Guided construction of single cell reference for human and mouse lung.

Accurate cell type identification is a key and rate-limiting step in single-cell data analysis. Single-cell references with comprehensive cell types, reproducible and functionally validated cell identities, and common nomenclatures are much needed by the research community for automated cell type annotation, data integration, and data sharing. Here, we develop a computational pipeline utilizing the LungMAP CellCards as a dictionary to consolidate single-cell transcriptomic datasets of 104 human lungs and 17 mouse lung samples to construct LungMAP single-cell reference (CellRef) for both normal human and mouse lungs. CellRefs define 48 human and 40 mouse lung cell types catalogued from diverse anatomic locations and developmental time points. We demonstrate the accuracy and stability of LungMAP CellRefs and their utility for automated cell type annotation of both normal and diseased lungs using multiple independent methods and testing data. We develop user-friendly web interfaces for easy access and maximal utilization of the LungMAP CellRefs.

Lymphangioleiomyomatosis (LAM) Cell Atlas.

Lymphangioleiomyomatosis (LAM) is a rare lung disease of women, causing cystic remodelling of the lung and progressive respiratory failure. The cellular composition, microenvironment and cellular interactions within the LAM lesion remain unclear. To facilitate data sharing and collaborative LAM research, we performed an integrative analysis of single-cell data compiled from lung, uterus and kidney of patients with LAM from three research centres and developed an LAM Cell Atlas (LCA) Web-Portal. The LCA offers a variety of interactive options for investigators to search, visualise and reanalyse comprehensive single-cell multiomics data sets to reveal dysregulated genetic programmes at transcriptomic, epigenomic and cell-cell connectome levels.

LungMAP Portal Ecosystem: Systems-Level Exploration of the Lung.

An improved understanding of the human lung necessitates advanced systems models informed by an ever-increasing repertoire of molecular omics, cellular, imaging, and pathological datasets. To centralize and standardize information across broad lung research efforts we expanded the LungMAP.net website into a new gateway portal. This portal connects a broad spectrum of research networks, bulk and single-cell multi-omics data and a diverse collection of image data that span mammalian lung development, and disease. The data are standardized across species and technologies using harmonized data and metadata models that leverage recent advances including those from the Human Cell Atlas, diverse ontologies, and the LungMAP CellCards initiative. To cultivate future discoveries, we have aggregated a diverse collection of single-cell atlases for multiple species (human, rhesus, mouse), to enable consistent queries across technologies, cohorts, age, disease, and drug treatment. These atlases are provided as independent and integrated queryable datasets, with an emphasis on dynamic visualization, figure generation, re-analysis, cell-type curation, and automated reference-based classification of user-provided single-cell genomics datasets (Azimuth). As this resource grows, we intend to increase the breadth of available interactive interfaces, supported data types, data portals and datasets from LungMAP and external research efforts.

Spatially adaptive blind deconvolution methods for optical coherence tomography.

Optical coherence tomography (OCT) is a powerful noninvasive imaging technique for detecting microvascular abnormalities. Following optical imaging principles, an OCT image will be blurred in the out-of-focus domain. Digital deconvolution is a commonly used method for image deblurring. However, the accuracy of traditional digital deconvolution methods, e.g., the Richardson-Lucy method, depends on the prior knowledge of the point spread function (PSF), which varies with the imaging depth and is difficult to determine. In this paper, a spatially adaptive blind deconvolution framework is proposed for recovering clear OCT images from blurred images without a known PSF. First, a depth-dependent PSF is derived from the Gaussian beam model. Second, the blind deconvolution problem is formalized as a regularized energy minimization problem using the least squares method. Third, the clear image and imaging depth are simultaneously recovered from blurry images using an alternating optimization method. To improve the computational efficiency of the proposed method, an accelerated alternating optimization method is proposed based on the convolution theorem and Fourier transform. The proposed method is numerically implemented with various regularization terms, including total variation, Tikhonov, and l1 norm terms. The proposed method is used to deblur synthetic and experimental OCT images. The influence of the regularization term on the deblurring performance is discussed. The results show that the proposed method can accurately deblur OCT images. The proposed acceleration method can significantly improve the computational efficiency of blind demodulation methods.

The balance between protective and pathogenic immune responses to pneumonia in the neonatal lung is enforced by gut microbiota.

Although modern clinical practices such as cesarean sections and perinatal antibiotics have improved infant survival, treatment with broad-spectrum antibiotics alters intestinal microbiota and causes dysbiosis. Infants exposed to perinatal antibiotics have an increased likelihood of life-threatening infections, including pneumonia. Here, we investigated how the gut microbiota sculpt pulmonary immune responses, promoting recovery and resolution of infection in newborn rhesus macaques. Early-life antibiotic exposure interrupted the maturation of intestinal commensal bacteria and disrupted the developmental trajectory of the pulmonary immune system, as assessed by single-cell proteomic and transcriptomic analyses. Early-life antibiotic exposure rendered newborn macaques more susceptible to bacterial pneumonia, concurrent with increases in neutrophil senescence and hyperinflammation, broad inflammatory cytokine signaling, and macrophage dysfunction. This pathogenic reprogramming of pulmonary immunity was further reflected by a hyperinflammatory signature in all pulmonary immune cell subsets coupled with a global loss of tissue-protective, homeostatic pathways in the lungs of dysbiotic newborns. Fecal microbiota transfer was associated with partial correction of the broad immune maladaptations and protection against severe pneumonia. These data demonstrate the importance of intestinal microbiota in programming pulmonary immunity and support the idea that gut microbiota promote the balance between pathways driving tissue repair and inflammatory responses associated with clinical recovery from infection in infants. Our results highlight a potential role for microbial transfer for immune support in these at-risk infants.

Inflammatory blockade prevents injury to the developing pulmonary gas exchange surface in preterm primates.

Perinatal inflammatory stress is associated with early life morbidity and lifelong consequences for pulmonary health. Chorioamnionitis, an inflammatory condition affecting the placenta and fluid surrounding the developing fetus, affects 25 to 40% of preterm births. Severe chorioamnionitis with preterm birth is associated with significantly increased risk of pulmonary disease and secondary infections in childhood, suggesting that fetal inflammation may markedly alter the development of the lung. Here, we used intra-amniotic lipopolysaccharide (LPS) challenge to induce experimental chorioamnionitis in a prenatal rhesus macaque (Macaca mulatta) model that mirrors structural and temporal aspects of human lung development. Inflammatory injury directly disrupted the developing gas exchange surface of the primate lung, with extensive damage to alveolar structure, particularly the close association and coordinated differentiation of alveolar type 1 pneumocytes and specialized alveolar capillary endothelium. Single-cell RNA sequencing analysis defined a multicellular alveolar signaling niche driving alveologenesis that was extensively disrupted by perinatal inflammation, leading to a loss of gas exchange surface and alveolar simplification, with notable resemblance to chronic lung disease in newborns. Blockade of the inflammatory cytokines interleukin-1ß and tumor necrosis factor-α ameliorated LPS-induced inflammatory lung injury by blunting stromal responses to inflammation and modulating innate immune activation in myeloid cells, restoring structural integrity and key signaling networks in the developing alveolus. These data provide new insight into the pathophysiology of developmental lung injury and suggest that modulating inflammation is a promising therapeutic approach to prevent fetal consequences of chorioamnionitis.

A census of the lung: CellCards from LungMAP.

The human lung plays vital roles in respiration, host defense, and basic physiology. Recent technological advancements such as single-cell RNA sequencing and genetic lineage tracing have revealed novel cell types and enriched functional properties of existing cell types in lung. The time has come to take a new census. Initiated by members of the NHLBI-funded LungMAP Consortium and aided by experts in the lung biology community, we synthesized current data into a comprehensive and practical cellular census of the lung. Identities of cell types in the normal lung are captured in individual cell cards with delineation of function, markers, developmental lineages, heterogeneity, regenerative potential, disease links, and key experimental tools. This publication will serve as the starting point of a live, up-to-date guide for lung research at https://www.lungmap.net/cell-cards/. We hope that Lung CellCards will promote the community-wide effort to establish, maintain, and restore respiratory health.

Glucocorticoid regulates mesenchymal cell differentiation required for perinatal lung morphogenesis and function.

While antenatal glucocorticoids are widely used to enhance lung function in preterm infants, cellular and molecular mechanisms by which glucocorticoid receptor (GR) signaling influences lung maturation remain poorly understood. Deletion of the glucocorticoid receptor gene (Nr3c1) from fetal pulmonary mesenchymal cells phenocopied defects caused by global Nr3c1 deletion, while lung epithelial- or endothelial-specific Nr3c1 deletion did not impair lung function at birth. We integrated genome-wide gene expression profiling, ATAC-seq, and single cell RNA-seq data in mice in which GR was deleted or activated to identify the cellular and molecular mechanisms by which glucocorticoids control prenatal lung maturation. GR enhanced differentiation of a newly defined proliferative mesenchymal progenitor cell (PMP) into matrix fibroblasts (MFBs), in part by directly activating extracellular matrix-associated target genes, including Fn1, Col16a4, and Eln and by modulating VEGF, JAK-STAT, and WNT signaling. Loss of mesenchymal GR signaling blocked fibroblast progenitor differentiation into mature MFBs, which in turn increased proliferation of SOX9+ alveolar epithelial progenitor cells and inhibited differentiation of mature alveolar type II (AT2) and AT1 cells. GR signaling controls genes required for differentiation of a subset of proliferative mesenchymal progenitors into matrix fibroblasts, in turn, regulating signals controlling AT2/AT1 progenitor cell proliferation and differentiation and identifying cells and processes by which glucocorticoid signaling regulates fetal lung maturation.

Integration of transcriptomic and proteomic data identifies biological functions in cell populations from human infant lung.

Systems biology uses computational approaches to integrate diverse data types to understand cell and organ behavior. Data derived from complementary technologies, for example transcriptomic and proteomic analyses, are providing new insights into development and disease. We compared mRNA and protein profiles from purified endothelial, epithelial, immune, and mesenchymal cells from normal human infant lung tissue. Signatures for each cell type were identified and compared at both mRNA and protein levels. Cell-specific biological processes and pathways were predicted by analysis of concordant and discordant RNA-protein pairs. Cell clustering and gene set enrichment comparisons identified shared versus unique processes associated with transcriptomic and/or proteomic data. Clear cell-cell correlations between mRNA and protein data were obtained from each cell type. Approximately 40% of RNA-protein pairs were coherently expressed. While the correlation between RNA and their protein products was relatively low (Spearman rank coefficient rs ~0.4), cell-specific signature genes involved in functional processes characteristic of each cell type were more highly correlated with their protein products. Consistency of cell-specific RNA-protein signatures indicated an essential framework for the function of each cell type. Visualization and reutilization of the protein and RNA profiles are supported by a new web application, "LungProteomics," which is freely accessible to the public.

Single cell RNA analysis identifies cellular heterogeneity and adaptive responses of the lung at birth.

The respiratory system undergoes a diversity of structural, biochemical, and functional changes necessary for adaptation to air breathing at birth. To identify the heterogeneity of pulmonary cell types and dynamic changes in gene expression mediating adaptation to respiration, here we perform single cell RNA analyses of mouse lung on postnatal day 1. Using an iterative cell type identification strategy we unbiasedly identify the heterogeneity of murine pulmonary cell types. We identify distinct populations of epithelial, endothelial, mesenchymal, and immune cells, each containing distinct subpopulations. Furthermore we compare temporal changes in RNA expression patterns before and after birth to identify signaling pathways selectively activated in specific pulmonary cell types, including activation of cell stress and the unfolded protein response during perinatal adaptation of the lung. The present data provide a single cell view of the adaptation to air breathing after birth.

Cell type-resolved human lung lipidome reveals cellular cooperation in lung function.

Cell type-resolved proteome analyses of the brain, heart and liver have been reported, however a similar effort on the lipidome is currently lacking. Here we applied liquid chromatography-tandem mass spectrometry to characterize the lipidome of major lung cell types isolated from human donors, representing the first lipidome map of any organ. We coupled this with cell type-resolved proteomics of the same samples (available at Lungmap.net). Complementary proteomics analyses substantiated the functional identity of the isolated cells. Lipidomics analyses showed significant variations in the lipidome across major human lung cell types, with differences most evident at the subclass and intra-subclass (i.e. total carbon length of the fatty acid chains) level. Further, lipidomic signatures revealed an overarching posture of high cellular cooperation within the human lung to support critical functions. Our complementary cell type-resolved lipid and protein datasets serve as a rich resource for analyses of human lung function.

Lung Gene Expression Analysis (LGEA): an integrative web portal for comprehensive gene expression data analysis in lung development.

'LungGENS', our previously developed web tool for mapping single-cell gene expression in the developing lung, has been well received by the pulmonary research community. With continued support from the 'LungMAP' consortium, we extended the scope of the LungGENS database to accommodate transcriptomics data from pulmonary tissues and cells from human and mouse at different stages of lung development. Lung Gene Expression Analysis (LGEA) web portal is an extended version of LungGENS useful for the analysis, display and interpretation of gene expression patterns obtained from single cells, sorted cell populations and whole lung tissues. The LGEA web portal is freely available at http://research.cchmc.org/pbge/lunggens/mainportal.html.

Single-cell RNA sequencing identifies diverse roles of epithelial cells in idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a lethal interstitial lung disease characterized by airway remodeling, inflammation, alveolar destruction, and fibrosis. We utilized single-cell RNA sequencing (scRNA-seq) to identify epithelial cell types and associated biological processes involved in the pathogenesis of IPF. Transcriptomic analysis of normal human lung epithelial cells defined gene expression patterns associated with highly differentiated alveolar type 2 (AT2) cells, indicated by enrichment of RNAs critical for surfactant homeostasis. In contrast, scRNA-seq of IPF cells identified 3 distinct subsets of epithelial cell types with characteristics of conducting airway basal and goblet cells and an additional atypical transitional cell that contributes to pathological processes in IPF. Individual IPF cells frequently coexpressed alveolar type 1 (AT1), AT2, and conducting airway selective markers, demonstrating "indeterminate" states of differentiation not seen in normal lung development. Pathway analysis predicted aberrant activation of canonical signaling via TGF-ß, HIPPO/YAP, P53, WNT, and AKT/PI3K. Immunofluorescence confocal microscopy identified the disruption of alveolar structure and loss of the normal proximal-peripheral differentiation of pulmonary epithelial cells. scRNA-seq analyses identified loss of normal epithelial cell identities and unique contributions of epithelial cells to the pathogenesis of IPF. The present study provides a rich data source to further explore lung health and disease.

Systems biology evaluation of cell-free amniotic fluid transcriptome of term and preterm infants to detect fetal maturity.

BACKGROUND: Amniotic fluid (AF) is a proximal fluid to the fetus containing higher amounts of cell-free fetal RNA/DNA than maternal serum, thereby making it a promising source for identifying novel biomarkers that predict fetal development and organ maturation. Our aim was to compare AF transcriptomic profiles at different time points in pregnancy to demonstrate unique genetic signatures that would serve as potential biomarkers indicative of fetal maturation. METHODS: We isolated AF RNA from 16 women at different time points in pregnancy: 4 from 18 to 24 weeks, 6 from 34 to 36 weeks, and 6 from 39 to 40 weeks. RNA-sequencing was performed on cell-free RNA. Gene expression and splicing analyses were performed in conjunction with cell-type and pathway predictions. RESULTS: Sample-level analysis at different time points in pregnancy demonstrated a strong correlation with cell types found in the intrauterine environment and fetal respiratory, digestive and external barrier tissues of the fetus, using high-confidence cellular molecular markers. While some RNAs and splice variants were present throughout pregnancy, many transcripts were uniquely expressed at different time points in pregnancy and associated with distinct neonatal co-morbidities (respiratory distress and gavage feeding), indicating fetal immaturity. CONCLUSION: The AF transcriptome exhibits unique cell/organ-selective expression patterns at different time points in pregnancy that can potentially identify fetal organ maturity and predict neonatal morbidity. Developing novel biomarkers indicative of the maturation of multiple organ systems can improve upon our current methods of fetal maturity testing which focus solely on the lung, and will better inform obstetrical decisions regarding delivery timing.

'LungGENS': a web-based tool for mapping single-cell gene expression in the developing lung.

We developed LungGENS (Lung Gene Expression iN Single-cell), a web-based bioinformatics resource for querying single-cell gene expression databases by entering a gene symbol or a list of genes or selecting a cell type of their interest. Gene query provides quantitative RNA expression of the gene of interest in each lung cell type. Cell type query returns associated selective gene signatures and genes encoding cell surface markers and transcription factors in interactive heatmap and tables. LungGENS will be broadly applicable in respiratory research, providing a cell-specific RNA expression resource at single-cell resolution. LungGENS is freely available for non-commercial use at https://research.cchmc.org/pbge/lunggens/default.html.