Here, we present a protocol to obtain three-color smFRET data and its analysis with a 3D ensemble Hidden Markov Model. With this approach, scientists can extract kinetic information from complex protein systems, including cooperativity or correlated interactions.
Single-molecule Förster resonance energy transfer (smFRET) has become a widely used biophysical technique to study the dynamics of biomolecules. For many molecular machines in a cell proteins have to act together with interaction partners in a functional cycle to fulfill their task. The extension of two-color to multi-color smFRET makes it possible to simultaneously probe more than one interaction or conformational change. This not only adds a new dimension to smFRET experiments but it also offers the unique possibility to directly study the sequence of events and to detect correlated interactions when using an immobilized sample and a total internal reflection fluorescence microscope (TIRFM). Therefore, multi-color smFRET is a versatile tool for studying biomolecular complexes in a quantitative manner and in a previously unachievable detail.
Here, we demonstrate how to overcome the special challenges of multi-color smFRET experiments on proteins. We present detailed protocols for obtaining the data and for extracting kinetic information. This includes trace selection criteria, state separation, and the recovery of state trajectories from the noisy data using a 3D ensemble Hidden Markov Model (HMM). Compared to other methods, the kinetic information is not recovered from dwell time histograms but directly from the HMM. The maximum likelihood framework allows us to critically evaluate the kinetic model and to provide meaningful uncertainties for the rates.
By applying our method to the heat shock protein 90 (Hsp90), we are able to disentangle the nucleotide binding and the global conformational changes of the protein. This allows us to directly observe the cooperativity between the two nucleotide binding pockets of the Hsp90 dimer.
Many proteins fulfill their function in dynamic complexes with other molecules, mediated by conformational changes and transient associations on a broad range of timescales1,2,3. Coupled to an external energy source (e.g., ATP) these dynamic interactions can lead to directionality in a functional cycle and ultimately maintain the non-equilibrium steady-state in a cell, the prerequisite for life.
In order to fully understand these molecular machines, a static description guided by structural studies is not sufficient. In addition, it is essential to have knowledge of the underlying kinetic model and to determine the kinetic rate constants. Several existing methods allow researchers to study the dynamics of binary interactions between two molecules of interest, e.g., surface plasmon resonance, relaxation methods with a spectroscopic readout (e.g., jump or stopped-flow techniques), and nuclear magnetic resonance. However, their applicability is in most cases limited to simple two-state systems (e.g., one bound and one unbound state) due to the averaging inherent to bulk experiments. In cases where more states or intermediates are involved, they yield only a complex mixture of the rate constants. Single-molecule methods such as optical or magnetic tweezers or two-color smFRET, i.e., one donor and one acceptor fluorophore, with a surface-immobilized sample can recover the rate constants for all observed conformational changes. However, when it comes to interactions affecting more than one binding site, these methods remain limited and the information on the possible correlation of the two (or more) interactions will only be accessible via indirect conclusions from a set of experiments.
Multi-color smFRET4,5,6,7,8,9 offers the opportunity to study the interaction between these components directly, at real time and under near-physiological conditions10. This permits one to investigate for example, the conformation-dependent binding of a ligand or another protein8,9,11. The overall approach presented here is to label the protein(s) of interest at specific positions, to attach one protein to the surface of the measurement chamber, and to track the fluorescence intensity over time on a prism-type TIRFM (for details see 9,12). The spatial proximity of the different dyes can then be determined from the energy transfer between them. Labeling strategies may vary from protein to protein (reviewed in 13) and guidelines to avoid artifacts in smFRET measurements exist14.
Since a donor dye may transfer energy to different acceptor dyes in a multi-color smFRET experiment, the relative position of all dyes is not accessible from excitation of one dye alone15,16. But in combination with alternating laser excitation (ALEX17, and reviewed in 18) this method provides all spatio-temporal information at sub-second and sub-nanometer resolution.
In principal, high resolution structural information can be achieved by using the inter-dye distances calculated from the combination of all fluorescence intensities in a multi-color smFRET experiment with ALEX. However, here we focus on state identification and separation as well as the extraction of kinetic models, where multi-color smFRET is indispensable. When "only" structure determination by triangulation is desired, a set of simpler two-color smFRET experiments with high signal-to-noise ratio can be performed12,19.
We use the partial fluorescence () as a proxy for the energy transfer between two fluorophores7. The PF is calculated from the fluorescence intensity analogous to the FRET efficiency of a two-color experiment:
Where, is the intensity in emission channel em after excitation with color ex, and c is the acceptor with the longest wavelength. Detection channels represent the same position in the sample chamber but record different spectral ranges of the fluorescence light. The same identifier for excitation and emission are used in this protocol (i.e., "blue," "green," and "red").
Because of experimental shortcomings the measured fluorescence intensities depend not only on the energy transfer but also on fluorophore and setup properties. In order to obtain the true energy transfer efficiency between two fluorophores, the measured intensities have to be corrected. The following procedure is based on reference9. Correction factors for apparent leakage (lk, i.e., the detection of photons from a fluorophore in a channel designated for another dye) and apparent gamma (ag, i.e., the fluorescence quantum yield of the dye and the detection efficiency of the channel) are obtained from single-molecule traces that show an acceptor bleaching event.
The leakage of the donor dye into every possible acceptor channel is calculated from all data points in the recorded fluorescence traces where the acceptor dye bleached but the donor is still fluorescent ():
The median of the leakage histogram is used as the apparent leakage factor. After correction for leakage, the apparent gamma factor is determined from the same set of traces. It is calculated by dividing the change of fluorescence in the acceptor channel by the change of fluorescence in the donor channel upon bleaching of the acceptor dye:
Where c again is the detection channel for the acceptor with the longest wavelength. The median of the resulting distribution is used as the apparent correction factor.
The corrected intensities in each channel are obtained by:
The PF is then calculated according to:
Different populations can be separated in the multi-dimensional space spanned by the PFs. The position and width of each state is determined by fitting the data with multi-dimensional Gaussian functions. Subsequent optimization of one global HMM based on all PF traces provides a quantitative description of the observed kinetics. Even small changes of the rates are detectable.
HMMs provide a way of inferring a state model from a collection of noisy time traces. The system is considered to be in one of a set of discrete, hidden states at any given time and the actual observation (i.e., the emission) is a probabilistic function of this hidden state20. In the case of TIRFM smFRET data, the emission probabilities bi per state i can be modeled by continuous Gaussian probability density functions. At regularly spaced discrete time points, transitions from one to another state can occur according to the transition probability that is time-invariant and only depends on the current state. The transition matrix A contains these transition probabilities aij between all hidden states. The initial state distribution gives the state-specific probabilities for the first time point of a time trace. Using a maximum-likelihood approach, these parameters can be optimized to best describe the data with the Forward-Backward and Baum-Welch algorithms20,21. This yields the maximum likelihood estimators (MLE). Finally, the state sequence that most likely produced the trajectory of observations can be inferred with the Viterbi algorithm. In contrast to other HMM analyses of smFRET data24,25,26 we do not use the HMM as a mere "smoothing" of the data but extract the kinetic state model from the data set without the need for fitting dwell time histograms27. HMM analysis is done with in-house scripts using Igor Pro. Implementation of the code is based on reference21. We provide a software kit and exemplary data on our webpage in order to follow sections 5 and 6 of this protocol (https://www.singlemolecule.uni-freiburg.de/software/3d-fret). Full software is available upon request.
Time points in the data with PF <-1 or PF >2 in any detection channel are assigned the minimal emission probability for all states (10-200). This prevents artificial transitions at these data points.
The parameters for the emission probabilities are obtained from the fit of the 3D PF histogram with Gaussian functions as described in step 5.7. These parameters are kept fixed during the optimization of the HMM.
In the presented approach, the initial state distribution vector and the transition matrix are used globally to describe the entire ensemble of traces. They are updated based on all N molecules from the data set according to reference27.
Start parameters for the initial state distribution are determined from 2D projections of the PF histogram (step 5.3) and the transition probabilities are set to 0.05 with the exception of the probabilities to stay in the same state, which are chosen such that the probability to leave a certain state is normalized to unity.
A likelihood profiling method is used to give confidence intervals (CIs) for all transition rates21,22, which serve as meaningful estimates for their uncertainty. To calculate the bounds of the CI for a specific rate, the transition probability of interest is fixed to a value other than the MLE. This yields the test model λ'. A likelihood ratio (LR) test of the likelihood given the data set 0 is performed according to:
The 95% confidence bound for the parameter is reached when LR exceeds 3.841, the 95% quantile of a x2-distribution with one degree of freedom22,23.
The power of the method is demonstrated using the Hsp90. This abundant protein is found in bacteria and eukaryotes and is part of the cellular stress response28. It is a promising drug target in cancer treatment29. Hsp90 is a homodimer with one nucleotide binding pocket in the N-terminal domain of each subunit30. It can undergo transitions between at least two globally distinct conformations, one closed and one N-terminal open, V-shaped conformation19,31,32. The dimeric nature directly raises the question of the interplay between the two nucleotide binding sites in Hsp90.
In the following, we provide a step-by-step protocol for the data acquisition and analysis of a three-color smFRET experiment on yeast Hsp90 and nucleotide. The conformation-dependent binding of fluorescently labeled AMP-PNP (AMP-PNP*, a non-hydrolyzable analog of ATP) is analyzed. The application of the described procedure permits the study of the nucleotide binding and at the same time the conformational changes of Hsp90 and thereby reveals the cooperativity between the two nucleotide binding pockets of Hsp90.
1. Setup and Prerequisites
2. Measurement
3. Selection of Single-molecule Traces
4. Calculation of the Partial Fluorescence Traces
5. Population Selection and 3D Histogram Fitting
6. Kinetic Analysis with 3D Ensemble HMM
Multi-color smFRET measurements allow the direct detection of correlation between two or more distinct interaction sites. This renders the technique unique to investigate multi-component systems, such as protein complexes. We focus on the presentation of a three-color smFRET experiment here, which serves as an illustrative example.
The general workflow of the method is shown in Figure 1. The first part comprises the recording of multi-color smFRET data on a prism-type TIRF microscope. The surface attachment strategy and the schematics of the setup are depicted in Figure 2A. For a more detailed description of the setup, refer to reference9. The second part of the presented method focuses on the data analysis. Exemplary fluorescence intensity traces are shown in Figure 2B. Suitable time traces show: (i) a clear bleaching step for both fluorophores attached to Hsp90, (ii) flat intensity plateaus, (iii) anti-correlated behavior in the corresponding channels, and (iv) at least one binding event of AMP-PNP* (Figure 3). More than 400 molecules that meet the selection criteria were selected to yield reliable statistics. In the studied system, five states can be distinguished by the fluorescence intensities, with four states being functionally distinct (Figure 2C).
From the fluorescence intensity traces the partial fluorescence (PF, the extension of the FRET efficiency for multi-color smFRET experiments) can be calculated (Figure 4A). The PF is related to the proximity of the dyes. In a three-color smFRET experiment, the data spans a 3D space (Figure 5B). 2D projections of the 3D histogram have proven to be useful for state separation (Figure 4B, C). In a successful experiment, all states that are theoretically expected under the applied experimental conditions are distinguishable by their PF in the 2D projections.
The relative populations of the states are determined from the 2D projections by drawing free-hand polygons that enclose the peak (Figure 5C). This approach was found to be accurate and reliable9. The emission probabilities for the HMM are obtained by fitting the 3D PF histogram with the sum of 3D Gaussian functions, where S is the number of distinguishable states (five in the presented case; Figure 5D). For this fit to converge properly, only the relative population pi of each state i has to be held constant while the position and width of the Gaussians are free.
One ensemble HMM is optimized over the full data set with the emission probabilities fixed to the parameters obtained from the Gaussian fit (Figure 6). In order to get a measure for the uncertainty of the extracted transition probabilities, the 95% CI for each transition is determined (Figure 7).
In addition, the average time that the labeled reporter nucleotide AMP-PNP* remains bound to Hsp90 can be extracted under different experimental conditions (Figure 8A). This helps to further reduce the complexity of the results. To do so, states that represent AMP-PNP* bound and unbound conformations are collapsed in the state trajectories, respectively. From this the average dwell time for the AMP-PNP* dissociation can be calculated (Figure 8B).
By comparing the kinetics observed in the absence and the presence of additional, unlabeled AMP-PNP, unique information on the correlation between the conformational changes of Hsp90 and the nucleotide state can be gained. This makes it possible to directly study the cooperativity between the two nucleotide binding pockets of Hsp90. Besides, it circumvents the need for titration experiments that measure the binding site occupation as a function of the substrate concentration (e.g., Hill plots). For highly dynamic protein systems such as Hsp90, this approach is also sensitive to small changes in the rates11.
Figure 1: General workflow of the data acquisition and analysis. Data is acquired on a multi-color smFRET total internal reflection fluorescence microscope (TIRFM). After trace selection and calculation of the partial fluorescence (PF), the 3D PF histogram and 2D projections thereof are compiled. Using the 2D projections, the population of all distinguishable states can be determined. These are used as a constraint for a 3D Gaussian fit to the PF histogram. The Gaussian probability density functions serve as emission probabilities for the subsequent 3D ensemble Hidden Markov Modeling (HMM). This yields the kinetic description of the system. Please click here to view a larger version of this figure.
Figure 2: Scheme of the data acquisition. (A) Pictogram of the studied system consisting of an Hsp90 dimer (yellow ovals represent the domain structure) attached to the surface with the labels Atto488 (blue) and Atto550 (green) and the reporter nucleotide AMP-PNP* in solution, labeled with Atto647N (red). Data are recorded on a prism-type TIRF microscope with alternating laser excitation (ALEX). (B) Exemplary fluorescence intensity (Fl. Int.) traces after blue and green excitation. (C) Pictograms of the distinguishable conformational states of Hsp90 (S0, S1, S2, S3, S4) and their respective identifier for the functional state (O, C, O*, C*) used in this work. Fluorophore positions are indicated in blue, green, and red. Two populations represent the same functional state, namely open Hsp90 with AMP-PNP* bound (S2 and S3). Please click here to view a larger version of this figure.
Figure 3: Selection criteria. (A) A molecule selected for further analysis. (B, C) Molecules not selected for further analysis. (B) No flat plateaus and Atto550 is in a dark state around 30 s after green excitation (indicated by arrows). (C) Multiple bleaching steps after green excitation (indicated by arrows). Fl. Int.: fluorescence intensity, blue: Atto488, green: Atto550, red Atto647N, ex: excitation. Please click here to view a larger version of this figure.
Figure 4: Calculation of the PF traces and representative histograms. (A) Representative fluorescence intensity (Fl. Int.) traces and the corresponding partial fluorescence (PF) traces. (B) Two 2D projections of the 3D PF histogram for the measurement in absence of additional, unlabeled nucleotide. (C) The same projections for the experiment in the presence of additional 250 µM AMP-PNP. Please click here to view a larger version of this figure.
Figure 5: Population selection process. (A) The position of the five distinguishable populations in the two 2D projections. (B) Copy of the pictograms in Figure 2C. (C) Representative 3D scatter plot of the PF data. Coloration of the data points is only for visualization. The same color code as in B is used. (D) Determination of the relative populations is done by drawing free-hand polygons around the peaks in the 2D histogram. Using a combination of the two projections depicted in Figure 3, all five populations can be distinguished. (E) Results of the 3D Gaussian fit to the histogram of the PF data shown in A. Depicted are the isosurfaces at FWHM, which represent the five different populations. Please click here to view a larger version of this figure.
Figure 6: Flow chart of the ensemble 3D HMM optimization. One model is optimized for all molecules from the data set. The start values are given by the input model (with a pre-defined number of states). The likelihood of the model given the data (the 3D PF traces) is rated with the Forward-Backward (FB) algorithm. The Baum-Welch (BW) algorithm yields a local maximum likelihood estimate (MLE) of the parameters. The global MLE then can be found iteratively. The Viterbi algorithm computes the most likely state trajectory given a model. Please click here to view a larger version of this figure.
Figure 7: Meaningful uncertainty estimation with CI. (A) Determination of the CI for one exemplary rate constant. The likelihood ratio (LR) of the test model compared to the maximum likelihood estimator (MLE) model is calculated in the region around the MLE for the rate constant. The 95% confidence bound is reached when the LR exceeds 3.841 (horizontal dark grey line). (B) The extracted rate constants without additional nucleotide (red) and with AMP-PNP (blue) and their 95% CI. Please click here to view a larger version of this figure.
Figure 8: The average dwell time of the reporter nucleotide AMP-PNP* bound to Hsp90 is prolonged in presence of additional nucleotide. (A) Pictogram of the observed dissociation of labeled AMP-PNP* from the Hsp90 dimer, averaging over all conformations of Hsp90. The domain structure of Hsp90 is depicted by yellow ovals and the conformational flexibility is indicated by overlaying an open and closed dimer. (B) Average dwell time of AMP-PNP* bound to Hsp90 in absence of additional nucleotide (red) and in presence of 250 µM unlabeled AMP-PNP (blue). Please click here to view a larger version of this figure.
We present the experimental procedure to obtain three-color smFRET data for a complex protein system and a step-by-step description of the analysis of these measurements. This approach offers the unique possibility to directly assess the correlation between multiple interaction sites or conformational changes.
In order to obtain suitable multi-color single-molecule data on proteins it is important to perform reproducible measurements at a low noise level. This can be achieved by using an efficient and reliable surface passivation protocol for the flow chamber9. A sufficient density of molecules immobilized to the surface should be used to increase the yield of molecules per recorded movie. This means that fluorescence light from single molecules should fall on about 5 – 10% of the pixels in the recorded images. At the same time overloading should be prevented, because it would result in an overlap of neighboring molecules. When a labeled species is free in solution (AMP-PNP* in the presented study), make sure the concentration is low enough to not interfere with the measurement by direct excitation from lasers with shorter wavelength (i.e., typically well below a micromolar concentration). Also, ensure that the concentration of these compounds is not lower than intended due to unspecific binding to interfaces. Additionally, the optimal excitation power represents a trade-off between the signal-to-noise ratio and the photobleaching of the fluorophores.
No oxygen scavenging system is employed in the presented protocol. The used Atto-Tec dyes do not show significant blinking on the time scale of the experiment (70 ms illumination per excitation cycle) and no decrease of the photo bleach rate was observed by a scavenger system consisting of glucose oxidase, catalase, and Trolox33,34. Also, absence of oxygen scavenger avoids artifacts due to unspecific protein interactions, as oxygen scavenging systems usually contain protein components up to a micromolar concentration35.
Another critical step is the trace selection. The size of the area for summing the intensity of a single fluorophore is chosen to be as small as possible but still larger than the point spread function of the setup. Be aware that a flat plateau in the joint intensity traces can only be expected if the detection channels have similar apparent gamma factors, since the traces are not corrected at this stage of the analysis. Only molecules that meet well-defined criteria for the fluorescence intensity traces are included in the analysis (Section 3). Molecules with a poor signal-to-noise ratio in the PF traces are excluded from further analysis and do not affect data evaluation as clear selection criteria are used.
Depending on the data quality, alternative approaches provide optimal state allocation. A free multi-dimensional ensemble HMM based either on the fluorescence intensity traces27 or the PF traces could be applied to allocate the underlying states by optimizing the corresponding emission probabilities (e.g., Gaussian PDFs) while at the same time optimizing the kinetic model. Both approaches have their advantages and disadvantages. PF traces may contain unfavorable spikes while fluorescence intensity levels vary from molecule to molecule. The presented protocol solves this issue by preventing artificial transitions when spikes occur and by estimating the emission probabilities from a 3D Gaussian fit to the PF histogram and only optimizing the transition probabilities in the subsequent ensemble HMM run.
The presented approach is limited by the need for a labeled interaction partner that binds with a comparably high affinity in order to be compatible with the low concentrations necessary for conventional single-molecule measurements. This may be overcome by strategies to increase the local concentration (tethering of the interaction partners36, encapsulation in vesicles37,38) or methods to decrease the excited volume (e.g., zero-mode waveguides39). In addition, labeling positions have to be chosen with care and control experiments are necessary to exclude severe side-effects of the fluorophores. In case of Hsp90, this is usually checked by an ATPase assay, which proves the enzymatic activity40. If available, structural information from crystal or NMR structure should be considered. Preferred positions are in surface-exposed loops, away from interaction interfaces and not buried in a protein surface pocket. This ensures a large accessible volume for the dye.
The framework is suited for a multi-color smFRET experiment with an arbitrary number of colors. We focus on three-color measurements here, as these already provide the feature that renders them distinct from other single-molecule experiments, namely the possibility to directly observe correlation between two processes (e.g., binding of an interaction partner and conformational changes). This information is inaccessible in a standard two-color smFRET experiment but is of fundamental interest for understanding the function of any molecular machine.
The presented experiment and analysis is capable of characterizing protein systems, which might display dynamics on a wide range of time scales between states that are highly flexible themselves3. This contrasts previously published multi-color smFRET experiments, which focused on DNA or RNA model systems, such as Holliday junctions4, exhibiting transitions between (mostly two) well-defined states. In protein systems, the signal-to-noise ratio is lower and extracting the complete set of transition probabilities is difficult because of the limited temporal bandwidth in smFRET experiments due to photobleaching. Here, we show that – if applied carefully and with reasonable constraints – the obstacles in measuring protein systems can be overcome with multi-color smFRET using multi-dimensional state allocation and ensemble hidden Markov analysis9.
The authors have nothing to disclose.
This work is funded by the German Research Foundation (INST 39/969-1) and the European Research Council through the ERC Grant Agreement n. 681891.
Setup | |||
vibration-damped optical table | Newport, Irvine, CA, USA | RS2000 | |
OBIS 473nm LX 75mW LASER | Coherent Inc, Santa Clara, CA, USA | 1185052 | |
OBIS 532nm LS 50mW LASER | Coherent Inc, Santa Clara, CA, USA | 1261779 | |
OBIS 594nm LS 60mW LASER | Coherent Inc, Santa Clara, CA, USA | 1233470 | |
OBIS 637nm LX 140mW LASER | Coherent Inc, Santa Clara, CA, USA | 1196625 | |
laser control unit | Coherent Inc, Santa Clara, CA, USA | 1234465 | Scientific Remote |
aspheric telescope lenses | Thorlabs Inc, Newton, New Jersey, USA | d=25.4mm, f=50mm and f=100mm | |
CF ex1 | AHF analysentechnik AG, Tübingen, Germany | ZET 473/10 | cleanup filter excitation |
CF ex2 | AHF analysentechnik AG, Tübingen, Germany | ZET 532/10 | cleanup filter excitation |
CF ex3 | AHF analysentechnik AG, Tübingen, Germany | ZET 594/10 | cleanup filter excitation |
CF ex4 | Thorlabs Inc, Newton, New Jersey, USA | FL635-10 | cleanup filter excitation |
DM ex1 | AHF analysentechnik AG, Tübingen, Germany | ZQ594RDC | dichroic mirror excitation |
DM ex2 | AHF analysentechnik AG, Tübingen, Germany | 570DCXR | dichroic mirror excitation |
DM ex3 | AHF analysentechnik AG, Tübingen, Germany | ZQ491RDC | dichroic mirror excitation |
AOTFnC-Vis | AA Opto-Electronic, Orsay, France | ||
λ/4 plate | Thorlabs Inc, Newton, New Jersey, USA | AQWP05M-600 | |
CFI Apo TIRF 100x | Nikon Instruments Inc, Melville, NY, USA | high-NA objective | |
piezo focus positioner MIPOS 250 CAP | piezosystem jena GmbH, Jena, Germany | Piezo Controller NV 40/1 CLE | |
piezo stepper | Newport, Irvine, CA, USA | PZA12 | PZC200-KT NanoPZ Actuator Kit |
achromatic aspheric lenses | Qioptiq Photonics GmbH & Co. KG, Göttingen, Germany | G322-304-000 | d=50mm, f=200mm |
adjustable optical slit | Owis GmbH, Staufen i. Br., Germany | 27.160.1212 | max. aperture 12 x 12 mm |
DM det1 | AHF analysentechnik AG, Tübingen, Germany | T 600 LPXR | dichroic mirror detection |
DM det2 | AHF analysentechnik AG, Tübingen, Germany | H 560 LPXR superflat | dichroic mirror detection |
DM det3 | AHF analysentechnik AG, Tübingen, Germany | HC BS R635 | dichroic mirror detection |
BP det1 | AHF analysentechnik AG, Tübingen, Germany | 525/40 BrightLine HC | bandpass filter detection |
BP det2 | AHF analysentechnik AG, Tübingen, Germany | 586/20 BrightLine HC | bandpass filter detection |
BP det3 | AHF analysentechnik AG, Tübingen, Germany | 631/36 BrightLine HC | bandpass filter detection |
BP det4 | AHF analysentechnik AG, Tübingen, Germany | 700/75 ET Bandpass | bandpass filter detection |
optical shutters detection | Vincent Associates, Rochester, NY, USA | Uniblitz VS25S2T0 | |
EMCCD iXon Ultra 897 | Andor Technology Ltd, Belfast, Northern Ireland | ||
digital I/O card, PCIe-6535 | National Instruments, Austin, Texas, USA | ||
syringe pump | Harvard Apparatus, Holliston, MA, USA | PHD22/2000 | |
Name | Company | Catalog Number | コメント |
Flow chamber | |||
quartz slides | G. Finkenbeiner Inc, Waltham, MA, USA | Spectrosil2000, h=3mm | |
TEGADERM film | 3M Deutschland GmbH, Neuss, Germany | 1626W | 10 x 12cm |
spray adhesive | 3M Deutschland GmbH, Neuss, Germany | Photo Mount 050777 | |
glycerol | Carl Zeiss AG, Oberkochen, Germany | Immersol G | |
immersion oil | OLYMPUS EUROPA SE & CO. KG, Hamburg, Germany | IMMOIL-F30CC | |
prism | Vogelsberger Quarzglastechnik GmbH, Hauzenberg, Germany | Suprasil1 | |
aluminium prism holder | custom built | ||
hollow setscrews | Thorlabs Inc, Newton, New Jersey, USA | with custom drilling | |
Tygon S3 E-3603 tubing | neoLab Migge GmbH, Heidelberg, Germany | 2-4450 | ACF00001 |
PTFE tubing | Bohlender GmbH, Grünsfeld, Germany | S1810-08 | |
Name | Company | Catalog Number | コメント |
Sample | |||
yeast Hsp90 D61C, Q385C_biotin | UniProt ID P02829 | ||
Maleimide derivatives of Atto488, Atto550 | ATTO-TEC GmbH, Siegen, Germany | ||
AMP-PNP* | Jena Bioscience, Jena, Germany | γ-[(6-Aminohexyl)-imido]-AMP-PNP-Atto647N | |
Fluospheres | Thermo Fisher Scientific, Waltham, MA, USA | F8764 | amine-modified, 0.2 μm, yellow-green fluorescent |
Name | Company | Catalog Number | コメント |
Software | |||
Andor Solis | Andor Technology Ltd, Belfast, Northern Ireland | version 4.30 | |
LabVIEW | National Instruments, Austin, Texas, USA | version 2012, 32bit; misc. hardware control | |
MDS control software | AA Opto-Electronic, Orsay, France | version 2.03a | |
Coherent Connection | Coherent Inc, Santa Clara, CA, USA | version 3 | |
Igor Pro | WaveMetrics Inc, Portland, OR, USA | version 6.37 |