Abstract
Single-cell approaches have shone a spotlight on discrete context-specific tissue macrophage states, deconstructed to their most minute details. Machine-learning (ML) approaches have recently challenged that dogma by revealing a context-agnostic continuum of states shared across tissues. Both approaches agree that 'brake' and 'accelerator' macrophage subpopulations must be balanced to achieve homeostasis. Both approaches also highlight the importance of ensemble fluidity as subpopulations switch between wide ranges of accelerator and brake phenotypes to mount the most optimal wholistic response to any threat. A full comprehension of the rules that govern these brake and accelerator states is a promising avenue because it can help formulate precise macrophage re-education therapeutic strategies that might selectively boost or suppress disease-associated states and phenotypes across various tissues.