ArticleeLife2022
Non-invasive classification of macrophage polarisation by 2P-FLIM and machine learning.
Article in eLife, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Illuminating Immunity: A Systematic Review of Immune Cell Autofluorescence.Journal of biophotonics · 2025Pooled it
- The morpho-kinetic landscape of macrophage modes during wound healing in zebrafish.Scientific reports · 2026Article
- Comparative analysis of RT-qPCR, flow cytometry, and Di-4-ANEPPDHQ fluorescence for distinguishing macrophages phenotypes.Biochemistry and biophysics reports · 2025Article
- Engineering macrophage responses through 3D scaffold microarchitecture.Materials today. Bio · 2025Article
- Optical express-biopsy of gliomas using macroscopic fluorescence lifetime imaging.Biomedical optics express · 2025Article
- Machine learning-assisted decoding of temporal transcriptional dynamics via fluorescent timer.Nature communications · 2025Article
- FLIMB: fluorescence lifetime microendoscopy for metabolic and functional imaging of femoral marrow at subcellular resolution.Biomedical optics express · 2025Article
- Multispectral autofluorescence for label free classification of immune cell type and activation/polarization status.Scandinavian journal of immunology · 2025Article
- Two-photon NAD(P)H-FLIM reveals unperturbed energy metabolism of Ascaris suum larvae, in contrast to host macrophages upon artemisinin derivatives exposure.Scientific reports · 2025Article
- Unravelling approaches to study macrophages: from classical to novel biophysical methodologies.PeerJ · 2025Review
- Chronic compression induces transcriptional, metabolic, and functional state changes in macrophages that recapitulate tumor-associated phenotypes.Frontiers in immunology · 2025Article
- Article
- Imaging immunometabolismImmunometabolism (Cobham, Surrey) · 2024Article
- Applications of machine learning in time-domain fluorescence lifetime imaging: a review.Methods and applications in fluorescence · 2024Review
- 2P-FLIM unveils time-dependent metabolic shifts during osteogenic differentiation with a key role of lactate to fuel osteogenesis via glutaminolysis identified.Stem cell research & therapy · 2023Article
- Probing organoid metabolism using fluorescence lifetime imaging microscopy (FLIM): The next frontier of drug discovery and disease understanding.Advanced drug delivery reviews · 2023Review
- Prediction of six macrophage phenotypes and their IL-10 content based on single-cell morphology using artificial intelligence.Frontiers in immunology · 2023Article
- Computational approaches in rheumatic diseases - Deciphering complex spatio-temporal cell interactions.Computational and structural biotechnology journal · 2023Review
- Time-Dependent Anabolic Response of hMSC-Derived Cartilage Grafts to Hydrostatic Pressure.Journal of tissue engineering and regenerative medicine · 2023Article
- Comparison of phasor analysis and biexponential decay curve fitting of autofluorescence lifetime imaging data for machine learning prediction of cellular phenotypes.Frontiers in bioinformatics · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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