Image based machine learning for identification of macrophage subsets
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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Published: |
Nature Publishing Group
2017
|
| Online Access: | https://eprints.nottingham.ac.uk/42663/ |
| _version_ | 1848796539383709696 |
|---|---|
| author | Rostam, Hassan Reynolds, Paul M. Alexander, Morgan R. Gadegaard, Nikolaj Ghaemmaghami, Amir M. |
| author_facet | Rostam, Hassan Reynolds, Paul M. Alexander, Morgan R. Gadegaard, Nikolaj Ghaemmaghami, Amir M. |
| author_sort | Rostam, Hassan |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:49:35Z |
| format | Article |
| id | nottingham-42663 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:49:35Z |
| publishDate | 2017 |
| publisher | Nature Publishing Group |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-426632020-05-04T18:50:27Z https://eprints.nottingham.ac.uk/42663/ Image based machine learning for identification of macrophage subsets Rostam, Hassan Reynolds, Paul M. Alexander, Morgan R. Gadegaard, Nikolaj Ghaemmaghami, Amir M. 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. Nature Publishing Group 2017-06-14 Article PeerReviewed Rostam, Hassan, Reynolds, Paul M., Alexander, Morgan R., 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 https://www.nature.com/articles/s41598-017-03780-z doi:10.1038/s41598-017-03780-z doi:10.1038/s41598-017-03780-z |
| spellingShingle | Rostam, Hassan Reynolds, Paul M. Alexander, Morgan R. Gadegaard, Nikolaj Ghaemmaghami, Amir M. Image based machine learning for identification of macrophage subsets |
| title | 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_short | Image based machine learning for identification of macrophage subsets |
| title_sort | image based machine learning for identification of macrophage subsets |
| url | https://eprints.nottingham.ac.uk/42663/ https://eprints.nottingham.ac.uk/42663/ https://eprints.nottingham.ac.uk/42663/ |