Non-invasive classification of macrophage polarisation by 2P-FLIM and machine learning Article Swipe
Nuno Neto
,
Sinead A O’Rourke
,
Mimi Zhang
,
Hannah K. Fitzgerald
,
Aisling Dunne
,
Michael G. Monaghan
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.7554/elife.77373
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.7554/elife.77373
In this study, we utilise fluorescence lifetime imaging of NAD(P)H-based cellular autofluorescence as a non-invasive modality to classify two contrasting states of human macrophages by proxy of their governing metabolic state. Macrophages derived from human blood-circulating monocytes were polarised using established protocols and metabolically challenged using small molecules to validate their responding metabolic actions in extracellular acidification and oxygen consumption. Large field-of-view images of individual polarised macrophages were obtained using fluorescence lifetime imaging microscopy (FLIM). These were challenged in real time with small-molecule perturbations of metabolism during imaging. We uncovered FLIM parameters that are pronounced under the action of carbonyl cyanide-p-trifluoromethoxyphenylhydrazone (FCCP), which strongly stratifies the phenotype of polarised human macrophages; however, this performance is impacted by donor variability when analysing the data at a single-cell level. The stratification and parameters emanating from a full field-of-view and single-cell FLIM approach serve as the basis for machine learning models. Applying a random forests model, we identify three strongly governing FLIM parameters, achieving an area under the receiver operating characteristics curve (ROC-AUC) value of 0.944 and out-of-bag (OBB) error rate of 16.67% when classifying human macrophages in a full field-of-view image. To conclude, 2P-FLIM with the integration of machine learning models is showed to be a powerful technique for analysis of both human macrophage metabolism and polarisation at full FoV and single-cell level.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.7554/elife.77373
- OA Status
- gold
- Cited By
- 32
- References
- 63
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4306648660Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.7554/elife.77373Digital Object Identifier
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Non-invasive classification of macrophage polarisation by 2P-FLIM and machine learningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-09-26Full publication date if available
- Authors
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Nuno Neto, Sinead A O’Rourke, Mimi Zhang, Hannah K. Fitzgerald, Aisling Dunne, Michael G. MonaghanList of authors in order
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https://doi.org/10.7554/elife.77373Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.7554/elife.77373Direct OA link when available
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Fluorescence-lifetime imaging microscopy, Autofluorescence, Biological system, Computer science, Artificial intelligence, Fluorescence, Cell, Biophysics, Chemistry, Physics, Nuclear magnetic resonance, Biology, Biochemistry, OpticsTop concepts (fields/topics) attached by OpenAlex
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32Total citation count in OpenAlex
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2025: 14, 2024: 4, 2023: 14Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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