Machine learning identifies signatures of macrophage reactivity and tolerance that predict disease outcomes

Background Single-cell transcriptomic studies have greatly improved organ-specific insights into macrophage polarization states are essential for the initiation and resolution of inflammation in all tissues; however, such insights are yet to translate into therapies that can predictably alter macrophage fate. Method Using machine learning algorithms on human macrophages, here we reveal the continuum of polarization states that is shared across diverse contexts. A path, comprised of 338 genes accurately identified both physiologic and pathologic spectra of reactivity and tolerance, and remained relevant across tissues, organs, species, and immune cells (>12,500 diverse datasets). Findings This 338-gene signature identified macrophage polarization states at single-cell resolution, in physiology and across diverse human diseases, and in murine pre-clinical disease models. The signature consistently outperformed conventional signatures in the degree of transcriptome-proteome overlap, and in detecting disease states; it also prognosticated outcomes across diverse acute and chronic diseases, e.g., sepsis, liver fibrosis, aging, and cancers. Crowd-sourced genetic and pharmacologic studies confirmed that model-rationalized interventions trigger predictable macrophage fates. Interpretation These findings provide a formal and universally relevant definition of macrophage states and a predictive framework (http://hegemon.ucsd.edu/SMaRT) for the scientific community to develop macrophage-targeted precision diagnostics and therapeutics.

Automated summary

Through machine learning algorithms on human macrophages, researchers have identified a continuum of polarization states and proposed a 338-gene signature for these states. This signature accurately identified macrophage polarization states in both physiological and pathological contexts, and showed superiority in predicting disease outcomes. These findings provide a predictive framework for the development of macrophage-targeted precision diagnostics and therapeutics.