Myocardial infarction is the most common form of acute cardiac injury attributing to heart failure. While there have been significant advances in current therapies, mortality and morbidity remain high. Emphasis on inflammation and extracellular matrix remodeling as key pathological factors has brought to light new potential therapeutic targets including macrophages which are central players in the inflammatory response following myocardial infarction. Blood derived and tissue resident macrophages exhibit both a pro- and anti-inflammatory phenotype, essential for removing injured tissue and facilitating repair, respectively. Sustained activation of pro-inflammatory macrophages evokes extensive remodeling of cardiac tissue through secretion of matrix proteases and activation of myofibroblasts. As the heart continues to employ methods of remodeling and repair, a destructive cycle prevails ultimately leading to deterioration of cardiac function and heart failure. This review summarizes not only the traditionally accepted role of macrophages in the heart but also recent advances that have deepened our understanding and appreciation of this dynamic cell. We discuss the role of macrophages in normal and maladaptive matrix remodeling, as well as studies to date which have aimed to target the inflammatory response in combatting excessive matrix deposition and subsequent heart failure.

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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