Till Sörensen

immunoClust--An automated analysis pipeline for the identification of immunophenotypic signatures in high-dimensional cytometric datasets.

Multiparametric fluorescence and mass cytometry offers new perspectives to disclose and to monitor the high diversity of cell populations in the peripheral blood for biomarker research. While high‐end cytometric devices are currently available to detect theoretically up to 120 individual parameters at the single cell level, software tools are needed to analyze these complex datasets automatically in acceptable time and without operator bias or knowledge. We developed an automated analysis pipeline, immunoClust, for uncompensated fluorescence and mass cytometry data, which consists of two parts. First, cell events of each sample are grouped into individual clusters. Subsequently, a classification algorithm assorts these cell event clusters into populations comparable between different samples. The clustering of cell events is designed for datasets with large event counts in high dimensions as a global unsupervised method, sensitive to identify rare cell types even when next to large populations. Both parts use model‐based clustering with an iterative expectation maximization algorithm and the integrated classification likelihood to obtain the clusters. A detailed description of both algorithms is presented. Testing and validation was performed using 1) blood cell samples of defined composition that were depleted of particular cell subsets by magnetic cell sorting, 2) datasets of the FlowCAP III challenges to identify populations of rare cell types and 3) high‐dimensional fluorescence and mass‐cytometry datasets for comparison with conventional manual gating procedures. In conclusion, the immunoClust‐algorithm is a promising tool to standardize and automate the analysis of high‐dimensional cytometric datasets. As a prerequisite for interpretation of such data, it will support our efforts in developing immunological biomarkers for chronic inflammatory disorders and therapy recommendations in personalized medicine. immunoClust is implemented as an R‐package and is provided as source code from www.bioconductor.org.

Monocyte alterations in rheumatoid arthritis are dominated by preterm release from bone marrow and prominent triggering in the joint

Objective Rheumatoid arthritis (RA) accompanies infiltration and activation of monocytes in inflamed joints. We investigated dominant alterations of RA monocytes in bone marrow (BM), blood and inflamed joints. Methods CD14+ cells from BM and peripheral blood (PB) of patients with RA and osteoarthritis (OA) were profiled with GeneChip microarrays. Detailed functional analysis was performed with reference transcriptomes of BM precursors, monocyte blood subsets, monocyte activation and mobilisation. Cytometric profiling determined monocyte subsets of CD14++CD16−, CD14++CD16+ and CD14+CD16+ cells in BM, PB and synovial fluid (SF) and ELISAs quantified the release of activation markers into SF and serum. Results Investigation of genes differentially expressed between RA and OA monocytes with reference transcriptomes revealed gene patterns of early myeloid precursors in RA-BM and late myeloid precursors along with reduced terminal differentiation to CD14+CD16+monocytes in RA-PB. Patterns associated with tumor necrosis factor/lipopolysaccharide (TNF/LPS) stimulation were weak and more pronounced in RA-PB than RA-BM. Cytometric phenotyping of cells in BM, blood and SF disclosed differences related to monocyte subsets and confirmed the reduced frequency of terminally differentiated CD14+CD16+monocytes in RA-PB. Monocyte activation in SF was characterised by the predominance of CD14++CD16++CD163+HLA-DR+ cells and elevated concentrations of sCD14, sCD163 and S100P. Conclusion Patterns of less mature and less differentiated RA-BM and RA-PB monocytes suggest increased turnover with accelerated monocytopoiesis, BM egress and migration into inflamed joints. Predominant activation in the joint indicates the action of local and primary stimuli, which may also promote adaptive immune triggering through monocytes, potentially leading to new diagnostic and therapeutic strategies.

Genomic stratification by expression of HLA-DRB4 alleles identifies differential innate and adaptive immune transcriptional patterns - A strategy to detect predictors of methotrexate response in early rheumatoid arthritis

Effective drug selection is the current challenge in rheumatoid arthritis (RA). Treatment failure may follow different pathomechanisms and therefore require investigation of molecularly defined subgroups. In this exploratory study, whole blood transcriptomes of 68 treatment-naïve early RA patients were analyzed before initiating MTX. Subgroups were defined by serologic and genetic markers. Response related signatures were interpreted using reference transcriptomes of various cell types, cytokine stimulated conditions and bone marrow precursors. HLA-DRB4-negative patients exhibited most distinctive transcriptional differences. Preponderance of transcripts associated with phagocytes and bone marrow activation indicated response and transcripts of T- and B-lymphocytes non-response. HLA-DRB4-positive patients were more heterogeneous, but also linked failure to increased adaptive immune response. RT-qPCR confirmed reliable candidate selection and independent samples of responders and non-responders the functional patterning. In summary, genomic stratification identified different molecular pathomechanisms in early RA and preponderance of innate but not adaptive immune activation suggested response to MTX therapy.

A6.07 Tissue- and cell-specific transcriptomes indicate systemic nature of ra and revealed combinations of protein biomarkers relevant for disease characterisation in serum

Background and objectives Clinical signs and symptoms, radiographic changes and routine laboratory tests have indispensable roles in diagnosis of rheumatoid arthritis (RA). Nevertheless, a high degree of heterogeneity between RA patients and increasing options of treatment require the identification of objective criteria relevant for diagnosis and therapeutic stratification of patients. This study focused on global approaches in dissecting inflammation in RA including transcriptome analyses of synovial tissue, blood and bone marrow monocytes and proteome analyses of selected molecules in serum from long-lasting and early RA. Materials and methods Gene-expression profiling of synovial tissues, blood and bone marrow monocytes of RA and osteoarthritis (OA) patients were performed by Affymetrix microarrays. Based on transcriptome data, 28 molecules were selected for protein analyses by ELISA and multiplex immunoassays in sera from patients with long-lasting RA (n = 17) and OA (n = 16), early RA (n = 10) and healthy donors (n = 14). Results Transcriptome analyses of synovial tissues from RA and OA patients showed the most prominent differences between these two diseases and identified more than 1000 differentially expressed genes. More subtle differences were disclosed by gene-expression profiling of blood and bone marrow monocytes from RA and OA with 300 and 150 differentially expressed genes, respectively. From RA tissue- and cell-specific transcriptomes 28 genes were selected for protein analyses in serum from RA and OA patients including: chemokines (CXCL13, CCL18), adhesion molecules (VCAM1, ICAM1, E- and P-Selectins), enzymes (MMP3, A1AT), alarmins (S100P and S1008/9) and the soluble form of cell surface molecules (CD14, CD163). Out of 28 markers only 16 reached statistical significance to discriminate long-lasting RA from OA. A combination of 5 markers was able to correctly classify long-lasting RA. However, the same combination of markers identified only one-third of early RA patients. Conclusions Tissue- and cell-specific transcriptomes demonstrated the systemic nature of RA. Proteome analyses of serum from long-lasting RA patients confirmed transcriptome data and showed that molecular patterns determined by the combination of inflammatory and cell-specific markers are required for disease stratification. In early RA transcriptome data outperformed proteome data suggesting that focus on transcriptional alterations is more sensitive approach for disease management of early RA.

Biomarkers in rheumatology. Biomarkers and imaging for the diagnosis and stratification of rheumatoid arthritis and spondylarthritis in the ArthroMark network funded by the Federal Ministry of Education and Research

The introduction of biologics has continuously increased the demand for biomarkers for early diagnosis and therapeutic stratification. ArthroMark, a research network funded by the Federal Ministry of Education and Research, aims to establish such biomarkers for rheumatoid arthritis and spondyloarthritides. Biobanks and previous work on genotyping, gene expression and autoreactivity profiling build the basis. Bioinformatic networks will help to harmonize the investigations and a clinical study with modern imaging techniques to characterize the functional relevance of the new biomarkers as effectively as possible. To validate the markers for diagnostic application the network aims to expand gradually.ZusammenfassungSeit Einführung der Biologika sind Biomarker zur Frühdiagnostik und Therapieentscheidung von zunehmender Bedeutung. Sie sollen im BMBF-geförderten Forschungsverbund ArthroMark für die rheumatoide Arthritis und die Spondylarthritis etabliert werden. Biobanken und Vorarbeiten zu Genotypen, Genexpressions- und Autoantigenprofilen dienen als Grundlage. Bioinformatische Vernetzung soll die Untersuchungen aufeinander abstimmen und eine Studie mit modernen Bildgebungsverfahren die funktionelle Bedeutung der neuen Biomarker bestmöglich charakterisieren. Zur Validierung der diagnostischen Marker wird eine schrittweise Erweiterung des Netzwerkes angestrebt.AbstractThe introduction of biologics has continuously increased the demand for biomarkers for early diagnosis and therapeutic stratification. ArthroMark, a research network funded by the Federal Ministry of Education and Research, aims to establish such biomarkers for rheumatoid arthritis and spondyloarthritides. Biobanks and previous work on genotyping, gene expression and autoreactivity profiling build the basis. Bioinformatic networks will help to harmonize the investigations and a clinical study with modern imaging techniques to characterize the functional relevance of the new biomarkers as effectively as possible. To validate the markers for diagnostic application the network aims to expand gradually.

An explorative study on deep profiling of peripheral leukocytes to identify predictors for responsiveness to anti-tumour necrosis factor alpha therapies in ankylosing spondylitis: natural killer cells in focus

BackgroundTherapeutic targeting of tumour necrosis factor (TNF)-α is highly effective in ankylosing spondylitis (AS) patients. However, since one-third of anti-TNF-treated AS patients do not show an adequate clinical response there is an urgent need for new biomarkers that would aid clinicians in their decision-making to select appropriate therapeutic options. Thus, the aim of this explorative study was to identify cell-based biomarkers in peripheral blood that could be used for a pre-treatment stratification of AS patients.MethodsA high-dimensional, multi-parametric flow cytometric approach was applied to identify baseline predictors in 31 AS patients before treatment with the TNF blockers adalimumab (TNF-neutralisation) and etanercept (soluble TNF receptor).ResultsAs the major result, the frequencies of natural killer (NK) cells, and in particular CD8-positive (CD8+) NK cell subsets, were most predictive for therapeutic outcome in AS patients. While an inverse correlation between classical CD56+/CD16+ NK cells and reduction of disease activity was observed, the CD8+ NK cell subset behaved in the opposite direction. At baseline, responders showed significantly increased frequencies of CD8+ NK cells compared with non-responders.ConclusionsThis is the first study demonstrating that the composition of the NK cell compartment has predictive power for prediction of therapeutic outcome for anti-TNF-α blockers, and we identified CD8+ NK cells as a potential new player in the TNF-α-driven chronic inflammatory immune response of AS.

SAT0249 Reduction of monocyte activation by bowel cleanse and one week fasting suggests permanent pathogenetic triggering from the gut in rheumatoid arthritis

Background: Fasting can improve clinical disease activity in rheumatoid arthritis (RA) [1], but mechanism involved are not clear. Recently, we demonstrated that monocytes in RA express transcriptome patterns of increased myelopoiesis, premature egress from bone marrow and reduced circulation time as indicators of permanent activation of the innate immune response [2]. Objectives: We investigated the influence of bowel cleanse and fasting on monocyte subpopulations in the blood to determine the extent of microbiota and gut immunity related triggering of chronic inflammation in RA. Methods: RA patients (n=22) and controls (n=12, metabolic syndrome), who presented for fasting according to the Buchinger procedure (bowel cleanse with colonoscopy fluid), were analyzed for DAS28, CrP, differential blood count and high resolution cytometric phenotyping at baseline, day 3, day 7 (end of fasting) and day 10. ImmunoClust was applied for automated cell clustering [3]. Results: Disease activity was strikingly decreased after fasting in virtually all RA patients (DAS28 from 4.24 to 3.17, p<0.00005) with significant reduction already after 3 days (p<0.01). This was accompanied by a significant decline of CrP and ESR. Differential blood count revealed a slight decrease in total leukocytes and significant reduction of lymphocytes and eosinophils in RA. However, these blood changes were also observed but on a lower level in the metabolic controls. The most dominant and RA specific effect was a significant reduction of total monocytes when compared to RA baseline or to controls at day 10. Deep profiling of the monocyte compartment revealed reduced non-classical (CD14+CD16+) and intermediate (CD14++CD16+) monocytes prior to fasting in RA compared to controls and confirmed previous results [2]. Bowel cleanse and fasting induced a significant increase of these two monocyte subpopulations by absolute counts and even more by percentage of total monocytes. This indicates reduced recruitment to inflamed tissue and prolonged circulation with more cells differentiating from classical to non-classical monocytes in the blood [4]. The decrease of lymphocytes in RA patients after fasting was characterized by a dominant reduction of naive T-, B-cells and CD16- NK-cells along with a relative increase in memory lymphocytes and CD16+ NK-cells. These effects were also observed but less pronounced in controls. Conclusions: Bowel cleanse and fasting in RA induces a reduction of inflammation related to monocyte activation and turnover immediately within few days. Changes in the monocyte compartment were specific for RA compared to controls and dominated the immunological changes, suggesting that innate triggering mechanisms from gut and its microbiota are etiologically relevant in RA. References [1] Kjeldsen-Kragh J, et al. Lancet1991;338(8772):899. [2] Smiljanovic B, et al. Ann Rheum Dis2018;77(2):300. [3] Sörensen T, et al. Cytometry A2015;87(7):603. [4] Tak T, et al. Blood2017;130(12):1474. Acknowledgements: Technical assitance: Silvia Pade, Barbara Walewska Funding: German Federal Ministry of Education and Research grant ArthoMark (01EC1009A), Corona-Stiftung grant BioFast (S199/10063/2016). Disclosure of Interest: None declared

05.08 Increased turnover of monocytes in patients with rheumatoid arthritis identified by transcriptome and cytometric profiling

Background Targeting molecules involved in monocyte activation is an important treatment strategy for RA. In this study we aimed to determine monocyte maturation and activation from bone marrow (BM) via blood into synovial fluid (SF) by investigating monocytes transcriptomes and by cytometric profiling of classical (CD14++CD16-), intermediated (CD14++CD16+) and non-classical (CD14+CD16+) monocytes. Materials and methods CD14+ cells from BM and blood of RA and osteoarthritis (OA) patients were profiled with Affymetrix microarrays. A detailed functional analysis was performed with reference transcriptomes of BM precursors, monocyte blood subsets, monocyte activation and egress from BM induced by G-CSF (granulocyte colony-stimulating factor). Cytometric profiling of CD14, CD16, HLA-DR and CD163 expression were used to determine monocyte subsets and to follow their activation and differentiation in BM, blood and SF. Results Transcriptomes of RA-BM monocytes exhibited i) pronounce gene pattern of early myeloid precursors from BM and ii) weak gene pattern of late myeloid precursors from BM. Transcriptomes of RA blood monocytes demonstrated i) pattern of late myeloid precursors from BM and ii) reduced pattern of terminally differentiated CD14+CD16+ monocytes from blood. Cytometric profiling of BM, blood and SF monocytes in RA and OA showed that all three body compartments have their own distribution of monocyte subsets. BM was characterised with classical and intermediate subsets and both subsets showed decreased CD16 expression in RA when compared to OA. As expected, blood was characterised with three subsets, and RA blood showed decreased CD14 and HLA-DR expression on classical monocytes and reduced frequency of non-classical subset. In RA-SF, classical monocytes were absent, intermediate were most dominant and cell-phenotype with low CD16 expression but similar to non-classical monocytes was related to macrophages. Cell frequency of intermediate subset in SF positively correlated with inflammation (ESR; R>0.85) and showed the highest expression of HLA-DR, CD14, CD163. Conclusions Monocyte turnover is increased in RA and characterised with accelerated monocytopoiesis, faster BM egress and migration into inflamed joints. Permanent monocyte activation in the joint and their role in linking innate and adaptive immunity, which is targeted by biologics, emphasises their high diagnostic value and relevance for therapeutic stratification.

A6.11 Immunoclust based analysis of cytometric profiles reveals immunophenotypic changes in synovial fluid compared to peripheral blood cells in rheumatoid arthritis

Background and objectives Flow cytometry offers quantification of multidimensional characteristics at single cell level for millions of cells. Multiplex flow cytometry or mass cytometry enable to screen for dozens of antigens on a single cell. Conventional analysis of such data requires user defined gating and is time consuming. Using the new bioinformatics tools immunoClust for automated and user-independent analysis, we investigated the complexity of phenotypic changes of immune cells upon migration from peripheral blood (PB) to synovial fluid (SF) in rheumatoid arthritis (RA). Materials and methods Seven paired samples of PB and SF from RA patients were stained in 10 different antibody cocktails and data investigated by the newly developed immunoClust pipeline for sample specific populations and differences between PB and SF. Population clustering and comparative meta-clustering assume finite mixture models and use Expectation Maximisation (EM)-iterations with integrated classification likelihood (ICL) criterion to stabilise the number of reasonable clusters. For meta-clustering, a probability measure on Gaussian distributions was invented, which is based on the Bhattacharyya Coefficients. Meta-clusters were manually annotated and classified. The clustering tools of immunoClust are available as open source R-package in Bioconductor. Results Automated clustering with 46 different surface markers detected all major leukocyte subsets and several activation markers in PB and SF samples including neutrophils, eosinophils, T-cells and sub-populations, monocytes, B-cells, NK-cells and dendritic cells. The comparison revealed about 10 highly significant changes per staining cocktail. For example the percentage of monocytes/macrophages was doubled in SF and dominated by CD16+ cells, the frequencies of effector/memory subpopulations of lymphocytes were increased and naïve T-cells and B-cells were almost completely absent. In addition several unexpected populations like CCR7+ monocytes were found in SF only. Conclusion In conclusion, the results give a reasonable starting point to face the next field of research for marker detection and prediction analysis. The data will be further exploited for changes in cell activation and differentiation in SF in order to screen for these populations also in PB. This approach is not only applicable to fluorescence-based flow data but could be also used for multi-parametric data sets generated by mass spectrometry-based cytometry (CyTOF).

A8.20 Bioconpages - comparison of DNA methylation and gene expression in different immune cells

Background and Objective Site specific methylation of DNA may contribute to the regulation of gene expression. Microarray based analysis of methylation refers to CpG site selected by a biostatistic algorithm without proof for actual involvement. To test for putatively effective CpG sites in immunity, we compared methylation with transcription in parallel in different sorted immune cell types. In order to perform primary analysis and to map corresponding results, software tools and an online database were developed. Materials and Methods Cells from 4 healthy donors were sorted by FACS technology for naive and activated/memory T-cells and B-cells, NK-cells, monocytes, and granulocytes. Genome wide DNA methylation was assessed using the HumanMethylation450 BeadChip platform and Genome Studio (Illumina). Transcriptomes were determined with Affymetrix HG-U133 Plus 2.0 GeneChips. A tool has been implemented in Java and R. In a first step the program checks the quality of each microarray and normalizes the data (Affymetrix & Illimunina). Afterwards the program imports and analyses the transcription and methylation data to determine high and low transcribed genes, match them with the status of DNA methylation and save the results as. txt and. jpg files. The tool will be provided on our homepage http://www.charite-bioinformatik.de. Results As an example, one of the performed analyses compared monocytes and T-cells. We found 4.624 genes, which showed differences in gene expression and 19.261 different DNA methylation sites. Between closer related cells like naive and activated/memory cells of the same lymphocyte subtype (CD4+ T-cells) the number decrease to 638 genes and 9.412 sites. Comparing monocytes against T-cells, corresponding changes of expression and methylation were found in only 629 of 1951 increased and in 279 of 2673 decreased expressed genes. These results and other comparisons will be presented in the BioConpages database. The database can be searched by GeneID and to retrieve information of the corresponding transcription signals and percentage of methylation in the different cell types. In general, when selecting genes differentially expressed in immune cells, only around 10% of all CpG sites annotated to a single gene were compatible with the differential expression pattern in immune cells. Conclusions This type of screening enables to preselect CpG sites putatively involved in differntiation of immune cells. Thus, corresponding information of transcription and methylation is indispensible to infer methylation associated gene regulation. This applies not only for microarray but also for sequencing approaches.