Automated tracking of label-free cells with enhanced recognition of whole tracks View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Naim Al-Zaben, Anna Medyukhina, Stefanie Dietrich, Alessandra Marolda, Kerstin Hünniger, Oliver Kurzai, Marc Thilo Figge

ABSTRACT

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. More... »

PAGES

3317

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-019-39725-x

DOI

http://dx.doi.org/10.1038/s41598-019-39725-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112471702

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30824740


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