An introduction to Fluorescence Cross Correlation Spectroscopy

January 27, 2012

Studying protein properties

Fluorescence cross-correlation spectroscopy (FCCS) is a powerful biophysical method to study protein-protein interactions with the help of fluorescent labels and a confocal microscope. During my work I found it a bit disappointing not to know some of the theory behind FCCS. Knowing how FCCS works and how to interpret the results is, however, essential for making sense out of the previous biochemical work. In essence FCCS measures the photons which are emitted from a fluorescent molecule after excitation through a laser which is attached to a biomolecule. Most of the time this is a protein. Another fluorescent label is attached to a second protein which is hypothesized to interact with the first one, but emits photons of a different wavelength than the first fluorophores. The confocal microscope only observes a femtoliter volume in which the concentration of the two proteins is in the nanomolar range. Proteins diffusing in and out of the focal volume are detected by detecting the emitted photons. Since big molecules diffuse slower than small ones, two different “fluorescence vs. time” curves result. If both proteins interact, one might observe a third curve which indicates even slower diffusion. However, in practice it is not so trivial to interpret the FCCS results. This is the approach of a master student to make some sense out of the matter and therefore not all translations from the mathematical formulas into English might be 100% correct. Comments on mistakes or misunderstandings are greatly appreciated!

Principle of a confocal microscope with a cross-correlation setup

In order to study protein-protein interactions a dual-color setup (Fig. 1) is the method of choice, since differences in diffusion time for two molecules are hard to measure accurately if the molecular masses do not differ by factor four at least. To be able to excite the two different fluorophores two different excitation wavelengths are required. Two lasers fulfill this task. For a dual-color setup an additional normal mirror and an additional dicroic mirror are necessary. A dicroic mirror reflects light of one wavelength spectrum, but allows passage of another (Fig. 1). Via such a mirror therefore both laser beams can be focused on the sample volume (Fig. 2). After excitation the fluorophores emit photons of a higher wavelength due to photochemical energy losses. A second dicroic mirror allows only the passage of the desired emitted wavelength spectra. Since this spectrum contains the emission wavelengths of both fluorophores they need to be split by applying a third dicroic mirror which reflects the orange light in this examples and allows passage of the green light. Two emission filters that are located in the now separated light beams further “purify” the incoming light. Lenses now focus the light on a photoreceptor with one-photon sensitivity.

Fig. 1: Functional parts of a confocal microscope suitable for FCCS while making use of a dual-color setup (1).

Molecules now pass through the focal volume (Fig. 2). This leads to random photon noise which is depicted in Fig. 3. Sometimes signals arise that have a higher intensity (photons/time) than the noise which is always present. The strength of the statistical method behind FCCS is to extract meaning out of these fluctuations. By correlating the intensity fluctuations to the noise information about the molecule concentration (number of molecules) and molecular size (diffusion time) can be obtained. A brief overview about how this is done can be found below.

Fig. 2: Molecules of different sizes pass through the focal volume (with circle) of the microscope and emit photons. The longer the diffusion takes, the more photons are emitted and detected (2).

What is measured and what it means

When molecules move through the focal volume they emit photons that are detected by for example a photomultiplier device (see above). The measured amount of photons is plotted against the time which yields an intensity curve that can be seen in the top left corner of Fig. 3. Since there are always photons detected that do not originate from the sample molecule, but are for example reflected there is a constant mean intensity measured, which is called noise. However, the signal also consists out of a fluctuating contribution which is significantly higher than the average signal. These fluctuations are the photons that are emitted by the protein linked fluorophores and therefore quite some informational value is linked to them. The two curves in the top portion of Fig. 2 essentially depict the same (photon amount per time), however the curves differ because different molecules are responsible for them. The left curve shows a small molecule which diffuses fast, the right curve a bigger and therefore slower molecule.  When now the signal at one point in time (a fluctuation) is correlated to the average intensity over an increasing time period, a so-called auto-correlation curve results (Fig. 3, bottom curve). With the average measuring time being the same as the amount of time at which the fluctuating signal was measured the correlation is of course 1 (100%). However, when average intervals are increased the results resemble more and more the background noise and the measured fluctuation intensity at one point does not correlate with the average anymore or at least not perfectly (smaller than 1, bigger than 0). Plotting this number on a time log scale results in the autocorrelation curve shown below, which also depicts the decreasing correlation value between the measured signals as blue are between the curves.
In order to detect nice fluctuations in the first place the concentration of the to-be measured molecule in the focal volume needs to be in the nanomolar range. Otherwise the fluctuations combine and become background noise themselves. Just on a higher level.
The measured data, of course, in most cases does not perfectly overlay the theoretically determined curve which originates from the autocorrelation function. In order to achieve mathematical fitting is required. For FCCS a so-called nonlinear least square algorithm can be used which will not be explained here.

Behind the scenes of cross-correlation

The value that can be extracted from such a fitted curve is, that curves, when compared to each other, are differing. This is the result of different molecular properties like the concentration, the size (diffusion time) and triplet state. These features allow the characterization of the molecule.

Fig. 3: Schematic overview of the origin of the actual autocorrelation curve from detected photon intensities plotted against time and correlated with average values of different time spans. For details see the text (modified overview with graphical elements from (1) and (3)).

As already mentioned above, different molecular properties lead to (slightly) different curves. If, however the diffusion coefficients (size) of two molecules are very similar, also the resulting cross-correlation curves will be hardly distinguishable. Still, it is possible two examine the potential interaction of two molecules at the same time if both molecules are labeled with fluorophores that emit photons of different wavelengths. Fig. 1 shows how these photons with different wavelengths are detected individually. Mathematically these slightly different curves (they, however, DO DIFFER in wavelength origin) can now be correlated with each other (cross-correlated) which yields information about potential dimer formation if the “new” combined curve cannot be correlated significantly to one of the previous ones.

Taken together FCCS is a relatively easy method to handle and delivers fast clues about molecular interaction on a single-molecule level. A prerequisite is, however, that the previous cloning and biochemical steps worked and that the proteins are pure, nicely labeled, functional and non-agregated. Once this has been achieved great discoveries are possible 😉

(1)    Schwille & Haustein, Fluorescence Correlation Spectroscopy – An introduction to its concepts and applications, http://www.biophysics.org/Portals/1/PDFs/Education/schwille.pdf.
(2)     Jay Unruh, Fluorescence Correlation Spectroscopy, http://research.stowers institute.org/microscopy/external/Technology/FCS/index.htm.
(3)    Invitrogen, Fluorescence Correlation Spectroscopy (FCS) – Note 1.3, http://www.invitrogen.com/site/us/en/home/References/Molecular-Probes-The-Handbook/Technical-Notes-and-Product-Highlights/Fluorescence-Correlation-Spectroscopy-FCS.html

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One Response to “An introduction to Fluorescence Cross Correlation Spectroscopy”

  1. […] fluctuations in fluorescence intensity has briefly been described in my blog and can be found here. With just knowing (A) and (B) many interesting things can be calculated in a short amount of […]

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