Freitag, 22. September 2017

Computational imaging in cell biology

Eils et al. (2003): Computational imaging in cell biology

This paper deals with "computational methods that (semi-) automatically quantify objects, distances, concentrations, and velocities of cells and subcellular structures" and thus generate quantitative data that "provide the basis for mathematical modeling of protein kinetics and biochemical signaling networks".

In the introduction, the authors write:
Fluorescent dyes such as fluorescein and rhodamine, together with recombinant fluorescent protein technology and voltage- and pH-sensitive dyes allow virtually any cellular structure to be tagged. In combination with techniques in live cells like FRAP and fluorescence resonance energy transfer, it is now possible to obtain spatio-temporal, biochemical, and biophysical information about the cell in a manner not imaginable before.
This is continued by an elaboration on "methods for segmentation and tracking of cells".
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 tracking a large number of small particles that move individually and independently from each other, single particle tracking approaches are most appropriate.
For the determination of more complex movement, two independent approaches were initially developed, but recently have been merged. Optical flow methods estimate the local motion directly from local gray value changes in image sequences. Image registration aims at identifying and allocating certain objects in the real world as they appear in an internal computer model. The main application of image registration in cell biology is the automated correction of rotational and translational movements over time (rigid transformation). This allows the identification of local dynamics, in particular when the movement is a result of the superposition of two or more independent dynamics. Registration also helps to identify global movements when local changes are artifacts and should be neglected.
Several paragraphs follow that explain how these methods work. The paper also mentions computer vision, visualization and quantitative image analysis.
A great advantage of the combination of segmentation and surface reconstruction is the immediate access to quantitative information that corresponds to visual data. These approaches were designed to deal particularly with the high degree of anisotropy typical for 4-D live-cell recordings and to directly estimate quantitative parameters, e.g., the gray values in the segmented area of corresponding images can be measured to determine the amount and concentration of fluorescently labeled proteins in the segmented cellular compartments.
A challenge for future work is to better understand the biomechanical behavior of cellular structures, e.g., cellular membranes, by fitting a biophysical model to the data - an approach already successfully implemented in various fields of medical image analysis.
Finally, the paper mentions a couple of applications and concludes:
In combination with models of biochemical processes and regulatory networks, computational imaging as part of the emerging field of systems biology will lead to the identification of novel principles of cellular regulation derived from the huge amount of experimental data that are currently generated.

Keine Kommentare:

Kommentar veröffentlichen