Image based machine learning for identification of macrophage subsets

Rostam, Hassan; Reynolds, Paul M.; Alexander, Morgan R.; Gadegaard, Nikolaj; Ghaemmaghami, Amir M.

Abstract

Macrophages play a crucial rule in orchestrating immune responses against pathogens and foreign materials. Macrophages have remarkable plasticity in response to environmental cues and are able to acquire a spectrum of activation status, best exemplified by pro-inflammatory (M1) and anti-inflammatory (M2) phenotypes at the two ends of the spectrum. Characterisation of M1 and M2 subsets is usually carried out by quantification of multiple cell surface markers, transcription factors and cytokine profiles. These approaches are time consuming, require large numbers of cells and are resource intensive. In this study, we used machine learning algorithms to develop a simple and fast imaging-based approach that enables automated identification of different macrophage functional phenotypes using their cell size and morphology. Fluorescent microscopy was used to assess cell morphology of different cell types which were stained for nucleus and actin distribution using DAPI and phalloidin respectively. By only analysing their morphology we were able to identify M1 and M2 phenotypes effectively and could distinguish them from naïve macrophages and monocytes with an average accuracy of 90%. Thus we suggest high-content and automated image analysis can be used for fast phenotyping of functionally diverse cell populations with reasonable accuracy and without the need for using multiple markers.

Citation

Rostam, H., Reynolds, P. M., Alexander, M. R., Gadegaard, N., & Ghaemmaghami, A. M. (2017). Image based machine learning for identification of macrophage subsets. Scientific Reports, 7(1), Article 3521. https://doi.org/10.1038/s41598-017-03780-z

• Journal Article Type: Article
• Acceptance Date: May 1, 2017
• Online Publication Date: Jun 14, 2017
• Publication Date: 2017-12
• Deposit Date: Jun 14, 2017
• Publicly Available Date: Jun 14, 2017
• Journal: Scientific Reports
• Electronic ISSN: 2045-2322
• Publisher: Nature Publishing Group
• Peer Reviewed: Peer Reviewed
• Volume: 7
• Issue: 1
• Article Number: 3521
• DOI: https://doi.org/10.1038/s41598-017-03780-z
• Public URL: https://nottingham-repository.worktribe.com/output/866401
• Publisher URL: https://www.nature.com/articles/s41598-017-03780-z
• Contract Date: Jun 14, 2017