CASTLE: cell adhesion with supervised training and learning environment

2020-07-29

DOI：10.1088/1361-6463/ab9e35

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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 asin vitrostaining. 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 andk-means clustering, to distinguish morphology currently inaccessible with manual methods. All scripts and documentation are open-source and available at the corresponding GitLab project.

Keywords:

machine learning

cell adhesion

cell morphology

galectins

imagej

ilastik

image analysis

CASTLE: cell adhesion with supervised training and learning environment

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