CASTLE: cell adhesion with supervised training and learning environment

Research output: Contribution to journalArticlepeer-review

Authors

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.

Details

Original languageEnglish
Article number424002
Number of pages17
JournalJournal of Physics D: Applied Physics
Volume53
Issue number42
Early online date29 Jul 2020
Publication statusPublished - 14 Oct 2020

Keywords

  • cell adhesion, cell morphology, galectins, ilastik, image analysis, imagej, machine learning