The Idiopathic Pulmonary Fibrosis Cell Atlas
2020; American Physical Society; Volume: 319; Issue: 6 Linguagem: Inglês
10.1152/ajplung.00451.2020
ISSN1522-1504
AutoresNir Neumark, Carlos Cosme, Kadi-Ann Rose, Naftali Kaminski,
Tópico(s)Systemic Sclerosis and Related Diseases
ResumoEditorialThe Idiopathic Pulmonary Fibrosis Cell AtlasNir Neumark, Carlos Cosme Jr., Kadi-Ann Rose, and Naftali KaminskiNir NeumarkSection of Pulmonary, Critical Care, and Sleep Medicine, Yale University School of Medicine, New Haven, ConnecticutInterdepartmental Program in Computational Biology and Bioinformatics, Yale University School of Medicine, New Haven, Connecticut,* Carlos Cosme Jr.Section of Pulmonary, Critical Care, and Sleep Medicine, Yale University School of Medicine, New Haven, Connecticut,* Kadi-Ann RoseSection of Pulmonary, Critical Care, and Sleep Medicine, Yale University School of Medicine, New Haven, Connecticut, and Naftali KaminskiSection of Pulmonary, Critical Care, and Sleep Medicine, Yale University School of Medicine, New Haven, ConnecticutPublished Online:30 Nov 2020https://doi.org/10.1152/ajplung.00451.2020This is the final version - click for previous versionMoreSectionsPDF (1 MB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInEmailWeChat INTRODUCTIONIdiopathic pulmonary fibrosis (IPF) is a fatal lung disease that is characterized by replacement of the normal lung anatomy with active remodeling and deposition of extracellular matrix (ECM) accompanied by a shift in lung cellular communities. The most common outcome of this debilitating disease is respiratory failure and subsequent death. The disease predominately afflicts the middle aged and elderly and has a median life expectancy after diagnosis of 3.8 yr. Although two FDA-approved drugs consistently slow down disease progression, lung transplantation is still the only curative treatment.Historically, much of our understanding of pulmonary fibrosis has been derived from detailed pathological analysis of human lungs that elucidated the unique morphological characteristics of IPF and observations derived from animal models of disease. In the last decade insights derived from genetic studies and from transcriptomic studies of the human lung have given investigators a better appreciation of the complexity and extent of the molecular and cellular changes that determine the lung phenotype in IPF (11). However, a detailed description of all the cell-specific molecular changes in the human fibrotic lung was until recently still lacking.The advent of single-cell RNA sequencing (scRNAseq) has afforded researchers the opportunity to comprehensively interrogate tissues at cell-level resolution (8). In contrast to the bulk RNA sequencing protocols that analyzed gene expression in a whole piece of tissue, which averaged the signal of multiple cells without distinction of the cellular source of the signal, scRNAseq profiles expression of each individual cell within a sample. This technology allows researchers to closely examine every single individual cell within a sequenced sample to identify their cell types, functions, and ultimately the molecular signatures and cellular interactions guiding their function within the context of that tissue and human disease. With such breadth and depth, scRNAseq is a powerful tool that is in particular appealing to better untangle the intricate multicellular complexity of the human IPF lung.Several groups embarked independently on exploiting the power of scRNAseq within the context of IPF to create a comprehensive transcriptomic "map" of all human cells in patients with IPF (1–6, 10). The insights from these papers are substantial and range from establishing evidence of profibrotic attributes of distinct cell populations to identification of novel and ectopic cell populations in the human IPF lung. But the "big-data" nature of scRNAseq poses distinct challenges; in a single experiment of a single sample, investigators can profile 5,000–10,000 cells, with a few thousand transcripts per cell, and usually investigators will run multiple samples. In the lung, if no enrichment techniques are used, we are dealing, based on one estimate, with ∼40 known cell types including epithelial, endothelial, mesenchymal, and myelomonocytic populations, and maybe 10–15 new ones (9). Thus, a detailed description of the cell- and disease-specific changes in gene expression is beyond the scope of any single paper or the bandwidth and research interests of a single research group. And although the data are usually deposited in public data repositories, mining the data requires significant expertise in bioinformatics and computational biology, as well as access to powerful computer systems and storage systems. Aiming to make IPF scRNAseq data accessible to the scientific and medical community, we decided to create an easy-to-use data dissemination portal called the IPF Cell Atlas (www.ipfcellatlas.com). With this interactive and easy-to-use web tool, users have access to a variety of embedded visualization tools that allow exploration of several independently procured single-cell data sets on IPF. We have designed this website to be easily accessible and intuitive for any user, regardless of their technical background or computational skill. We believe that improving accessibility to the newest and most advanced data on IPF will have a profound impact on our understanding of IPF and will accelerate development of therapeutic solutions for this devastating disease.SINGLE-CELL STUDIES INCLUDED IN THE IPF CELL ATLASHere, we focus our attention on the main findings of four groups whose data are currently available on the IPF Cell Atlas (Table 1). In 2018, Reyfman et al. (6) applied scRNAseq to eight control lung samples and eight pulmonary fibrosis explants, of which four were IPF, two systemic sclerosis, one polymyositis, and one chronic hypersensitivity pneumonitis. Single-cell sequencing of their 16 samples returned a data set of 76,070 cells that clustered into 16 resident lung cell types based on canonical markers and performed bulk RNA seq and histological validations. The authors identified a novel fibrotic lung macrophage population that was found only in patients with pulmonary fibrosis. These cells expressed profibrotic molecules such as CHI3L1, MARCKS, IL1RN, PLA2G7, MMP9, and SPP1 as well as MAFB1 and SIGLEC1. In addition, they provided evidence that supported previous findings of increased epithelial cell heterogeneity within the IPF lung (10). A subsequent paper by Morse et al. (4) also reported the existence of this profibrotic alveolar macrophage in IPF patients. Their scRNAseq study, which analyzed the transcriptomes of 24,220 cells from the upper and lower lobes of seven control and three IPF samples, identified three distinct pulmonary macrophage subsets in the IPF lung: FABP4hi macrophages, SPP1hi/MERTK macrophages, and FCN1hi macrophages. The SPP1hi/MERTK macrophages displayed characteristics akin to the profibrotic alveolar macrophages identified in Reyfman et al. (6) and were identified by Morse et al. (4) to accumulate predominately in the lower lobes of the IPF lung. This year, the scale of IPF scRNAseq data was substantially augmented when two papers published in the same issue of Science Advances (1, 3) reported the sequencing results of over 400,000 cells from 105 human lungs. In the Habermann et al. (3) study, scRNAseq was applied to single-cell suspensions of 10 nonfibrotic and 20 pulmonary fibrosis lungs to produce a data set of 114,396 sequenced cells clustered into 31 distinct cell subsets. Our group (1) profiled 312,928 cells from distal lung parenchyma samples obtained from 32 IPF lungs, 18 chronic obstructive pulmonary disease (COPD) lungs, and 28 control donor lungs. Both groups observed epithelial cell heterogeneity, profibrotic macrophages, and fibroblast and myofibroblast subtypes in the fibrotic lung. Most interesting is the parallel observation by both groups of a novel pathological epithelial cell population coexpressing basal, epithelial, mesenchymal, senescence, and developmental markers (1, 3). Whereas Habermann et al. identified these cells as KRT5−/KRT17+ pathological, ECM-producing epithelial cells, we have named this population "aberrant basaloid cells" and demonstrated that these cells were localized at the edge of the myofibroblastic foci in the IPF lung. In addition, we identified a novel COL15-expressing population of vascular endothelial cells that in IPF are prominent in the distal lung, in areas of fibrosis and remodeling, but in the nonfibrotic lung are restricted to peribronchial regions and are never seen in the lung parenchyma (1). What we also noticed was a substantial agreement between most data sets: the aberrant basaloid cells can be found in at least three of the data sets (1, 3, 6), as are the profibrotic macrophages. Impressively, despite differences in dissociation techniques and analytic approaches, the results in these four data sets are congruent. The novel macrophage, epithelial, and endothelial cell populations are seen, as well as the overall phenomenon of proximalization of the distal lung with replacement of alveolar epithelial cells and alveolar endothelial capillary populations, with epithelial and endothelial cells that are usually restricted to the airways. Overall, it can be observed from these single-cell works on IPF that scRNAseq is robust and reproducible and holds significant promise for understanding of IPF and, by extension, developing novel therapeutic and diagnostic approaches to the disease. Looking at these data sets, it became very obvious to us that the discoveries reported by the original groups represent only the tip of the iceberg—and that creating an easy-to-use data mining tool is required to realize the full discovery potential of these data sets.Table 1. Studies included in www.IPFCellAtlas.comPublicationName on IPF Cell AtlasGEO Accession No.Cell NumberSubjects by DiseaseHighlighted FindingsReyfman et al., AJRCCM 2019 (6)MisharinGSE12161176,0705 IPF2 SSC1 PM1 CHP8 ControlProfibrotic alveolar macrophages; epithelial cell heterogeneity in fibrotic lungsMorse et al., ERJ 2019 (4)LafyatisGSE12803347,7713 IPFUpper and Lower lobe from each7 ControlProfibrotic alveolar macrophages (SPP1+/MERTK+); epithelial cell heterogeneity in fibrotic lungsAdams et al., Science Advances 2020 (1)Kaminski/RosasGSE136831312,92832 IPF28 Control18 COPDAberrant basaloid cells; invasive fibroblasts and myofibroblasts; COL15+ endothelial cells in IPF; profibrotic alveolar macrophages; altered regulatory networksHabermann et al., Science Advances 2020 (3)Banovich/KropskiGSE135893114,39612 IPF3 CHP2 NSIP1 Sarcoidosis10 ControlPathological KRT5−/KRT17+ epithelial cells; multiple fibroblast subtypesTotal: 551,000 cells from 134 individuals [52 idiopathic pulmonary fibrosis (IPF), 4 chronic hypersensitivity pneumonitis (CHP), 2 nonspecific interstitial pneumonitis (NSIP), 2 systemic sclerosis (SSC), 1 polymyositis (PM), 1 sarcoidosis, 18 chronic obstructive pulmonary disease (COPD), 54 control]. AJRCCM, American Journal of Respiratory and Critical Care Medicine; ERJ, European Respiratory Journal.THE IPF CELL ATLAS WEBSITEOwing to the "big data" nature of scRNAseq, analysis of these data sets is inherently computational and requires a heightened level of skill and comfort with programming, data science, and statistics, in addition to understanding of the biological fields relevant to the data sets being analyzed. For many biological and medical researchers, the need to develop these skills can be a significant barrier to access single-cell data. While some advocate expanding education in quantitative disciplines to address this, we feel that tools that provide the research community easy and intuitive access to the swaths of biological data available are urgently required. Within the context of single-cell work on IPF, we sought to minimize the barriers by creating an easy-to-navigate web interface that provides researchers public access to the four major single-cell IPF data sets mentioned above with several standardized visualization tools for independent differential gene expression analysis. Any scientist, regardless of their computational skill or physical location, can easily access this website for independent data mining of the data sets and novel expression pattern exploration. Our website, known as the Idiopathic Pulmonary Fibrosis Cell Atlas (or IPF Cell Atlas for short), can be accessed at www.ipfcellatlas.com.To code the front-end web interface and back-end database-interacting functions, we utilized the Shiny package (https://shiny.rstudio.com/) in the R programming language (9). As most of our single-cell data analysis is performed in the R language, having the ability to code the website in a familiar language both greatly reduced website development time and improved integration of figure-generating code that used the ggplot2 R package (11). For fast access to gene expression levels, cell-specific gene expression data were stored on a MySQL database hosted on an Amazon Web Services (AWS) cloud-based server. Database-interacting functions were coded into the back end of the IPF Cell Atlas to facilitate querying of our MySQL database whenever a user inputs a gene into a search box. A diagram of the IPF Cell Atlas' "front-back-front" input processing model is provided in Fig. 1.Fig. 1.Flowchart of the IPF Cell Atlas gene search and plot generation pipeline. Briefly, a user will input a gene into the gene search box provided in all analysis tools and will click the search button after toggling the tool-specific plotting options. The website then runs a query of our gene expression-containing database to selectively pull out the data for the input gene. The expression data are appended to the cell metadata for a given data set before being run through R code from the ggplot2 package to output the figure.Download figureDownload PowerPointOne of our main goals when designing the IPF Cell Atlas was to create a web interface that was clean, clear, and easily interpretable for all users regardless of their technical background. For navigation, the portal uses a series of embedded drop-down menus that users can click to move through the various available data sets and visualization tools. The upper panel contains drop-down tabs for each of the four scRNAseq data sets currently available on the website as well as a "Tools" tab for data set comparison and a "Models" tab for a transcriptional regulatory model of IPF. Several visualization tools are available that are meant to facilitate a multifaceted analysis of each of the data sets. These tools include the UMAP (Uniform Manifold Approximation and Projection) Explorer, Gene Explorer, Cell Explorer, and Batch Explorer (Fig. 2). The UMAP Explorer plots the expression of an input gene within each specific cell identified in our data set on a two-dimensional UMAP plot where individual cells are grouped by cell type annotation. This feature is particularly useful for qualitatively assessing cell-specific gene expression patterns in comparison to other cell types identified in each of the data sets. Within this tool, the user can toggle the cell groupings (epithelial, stromal, mesenchymal, immune) that they are most interested in. The Gene Explorer feature provides a more quantitative assessment of differential gene expression patterns and allows the user to plot gene expression between control and IPF samples as a violin plot, bar graph, density plot, or box plot by subject. The Batch Explorer, which is currently limited to the Kaminski/Rosas data set, allows the user to plot differential gene expression between control and IPF samples for up to eight genes of interest in a dot plot format. These types of plots can be particularly informative given that they also provide estimates for the percentage of a given cell type expression of a given gene as well as the average expression of that gene within the given cell type and disease state. Finally, the Cell Explorer function, which is also currently limited to the Kaminski/Rosas data set, allows the user to select a specific cell type for which to plot the average gene expression of all genes within that cell type for the control samples (x-axis) versus the IPF samples (y-axis). Users can input a gene to specifically highlight among the mass of points. In addition to these visualization tools, users who feel their own independent computational analysis of each data set would be more fruitful can also freely download the data from the Gene Expression Omnibus (GEO), in which links to each GEO page are provided in the drop-down menu of each data set.Fig. 2.Screenshots of analysis tools that are available on the IPF Cell Atlas website. A: UMAP Explorer feature that allows the user to visualize gene expression of an input gene within a selected cell grouping (i.e., epithelial, immune, stromal, mesenchymal, immune; data set dependent) on a 2-dimensional UMAP projection clustered by cell type. B: Batch Explorer features that plot a dot plot of differential gene expression for up to 8 genes within a selected cell grouping. In addition to expression differences between control and IPF samples, the dot plot provides information on the average level of expression of each gene per cell type as well as the percentage of cells of that cell type expressing each gene. C: data set comparison tool for comparing gene expression on the different UMAPs generated for each of the data sets. D: Gene Explorer feature that allows a user to input a gene and observe cell-specific differential expression patterns in a violin plot, bar graph, density plot, or box plot format. Users can toggle the type of plot and whether to display results that compare control to IPF.Download figureDownload PowerPointA STEP-BY-STEP USAGE GUIDEAccessing and taking full advantage of the website is straightforward. Upon entering the website at www.ipfcellatlas.com, a user must first decide a few things: 1) the gene(s) whose expression patterns are of interest, 2) the visualization tool that will best display the data, and 3) the specific data set for which to explore and analyze. After these questions are resolved, a user can then begin exploring the data provided on the website easily. As a step-by-step example, suppose a user wants to see a bar graph showing expressional differences of Col4a1 in all identified cell types between control and IPF samples of the Kaminski/Rosas data set. Here, 1) the gene of interest is Col4a1, 2) the visual aid is a bar graph that is found in the Gene Explorer tool, and 3) the data set of interest is "Kaminski/Rosas." First, the hypothetical user will click the "Kaminski/Rosas" tab on the upper panel to display the drop-down menu of visualization tools. Next, the user will click the Gene Explorer tool from the drop-down menu and will be routed to a new page containing input and toggle boxes on the left of the screen and a display panel in the center. In the case of the Gene Explorer, the user will be greeted by a gene input box, a toggle switch for the type of plot (violin plot, bar graph, density plot, or box plot), and a toggle switch for splitting these plots by disease to compare control and IPF samples. Because the user wants to see a bar graph comparing Col4a1 expression between control and IPF samples, they will now need to input "Col4a1" into the "Gene search term" box, then click the "Bar" option under "Plot type" to output a bar graph and then the "Break by Disease" checkbox at the bottom of the panel to split the bars for each cell type by control and IPF. Finally, the user will click "Search" for the website to run the query on Col4a1 and output a split a bar graph of identified cells of the Kaminski/Rosas data set that is split by control and IPF. The same general step-by-step process is applicable, with some minor adjustments, to the other visualization tools available on the portal.WEBSITE USAGE BY THE NUMBERSSince being made public in September of 2019, the IPF Cell Atlas portal has seen more than 4,000 unique users from five continents and 56 countries, with ∼60% of users coming from the United States per Google Analytics (Fig. 3). There are on average 500 unique visitors to the website each month. July 2020 saw our portal reach a new record for monthly users with 794 unique visitors. Over 12,000 sessions on the portal have been engaged that have led to over 100,000 searches of more than 7,400 unique genes. At the time of writing this editorial, the most frequently searched gene is ACE2, the COVID-19 cellular receptor. This is, of course, due to the COVID-19 pandemic. ACE2 had zero searches in November 2019 but increased to be the top searched gene by August 2020 with 1,966 searches (Fig. 3). Other top searched genes include the myofibroblast marker ACTA2 (942 searches), the senescence marker CDKN2A (662 searches), the cytokine IL11 (623 searches), and MUC5B, the gene whose promoter variant is the most associated gene variant with IPF (530 searches). Although the top genes searched may represent current and emerging interest, we find the fact that 7,400 unique genes have been accessed by over 4,000 individual users most exciting, as it represents the diversity, extent, and reach of interest the IPF Cell Atlas generated.Fig. 3.Website usage statistics provided through Google Analytics. A: heatmap of unique users to the portal from across the globe. B: pie charts of the top 5 searched genes before the pandemic (November 2019) and during the pandemic (August 2020). Percentages shown represent the percentage of searches of that gene within the 5 genes shown. C: line graph displaying the number of unique users accessing the website each month since it was made public.Download figureDownload PowerPointFUTURE WORK AND CONCLUDING REMARKSThe IPF Cell Atlas in its current form represents the first iteration of what could be a significantly more ambitious project. Based on current use it seems that the IPF Cell Atlas is indeed expanding the access of the academic community to single-cell data from human lung diseases, and, as the ACE2 results suggest, investigators utilize this atlas for scientific objectives that could not have even been predicted when the atlas was designed. We plan to continue to develop the website to include integrating new single-cell data sets and analytical tools as they are made available. New analysis tools may include a heatmap generator, differential cell-cell interaction networks, and improved support for cross-data set gene expression comparison, therapeutic signature exploration, and integrating data from other IPF-specific OMICs approaches, especially spatial transcriptomic as well as single-cell epigenomic, proteomic, and metabolomic data. However, for the lung community at large to benefit, the IPF Cell Atlas should also be expanded in scope; IPF is after all only one of many diffuse parenchymal lung diseases. Thus, data sets from other diffuse parenchymal lung diseases, as well as reference data sets, should be included (7). This would pose significant challenges, as presently there is little agreement about essential information required to harmonize data such as minimal sample attributes (metadata) that need to be provided for each sample, conventions for cell type and state definitions, clear delineation of anatomical section collection, and an effort to develop representative sampling of genders, ethnicities, races, exposures, disease stages, presentations, and other clinically relevant attributes (8). As more data sets are added and the diversity of the populations and diseases widens, the value of user-friendly, simple to use, single-cell data portals will increase, to allow exploration of the multicellular complexities of human advanced lung disease, facilitate discovery and hypothesis generation, and eventually be a significant milestone in the campaign to cure IPF, as well as other diffuse parenchymal lung diseases.GRANTSThis work was supported by a generous donation from Three Lake Partners for the construction of the IPF Cell Atlas and by NIH National Heart, Lung, and Blood Institute Grants R01 HL-127349, R01 HL-141852, and U01 HL-145567 to N.K.DISCLOSURESN.K. served as a consultant to Biogen Idec, Boehringer Ingelheim, Third Rock, Pliant, Samumed, NuMedii, Theravance, LifeMax, Three Lake Partners, Optikira, and Astra Zeneca over the last 3 years, reports equity in Pliant, a grant from Veracyte, and nonfinancial support from MiRagen and Astra Zeneca, and has intellectual property on novel biomarkers and therapeutics in IPF licensed to Biotech. None of the other authors has any conflicts of interest, financial or otherwise, to disclose.AUTHOR CONTRIBUTIONSN.N., C.C. and N.K. prepared figures; N.N., C.C. and N.K. drafted manuscript; N.N., C.C., K.R. and N.K. edited and revised manuscript; C.C. and N.K. approved final version of manuscript.REFERENCES1. Adams TS, Schupp JC, Poli S, Ayaub EA, Neumark N, Ahangari F, Chu SG, Raby BA, DeIuliis G, Januszyk M, Duan Q, Arnett HA, Siddiqui A, Washko GR, Homer R, Yan X, Rosas IO, Kaminski N. Single-cell RNA-seq reveals ectopic and aberrant lung-resident cell populations in idiopathic pulmonary fibrosis. Sci Adv 6: eaba1983, 2020. doi:10.1126/sciadv.aba1983. Crossref | PubMed | ISI | Google Scholar2. Carraro G, Mulay A, Yao C, Mizuno T, Konda B, Petrov M, Lafkas D, Arron JR, Hogaboam CM, Chen P, Jiang D, Noble PW, Randell SH, McQualter JL, Stripp BR. Single cell reconstruction of human basal cell diversity in normal and IPF lung. 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JCI Insight 1: e90558, 2016. doi:10.1172/jci.insight.90558. Crossref | PubMed | ISI | Google Scholar11. Yu G, Ibarra GH, Kaminski N. Fibrosis: lessons from OMICS analyses of the human lung. Matrix Biol 68-69: 422–434, 2018. doi:10.1016/j.matbio.2018.03.014. Crossref | PubMed | ISI | Google ScholarAUTHOR NOTES*N. Neumark and C. Cosme Jr. contributed equally to this work.Correspondence: N. Kaminski (naftali.[email protected]edu). Download PDF Previous Back to Top Next FiguresReferencesRelatedInformationCited ByFrom COVID to fibrosis: lessons from single-cell analyses of the human lung13 June 2022 | Human Genomics, Vol. 16, No. 1Microenvironmental sensing by fibroblasts controls macrophage population size5 August 2022 | Proceedings of the National Academy of Sciences, Vol. 119, No. 32CRTH2 Mediates Profibrotic Macrophage Differentiation and Promotes Lung FibrosisAmerican Journal of Respiratory Cell and Molecular Biology, Vol. 67, No. 2Insights gained in the pathology of lung disease through single‐cell transcriptomics17 June 2022 | The Journal of Pathology, Vol. 257, No. 4Targeting monocytes/macrophages in fibrosis and cancer diseases: Therapeutic approachesPharmacology & Therapeutics, Vol. 234Proteomic biomarkers of progressive fibrosing interstitial lung disease: a multicentre cohort analysisThe Lancet Respiratory Medicine, Vol. 10, No. 6LncRNA DACH1 protects against pulmonary fibrosis by binding to SRSF1 to suppress CTNNB1 accumulationActa Pharmaceutica Sinica B, Vol. 70Idiopathic pulmonary fibrosis: Current and future treatment10 January 2022 | The Clinical Respiratory Journal, Vol. 16, No. 2Lung fibrosis is induced in ADAR2 overexpressing mice via HuR‐induced CTGF signaling5 January 2022 | The FASEB Journal, Vol. 36, No. 2Development of human alveolar epithelial cell models to study distal lung biology and diseaseiScience, Vol. 25, No. 2Modeling of lung phenotype of Hermansky–Pudlak syndrome type I using patient-specific iPSCs4 November 2021 | Respiratory Research, Vol. 22, No. 1Disease modeling of pulmonary fibrosis using human pluripotent stem cell-derived alveolar organoidsStem Cell Reports, Vol. 16, No. 12Comprehensive epigenomic profiling of human alveolar epithelial differentiation identifies key epigenetic states and transcription factor co-regulatory networks for maintenance of distal lung identity18 December 2021 | BMC Genomics, Vol. 22, No. 1Beyond the transcription factor: the under-studied role of epigenomics in lung differentiationEpigenomics, Vol. 13, No. 23Mendelian randomisation highlights hypothyroidism as a causal determinant of idiopathic pulmonary fibrosisEBioMedicine, Vol. 73Disease pathology in fibrotic interstitial lung disease: is it all about usual interstitial pneumonia?The Lancet, Vol. 398, No. 10309The Role of Herpes Viruses in Pulmonary Fibrosis22 July 2021 | Frontiers in Medicine, Vol. 8Resident Interstitial Lung Fibroblasts and their Role in Alveolar Stem Cell Niche Development, Homeostasis, Injury, and Regeneration24 February 2021 | Stem Cells Translational Medicine, Vol. 10, No. 7PTX3 Regulation of Inflammation, Hemostatic Response, Tissue Repair, and Resolution of Fibrosis Favors a Role in Limiting Idiopathic Pulmonary Fibrosis21 June 2021 | Frontiers in Immunology, Vol. 12Molecular Markers and the Promise of Precision Medicine for Interstitial Lung DiseaseClinics in Chest Medicine, Vol. 42, No. 2Toll-Interacting Protein in Pulmonary Diseases. Abiding by the Goldilocks PrincipleAmerican Journal of Respiratory Cell and Molecular Biology, Vol. 64, No. 5Regulatory Immune Cells in Idiopathic Pulmonary Fibrosis: Friends or Foes?22 April 2021 | Frontiers in Immunology, Vol. 12 More from this issue > Volume 319Issue 6December 2020Pages L887-L892 Copyright & PermissionsCopyright © 2020 the American Physiological Societyhttps://doi.org/10.1152/ajplung.00451.2020PubMed32996785History Received 21 September 2020 Accepted 22 September 2020 Published online 30 November 2020 Published in print 1 December 2020 Keywordsdata sharingIPFsingle-cell RNAseqsystems biology Metrics Downloaded 6,699 times
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