Although the highly dynamic and mosaic organization of the plasma membrane is well-recognized, depicting a resolved, global view of this organization remains challenging. We present an analytical single-particle tracking (SPT) method and tool, multiple-target tracing (MTT), that takes advantage of the high spatial resolution provided by single-fluorophore sensitivity. MTT can be used to generate dynamic maps at high densities of tracked particles, thereby providing global representation of molecular dynamics in cell membranes. Deflation by subtracting detected peaks allows detection of lower-intensity peaks. We exhaustively detected particles using MTT, with performance reaching theoretical limits, and then reconnected trajectories integrating the statistical information from past trajectories. We demonstrate the potential of this method by applying it to the epidermal growth factor receptor (EGFR) labeled with quantum dots (Qdots), in the plasma membrane of live cells. We anticipate the use of MTT to explore molecular dynamics and interactions at the cell membrane.
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We thank H.T. He, L. Leserman, P. Réfrégier, F. Conchonaud, S. Mailfert and M. Fallet for helpful discussions and suggestions, and E. Witty (AngloScribe) for language editing. This project was supported by grants from Institut National de la Santé et de la Recherche Médicale, Centre National de la Recherche Scientifique, Agence Nationale de la Recherche and European regional development fund.
Supplementary Figures 1–5, Supplementary Methods, Supplementary Table 1 and Supplementary Note (PDF 4011 kb)
MTT computation on experimental data. The left part is the raw image corresponding to the cell depicted in Fig. 5b and the right part illustrates the MTT process, corresponding to the 120 × 80 pixels area zoomed in on in Supplementary Fig. 2. Two circles (with pseudo-colors for clarity purpose) delimit the concentric reconnection domains associated to local and maximal diffusion of each particle, eventually increasing after blink (Fig. 3). Images were acquired as described, at 36 ms acquisition time (28 fps). (MOV 1242 kb)
Spatial variations of EGFR confinement. Each frame of the video was independently computed as in Fig. 5c, from an acquisition stack of 1,000 frames (corresponding to 36 s) as a contour plot of the local confinement level. Contours were calculated by cubic interpolation through (x, y, Log(Lconf)) points using Matlab. Each pixel can account for up to tens of trajectories (Fig. 6c). In these cases, the mean value of Lconf was used. To generate a movie instead of a map, contours were generated frame by frame and recorded at 2× reduced speed (14 fps). Using high-density labeling, non-labeled regions corresponded to a reasonably low fraction of the membrane. They were either interpolated or left as gaps in the surface, according to their surrounding values. Temporal smoothing over 3 frames was performed to improve visual quality. Non-specific binding to the coverslip around the cell was discarded by computing the cell contour using the DIC image. Image size is 61.5 × 51.25 μm, at 100× magnification, using the same color-scale as in Fig. 5c. (MOV 2822 kb)
3× zoom of Supplementary Video 2. This video was recorded at 2× reduced speed (14 fps). Image width is 20.5 μm. (MOV 3239 kb)
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