Abstract
Different types of microscopy are used to uncover signatures of cell adhesion and mechanics. Automating the identification and analysis often involve sacrificial routines of cell manipulation such as in vitro staining. Phase-contrast microscopy (PCM) is rarely used in automation due to the difficulties with poor quality images. However, it is the least intrusive method to provide insights into the dynamics of cells, where other types of microscopy are too destructive to monitor. In this study, we propose an efficient workflow to automate cell counting and morphology in PCM images. We introduce Cell Adhesion with Supervised Training and Learning Environment (CASTLE), available as a series of additional plugins to ImageJ. CASTLE combines effective techniques for phase-contrast image processing with statistical analysis and machine learning algorithms to interpret the results. The proposed workflow was validated by comparing the results to a manual count and manual segmentation of cells in images investigating different adherent cell types, including monocytes, neutrophils and platelets. In addition, the effect of different molecules on cell adhesion was characterised using CASTLE. For example, we demonstate that Galectin-9 leads to differences in adhesion of leukocytes. CASTLE also provides information using machine learning techniques, namely principal component analysis and k-means clustering, to distinguish morphology currently inaccessible with manual methods. All scripts and documentation are open-source and available at the corresponding GitLab project.
Highlights
We propose an efficient workflow to automate cell counting and morphology in Phase-contrast microscopy (PCM) images
We focus most of our discussion to the analysis of polymorphonuclear leukocytes (PMNs) and peripheral blood mononuclear cells (PBMCs) depending on concentrations of Gal-9, since these data sets are the most challenging to the algorithms and involve different cell types with quantitatively varying concentrations of molecules
We demonstrate the applicability of our workflow to two other studies of cell adhesion: one investigating the effect of ICAM-1 on PMN adhesion, and the other investigating the adhesion of platelets in dependence on von Willebrand factor (vWF) and adenosine diphosphate (ADP)
Summary
The function of almost all cell types, including leukocytes, platelets, and cancer cells, critically involves a number of mechanical processes [1]; these include the regulation of cellcell and cell-matrix adhesion, flow sensing and changes in. To investigate the effect of specific adhesion molecules on leukocyte behaviour, e.g. those appearing on the endothelium, biologists often employ flow based adhesion assays where cells adhere to coated substrates, or to endothelial monolayers. To identify adhesive states or morphological changes during migration, experimentalists frequently analyse a large number of cells under different conditions
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