Image based machine learning for identification of macrophage subsets

Rostam, Hassan and Reynolds, Paul M. and Alexander, Morgan R. and Gadegaard, Nikolaj and Ghaemmaghami, Amir M. (2017) Image based machine learning for identification of macrophage subsets. Scientific Reports, 7 . 3521/1-3521/11. ISSN 2045-2322

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Available under Licence Creative Commons Attribution.
Download (2MB) | Preview

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.

Item Type: Article
Schools/Departments: University of Nottingham, UK > Faculty of Medicine and Health Sciences > School of Life Sciences
University of Nottingham, UK > Faculty of Science > School of Pharmacy
Identification Number: 10.1038/s41598-017-03780-z
Depositing User: Eprints, Support
Date Deposited: 14 Jun 2017 14:17
Last Modified: 13 Oct 2017 00:41
URI: http://eprints.nottingham.ac.uk/id/eprint/42663

Actions (Archive Staff Only)

Edit View Edit View