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Computational imaging in cell biology.

Eils R, Athale C - J. Cell Biol. (2003)

Bottom Line: Such quantitative data provide the basis for mathematical modeling of protein kinetics and biochemical signaling networks that, in turn, open the way toward a quantitative view of cell biology.We will present live-cell studies that would have been impossible without computational imaging.These applications illustrate the potential of computational imaging to enhance our knowledge of the dynamics of cellular structures and processes.

View Article: PubMed Central - PubMed

Affiliation: Intelligent Bioinformatics Systems Division, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany. r.eils@dkfz.de

ABSTRACT
Microscopy of cells has changed dramatically since its early days in the mid-seventeenth century. Image analysis has concurrently evolved from measurements of hand drawings and still photographs to computational methods that (semi-) automatically quantify objects, distances, concentrations, and velocities of cells and subcellular structures. Today's imaging technologies generate a wealth of data that requires visualization and multi-dimensional and quantitative image analysis as prerequisites to turning qualitative data into quantitative values. Such quantitative data provide the basis for mathematical modeling of protein kinetics and biochemical signaling networks that, in turn, open the way toward a quantitative view of cell biology. Here, we will review technologies for analyzing and reconstructing dynamic structures and processes in the living cell. We will present live-cell studies that would have been impossible without computational imaging. These applications illustrate the potential of computational imaging to enhance our knowledge of the dynamics of cellular structures and processes.

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The typical workflow in computational imaging is presented. Once the images have been acquired by microscopy and preprocessed to improve the signal-to-noise ratio, they can be directly visualized by methods like volume rendering. For multiple objects in motion, single particle tracking, in which a particle is tracked over different time-steps, is the most direct method used. It provides access to parameters such as velocity, acceleration, and diffusion coefficients. Segmentation is the basis for both surface rendering and kinetic measurements. Surface rendering is obtained after segmentation of contours in each individual section and gives rise to volumetric measurements such as volume and surface area. Measurements of concentration changes for segmented areas in FRAP or fluorescence loss in photobleaching experiments give rise to estimates of kinetic parameters such as diffusion and binding coefficients. Image registration is used to measure elastic or rigid changes of form. It is also often used to correct for global movement before further quantitative analysis. The estimation of flow of gray values is an approach to quantify mobility in continuous space. All these processes lead to accurate estimates of quantitative parameters.
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fig1: The typical workflow in computational imaging is presented. Once the images have been acquired by microscopy and preprocessed to improve the signal-to-noise ratio, they can be directly visualized by methods like volume rendering. For multiple objects in motion, single particle tracking, in which a particle is tracked over different time-steps, is the most direct method used. It provides access to parameters such as velocity, acceleration, and diffusion coefficients. Segmentation is the basis for both surface rendering and kinetic measurements. Surface rendering is obtained after segmentation of contours in each individual section and gives rise to volumetric measurements such as volume and surface area. Measurements of concentration changes for segmented areas in FRAP or fluorescence loss in photobleaching experiments give rise to estimates of kinetic parameters such as diffusion and binding coefficients. Image registration is used to measure elastic or rigid changes of form. It is also often used to correct for global movement before further quantitative analysis. The estimation of flow of gray values is an approach to quantify mobility in continuous space. All these processes lead to accurate estimates of quantitative parameters.

Mentions: Live-cell image analysis started with the earliest microscopists. Although most of these measurements were based on manual inspection and intervention, with the advent of fluorescence microscopy, many studies also involved quantitative imaging of living cells either using video or CCD cameras (Inoue, 1981; Allen and Allen, 1983). In the early years of live-cell microscopy, methods for segmentation and tracking of cells (Berg and Brown, 1972; Berns and Berns, 1982) were rapidly developed and adapted from other areas. Nowadays, techniques for fully automated analysis and time–space visualization of time series from living cells involve either segmentation and tracking of individual structures, or continuous motion estimation (for an overview, see Fig. 1) . For tracking a large number of small particles that move individually and independently from each other, single particle tracking approaches are most appropriate (Qian et al., 1991).


Computational imaging in cell biology.

Eils R, Athale C - J. Cell Biol. (2003)

The typical workflow in computational imaging is presented. Once the images have been acquired by microscopy and preprocessed to improve the signal-to-noise ratio, they can be directly visualized by methods like volume rendering. For multiple objects in motion, single particle tracking, in which a particle is tracked over different time-steps, is the most direct method used. It provides access to parameters such as velocity, acceleration, and diffusion coefficients. Segmentation is the basis for both surface rendering and kinetic measurements. Surface rendering is obtained after segmentation of contours in each individual section and gives rise to volumetric measurements such as volume and surface area. Measurements of concentration changes for segmented areas in FRAP or fluorescence loss in photobleaching experiments give rise to estimates of kinetic parameters such as diffusion and binding coefficients. Image registration is used to measure elastic or rigid changes of form. It is also often used to correct for global movement before further quantitative analysis. The estimation of flow of gray values is an approach to quantify mobility in continuous space. All these processes lead to accurate estimates of quantitative parameters.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2172925&req=5

fig1: The typical workflow in computational imaging is presented. Once the images have been acquired by microscopy and preprocessed to improve the signal-to-noise ratio, they can be directly visualized by methods like volume rendering. For multiple objects in motion, single particle tracking, in which a particle is tracked over different time-steps, is the most direct method used. It provides access to parameters such as velocity, acceleration, and diffusion coefficients. Segmentation is the basis for both surface rendering and kinetic measurements. Surface rendering is obtained after segmentation of contours in each individual section and gives rise to volumetric measurements such as volume and surface area. Measurements of concentration changes for segmented areas in FRAP or fluorescence loss in photobleaching experiments give rise to estimates of kinetic parameters such as diffusion and binding coefficients. Image registration is used to measure elastic or rigid changes of form. It is also often used to correct for global movement before further quantitative analysis. The estimation of flow of gray values is an approach to quantify mobility in continuous space. All these processes lead to accurate estimates of quantitative parameters.
Mentions: Live-cell image analysis started with the earliest microscopists. Although most of these measurements were based on manual inspection and intervention, with the advent of fluorescence microscopy, many studies also involved quantitative imaging of living cells either using video or CCD cameras (Inoue, 1981; Allen and Allen, 1983). In the early years of live-cell microscopy, methods for segmentation and tracking of cells (Berg and Brown, 1972; Berns and Berns, 1982) were rapidly developed and adapted from other areas. Nowadays, techniques for fully automated analysis and time–space visualization of time series from living cells involve either segmentation and tracking of individual structures, or continuous motion estimation (for an overview, see Fig. 1) . For tracking a large number of small particles that move individually and independently from each other, single particle tracking approaches are most appropriate (Qian et al., 1991).

Bottom Line: Such quantitative data provide the basis for mathematical modeling of protein kinetics and biochemical signaling networks that, in turn, open the way toward a quantitative view of cell biology.We will present live-cell studies that would have been impossible without computational imaging.These applications illustrate the potential of computational imaging to enhance our knowledge of the dynamics of cellular structures and processes.

View Article: PubMed Central - PubMed

Affiliation: Intelligent Bioinformatics Systems Division, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany. r.eils@dkfz.de

ABSTRACT
Microscopy of cells has changed dramatically since its early days in the mid-seventeenth century. Image analysis has concurrently evolved from measurements of hand drawings and still photographs to computational methods that (semi-) automatically quantify objects, distances, concentrations, and velocities of cells and subcellular structures. Today's imaging technologies generate a wealth of data that requires visualization and multi-dimensional and quantitative image analysis as prerequisites to turning qualitative data into quantitative values. Such quantitative data provide the basis for mathematical modeling of protein kinetics and biochemical signaling networks that, in turn, open the way toward a quantitative view of cell biology. Here, we will review technologies for analyzing and reconstructing dynamic structures and processes in the living cell. We will present live-cell studies that would have been impossible without computational imaging. These applications illustrate the potential of computational imaging to enhance our knowledge of the dynamics of cellular structures and processes.

Show MeSH
Related in: MedlinePlus