Hassan Rostam
Image based machine learning for identification of macrophage subsets
Rostam, Hassan; Reynolds, Paul M.; Alexander, Morgan R.; Gadegaard, Nikolaj; Ghaemmaghami, Amir M.
Authors
Paul M. Reynolds
MORGAN ALEXANDER MORGAN.ALEXANDER@NOTTINGHAM.AC.UK
Professor of Biomedical Surfaces
Nikolaj Gadegaard
Professor AMIR GHAEMMAGHAMI AMIR.GHAEMMAGHAMI@NOTTINGHAM.AC.UK
Professor of Immunology and Immuno- Bioengineering
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 |
Files
Rostam_et_al-2017-Scientific_Reports (1).pdf
(2.2 Mb)
PDF
Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by/4.0
You might also like
High throughput screening for biomaterials discovery
(2014)
Journal Article
Strategies for MCR image analysis of large hyperspectral data-sets
(2012)
Journal Article
Modelling human embryoid body cell adhesion to a combinatorial library of polymer surfaces
(2012)
Journal Article
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: discovery-access-systems@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search