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Precision management of fibrotic lung diseases is challenging due to their diverse clinical trajectories and lack of reliable biomarkers for risk stratification and therapeutic monitoring. Here, we validated the accuracy of CMKLR1 as an imaging biomarker of the lung inflammation-fibrosis axis. By analyzing single-cell RNA sequencing datasets, we demonstrated CMKLR1 expression as a transient signature of monocyte-derived macrophages (MDMf) enriched in patients with idiopathic pulmonary fibrosis (IPF). Consistently, we identified MDMf as the major driver of the uptake of CMKLR1-targeting peptides in a murine model of bleomycin-induced lung fibrosis. Furthermore, CMKLR1- targeted positron emission tomography in the murine model enabled quantification and spatial mapping of inflamed lung regions infiltrated by CMKLR1-expressing macrophages and emerged as a robust predictor of subsequent lung fibrosis. Last, high CMKLR1 expression by bronchoalveolar lavage cells identified an inflammatory endotype of IPF with poor survival. Our investigation supports the potential of CMKLR1 as an imaging biomarker for endotyping and risk stratification of fibrotic lung diseases. [ABSTRACT FROM AUTHOR]

Background: Single-cell RNA sequencing (scRNA-seq) technology has enabled assessment of transcriptome-wide changes at single-cell resolution. Due to the heterogeneity in environmental exposure and genetic background across subjects, subject effect contributes to the major source of variation in scRNA-seq data with multiple subjects, which severely confounds cell type specific differential expression (DE) analysis. Moreover, dropout events are prevalent in scRNA-seq data, leading to excessive number of zeroes in the data, which further aggravates the challenge in DE analysis. Results: We developed iDESC to detect cell type specific DE genes between two groups of subjects in scRNA-seq data. iDESC uses a zero-inflated negative binomial mixed model to consider both subject effect and dropouts. The prevalence of dropout events (dropout rate) was demonstrated to be dependent on gene expression level, which is modeled by pooling information across genes. Subject effect is modeled as a random effect in the log-mean of the negative binomial component. We evaluated and compared the performance of iDESC with eleven existing DE analysis methods. Using simulated data, we demonstrated that iDESC had well-controlled type I error and higher power compared to the existing methods. Applications of those methods with well-controlled type I error to three real scRNA-seq datasets from the same tissue and disease showed that the results of iDESC achieved the best consistency between datasets and the best disease relevance. Conclusions: iDESC was able to achieve more accurate and robust DE analysis results by separating subject effect from disease effect with consideration of dropouts to identify DE genes, suggesting the importance of considering subject effect and dropouts in the DE analysis of scRNA-seq data with multiple subjects. [ABSTRACT FROM AUTHOR]

VISTA (V domain immunoglobulin suppressor of T cell activation, also called PD-1H [programmed death-1 homolog]), a novel immune regulator expressed on myeloid and T lymphocyte lineages, is upregulated in mouse and human idiopathic pulmonary fibrosis (IPF). However, the significance of VISTA and its therapeutic potential in regulating IPF has yet to be defined. To determine the role of VISTA and its therapeutic potential in IPF, the expression profile of VISTA was evaluated from human single-cell RNA sequencing data (IPF Cell Atlas). Inflammatory response and lung fibrosis were assessed in bleomycin-induced experimental pulmonary fibrosis models in VISTA-deficient mice compared with wild-type littermates. In addition, these outcomes were evaluated after VISTA agonistic antibody treatment in the wild-type pulmonary fibrosis mice. VISTA expression was increased in lung tissue-infiltrating monocytes of patients with IPF. VISTA was induced in the myeloid population, mainly circulating monocyte-derived macrophages, during bleomycin-induced pulmonary fibrosis. Genetic ablation of VISTA drastically promoted pulmonary fibrosis, and bleomycin-induced fibroblast activation was dependent on the interaction between VISTA-expressing myeloid cells and fibroblasts. Treatment with VISTA agonistic antibody reduced fibrotic phenotypes accompanied by the suppression of lung innate immune and fibrotic mediators. In conclusion, these results suggest that VISTA upregulation in pulmonary fibrosis may be a compensatory mechanism to limit inflammation and fibrosis, and stimulation of VISTA signaling using VISTA agonists effectively limits the fibrotic innate immune landscape and consequent tissue fibrosis. Further studies are warranted to test VISTA as a novel therapeutic target for the IPF treatment. [ABSTRACT FROM AUTHOR]

Patients with chronic lung disease (CLD) have an increased risk for severe coronavirus disease-19 (COVID-19) and poor outcomes. Here, we analyze the transcriptomes of 611,398 single cells isolated from healthy and CLD lungs to identify molecular characteristics of lung cells that may account for worse COVID-19 outcomes in patients with chronic lung diseases. We observe a similar cellular distribution and relative expression of SARS-CoV-2 entry factors in control and CLD lungs. CLD AT2 cells express higher levels of genes linked directly to the efficiency of viral replication and the innate immune response. Additionally, we identify basal differences in inflammatory gene expression programs that highlight how CLD alters the inflammatory microenvironment encountered upon viral exposure to the peripheral lung. Our study indicates that CLD is accompanied by changes in cell-type-specific gene expression programs that prime the lung epithelium for and influence the innate and adaptive immune responses to SARS-CoV-2 infection. Patients with chronic lung disease (CLD) have an increased risk for severe coronavirus disease-19 and poor outcomes. Here the authors compare the transcriptomes of single cells isolated from healthy and CLD lungs to identify molecular characteristics of lung cells that may account for worse COVID-19 outcomes in these patients. [ABSTRACT FROM AUTHOR]

Reference 1 Liu Y. iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects. Correction: iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects. [Extracted from the article]