Biology subjects

Koch, I.

Publications and source records attributed to Koch, I..

18 recordsLinked to original sources

bioRxiv2026

Crystal structure of E. coli Nissle 1917 flagellin reveals novel features that modulate bacterial motility but not TLR5 recognition

The probiotic E. coli Nissle 1917 (EcN) strain is known to promote intestinal homeostasis via flagellin, the protomer of its motility apparatus, the flagellum. The flagellin of EcN shows atypical features, namely a hypervariable region (HVR), whose structure and significance have remained elusive. We therefore determined the crystal structure of the E. coli Nissle 1917 flagellin FliC at a resolution of 1.2 [A] which revealed an unusual domain architecture: the canonical D1 domain was found connected by an extended linker to an extensive HVR whose D2, D3 and D4 domains form an outer domain (OD) which surrounds the filament core comprised of conserved domains D0-D1. Using both recombinant proteins and gene-edited EcN strains expressing mutant flagellins, the functional requirement for these unique features was subsequently studied for effects on immune recognition on intestinal epithelial and immune cells, as well as on flagellar protein expression, assembly and bacterial motility. While human and mouse TLR5 immune recognition of flagellar proteins or intact bacteria was only moderately affected by removal of linker or D4, especially linker removal reduced protein stability and bacterial motility in both soft agar and liquid media swimming assays. Interestingly, depending on the environment, D4 or HVR removal had different effects on motility and surface structure. Finally, a site-directed mutagenesis approach highlighted that loss of TLR5 recognition strictly entails loss of motility but not vice versa. Our data indicate that specific HVRs/OD might be relevant for motility of E. coli Nissle 1917 in specialized environments, but not for immune recognition. Moreover, we find mutational tolerance is greater for immune recognition than for motility, providing new insights into bacterial adaptation to the host environment.

microbiology↗

bioRxiv2024

Aerosol deposition affects water uptake and water loss of beech leaves

The deposition of aerosols on leaves could significantly influence plant-atmosphere-interaction through the formation of very thin aqueous films that allow the transport of liquid water through the stomata. Such films can be formed by deliquescence and dynamic expansion of hygroscopic aerosols ( hydraulic activation of stomata). Two processes that may be associated with stomatal liquid water transport are foliar water uptake (FWU) and the contribution of leaky stomata to minimum epidermal conductance (gmin). We investigated whether ambient aerosols affect FWU and gmin of Fagus sylvatica seedlings. Plants were grown in ventilated greenhouses with ambient air or filtered, almost aerosol-free air. The gmin was determined using leaf drying curves. FWU was investigated gravimetrically and with deuterium- enriched water, starting from different leaf water potentials, by spraying freshly-cut or pre-dried leaves (60 minutes). The presence of aerosols in the environment increased gmin by about 47%, confirming previous measurements in other species. Aerosols also increased FWU measured by deuterium uptake. FWU was higher for freshly-cut leaves than for pre-dried leaves, despite the lower leaf water potential. No gravimetric weight gain could be detected. Both the gmin and FWU results are consistent with bidirectional stomatal transport of liquid water along aerosol-induced pathways. The FWU result could also have been generated by water vapor through reverse transpiration, although the functional contribution of the aerosols would remain unclear. At low leaf water potential, the pathway may dry out and become less functional for FWU, whereas it may still be noticeable as stomatal leakage, given the strong gradient of water potential from the leaf interior to the atmosphere.

plant biology↗

bioRxiv2024

Kinetic and structure-based comparisons of silent and stimulatory flagellin interactions with TLR5

The bacterial protein flagellin is the sole ligand of the innate immune receptor Toll- like receptor 5 (TLR5). Flagellins with strong agonism bind TLR5 at their D1 and D0 domains, while poor agonist "silent" flagellins bind with D1 only. However, D0-TLR5 interactions insufficiently explain the silent phenotype. Here, we characterize the D1 domain binding kinetics of the silent flagellin RhFlaB compared to the strong canonical agonist StFliC. Using Surface Plasmon Resonance, we show that the RhFlaB-D1 binds more strongly than StFliC-D1, but forms shorter-lived complexes. Cryo-EM analysis of RhFlaB showed its D1 primary interface (PI) contains two distinct hydrophobic pockets, which should facilitate rapid association, while its D1s secondary interface (SI) is dominated by negatively charged residues, which likely impede residue-residue interactions. Comparisons with an existing StFliC-D1 structure suggest that strong agonism requires strong binding at both the PI and SI of the D1, and silent flagellins evade detection partly through weaker SI interactions. These findings offer a foundation for designing targeted interventions that can modulate TLR5 activity to influence immunity, inflammation, or even gut microbiome composition.

biochemistry↗

bioRxiv2024

The transaminase-omega-amidase pathway is a redox switch in glutamine metabolism that generates alpha-ketoglutarate

Oxidative stress is caused by short-lived molecules and metabolic changes belong to the fastest cellular responses. Here we studied how the endothelial cell metabolome reacts to acute oxidative challenges (menadione or H2O2) to identify redox-sensitive metabolic enzymes. H2O2 selectively increased -ketoglutaramate (KGM), a largely uncharacterized metabolite produced by glutamine transamination and a yet unrecognized intermediate of endothelial glutamine catabolism. The enzyme nitrilase-like 2 {omega}-amidase (NIT2) converts KGM to -ketoglutarate (KG). Reversible oxidation of specific cysteine in NIT2 by H2O2 inhibited its catalytic activity. Furthermore, a variant in the NIT2 gene that decreases its expression is associated with high plasma KGM level in humans. Endothelial-specific knockout mice of NIT2 exhibited increased levels of KGM and impaired angiogenesis. Knockout of NIT2 impaired endothelial cell proliferation and sprouting and induced senescence. In conclusion, we show that the glutamine transaminase-{omega}-amidase pathway is a metabolic switch in which NIT2 is the redox-sensitive enzyme. The pathway is modulated in humans and functionally important for endothelial glutamine metabolism.

physiology↗

bioRxiv2023

Stochastic modeling of the dynamics of Salmonella infection of epithelial cells

Bacteria of the Salmonella genus are intracellular pathogens, which cause gastroenteritis and typhoid fever in animals and humans, and are responsible for millions of infections and thousands of deaths across the world every year. Furthermore, Salmonella has played the role of a model organism for studying host-pathogen interactions. Taking these two aspects into account, enormous efforts in the literature are devoted to study this intracellular pathogen. Within epithelial cells, there are two distinct subpopulations of Salmonella: (i) a large fraction of Salmonella, which are enclosed by vacuoles, and (ii) a small fraction of hyper-replicating cytosolic Salmonella. Here, by considering the infection of epithelial cells by Salmonella as a discrete-state, continuous-time Markov process, we propose a stochastic model of infection, which includes the invasion of Salmonella into the epithelial cells by a cooperative strategy, the replication inside the Salmonella-containing vacuole, and the bacterial proliferation in the cytosol. The xenophagic degradation of cytosolic bacteria is considered, too. The stochastic approach provides important insights into stochastic variation and heterogeneity of the vacuolar and cytosolic Salmonella populations on a single-cell level over time. Specifically, we predict the percentage of infected human epithelial cells depending on the incubation time and the multiplicity of infection, an d the bacterial load of the infected cells at different post-infection times.

systems biology↗

bioRxiv2022

Protein Fold Classification using Graph Neural Network and Protein Topology Graph

Protein fold classification reveals key structural information about proteins that is essential for understanding their function. While numerous approaches exist in the literature that classifies protein fold from sequence data using machine learning, there is hardly any approach that classifies protein fold from the secondary or tertiary structure data using deep learning. This work proposes a novel protein fold classification technique based on graph neural network and protein topology graphs. Protein topology graphs are constructed according to definitions in the Protein Topology Graph Library from protein secondary structure level data and their contacts. To the best of our knowledge, this is the first approach that applies graph neural network for protein fold classification. We analyze the SCOPe 2.07 data set, a manually and computationally curated database that classifies known protein structures into their taxonomic hierarchy and provides predefined labels for a certain number of entries from the Protein Data Bank. We also analyze the latest version of the CATH data set. Experimental results show that the classification accuracy is at around 82% - 100% under certain settings. Due to the rapid growth of structural data, automating the structure classification process with high accuracy using structural data is much needed in the field. This work introduces a new paradigm of protein fold classification that meets this need. The implementation of the model for protein fold classification and the datasets are available here https://github.com/SuriDipannitaSayeed/ProteinFoldClassification.git Author summaryClassification of protein structures is traditionally done using manual curation, evolutionary relationship, or sequence comparison-based methods. Applying machine learning and deep learning to protein structure classification is a comparatively new trend that holds great promises for automating the structure classification process. Advance deep learning technique like Graph Neural Network is still unexplored in this respect. SCOP and CATH are two traditional databases that provide the hierarchical taxonomic classification of protein structures. This work provides a novel computational approach that classifies protein folds in SCOP and CATH with graph neural network, performing a graph classification task.

bioinformatics↗

bioRxiv2022

High-resolution epigenetic profiling identifies novel regulators of COPD in human lung fibroblasts

Patients with chronic obstructive pulmonary disease (COPD) are still waiting for curative treatments. Considering the environmental cause of COPD (e.g., cigarette smoke) and disease phenotypes, including stem-cell senescence and impaired differentiation, we hypothesized that COPD will be associated with altered epigenetic signaling in lung cells. We generated genome-wide DNA methylation maps at single CpG resolution of primary human lung fibroblasts (HLFs) isolated from distal parenchyma of ex-smoker controls and COPD patients, with both mild and severe disease. The epigenetic landscape is markedly changed in lung fibroblasts across COPD stages, with DNA methylation changes occurring predominantly in regulatory regions, including promoters and enhancers. RNA sequencing of matched fibroblasts demonstrated dysregulation of genes involved in proliferation, DNA repair, and extracellular matrix organization. Notably, we identified epigenetic and transcriptional dysregulation already in mild COPD patients, providing unique insights into early disease. Integration of profiling data identified 110 candidate regulators of disease phenotypes, including epigenetic factors. Using phenotypic screens, we verified the regulator capacity of multiple candidates and linked them to repair processes in the human lung. Our study provides first integrative high-resolution epigenetic and transcriptomic maps of human lung fibroblasts across stages of COPD. We reveal novel transcriptomic and epigenetic signatures associated with COPD onset and progression and identify new candidate regulators involved in the pathogenesis of chronic respiratory diseases. The presence of various epigenetic factors among the candidates demonstrates that epigenetic regulation in COPD is an exciting research field that holds promise for novel therapeutic avenues for patients.

cell biology↗

bioRxiv2021

Mathematical modeling of the molecular switch of TNFR1-mediated signaling pathways using Petri nets

The paper describes a mathematical model of the molecular switch of cell survival, apoptosis, and necroptosis in cellular signaling pathways initiated by tumor necrosis factor 1. Based on experimental findings in the current literature, we constructed a Petri net model in terms of detailed molecular reactions for the molecular players, protein complexes, post-translational modifications, and cross talk. The model comprises 118 biochemical entities, 130 reactions, and 299 connecting edges. Applying Petri net analysis techniques, we found 279 pathways describing complete signal flows from receptor activation to cellular response, representing the combinatorial diversity of functional pathways.120 pathways steered the cell to survival, whereas 58 and 35 pathways led to apoptosis and necroptosis, respectively. For 65 pathways, the triggered response was not deterministic, leading to multiple possible outcomes. Based on the Petri net, we investigated the detailed in silico knockout behavior and identified important checkpoints of the TNFR1 signaling pathway in terms of ubiquitination within complex I and the gene expression dependent on NF-{kappa}B, which controls the caspase activity in complex II and apoptosis induction.

systems biology↗

bioRxiv2021

The proteasome regulator PSME4 drives immune evasion and abrogates anti-tumor immunity in NSCLC

Protein degradation by proteasomes is important for the immune response against tumors. Antigens generated by the proteasome promote immune cell infiltration into tumors and improve tumors responses to immunotherapy. For example, immunoproteasomes - a subset of proteasomes induced by inflammatory signals - may improve the response of melanomas to immune checkpoint inhibitors (ICI) by eliciting tumor inflammation. Yet, it is unclear whether and how protein degradation by proteasomes impacts cancer progression and contributes to immune evasion and resistance. Here, we profile the proteasome-cleaved peptides in lung cancers and find that PSME4 serves as a novel inhibitory regulator of the immunoproteasome, playing an anti-inflammatory role in cancer. Biochemical assays combined with scRNA-seq, immunopeptidomics and in vivo analyses demonstrate that PSME4 promotes an immunosuppressive environment around the tumor and abrogates anti-tumor immunity by inhibiting antigen presentation and attenuating tumor inflammation. Furthermore, we find that PSME4 expression is correlated with responsiveness to ICI across several cancer types. Our findings suggest that PSME4-mediated regulation of proteasome activity is a novel mechanism of immune evasion in non-small-cell lung carcinoma and may be targeted therapeutically for restoring anti-tumor immunity. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=139 SRC="FIGDIR/small/464690v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@e87910org.highwire.dtl.DTLVardef@641843org.highwire.dtl.DTLVardef@175092corg.highwire.dtl.DTLVardef@ad34c2_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIMapping the degradation landscape in Non-Small Cell Lung Cancer (NSCLC) uncovers altered proteasome activity and composition C_LIO_LIProteasome regulator PSME4 plays an anti-inflammatory role in NSCLC by attenuating immunoproteasome activity C_LIO_LIPSME4 restricts tumor antigen presentation and cytokine secretion, defining a cold tumor environment C_LIO_LIPSME4 drives tumor immune evasion and is associated with resistance to immunotherapy C_LI

cancer biology↗

bioRxiv2021

Tumor-secreted versican co-opts myeloid IKKβ during metastasis

The mechanisms tumor cells use to hijack the immune system are largely uncharted. Here we used bioluminescent nuclear factor (NF)-{kappa}B reporter mice and macrophages to discover that metastatic tumors trigger NF-{kappa}B activation in host macrophages, dependent on mutant KRAS signaling and delivered via secretory versican. Versican activates NF-{kappa}B in tumor-associated macrophages via inhibitor of NF-{kappa}B kinase (IKK) {beta}, resulting in release of interleukin (IL)-1{beta} into the tumor microenvironment. Versican silencing in cancer cells or conditional IKK{beta} deletion in macrophages prevents myeloid NF-{kappa}B activation and metastasis. Versican is overexpressed and/or mutated in human cancers and metastatic effusions with KRAS mutations, predicts poor survival, can aid in the development of diagnostic platforms for pleural metastasis, and is druggable via toll-like receptor (TLR) 1/2 inhibition. The data indicate a cardinal role for tumor-derived versican in establishing cross-talk with macrophage IKK{beta} during metastasis and may foster the development of new therapies and diagnostic tools.

cancer biology↗

bioRxiv2020

KRAS signalling in malignant pleural mesothelioma

Malignant pleural mesothelioma (MPM) arises from mesothelial cells lining the pleural cavity of asbestos-exposed individuals and rapidly leads to the development of pleural effusion and death. MPM harbours loss-of-function mutations in genes like BAP1, NF2, CDKN2A, and TP53, but isolated deletion of these genes alone in mice does not cause MPM and mouse models of the disease are sparse. Here we show that a significant proportion of human MPM harbour point mutations and copy number alterations in the KRAS proto-oncogene. These mutations are likely pathogenic, since ectopic expression of mutant KRASG12D in the pleural mesothelium of conditional mice causes MPM. Murine MPM cell lines derived from these tumours carry the initiating KRASG12D lesions, secondary Bap1 alterations, and human MPM-like gene expression profiles. Moreover, they are transplantable and actionable by KRAS inhibition. Our results indicate that KRAS mutations likely play an important and underestimated role in MPM, which warrants further exploration.

physiology↗

bioRxiv2020

Versatile workflow for cell type resolved transcriptional and epigenetic profiles from cryopreserved human lung.

The complexity of the lung microenvironment together with changes in cellular composition during disease progression make it exceptionally hard to understand the molecular mechanisms leading to the development of chronic lung diseases. Although recent advances in cell type resolved and single-cell sequencing approaches hold great promise for studying complex diseases, their implementation greatly relies on local access to fresh tissue, as traditional methods to process and store tissue do not allow viable cell isolation. To overcome these hurdles, we developed a novel, versatile workflow that allows long-term storage of human lung tissue with high cell viability, permits thorough sample quality check before cell isolation, and is compatible with next generation sequencing-based profiling, including single-cell approaches. We demonstrate that cryopreservation is suitable for isolation of multiple cell types from different lung locations and is applicable to both healthy and diseased tissue, including COPD and tumor samples. Basal cells isolated from cryopreserved airways retain the ability to differentiate, indicating that cellular identity is not altered by cryopreservation. Importantly, using RNA sequencing (RNA-seq) and Illumina EPIC Array, we show that genome-wide gene expression and DNA methylation signatures are preserved upon cryopreservation, emphasizing the suitability of our workflow for -omics profiling of human lung cells. In addition, we obtained high-quality single-cell RNA sequencing data of cells isolated from cryopreserved human lung, demonstrating that cryopreservation empowers single-cell approaches. Overall, thanks to its simplicity, our cryopreservation workflow is well-suited for prospective tissue collection by academic collaborators and biobanks, opening worldwide access to human tissue.

cell biology↗

bioRxiv2020

Experiments on localization accuracy with non-individual and individual HRTFs comparing static and dynamic reproduction methods

Binaural reproduction can be used in listening experiments under real-life conditions to achieve a high realism and good reproducibility. In recent years a clear trend to more individual reproduction can be observed as the ability to measure individual head-related-transfer-functions (HRTFs) is becoming more widespread. The question of the accuracy and reproduction methods needed for a realistic playback however has not been sufficiently answered. To evaluate an appropriate approach for binaural reproduction via headphones different head-related-transfer-functions (HRTFs) and reproduction methods were compared in this paper. In a listening test eleven explicitly trained participants were asked to localize eleven sound sources positioned in the right hemisphere using the proximal pointing method. Binaural stimuli based on individually measured HRTFs were compared to those of an artificial head in a static reproduction of stimuli and in three dynamic reproduction methods of different resolutions (5{degrees}, 2.5{degrees} and 1{degrees}). Unsigned errors in azimuth and elevation as well as front-back-confusions and in-head-localization were observed. Dynamic reproduction of any resolution applied turned out fundamental for a reduction of undesired front-back-confusions and in-head-localization. Individually measured HRTFs showed a smaller effect on localization accuracy compared to the influence of dynamic sound reproduction. They were mainly observed to reduce the front-back-confusion rate.

neuroscience↗

bioRxiv2018

Quality assessment of tissue samples stored in a specialized human lung biobank

Human sample, from patients or healthy donors, are a valuable link between basic research and clinic. Especially in translational research, they play an essential role in understanding development and progression of diseases as well as in developing new diagnostic and therapeutic tools. Stored in biobanks, fast access to appropriate material becomes possible. However, biobanking in a clinical context faces several challenges. In practice, collecting samples during clinical routine does not allow to strictly adhere to protocols of sample collection in all aspects. This may influence sample quality to variable degrees. Time from sample draw to asservation is a variable factor, and influences of prolonged storage at ambient temperature of tissues are not well understood. We investigated whether delays between 5 minutes and 3 hours, and the use of RNAlater RNA-preserving reagent would lead to a relevant drop in sample quality, measured by quantitative mRNA expression analysis. Our findings suggest that even under ambient conditions, delays up to 3 hours do not have a major impact on sample quality as long as the tissue remains intact.

molecular biology↗

bioRxiv2018

The impacts of landscape composition, marginality and climatic stability on the patterns of endemism of Cerrado woody plants

AimAlthough various theories have been proposed to explain the outstanding endemism of plants in the Cerrado, four hypotheses about the mechanisms of diversification and distribution are most supported: (1) plateau/valley, (2) stable/unstable climate, (3) core/peripheral distribution, and (4) soil fertility. The first argues that plateaus harbor more ancient lineages than valleys and therefore presents higher endemism. The second theory suggests that climatic stable environments maintained more paleoendemic species. The third scenario attributes the distribution of endemism to gradients of conditions available to locally adapted species and predicts higher endemism in nuclear than in marginal areas. The last theory suggests that lower fertility soils account for higher endemism due to the habitat specialization of its species. We compared endemism patterns with the predictions of each theory to discuss their importance.\n\nLocationBrazil.\n\nTime periodQuaternary.\n\nMajor taxa studiedAngiosperms.\n\nMethodsWe mapped the endemism using records of 311 plant species of the Cerrado and applied spatial analysis and distribution models to summarize the importance of each predictor of endemism.\n\nResultsWe identified 28 areas in which the higher endemism of Cerrado plants were concentrated and presented a map of its distribution. We found correlations among endemism, climate stability, elevation, and marginality, which supported the plateau/valley, core/peripheral, and stable/unstable hypotheses. No association between soil fertility and endemism was detected. We propose that plateaus are more stable climatic environments, and this characteristic along with their elevation and centrality are predictive of endemism.\n\nMain conclusionsWe concluded that most of the endemism is concentrated in overlapping areas of stability of species, which are concentrated in higher elevation central regions. Soil fertility was not linked to endemism. We recommend that central plateaus in the Cerrado require special attention in conservation to optimize the protection of endemic species in the biome.

ecology↗

bioRxiv2018

Mapping the genetics of neuropsychological traits to the molecular network of the human brain using a data integrative approach

MotivationComplex neuropsychiatric conditions including autism spectrum disorders are among the most heritable neurodevelopmental disorders with distinct profiles of neuropsychological traits. A variety of genetic factors modulate these traits (phenotypes) underlying clinical diagnoses. To explore the associations between genetic factors and phenotypes, genome-wide association studies are broadly applied. Stringent quality checks and thorough downstream analyses for in-depth interpretation of the associations are an indispensable prerequisite. However, in the area of neuropsychology there is no framework existing, which besides performing association studies also affiliates genetic variants at the brain and gene network level within a single framework.\n\nResultsWe present a novel bioinformatics approach in the field of neuropsychology that integrates current state-of-the-art tools, algorithms and brain transcriptome data to elaborate the association of phenotype and genotype data. The integration of transcriptome data gives an advantage over the existing pipelines by directly translating genetic associations to brain regions and developmental patterns. Based on our data integrative approach, we identify genetic variants associated with Intelligence Quotient (IQ) in an autism cohort and found their respective genes to be expressed in specific brain areas.\n\nConclusionOur data integrative approach revealed that IQ is related to early down-regulated and late up-regulated gene modules implicated in frontal cortex and striatum, respectively. Besides identifying new gene associations with IQ we also provide a proof of concept, as several of the identified genes in our analysis are candidate genes related to intelligence in autism, intellectual disability, and Alzheimers disease. The framework provides a complete extensive analysis starting from a phenotypic trait data to its association at specific brain areas at vulnerable time points within a timespan of four days.\n\nAvailability and ImplementationOur framework is implemented in R and Python. It is available as an in-house script, which can be provided on demand.\n\nContactafsheen.yousaf@kgu.de

bioinformatics↗

bioRxiv2018

isiKnock: in silico knockouts in biochemical pathways

SummaryisiKnock is a new software that automatically conducts in silico knockouts for mathematical models of biochemical pathways. The software allows for the prediction of the behavior of biological systems after single or multiple knockout. The implemented algorithm applies transition invariants and the novel concept of Manatee invariants. A knockout matrix visualizes the results. The tool enables the analysis of dependencies, for example, in signal flows from the receptor activation to the cell response at steady state.\n\nAvailability and ImplementationisiKnock is an open-source tool, freely available at http://www.bioinformatik.uni-frankfurt.de/tools/isiKnock/index.php. It requires at least Java 8 and runs under Microsoft Windows, Linux, and Mac OS.\n\nContactina.koch@bioinformatik.uni-frankfurt.de

bioinformatics↗

bioRxiv2017

Bioinformatics analysis quantifies neighborhood preferences of cancer cells in Hodgkin lymphoma.

MotivationHodgkin lymphoma is a tumor of the lymphatic system and represents one of the most frequent lymphoma in the Western world. It is characterized by Hodgkin cells and Reed-Sternberg cells, which exhibit a broad morphological spectrum. The cells are visualized by immunohistochemical staining of tissue sections. In pathology, tissue images are mainly manually evaluated, relying on the expertise and experience of pathologists. Computational quantification methods become more and more essential to evaluate tissue images. In particular, the distribution of cancer cells is of great interest.\n\nResultsHere, we systematically quantified and investigated cancer cell properties and their spatial neighborhood relations by applying statistical analyses to whole slide images of Hodgkin lymphoma and lymphadenitis, which describes a non-cancerous inflammation of the lymph node. We differentiated cells by their morphology and studied the spatial neighborhood relation of more than 400,000 immunohistochemically stained cells. We found that, according to their morphological features, the cells exhibited significant preferences for and aversions to cells of specific profiles as nearest neighbor. We quantified differences between Hodgkin lymphoma and lymphadenitis concerning the neighborhood relations of cells and the sizes of cells. The approach can easily be applied to other cancer types.\n\nContactina.koch@bioinformatik.uni-frankfurt.de

bioinformatics↗