Abstract
Fluorescence correlation spectroscopy (FCS), is a widely used tool routinely exploited for in vivo and in vitro applications. While FCS provides estimates of dynamical quantities, such as diffusion coefficients, it demands high signal to noise ratios and long time traces, typically in the minute range. In principle, the same information can be extracted from microseconds to seconds long time traces; however, an appropriate analysis method is missing. To overcome these limitations, we adapt novel tools inspired by Bayesian nonparametrics, which starts from the direct analysis of the observed photon counts. With this approach, we are able to analyze time traces, which are too short to be analyzed by existing methods, including FCS. Our new analysis extends the capability of single molecule fluorescence confocal microscopy approaches to probe processes several orders of magnitude faster and permits a reduction of phototoxic effects on living samples induced by long periods of light exposure.
Similar content being viewed by others
Introduction
Owing to its flexibility and limited invasiveness for in vivo applications, singlemolecule fluorescence confocal microscopy^{1,2,3,4} is one of the most widely used experimental techniques of modern biophysics. In this technique, fluorescent molecules are allowed to freely diffuse into a volume illuminated by a tightly focused laser beam of a conventional singlefocus confocal setup. Molecular motion inside the illuminated volume generates fluctuations in the emitted fluorescence that is recorded and subsequently temporally autocorrelated^{1,2,3,4} or, jointly spatiotemporally autocorrelated^{5,6,7}, to deduce physical quantities of interest. For example, fluorescence correlation spectroscopy (FCS)^{1,2} as well as complementary methods—such as fluorescence crosscorrelation spectroscopy (FCCS)^{8}, scanning FCS^{9,10}, spot variation fluorescence correlation spectroscopy^{11}, fluorescence resonance energy transferfluorescence correlation spectroscopy (FRETFCS)^{12,13}, etc—estimate diffusion coefficients, reaction kinetic, binding affinities, and concentrations of labeled molecules^{14,15}.
Although singlemolecule fluorescence confocal microscopy data are acquired on the nano to millisecond timescales (nsms), fluorescence correlation methods typically require the analysis of long time traces, several seconds to tens of minutes long, depending on the molecular concentrations and emission properties of the fluorophores employed^{16,17}. These traces, capturing multiple molecule traversals of the confocal volume, provide the statistics needed for the postprocessing steps used in traditional FCS analysis^{16} (e.g. autocorrelation, and nonlinear fitting to theoretical curves). However, processing steps like these downgrade the quality of the available data and demand either relatively high concentrations or excessively long time traces to yield reliable estimates. The same downgrades are encountered even with lesstraditional FCS analyses, including Bayesian approaches^{18,19,20,21,22}, which also rely on autocorrelations.
In principle, within milliseconds, for the fluorophore concentrations and confocal volumes used in most experiments^{1,2,23}, thousands of data points are already available. Accordingly, if one could, somehow, estimate diffusion coefficients within tens of ms with the same accuracy as FCS, one could hypothetically use tens of minutes worth of data to discriminate between very small differences in diffusion coefficients. Alternatively, one could opt for shorter traces in the first place and, in doing so, reduce the sample’s light exposure to only a few milliseconds, thereby minimizing phototoxic effects, which remain a severe limitation of fluorescence microscopy^{24,25,26}.
Exploiting data on millisecond timescales would require a method that, simultaneously and selfconsistently, estimates the number of fluorescent molecules at any given time within the (inhomogenously) illuminated volume and deduce their dynamical properties based on their photon emissions, which, in turn, depend on their evolving location within the confocal volume. The mathematics to do so in a rigorous and efficient manner have, so far, been unavailable as analyzing ms traces would demand that we consider all possible populations of molecules responsible for the observed traces, their diffusion coefficients, and every possible location (and, thus, photon emission rate) of those molecules at any given time.
Indeed, with current technology, this global optimization is prohibitively computationally expensive. To wit, maximum likelihood approaches^{15,27}, popular in a variety of applications, are excluded as they require that the, otherwise unknown, population of molecules in the confocal volume at any given time be specified in advance by other means. These considerations motivate an entirely new framework for FCS.
Here, we introduce a novel approach that exploits Bayesian nonparametrics^{15,28,29}, a branch of statistics first suggested^{30} in 1973 and only broadly popularized in physical applications over the last few years^{15,28,29,31,32,33,34,35,36,37}. This approach allows us to account for an arbitrary number of molecules responsible for emitting detected photons. With the proposed method, we are able to estimate physical variables, otherwise determined from FCS, with: (i) significantly shorter time traces; and (ii) nearly singlemolecule resolution. Furthermore, our overall framework is generalizable and can estimate not only diffusion coefficients and molecular populations but also track molecules through time as well as determine their molecular brightness and the background photon emission rate.
Results
Overview
The method we propose for the analysis of traces from singlemolecule fluorescence confocal microscopy follows the Bayesian paradigm^{15,27,29,38}. Within this paradigm, our goal is to estimate posterior probability distributions over unknown parameters such as diffusion coefficients as well as molecular populations over time.
In this section, we first demonstrate and validate our method by computing posterior distributions using synthetic (simulated) traces mimicking the properties of real singlemolecule fluorescence confocal experiments. We subsequently benchmark our estimates with traces from control in vitro experiments. A comprehensive summary of the results can be found in Supplementary Table 1.
Demonstration and validation with simulated data
To demonstrate the robustness of our method, we simulate fluorescent time traces under a broad range of: (i) numbers of labeled molecules in the effective volume, Fig. 1; (ii) diffusion coefficients, Fig. 2a; (iii) trace lengths, Fig. 2b; and (iv) molecular brightness, Fig. 3. As, the majority of our time traces are too short to be meaningfully analyzed with traditional FCS, we compare our posteriors directly to the ground truth that we used in the simulations.
The posteriors we obtain, in all figures, are informed from the analysis of a single trace. In those, the breadth of the posterior (i.e., variance), which is a measure of the accuracy of our estimate, indicates the uncertainty introduced by the finiteness of the data and the inherent noise in this single time trace.
To begin, in Fig. 1 we simulate a 3D Gaussian confocal volume of size ω_{xy} = 0.3 μm and ω_{z} = 1.5 μm and one molecule inside the effective volume (Fig. 1a) or five molecules inside the effective volume (Fig. 1c) diffusing at 10 μm^{2} s^{−1} for a total period of 100 ms. The corresponding full joint posteriors are shown in Supplementary Figs 1 and 2.
As can be seen in Fig. 1, a low intensity leads to a wide estimate of the diffusion coefficient. However, the higher the intensity, the sharper (i.e., more conclusive) the estimate of the diffusion coefficient becomes (e.g., note a narrower posterior in Fig. 1d as compared with Fig. 1b). Thus, diffusion coefficients are determined more accurately when the number of labeled molecules are higher. Accordingly, the most difficult data to analyze are those where concentrations of molecules are so low that, on average, only one molecule ventures, albeit rarely, into the effective region of the confocal volume where it can be appreciably excited. Put differently, for an equal length time trace, the posterior estimate over the diffusion coefficient is broader (i.e., less conclusive) for lower numbers of molecules inside the effective volume, Fig. 1b, than it is for larger numbers of molecules, Fig. 1d.
Following a similar reasoning, the slower a molecule diffuses, the more photons are collected, leading to a sharper posterior estimate of the corresponding diffusion coefficient, Fig. 2a. Likewise, the longer the trace is, Fig. 2b, or the greater the molecular brightness is, Fig. 3, the sharper the diffusion coefficient estimate becomes.
We emphasize that our definition of molecular brightness is based on the maximum number of detected photons emitted from a single fluorophore when it is located at the center of the confocal volume and we provide more details in the Supplementary Note 3.
In Fig. 3 we demonstrate the robustness of the diffusion coefficient estimates when varying the molecular brightness. Although we keep the background photon emission rate fixed, we simulate gradually dimmer fluorophores such as those encountered in experiments under lower laser powers, until the molecular signature is virtually lost. As can be seen, such traces lead to broader posterior estimates over diffusion coefficients, as one would expect, as these traces are associated with greater uncertainty. Also, as such traces lead to a weaker (i.e., less constraining) likelihood, the posterior resembles more closely the prior (similar to every Bayesian methods) and naturally starts to deviate from the exact value.
Experimental data with elongated confocal volume shapes
Here, we apply our method on experimental traces captured with an elongated confocal volume that we approximate by a cylinder. To do so, we apply our method on fluorescent beads (with average diameter of 45 nm) diffusing in water. We benchmark our estimated diffusion coefficients against the Stokes–Einstein prediction and results from FCS. In particular, Fig. 4 illustrates our method’s performance in the analysis of traces too short to be meaningfully analyzed by FCS. The FCS formulation we used here can be found in Supplementary Note 3 and additional results can be found in Supplementary Fig. 10.
Experimental data with elliptical confocal volume shapes
Next, we apply our method on experimental time traces derived from singlemolecule fluorescence confocal microscopy. In our setup, we monitor Cy3 dyes, which diffuse freely in a mixture of water and glycerol. We benchmark our estimated diffusion coefficients against two values: those predicted by the Stokes–Einstein formula^{39}, which is parametrized by physical quantities such as temperature and viscosity; and those estimated by FCS. To analyze the data using FCS reliably, we used the full (6 min) trace available.
In benchmarking, we obtained and analyzed measurements with different: (i) numbers of Cy3 dyes inside the effective volume (tuned by varying Cy3 concentration); (ii) trace lengths; (ii) diffusion coefficients (tuned by adjusting the viscosity of the solution); and (iii) molecular brightness (tuned by adjusting the laser power).
Just as before, the slower a molecule diffuses, the more time it spends in the vicinity of the confocal volume, so the more photons are collected, thereby leading to sharper posterior estimates for the diffusion coefficient; as seen on Fig. 5a.
In Fig. 5b, we illustrate the effect of different time trace length. In this case, we reach the same accuracy as FCS with 100 × times less data. Consistent with the synthetic data shown earlier, we obtain a broader posterior over diffusion coefficients when the number of dyes inside the effective volume is low and sharper posteriors for higher numbers of dyes.
For example, in Fig. 5c–f, we illustrate the effect of different dye concentrations where a trace with stronger signal, anticipated when concentrations are higher, leads to better diffusion coefficient estimates (and thus sharper posteriors) on traces of equal length owing to the higher number of labeled molecules inside the confocal volume. The corresponding full joint posteriors of Fig. 5 are shown in Supplementary Figs 6 and 7.
In general, a posterior’s sharpness depends strongly on the number of molecules in a time trace, their respective locations, and thus their photon emission rates. As the molecular population near the center of the confocal volume may exhibit strong fluctuations, the width of the posterior may also fluctuate from trace to trace, especially when the individual traces are short. Thus, the individual posteriors become sharper only on average as we move to higher numbers of molecules inside the effective volume or molecular brightness.
To test our method beyond free beads and dyes, we used labeled proteins, namely freely diffusing streptavidin labeled by Cy3. Similar to the previous cases, we tested a range of concentrations, diffusion coefficients, and laser powers. Figure 6 summarizes characteristic results and compares our analyses against the results of FCS, which is applied on longer time traces (6 min). As can be seen, even in this case our method provides acceptable estimates of the diffusion coefficient with 100 × times fewer data points than FCS.
Additional results
In addition to cases involving a single diffusion coefficient that we have considered thus far, our method can be generalized to treat multiple diffusion coefficients as well. To show this, we artificially mixed (summed) and analyzed experimental traces where dyes diffuse in different amounts of glycerol and so they exhibit different diffusion coefficients. On account of the additivity of photon emissions and detections, artificial mixing of traces allows us to obtain realistic traces of different diffusive species that can be analyzed as if they were diffusing simultaneously within the same confocal volume and separately as well. In Fig. 7, we compare the analysis of intensities created by mixing traces containing slow and fast diffusing Cy3 (94% and 75% glycerol/water, respectively). As can be seen, our estimates obtained under simultaneous diffusion compare favorably to the estimates under separate diffusion, indicating that our method can also identify robustly multiple diffusion coefficients at once. The full joint posterior distributions corresponding to Fig. 7 and additional data are shown in Supplementary Figs 12 and 13.
For all cases described so far, using our method, we estimated more than just diffusion coefficients. For example, we also estimate the population of molecules contributing photons to the traces, their instantaneous photon emission rates and locations relative to the center of the confocal volume, as well as the background photon emission rate. A moredetailed report of our estimates, with presentations of full joint posterior distributions, can be found in the Supplementary Figs 3, 4, 8, and 9.
Discussion
Singlemolecule fluorescence confocal microscopy has the potential to reveal dynamical information at timescales that may be as short as a hundred milliseconds. Here, we have exploited Bayesian nonparametrics to overcome the limitations of specifically fluorescent correlative methods in utilizing short, ≈ 10 ms, and noisy time traces to deduce molecular properties such as diffusion coefficients. Exploiting new analysis, to obtain reliable results from such short traces or excessively noisy traces as those obtained under low laser power, is key to minimizing photodamage inherent to all methods relying on illumination and especially critical to gaining insight on rapid or lightsensitive processes^{24,26}. The analysis of similarly short traces is also required when monitoring nonequilibrium processes that remain stationary or approximately stationary over only short periods of time. Furthermore, novel analysis with increased sensitivity may exploit the entirety of longer traces to tease out subtle dynamical features (such as deducing multiple diffusion coefficients at once).
The deep implication of our method is that it places singlemolecule fluorescence confocal microscopy at a competitive advantage over widefield techniques used in single particle tracking. Indeed, widefield techniques provide high, superresolved, spatial accuracy^{15}, but with diminished temporal resolution, as molecule localization requires the collection of sufficient photons obtained only after long frame exposures^{15}. Such a requirement is especially problematic for photosensitive or rapidly diffusing biomolecules^{15}.
By contrast to widefield microscopy, singlemolecule fluorescence confocal microscopy yields minimal spatial resolution. However, as our analysis shows, although spatial resolution may be diminished, reduced photodamage and exceptionally high temporal resolution can be achieved instead.
Since their inception, over half a century ago, correlative methods, such as FCS, have demanded very long traces in order to extract dynamical features from singlemolecule fluorescence confocal microscopy data^{2,11,40,41,42,43}. In this study, we have developed a principled framework capable of taking advantage of all spatiotemporal information nested within time traces of photon counts and, together with novel mathematics, we have reformulated the analysis of singlemolecule fluorescence confocal microscopy data.
Existing methods, even those that apply Bayesian techniques such as FCSBayes^{18,19,20,21,22}, still utilize autocorrelation functions. Therefore, they demand equally long time traces as FCS and implicitly assume that the molecular process probed (e.g., diffusion) remains stationary over the portion of the trace analyzed. By contrast, our method only requires short traces and therefore it avoids stationarity or equilibrium requirements on timescales longer than those of the segments analyzed. In addition, our method also: (i) provides interpretable estimation of errors (i.e., posterior variance) determined exclusively from the information content of the trace supplied (i.e., length and noise) as opposed to ad hoc metrics residuals (i.e., chi square); (ii) tracks instantaneous molecule photon emissions and locations; and (iii) estimates the molecular brightness and background photon emission rates which, if left undetermined, can introduce biases.
As our method is formulated exclusively in the timedomain, it offers a versatile framework for further modifications. For example, it is possible to adapt the present formulation to incorporate scanning FCS^{9,10,44} which involves moving the confocal volume or incorporate demanding illumination profiles, such as those arising in two photon excitation^{42,45}, TIRF microscopy^{41} or even Airy patterns^{46} with or without aberrations^{47} by changing the specified point spread function (see Methods section). In addition, it is possible to extend our framework to treat multiple diffusion coefficients (see Supplementary Note 5), confining forces or photon emission kinetics as would be relevant for FCSFRET^{48,49} and FLIM^{50,51} applications. Also, our method could be extended to handle more complex photophysics^{23,52,53,54}, and, as we explicitly track individual molecules over time, extensions appropriate for fast bimolecular reaction kinetics are also conceivable.
Methods
Model overview
Here we describe the formulation and mathematical foundation of our model. Our overarching goal is to start from an experimental time series of photon counts, \(\bar w = (w_1,w_2,...,w_K)\) where w_{k} denotes the photon intensity assessed at time t_{k} (which includes both background photons as well as photons derived from the labeled molecules of interest), and derive estimates of kinetic quantities such as molecular locations with respect to the center of the confocal volume as well as diffusion coefficients.
To derive estimates for the desired quantities, we need to compute intermediate quantities which include: (i) molecular brightness; (ii) background photon emission rate; and, most importantly, (iii) the unknown population of moving molecules and their relative locations with respect to the center of the confocal volume. Below we explain each one of these in detail. Computational details and a working implementation of the entire method are available in the Supplementary Notes 3 and 4. For convenience, we summarize our notation, abbreviations and mathematical definitions in Supplementary Tables 2–4.
Model description
The starting point of our analysis is the raw data, namely the photon counts. As our current focus is on deducing dynamical information on timescales exceeding ≈ 1 μs, we ignore triplet state and photon antibunching effects that occur on vastly different timescales^{16,55,56}.
At the timescale of interest, individual photon detections, assuming saturation is not reached, happen stochastically and independently from each other. Accordingly, the total number of photon counts w_{k} between successive assessments follows Poisson^{15,27} (shot noise) statistics
where μ_{back} is a background photon emission rate and \(\mathop {\sum}\limits_n {\mu _k^n}\) gathers the photon emission rates \(\mu _k^n\) from individual fluorescent molecules that we index with n = 1, 2, …. The number of molecules involved in the summation above is to be determined. This is the key reason we invoke Bayesian nonparametrics in the model inference section (see below). As we only collect a small fraction of the total photons emitted by the fluorescent molecules, as we describe above, in our framework \(\mu _k^n\) coincides with the emission rate of detected photons, as opposed to the true photon emission rate, which might be larger.
Each rate \(\mu _k^n\) depends on the position \((x_k^n,y_k^n,z_k^n)\) of the corresponding molecule relative to the center of the confocal volume as well as other features such as laser intensity, laser wavelength, quantum yield, and camera pinhole size^{57}. Similar to other studies^{40,58,59}, we combine all these effects into a characteristic point spread function (PSF) that combines excitation and emission PSFs
The parameter μ_{mol} represents the molecular brightness and, as we discuss in the Supplementary Note 3, it is related to the maximum photon emission rate of a single molecule that is located at the center of the confocal volume. Specific choices of PSF models, such as Gaussian or GaussianLorentzian, are also detailed in the Supplementary Note 3 and a comparison of the different PSF models is shown in Supplementary Fig. 5.
Finally, we associate individual molecular locations across time by adopting a motion model. Here we assume that molecules are purely diffusive and arrive at
where D denotes the diffusion coefficient, which we assume is the same for all molecules. As we explain in the Supplementary Note 4, these probabilities result directly from the diffusion equation. A graphical summary of the entire formulation is shown on Fig. 8.
In addition, in the Supplementary Note 5 and Supplementary Fig. 16, we illustrate how this motion model can be generalized to capture more than one diffusion coefficients.
Model inference
The quantities that we want to estimate, for example the diffusion coefficient D, molecular locations through time \((x_k^n,y_k^n,z_k^n)\), molecular brightness μ_{mol} and background photon emission rate μ_{back}, or the molecular population, are introduced as model variables in the preceding formulation. To estimate values for these variables, we follow the Bayesian paradigm^{15,28,38,59}.
Variables such as D, μ_{mol}, and μ_{back} are parameters of the model and, as such, require priors. Choices for these priors are straightforward and, for interpretational and computational convenience, we adopt the distributions described in the Supplementary Note 4.
In addition, we must place priors on the initial molecular locations, \((x_1^n,y_1^n,z_1^n)\), i.e., the locations of the molecules at the onset of the measurement period. Specifying a prior on initial molecular locations also entails specifying a prior on the molecular population.
In particular, to allow the dimensionality or, alternatively, the complexity of our model to fluctuate based on the number of molecules that contribute to the fluorescent trace, we abandon traditional Bayesian parametric priors and turn to the nonparametric formulation described below.
Before we proceed any further, we recast Eq. (2) as
The newly introduced variables b^{n}, one for each model molecule, may take only values 1 or 0. In particular, the possibility that b^{n} = 0, coinciding with the case where molecules do not contribute to the observation, allows us to introduce an arbitrarily large number of molecules, technically an infinite number. With the introduction of b^{n}, we can estimate the number of molecules that contribute photons (termed “active” to distinguish them from those that do not contribute termed “inactive”) simultaneously with the rest of the parameters simply by treating each b^{n} as a separate parameter and estimating its value (of 1 for active molecules and 0 for inactive ones).
To estimate b^{n}, we place a prior b^{n} ~ Bernoulli(q^{n}) and subsequently a hyperprior on q^{n} in order to learn precisely how many model molecules are active. For the latter, we choose q^{n} ~ Beta(A_{q}, B_{q}) with hyperparameters A_{q} and B_{q}. Both steps can be combined by invoking the newly developed BetaBernoulli process^{36,60} which is described in more detail in the Supplementary Note 4.
Once the choices for the priors above are made, we form a joint posterior probability distribution \(p(D,\mu _{{\mathrm{mol}}},\mu _{{\mathrm{back}}},\{ x_k^n,y_k^n,z_k^n,b^n,q^n\} _k^n\bar w)\) encompassing all unknown variables which we may wish to determine.
The nonlinearities in the PSF with respect to variables \(\{ x_k^n,y_k^n,z_k^n\} _k^n\) and the nonparametric prior on {b^{n}, q^{n}}^{n} exclude analytic forms for our posterior. For this reason, we develop a computational scheme exploiting Markov chain Monte Carlo^{38,61} that can be used to generate pseudorandom samples from this posterior.
The main bottleneck of a naive implementation of our method, as compared with correlative methods, is its higher computational cost. As we explain in the Supplementary Note 4, to have computations run on an average desktop computer, we adopt mathematical approximations (e.g., photon binning, Anscombe transform^{62} and filter updates^{63,64}) that are tested on the synthetic data presented. Specifically, the time trace preparation is described in the Supplementary Note 3 and Supplementary Fig. 14.
A working implementation of the framework described in this study is provided in the source code and the graphical user interface (GUI) is shown on Supplementary Fig. 15.
Data acquisition
Synthetic data: We obtain the synthetic data presented in the Results section by standard pseudorandom computer simulations^{65,66,67} that mimic the common singlemolecule fluorescence confocal setup. We provide details and complete parameter choices in the Supplementary Note 3, Supplementary Tables 5 and 6.
Experimental data: For the experimental data acquired with elongated confocal volumes, a stock solution of Cy3B (monoreactive NHS ester, GE Healthcare) was prepared by dissolving a small amount of solid in 1 mL of doublydistilled water, and its concentration was determined from the absorbance of the solution using the extinction coefficient provided by the vendors. A 10 nm solution was then prepared by appropriate dilution of the stock and measured on a silicone perfusion chamber mounted on a glass coverslip. Fluorescent beads were purchased from ThermoFisher (Catalog number: F8792. Lot number: 1604237). The average diameter was 0.046 μm as indicated in the certificate of analysis provided by the vendors. Suspensions for FCS measurements were prepared by adding 3 μL of stock solution (9.4 × 1014 particles/mL) to 1 mL of water and sonicating the mixture for 20 min. Measurements were carried out using a homebuilt instrument. A 532 nm continuouswave laser (Compass 215M10, Coherent, Santa Clara, CA) was attenuated to 100 μW and focused onto an PlanApo 100 ×, 1.4 NA, oilimmersion, objective (Olympus, Center Valley, PA). Emitted fluorescence was collected using the same objective and then passed through a 50 μm pinhole to reject the outoffocus light. The signal was detected using a silicon avalanche photodiode (SPCMAQR14; PerkinElmer, Fremont, CA). A bandpass filter (Omega 3RD560620) in front of the detector was employed to further reduce the background signal and an ALV correlator card (ALV 5000/EPP, ALVGmbH, Langen, Germany) was used to correlate the detected fluorescence signal. Data for our analysis were acquired with 100 μs resolution using a PCI6602 acquisition card (National Instruments, Austin, TX). Measurements have been tested for saturation separately and shown on Supplementary Fig. 11.
For the experimental data acquired with elliptical confocal volumes, Cy3 dye and Cy3labeled streptavidin solutions were prepared by suspending Cy3 or streptavidin in glycerol/buffer (pH 7.5, 10 mm TrisHCl,100 mm NaCl and 10 mm KCl, 2.5 mm CaCl_{2}) at different v/v, to a final concentration of either 100 pm or 1 nm. The solutions were added onto a glassbottomed fluidcell, mounted on a custom designed singlemolecule fluorescence confocal microscope^{68,69} and a 532 nm laser beam was focused to a diffractionlimited spot on the glass coverslip of the fluidcell using a × 60, 1.42 NA, oilimmersion objective (Olympus). The laser power was measured before the objective and the beam was reflected by a dichroic and focused by the objective on to the sample. The dichroic reflected 95% of the intensity on to the objective. Emitted fluorescence was collected by the same objective and focused onto the detection face of a Single Photon Avalanche Diode (SPAD, Micro Photon Devices) that has a maximum count rate of 11.8 Mc/s. A bandpass filter was placed in front of the detector to transmit only the fluorescence from Cy3 and to block the backscattered excitation light. TTL pulses, triggered by the arrival of individual photons on the SPAD, were timestamped and recorded at 80 MHz by a field programmable gated array (FPGA, NI Instruments) using custom LabVIEW software^{69} and initially binned at 100 μs.
Data availability
The data generated and analyzed in this study are freely available from the corresponding author upon request.
Code availability
Source code and GUI versions of the methods developed herewith are freely available to download at [https://cbp.asu.edu/content/stevepresselab] or [https://github.com/sinajazani/FCS].
References
Elson, E. L. & Magde, D. Fluorescence correlation spectroscopy. i. conceptual basis and theory. Biopolymers 13, 1–27 (1974).
Magde, D., Elson, E. L. & Webb, W. W. Fluorescence correlation spectroscopy. ii. an experimental realization. Biopolymers 13, 29–61 (1974).
Bright, G. R., Fisher, G. W., Rogowska, J. & Taylor, D. L. Fluorescence ratio imaging microscopy. Methods Cell Biol. 30, 157–192 (1989).
Fitzpatrick, J. A. & Lillemeier, B. F. Fluorescence correlation spectroscopy: linking molecular dynamics to biological function in vitro and in situ. Curr. Opin. Struct. Biol. 21, 650–660 (2011).
Digman, M. A. & Gratton, E. Lessons in fluctuation correlation spectroscopy. Annu. Rev. Phys. Chem. 62, 645–668 (2011).
Di Rienzo, C., Gratton, E., Beltram, F. & Cardarelli, F. Fast spatiotemporal correlation spectroscopy to determine protein lateral diffusion laws in live cell membranes. Proc. Natl Acad. Sci. 110, 12307–12312 (2013).
Digman, M. A. et al. Measuring fast dynamics in solutions and cells with a laser scanning microscope. Biophys. J. 89, 1317–1327 (2005).
Schwille, P., MeyerAlmes, F.J. & Rigler, R. Dualcolor fluorescence crosscorrelation spectroscopy for multicomponent diffusional analysis in solution. Biophys. J. 72, 1878–1886 (1997).
Petrášek, Z. & Schwille, P. Precise measurement of diffusion coefficients using scanning fluorescence correlation spectroscopy. Biophys. J. 94, 1437–1448 (2008).
Petrášek, Z. & Schwille, P. Scanning fluorescence correlation spectroscopy. In Single molecules and nanotechnology, 83–105 (Springer 2008).
Ruprecht, V., Wieser, S., Marguet, D. & Schütz, G. J. Spot variation fluorescence correlation spectroscopy allows for superresolution chronoscopy of confinement times in membranes. Biophys. J. 100, 2839–2845 (2011).
Remaut, K. et al. Fretfcs as a tool to evaluate the stability of oligonucleotide drugs after intracellular delivery. J. Control. Release 103, 259–271 (2005).
Torres, T. & Levitus, M. Measuring conformational dynamics: a new fcsfret approach. J. Phys. Chem. B 111, 7392–7400 (2007).
Tsekouras, K., Siegel, A. P., Day, R. N. & Pressé, S. Inferring diffusion dynamics from fcs in heterogeneous nuclear environments. Biophys. J. 109, 7–17 (2015).
Lee, A., Tsekouras, K., Calderon, C., Bustamante, C. & Pressé, S. Unraveling the thousand word picture: san introduction to superresolution data analysis. Chem. Rev. 117, 7276–7330 (2017).
Rigler, R. & Elson, E. S. Fluorescence correlation spectroscopy: theory and applications, vol. 65 (Springer Science & Business Media 2012).
Enderlein, J., Gregor, I., Patra, D. & Fitter, J. Statistical analysis of diffusion coefficient determination by fluorescence correlation spectroscopy. J. Fluoresc. 15, 415–422 (2005).
Guo, S.M. et al. Bayesian approach to the analysis of fluorescence correlation spectroscopy data ii: application to simulated and in vitro data. Anal. Chem. 84, 3880–3888 (2012).
He, J., Guo, S.M. & Bathe, M. Bayesian approach to the analysis of fluorescence correlation spectroscopy data i: theory. Anal. Chem. 84, 3871–3879 (2012).
Kügel, W., Muschielok, A. & Michaelis, J. Bayesianinferencebased fluorescence correlation spectroscopy and singlemolecule burst analysis reveal the influence of dye selection on dna hairpin dynamics. Chemphyschem 13, 1013–1022 (2012).
Murphy, K. R., Stedmon, C. A., Wenig, P. & Bro, R. Openfluor–an online spectral library of autofluorescence by organic compounds in the environment. Anal. Methods 6, 658–661 (2014).
Sun, G. et al. Bayesian model selection applied to the analysis of fluorescence correlation spectroscopy data of fluorescent proteins in vitro and in vivo. Anal. Chem. 87, 4326–4333 (2015).
Stasevich, T. J. et al. Crossvalidating frap and fcs to quantify the impact of photobleaching on in vivo binding estimates. Biophys. J. 99, 3093–3101 (2010).
Liu, Z., Lavis, L. D. & Betzig, E. Imaging livecell dynamics and structure at the singlemolecule level. Mol. Cell 58, 644–659 (2015).
Purschke, M., Rubio, N., Held, K. D. & Redmond, R. W. Phototoxicity of hoechst 33342 in timelapse fluorescence microscopy. Photochem. Photobiol. Sci. 9, 1634–1639 (2010).
Magidson, V. & Khodjakov, A. Circumventing photodamage in livecell microscopy. In Methods in cell biology, 114, 545–560 (Elsevier 2013).
Von Toussaint, U. Bayesian inference in physics. Rev. Mod. Phys. 83, 943 (2011).
Tavakoli, M., Taylor, J. N., Li, C.B., Komatsuzaki, T. & Pressé, S. Single Molecule Data Analysis: An Introduction, chapter 4, 205–305 (John Wiley & Sons 2017).
Hines, K. E. A primer on bayesian inference for biophysical systems. Biophys. J. 108, 2103–2113 (2015).
Ferguson, T. S. A bayesian analysis of some nonparametric problems. Ann. Stat. 1, 209–230 (1973).
Sgouralis, I. & Pressé, S. An introduction to infinite hmms for singlemolecule data analysis. Biophys. J. 112, 2021–2029 (2017).
Sgouralis, I. & Pressé, S. Icon: an adaptation of infinite hmms for time traces with drift. Biophys. J. 112, 2117–2126 (2017).
Sgouralis, I., Whitmore, M., Lapidus, L., Comstock, M. J. & Pressé, S. Single molecule force spectroscopy at high data acquisition: a bayesian nonparametric analysis. J. Chem. Phys. 148, 123320 (2018).
Calderon, C. P. & Bloom, K. Inferring latent states and refining force estimates via hierarchical dirichlet process modeling in single particle tracking experiments. PLoS ONE 10, e0137633 (2015).
Hines, K. E., Bankston, J. R. & Aldrich, R. W. Analyzing singlemolecule time series via nonparametric bayesian inference. Biophys. J. 108, 540–556 (2015).
Paisley, J. & Carin, L. Nonparametric factor analysis with beta process priors. In Proceedings of the 26th Annual International Conference on Machine Learning, 777–784 (ACM 2009).
Sgouralis, I., Nebenführ, A. & Maroulas, V. A bayesian topological framework for the identification and reconstruction of subcellular motion. SIAM J. Imaging Sci. 10, 871–899 (2017).
Gelman, A. et al. Bayesian Data Analysis (CRC press Boca Raton, FL, 2014).
Berg, H. C. Random WQalks in biology (Princeton University Press 1993).
Wohland, T., Rigler, R. & Vogel, H. The standard deviation in fluorescence correlation spectroscopy. Biophys. J. 80, 2987–2999 (2001).
Hassler, K. et al. Total internal reflection fluorescence correlation spectroscopy (tirfcs) with low background and high countrate per molecule. Opt. Express 13, 7415–7423 (2005).
Berland, K. M., So, P. & Gratton, E. Twophoton fluorescence correlation spectroscopy: method and application to the intracellular environment. Biophys. J. 68, 694–701 (1995).
Rigler, R., Mets, Ü., Widengren, J. & Kask, P. Fluorescence correlation spectroscopy with high count rate and low background: analysis of translational diffusion. Eur. Biophys. J. 22, 169–175 (1993).
Enderlein, J., Gregor, I., Patra, D. & Fitter, J. Art and artefacts of fluorescence correlation spectroscopy. Curr. Pharm. Biotechnol. 5, 155–161 (2004).
Dertinger, T. et al. Twofocus fluorescence correlation spectroscopy: a new tool for accurate and absolute diffusion measurements. Chemphyschem 8, 433–443 (2007).
Born, M. & Wolf, E. Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Lght (Elsevier 2013).
Gibson, S. F. & Lanni, F. Experimental test of an analytical model of aberration in an oilimmersion objective lens used in threedimensional light microscopy. JOSA A 9, 154–166 (1992).
Pirchi, M. et al. Photonbyphoton hidden markov model analysis for microsecond singlemolecule fret kinetics. J. Phys. Chem. B 120, 13065–13075 (2016).
Bronson, J. E., Fei, J., Hofman, J. M., Gonzalez, R. L. Jr. & Wiggins, C. H. Learning rates and states from biophysical time series: a bayesian approach to model selection and singlemolecule fret data. Biophys. J. 97, 3196–3205 (2009).
van Munster, E. B. & Gadella, T. W. Fluorescence lifetime imaging microscopy (flim). In Microscopy techniques, 143–175 (Springer 2005).
Bastiaens, P. I. & Squire, A. Fluorescence lifetime imaging microscopy: spatial resolution of biochemical processes in the cell. Trends Cell Biol. 9, 48–52 (1999).
Song, L., Varma, C., Verhoeven, J. & Tanke, H. J. Influence of the triplet excited state on the photobleaching kinetics of fluorescein in microscopy. Biophys. J. 70, 2959–2968 (1996).
Soumpasis, D. Theoretical analysis of fluorescence photobleaching recovery experiments. Biophys. J. 41, 95–97 (1983).
Sgouralis, I. et al. A bayesian nonparametric approach to single molecule förster resonance energy transfer. J. Phys.Chem. B 123, 675–688 (2018).
Widengren, J., Mets, U. & Rigler, R. Fluorescence correlation spectroscopy of triplet states in solution: a theoretical and experimental study. J. Phys. Chem. 99, 13368–13379 (1995).
Grußmayer, K. S., Kurz, A. & Herten, D.P. Singlemolecule studies on the label number distribution of fluorescent markers. Chemphyschem 15, 734–742 (2014).
Brakenhoff, G., Visscher, K. & Van der Voort, H. Size and shape of the confocal spot: control and relation to 3d imaging and image processing. In Handbook of biological confocal microscopy, 87–91 (Springer 1990).
Chen, Y., Müller, J. D., So, P. T. & Gratton, E. The photon counting histogram in fluorescence fluctuation spectroscopy. Biophys. J. 77, 553–567 (1999).
Jazani, S., Sgouralis, I. & Pressé, S. A method for single molecule tracking using a conventional singlefocus confocal setup. J. Chem. Phys. 150, 123320 (2019).
Tsekouras, K., Custer, T. C., Jashnsaz, H., Walter, N. G. & Pressé, S. A novel method to accurately locate and count large numbers of steps by photobleaching. Mol. Biol. Cell 27, 3601–3615 (2016).
Robert, C. & Casella, G. Introducing Monte Carlo Methods with R (Springer Science & Business Media 2009).
Anscombe, F. J. The transformation of poisson, binomial and negativebinomial data. Biometrika 35, 246–254 (1948).
Menegaz, H. M., Ishihara, J. Y., Borges, G. A. & Vargas, A. N. A systematization of the unscented kalman filter theory. IEEE Trans. Autom. control 60, 2583–2598 (2015).
Wan, E. A. & Van Der Merwe, R. The unscented kalman filter for nonlinear estimation. In Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. ASSPCC. The IEEE 2000, 153–158 (Ieee 2000).
Ibe, O. C. Elements of Random Walk and Diffusion Processes (John Wiley & Sons, Inc., Hoboken, New Jersey 2013).
Higham, D. J. An algorithmic introduction to numerical simulation of stochastic differential equations. SIAM Rev. 43, 525–546 (2001).
Erban, R. & Chapman, S. J. Stochastic modelling of reaction–diffusion processes: algorithms for bimolecular reactions. Phys. Biol. 6, 046001 (2009).
Li, H., Yen, C.F. & Sivasankar, S. Fluorescence axial localization with nanometer accuracy and precision. Nano. Lett. 12, 3731–3735 (2012).
Schmidt, P. D., Reichert, B. H., Lajoie, J. G. & Sivasankar, S. Method for high frequency tracking and subnm sample stabilization in single molecule fluorescence microscopy. Sci. Rep. 8, 13912 (2018).
Acknowledgements
S.P. acknowledges support from NSF CAREER grant MCB1719537. S.S. acknowledge support from the National Institute of Health grant R01GM121885. M.L. thanks Anirban Purohit for assistance with the experiments.
Author information
Authors and Affiliations
Contributions
S.J. developed analysis software and analyzed data; S.J., I.S. developed computational tools; O.S., S.S., M.L. contributed experimental data; S.J., I.S., S.P. conceived research; S.P. oversaw all aspects of the projects. S.J. and I.S. contributed equally to this work.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Peer review information: Nature Communications thanks Thorsten Wohland, Ingo Gregor and other anonymous reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Jazani, S., Sgouralis, I., Shafraz, O.M. et al. An alternative framework for fluorescence correlation spectroscopy. Nat Commun 10, 3662 (2019). https://doi.org/10.1038/s41467019115742
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41467019115742
This article is cited by

Singlemolecule FRET for probing nanoscale biomolecular dynamics
Nature Reviews Physics (2024)

Quantifying biomolecular organisation in membranes with brightnesstransit statistics
Nature Communications (2024)

Current capabilities and future perspectives of FCS: superresolution microscopy, machine learning, and in vivo applications
Communications Biology (2023)

Nanomaterialbased contrast agents
Nature Reviews Methods Primers (2023)

Diffractionlimited molecular cluster quantification with Bayesian nonparametrics
Nature Computational Science (2022)