Cellular Atlas of Senescent Lineages in Radiation- or Immunotherapy-Induced Lung Injury by Single-Cell RNA-Sequencing Analysis

Although the combination of immunotherapy and radiation therapy to treat various malignancies is rapidly expanding, concerns regarding increased pulmonary toxicities remain. The mechanisms of immunotherapy- and irradiation-induced lung injury involve a complex interplay of cell types and signaling pathways, much of which remains to be elucidated.C57/BL6 mice were treated with a single fraction (20 Gy) of radiation therapy to the right lung or 200 μg anti-Programmed cell death protein 1 antibody twice a week. At 7, 30, and 60 days after treatment, the lung tissues were obtained for unbiased single-cell RNA sequencing or histologic staining. The Seurat analysis pipeline, Cellchat, Monocol, and Single-Cell rEgulatory Network Inference and Clustering were used to define cell types, mechanisms, and mediators driving pathologic remodeling in response to this lung injury. Reverse transcription polymerase chain reaction, immunofluorescent staining, and multiplex immunohistochemistry were applied to validate the key results.Thirty distinct cell subsets encompassing 75,396 cells were identified. A comprehensive investigation of cell-cell crosstalk revealed that monokine signals derived from senescent fibroblasts were substantially elevated after lung injury. Independent analytical strategies revealed that senescence-like subtypes of fibroblasts, alveolar epithelial cells, B cells, and myeloid immune cells were functionally pathologic, with high expression of senescence-signature proteins, especially Apolipoprotein E, during injury response. Senescence markers were also elevated in irradiated human cell lines, mouse cell lines (B3T3 and L929), and the publicly available human pulmonary fibrosis data set.These findings demonstrate that the accumulation of senescence-like fibroblasts, macrophages, and alveolar epithelial cells is the primary common pathologic mechanism of immunotherapy- and irradiation-induced lung injury. These high-resolution transcriptomic data provide novel insights into therapeutic opportunities to predict or prevent therapy-induced lung injury.