] M. Geissbuehler, T. Spielmann, A. Formey, I. Ma¨rki, M. Leutenegger, B. Hinz, K. Johnsson, D. Van De Ville, and T. Lasser, Biophysical Journal 98, 339 (2010).


9

[23] T. Spielmann, L. Xu, A. K. B. Gad, S. Johansson, and J. Widengren, The FEBS Journal 281, 1317 (2014). [24] T. Spielmann, H. Blom, M. Geissbuehler, T. Lasser, and J. Widengren, The Journal of Physical Chemistry B 114, 4035 (2010). [25] M. Wagner, P. Weber, T. Bruns, W. S. L. Strauss, R. Wittig, and H. Schneckenburger, International Journal of Molecular Sciences 11, 956 (2010). [26] J. Huisken and D. Y. R. Stainier, Development 136, 1963 (2009). [27] J. Huisken, J. Swoger, F. Del Bene, J. Wittbrodt, and E. H. K. Stelzer, Science 305, 1007 (2004). [28] J. G. Ritter, R. Veith, J.-P. Siebrasse, and U. Kubitscheck, Optics Express 16, 7142 (2008). [29] A. H. Voie, D. H. Burns, and F. A. Spelman, Journal of Microscopy 170, 229 (1993). [30] C. J. Engelbrecht and E. H. Stelzer, Optics Letters 31, 1477 (2006). [31] F. C. Zanacchi, Z. Lavagnino, M. P. Donnorso, A. D. Bue, L. Furia, M. Faretta, and A. Diaspro, Nature Methods 8, 1047 (2011). [32] P. J. Keller, A. D. Schmidt, A. Santella, K. Khairy, Z. Bao, J. Wittbrodt, and E. H. K. Stelzer, Nature Methods 7, 637 (2010). [33] T. Wohland, X. Shi, J. Sankaran, and E. H. K. Stelzer, Optics Express 18, 10627 (2010). [34] J. Buchholz, J. W. Krieger, G. Mocsa´r, B. Kreith, E. Charbon, G. Va´mosi, U. Krebschull, and J. Langowski, Optics express 20, 17767 (2012). [35] J. Buchholz, J. W. Krieger, A. Pernus, E. Charbon, U. Kebschull, and J. Langowski, Biophysical Journal 102, 207a (2012). [36] A. Chmyrov, T. Sandeń, and J. Widengren, The Journal of Physical Chemistry. B 114, 11282 (2010). [37] I. Stein, S. Capone, J. H. Smit, F. Baumann, T. Cordes, and P. Tinnefeld, ChemPhysChem 13, 931 (2011). [38] A. Chmyrov, T. Sandeń, and J. Widengren, The Journal of Physical Chemistry B 11282 (2010). [39] H. Blom, A. Chmyrov, K. Hassler, L. M. Davis, and J. Widengren, The Journal of Physical Chemistry. A 113, 5554 (2009). [40] S. Reindl and A. Penzkofer, Chemical Physics 213, 429 (1996). [41] A. Penzkofer and A. Beidoun, Chemical Physics 177, 203 (1993). [42] M. Bra¨ndeń, T. Sandeń, P. Brzezinski, and J. Widengren, Proceedings of the National Academy of Sciences of the United States of America 103, 19766 (2006). [43] T. Sandeń, L. Salomonsson, P. Brzezinski, and J. Widengren, Proceedings of the National Academy of Sciences of the United States of America 107, 4129 (2010).


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