Non-invasive classification of macrophage polarisation by 2P-FLIM and machine learning.
Nuno G B NetoSinead A O'RourkeMimi ZhangHannah K FitzgeraldAisling DunneMichael G MonaghanPublished in: eLife (2022)
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.
Keyphrases
- machine learning
- endothelial cells
- single cell
- high resolution
- small molecule
- induced pluripotent stem cells
- deep learning
- rna seq
- pluripotent stem cells
- artificial intelligence
- high throughput
- single molecule
- big data
- adipose tissue
- dendritic cells
- optical coherence tomography
- convolutional neural network
- energy transfer