Image based Machine Learning for identification of macrophage subsets
Open accessAbstract
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.
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Pro-inflammatory M1 macrophages appear large and round-shaped, while anti-inflammatory M2 macrophages show an elongated shape.
“Previous studies indicated that pro-inflammatory M1 macrophages appeared large, round-shape morphology, while anti-inflammatory M2 macrophages showed elongated shape [49, 50].”
From A tailored bioactive 3D porous poly(lactic-acid)-exosome scaffold with osteo-immunomodulatory and osteogenic differentiation properties (2022) · cites this paper as [49]
The Introduction reports a prior study of murine macrophages in which M1 cells were large and rounded and M2 cells were stretched and elongated. The claim omits that population qualifier; the paper’s own Results describe human M1 cells as smaller and M2 cells as the largest, so the pattern should not be presented as a general finding. (LLM, conf=0.86)
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- Authors
- Rostam, H. M., Reynolds, P. M., Alexander, M. R., Gadegaard, N., & Ghaemmaghami, A. M.
- Affiliations
- University of Nottingham (GB)
- University of Glasgow (GB)
- Corresponding author
- Gadegaard, Nikolaj
- Ghaemmaghami, Amir M.
- Venue
- Scientific Reports
- Published
- 2017-06-14
- Volume
- 7
- Issue
- 1
- Pages
- 3521-3521
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC5471192
- OpenAlex
- W2624032785
- Cited by
- 196
- References
- 56
- Citations by year
- 2026: 15 · 2025: 32 · 2024: 30 · 2023: 31 · 2022: 35 · 2021: 19 · 2020: 19 · 2019: 8 · 2018: 5 · 2017: 2
- FWCI
- 5.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Nottingham ePrints (University of Nottingham) (submittedVersion, cc-by)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- Funders
- European Commission (648892, 602694)
- University of Glasgow
- Engineering and Physical Sciences Research Council (EP/N006615/1)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Life Sciences › Immunology and Microbiology › Immunology › Immune cells in cancer
- Keywords
- macrophage polarization, M1 macrophages, M2 macrophages, cell morphology, cell size, automated image analysis, high-content imaging, monocytes, macrophage plasticity, machine learning classification
- MeSH terms
- Machine Learning, Cytological Techniques, Humans, Image Processing, Computer-Assisted, Macrophages, Microscopy, Fluorescence, Staining and Labeling, Cell Size, Cell Shape
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