Image segmentation and dynamic lineage analysis in single-cell fluorescence microscopy - PubMed (original) (raw)

Comparative Study

Image segmentation and dynamic lineage analysis in single-cell fluorescence microscopy

Quanli Wang et al. Cytometry A. 2010 Jan.

Abstract

An increasingly common component of studies in synthetic and systems biology is analysis of dynamics of gene expression at the single-cell level, a context that is heavily dependent on the use of time-lapse movies. Extracting quantitative data on the single-cell temporal dynamics from such movies remains a major challenge. Here, we describe novel methods for automating key steps in the analysis of single-cell, fluorescent images-segmentation and lineage reconstruction-to recognize and track individual cells over time. The automated analysis iteratively combines a set of extended morphological methods for segmentation, and uses a neighborhood-based scoring method for frame-to-frame lineage linking. Our studies with bacteria, budding yeast and human cells, demonstrate the portability and usability of these methods, whether using phase, bright field or fluorescent images. These examples also demonstrate the utility of our integrated approach in facilitating analyses of engineered and natural cellular networks in diverse settings. The automated methods are implemented in freely available, open-source software.

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Figures

Figure 1

Figure 1

Preprocessing to convert the input grey-scale phase image into a hybrid image. (a) Sample grey-scale input images from E. coli experiment at time point t = 10,t = 25 and t = 40 respectively. (b) Resulting hybrid images after applying the modified range filter to identify initial backgrounds (colored in green). (c) Resulting hybrid images after applying the high-pass filter to (b) to further identif border regions (colored in blue). (d) the resulting hybrid images after applying a threshold filter to (c) to mark more pixels as borders.

Figure 2

Figure 2

Iterative segmentation. (a) Result of using a hybrid quantile filter on images in Figure 1d. (b) Hybrid images after further smoothing, thickening and re-smoothing to (a). (c) The binary masks of the blobs with lower scores in (b). (d) The blobs with higher scores in (b). (e) The final segmentation result with (arbitrary) cell coloring for visual clarity.

Figure 2

Figure 2

Iterative segmentation. (a) Result of using a hybrid quantile filter on images in Figure 1d. (b) Hybrid images after further smoothing, thickening and re-smoothing to (a). (c) The binary masks of the blobs with lower scores in (b). (d) The blobs with higher scores in (b). (e) The final segmentation result with (arbitrary) cell coloring for visual clarity.

Figure 3

Figure 3

Neighborhood based cell tracking. (a,b) Two images at consecutive times with the same cell labeled in blue. (c) Result of overlaying the segmented images from a and (b). (d,e) Neighborhood of the cell identified in (a) and (b) respectively, labeled in blue and cyan. (f) The overlapping position where the best scores are obtained for the labeled cells in and (b).

Figure 4

Figure 4

Cell tracking and lineage tree reconstruction. (a) The combined score matrix displayed as a heat map, with warm color indicating good matching. (b) Heat map for the final correspondence matrix. (c) Complete lineage tree for cell number 1 tracked in images 1-25.

Figure 5

Figure 5

Comparison with other leading algorithms. (a) Heat map of score matrix analogous to Figure 4(a) using the method of (20). (b) Tracking result for human cells compared to that of (26).

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