Background: We hypothesize that macrophage heterogeneity is an unexploited source of therapeutic targets for vascular inflammation. Interferon-gamma (IFNγ) stimulated primary human macrophages M(IFNγ) is a widely used in vitro model for proinflammatory macrophages. However, typical activation-induced transcript profiling assumes a homogenous macrophage population. Our goal is to evaluate the extent of heterogeneity of activated macrophages to devise a strategy for precision medicine for inflammatory vascular disease. Methods: Using unbiased single-cell RNA sequencing (scRNA-seq), systems biology, and machine learning, we examined inter-subgroup differences of human primary M(IFNγ) (4 donors). Network analysis, kinetic proteomics, and in vitro assays (n=3-6) characterized the clusters, followed by validation in human carotid atherosclerotic plaques (n=13). scRNAseq data analysis in the L1000 CDS 2 drug-gene network computationally identified drugs that may potentiate or suppress each cluster. Results: The scRNA-seq demonstrated 3 distinct subpopulations: Clusters 1, 2, and 3 (C1, 2, and 3). C3 showed increased proinflammatory chemokine production, protein synthesis, and glycolysis. C1 was more efferocytotic/phagocytic, chemotactic, and less inflammatory. C2 is intermediate between C1 and C3. Histological analysis localized C1 and C3-like macrophages in different areas of the plaques ( Fig. 1A ). In addition, we used targeted scRNAseq (n=4) to analyze M(IFNγ) treated with an L1000-derived drug BI-2536 (Polo-like kinase inhibitor). As predicted, BI-2536 shifted the phenotypic heterogeneity of M(IFNγ) towards less inflammatory characteristics ( Fig. 1B ) which were further validated with bulk qPCR & ELISA (n=8). Conclusion: Our study presents a novel strategy for precision medicine that leverages single-cell data and gene interaction networks to identify modulators of macrophage heterogeneity as new anti-inflammatory therapies.

In the Supplementary Information file originally published with this Article, there are typographical errors.In the section under 'Convergence' , "In order to evaluate such convergence, we fit the last tγ time steps of the evolution to a linear trend, α β ( ) = +  c t t using the QR decomposition method"should read:"In order to evaluate such convergence, we fit the last tγ time steps of the evolution to a linear trend, α β ( ) = + c t t using the QR decomposition method"In the section under ' Analysis of Fluctuations' , "For each one of the I repetitions of the experiment we fit a linear model to the final tγ time steps of the simulation,should read:"For each one of the I repetitions of the experiment we fit a linear model to the final tγ time steps of the simulation,Lastly, Equation (2.1),

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