Alzheimer's disease protease-containing plasma extracellular vesicles transfer to the hippocampus via the choroid plexus - Lee, Jung Hyun; Ostalecki, Christian; Oberstein, Timo; Schierer, Stefan; Zinser, Elisabeth; Eberhardt, Martin; Blume, Katja; Plosnita, Bianca; Stich, Lena; Bruns, Heiko; Coras, Roland; Vera González, Julio; et al. (EBioMedicine, 2022)
Background: Plasma extracellular vesicles (pEV) can harbor a diverse array of factors including active proteases and the amyloid-precursor-protein (APP) cleavage product Aβ, involved in plaque formation in Alzheimer`s diseases (AD). A potential role of such vesicles in AD pathology is unexplored. Methods: In a case-control study of randomly selected patients with AD and other neurological diseases (n = 14), and healthy controls (n = 7), we systematically analyzed the content of pEV, using different assay systems. In addition, we determined their entry path into brain tissue, employing animal (mice) injection experiments with ex vivo generated EV that were similar to AD-pEV, followed by multi antigen analysis (MAA) of brain tissue (n = 4 per condition). The results were compared with an IHC staining of human brain tissue in a small cohort of AD patients (n = 3) and controls with no neurodegenerative diseases (n = 3). Findings: We show that pEV levels are considerably upregulated in AD patients. Besides numerous inflammatory effectors, AD-pEV contained α-, β- and γ-secretases, able to cleave APP in in target cells. In vitro generated EV with similar characteristics as AD-pEV accumulated in the choroid plexus (CP) of injected animals and reached primarily hippocampal neurons. Corroborating findings were made in human brain samples. An inhibitor of hyaluronic-acid-synthetase (HAS) blocked uploading of proteases and Hyaluronan onto EV in vitro and abolished CP targeting in animal injection experiments. Interpretation: We conclude that protease-containing pEV could be part of a communication axis between the periphery and the brain that could be become detrimental depending on pEV concentration and duration of target cell impact.
CRIS-Team Scopus Importer:2022-03-04
Keywords: Alzheimer's disease; APP; Aβ; BACE; Choroid plexus; Hippocampus; Hyaluronan; Hyaluronic acid synthetase; Plasma extracellular vesicles; Secretases

Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data using Graph Neural Networks - Rivera Monroy, Luis Carlos; Rist, Leonhard; Eberhardt, Martin; Ostalecki, Christian; Bauer, Andreas; Vera, Julio; Breininger, Katharina; Maier, Andreas (Bildverarbeitung für die Medizin 2024, 2024)
This study leverages graph neural networks to integrate MELC data with Radiomic-extracted features for melanoma classification, focusing on cellwise analysis. It assesses the effectiveness of gene expression profiles and Radiomic features, revealing that Radiomic features, particularly when combined with UMAP for dimensionality reduction, significantly enhance classification performance. Notably, using Radiomics contributes to increased diagnostic accuracy and computational efficiency, as it allows for the extraction of critical data from fewer stains, thereby reducing operational costs. This methodology marks an advancement in computational dermatology for melanoma cell classification, setting the stage for future research and potential developments.<br

Graph neural networks in multi-stained pathological imaging: extended comparative analysis of Radiomic features - Rivera Monroy, Luis Carlos; Rist, Leonhard; Ostalecki, Christian; Bauer, Andreas; Vera, Julio; Breininger, Katharina; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2024)
Purpose This study investigates the application of Radiomic features within graph neural networks (GNNs) for the classification of multiple-epitope-ligand cartography (MELC) pathology samples. It aims to enhance the diagnosis of often misdiagnosed skin diseases such as eczema, lymphoma, and melanoma. The novel contribution lies in integrating Radiomic features with GNNs and comparing their efficacy against traditional multi-stain profiles. Methods We utilized GNNs to process multiple pathological slides as cell-level graphs, comparing their performance with XGBoost and Random Forest classifiers. The analysis included two feature types: multi-stain profiles and Radiomic features. Dimensionality reduction techniques such as UMAP and t-SNE were applied to optimize the feature space, and graph connectivity was based on spatial and feature closeness. Results Integrating Radiomic features into spatially connected graphs significantly improved classification accuracy over traditional models. The application of UMAP further enhanced the performance of GNNs, particularly in classifying diseases with similar pathological features. The GNN model outperformed baseline methods, demonstrating its robustness in handling complex histopathological data. Conclusion Radiomic features processed through GNNs show significant promise for multi-disease classification, improving diagnostic accuracy. This study’s findings suggest that integrating advanced imaging analysis with graph-based modeling can lead to better diagnostic tools. Future research should expand these methods to a wider range of diseases to validate their generalizability and effectivenes
Keywords: Radiomics; Multi-array imaging; Graph neural network; MELC; Histopathological analysis

Mapping bone marrow metastasis in neuroblastoma by deep multiplex imaging and transcriptomics - Lazic, Daria; Kromp, Florian; Rifatbegovic, Fikret; Repiscak, Peter; Mivalt, Filip; Halbritter, Florian; Bernkopf, Marie; Bileck, Andrea; Ussowicz, Marek; Ambros, Inge M.; Ambros, Peter F.; Gerner, Christopher; et al. (CANCER RESEARCH, 2022)
CRIS-Team WoS Importer:2023-04-07

Melanoma Segmentation and Classification Employing Imaging and Graph Encodings - Rivera Monroy, Luis Carlos; Eberhardt, Martin; Ostalecki, Christian; Baur, Andreas; Vera, Julio; Maier, Andreas (Proceedings - 2022 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2022, 2022)
Histopathology imaging is crucial for the diagnosis and the decision on treating associated tissue diseases. For this reason, Computer-assisted approaches have gained popularity and shown promise in tasks such as segmentation and classification. However, collecting a significant amount of data and high-quality annotations remains a challenge. This work describes a pipeline for toponomy profiling on the population of cells that combines melanoma multidimensional fluorescent imaging, a supervised segmentation of the region of interest, and graph unsupervised embedded representation.The Multi-Epitope-Ligand Cartography (MELC) provides a range of data shown to be optimal for solving supervised and unsuper-vised tasks. This imaging technique and the methodology, such as the one presented here, are promising for further improvement in the study of diagnostic techniques and therapies for skin diseases, such as melanoma.
CRIS-Team Scopus Importer:2023-03-07
Keywords: melanoma; microscopy; neural networks; segmentation

Multi-antigen imaging reveals inflammatory dc, adam17 and neprilysin as effectors in keloid formation - Rath, Mathias; Pitiot, Alain; Kirr, Michael; Fröhlich, Waltraud; Plosnita, Bianca; Schliep, Stefan; Bauerschmitz, Jürgen; Baur, Andreas; Ostalecki, Christian (International Journal of Molecular Sciences, 2021)
Keloid is an aberrant scarring process of the skin, characterized by excessive extracellular matrix synthesis and deposition. The pathogenesis of this prevalent cutaneous disorder is not fully understood; however, a persistent inflammatory process is observed. To obtain more insight into this process, we analyzed lesional, perilesional and healthy tissue using multi-antigen-analysis (MAA) in conjunction with a data mining approach. Here, we demonstrate that monocyte-derived inflammatory dendritic cells (CD1a+, CD11c+, CD14+) and activated CD4+ T lymphocytes (CD45 RO+) dominated the immune infiltration in keloids while associating with fibroblasts. In perilesional tissue, precursor immune cells were dominant in the perivascular area, suggesting that they were attracted by an immune process, potentially in the lesional area. Supporting this hypothesis, only in keloid lesions, high levels of ADAM10/17 and Neprilysin (CD10) were observed in both fibroblasts and leukocytes. The spatial proximity of these two cell types, which could be confirmed by image analysis only in lesional tissue, could be a potential factor leading to the activation of fibroblasts. Our findings provide new insight into the pathogenesis of keloid formation and reveal metalloproteinases as a target for therapeutical intervention.
CRIS-Team Scopus Importer:2021-09-10
Keywords: Dendritic cells; Multiplex imaging; Proteases; Scar; Single-cell analysis; Tumor biology; Tumor microenvironment

Multi-level cancer profiling through joint cell-graph representations - Rivera Monroy, Luis Carlos; Rist, Leonhard; Wilm, Frauke; Ostalecki, Christian; Baur, Andreas; Vera González, Julio; Breininger, Katharina; Maier, Andreas (Smart Health, 2024)
Computer-aided analysis of digitized pathology samples has significantly advanced with the rapid progression of machine and Deep Learning (DL) methods. However, most existing approaches primarily focus on features extracted from patches due to the large image sizes. This focus limits the ability of Convolutional Neural Networks (CNNs) to capture global information from the samples, resulting in an incomplete phenotypical and topological representation and thereby restricting the diagnostic capabilities of these methods. The recent emergence of Graph Neural Networks (GNNs) offers new opportunities to overcome these limitations through graph-driven representations of pathological samples. This work introduces a graph-based framework that encompasses diverse cancer types and integrates different imaging modalities. In this framework, histopathology samples are represented as graphs, and a pipeline facilitating cell-wise and disease classification is developed. The results support this motivation: for cell-wise classification, we achieved an average accuracy of , and for disease-wise classification, an average accuracy of 83%, outperforming reference models such as XGBoost and standard CNNs. This approach not only provides flexibility in combining various diseases but also extends to integrating different staining technique
Keywords: Fluorescence microscopy imageGraph deep learningMultiplex imagingDigital pathology

Spatial cell graph analysis reveals skin tissue organization characteristic for cutaneous T cell lymphoma - Sarkar, Suryadipto; Möller, Anna; Hartebrodt, Anne; Erdmann, Michael; Ostalecki, Christian; Baur, Andreas; Blumenthal, David B. (npj Systems Biology and Applications, 2024)