A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts

Many biological processes occur at the sites of cell-cell interactions, e.g., cell-cell adhesion^1,2,3, cell-cell fusion⁴ and cellular recognition⁵. Such events are particularly important during the development of multicellular organisms and for cell-cell communication, e.g., during immune responses. These processes are typically mediated by proteins that are localized at the surface, i.e., at the plasma membrane (PM) of neighboring cells and undergo specific interactions at the cell-cell contact that are precisely regulated in space and time. In many cases, these interactions are direct homo- or heterotypic protein-protein trans interactions, but may also involve ions or ligands acting as extracellular linkers¹. Although of fundamental importance, there is a lack of assays probing these specific protein-protein interactions directly in the native environment of living cells. Many methods either require cell disruption (e.g., biochemical assays such as co-immunoprecipitation⁶), fixation (e.g., some of the super-resolution optical microscopy techniques and electron microscopy of cell-cell contacts⁷), or are non-specific, e.g., aggregation/ adhesion assays^8,9. To overcome this issue, fluorescence techniques have been implemented based on fluorescence resonance energy transfer (FRET)¹⁰ or fluorescence complementation¹¹. However, to achieve sufficiently small distances between fluorophores, these methods require fluorescent labels on the extracellular side of the proteins¹⁰, potentially interfering with trans interactions.

Here, we present an alternative fluorescence-based assay for protein-protein interactions at cell-cell contacts. This approach combines fluorescence cross-correlation approaches (scanning fluorescence cross-correlation spectroscopy (sFCCS), cross-correlation number and brightness (ccN&B)) and mixing of cells expressing a fusion construct of the protein of interest, e.g., an adhesion receptor. The investigated receptors in the two interacting cells are labeled with two spectrally separated fluorescent proteins (FPs), from the intracellular side (see Figure 1A).

The employed methods are based on the statistical analysis of fluorescence fluctuations induced by the diffusive motion of fluorescent fusion proteins through the focal volume of a confocal laser scanning microscope. More in detail, the assay probes the co-diffusion of the proteins of interest in both neighboring PMs at cell-cell contacts. If the proteins undergo trans interactions, these trans complexes will carry fluorescent proteins emitting in both spectral channels, causing correlated fluorescence fluctuations of both emitters. On the other hand, if no binding occurs, the number fluctuations of proteins in facing PMs will be independent, causing no correlated fluctuations. The acquisition can be performed in two ways: 1) sFCCS is based on a line-shaped scan across the cell-cell contact and effectively probes the interactions in a spot located in the contact region. Through a temporal analysis of fluorescence fluctuations, sFCCS provides also dynamics information, i.e., the diffusion coefficients of protein complexes; 2) ccN&B is based on a pixel-wise analysis of a sequence of images acquired at the cell-cell contact regions. It has capability to probe and map interactions along the whole contact region (in one focal plane), but does not provide information on dynamics. Both methods can be combined with an analysis of the molecular brightness, i.e., the average fluorescence signal emitted in the time unit by single diffusing protein complexes and, thus, provide estimates of the stoichiometry of protein complexes at cell-cell contacts.

In this article, we provide detailed protocols for sample preparation, instrument calibration, data acquisition and analysis to perform the presented assay on a commercial confocal laser scanning microscope. The experiments can be performed on any instrument equipped with photon counting or analog detectors and an objective with high numerical aperture. We further discuss critical steps of the protocol and provide correction schemes for several processes causing artefactual signal fluctuations, e.g., detector noise, photobleaching or cell movement. Originally developed to probe interactions between adherent cells, the assay may be modified for suspension cells, or adapted to model membrane systems, e.g., giant unilamellar vesicles (GUVs) or giant plasma membrane vesicles (GPMVs), allowing the quantification of interactions in different lipid environments or in the absence of an organized cytoskeleton^12,13.

Scanning fluorescence cross-correlation spectroscopy is a modified version of fluorescence cross-correlation spectroscopy¹⁴ and was specifically designed to probe slow diffusive dynamics in lipid membranes¹⁵. It is based on a line scan acquisition perpendicular to the PM containing the fluorescent proteins of interest. To probe interactions of two differently labeled protein species, the acquisition is performed in two spectral channels using two laser lines and two detection windows for spectrally separated fluorophores. Due to the slow diffusion dynamics of proteins in the PM (D≤~1 µm²/s), a cross-talk-free measurement can be performed by alternating the excitation scheme from line to line¹⁵. The analysis starts with: 1) an alignment algorithm correcting for lateral cell movement based on block-wise averaging of ~1000 lines, 2) determination of the position with maximum fluorescence signal, i.e., the PM position, in each block and 3) shifting of all blocks to a common origin^12,15, separately in each channel. Then, an automatic selection of pixels corresponding to the PM is performed by selecting the central region from a Gaussian fit of the sum of all aligned lines (i.e., center ± 2.5σ). Integration of the signal in each line yields the membrane fluorescence time series F(t) in each channel (g = green channel, r = red channel). Note that the pixel size has to be small enough, e.g., <200 nm, to reconstruct the shape of the point spread function and find its center, corresponding to the position of the PM. In the presence of substantial photobleaching, the fluorescence time series in each channel may be modeled with a double-exponential function and then corrected with the following formula:¹⁶

.    (1)

It is important to note that this formula effectively corrects both the amplitudes and diffusion times obtained from correlation analysis of F(t)^c, compared to parameter estimates that would be obtained from the uncorrected F(t). Then, the auto- and cross-correlation functions (ACFs/ CCFs) of the fluorescence signals are calculated:

, (2)

, (3)

where δF_i = F_i(t) – F_i(t) and i = g,r.

A two-dimensional diffusion model is then fitted to all correlation functions (CFs):

.   (4)

Here, N denotes the number of fluorescent proteins in the observation volume and τ_d the diffusion time for each channel. This model takes into account that in the described experimental setting, diffusion of proteins in the PM occurs in the x-z plane, in contrast to the commonly used configuration of fluorescence correlation spectroscopy (FCS) experiments on membranes probing diffusion in the x-y plane of the confocal volume¹⁷. The waist w₀ and the structure factor S, describing the elongation w_z of the focal volume in z, S = w_z/w₀, are obtained from a point FCS calibration measurement performed with spectrally similar dyes and same optical settings using already available values for the diffusion coefficient D_dye:

,    (5)

where τ_d,dye is the measured average diffusion time of the dye molecules, obtained from fitting a model for three-dimensional diffusion to the data, taking into account transitions of a fraction T of all N molecules to a triplet state with a time constant τ_τ:

.   (6)

Finally, diffusion coefficients (D), molecular brightness values (ε) and the relative cross-correlation of sFCCS data (rel.cc.) are calculated as follows:

,    (7)

 ,   (8)

,   (9)

where G_cross(0) is the amplitude of the cross-correlation function and is the amplitude of the autocorrelation function in the i-th channel.

This definition of the relative cross-correlation, i.e. using max instead of mean in Equation 9, takes into account that the maximum number of complexes of two protein species present at different concentrations is limited by the species present in a lower number.

Cross-correlation number and brightness is based on a moment analysis of the fluorescence intensity for each pixel of an image stack acquired over time at a fixed position in the sample, typically consisting of ~100-200 frames, with two spectral channels (g = green channel, r = red channel). From the temporal mean I_i and variance  , the molecular brightness ε_i and number n_i are calculated in each pixel and spectral channel (i = g, r)¹⁸:

,   (10)

. (11)

It is important to note that the given equations apply to the ideal case of a true photon-counting detector. For analog detection systems, the following equations apply^19,20:

,   (12)

.   (13)

Here, S is the conversion factor between detected photons and the recorded digital counts, is the readout noise and offset refers to the detector intensity offset. Generally, these quantities should be calibrated, for any detector type, based on measuring the detector variance as a function of intensity for steady illumination¹⁹, e.g., a reflective metal surface or dried dye solution. The offset can be determined by measuring the count rate for a sample without excitation light. By performing a linear regression of the detector-associated variance versus intensity (I) plot, S and can be determined¹⁹:

.   (14)

Finally, the cross-correlation brightness is calculated in each pixel and is defined in general as²¹

,   (15)

where is the cross-variance .

In order to filter long-lived fluctuations, all ccN&B calculations are performed following a boxcar filtering, independently for each pixel²². Briefly, n_i, ε_i (i = g, r) and B_cc are calculated in sliding segments of e.g., 8-15 frames. The values thus obtained can be then averaged to obtain the final pixel number and brightness values.

Stoichiometry analysis
In order to estimate the stoichiometry of protein complexes at cell-cell contacts, the molecular brightness can be separately analyzed in each spectral channel for the sFCCS or ccN&B data. In sFCCS, one brightness value is obtained per measurement in each channel. In ccN&B, a brightness histogram of all pixels corresponding to the cell-cell contact is obtained and the average (or median) value can be used as representative brightness for the measurement. By performing the same analysis on a monomeric reference, all brightness values can be normalized to directly obtain the average oligomeric state of the detected protein complexes. At this point, it is important to correct for the presence of non-fluorescent FPs that may result in an underestimation of the oligomeric state. This is typically performed by measuring the brightness of a homo-dimeric reference protein^23,24 using one-color sFCS or number and brightness (N&B).