Image based Machine Learning for identification of macrophage subsets

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-inf...

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Autores principales:	Hassan M. Rostam, Paul M. Reynolds, Morgan R. Alexander, Nikolaj Gadegaard, Amir M. Ghaemmaghami
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c40e5b4b86d9463c9c5fe172079ddcaf
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spelling	oai:doaj.org-article:c40e5b4b86d9463c9c5fe172079ddcaf2021-12-02T16:06:00ZImage based Machine Learning for identification of macrophage subsets10.1038/s41598-017-03780-z2045-2322https://doaj.org/article/c40e5b4b86d9463c9c5fe172079ddcaf2017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-03780-zhttps://doaj.org/toc/2045-2322Abstract 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.Hassan M. RostamPaul M. ReynoldsMorgan R. AlexanderNikolaj GadegaardAmir M. GhaemmaghamiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hassan M. Rostam Paul M. Reynolds Morgan R. Alexander Nikolaj Gadegaard Amir M. Ghaemmaghami Image based Machine Learning for identification of macrophage subsets
description	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.
format	article
author	Hassan M. Rostam Paul M. Reynolds Morgan R. Alexander Nikolaj Gadegaard Amir M. Ghaemmaghami
author_facet	Hassan M. Rostam Paul M. Reynolds Morgan R. Alexander Nikolaj Gadegaard Amir M. Ghaemmaghami
author_sort	Hassan M. Rostam
title	Image based Machine Learning for identification of macrophage subsets
title_short	Image based Machine Learning for identification of macrophage subsets
title_full	Image based Machine Learning for identification of macrophage subsets
title_fullStr	Image based Machine Learning for identification of macrophage subsets
title_full_unstemmed	Image based Machine Learning for identification of macrophage subsets
title_sort	image based machine learning for identification of macrophage subsets
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/c40e5b4b86d9463c9c5fe172079ddcaf
work_keys_str_mv	AT hassanmrostam imagebasedmachinelearningforidentificationofmacrophagesubsets AT paulmreynolds imagebasedmachinelearningforidentificationofmacrophagesubsets AT morganralexander imagebasedmachinelearningforidentificationofmacrophagesubsets AT nikolajgadegaard imagebasedmachinelearningforidentificationofmacrophagesubsets AT amirmghaemmaghami imagebasedmachinelearningforidentificationofmacrophagesubsets
_version_	1718385174061776896

Image based Machine Learning for identification of macrophage subsets

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