Automated microscopy has given researchers access to great amounts of live cell imaging data from in vitro and in vivo experiments. Much focus has been put on extracting cell tracks from such data using a plethora of segmentation and tracking algorithms, but further analysis is normally required to draw biologically relevant conclusions. Such relevant conclusions may be whether the migration is directed or not, whether the population has homogeneous or heterogeneous migration patterns. This review focuses on the analysis of cell migration data that are extracted from time lapse images. We discuss a range of measures and models used to analyze cell tracks independent of the biological system or the way the tracks were obtained. For single-cell migration, we focus on measures and models giving examples of biological systems where they have been applied, for example, migration of bacteria, fibroblasts, and immune cells. For collective migration, we describe the model systems wound healing, neural crest migration, and Drosophila gastrulation and discuss methods for cell migration within these systems. We also discuss the role of the extracellular matrix and subsequent differences between track analysis in vitro and in vivo. Besides methods and measures, we are putting special focus on the need for openly available data and code, as well as a lack of common vocabulary in cell track analysis. © 2017 International Society for Advancement of Cytometry.

Migration and interactions of immune cells are routinely studied by time-lapse microscopy of in vitro migration and confrontation assays. To objectively quantify the dynamic behavior of cells, software tools for automated cell tracking can be applied. However, many existing tracking algorithms recognize only rather short fragments of a whole cell track and rely on cell staining to enhance cell segmentation. While our previously developed segmentation approach enables tracking of label-free cells, it still suffers from frequently recognizing only short track fragments. In this study, we identify sources of track fragmentation and provide solutions to obtain longer cell tracks. This is achieved by improving the detection of low-contrast cells and by optimizing the value of the gap size parameter, which defines the number of missing cell positions between track fragments that is accepted for still connecting them into one track. We find that the enhanced track recognition increases the average length of cell tracks up to 2.2-fold. Recognizing cell tracks as a whole will enable studying and quantifying more complex patterns of cell behavior, e.g. switches in migration mode or dependence of the phagocytosis efficiency on the number and type of preceding interactions. Such quantitative analyses will improve our understanding of how immune cells interact and function in health and disease.

Summary Mucormycoses are life‐threatening infections that affect patients suffering from immune deficiencies. We performed phagocytosis assays confronting various strains of Lichtheimia species with alveolar macrophages, which form the first line of defence of the innate immune system. To investigate 17 strains from four different continents in a comparative fashion, transmitted light and confocal fluorescence microscopy was applied in combination with automated image analysis. This interdisciplinary approach enabled the objective and quantitative processing of the big volume of image data. Applying machine‐learning supported methods, a spontaneous clustering of the strains was revealed in the space of phagocytic measures. This clustering was not driven by measures of fungal morphology but rather by the geographical origin of the fungal strains. Our study illustrates the crucial contribution of machine‐learning supported automated image analysis to the qualitative discovery and quantitative comparison of major factors affecting host–pathogen interactions. We found that the phagocytic vulnerability of Lichtheimia species depends on their geographical origin, where strains within each geographic region behaved similarly, but strongly differed amongst the regions. Based on this clustering, we were able to also classify clinical isolates with regard to their potential geographical origin.