Abstract
Cell migration is the driving force behind the dynamics of many diverse biological processes. Even though microscopy experiments are routinely performed today by which populations of cells are visualized in space and time, valuable information contained in image data is often disregarded because statistical analyses are performed at the level of cell populations rather than at the single-cell level. Image-based systems biology is a modern approach that aims at quantitatively analyzing and modeling biological processes by developing novel strategies and tools for the interpretation of image data. In this study, we take first steps towards a fully automated characterization and parameter-free classification of cell track data that can be generally applied to tracked objects as obtained from image data. The requirements to achieve this aim include: (i) combination of different measures for single cell tracks, such as the confinement ratio and the asphericity of the track volume, and (ii) computation of these measures in a staggered fashion to retrieve local information from all possible combinations of track segments. We demonstrate for a population of synthetic cell tracks as well as for in vitro neutrophil tracks obtained from microscopy experiment that the information contained in the track data is fully exploited in this way and does not require any prior knowledge, which keeps the analysis unbiased and general. The identification of cells that show the same type of migration behavior within the population of all cells is achieved via agglomerative hierarchical clustering of cell tracks in the parameter space of the staggered measures. The recognition of characteristic patterns is highly desired to advance our knowledge about the dynamics of biological processes.
Highlights
Image-based systems biology is a growing field of research that involves the development of methods for the quantitative analysis and modeling of information contained in microscopic images
We present the results on the automated characterization and parameter-free classification for synthetic cell tracks generated on the computer as well as for neutrophil migration observed in microscopy experiments
Cell population analyses obscure heterogeneity in cell track data A statistical analysis was performed for a population of I~500 synthetic cell tracks that were generated in silico as outlined in the Methods section
Summary
Image-based systems biology is a growing field of research that involves the development of methods for the quantitative analysis and modeling of information contained in microscopic images. In order to capture important details of a biological process under consideration and to arrive at quantitative predictions, it is generally required that algorithms capable of analyzing the specific experimental data have to be developed first [2,3]. While the time-dependent positions of cells are recorded in microscopy experiments at the single-cell level, in many cases the subsequent analysis is performed by statistical means at the level of the cell population, where the absolute cell positions in the biological sample and the relative temporal offset between cell tracks are integrated out. Analyzing image data obtained at the single-cell level by statistical means at the level of the cell population may strongly reduce the predictive power of the analysis and may possibly even lead to incorrect conclusions with regard to spatio-temporal changes in the cellular migration behavior
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