Clonal Expansion and Epigenetic Inheritance Shape Long-Lasting NK cell Memory

Die Selektion klonal expandierender Zellen mit einzigartigen, somatisch rekombinierten Anti-gen-Rezeptoren und die Langlebigkeit der daraus hervorgehenden Gedächtniszellen sind definierende Eigenschaften adaptiver Immunität. Dahingegen ist das angeborene Immunsystem da-rauf programmiert, mittels einer breiten Palette konservierter Rezeptoren möglichst schnell auf Pathogene zu reagieren und wird dabei auf Populationsebene epigenetisch geprägt. In dieser Arbeit möchte ich dieses Paradigma auf der Basis von Natürlichem Killer (NK) Zell-Gedächtnis an das humane Zytomegalievirus (HCMV) als Beispiel für Pathogen-spezifische Anpassung innerhalb des angeborenen Immunsystems herausfordern. Indem wir multiparametrische Einzel-zellanalysen zur Kartierung von ex vivo NK Zellen mit endogenen Barcodes in Form von soma-tischen Mutationen in mitochondrialer DNA (mtDNA) verknüpfen, können wir drastische klonale Expansionen adaptiver NK Zellen in HCMV+ Spendern nachweisen. NK-Zell-Klonotypen waren durch eine ihnen gemeinsame, inflammatorische Gedächtnissignatur mit AP1 Motiven gekennzeichnet, die eine Reihe einzigartiger Chromatin-Regionen mit Klon-spezifischer Zugänglichkeit überlagerte. NK-Zell-Klone wurden über einen Zeitraum von bis zu 19 Monaten stabil aufrechterhalten und behielten dabei ihre charakteristischen, Klon-spezifischen epigenetischen Signaturen, was die entscheidende Rolle klonaler Vererbung von Chromatin-Zugänglichkeit für die Prägung des epigenetischen Gedächtnis-Repertoires unterstreicht. Insgesamt identifiziert diese Arbeit zum ersten Mal klonale Expansion und Persistenz innerhalb des angeborenen Immunsystems im Menschen und deutet daraufhin, dass diese zentralen Mechanismen zur Ausbildung von immunologischem Gedächtnis evolutionär unabhängig von diversifizierten Antigen-Rezeptoren entstanden sind.A hallmark of adaptive immunity is the clonal selection and expansion of cells with somatically diversified receptors and their long-term maintenance as memory cells. The innate immune system, in contrast, is wired to rapidly respond to pathogens via a broad set of germline-encoded receptors, acquiring epigenetic imprinting at the population level. The presented work challenges this paradigm by studying Natural Killer (NK) cell memory to human Cytomegalovirus (HCMV) infection as an example of pathogen-specific adaptation within the innate immune system. Leveraging single-cell multi-omic maps of ex vivo NK cells and somatic mitochondrial DNA (mtDNA) mutations as endogenous barcodes, we reveal drastic clonal expansions of adaptive NK cells in HCMV+ individuals. NK cell clonotypes were characterized by a convergent inflammatory memory signature driven by AP1 transcription factor activity, superimposed on a private set of clone-specific accessible chromatin regions. Strikingly, NK cell clones were stably maintained in their specific epigenetic states for up to 19 months, revealing that clonal inheritance of chromatin accessibility shapes the epigenetic memory repertoire. Together, this work presents the first identification of clonal expansion and persistence within the human innate immune system, suggesting these central mechanisms of immune memory have evolved independently of antigen-receptor diversification

Jointly Trained Variational Autoencoder for Multi-Modal Sensor Fusion

Korthals T, Hesse M, Leitner J, Melnik A, Rückert U. Jointly Trained Variational Autoencoder for Multi-Modal Sensor Fusion. In: 22st International Conference on Information Fusion, (FUSION) 2019, Ottawa, CA, July 2-5, 2019. 2019: 1-8

Fiducial Marker based Extrinsic Camera Calibration for a Robot Benchmarking Platform

Korthals T, Wolf D, Rudolph D, Hesse M, Rückert U. Fiducial Marker based Extrinsic Camera Calibration for a Robot Benchmarking Platform. In: European Conference on Mobile Robots, ECMR 2019, Prague, CZ, September 4-6, 2019. 2019: 1-6.Evaluation of robotic experiments requires physical robots as well as position sensing systems. Accurate systems detecting sufficiently all necessary degrees of freedom, like the famous Vicon system, are commonly too expensive. Therefore, we target an economical multi-camera based solution by following these three requirements: Using multiple cameras to track even large laboratory areas, applying fiducial marker trackers for pose identification, and fuse tracking hypothesis resulting from multiple cameras via extended Kalman filter (i.e. ROS's robot\_localization). While the registration of a multi-camera system for collaborative tracking remains a challenging issue, the contribution of this paper is as follows: We introduce the framework of Cognitive Interaction Tracking (CITrack). Then, common fiducial marker tracking systems (ARToolKit, AprilTag, ArUco) are compared with respect to their maintainability. Lastly, a graph-based camera registration approach in SE(3), using the fiducial marker tracking in a multi-camera setup, is presented and evaluated

High-throughput characterization of HLA-E-presented CD94/NKG2x ligands reveals peptides which modulate NK cell activation

HLA-E is a non-classical class I MHC protein involved in innate and adaptive immune recognition. While recent studies have shown HLA-E can present diverse peptides to NK cells and T cells, the HLA-E repertoire recognized by CD94/NKG2x has remained poorly defined, with only a limited number of peptide ligands identified. Here we screen a yeast-displayed peptide library in the context of HLA-E to identify 500 high-confidence unique peptides that bind both HLA-E and CD94/NKG2A or CD94/NKG2C. Utilizing the sequences identified via yeast display selections, we train prediction algorithms and identify human and cytomegalovirus (CMV) proteome-derived, HLA-E-presented peptides capable of binding and signaling through both CD94/NKG2A and CD94/NKG2C. In addition, we identify peptides which selectively activate NKG2C+ NK cells. Taken together, characterization of the HLA-E-binding peptide repertoire and identification of NK activity-modulating peptides present opportunities for studies of NK cell regulation in health and disease, in addition to vaccine and therapeutic design

Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation

Korthals T, Kragh M, Christiansen P, Karstoft H, Jørgensen RN, Rückert U. Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation. Frontiers in Robotics and AI. 2018;5: 26.Today, agricultural vehicles are available that can automatically perform tasks such as weed detection and spraying, mowing, and sowing while being steered automatically. However, for such systems to be fully autonomous and self-driven, not only their specific agricultural tasks must be automated. An accurate and robust perception system automatically detecting and avoiding all obstacles must also be realized to ensure safety of humans, animals, and other surroundings. In this paper, we present a multi-modal obstacle and environment detection and recognition approach for process evaluation in agricultural fields. The proposed pipeline detects and maps static and dynamic obstacles globally, while providing process-relevant information along the traversed trajectory. Detection algorithms are introduced for a variety of sensor technologies, including range sensors (lidar and radar) and cameras (stereo and thermal). Detection information is mapped globally into semantical occupancy grid maps and fused across all sensors with late fusion, resulting in accurate traversability assessment and semantical mapping of process-relevant categories (e.g., crop, ground, and obstacles). Finally, a decoding step uses a Hidden Markov model to extract relevant process-specific parameters along the trajectory of the vehicle, thus informing a potential control system of unexpected structures in the planned path. The method is evaluated on a public dataset for multi-modal obstacle detection in agricultural fields. Results show that a combination of multiple sensor modalities increases detection performance and that different fusion strategies must be applied between algorithms detecting similar and dissimilar classes

Coordinated Heterogeneous Distributed Perception based on Latent Space Representation

Korthals T, Leitner J, Rückert U. Coordinated Heterogeneous Distributed Perception based on Latent Space Representation. CoRR. 2018.We investigate a reinforcement approach for distributed sensing based on the latent space derived from multi-modal deep generative models. Our contribution provides insights to the following benefits: Detections can be exchanged effectively between robots equipped with uni-modal sensors due to a shared latent representation of information that is trained by a Variational Auto Encoder (VAE). Sensor-fusion can be applied asynchronously due to the generative feature of the VAE. Deep Q-Networks (DQNs) are trained to minimize uncertainty in latent space by coordinating robots to a Point-of-Interest (PoI) where their sensor modality can provide beneficial information about the PoI. Additionally, we show that the decrease in uncertainty can be defined as the direct reward signal for training the DQN

AMiRo: A Modular & Customizable Open-Source Mini Robot Platform

Herbrechtsmeier S, Korthals T, Schöpping T, Rückert U. AMiRo: A Modular & Customizable Open-Source Mini Robot Platform. Presented at the 20th International Conference on System Theory, Control and Computing, Sinaia.AMiRo is a novel modular robot platform that can be easily extended and customized in hardware and software. Built up of electronic modules that fully exploit recent technology and open-source software for sensor processing, actuator control, and cognitive processing, the robot facilitates rich autonomous behaviors. Further contribution lies in the completely open- source software habitat: from low-level microcontroller imple- mentations, over high-level applications running on an embedded processor, up to hardware accelerated algorithms using pro- grammable logic. This paper describes in detail the motivation, system architecture, and software design of the AMiRo, which surpasses state-of-the-art competitors

AMiRo: A Mini Robot as Versatile Teaching Platform

Schöpping T, Korthals T, Hesse M, Rückert U. AMiRo: A Mini Robot as Versatile Teaching Platform. In: Proceedings of the 9th International Conference on Robotics in Education, RiE 2018. Advances in Intelligent Systems and Computing. Vol 829. Springer; 2018: 177-188.Since robots become increasingly ubiquitous and system complexity increases, teaching university students in robotics is essential for modern studies in computer science. This work thus presents the education curriculum around the Autonomous Mini Robot (AMiRo) as a solution to this challenge. The goal is to provide insights to the various fields related to robotics and allow students to specialize in a wide range of topics, depending on their interests. Concept as well as platform have been evaluated and the results reveal a generally positive feedback as well as some issues, for which according solutions are proposed

Occupancy Grid Mapping with Highly Uncertain Range Sensors based on Inverse Particle Filters

Korthals T, Barther M, Schöpping T, Herbrechtsmeier S, Rückert U. Occupancy Grid Mapping with Highly Uncertain Range Sensors based on Inverse Particle Filters. In: Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics. 2016: 192-200.A huge number of techniques for detecting and mapping obstacles based on LIDAR and SONAR exist, though not taking approximative sensors with high levels of uncertainty into consideration. The proposed mapping method in this article is undertaken by detecting surfaces and approximating objects by distance using sensors with high localization ambiguity. Detection is based on an Inverse Particle Filter, which uses readings from single or multiple sensors as well as a robot’s motion. This contribution describes the extension of the Sequential Importance Resampling filter to detect objects based on an analytical sensor model and embedding into Occupancy Grid Maps. The approach has been applied to the autonomous mini robot AMiRo in a distributed way. There were promising results for its low-power, low-cost proximity sensors in various real life mapping scenarios, which outperform the standard Inverse Sensor Model approach

Evidence Grid Based Information Fusion for Semantic Classifiers in Dynamic Sensor Networks

Korthals T, Krause T, Rückert U. Evidence Grid Based Information Fusion for Semantic Classifiers in Dynamic Sensor Networks. In: Niggemann O, Beyerer J, eds. Machine Learning for Cyber Physical Systems. Berlin, Heidelberg: Springer Science + Business Media; 2015: 9-14