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Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 21, Iss. 20 — Oct. 7, 2013
  • pp: 23543–23555
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High countrate real-time FCS using F2Cor

Emmanuel Schaub  »View Author Affiliations


Optics Express, Vol. 21, Issue 20, pp. 23543-23555 (2013)
http://dx.doi.org/10.1364/OE.21.023543


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Abstract

We present a fluorescence correlation spectroscopy setup based on a software correlator. The setup can process autocorrelation curves in real-time at countrate as high as 8MHz, with time resolution of 1µs. It uses the F2Cor autocorrelation algorithm, a low cost counting board and a desktop computer. Symmetrical normalization, which improves the signal to noise ratio of the FCS curve for large values of the lag-time, is adapted to the F2Cor algorithm. A new acquisition mode, which we call oscilloscope-mode, is presented. It takes advantage of the flexibility F2Cor, and proves to be very useful for optical setup adjustment. As an application of this setup, we performed FCS measurements on a reference tetramethylrhodamine solution at high concentration, up to 2.5µM, which extend to the micromolar range the concentration applicable in FCS, using a conventional optical setup. At such high countrates the FCS curves need to be corrected for dead-time of the photo-detector, which was done successfully.

© 2013 Optical Society of America

1. Introduction

Fluorescence correlation spectroscopy (FCS) is a well established technique [1

1. E. Elson and D. Magde, “Fluorescence correlation spectroscopy. I. Conceptual basis and theory,” Biopolymers 13(1), 1–27 (1974). [CrossRef]

3

3. P. Schwille and J. Ries, “Principles and applications of fluorescence correlation spectroscopy (FCS),” in Biophotonics: Spectroscopy, Imaging, Sensing, and Manipulation (Springer, 2011), pp. 63–85.

], which is used to measure local concentration, mobility and interactions of fluorescent species in solution. The calculation of the autocorrelation function of the fluorescence signal can be performed using either a hardware correlator [4

4. ALV-5000 multiple tau digital correlator reference manual (ALV gmbh, 1993).

] or a software correlator [5

5. T. A. Laurence, S. Fore, and T. Huser, “Fast, flexible algorithm for calculating photon correlations,” Opt. Lett. 31(6), 829–831 (2006). [CrossRef] [PubMed]

9

9. L. L. Yang, H. Y. Lee, M. K. Wang, X. Y. Lin, K. H. Hsu, Y. R. Chang, W. Fann, and J. D. White, “Real-time data acquisition incorporating high-speed software correlator for single-molecule spectroscopy,” J. Microsc. 234(3), 302–310 (2009). [CrossRef] [PubMed]

]. Software correlators give more flexibility and the cost is reduced. On the other hand, hardware correlators can calculate and display autocorrelations trace in real-time even at high fluorescence countrate.

The multiple-tau correlation technique has been regularly used in hardware correlators [4

4. ALV-5000 multiple tau digital correlator reference manual (ALV gmbh, 1993).

, 10

10. K. Schätzel, “Correlation techniques in dynamic light scattering,” Appl. Phys. B 42, 193–213 (1987). [CrossRef]

]. It consists on calculating the autocorrelation function (ACF) for geometrically increasing values of the lag time over a large range. The conventional implementation uses a bin-and-multiply approach (B&M): photon data are binned and multiplied over time intervals of a fraction of the considered lag time. Compared to linear correlators, the number of operations is dramatically reduced, and one can access a high dynamics of correlation times. This approach was also implemented using a field programmable gate array (FPGA) [11

11. W. Liu, J. Shen, and X. Sun, “Design of multiple-tau photon correlation system implemented by FPGA ,” Software and Systems, 410-414 (2008).

]. In 2000, Eid et al developed a dedicated electronic ISA acquisition card [12

12. J. S. Eid, J. D. Muller, and E. Gratton, “Data acquisition card for fluctuation correlation spectroscopy allowing full access to the detected photon sequence,” Rev. Sci. Instrum. 71(2), 361–368 (2000). [CrossRef]

], which allows full access to the photon sequence. They presented FCS results as well as photon counting histogram measurements (PCH), but didn't give details about the algorithm they used. In 2001 Magatti et al [6

6. D. Magatti and F. Ferri, “Fast multi-tau real-time software correlator for dynamic light scattering,” Appl. Opt. 40(24), 4011–4021 (2001). [CrossRef] [PubMed]

] presented the first multiple-tau software correlator. It used the same B&M principle as hardware correlators. Using a National Instrument PCI-6602 timer/counter board, it could process data in real-time for lag times higher than 5µs, and in batch processing for lower lag time values. In 2003 they proposed a new version [7

7. D. Magatti and F. Ferri, “25 ns software correlator for photon and fluorescence correlation spectroscopy,” Rev. Sci. Instrum. 74(2), 1135–1144 (2003). [CrossRef]

], still using a PCI-6602, which could process data in full real-time for all values of the lag time, as low as 25ns, but for countrates lower than 30kHz. In 2006 Laurence et al [5

5. T. A. Laurence, S. Fore, and T. Huser, “Fast, flexible algorithm for calculating photon correlations,” Opt. Lett. 31(6), 829–831 (2006). [CrossRef] [PubMed]

] proposed a very flexible algorithm, with arbitrary lag time spacing and bin width. The execution speed is comparable to the B&M approach. For their measurements, they also used a PCI-6602. The calculation was performed offline. In 2009, Yang et al introduced a new algorithm which we name simple correlation histogram algorithm (SCH), the calculation time of which is independent of the time resolution. Using a PC-6602 they could process FCS curves in real-time but only up to 500Hz, because of the small buffer size of the board. However the algorithm itself, which is very simple, can process photon data at rates higher than 1MHz, depending on the maximum lag time value. Recently, we proposed a new photon correlation algorithm, named F2Cor [13

13. E. Schaub, “F2Cor: fast 2-stage correlation algorithm for FCS and DLS,” Opt. Express 20(3), 2184–2195 (2012). [CrossRef] [PubMed]

], which is the fastest to date. It combines both the B&M and the SCH approach: the ACF is calculated using the SCH approach for short values of the lag time, while conventional B&M approach is used for larger values of the lag time. The optimal limit between the two modes depends on the countrate. The calculation time scales linearly with the number of photons. Using a laptop PC computer, and a single microprocessor core, it can process the ACF of photon file as fast as 0.1µs/photon. However in [13

13. E. Schaub, “F2Cor: fast 2-stage correlation algorithm for FCS and DLS,” Opt. Express 20(3), 2184–2195 (2012). [CrossRef] [PubMed]

] the calculation was performed offline, using pre-recorded data files.

In this paper, we present an FCS setup which integrates the F2cor algorithm and uses the low cost PCI-6602 counting board. It can calculate in full real-time ACF at countrates as high as 8 MHz, with a 1µs resolution time, which is only limited by the data transfer rate between the acquisition board and the computer.

In a section 2 we present the experimental setup, and the way the data are acquired and processed in real-time. In section 3 we present the symmetrical normalization scheme and its adaptation to F2Cor. In section 4 we present what we call the oscilloscope mode FCS, which can monitor the instantaneous FCS curve as a function of the time, which is very useful to optimize the settings of the setup. In section 5 we show measurements performed on a tetramethylrhodamine reference dye solution as a function of the concentration. In particular we show that our setup, thanks to its high countrate capability, is able to perform FCS measurements in real-time, at concentration as high as 2.5µM, which to our knowledge was never attained before using a conventional optical setup. Finally we propose what might be the future developments of this technique and potential applications.

2. Setup

The measurements were performed on a Leica DMIRE2 SP2 confocal microscope (PIXEL facility of University of Rennes 1, France). The optical setup is represented in Fig. 1
Fig. 1 Optical setup. The 561nm laser beam is focused into the dye solution using a 1.2NA water immersion x60 objective. The fluorescence signal is collected by the same objective. It is transmitted by the acousto-optic beam splitter (AOBS) and it is focused onto the confocal pinhole. The fluorescence passes trough a 570-625 nm band-pass interference filter. It is focused onto the core of a multimode optical fiber and detected by a single photon avalanche photodiode (SPAD).
. The excitation wavelength was 561nm, the power of which is adjusted using an acousto-optic beam splitter (AOBS). The laser light is focused into the sample using a 1.2NA water immersion x60 objective (UPLSAPO60XW/1.20/WD:0.28mm, Olympus). The fluorescence signal is detected in the descanned mode, using the so-called X1 port of the scanning head, with a 570-625 nm band-pass interference filter. The fluorescence is focused onto the core of a multimode optical fiber and detected by a single photon avalanche photodiode (SPAD) (SPCM-AQR-12-FC, Perkin-Elmer), which generates TTL photo-pulses.

Figure 2
Fig. 2 Diagram of the acquisition chain. It contains a single photon avalanche diode (SPAD), and a computer equipped with a PCI counter board (NI PCI6602). LabVIEW manages the acquisition and the user interface and uses the F2Cor library to compute the autocorrelation function (ACF).
represents the diagram of the acquisition chain. The SPAD is connected to a low cost general purpose PCI counter/timer board (NI-PCI6602, National Instruments). To acquire and process the data we use an ordinary desktop PC (AMD Phenom 2, 4GB RAM, windows XP). Both the PCI6602 and Eid's card [12

12. J. S. Eid, J. D. Muller, and E. Gratton, “Data acquisition card for fluctuation correlation spectroscopy allowing full access to the detected photon sequence,” Rev. Sci. Instrum. 71(2), 361–368 (2000). [CrossRef]

] can acquire the photons following either the photon mode (PM) or the time mode (TM). In PM the board records the arrival time of each photon and send it to the host computer. In TM, which is the acquisition mode we use, a counter counts the number of photo-pulses during each integration time, which is defined by an internally generated clock. More precisely, the PCI-6602 has eight counters, and two of them are used in our application. Counter 0 is used to generate the internal reference frequency, while Counter 1 counts the number of photo-pulses arriving at its source terminal between two consecutive gate rising edges. The TTL output of the SPAD is connected to the source of Counter 1, while the internally generated clock is connected to the gate of Counter 1. The maximum clock frequency is limited by the transfer rate between the counter board and the host computer. We use a binning time ofΔt0=1µs (a sustained throughput frequency as high as 1 MHz can be obtained by reducing the number of interrupts the counter board will generate [14

14. DMA Performance Improvements for TIO-based Devices,” http://digital.ni.com/public.nsf/allkb/1B64310FAE9007C086256A1D006D9BBF.

]). The data are continuously read by the host computer, with no loss of photons, even at very high count rate, except for those lost due to the dead-time of the SPAD.

While the time resolution in the PM mode is limited by the internal timebase of the acquisition board, in TM the time resolution is the binning time Δt0, which is limited by the DMA transfer rate between the acquisition board and the host computer. In FCS, when only considering motion of molecules and not faster processes such as triplet kinetics or chemical reaction kinetics, a time resolution of 1µs is quite enough (as we will see in the results section, the typical correlation time of a small dye in water using a high N.A. objective is about 30-40µs). In addition the portion of the FCS curve in the microsecond range is generally affected by the after-pulse effect so that this part of the curve is often discarded [15

15. S. A. Kim, K. G. Heinze, and P. Schwille, “Fluorescence correlation spectroscopy in living cells,” Nat. Methods 4(11), 963–973 (2007). [CrossRef] [PubMed]

]. Thus in most applications a 1µs time resolution is not a limiting factor.

As the F2cor algorithm works in the so-called photon mode (PM), it requires photon arrival times. The TM raw photon data have first to be converted online into photon arrival times, which is straightforward: If, for example, there are 12 photons occurring in a particular 1µs interval at time t = 123678 µs, the number “12” is converted into a sequence of 12 numbers, all of which are 123678. The value of K, which is the level of the lag time from which F2Cor uses the B&M scheme [13

13. E. Schaub, “F2Cor: fast 2-stage correlation algorithm for FCS and DLS,” Opt. Express 20(3), 2184–2195 (2012). [CrossRef] [PubMed]

], must be set manually by the user before starting the measurement.

3. Symmetrical normalization

We upgraded F2Cor to implement the so-called symmetrical normalization scheme [16

16. K. Schätzel, M. Drewel, and S. Stimac, “Photon correlation measurements at large lag times: improving statistical accuracy,” J. Mod. Opt. 35(4), 711–718 (1988). [CrossRef]

], which provides a better signal-to-noise ratio for large values of the lag time.

Before presenting the symmetrical normalization, we need to introduce some of the variables and quantities used by the F2Cor algorithm: We precise for which lag time series the autocorrelation is performed [Eq. (1)], how the fluorescence signal is represented depending on the level of the correlator [Eq. (2)]. As F2Cor is a two stage correlator, we present the unnormalized ACF provided by the B&M higher stage [Eq. (3)] and by the SCH lower stage [Eq. (4)]. How these results are calculated is explained in [13

13. E. Schaub, “F2Cor: fast 2-stage correlation algorithm for FCS and DLS,” Opt. Express 20(3), 2184–2195 (2012). [CrossRef] [PubMed]

]. In Eq. (5) we give the general definition of the normalized ACF. Then we introduce the symmetrical normalization, the definition of which is given in Eq. (6) for a linear correlator. Finally we apply this definition to the two stages of the F2Cor correlator [Eq. (7-10)].

The result provided by F2Cor follows the multiple-tau scheme. More precisely, the correlator is linear by level, and the time resolution is lowered by a factor of two from one level to the next.

The ACF is calculated for the following lag times in Δt0units:
{τn}n=1,,8kmax+15={2kl|l{1,2,,15}fork=0l{8,9,,15}for1kkmax}={1,2,,15,16,18,,30,32,36,,60,,8×2kmax,,15×2kmax},
(1)
where k is the level of the lag timeτ=2kl.

Let I(n) be the raw fluorescence signal that is the number of photons detected for nΔt0t<(n+1)Δt0.

Let L be the photon record length in Δt0 unit. 0t<LΔt0.

We denote Ik,m the binned fluorescence signal at level k that is the number of photons for.
2kmΔt0t<2k(m+1)Δt0.Ik,m=i=02k1I(2km+i).
(2)
F2Cor uses two estimators to calculate the unnormalized ACF [13

13. E. Schaub, “F2Cor: fast 2-stage correlation algorithm for FCS and DLS,” Opt. Express 20(3), 2184–2195 (2012). [CrossRef] [PubMed]

]. We denote K the level of the lag time from which F2Cor uses the B&M scheme.

ForkK:
GB&M(τ=2kl)=1L/2klm=0L/2kl1Ik,mIk,m+l,
(3)
where the bracket is the integer part of the enclosed expression (floor). I(n)I(n+τ)¯|B&M=GB&M(τ)/22k is an estimator ofI(n)I(n+τ).

For shorter values of the lag time F2Cor uses the SCH scheme:
GTr(τ=2kl)=G0(τ)+i=12k12ki2k(G0(τi)+G0(τ+i)),
(4)
where G0(τ)=i=0Lτ1I(i)I(i+τ).

Compared toGB&M(τ), I(n)I(n+τ)¯|Tr=GTr(τ)/(2k(Lτ)) is an improved estimator of I(n)I(n+τ).

The normalized ACF is defined by:
g(τ)=I(n)I(n+τ)I(n)2.
(5)
For a linear correlator, the symmetrically normalized ACF estimator is defined by [16

16. K. Schätzel, M. Drewel, and S. Stimac, “Photon correlation measurements at large lag times: improving statistical accuracy,” J. Mod. Opt. 35(4), 711–718 (1988). [CrossRef]

]:
g^sym(τ)=1Lτi=0Lτ1I(i)I(i+τ)(1Lτi=0Lτ1I(i))(1Lτi=0Lτ1I(i+τ)).
(6)
In the B&M scheme, which is used in F2Cor for large values of the lag time, each level k is a linear correlator. The symmetrically normalized ACF estimator is then [17

17. T. Wohland, R. Rigler, and H. Vogel, “The standard deviation in fluorescence correlation spectroscopy,” Biophys. J. 80(6), 2987–2999 (2001). [CrossRef] [PubMed]

]:
g^B&M(τ=2kl)=GB&M(τ)Mdir(τ)Mdel(τ),
(7)
where Mdir(τ) and Mdel(τ) are respectively the direct and delayed monitors:
Mdir(τ=2kl)=(1L/2klm=0L/2kl1Ik,m)Mdel(τ=2kl)=(1L/2klm=0L/2kl1Ik,m+l).
(8)
As I(n)I(n+τ)¯|Tr is an improved estimator of I(n)I(n+τ) compared to I(n)I(n+τ)¯|B&M, we define the following symmetrically normalized ACF estimator, for short values of the lag time:
g^Tr(τ=2kl)=(2k/(Lτ))GTr(τ)Mdir(τ)Mdel(τ).
(9)
Finally,
g^(τ=2kl)={g^Tr(τ)fork<Kg^B&M(τ)forkK.
(10)
To illustrate the improvement provided by the symmetrical normalization on the signal-to-noise ratio of the correlation curve, we have simulated shot noise signals (Poisson noise) of 10s in duration and of mean countrate of 1MHz. Figure 4(a)
Fig. 4 Effect of the symmetrical normalization on the ACF of simulated shot noise signals (countrate of 1MHz, signal duration of 10s). (a) ACF of 3 simulations without symmetrical normalization. (b) ACF of 3 simulations with symmetrical normalization. (c) Standard deviation obtained from 10 simulations without symmetrical normalization (yellow) and with symmetrical normalization (white). (d) Improvement of the signal-to-noise ratio provided by the symmetrical normalization, expressed as the ratio between the standard deviation of the ACF with symmetrical normalization to that without symmetrical normalization.
represents the ACF without symmetrical normalization obtained from 3 simulations while Fig. 4(b) represents that obtained with symmetrical normalization. Clearly in this latter case the signal is less noisy at large lag times. In Fig. 4(c) we have plotted the standard deviation obtained from 10 simulations in both cases (yellow: without symmetrical normalization, white: with symmetrical normalization). We see that from about 5ms the standard deviation starts growing without symmetrical normalization, while it continues decreasing with symmetrical normalization. In Fig. 4(d) we have plotted the ratio between the standard deviation of the ACF with symmetrical normalization to that without symmetrical normalization, which expresses the signal-to-noise improvement provided by the symmetrical normalization.

4. Oscilloscope mode FCS

Since our correlator is a software correlator, it is flexible. We make use of this flexibility to provide a new acquisition mode, which we name oscilloscope mode (OM), and which is especially useful to optimize the setup. Before starting measurements, the FCS setup requires careful adjustment: alignment, correction collar, pinhole diameter using a reference dye solution. For this purpose one wishes one could continuously visualize the FCS curve while adjusting the system. This is precisely what OM allows. It calculates and displays continuously the FCS curve over a sliding time window. If the time window is too short, the FCS curve is too noisy, while a very long time makes the FCS curve display not responsive enough. In practice, the FCS curve is updated every ΔT0 = 0.1s, and the time window is ΔT = 2s in duration.

Repeating a correlation calculation every 0.1s over a ΔT=2s time interval would require a very high calculation time. In the following, we will show how we can calculate this OM ACF, which takes into account the symmetrical normalization, by simply using intermediate results of the full time ACF calculation, which are stored as the measurement progresses. For that purpose, first we write the expression of the symmetrically normalized OM ACF we want to calculate: As F2Cor is a two stage correlator we need to consider successively the B&M stage and the SCH stage. The expression of the symmetrically normalized OM ACF for the B&M stage is given in Eq. (11). For the SCH stage we first derivate the unnormalized OM ACF [Eq. (13)]. Then making the connection between the B&M stage and the SCH stage we derivate the final result [Eq. (15)] which uses intermediate results provided by F2Cor [Eq. (14)].

In the B&M mode the symmetrically normalized ACF at time LΔt0averaged over a ΔT time window is defined as:
g^ΔT(τ=2kl)=1L/2k(LΔT)/2km=(LΔT)/2klL/2kl1Ik,mIk,m+l(m=(LΔT)/2klL/2kl1Ik,mL/2k(LΔT)/2k)(m=(LΔT)/2klL/2kl1Ik,m+lL/2k(LΔT)/2k).
(11)
We want to use the same normalization scheme for the SCH mode.
LetGΔT,Tr(τ)=GΔT,0(τ)+i=12k12ki2k(GΔT,0(τi)+GΔT,0(τ+i)),
(12)
where GΔT,0(τ)=j=LτΔTLτ1I(j)I(j+τ).

From Eq. (4) we find
GΔT,Tr(τ)=GTr(τ,L)GTr(τ,Lτ).
(13)
In Eq. (11) m=(LΔt)/2klL/2kl1Ik,mIk,m+l is a linear combination of terms of the formI(m)I(n), the total weight of which is 22k(L/2k(LΔT)/2k).GΔT,Tr(τ) is also a linear combination of terms of the form I(m)I(n), the total weight of which is 2kΔT.

During the normal full length FCS calculation, every ΔT0, the following intermediate results are stored:
R(τ=2kl,L)={2k(L/2k(LΔT)/2k)ΔTGTr(τ,L)ifk<Km=0L/2kl1Ik,mIk,m+lifkKSdir(τ=2kl,L)=m=0L/2kl1Ik,m.Sdel(τ=2kl,L)=m=0L/2kl1Ik,m+l
(14)
Finally, the symmetrically normalized ACF at time LΔt0averaged over a ΔT time window is then:
g^ΔT(τ=2kl)=(L/2k(LΔT)/2k)(R(τ,L)R(τ,LΔT))(Sdir(τ,L)Sdir(τ,LΔT))(Sdel(τ,L)Sdel(τ,LΔT)).
(15)
The calculation of (15) is immediate and does not require any additional correlation processing other than that performed during the online full length FCS calculation.

5. Measurements on a reference dye

We wanted to evaluate the performance of our system at high countrate resulting from a high concentration reference dye solution.

First we measured the ACF on a reference dye solution of 5-Carboxytetramethylrhodamine (TMR) (reference dye sampler kit R-14782, Invitrogen), in the micromolar concentration range. Figure 5 shows the results we got for concentration ranging from 0.5 to 2.5µM. The excitation wavelength was 561nm, the power at the sample was 4.0µW, and the collection time was 60s. For an easier comparison, we display C×G the product of the concentration and ACF.

Ideally all curves should superimpose. However we see that the higher the concentration, the lower the amplitude of C×G. At high countrate, dead-time tddof the SPAD, which is the time after each detected photon during which the SPAD won't be able to detect another photon, is not negligible. To the first order, the ACF is modified as follow [18

18. D. E. Koppel, “Statistical accuracy in fluorescence correlation spectroscopy,” Phys. Rev. A 10(6), 1938–1945 (1974). [CrossRef]

]:
G(τ)(12tddnΔt0)Gideal(τ),
(16)
which is valid for 2tddn/Δt0<<1, where n is the average number of photons in the Δt0 counting interval.

Thus, to the first order, the dead-time will only increase the apparent number of molecules that is why the measured correlation time is constant as a function of the concentration [inset of Fig. 6
Fig. 6 FCS measurements of TMR solutions in water for a concentration range of 0.5-2.5µM. The excitation power is 4µW at the sample and the measurement time is 60s. To normalize the ACF with respect to the number of molecules in the observation volume, the ACF is multiplied by the concentration. The curves don't superimpose because of the dead-time of the SPAD. The inset shows the correlation time which is constant as a function of the concentration.
].

Figure 7
Fig. 7 Measured countrate as a function of the concentration of the TMR dye solutions.
represents the countrate as a function of the concentration. For a negligible dead-time, the curve would be linear.

The relation between the expected countrate nand the measured one m is [19

19. L. Neri, S. Tudisco, F. Musumeci, A. Scordino, G. Fallica, M. Mazzillo, and M. Zimbone, “Note: Dead time causes and correction method for single photon avalanche diode devices,” Rev. Sci. Instrum. 81(8), 086102–086103 (2010). [CrossRef] [PubMed]

]:
m=n1+ntdd.
(17)
In our reference dye solution the expected countrate is proportional to the concentration n=αC, so that tdd can be simply measured by fitting the curve of Fig. 7 by the following equation:
m=αC1+αCtdd
(18)
We found tdd = 36ns. Using Eq. (16) we were able to correct the dead-time effect. The result is presented in Fig. 8
Fig. 8 FCS measurements of TMR with correction for the dead-time effect. All curves superimpose. The curve for 2.5µM slightly deviates from the average because the validity condition of the correction is not well respected. The inset shows the particle number as a function of the concentration before (⬜) and after (⬛) dead-time correction. In this latter case the dependency is linear.
, where we see that all curves superimpose. We see however that at 2.5µM the curve slightly departs from the average. This is because the validity condition of the correction 2tddn/Δt0<<1 is not well fulfilled anymore (1/2tdd = 14MHz).

6. Discussion and prospects

To perform FCS measurements in the micromolar range using a setup which cannot operate at high countrate, one would need to reduce the excitation power by a factor we call R. This reduced excitation power has several consequences. First the individual brightness of the molecules will be reduced by a factor R. The signal-to-noise of the FCS curve depends quadratically on the brightness so that to maintain the same signal-noise-ratio, one would need to increase the measurement time by a factor R2. Our system operates satisfactorily up to 8MHz. To get the same result on a system operating at 1MHz, one would need to increase the measurement time by a factor 64. For example, the acquisition time for the measurements of Fig. 8 was 60s for each curve. The measurement time would then need to be increased to more than one hour in the case of a system operating up to 1MHz. Another consequence of the reduced molecular brightness is that the autocorrelation curve will be more affected by the after-pulse effect. Indeed, if we performed FCS on a solution of fluorescent beads, the after-pulse effect would not be seen on the ACF curve because fluorescent beads are very bright particles. In opposite, if we performed FCS on an extremely highly concentrated solution (in the millimolar range) at very low excitation power, we would only see on the ACF curve the effect of the after-pulse and that of the instability of the instrument. To avoid the after-pulse effect a correction using a calibration process would be necessary, or the cross-correlation between two detectors would have to be performed, which would further increase the measurement time.

In terms of applications, performing FCS measurements at high concentration could be useful to analyze reactions with weak affinity. In the case of living cells, the concentration of proteins can also be high. FCS measurements in the micromolar range have been performed using nanoapertures milled in a metallic film [20

20. J. Wenger, D. Gérard, P. F. Lenne, H. Rigneault, J. Dintinger, T. W. Ebbesen, A. Boned, F. Conchonaud, and D. Marguet, “Dual-color fluorescence cross-correlation spectroscopy in a single nanoaperture : towards rapid multicomponent screening at high concentrations,” Opt. Express 14(25), 12206–12216 (2006). [CrossRef] [PubMed]

], the effect of which is to reduce dramatically the observation volume. The advantage of our method is that we don't need coverslips with metallic coating, and in the case of measurements in living cell, the measurement could be performed at any point in the cell. The drawback is that because of the dead-time effect, the FCS curves need to be corrected. One might expect in the future SPAD will be available with shorter dead-time. Alternatively, one may use a multichannel photodetector, so that the countrate of each channel is much below saturation. Incidentally, using a dispersion prism one may this way perform spectrally resolved FCS. Ultimately the concentration range will be limited by the instability of the system (mechanical instability, laser fluctuation) [15

15. S. A. Kim, K. G. Heinze, and P. Schwille, “Fluorescence correlation spectroscopy in living cells,” Nat. Methods 4(11), 963–973 (2007). [CrossRef] [PubMed]

], but as shown in section 5, our current limit is due to the dead-time of the SPAD and to the limit of validity of our correction process.

In this paper, the oscilloscope mode we have introduced was used simply as a convenient mode for adjusting the system. Its use could be extended to implement time-resolved FCS. In the case of a non-stationary system, such as living cells, the concentration/mobility of the protein of interest may evolve as a function of time. Time-resolved FCS would then be able to continuously measure the concentration/mobility fluctuations as the system evolves. The FCS curve at a given time necessarily results from the averaging over a certain time window. The shorter the time window is, the better the time resolution is, but the lower the number of photon is. The signal-to-noise ratio of the FCS curve is essentially dependant on the brightness of the molecule (the ratio of the countrate and number of molecules in the observation volume). If the brightness of the molecules is high, the capability of performing real-time ACF measurement at high countrate is essential, if one want to work even at moderate concentration. In this respect F2Cor is advantageous. To have a high brightness, the choice of the dye is naturally very important, as well as a very high photon collection efficiency. One can also increase the excitation power, with two limiting factors: first the photobleaching, and possibly a saturation of the dye due to the triplet state. Photobleaching affects the correlation function, the apparent diffusion time of which is shortened. Besides, as biological cells have a limited volume compared to that of the excitation volume, photobleaching may deplete the stock of fluorescent molecules in the case long acquisitions. One way to reduce the negative effects of the photobleaching could be the use of scanning FCS (SFCS) [21

21. Z. Petrasek and P. Schwille, “Scanning fluorescence correlation spectroscopy,” in Single Molecules and Nanotechnology (Springer, 2008), pp. 83–105.

]. SFCS will reduce the distortion of the correlation curve and in addition, if the photobleaching is mediated by the triplet state, SFCS can reduce the photobleaching itself. In the case of very long acquisitions in living cells one might then combine SFCS and time-resolved FCS.

In this paper we used the PCI-6602 to construct an FCS setup at reduced cost, operating continuously in real-time at high countrate, with a time resolution of 1µs. However, our LabVIEW software, which integrate the F2Cor library, could easily be adapted to other single photon counting acquisition modules from various manufacturers (Beck&Hickl, Picoquant, Hamamatsu), which have better time resolution. All LabVIEW VIs as well as the F2Cor DLL library can be obtained by contacting the author.

7. Conclusion

We presented an FCS setup that can perform online ACF calculation at very high countrate, up to 8MHz, with minimum hardware. This high countrate capability results from the use of the counting board in the time mode, and from the use of F2Cor, a very fast software correlator. We have shown that henceforth real-time measurements in the micromolar range can be performed on a conventional optical setup, which should be useful for low affinity molecular interactions studies as well as measurements in living cells. In addition, this high countrate capability combined with the flexibility of the software approach opens up the way to time-resolved FCS, which will be useful to study non-stationary systems.

Acknowledgments

This work was supported by Région Bretagne, Rennes Métropole, Conseil Général d’Ille-et-Villaine and by European Regional Development Fund.

References and links

1.

E. Elson and D. Magde, “Fluorescence correlation spectroscopy. I. Conceptual basis and theory,” Biopolymers 13(1), 1–27 (1974). [CrossRef]

2.

D. Magde, E. L. Elson, and W. W. Webb, “Fluorescence correlation spectroscopy. II. An experimental realization,” Biopolymers 13(1), 29–61 (1974). [CrossRef] [PubMed]

3.

P. Schwille and J. Ries, “Principles and applications of fluorescence correlation spectroscopy (FCS),” in Biophotonics: Spectroscopy, Imaging, Sensing, and Manipulation (Springer, 2011), pp. 63–85.

4.

ALV-5000 multiple tau digital correlator reference manual (ALV gmbh, 1993).

5.

T. A. Laurence, S. Fore, and T. Huser, “Fast, flexible algorithm for calculating photon correlations,” Opt. Lett. 31(6), 829–831 (2006). [CrossRef] [PubMed]

6.

D. Magatti and F. Ferri, “Fast multi-tau real-time software correlator for dynamic light scattering,” Appl. Opt. 40(24), 4011–4021 (2001). [CrossRef] [PubMed]

7.

D. Magatti and F. Ferri, “25 ns software correlator for photon and fluorescence correlation spectroscopy,” Rev. Sci. Instrum. 74(2), 1135–1144 (2003). [CrossRef]

8.

M. Wahl, I. Gregor, M. Patting, and J. Enderlein, “Fast calculation of fluorescence correlation data with asynchronous time-correlated single-photon counting,” Opt. Express 11(26), 3583–3591 (2003). [CrossRef] [PubMed]

9.

L. L. Yang, H. Y. Lee, M. K. Wang, X. Y. Lin, K. H. Hsu, Y. R. Chang, W. Fann, and J. D. White, “Real-time data acquisition incorporating high-speed software correlator for single-molecule spectroscopy,” J. Microsc. 234(3), 302–310 (2009). [CrossRef] [PubMed]

10.

K. Schätzel, “Correlation techniques in dynamic light scattering,” Appl. Phys. B 42, 193–213 (1987). [CrossRef]

11.

W. Liu, J. Shen, and X. Sun, “Design of multiple-tau photon correlation system implemented by FPGA ,” Software and Systems, 410-414 (2008).

12.

J. S. Eid, J. D. Muller, and E. Gratton, “Data acquisition card for fluctuation correlation spectroscopy allowing full access to the detected photon sequence,” Rev. Sci. Instrum. 71(2), 361–368 (2000). [CrossRef]

13.

E. Schaub, “F2Cor: fast 2-stage correlation algorithm for FCS and DLS,” Opt. Express 20(3), 2184–2195 (2012). [CrossRef] [PubMed]

14.

DMA Performance Improvements for TIO-based Devices,” http://digital.ni.com/public.nsf/allkb/1B64310FAE9007C086256A1D006D9BBF.

15.

S. A. Kim, K. G. Heinze, and P. Schwille, “Fluorescence correlation spectroscopy in living cells,” Nat. Methods 4(11), 963–973 (2007). [CrossRef] [PubMed]

16.

K. Schätzel, M. Drewel, and S. Stimac, “Photon correlation measurements at large lag times: improving statistical accuracy,” J. Mod. Opt. 35(4), 711–718 (1988). [CrossRef]

17.

T. Wohland, R. Rigler, and H. Vogel, “The standard deviation in fluorescence correlation spectroscopy,” Biophys. J. 80(6), 2987–2999 (2001). [CrossRef] [PubMed]

18.

D. E. Koppel, “Statistical accuracy in fluorescence correlation spectroscopy,” Phys. Rev. A 10(6), 1938–1945 (1974). [CrossRef]

19.

L. Neri, S. Tudisco, F. Musumeci, A. Scordino, G. Fallica, M. Mazzillo, and M. Zimbone, “Note: Dead time causes and correction method for single photon avalanche diode devices,” Rev. Sci. Instrum. 81(8), 086102–086103 (2010). [CrossRef] [PubMed]

20.

J. Wenger, D. Gérard, P. F. Lenne, H. Rigneault, J. Dintinger, T. W. Ebbesen, A. Boned, F. Conchonaud, and D. Marguet, “Dual-color fluorescence cross-correlation spectroscopy in a single nanoaperture : towards rapid multicomponent screening at high concentrations,” Opt. Express 14(25), 12206–12216 (2006). [CrossRef] [PubMed]

21.

Z. Petrasek and P. Schwille, “Scanning fluorescence correlation spectroscopy,” in Single Molecules and Nanotechnology (Springer, 2008), pp. 83–105.

OCIS Codes
(070.4550) Fourier optics and signal processing : Correlators
(120.6200) Instrumentation, measurement, and metrology : Spectrometers and spectroscopic instrumentation
(170.2520) Medical optics and biotechnology : Fluorescence microscopy
(070.7145) Fourier optics and signal processing : Ultrafast processing

ToC Category:
Spectroscopy

History
Original Manuscript: July 16, 2013
Revised Manuscript: September 13, 2013
Manuscript Accepted: September 19, 2013
Published: September 26, 2013

Citation
Emmanuel Schaub, "High countrate real-time FCS using F2Cor," Opt. Express 21, 23543-23555 (2013)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-21-20-23543


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References

  1. E. Elson and D. Magde, “Fluorescence correlation spectroscopy. I. Conceptual basis and theory,” Biopolymers13(1), 1–27 (1974). [CrossRef]
  2. D. Magde, E. L. Elson, and W. W. Webb, “Fluorescence correlation spectroscopy. II. An experimental realization,” Biopolymers13(1), 29–61 (1974). [CrossRef] [PubMed]
  3. P. Schwille and J. Ries, “Principles and applications of fluorescence correlation spectroscopy (FCS),” in Biophotonics: Spectroscopy, Imaging, Sensing, and Manipulation (Springer, 2011), pp. 63–85.
  4. ALV-5000 multiple tau digital correlator reference manual (ALV gmbh, 1993).
  5. T. A. Laurence, S. Fore, and T. Huser, “Fast, flexible algorithm for calculating photon correlations,” Opt. Lett.31(6), 829–831 (2006). [CrossRef] [PubMed]
  6. D. Magatti and F. Ferri, “Fast multi-tau real-time software correlator for dynamic light scattering,” Appl. Opt.40(24), 4011–4021 (2001). [CrossRef] [PubMed]
  7. D. Magatti and F. Ferri, “25 ns software correlator for photon and fluorescence correlation spectroscopy,” Rev. Sci. Instrum.74(2), 1135–1144 (2003). [CrossRef]
  8. M. Wahl, I. Gregor, M. Patting, and J. Enderlein, “Fast calculation of fluorescence correlation data with asynchronous time-correlated single-photon counting,” Opt. Express11(26), 3583–3591 (2003). [CrossRef] [PubMed]
  9. L. L. Yang, H. Y. Lee, M. K. Wang, X. Y. Lin, K. H. Hsu, Y. R. Chang, W. Fann, and J. D. White, “Real-time data acquisition incorporating high-speed software correlator for single-molecule spectroscopy,” J. Microsc.234(3), 302–310 (2009). [CrossRef] [PubMed]
  10. K. Schätzel, “Correlation techniques in dynamic light scattering,” Appl. Phys. B42, 193–213 (1987). [CrossRef]
  11. W. Liu, J. Shen, and X. Sun, “Design of multiple-tau photon correlation system implemented by FPGA,” Software and Systems, 410-414 (2008).
  12. J. S. Eid, J. D. Muller, and E. Gratton, “Data acquisition card for fluctuation correlation spectroscopy allowing full access to the detected photon sequence,” Rev. Sci. Instrum.71(2), 361–368 (2000). [CrossRef]
  13. E. Schaub, “F2Cor: fast 2-stage correlation algorithm for FCS and DLS,” Opt. Express20(3), 2184–2195 (2012). [CrossRef] [PubMed]
  14. DMA Performance Improvements for TIO-based Devices,” http://digital.ni.com/public.nsf/allkb/1B64310FAE9007C086256A1D006D9BBF .
  15. S. A. Kim, K. G. Heinze, and P. Schwille, “Fluorescence correlation spectroscopy in living cells,” Nat. Methods4(11), 963–973 (2007). [CrossRef] [PubMed]
  16. K. Schätzel, M. Drewel, and S. Stimac, “Photon correlation measurements at large lag times: improving statistical accuracy,” J. Mod. Opt.35(4), 711–718 (1988). [CrossRef]
  17. T. Wohland, R. Rigler, and H. Vogel, “The standard deviation in fluorescence correlation spectroscopy,” Biophys. J.80(6), 2987–2999 (2001). [CrossRef] [PubMed]
  18. D. E. Koppel, “Statistical accuracy in fluorescence correlation spectroscopy,” Phys. Rev. A10(6), 1938–1945 (1974). [CrossRef]
  19. L. Neri, S. Tudisco, F. Musumeci, A. Scordino, G. Fallica, M. Mazzillo, and M. Zimbone, “Note: Dead time causes and correction method for single photon avalanche diode devices,” Rev. Sci. Instrum.81(8), 086102–086103 (2010). [CrossRef] [PubMed]
  20. J. Wenger, D. Gérard, P. F. Lenne, H. Rigneault, J. Dintinger, T. W. Ebbesen, A. Boned, F. Conchonaud, and D. Marguet, “Dual-color fluorescence cross-correlation spectroscopy in a single nanoaperture : towards rapid multicomponent screening at high concentrations,” Opt. Express14(25), 12206–12216 (2006). [CrossRef] [PubMed]
  21. Z. Petrasek and P. Schwille, “Scanning fluorescence correlation spectroscopy,” in Single Molecules and Nanotechnology (Springer, 2008), pp. 83–105.

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