Transferability of radiomic signatures from experimental to human interstitial lung disease

BACKGROUND Interstitial lung disease (ILD) defines a group of parenchymal lung disorders, characterized by fibrosis as their common final pathophysiological stage. To improve diagnosis and treatment of ILD, there is a need for repetitive non-invasive characterization of lung tissue by quantitative parameters. In this study, we investigated whether CT image patterns found in mice with bleomycin induced lung fibrosis can be translated as prognostic factors to human patients diagnosed with ILD. METHODS Bleomycin was used to induce lung fibrosis in mice (n_control = 36, n_experimental = 55). The patient cohort consisted of 98 systemic sclerosis (SSc) patients (n_ILD = 65). Radiomic features (n_histogram = 17, n_texture = 137) were extracted from microCT (mice) and HRCT (patients) images. Predictive performance of the models was evaluated with the area under the receiver-operating characteristic curve (AUC). First, predictive performance of individual features was examined and compared between murine and patient data sets. Second, multivariate models predicting ILD were trained on murine data and tested on patient data. Additionally, the models were reoptimized on patient data to reduce the influence of the domain shift on the performance scores. RESULTS Predictive power of individual features in terms of AUC was highly correlated between mice and patients (r = 0.86). A model based only on mean image intensity in the lung scored AUC = 0.921 ± 0.048 in mice and AUC = 0.774 (CI95% 0.677-0.859) in patients. The best radiomic model based on three radiomic features scored AUC = 0.994 ± 0.013 in mice and validated with AUC = 0.832 (CI95% 0.745-0.907) in patients. However, reoptimization of the model weights in the patient cohort allowed to increase the model's performance to AUC = 0.912 ± 0.058. CONCLUSION Radiomic signatures of experimental ILD derived from microCT scans translated to HRCT of humans with SSc-ILD. We showed that the experimental model of BLM-induced ILD is a promising system to test radiomic models for later application and validation in human cohorts

Staphylococcus aureus impairs dermal fibroblast functions with deleterious effects on wound healing.

Chronic wounds are a major disease burden worldwide. The breach of the epithelial barrier facilitates transition of skin commensals to invasive facultative pathogens. Therefore, we investigated the potential effects of Staphylococcus aureus (SA) on dermal fibroblasts as key cells for tissue repair. In co-culture systems combining live or heat-killed SA with dermal fibroblasts derived from the BJ-5ta cell line, healthy individuals, and patients with systemic sclerosis, we assessed tissue repair including pro-inflammatory cytokines, matrix metalloproteases (MMPs), myofibroblast functions, and host defense responses. Only live SA induced the upregulation of IL-1β/-6/-8 and MMP1/3 as co-factors of tissue degradation. Additionally, the increased cell death reduced collagen production, proliferation, migration, and contractility, prerequisite mechanisms for wound closure. Intracellular SA triggered inflammatory and type I IFN responses via intracellular dsDNA sensor molecules and MyD88 and STING signaling pathways. In conclusion, live SA affected various key tissue repair functions of dermal fibroblasts from different sources to a similar extent. Thus, SA infection of dermal fibroblasts should be taken into account for future wound management strategies

Long noncoding RNA H19X is a key mediator of TGF-beta-driven fibrosis

TGFβ is a master regulator of fibrosis, driving the differentiation of fibroblasts into apoptosis resistant myofibroblasts and sustaining the production of extracellular matrix (ECM) components. Here, we identify the nuclear lncRNA H19X as a master regulator of TGFβ-driven tissue fibrosis. H19X was consistently upregulated in a wide variety of human fibrotic tissues and diseases and was strongly induced by TGFβ, particularly in fibroblasts and fibroblast-related cells. Functional experiments following H19X silencing revealed that H19X is an obligatory factor for the TGFβ-induced ECM synthesis as well as differentiation and survival of ECM-producing myofibroblasts. We showed that H19X regulates DDIT4L gene expression, specifically interacting with a region upstream of DDIT4L gene and changing the chromatin accessibility of a DDIT4L enhancer. These events resulted in transcriptional repression of DDIT4L and, in turn, in increased collagen expression and fibrosis. Our results shed light on key effectors of the TGFβ-induced ECM remodeling and fibrosis

The discovAIR project:a roadmap towards the Human Lung Cell Atlas

The Human Cell Atlas (HCA) consortium aims to establish an atlas of all organs in the healthy human body at single-cell resolution to increase our understanding of basic biological processes that govern development, physiology and anatomy, and to accelerate diagnosis and treatment of disease. The lung biological network of the HCA aims to generate the Human Lung Cell Atlas as a reference for the cellular repertoire, molecular cell states and phenotypes, and the cell-cell interactions that characterise normal lung homeostasis in healthy lung tissue. Such a reference atlas of the healthy human lung will facilitate mapping the changes in the cellular landscape in disease. The discovAIR project is one of six pilot actions for the HCA funded by the European Commission in the context of the H2020 framework program. DiscovAIR aims to establish the first draft of an integrated Human Lung Cell Atlas, combining single-cell transcriptional and epigenetic profiling with spatially resolving techniques on matched tissue samples, as well as including a number of chronic and infectious diseases of the lung. The integrated Lung Cell Atlas will be available as a resource for the wider respiratory community, including basic and translational scientists, clinical medicine, and the private sector, as well as for patients with lung disease and the interested lay public. We anticipate that the Lung Cell Atlas will be the founding stone for a more detailed understanding of the pathogenesis of lung diseases, guiding the design of novel diagnostics and preventive or curative interventions

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1 + profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas. </p

An integrated cell atlas of the lung in health and disease

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas

Evaluation of 99mTc-rhAnnexin V-128 SPECT/CT as a diagnostic tool for early stages of interstitial lung disease associated with systemic sclerosis

Background Given the need for early detection of organ involvement in systemic sclerosis, we evaluated 99mTc-rhAnnexin V-128 for the detection of early stages of interstitial lung disease (ILD) in respective animal models using single photon emission computed tomography (SPECT/CT). Methods In bleomycin (BLM)-challenged mice, fos-related antigen 2 (Fra-2) transgenic (tg) mice and respective controls, lung injury was evaluated by analysis of hematoxylin and eosin (HE) and Sirius red staining, with semi-quantification of fibrosis by the Ashcroft score. Apoptotic cells were identified by TUNEL assay, cleaved caspase 3 staining and double staining with specific cell markers. To detect early stages of lung remodeling by visualization of apoptosis, mice were injected intravenously with 99mTc-rhAnnexin V-128 and imaged by small animal SPECT/CT. For confirmation, biodistribution and ex vivo autoradiography studies were performed. Results In BLM-induced lung fibrosis, inflammatory infiltrates occurred as early as day 3 with peak at day 7, whereas pulmonary fibrosis developed from day 7 and was most pronounced at day 21. In accordance, the number of apoptotic cells was highest at day 3 compared with saline controls and then decreased over time. Epithelial cells (E-cadherin+) and inflammatory cells (CD45+) were the primary cells undergoing apoptosis in the earliest remodeling stages of experimental ILD. This was also true in the pathophysiologically different Fra-2 tg mice, where apoptosis of CD45+ cells occurred in the inflammatory stage. In accordance with the findings on tissue level, at day 3 in the BLM and at week 16 in the Fra-2 tg model, biodistribution and/or ex vivo autoradiography showed increased pulmonary uptake of 99mTc-rhAnnexin V-128 compared with controls. However, accumulation of the radiotracer and thus the signal intensity in lungs was too low to allow the differentiation of healthy and injured lungs in vivo. Conclusion At the tissue level, 99mTc-rhAnnexin V-128 successfully demonstrated early stages of ILD in two animal models by detection of apoptotic epithelial and/or inflammatory cells. In vivo, however, we did not detect early lung injury. It remains to be investigated whether the same applies to human ILD.ISSN:1465-9905ISSN:1465-9913ISSN:1478-6362ISSN:1478-635

Multilineage circuits of regenerative cell states.

Regenerating the lungs' architecture after injury requires rebuilding its fibroelastic extracellular matrix scaffold. Konkimalla et al. establish that regenerative cell states (RCSs) of both epithelial and mesenchymal origin are functionally linked and indispensable for this process. Experimental ablation of RCSs causes organ degeneration, whereas their induction causes organ fibrosis

Reply to: The potential and challenges of radiomics in uncovering prognostic and molecular differences in interstitial lung disease associated with systemic sclerosis

We thank L. Zhang and co-workers for their correspondence regarding our manuscript. In interstitial lung disease associated with systemic sclerosis (SSc-ILD), the lack of valid prognostic biomarkers hampers individualised patient management. Our study introduces radiomics as a novel non-invasive tool to acquire phenotypic, prognostic and molecular information derived from standard-of-care high-resolution computed tomography images with great potential to support clinical decision making in SSc-ILD [1]