Ying Tang

Abstract Pulmonary inflammation, which is characterized by the presence of perivascular macrophages, has been proposed as a key pathogenic driver of pulmonary hypertension (PH), a vascular disease with increasing global significance. However, the mechanisms of expansion of lung macrophages and the role of blood-borne monocytes in PH are poorly understood. Using multicolor flow cytometric analysis of blood in mouse and rat models of PH and patients with PH, an increase in blood monocytes was observed. In parallel, lung tissue displayed increased chemokine transcript expression, including those responsible for monocyte recruitment, such as Ccl2 and Cx3cl1, accompanied by an expansion of interstitial lung macrophages. These data indicate that blood monocytes are recruited to lung perivascular spaces and differentiate into inflammatory macrophages. Correspondingly, parabiosis between congenically different hypoxic mice demonstrated that most interstitial macrophages originated from blood monocytes. To define the actions of these cells in PH in vivo, we reduced blood monocyte numbers via genetic deficiency of cx3cr1 or ccr2 in chronically hypoxic male mice and by pharmacologic inhibition of Cx3cl1 in monocrotaline-exposed rats. Both models exhibited decreased inflammatory blood monocytes, as well as interstitial macrophages, leading to a substantial decrease in arteriolar remodeling but with a less robust hemodynamic effect. This study defines a direct mechanism by which interstitial macrophages expand in PH. It also demonstrates a pathway for pulmonary vascular remodeling in PH that depends upon interstitial macrophage-dependent inflammation yet is dissociated, at least in part, from hemodynamic consequences, thus offering guidance on future anti-inflammatory therapeutic strategies in this disease.

Abstract Organizing single cells along a developmental trajectory has emerged as a powerful tool for understanding how gene regulation governs cell fate decisions. However, learning the structure of complex single-cell trajectories with two or more branches remains a challenging computational problem. We present Monocle 2, which uses reversed graph embedding to reconstruct single-cell trajectories in a fully unsupervised manner. Monocle 2 learns an explicit principal graph to describe the data, greatly improving the robustness and accuracy of its trajectories compared to other algorithms. Monocle 2 uncovered a new, alternative cell fate in what we previously reported to be a linear trajectory for differentiating myoblasts. We also reconstruct branched trajectories for two studies of blood development, and show that loss of function mutations in key lineage transcription factors diverts cells to alternative branches on the a trajectory. Monocle 2 is thus a powerful tool for analyzing cell fate decisions with single-cell genomics.