An accurate and automated approach for the quantification of single-cell adhesion dynamics from microscopy images

Cell adhesion 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 and, however, are characterized by several drawbacks, including low temporal resolution and limited procedural standardization, which reduce their usefulness and accuracy. In this work, we propose an alternative analytical approach, based on live-cell imaging, which enables the study of cell adhesion dynamics at the single-cell level. 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 behavior in the adhesion process. Finally, we generalize our method by substituting the segmentation algorithm and showing that this approach can be integrated within routine laboratory analytical procedures and does not require high-performance microscopy and imaging setups. Our 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 characterization of cell adhesion. In addition to 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 by allowing detailed quantification of cell adhesion metrics.