A novel approach for the quantification of single-cell adhesion dynamics from microscopy images

Abstract

BackgroundCell adhesion, that is the ability to attach to a given substrate, is a key property of cancer cells, as it relates to their potential for dissemination and metastasis. The in vitro assays used to measure it, however, are characterized by several drawbacks, including low temporal resolution and limited procedural standardisation which reduce their usefulness and accuracy. ResultsIn this work, we propose an alternative analytical approach, based on live-cell imaging data, that yields comprehensive information on cell adhesion dynamics at the single-cell level. It relies on a segmentation routine, to identify the pixels belonging to each cell from time-lapse microscopy images acquired during the adhesion process. A tracking algorithm then enables the study of individual cell adhesion dynamics over time. The increased resolution afforded by this method was instrumental for the identification of cell division prior to attachment and the co-existence of markedly different proliferation rates across the culture, previously unidentified patterns of behaviour in the adhesion process. Finally, we generalize our method by substituting the segmentation algorithm of the instrument used to acquire the images, with a custom-made one, showing that this approach can be integrated within routine laboratory analytical procedures and does not necessarily require high-performance microscopy and imaging setups. ConclusionsOur new analytical approach improves the in vitro quantification of cell adhesion, enabling the study of this process with high temporal resolution and increased level of detail. The extension of the analysis to the single-cell level, additionally, uncovered the role of population variability and proliferation in this process. The simple and cost-effective procedure here described enables the accurate characterisation of cell adhesion. Beside improving our understanding of adhesion dynamics, its results could support the development of treatments targeting the ability of cancer cells to adhere to surrounding tissues.