Three tissue resident macrophage subsets coexist across organs with conserved origins and life cycles.

Resident macrophages orchestrate homeostatic, inflammatory, and reparative activities. It is appreciated that different tissues instruct specialized macrophage functions. However, individual tissues contain heterogeneous subpopulations, and how these subpopulations are related is unclear. We asked whether common transcriptional and functional elements could reveal an underlying framework across tissues. Using single-cell RNA sequencing and random forest modeling, we observed that four genes could predict three macrophage subsets that were present in murine heart, liver, lung, kidney, and brain. Parabiotic and genetic fate mapping studies revealed that these core markers predicted three unique life cycles across 17 tissues. TLF + (expressing TIMD4 and/or LYVE1 and/or FOLR2) macrophages were maintained through self-renewal with minimal monocyte input; CCR2 + (TIMD4 − LYVE1 − FOLR2 − ) macrophages were almost entirely replaced by monocytes, and MHC-II hi macrophages (TIMD4 − LYVE1 − FOLR2 − CCR2 − ), while receiving modest monocyte contribution, were not continually replaced. Rather, monocyte-derived macrophages contributed to the resident macrophage population until they reached a defined upper limit after which they did not outcompete pre-existing resident macrophages. Developmentally, TLF + macrophages were first to emerge in the yolk sac and early fetal organs. Fate mapping studies in the mouse and human single-cell RNA sequencing indicated that TLF + macrophages originated from both yolk sac and fetal monocyte precursors. Furthermore, TLF + macrophages were the most transcriptionally conserved subset across mouse tissues and between mice and humans, despite organ- and species-specific transcriptional differences. Here, we define the existence of three murine macrophage subpopulations based on common life cycle properties and core gene signatures and provide a common starting point to understand tissue macrophage heterogeneity.