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Dynamic multiple-target tracing to probe spatiotemporal cartography of cell membranes

Nature Methods volume 5, pages 687–694 (2008)Cite this article

Abstract

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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Figure 1: Schematic overview of MTT.
Figure 2: Exhaustive particle detection principle.
Figure 3: Multiple-target reconnection.
Figure 4: Performances of the deflation algorithm for detection, estimation and reconnection.
Figure 5: Detection and mapping of confinement at the cell membrane.
Figure 6: Confinement quantification.

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Acknowledgements

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.

Author information

Authors and Affiliations

  1. Centre d'Immunologie de Marseille-Luminy, Université de la Méditerranée, UMR 6102, Parc scientifique de Luminy, Case 906, Marseille, F-13288, cedex 09, France

    Arnauld Sergé & Didier Marguet

  2. Institut National de la Santé et de la Recherche Médicale, Unité Mixte de Recherche 631, UMR 6102, Parc scientifique de Luminy, Case 906, Marseille, F-13288, cedex 09, France

    Arnauld Sergé & Didier Marguet

  3. Centre National de la Recherche Scientifique, UMR 6102, Parc scientifique de Luminy, Case 906, Marseille, F-13288, cedex 09, France

    Arnauld Sergé & Didier Marguet

  4. Institut Fresnel, Université Paul Cézanne, UMR 6133, Domaine Universitaire de Saint Jérôme, Marseille, F-13397, cedex 20, France

    Nicolas Bertaux & Hervé Rigneault

  5. Centre National de la Recherche Scientifique, UMR 6133, Domaine Universitaire de Saint Jérôme, Marseille, F-13397, cedex 20, France

    Nicolas Bertaux & Hervé Rigneault

  6. École Centrale Marseille, Technopôle de Château-Gombert, 38, rue Frédéric Joliot Curie, Marseille, F-13451, cedex 20, France

    Nicolas Bertaux

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  1. Arnauld Sergé
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  4. Didier Marguet
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Contributions

H.R. and D.M. designed the research; A.S. and N.B. performed the research, developed new analytical tools and analyzed data; A.S. and D.M. wrote the paper.

Corresponding author

Correspondence to Didier Marguet.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5, Supplementary Methods, Supplementary Table 1 and Supplementary Note (PDF 4011 kb)

Supplementary Video 1

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)

Supplementary Video 2

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)

Supplementary Video 3

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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Sergé, A., Bertaux, N., Rigneault, H. et al. Dynamic multiple-target tracing to probe spatiotemporal cartography of cell membranes. Nat Methods 5, 687–694 (2008). https://doi.org/10.1038/nmeth.1233

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