In this paper, we described the implementation and performance of RICS on a one-photon Zeiss LSM510 META confocal laser-scanning microscope with analog detection. The detector autocorrelation time imposes an upper limit onto the diffusion coefficient that can be measured. The determination of fast diffusion requires high scan speeds, leading to increased spatial correlation induced by the detector. The detector electronics does not have enough time to reset itself before collecting the next data point. As a result, the residual signal from the previous data point will be correlated with the signal from the following data point. It is important to eliminate these bleed-through noise data points before fitting the spatial ACF since they can result in a lack of convergence of the fitting functions.16
For the diffusion coefficients considered here, it was possible to discard the spatial correlations along the x-axis (ψ = 0) allowing the recovery of diffusion times ranging up to several tens of μm2
/s. However, dependent on the sample properties and the corresponding instrument settings the dynamic range for our instrument would allow spatial correlations along the x-axis (ψ = 0) for long pixel dwell times τp
= 163.84 and 102.4 μs (scanspeeds 1 and 2) and for gain settings below 1000.
The accuracy of the RICS approach to determine diffusion coefficients was assessed in isotropic viscous solutions of 175 nm fluorescent beads. Data were analyzed with both the UCI and UH RICS software. Both software programs yielded similar results, which were in good agreement with the Stokes-Einstein free translational diffusion model (), indicating that the RICS method and the in-house software for data analysis are compatible for well-characterized, chemically and optically homogeneous samples.
The applicability of RICS to monitor 2D diffusion was evaluated by performing measurements on the top membrane of POPC/DiI-C18
(5) GUVs, a model membrane system also characterized by means of beam expander FCS. FRAP measurements in GUVs showed that nearly all DiI-C18
(5) molecules (M = 97 ± 2 % are mobile. The mean D value (D = 7 ± 3 μm2
/s) obtained by RICS was in good agreement with the D values deduced from the beam expander FCS diffusion law fit (Deff
= 8.02 ± 0.01 μm2
/s). These values are also in good agreement with those reported in the literature for DiI-C18
in DOPC (1,2-dioleoyl-sn-glycero-3-phosphatidylcholine) GUVs (D = 7.0 ± 1.2 μm2
FRAP measurements performed on DiI-C18
in POPC supported phospholipid bilayers undergoing minimal interactions with the substrate yield a diffusion coefficient that is equal to the value we obtained via RICS in POPC GUVs (D = 7.5 ± 1.2 μm2
Consistent results were obtained for the mapped diffusion coefficient values for varying sizes of the analysis ROI as long as the GUV perimeter was avoided
RICS was applied to monitor the diffusion of DiI-C18
(5) in the membrane of primary OLGs derived from neonatal rat brain (i.e. shake-off cells). Measurements in cell membranes are obviously more challenging than those in homogeneous solutions or in fluid phase model membrane systems. The images of cell membranes often exhibit large immobile structures, possibly cytosolic vesicles, which will appear as long-range spatial correlations in the autocorrelation spectrum and which may hide other potentially more meaningful information.14,16
These immobile structures, however, were filtered out of the images by applying a moving average window of two frames before calculation of the autocorrelation spectra. Note that the relationship between G(0,0) and the number of particles is no longer valid when the subtraction algorithm (overall or moving average) is applied and the G(0,0) value can no longer be used as a direct measure of the molecular concentration.14,16
Fitting the autocorrelation spectra calculated for DiI-C18
(5) in shake-off cells to a single free diffusing component gave diffusion coefficients (D = 0.12–4 μm2
/s) that compare well with the FRAP measurements performed here (0.4 ± 0.2 μm2
/s ≤ D ≤ 2.8 ± 0.3 μm2
/s) as well as D values reported for FCS (D = 3 ± 1 μm2
; D = 4.5 ± 0.8 μm2
) and FRAP measurements (D = 1–4 μm2
) on DiI-C18
in other cell membranes. However, comparison between FRAP and RICS results cannot directly be made, even when the region of interest is of comparable size. In a FRAP experiment, the mobility of the fluorescent molecules in the non-bleached area is indirectly sampled as well. The value of D obtained in RICS is more locally defined. RICS is therefore more appropriate for detecting different molecular mobilities in spatially different areas of the cell membrane. This allows for mapping of the diffusion coefficient over the cell membrane, as shown in for DiI-C18
(5) in the membrane of shake-off OLGs. Note that ROIs near or crossing cell membranes were not analyzed.14
On the other hand, since the current implementation of RICS does not allow for determination of immobile fractions, FRAP measurements are useful to quantify the mobile fraction of the molecules under study.
Interpretation of the mapped diffusion coefficients over the cell membrane is not obvious at this very moment. As has been observed, processes of neighboring cells which extend under the cell areas might be simultaneously imaged. However, by selecting areas that upon visual inspection show a homogeneous fluorescence intensity, diffusion coefficient values are found which correspond with those obtained for DiI-C18(5) using FRAP. Higher diffusion coefficient values seem to be often associated with areas where bright spots near or inside cell processes or near the perimeter of the cell are present. Further exploration is obviously required and might imply drug induced modification of intracellular structures.
In order to accurately calculate and fit the autocorrelation spectrum, it is important to collect a sufficient number of images. Note that the number of frames is tightly connected to the size of the analysis ROI. For smaller ROIs, more frames are needed, thereby increasing spatial resolution at the expense of temporal resolution, and vice versa. An alternative way to reduce the exposure of cells to laser light is by keeping the laser power low, which usually involves a high detector gain, possibly resulting in noisy images. This thermal noise due to the high detector gain, however, is truly random and will not contribute to the autocorrelation spectrum.16
. Immobile or slowly moving sub-cellular structures, on the other hand, do strongly influence the spatial correlations. When measuring in living motile OLG cells, it is therefore imperative to subtract these features by applying a moving average immobile fraction removal algorithm before calculating autocorrelation spectra.
The acquisition of fewer frames requires less collection time allowing to observe cellular changes on a shorter timescale. However, this is at the expense of the quality of the collected data.