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. 2010 Oct 15;5(10):e13190.
doi: 10.1371/journal.pone.0013190.

A patch-based method for repetitive and transient event detection in fluorescence imaging

Affiliations

A patch-based method for repetitive and transient event detection in fluorescence imaging

Jérôme Boulanger et al. PLoS One. .

Abstract

Automatic detection and characterization of molecular behavior in large data sets obtained by fast imaging in advanced light microscopy become key issues to decipher the dynamic architectures and their coordination in the living cell. Automatic quantification of the number of sudden and transient events observed in fluorescence microscopy is discussed in this paper. We propose a calibrated method based on the comparison of image patches expected to distinguish sudden appearing/vanishing fluorescent spots from other motion behaviors such as lateral movements. We analyze the performances of two statistical control procedures and compare the proposed approach to a frame difference approach using the same controls on a benchmark of synthetic image sequences. We have then selected a molecular model related to membrane trafficking and considered real image sequences obtained in cells stably expressing an endocytic-recycling trans-membrane protein, the Langerin-YFP, for validation. With this model, we targeted the efficient detection of fast and transient local fluorescence concentration arising in image sequences from a data base provided by two different microscopy modalities, wide field (WF) video microscopy using maximum intensity projection along the axial direction and total internal reflection fluorescence microscopy. Finally, the proposed detection method is briefly used to statistically explore the effect of several perturbations on the rate of transient events detected on the pilot biological model.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Dynamics of YFP-Langerin in M10 stable cell lines.
Maximum Intensity Projection of time lapse video (about 0.8–1 fps) of Langerin-YFP fluorescence (A) and thumbnails temporal series (B) corresponding to the boxed area in (A), recorded in full field illumination fluorescent video-microscopy. Large white arrow shows sudden appearance of fluorescence concentration while thin red arrows identifies lateral movement of a vesicle. The same cell line was imaged using a TIRF microscopy set-up equipped with a very high sensitive EM-CCD camera and a typical Maximum Intensity Projection of a TIRF microscopy video sequence (10fps) is presented in (C). Note that the time regime is different between A and B and that long distance trajectories as detectable in (A) are not present in (C). Thumbnails temporal series (D) of the boxed area in (C) illustrate the time delay between appearance of a fixed spot loaded with Langerin-YFP corresponding to a vesicle entering the evanescent field and docking at a specific site of the cell plasma membrane, its subsequent diffusion and progressive disappearance (D, white arrow).
Figure 2
Figure 2. Histogram of the maximum intensity projection (MIP) image of a WF image sequence.
The two Gaussian components of the mixture have been estimated using an Expectation-Maximization algorithm.
Figure 3
Figure 3. Estimation of the bleaching time constant.
Figure 4
Figure 4. Distribution of the minimum distance between patches.
The size of the patches is formula image pixels and the sets contains 17 samples corresponding to search windows of size formula image. The density of Generalized Extreme Value distribution with the following estimated parameters: formula image, formula image and formula image is shown in red.
Figure 5
Figure 5. First frame of a typical synthetic image sequence used for testing the detection method.
Figure 6
Figure 6. The signal-to-noise ratio influences the number of true detections and false alarms.
The number of true detections formula image and the number of false alarms formula image are drawn as a function of the signal-to-noise ratios for the proposed SSD method and for a FD approach using a FDR and a FWER control. The test has been performed on sequences similar to the one shown in figure 5.
Figure 7
Figure 7. Validation of the proposed method through biological, drugs and physical perturbation.
A detection map in a single image corresponding to a 2D projection of a 3D stack (A) and a Maximum Intensity Projection of Video S2 acquired in the WF mode (B) are represented. Green circles identify appearing spot and red circles disappearing spots. The cell contour is delineated in blue and obtained by automatic segmentation. Thumbnails sequence in (C) shows automatic detection of suddenly disappearing structure (red circles and arrows) and appearing spots (green circles and arrows) at the frame rate of one 3D stack per second.
Figure 8
Figure 8. Distribution of the rate of detected events at the single cell level.
The FD approach and the proposed SSD approach have been applied in the same conditions of bleaching compensation and variance stabilization.

References

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