Characterization of protein dynamics in asymmetric cell division by scanning fluorescence correlation spectroscopy

Abstract

The development and differentiation of complex organisms from the single fertilized egg is regulated by a variety of processes that all rely on the distribution and interaction of proteins. Despite the tight regulation of these processes with respect to temporal and spatial protein localization, exact quantification of the underlying parameters, such as concentrations and distribution coefficients, has so far been problematic. Recent experiments suggest that fluorescence correlation spectroscopy on a single molecule level in living cells has great promise in revealing these parameters with high precision. The optically challenging situation in multicellular systems such as embryos can be ameliorated by two-photon excitation, where scattering background and cumulative photobleaching is limited. A more severe problem is posed by the large range of molecular mobilities observed at the same time, as standard FCS relies strongly on the presence of mobility-induced fluctuations. In this study, we overcame the limitations of standard FCS. We analyzed in vivo polarity protein PAR-2 from eggs of Caenorhabditis elegans by beam-scanning FCS in the cytosol and on the cortex of C. elegans before asymmetric cell division. The surprising result is that the distribution of PAR-2 is largely uncoupled from the movement of cytoskeletal components of the cortex. These results call for a more systematic future investigation of the different cortical elements, and show that the FCS technique can contribute to answering these questions, by providing a complementary approach that can reveal insights not obtainable by other techniques.

FIGURE 1

Diffusion coefficients of GFP∷PAR-2 (A and C) and NMY-2∷GFP (B and D) in the cytosol. The measurement volume was positioned into different parts of the cytosol, while focused into the midplane of the embryo (A and B), and the measured autocorrelation curves were fitted to Eq. 2. The distributions of the obtained diffusion coefficients D are shown in panels C (GFP∷PAR-2) and D (NMY-2∷GFP). (Scale bar, 10 μm. a, anterior; p, posterior.)

FIGURE 2

Nonuniform fluorescence pattern of GFP∷PAR-2 (A and C) and NMY-2∷GFP (B and D) on the cortex. The objective was focused near the coverslip onto the flattened part of the embryo. The circles in panels A and B indicate the scan path for sFCS measurements. (C and D) The fluctuating fluorescence intensity recorded in the sFCS measurement is displayed in a two-dimensional plot, where the horizontal axis corresponds to one revolution (scan period T), and the vertical axis to subsequent revolutions during the course of measurement (from top to bottom). The columns in the plot then show the fluorescence fluctuations at individual positions along the scanned circle. (C) GFP∷PAR-2, D: NMY-2∷GFP. (Scale bar, 10 μm. a, anterior; p, posterior.)

FIGURE 3

The experimental fluorescence autocorrelations from sFCS measurements (shaded). The amplitudes of the peaks obtained from fits to Eq. 7 are indicated by solid dots. (A) GFP∷PAR-2, (B) NMY-2∷GFP.

FIGURE 4

Comparison of sFCS autocorrelations of GFP∷PAR-2 (black) and NMY-2∷GFP (thick gray) on the cortex and FCS autocorrelations of GFP∷PH (thin gray) on the membrane from measurements in several embryos. The sFCS autocorrelations are formed by the fitted amplitudes of the peaks as shown in Fig. 3.

FIGURE 5

Comparison of a typical sFCS autocorrelation (blue) of GFP∷PAR-2 (A) and NMY-2∷GFP (B) with several simple models of transport: uniform flow (green), binding/dissociation (magenta), normal diffusion (black), anomalous diffusion (gray), normal two-component diffusion (red), and flow with a Gaussian distribution of speeds, centered at (cyan). The parameters τ_i are the characteristic time constants of the relevant process.

FIGURE 6

sFCS autocorrelation of GFP∷PAR-2 displayed in spatiotemporal representation, and fits to three different models. The value x is the spatial and τ the temporal correlation coordinate. (A) The spatiotemporal autocorrelation. (B) The spatiotemporal autocorrelation normalized to the maximum at each τ value to emphasize the spatial broadening. (C and F) Fit to a one-component diffusion model and the residuals of the fit (χ² = 4.61 × 10⁻⁶). The white rectangle denotes the fitting range. (D and G) Fit to a two-component diffusion model and the fit residuals (χ² = 3.99 × 10⁻⁶). (E and H) Fit to a model with one diffusion and one binding/dissociation components, and the fit residuals (χ² = 4.37 × 10⁻⁶). The correlation coordinate x (corresponding to the distance along the scanned circle) can be equivalently expressed by the scan phase or by the time from the peak maximum τ. These three coordinates are related to each other in the following way: x = 2R sin(ωτ/2),

FIGURE 7

sFCS autocorrelation of NMY-2∷GFP displayed in spatiotemporal representation. (A) The spatiotemporal autocorrelation. (B) The spatiotemporal autocorrelation normalized to the maximum at each τ.