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Computational trajectory inference enables the reconstruction of cell state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for single-cell fate mapping in diverse scenarios, including regeneration, reprogramming and disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, taking into account the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in velocity vectors. On pancreas development data, CellRank automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. Applied to lineage-traced cellular reprogramming data, predicted fate probabilities correctly recover reprogramming outcomes. CellRank also predicts a new dedifferentiation trajectory during postinjury lung regeneration, including previously unknown intermediate cell states, which we confirm experimentally.

Systemic sclerosis (SSc) is a severe multi-organ disease with interstitial lung disease (ILD) being the major cause of death. While targeted therapies are emerging, biomarkers for sub-stratifying patients based on individual profiles are lacking. Herein, we investigated how levels of serum metabolites correlated with different stages of SSc and SSc-ILD. Serum samples of patients with SSc without ILD, stable and progressive SSc-ILD as well as of healthy controls (HC) were analysed using liquid targeted tandem mass spectrometry. The best discriminating profile consisted of 4 amino acids (AA) and 3 purine metabolites. l-tyrosine, l-tryptophan, and 1-methyl-adenosine distinguished HC from SSc patients. l-leucine, l-isoleucine, xanthosine, and adenosine monophosphate differentiated between progressing and stable SSc-ILD. In SSc-ILD, both, l-leucine and xanthosine negatively correlated with changes in FVC% predicted. Additionally, xanthosine was negatively correlated with changes in DLco% predicted and positively with the prognostic GAP index. Validation of l-leucine and l-isoleucine by an enzymatic assay confirmed both the sub-stratification of SSc-ILD patients and correlation with lung function and prognosis score. Serum metabolites may have potential as biomarkers for discriminating SSc patients based on the presence and severity of ILD. Confirmation in larger cohorts will be needed to appreciate their value for routine clinical care.

ObjectiveTo retrospectively evaluate if texture-based radiomics features are able to detect interstitial lung disease (ILD) and to distinguish between the different disease stages in patients with systemic sclerosis (SSc) in comparison with mere visual analysis of high-resolution computed tomography (HRCT).MethodsSixty patients (46 females, median age 56 years) with SSc who underwent HRCT of the thorax were retrospectively analyzed. Visual analysis was performed by two radiologists for the presence of ILD features. Gender, age, and pulmonary function (GAP) stage was calculated from clinical data (gender, age, pulmonary function test). Data augmentation was performed and the balanced dataset was split into a training (70%) and a testing dataset (30%). For selecting variables that allow classification of the GAP stage, single and multiple logistic regression models were fitted and compared by using the Akaike information criterion (AIC). Diagnostic accuracy was evaluated from the area under the curve (AUC) from receiver operating characteristic (ROC) analyses, and diagnostic sensitivity and specificity were calculated.ResultsValues for some radiomics features were significantly lower (p< 0.05) and those of other radiomics features were significantly higher (p= 0.001) in patients with GAP2 compared with those in patients with GAP1. The combination of two specific radiomics features in a multivariable model resulted in the lowest AIC of 10.73 with an AUC of 0.96, 84% sensitivity, and 99% specificity. Visual assessment of fibrosis was inferior in predicting individual GAP stages (AUC 0.86; 83% sensitivity; 74% specificity).ConclusionThe correlation of radiomics with GAP stage, but not with the visually defined features of ILD-HRCT, implies that radiomics might capture features indicating severity of SSc-ILD on HRCT, which are not recognized by visual analysis.Key Points• Radiomics features can predict GAP stage with a sensitivity of 84% and a specificity of almost 100%.• Extent of fibrosis on HRCT and a combined model of different visual HRCT-ILD features perform worse in predicting GAP stage.• The correlation of radiomics with GAP stage, but not with the visually defined features of ILD-HRCT, implies that radiomics might capture features on HRCT, which are not recognized by visual analysis.

Organ- and body-scale cell atlases have the potential to transform our understanding of human biology. To capture the variability present in the population, these atlases must include diverse demographics such as age and ethnicity from both healthy and diseased individuals. The growth in both size and number of single-cell datasets, combined with recent advances in computational techniques, for the first time makes it possible to generate such comprehensive large-scale atlases through integration of multiple datasets. Here, we present the integrated Human Lung Cell Atlas (HLCA) combining 46 datasets of the human respiratory system into a single atlas spanning over 2.2 million cells from 444 individuals across health and disease. The HLCA contains a consensus re-annotation of published and newly generated datasets, resolving under- or misannotation of 59% of cells in the original datasets. The HLCA enables recovery of rare cell types, provides consensus marker genes for each cell type, and uncovers gene modules associated with demographic covariates and anatomical location within the respiratory system. To facilitate the use of the HLCA as a reference for single-cell lung research and allow rapid analysis of new data, we provide an interactive web portal to project datasets onto the HLCA. Finally, we demonstrate the value of the HLCA reference for interpreting disease-associated changes. Thus, the HLCA outlines a roadmap for the development and use of organ-scale cell atlases within the Human 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 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 programme. 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 Human 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 Human 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.

TGF-beta 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 identified the nuclear long noncoding RNA (lncRNA) H19X as a master regulator of TGF-beta-driven tissue fibrosis. H19X was consistently upregulated in a wide variety of human fibrotic tissues and diseases and was strongly induced by TGF-beta, particularly in fibroblasts and fibroblast-related cells. Functional experiments following H19X silencing revealed that H19X was an obligatory factor for TGF-beta-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 the 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 TGF-beta-induced ECM remodeling and fibrosis.

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.

The correspondence of cell state changes in diseased organs to peripheral protein signatures is currently unknown. Here, we generated and integrated single‐cell transcriptomic and proteomic data from multiple large pulmonary fibrosis patient cohorts. Integration of 233,638 single‐cell transcriptomes (n = 61) across three independent cohorts enabled us to derive shifts in cell type proportions and a robust core set of genes altered in lung fibrosis for 45 cell types. Mass spectrometry analysis of lung lavage fluid (n = 124) and plasma (n = 141) proteomes identified distinct protein signatures correlated with diagnosis, lung function, and injury status. A novel SSTR2+ pericyte state correlated with disease severity and was reflected in lavage fluid by increased levels of the complement regulatory factor CFHR1. We further discovered CRTAC1 as a biomarker of alveolar type‐2 epithelial cell health status in lavage fluid and plasma. Using cross‐modal analysis and machine learning, we identified the cellular source of biomarkers and demonstrated that information transfer between modalities correctly predicts disease status, suggesting feasibility of clinical cell state monitoring through longitudinal sampling of body fluid proteomes., Multi‐modal integration of single‐cell RNA‐seq data from lung tissue and proteomic data from body fluids across independent lung fibrosis patient cohorts revealed biomarker signatures that correspond with cell state changes during disease progression.

BackgroundRadiomic features calculated from routine medical images show great potential for personalised medicine in cancer. Patients with systemic sclerosis (SSc), a rare, multiorgan autoimmune disorder, have a similarly poor prognosis due to interstitial lung disease (ILD). Here, our objectives were to explore computed tomography (CT)-based high-dimensional image analysis (“radiomics”) for disease characterisation, risk stratification and relaying information on lung pathophysiology in SSc-ILD.MethodsWe investigated two independent, prospectively followed SSc-ILD cohorts (Zurich, derivation cohort, n=90; Oslo, validation cohort, n=66). For every subject, we defined 1355 robust radiomic features from standard-of-care CT images. We performed unsupervised clustering to identify and characterise imaging-based patient clusters. A clinically applicable prognostic quantitative radiomic risk score (qRISSc) for progression-free survival (PFS) was derived from radiomic profiles using supervised analysis. The biological basis of qRISSc was assessed in a cross-species approach by correlation with lung proteomic, histological and gene expression data derived from mice with bleomycin-induced lung fibrosis.ResultsRadiomic profiling identified two clinically and prognostically distinct SSc-ILD patient clusters. To evaluate the clinical applicability, we derived and externally validated a binary, quantitative radiomic risk score (qRISSc) composed of 26 features that accurately predicted PFS and significantly improved upon clinical risk stratification parameters in multivariable Cox regression analyses in the pooled cohorts. A high qRISSc score, which identifies patients at risk for progression, was reverse translatable from human to experimental ILD and correlated with fibrotic pathway activation.ConclusionsRadiomics-based risk stratification using routine CT images provides complementary phenotypic, clinical and prognostic information significantly impacting clinical decision making in SSc-ILD.

Computational trajectory inference enables the reconstruction of cell-state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for mapping the fate of single cells in diverse scenarios, including perturbations such as regeneration or disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, derived from ratios of spliced to unspliced reads. CellRank takes into account both the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in RNA velocity vectors. On data from pancreas development, we show that it automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. CellRank also predicts a novel dedifferentiation trajectory during regeneration after lung injury, which we follow up experimentally by confirming the existence of previously unknown intermediate cell states.

Background:Management of patients with systemic sclerosis-associated interstitial lung disease (SSc-ILD) is complicated by high inter-patient variability. To date, no validated predictors of treatment response are available for routine use. High resolution computed tomography (HRCT)-based radiomics, i.e. the high-dimensional, quantitative analysis of imaging metadata, have previously been shown to be successful in discriminating (SSc-)ILD phenotypes in preclinical and clinical studies1. Since HRCT is an integral part of the routine work-up in SSc, HRCT-based radiomic features may hold potential as non-invasive biomarkers.Objectives:To predict treatment response using two-dimensional (2D) HRCT-based radiomics in SSc-ILD patients from a prospectively followed cohort.Methods:Inclusion criteria were diagnosis of SSc-ILD in HRCT, availability of a suitable chest HRCT scan within 12 months prior to initiation of a new treatment, and availability of clinical baseline and follow-up information. Treatment response was defined as the absence of all of the following over a follow-up period of 12-24 months: relative decrease in forced vital capacity (FVC) ≥5%, increase of ILD in HRCT as assessed by a radiologist, change in treatment regimen due to insufficient response, ILD-related death or lung transplantation. Of each pre-treatment HRCT, 6 slices (15±5 mm apart, starting from the basal lung margin) were manually segmented and 1513 2D radiomic features were extracted using the in-house software Z-Rad (Python 2.7). Features were Z-score transformed and pre-filtered for inter- and intra-reader robustness (intraclass correlation coefficient >0.85) and inter-feature correlation (Spearman’s rho Results:A total of 64 pre-treatment HRCTs from 54 patients were analyzed. In 9 patients, >1 asynchronous treatments were assessed, while 45 patients had only 1 eligible treatment approach. The response rate within the assessed follow-up period was 45.3% (n=29). For score generation, 13 radiomic features were selected and an optimal cut-off value of -0.1589 was determined. Univariate linear regression showed significant association between our categorical radiomics-based score and treatment response (p=0.007, area under the curve = 0.65 (0.51-0.79), sensitivity=0.90, specificity=0.43), whereby a high score was predictive for treatment response.No differences between patients with high (n=46) or low (n=18) scores were detected for baseline age (mean±SD=55.5±12.0 and 55.5±13.6 years, p=0.84), duration of SSc (mean±SD=6.2±8.4 and 4.7±4.4 years, p=0.79), time since ILD diagnosis (2.7±2.9 and 2.4±3.1 years, p=0.59), FVC (77.6±20.6 and 80.1±17.9, p=0.41) or DLco (54.4±21.0 and 57.6±18.9, p=0.40). Distribution of anti-Scl-70 positivity (45.7% vs. 55.6%, p=0.58) and diffuse cutaneous disease (47.7% vs. 61.1%, p=0.41) was not significantly different between patients with high and low scores, respectively, although a trend towards higher percentages in the high score group was observed.Conclusion:Our results indicate that, following validation in external cohorts, radiomics may be a promising tool for future pre-treatment patient stratification. Moreover, our radiomics-based score seems not to be associated with commonly studied clinical predictors such as anti-Scl-70 positivity or lung function, underlining a possible additive value to ‘traditional’ clinical parameters.References:[1]Schniering, J., et al. Resolving phenotypic and prognostic differences in interstitial lung disease related to systemic sclerosis by computed tomography-based radiomics. medRxiv [Preprint] doi:10.1101/2020.06.09.20124800 (2020).Disclosure of Interests:Chantal Meier: None declared, Malgorzata Maciukiewicz: None declared, Matthias Brunner: None declared, Janine Schniering: None declared, Hubert Gabrys: None declared, Anja Kühnis: None declared, Oliver Distler Speakers bureau: Speaker fee on Scleroderma and related complications: Bayer, Boehringer Ingelheim, Medscape, Novartis, Roche. Speaker fee on rheumatology topic other than Scleroderma: MSD, iQone, Novartis, Pfizer, Roche, Consultant of: Consultancy fee for Scleroderma and its complications: Abbvie, Acceleron Pharma, Amgen, AnaMar, Arxx Therapeutics, Bayer, Baecon Discovery, Boehringer, CSL Behring, ChemomAb, Corbus Pharmaceuticals, Horizon Pharmaceuticals, Galapagos NV, GSK, Glenmark Pharmaceuticals, Inventiva, Italfarmaco, iQvia, Kymera, Medac, Medscape, Mitsubishi Tanabe Pharma, MSD, Roche, Roivant Sciences, Sanofi, UCB. Consultancy fee for rheumatology topic other than Scleroderma: Abbvie, Amgen, Lilly, Pfizer, Grant/research support from: Research Grants to investigate the pathophysiology and potential treatment of Scleroderma and its complications: Kymera Therapeutics, Mitsubishi Tanabe, Thomas Frauenfelder: None declared, Stephanie Tanadini-Lang: None declared, Britta Maurer Speakers bureau: Speaker fees from Boehringer-Ingelheim, Grant/research support from: Grant/research support from AbbVie, Protagen, Novartis Biomedical Research, congress support from Pfizer, Roche, Actelion, mepha, and MSD

Radiomic features are quantitative data calculated from routine medical images and have shown great potential for disease phenotyping and risk stratification in cancer. Patients with systemic sclerosis (SSc), a multi-organ autoimmune disorder, have a similarly poor prognosis (10-year survival of 66%), due to interstitial lung disease (ILD) as the primary cause of death. Here, we present the analysis of 1,355 stable radiomic features extracted from computed tomography scans from 156 SSc-ILD patients, which describe distinct disease phenotypes and show prognostic power in two independent cohorts. We derive and externally validate a first quantitative radiomic risk score, qRISSc that accurately predicts progression-free survival in SSc-ILD and outperforms current clinical stratification measures. Correlation analysis with lung proteomics, histology and gene expression data in a cross-species approach demonstrates that qRISSc reverse translates into mice and captures the fibrotic remodeling process in experimental ILD.

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.

Background:The interstitial lung disease (ILD) associated with connective tissue diseases including systemic sclerosis (SSc) is heterogenous disease characterized by reduced survival of approximately 3 years (1). “Radiomics’’ is a field of research which describes the in-depth analysis of tissues by computational retrieval of high-dimensional quantitative features from medical images (2). Our previous study suggested capacity of radiomics features to differentiate between “high” and “low” risk groups for lung function decline in two independent cohorts (3).Objectives: •bTo develop robust, machine learning (ML) workflow for “radiomics” data in SSc-ILD to select optimal methods for prediction. •oTo predict the time to individual lung function decline defined as defined by the time to a relative decline of ≥ 15% in Forced Vital Capacity (FVC)% as previously (3), using workflow.Methods:We investigated two cohorts of SSc-ILD: 90 patients (76.7% female, median age 57.5 years) from the University Hospital Zurich and 66 patients (75.8% female, median age 61.0 years) from Oslo University Hospital’s. Patients were retrospectively selected if (3): a) diagnosed with early/mild SSc according to the Very Early Diagnosis of Systemic Sclerosis (VEDOSS) criteria, b) presence of ILD on HRCT as determined by a senior radiologist. For every subject, we defined 1,355 robust radiomic features from HRCT images. The follow-up period was defined as the time interval between baseline visit and the last available follow-up visit.We have developed a systematic computational workflow to build predictive ML models. To reduce the number of redundant radiomic features, we applied correlation thresholds. We applied distinct methods including 1) Lasso Penalized Regression for feature selection, and 2) Random Forest (RF) for modeling using the R package ‘caret’. To select the optimal ML model, we randomly divided derivation cohort into Training (70%) and Holdout (30%) sets and applied fivefold cross-validation (5kCV) for feature and classifier selection on Training set only.Results:We have investigated various methods to select the optimal set of predictive radiomic features. Since the ML model performance is affected by both, feature, and classifier selection, we assessed these factors first.Results from feature filtering and selection, suggested that the combination of correlation threshold of 0.9 with Lasso regression proved best. As we perform feature selection in 5k CV workflow, features present in at least 2 sets entered model optimization step.During model selection, we selected RF classifier. We detected positive correlation between actual and predicted values with Spearman’s rho = 0.313, p = 0.167 and Spearman’s rho = 0.341, p = 0.015 in Oslo and Holdout sets respectively, as shown on Figure 1. The percentage of variance remained modest for both Holdout (Rsq = 0.104) and Oslo (Rsq = 0.126) datasets.Figure 1.Performance of the best, RF classifier shown as scatterplot between actual and predicted values of individual time to lung decline.Conclusion:In summary, we: (1) developed ML workflow that allowed to select o optimal methodology for modeling (i.e., feature and classifier selection), and (2) provide models that predicted time to individual lung function decline, characterized by significant correlation between predicted and actual values.References:[1]Hansell DM, Goldin JG, King TE, Jr., Lynch DA, Richeldi L, Wells AU. CT staging and monitoring of fibrotic interstitial lung diseases in clinical practice and treatment trials: a position paper from the Fleischner Society. Lancet Respir Med. 2015;3(6):483-96.[2]Lambin, P. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48, 441–446 (2012).[3]Schniering J. et al. Resolving phenotypic and prognostic differences in interstitial lung disease related to systemic sclerosis by computed tomography-based radiomics. https://www.medrxiv.org/content/10.1101/2020.06.09.20124800v1Disclosure of Interests:None declared

Objectives: In this study, we aimed to assess the impact of different CT reconstruction kernels on the stability of radiomic features and the transferability between different diseases and tissue types. Three lung diseases were evaluated, i.e. non-small cell lung cancer (NSCLC), malignant pleural mesothelioma (MPM) and interstitial lung disease related to systemic sclerosis (SSc-ILD) as well as four different tissue types, i.e. primary tumor, largest involved lymph node ipsilateral and contralateral lung. Methods: Pre-treatment non-contrast enhanced CT scans from 23 NSCLC, 10 MPM and 12 SSc-ILD patients were collected retrospectively. For each patient, CT scans were reconstructed using smooth and sharp kernel in filtered back projection. The regions of interest (ROIs) were contoured on the smooth kernel-based CT and transferred to the sharp kernel-based CT. The voxels were resized to the largest voxel dimension of each cohort. In total, 1386 features were analyzed. Feature stability was assessed using the intraclass correlation coefficient. Features above the stability threshold >0.9 were considered stable. Results: We observed a strong impact of the reconstruction method on stability of the features (at maximum 26% of the 1386 features were stable). Intensity features were the most stable followed by texture and wavelet features. The wavelet features showed a positive correlation between percentage of stable features and size of the ROI (R2 = 0.79, p = 0.005). Lymph node radiomics showed poorest stability (Conclusion: The robustness of radiomic features is strongly affected by different reconstruction kernels. The effect is largely influenced by the tissue type and less by the disease type. Advances in knowledge: The study presents to our knowledge the most complete analysis on the impact of convolution kernel on the robustness of CT-based radiomics for four relevant tissue types in three different lung diseases.

Background: HRCT is a cornerstone for the diagnosis and monitoring of ILD. Recently, radiomics has emerged as a promising research field, which automatically extracts mineable quantitative data from medical images, aiming to identify imaging biomarkers. Aims: To evaluate 1) the potential of CT-based radiomics features for staging of experimental ILD and 2) the transferability of radiomics signatures to human ILD. Methods: Radiomics analysis was performed on HRCT scans from bleomycin (BLM)-treated mice and NaCl controls at days 3 and 14 after intratracheal BLM instillation (n=5 mice/group), and on human HRCT scans (mild ILD=20, advanced ILD=46). In total, 154 radiomics features were calculated on segmented lungs using the in-house developed software Z-Rad. A univariate logistic model was built for each feature to discriminate between mild and advanced fibrosis. Results: Radiomics features with good predictive performance (area under the receiver operating characteristic curve (AUC) >0.7 and p-value Conclusions: This proof-of-concept study suggests that 1) the well-established mouse model of BLM-induced ILD is a valuable model system to test defined hypotheses in radiomics research and 2) that radiomics features show great potential as quantitative imaging biomarkers for staging of ILD.

Background: Interstitial lung disease (ILD) is a life-threating complication of systemic sclerosis (SSc). There is an unmet need for validated, routinely available biomarkers for disease staging and individualized patient stratification. In that respect, high-resolution computed tomography (HRCT), routinely performed in the work-up of SSc-ILD patients, has great potential as a source for non-invasive imaging biomarkers. Recently, radiomics, the quantitative extraction of hundreds of radiologic image features, has emerged as a powerful tool for biomarker research and precision medicine approaches; however, so far, it has been applied predominantly in oncology. Objectives: To evaluate the performance of CT-based radiomics for disease detection, staging and as surrogate marker for tissue pathophysiology in the model of bleomycin (BLM)-induced lung fibrosis. Methods: To mimic different stages of SSc-ILD, small-animal CT imaging was performed at days 3, 7, and 14 after instillation of BLM or saline solution, respectively (n=5-6 mice/group). Trained observers manually segmented the left and right mouse lungs in the CT images, and 154 radiomics features were extracted including 17 histogram- and 137 texture-based features using the in-house developed software Z-Rad (Python 2.7). To link the radiomics features with the underlying pathophysiology, Pearson correlation analysis between radiomics and histological features (Ashcroft score, Sirius red and CD45 staining positivity) was performed. Results: CT imaging visualized morphological changes of the lung architecture in BLM-treated mice, as evidenced by a time-dependent increase of grey areas on chest CT images and a gradual increase in tissue density on density-masked lungs. Accordingly, 6 out of 17 classical histogram features (e.g. mean CT density, skewness and kurtosis) precisely distinguished diseased from healthy mice with area under the curve values (AUCs) of >0.87 (p 0.84; p In addition, texture-based, but not histogram-based features were associated (R2>0.5, p Conclusion: If confirmed in human SSc-ILD, texture-based radiomics features have great potential to provide biological and stage-specific quantitative information on lung (micro-)architecture and thus could serve as quantitative imaging biomarkers for guided decision making in SSc-ILD. Disclosure of Interests: : Janine Schniering: None declared, Matthias Brunner: None declared, Oliver Distler Grant/research support from: Prof. Distler received research funding from Actelion, Bayer, Boehringer Ingelheim and Mitsubishi Tanabe to investigate potential treatments of scleroderma and its complications, Consultant for: Prof. Distler has/had consultancy relationship within the last 3 years with Actelion, AnaMar, Bayer, Boehringer Ingelheim, ChemomAb, espeRare foundation, Genentech/Roche, GSK, Inventiva, Italfarmaco, iQvia, Lilly, medac, MedImmune, Mitsubishi Tanabe Pharma, Pharmacyclics, Novartis, Pfizer, Sanofi, Serodapharm and UCB in the area of potential treatments of scleroderma and its complications. In addition, he had/has consultancy relationship within the last 3 years with A. Menarini, Amgen, Abbvie, GSK, Mepha, MSD, Pfizer and UCB in the field of arthritides and related disorders, Matthias Guckenberger Grant/research support from: Grant/research support by Varian Medical Systems and Siemens Healthcare, Marta Bogowicz: None declared, Diem Vuong: None declared, Cristina Muller: None declared, Thomas Frauenfelder: None declared, Stephanie Tanadini-Lang Grant/research support from: Grant/research support by Varian Medical Systems and Siemens Healthcare, Britta Maurer Grant/research support from: Grant/research support from AbbVie, Protagen, Novartis; congress support from MSD, Pfizer, Roche, and Actelion

Background: Chronic digital ulcers (DU) are a major complication in systemic sclerosis (SSc). Non-healing wounds are characterized by persistent inflammation, defective re-epithelialization and impaired matrix remodeling, and are often accompanied by bacterial colonization. In DU, the breach of the basement membrane exposes tissue and cell layers to commensal skin bacteria. Objectives: To investigate whether interactions between commensal skin bacteria and dermal fibroblasts affect tissue repair mechanisms. Methods: Dermal fibroblasts isolated from healthy controls (HC) and patients with diffuse cutaneous SSc (dcSSc) (n=3, each) were co-cultured with Staphylococcus aureus (SA) for 3 h at using a lower inoculum rather representing colonization (1 x 105 CFU/ml) and a higher inoculum reflecting infection (1 x 107 CFU/ml). Thereafter, fibroblasts were cultured with flucloxacillin-containing medium to kill extracellular and adherent bacteria. For mechanistic studies, a fibroblast cell line (BJ-5ta) was used. IL-6, IL-8, pro-collagen Iα, and interferon (IFN)-β proteins in culture supernatants were measured by ELISA. Gene expression was assessed by quantitative PCR. Contractility was analyzed by collagen gel contraction assay. Wound closure assay was performed on a confluent cell layer with a uniform cell-free gap, generated by a cell culture insert. Expression of alpha-smooth muscle actin (α-SMA) was evaluated by Western blotting, cell proliferation with a colorimetric assay. Cell death was analyzed by live cell real time assay. Bacterial invasion was assessed by immunofluorescence staining using confocal microscopy and CFU recovery. Gene knockdown was performed using siRNA. Results: Exposure to 1 x 107 CFU/ml of SA influenced fibroblast function. In brief, exposure to SA increased the secretion of IL-6 and IL-8 in dermal fibroblasts by 1571/455-fold (HC/dcSSc; p=0.08/ Conclusion: Invasion of dermal fibroblasts by SA with activation of the STING and TLR9-MyD88 pathways is a key element in the impairment of tissue repair responses.

ObjectiveTo evaluate integrin αvβ3 (alpha-v-beta-3)-targeted and somatostatin receptor 2 (SSTR2)-targeted nuclear imaging for the visualisation of interstitial lung disease (ILD).MethodsThe pulmonary expression of integrin αvβ3 and SSTR2 was analysed in patients with different forms of ILD as well as in bleomycin (BLM)-treated mice and respective controls using immunohistochemistry. Single photon emission CT/CT (SPECT/CT) was performed on days 3, 7 and 14 after BLM instillation using the integrin αvβ3-targeting 177Lu-DOTA-RGD and the SSTR2-targeting 177Lu-DOTA-NOC radiotracer. The specific pulmonary accumulation of the radiotracers over time was assessed by in vivo and ex vivo SPECT/CT scans and by biodistribution studies.ResultsExpression of integrin αvβ3 and SSTR2 was substantially increased in human ILD regardless of the subtype. Similarly, in lungs of BLM-challenged mice, but not of controls, both imaging targets were stage-specifically overexpressed. While integrin αvβ3 was most abundantly upregulated on day 7, the inflammatory stage of BLM-induced lung fibrosis, SSTR2 expression peaked on day 14, the established fibrotic stage. In agreement with the findings on tissue level, targeted nuclear imaging using SPECT/CT specifically detected both imaging targets ex vivo and in vivo, and thus visualised different stages of experimental ILD.ConclusionOur preclinical proof-of-concept study suggests that specific visualisation of molecular processes in ILD by targeted nuclear imaging is feasible. If transferred into clinics, where imaging is considered an integral part of patients’ management, the additional information derived from specific imaging tools could represent a first step towards precision medicine in ILD.

Background:Interstitial lung disease (ILD) affects 60% of patients with systemic sclerosis (SSc) and is the primary cause of death. Medical imaging is an integral part of the routine work-up for diagnosis and monitoring of SSc-ILD and includes high-resolution computed tomography (HRCT). Radiomics is a novel research area that describes the in-depth analysis of tissue phenotypes in medical images with computational retrieval of quantitative, mineable metadata appropriate for statistical analyses.Objectives:To explore the performance of HRCT-derived radiomic features for the assessment of SSc-associated ILD (i.e. diagnosis, staging, and lung function).Methods:Radiomics analysis was performed on HRCT scans from 98 SSc patients, including n=33 SSc patients without ILD, n=33 with limited and n=32 with extensive ILD as defined by 0%, Results:In total, 1355/1386 radiomic features passed the test of robustness and were eligible for further, exploratory analyses. Radiomic features with good performance (area under the ROC curve (AUC) ≥ 0.7 and p-value ≤ 0.05) were considered as potential candidate discriminators. Under this criterion, we identified 288/1355 (21.3%) radiomic features that were significantly different between ILD and non-ILD patients and 409/1355 (30.2%) features that significantly discriminated between limited and extensive ILD (Fig. 1). For diagnosis, the texture featuredependence count entropywas the top parameter to distinguish ILD patients from healthy controls (AUC = 0.89, p = 1.83x10-10), whereas for staging the wavelet featureHHH long run high grey level emphasisproved to be best suited to separate limited from extensive ILD (AUC = 0.88, p = 7.76x10-9).Fig 1.Correlation analysis of the most significant (best performing) discriminative radiomic features with lung function revealed a significant negative correlation ofdependence count entropy(rho = -0.51, p = 9.89x10-8) andHHH long run high grey level emphasis(rho = -0.51, p = 1.73x10-5) with FVC% predicted.Conclusion:Our study adds novelty to the field of SSc-ILD showing that radiomic features have great potential as quantitative imaging biomarkers for diagnosis and staging of SSc-ILD and that they may reflect lung function. As the next step, we are planning to build predictive models, using machine learning, for diagnosis, staging, and lung function and validate them in external patient cohorts. If validated such models will pave the way for computer-aided management in SSc-ILD and thus improve patients’ outcome.References:[1]Gillies, R. J., Kinahan, P. E. & Hricak, H. Radiomics: Images Are More than Pictures, They Are Data. Radiology 278, 563-577, doi:10.1148/radiol.2015151169 (2016).Disclosure of Interests:Janine Schniering: None declared, Malgorzata Maciukiewicz: None declared, Hubert Gabrys: None declared, Matthias Brunner: None declared, Christian Blüthgen: None declared, Oliver Distler Grant/research support from: Grants/Research support from Actelion, Bayer, Boehringer Ingelheim, Competitive Drug Development International Ltd. and Mitsubishi Tanabe; he also holds the issued Patent on mir-29 for the treatment of systemic sclerosis (US8247389, EP2331143)., Consultant of: Consultancy fees from Actelion, Acceleron Pharma, AnaMar, Bayer, Baecon Discovery, Blade Therapeutics, Boehringer, CSL Behring, Catenion, ChemomAb, Curzion Pharmaceuticals, Ergonex, Galapagos NV, GSK, Glenmark Pharmaceuticals, Inventiva, Italfarmaco, iQvia, medac, Medscape, Mitsubishi Tanabe Pharma, MSD, Roche, Sanofi and UCB, Speakers bureau: Speaker fees from Actelion, Bayer, Boehringer Ingelheim, Medscape, Pfizer and Roche, Matthias Guckenberger: None declared, Thomas Frauenfelder: None declared, Stephanie Tanadini-Lang: None declared, Britta Maurer Grant/research support from: AbbVie, Protagen, Novartis, congress support from Pfizer, Roche, Actelion, and MSD, Speakers bureau: Novartis

Background:In fibrotic diseases, metabolic processes are altered with a tendency towards an anabolic state, which is partially reflected in serum. Circulating biomarkers for interstitial lung disease (ILD), the leading cause of death in systemic sclerosis (SSc), are still sparse and not established in routine care.Objectives:To assess the potential of serum metabolites as biomarkers for the presence and progression of SSc-ILD.Methods:Age and sex matched serum samples of SSc patients from the Zurich cohort and of healthy controls (HC) were analyzed. Progressive SSc-ILD was defined as either a relative decrease in forced vital capacity (FVC) >10%, a decrease in FVC of 5-9% and a concomitant decrease of carbon dioxide diffusion capacity >15%, or an increase of the extent of lung fibrosis on computed tomography from Results:In total, 85 metabolites were detected. Univariate analysis of all groups were suggestive of changes for 1-methyladenosine, L-tryptophan, L-tyrosine, L-leucine and xanthosine (p = 0.077, 0.028, 0.077, 0.028 and 0.032, respectively). In PLS-DA, HCs and SSc patients differed in their levels of L-tyrosine and L-tryptophan, while levels of L-threonine, 3-aminoisobutyric acid, adenosine monophosphate and xanthosine were changed when comparing non-ILD and SSc-ILD patients. Receiver operating curve (ROC) analysis of significant metabolites from uni- and multivariate testing resulted in separation of SSc patients from HCs by L-tyrosine (area under the curve (AUC) = 0.81, 95% confidence interval (CI): 0.67-0.96), L-tryptophan (AUC = 0.86, CI: 0.75-0.97) and 1-methyladenosine (AUC = 0.82, CI: 0.71-0.94). Progressive SSc-ILD patients were separated from stable patients by their levels of L-isoleucine, L-leucine, adenosine monophosphate and xanthosine (AUC = 0.83, 0.85, 0.79 and 0.77; CI: 0.66-1.00, 0.70-1.00, 0.60-0.97 and 0.55-0.99, respectively). Validation of increased values of the branched-chain amino acids L-leucine and L-isoleucine in progressive SSc-ILD vs. stable ILD using an enzymatic assay resulted in similar results as LC-MS/MS analysis, with higher values detected in progressive vs. stable patients (mean = 286.5 and 235.5 nM, respectively; p = 0.005). In ROC analysis (AUC = 0.81, CI: 0.62-1.00), a cut-off value of 250.3 nM separated stable from progressive patients with a sensitivity of 72.7% and a specificity of 83.3%.Conclusion:This study in SSc(-ILD) patients suggested alterations in serum metabolite levels corresponding with their current state of disease, indicating the potential use of serum metabolites as discriminating biomarkers upon further confirmation in larger multicenter studies.Disclosure of Interests:Chantal Meier: None declared, Katrin Freiburghaus: None declared, Cédric Bovet: None declared, Janine Schniering: None declared, Oliver Distler Grant/research support from: Grants/Research support from Actelion, Bayer, Boehringer Ingelheim, Competitive Drug Development International Ltd. and Mitsubishi Tanabe; he also holds the issued Patent on mir-29 for the treatment of systemic sclerosis (US8247389, EP2331143)., Consultant of: Consultancy fees from Actelion, Acceleron Pharma, AnaMar, Bayer, Baecon Discovery, Blade Therapeutics, Boehringer, CSL Behring, Catenion, ChemomAb, Curzion Pharmaceuticals, Ergonex, Galapagos NV, GSK, Glenmark Pharmaceuticals, Inventiva, Italfarmaco, iQvia, medac, Medscape, Mitsubishi Tanabe Pharma, MSD, Roche, Sanofi and UCB, Speakers bureau: Speaker fees from Actelion, Bayer, Boehringer Ingelheim, Medscape, Pfizer and Roche, Christos Nakas: None declared, Britta Maurer Grant/research support from: AbbVie, Protagen, Novartis, congress support from Pfizer, Roche, Actelion, and MSD, Speakers bureau: Novartis

Increased vascular permeability is an important hallmark of many diseases, including cancer, cerebral ischemia, and severe inflammatory disorders. In this regard, the noninvasive assessment of pathologically increased vascular permeability in vivo is of great interest. In this study, the potential of albumin- and transthyretin-binding radioligands was evaluated for imaging of vascular hyperpermeability. For this purpose, the bleomycin-induced lung injury model was used as a model of inflammation-associated vascular leakage. The plasma protein-binding ligands, which bind to albumin (DOTA-PPB-01) and transthyretin (DOTA-PPB-03), were radiolabeled and used for nuclear imaging and biodistribution studies. In this regard, 177Lu was employed as a surrogate nuclide for detailed preclinical investigations, including single-photon emission computed tomography (SPECT) studies, whereas 44Sc was proposed as a radionuclide for positron emission tomography (PET), which may be relevant for future clinical translation. M...

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. Electronic supplementary material The online version of this article (10.1186/s13075-018-1681-1) contains supplementary material, which is available to authorized users.

Background Interstitial lung disease (ILD) is a life-threatening complication in systemic sclerosis (SSc). Substantial research progress has identified distinct genomic and molecular subtypes in SSc-ILD and brought molecular targeted therapies within reach. However, personalized medicine approaches are still lacking since clinically applicable tools for individualized patient stratification are not yet available. Objectives To assess the possibility of imaging molecular targets as a biomarker for stage-dependent assessment of ILD in the mouse model of bleomycin-induced lung fibrosis. Methods Expression of integrin α v β 3 and folate receptor β (FR-β) was analyzed in lung tissues from patients with idiopathic pulmonary fibrosis (IPF), SSc-ILD, and healthy controls as well as from bleomycin treated mice and saline treated controls using immunohistochemistry and qPCR (n=5–11). SPECT (Single Photon Emission Computed Tomography) or PET (Positron Emission Tomography) was performed at days 3, 7, and 14 after bleomycin instillation using the integrin α v β 3 -specific 177 Lu-c(RGDfK)-ligand and the FR-β-specific 18 F-Azafol. Additionally, 18 F-FDG-PET and high resolution CT (HRCT) scans were performed. The specific lung uptake of the radiotracers over time was assessed by ex vivo SPECT or PET/CT scans and biodistribution studies. Results Expression of FR-β was significantly increased at the mRNA and protein level of SSc-ILD patients as compared to healthy subjects (p v β 3 was increased at the protein level of both SSc-ILD and IPF patients (p Similarly, in lungs of bleomycin treated mice, but not of controls, FR-β expression was increased time-dependently at the mRNA and protein level with higher expression at day 3 and day 7, the inflammatory stages of bleomycin-induced lung fibrosis (p v β 3 was upregulated at day 7 and day 14 at the protein, but not at the mRNA level in bleomycin treated mice, and thus not only in the inflammatory but also in the fibrotic stages (p 18 F-FDG-PET and HRCT detected time-dependent changes of metabolic activity and ILD morphology in bleomycin treated mice. However, compared with these routinely employed unselective imaging techniques, molecular targeted imaging of integrin α v β 3 and FR-β successfully visualized ILD and discriminated lung inflammation and/or fibrosis in a time-dependent manner and in correspondence with the expression changes at the tissue level. The specific lung uptake of 177 Lu-c(RGDfK)-ligand and 18 F-Azafol as compared with the unspecific uptake of 18 F-FDG in diseased lungs over time was demonstrated by biodistribution studies and ex vivo SPECT or PET/CT scans. Conclusions Our data suggest that stage-dependent visualization of ILD with radiotracers that target key markers of lung inflammation and/or fibrosis shows promise for clinical application. As opposed to unselective imaging techniques such as 18 F-FDG-PET and HRCT, the introduction of specific imaging biomarkers for individualized management of SSc-ILD patients could represent the first step towards precision medicine. Disclosure of Interest J. Schniering Grant/research support from: Swiss National Science Foundation (S-85605–02–01), M. Benesova Grant/research support from: Swiss National Science Foundation (S-85605–02–01), M. Brunner: None declared, C. Feghali-Bostwick: None declared, R. Schibli Grant/research support from: Merck & Cie, O. Distler Grant/research support from: Actelion, Bayer, Boehringer Ingelheim, Pfizer, Sanofi; Patent licensed: mir-29 for the treatment of systemic sclerosis, Consultant for: 4 D Science, Actelion, Active Biotec, Bayer, BiogenIdec, BMS, Boehringer Ingelheim, ChemomAb, EpiPharm, espeRare foundation, Genentech/Roche, GSK, Inventiva, Lilly, medac, Mepha, MedImmune, Mitsubishi Tanabe Pharma, Pharmacyclics, Pfizer, Sanofi, Serodapharm, Sinoxa, Speakers bureau: AbbVie, iQone Healthcare, Mepha, C. Muller Grant/research support from: Merck & Cie, B. Maurer Grant/research support from: AbbVie, Protagen, EMDO, Novartis, Pfizer, Roche, Actelion; Patent licensed: mir-29 for the treatment of systemic sclerosis

Background Interstitial lung disease (ILD), the primary cause of death in systemic sclerosis (SSc), often occurs early in the disease course, yet only becomes symptomatic when there is already substantial functional impairment and morphologic changes. Thus, there is an unmet need for early diagnosis. Apoptosis is considered the first pathophysiologic event in SSc-ILD. Monitoring of apoptotic processes with nuclear imaging, a sensitive, specific and noninvasive methodology, might be a promising new approach for the diagnosis of early SSc-ILD. Objectives To evaluate the radiotracer 99mTc-rhAnnexin V-128, which specifically targets a pathophysiologic key molecule of early apoptosis, for the detection of earliest stages of lung involvement in animal models of SSc-ILD with single photon emission computed tomography (SPECT/CT). Methods C57BL/6J mice were treated with a single intratracheal injection of bleomycin or saline. Animals were euthanized at days 3, 7, 14 and 21 post-injection (n=6). Lung injury was evaluated by analysis of HE and Sirius red staining. The Ashcroft score was applied for the semi-quantitative evaluation of fibrotic changes. Immunofluorescence using the TUNEL assay and double staining with specific cell markers were performed to determine apoptotic cells. Positive nuclei were quantified by manual and automatic counting with Image J analysis software. Three days after injection with bleomycin or saline, mice were injected with 99mTc-rhAnnexin V-128 (Advanced Accelerator Applications, Italy). After 1h, images were acquired using small animal SPECT/CT, followed by ex vivo autoradiography. Results In the model of bleomycin-induced lung fibrosis, inflammatory infiltrates (CD45+) occurred as early as day 3 with peak at day 7, whereas pulmonary fibrosis developed from day 7 as assessed by Sirius red staining and was most pronounced at day 21 (mean Ashcroft score=4.6, p=0.0286). Notably, the number of apoptotic cells evaluated by TUNEL staining, was highest at day 3 (mean ± SE=6.5±1.5positive cells/HPF, p=0.0436) compared with saline controls (mean ± SE=0.7±0.1, p=0.0095) and then decreased over time. To determine the type of apoptotic cells, we performed immunofluorescent co-stainings with different cell markers. Data displayed that endothelial cells (vWF+) and epithelial cells (cytokeratin+), but not inflammatory cells (CD45+) were the primary cells undergoing apoptosis in earliest inflammatory stages of ILD. In accordance with the findings on tissue level, at day 3 post-injection, we detected ex vivo with autoradiography, yet not with in vivo SPECT/CT, an increased pulmonary uptake of 99mTc-rhAnnexin V-128 in the lungs of bleomycin-induced mice compared with saline treated controls. Conclusions Apoptosis of epithelial and endothelial cells preceded the development of pulmonary inflammation and fibrosis in the model of bleomycin-induced lung fibrosis. Thus, the use of 99mTc-rhAnnexin V-128 might be a promising approach for the diagnosis of earliest stages of ILD. However, sensitivity of in vivo imaging has to be further improved. Disclosure of Interest L. Guo: None declared, J. Schniering Grant/research support from: Swiss National Science Foundation (S-85605–02–01), R. Schibli: None declared, A. Blanc: None declared, D. Chicco Employee of: Advanced Accelarator Applications, S. Ye: None declared, O. Distler Grant/research support from: Actelion, Bayer, Boehringer Ingelheim, Pfizer, Sanofi;patent licensed mir-29 for the treatment of systemic sclerosis, Consultant for: 4 D Science, Actelion, Active Biotec, Bayer, BiogenIdec, BMS, Boehringer Ingelheim, ChemomAb, EpiPharm, espeRare foundation, Genentech/Roche, GSK, Inventiva, Lilly, medac, Mepha, MedImmune, Mitsubishi Tanabe Pharma, Pharmacyclics, Pfizer, Sanofi, Serodapharm, Sinoxa, Speakers bureau: AbbVie, iQone Healthcare, Mepha, M. Behe: None declared, B. Maurer Grant/research support from: AbbVie, Protagen, EMDO, Novartis, Pfizer, Roche, Actelion. Patent licensed: mir-29 for the treatment of systemic sclerosis

Autoimmune connective tissue diseases (CTDs) have a propensity to affect multiple organ systems as well as physical function, quality of life, and survival. Their clinical heterogeneity, multisystem involvement, and low worldwide prevalence present challenges for researchers to establish a study design to help better understand the course and outcomes of CTDs. Systemic sclerosis (SSc) is a notable example of a CTD, wherein longitudinal cohort studies (LCS) have enabled us to elucidate disease manifestations, disease course, and risk and prognostic factors for clinically important outcomes, by embedding research in clinical practice. Nevertheless, further efforts are needed to better understand SSc especially with regard to recognizing organ involvement early, developing new therapies, optimizing the use of existing therapies, and defining treatment targets. The heterogeneous multi-organ nature of SSc would lend itself well to a structured model of care, wherein step-up treatment algorithms are used with the goal of attaining a prespecified treatment target. In this chapter, we discuss the rationale for a structured treatment approach in SSc and propose possible treatment algorithms for three of the more common disease manifestations, namely skin involvement, digital ulcers and gastrointestinal tract involvement. We discuss possible strategies for evaluating and implementing these algorithms in the setting of LCS. We conclude by presenting a research agenda for the development of structured models of care in SSc.

Background Interstitial lung disease (ILD) is one of the leading causes of death in systemic sclerosis (SSc). Since routine diagnostics such as CT and pulmonary function tests only detect impaired organ function and/or damage, there is an unmet need for the non-invasive diagnosis of ILD at earliest, possibly still reversible disease stages. Objectives To assess nuclear imaging for the detection of SSc-ILD by using radiotracers specifically targeting integrin α v β 3 as a pathophysiologic key molecule of early inflammation-dependent fibrosis in the murine model of bleomycin-induced pulmonary fibrosis and in the multisystemic Fra-2 transgenic (tg) mouse model of SSc. Methods Expression of integrin α v β 3 was analysed in lung sections from patients with SSc-ILD, idiopathic pulmonary fibrosis (IPF), healthy controls (n=5) as well as from bleomycin-treated mice, Fra-2 tg mice and respective controls (n=4) using immunohistochemistry. In vivo imaging was performed using 111 In-DOTA-RGD radioconjugates specifically targeting integrin α v β 3 . SPECT (single photon emission computed tomography) was performed using a small-animal SPECT/CT scanner (NanoSPECT/CT, Mediso). Animals were scanned at early disease time points to visualise inflammation-dependent pulmonary fibrosis. The pulmonary accumulation of the radiotracer was confirmed by ex vivo SPECT/CT, biodistribution, and autoradiography studies. Nonparametric non-related data were expressed as median (Q1,Q3) . For statistical analysis, the Mann-Whitney U test was applied. P-values Results In lung sections of patients with SSc-ILD and IPF, the expression of integrin α v β 3 was increased by 3.7-fold and 2.9-fold, respectively as compared to healthy controls (p v β 3 expression (upregulation by 5.7-fold and 4.8-fold, respectively) as we have similarly observed in SSc-ILD and IPF patients (p v β 3 was primarily expressed on endothelial cells, inflammatory cells and myofibroblasts as indicated by sequential stainings with cell type-specific markers. Notably, at day 7 after intratracheal bleomycin installation, the peak of pulmonary inflammation, nuclear SPECT/CT with 111 In-DOTA-RGD targeting integrin α v β 3 , successfully visualised pulmonary inflammation and incipient fibrosis in the model of bleomycin-induced pulmonary fibrosis. Similarly, imaging of integrin α v β 3 in Fra-2 tg mice at 13 weeks of age, the starting point of pulmonary fibrosis, showed a higher pulmonary uptake of the radiotracer in Fra-2 tg mice than in controls (Fig. A). Ex vivo SPECT/CT of isolated lungs, biodistribution and autoradiography studies confirmed the in vivo results and validated the specific radiotracer uptake in lungs from bleomycin-challenged mice and Fra-2 tg mice as compared to controls (Fig. B). Conclusions Our data provide the first evidence that targeting pathophysiologic key molecules using nuclear imaging methods for the visualization of inflammation-dependent fibrosis is a promising non-invasive approach for the early detection of lung involvement in SSc. Disclosure of Interest J. Schniering Grant/research support from: Swiss National Science Foundation (S-85605–02–01), S. Haller Grant/research support from: Swiss National Science Foundation (S-85605–02–01), Z. Guo Grant/research support from: respective institution, C. Feghali-Bostwick Grant/research support from: respective institution, R. Schibli Grant/research support from: respective institution, O. Distler Grant/research support from: Bayer, Sanofi, Ergonex, Boehringer Ingelheim, Actelion, Pfizer, Consultant for: 4 D Science, Actelion, Active Biotec, Bayer, BiogenIdec, BMS, Boehringer Ingelheim, EpiPharm, Ergonex, espeRare foundation, Genentech/Roche, GSK, Inventiva, Lilly, medac, MedImmune, Pharmacyclics, Pfizer, Serodapharm, Sinoxa, C. Muller Grant/research support from: respective institution, B. Maurer Grant/research support from: respective institution