Lukas Benjamin Glandorf


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Glandorf

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Lukas Benjamin

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0000-0003-4968-2906

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2024 - 20254
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Publications 1 - 4 of 4
  • Bessel beam optical coherence microscopy enables multiscale assessment of cerebrovascular network morphology and function
    Item type: Journal Article
    Glandorf, Lukas Benjamin; Wittmann, Bastian; Droux, Jeanne; et al. (2024)
    Light: Science & Applications
    Understanding the morphology and function of large-scale cerebrovascular networks is crucial for studying brain health and disease. However, reconciling the demands for imaging on a broad scale with the precision of high-resolution volumetric microscopy has been a persistent challenge. In this study, we introduce Bessel beam optical coherence microscopy with an extended focus to capture the full cortical vascular hierarchy in mice over 1000 x 1000 x 360 mu m3 field-of-view at capillary level resolution. The post-processing pipeline leverages a supervised deep learning approach for precise 3D segmentation of high-resolution angiograms, hence permitting reliable examination of microvascular structures at multiple spatial scales. Coupled with high-sensitivity Doppler optical coherence tomography, our method enables the computation of both axial and transverse blood velocity components as well as vessel-specific blood flow direction, facilitating a detailed assessment of morpho-functional characteristics across all vessel dimensions. Through graph-based analysis, we deliver insights into vascular connectivity, all the way from individual capillaries to broader network interactions, a task traditionally challenging for in vivo studies. The new imaging and analysis framework extends the frontiers of research into cerebrovascular function and neurovascular pathologies.
  • Quantitative Mapping of Functional Cerebrovascular Network Adaptations in Stroke Using Optical Coherence Microscopy
    Item type: Doctoral Thesis
    Glandorf, Lukas Benjamin (2025)
    Cerebral blood flow continuously delivers oxygen and energy substrates and removes metabolic waste, maintaining proper brain function. Despite the critical role of microvasculature in health and disease, our ability to monitor interconnected microvascular networks comprehensively in vivo remains limited. Consequently, fundamental aspects such as intertwined microvascular dynamics during cerebrovascular disorders like stroke remain incompletely understood. Optical coherence tomography (OCT) has revolutionized clinical ophthalmic imaging by providing rapid volumetric imaging with micrometer-scale resolution and label-free contrast. Beyond ophthalmology, OCT bridges the gap between macroscopic imaging techniques, like magnetic resonance imaging, and microscopic methods, such as multi-photon microscopy. Yet, OCT is primarily employed qualitatively or semi-quantitatively, limited by imaging artifacts, limited signal-to-noise ratios compared to fluorescence imaging, and lack of robust quantitative analysis frameworks. These limitations preclude detailed analyses of microvascular connectivity, topology, and blood flow dynamics in vivo, critical for preclinical and translational research. This dissertation addresses these limitations, aiming to enable quantitative in vivo study of large-scale interconnected microvascular networks with capillary-level resolution. Specifically, this work first evaluates digital adaptive optics and extended-focus optical coherence microscopy (xfOCM) as methods to enhance volumetric imaging capabilities at capillary resolution. Then, on the basis of an established xfOCM system, we develop a novel, integrated imaging and computational platform capable of capturing, segmenting, and quantitatively analyzing in vivo microvascular networks, including their topological features and detailed blood flow velocity and direction. Finally, an advanced iteration of this platform is devised, explicitly designed to investigate network-scale microvascular disruptions after stroke. Utilizing this framework, the thesis provides novel insights into post-stroke microvascular dysfunction, demonstrating distinct patterns of blood flow alterations and capillary stalling at unprecedented detail and scale. Ultimately, the developed methodology bridges single-capillary and mesoscale analysis, offering a powerful new tool to advance the understanding of various cerebrovascular pathologies, including stroke, Alzheimer's disease, and hypertension.
  • Pia-FLOW: Deciphering hemodynamic maps of the pial vascular connectome and its response to arterial occlusion
    Item type: Journal Article
    Glück, Chaim; Zhou, Quanyu; Droux, Jeanne; et al. (2024)
    Proceedings of the National Academy of Sciences of the United States of America
    The pial vasculature is the sole source of blood supply to the neocortex. The brain is contained within the skull, a vascularized bone marrow with a unique anatomical connection to the brain meninges. Recent developments in tissue clearing have enabled detailed mapping of the entire pial and calvarial vasculature. However, what are the absolute flow rate values of those vascular networks? This information cannot accurately be retrieved with the commonly used bioimaging methods. Here, we introduce Pia-FLOW, a unique approach based on large-scale transcranial fluorescence localization microscopy, to attain hemodynamic imaging of the whole murine pial and calvarial vasculature at frame rates up to 1,000 Hz and spatial resolution reaching 5.4 µm. Using Pia-FLOW, we provide detailed maps of flow velocity, direction, and vascular diameters which can serve as ground-truth data for further studies, advancing our understanding of brain fluid dynamics. Furthermore, Pia-FLOW revealed that the pial vascular network functions as one unit for robust allocation of blood after stroke.
  • Discerning Amyloid-β and Tau Pathologies with Learning-Based Quantum Sensing
    Item type: Journal Article
    Sundar, Shruti; Jabir , Marakkarakath Vadakkepurayil; Glandorf, Lukas Benjamin; et al. (2025)
    ACS Photonics
    Photon entanglement, a key feature of quantum correlations, provides a level of coherence absent in classical correlations, potentially offering new information when interacting with biological matter. One promising application is using entanglement decoherence to distinguish between healthy and diseased samples. However, achieving this requires efficient entangled photon sources capable of surviving through biological samples for reliable detection. In this work, we show the applicability of a polarization-entangled photon source as a label-free diagnostic tool for distinguishing between transgenic mouse models of amyloidosis and tauopathy and their respective control strains. We investigated cortical and hippocampal regions of these models, and our findings revealed greater preservation of entanglement in the transgenic samples compared to controls. To further enhance classification accuracy, we employed a supervised machine learning approach, achieving reliable distinctions between disease and control groups in unseen test samples. The quantum-based results were further validated through confocal imaging of the transgenic and control samples. These findings suggest that quantum sensing could serve as a label-free approach for distinguishing biological samples, with potential applications in the study of neurodegenerative disorders.
Publications 1 - 4 of 4