The cornea and cranial dura mater share sensory innervation. This link raises the possibility that pathological impulses mediated by corneal injury may be transmitted to the cranial dura, trigger dural perivascular/connective tissue nociceptor responses, and induce vascular and stromal alterations affecting dura mater blood and lymphatic vessel functionality. In this study, using a mouse model, we demonstrate for the first time that two weeks after the initial insult, alkaline injury to the cornea leads to remote pathological changes within the coronal suture area of the dura mater. Specifically, we detected significant pro-fibrotic changes in the dural stroma, as well as vascular remodeling characterized by alterations in vascular smooth muscle cell (VSMC) morphology, reduced blood vessel VSMC coverage, endothelial cell expression of the fibroblast specific protein 1, and significant increase in the number of podoplanin-positive lymphatic sprouts. Intriguingly, the deficiency of a major extracellular matrix component, small leucine-rich proteoglycan decorin, modifies both the direction and the extent of these changes. As the dura mater is the most important route for the brain metabolic clearance, these results are of clinical relevance and provide a much-needed link explaining the association between ophthalmic conditions and the development of neurodegenerative diseases.

Cancer cell adhesion to the endothelium is a crucial process in hematogenous metastasis, but how the integrity of the endothelial barrier and endothelial cell (EC) mechanical properties influence the adhesion between metastatic cancer cells and the endothelium remain unclear. In the present study, we have measured the adhesion between single cancer cells and two types of ECs at various growth states and their mechanical properties (elasticity) using atomic force microscopy single cell force spectroscopy. We demonstrated that the EC stiffness increased and adhesion with cancer cells decreased, as ECs grew from a single cell to a confluent state and developed cell-cell contacts, but this was reversed when confluent cells returned to a single state in a scratch assay. Our results suggest that the integrity of the endothelial barrier is an important factor in reducing the ability of the metastatic tumor cells to adhere to the vascular endothelium, extravasate and lodge in the vasculature of a distant organ where secondary metastatic tumors would develop.

Intracranial vascular networks including both blood and lymphatic vessels are essential for the brain homeostasis and may change in structure or density under pathological conditions. Investigations on intracranial vascular networks require vasculature integrity in prepared samples. In the present study, we have established a method for acquiring vascular network images on a whole-mount mouse dura mater with cranial bone. Intracardial perfusion on sacrificed six-week-old mice was carried out with fluorescently tagged soybean agglutinin (SBA) and anti-LYVE-1 antibody labeling blood and lymphatic vessel respectively. Following dissection and fixation, the dura mater with the skull was mounted on a cover glass. A series of individual images at 512 x 512 pixels were taken under a confocal microscope for subsequent generation of the whole-mount mosaic image. For images at 20x magnification, 224 individual images were needed to cover the whole sample, including frontal and parietal areas, coronal suture, transverse and sagittal sinuses. For the 4x magnification, 12 images were required to cover the same sample area. Images at higher magnification revealed more clear and precise vascular network structural details. This method is useful in studying the relationship between the structural status of intracranial vascular networks and neurological disorders.

Characterizing the spatial relationship between blood vessel and lymphatic vascular structures, in the mice dura mater tissue, is useful for modeling fluid flows and changes in dynamics in various disease processes. We propose a new deep learning-based approach to fuse a set of multi-channel single-focus microscopy images within each volumetric z-stack into a single fused image that accurately captures as much of the vascular structures as possible. The red spectral channel captures small blood vessels and the green fluorescence channel images lymphatics structures in the intact dura mater attached to bone. The deep architecture Multi-Channel Fusion U-Net (MCFU-Net) combines multi-slice regression likelihood maps of thin linear structures using max pooling for each channel independently to estimate a slice-based focus selection map. We compare MCFU-Net with a widely used derivative-based multiscale Hessian fusion method [8]. The multi-scale Hessian-based fusion produces dark-halos, non-homogeneous backgrounds and less detailed anatomical structures. Perception based no-reference image quality assessment metrics PIQUE, NIQE, and BRISQUE confirm the effectiveness of the proposed method.

The role of cranial dura mater microvascular networks, as means for maintaining brain fluid movement and balance, and as the source of significant initiators and/or contributors to neurological disorders, has been underappreciated until recently. These networks consist of both blood and lymphatic vessels. The goal of this study was to uncover microvascular pachymeningeal lymphatic structures that are not associated with venous sinuses and investigate their interrelationship with the blood microvascular networks and cranial bones. Using markers specific for blood and lymphatic endothelia, we demonstrate the existence of the complex non‐sinus‐associated lymphatic microvasculature. We show the interrelationship and possible connections between lymphatic vessels and dural blood vessels. Furthermore, we unveil vascular connections between dural microvascular networks and cranial bone marrow sinusoids. Our novel findings reveal the presence of lymphatic‐like structures that exist on their own and/or in close proximity to microvessels. Of particular interest are sub‐sets of vascular complexes with dual (lymphatic and blood) vessel identity representing a unique microenvironment within the cranial dura. The close association of the systemic blood circulation and meningeal lymphatics achieved in these complexes could facilitate fluid exchange between the two compartments and constitute an alternative route for CSF drainage.Support or Funding InformationThis work was supported in parts by the NIH National Institute of Neurological Disorders and Stroke (R01NS110915), the NIH National Cancer Institute (R01 CA160461), and the U.S. Army Research Laboratory project W911NF‐18‐2‐0285.

The contribution of cranial dura mater vascular networks, as means for maintaining brain fluid movement and balance, and as the source of significant initiators and/or contributors to neurological disorders, has been overlooked. These networks consist of both blood and lymphatic vessels. The latter were discovered recently and described as sinus-associated structures thus changing the old paradigm that central nervous system lacks lymphatics. In this study, using markers specific to blood and lymphatic endothelia, we demonstrate the existence of the complex non-sinus-associated pachymeningeal lymphatic vasculature. We further show the interrelationship and possible connections between lymphatic vessels and the dural blood circulatory system. Our novel findings reveal the presence of lymphatic-like structures that exist on their own and/or in close proximity to microvessels. Of particular interest are sub-sets of vascular complexes with dual (lymphatic and blood) vessel identity representing a unique microenvironment within the cranial dura. The close association of the systemic blood circulation and meningeal lymphatics achieved in these complexes could facilitate fluid exchange between the two compartments and constitute an alternative route for CSF drainage.

Automated curvilinear image segmentation is a crucial step to characterize and quantify the morphology of blood vessels across scale. We propose a dual pipeline RF OFB+U-NET that fuses U-Net deep learning features with a low level image feature filter bank using the random forests classifier for vessel segmentation. We modify the U-Net CNN architecture to provide a foreground vessel regression likelihood map that is used to segment both arteriole and venule blood vessels in mice dura mater tissues. The hybrid approach combining both hand-crafted and learned features was tested on 60 epifluorescence microscopy images and improved the segmentation of thin vessel structures by nearly 5% using the Dice similarity coefficient compared to U-Net.

Bone is a common site of metastasis for breast cancer and the mechanisms of metastasis are not fully elucidated. The purpose of our study was to characterize temporal and molecular dynamics of adhesive interactions between human breast cancer cells (HBCC) and human bone marrow endothelium (HBME) with piconewton resolution using atomic force microscopy (AFM). In adhesion experiments, a single breast cancer cell, MDA-MB-231 (MB231) or MDA-MB-435 (MB435) was attached to the AFM cantilever and brought into contact with a confluent HBME monolayer for different time periods (0.5 to 300 sec). The forces required to rupture individual molecular interactions and completely separate interacting cells were analyzed as measures of cell-cell adhesion. Adhesive interactions between HBME and either MB231 or MB435 cells increased progressively as cell-cell contact time was prolonged from 0.5 to 300 sec due to the time-dependent increase in the number and frequency of individual adhesive events, as well as to the involvement of stronger ligand-receptor interactions over time. Studies of the individual molecule involvement revealed that Thomsen-Friedenreich antigen (TF-Ag), galectin-3, integrin-β1, and integrin-α3 are all contributing to HBCC/HBME adhesion to various degrees in a temporally defined fashion. In conclusion, cell-cell contact time enhances adhesion of HBCC to HBME and the adhesion is mediated, in part, by TF-Ag, galectin-3, integrin-α3, and integrin-β1.

Adhesive interactions between living cells or ligand-receptor interactions can be studied at the molecular level using atomic force microscopy (AFM). Adhesion force measurements are performed with functionalized AFM probes. In order to measure single ligand-receptor interactions, a cantilever with a pyramidal tip is functionalized with a bio-recognized ligand (e.g., extracellular matrix protein). The ligand-functionalized probe is then brought into contact with a cell in culture to investigate adhesion between the respective probe-bound ligand and endogenously expressed cell surface receptors (e.g., integrins or other adhesion receptor). For experiments designed to examine cell-cell adhesions, a single cell is attached to a tipless cantilever which is then brought into contact with other cultured cells. Force curves are recorded to determine the forces necessary to rupture discrete adhesions between the probe-bound ligand and receptor, or to determine total adhesion force at cell-cell contacts. Here, we describe the procedures for measuring adhesions between (a) fibronectin and alpha 5 beta 1 integrin, and (b) breast cancer cells and bone marrow endothelial cells.

Segmentation and quantification of microvasculature structures are the main steps toward studying microvasculature remodeling. The proposed patch based semantic architecture enables accurate segmentation for the challenging epifluorescence microscopy images. Our pixel-based fast semantic network trained on random patches from different epifluorescence images to learn how to discriminate between vessels versus nonvessels pixels. The proposed semantic vessel network (SVNet) relies on understanding the morphological structure of the thin vessels in the patches rather than considering the whole image as input to speed up the training process and to maintain the clarity of thin structures. Experimental results on our ovariectomized - ovary removed (OVX) - mice dura mater epifluorescence microscopy images shows promising results in both arteriole and venule part. We compared our results with different segmentation methods such as local, global thresholding, matched based filter approaches and related state of the art deep learning networks. Our overall accuracy (> 98%) outperforms all the methods including our previous work (VNet). [1].

The pathogenesis of headaches is a matter of ongoing discussion of two major theories describing it either as a vascular phenomenon resulting from vasodilation or primarily as a neurogenic process accompanied by secondary vasodilation associated with sterile neurogenic inflammation. While summarizing current views on neurogenic and vascular origins of headache, this mini review adds new insights regarding how smooth muscle-free microvascular networks, discovered within dura mater connective tissue stroma (previously thought to be "avascular"), may become a site of initial insult generating the background for the development of headache. Deficiencies in estrogen-dependent control of microvascular integrity leading to plasma protein extravasation, potential activation of perivascular and connective tissue stroma nociceptive neurons, and triggering of inflammatory responses are described. Finally, possible avenues for controlling and preventing these pathophysiological changes are discussed.

JAA-F11 is a highly specific mouse monoclonal to the Thomsen-Friedenreich Antigen (TF-Ag) which is an alpha-O-linked disaccharide antigen on the surface of ~80% of human carcinomas, including breast, lung, colon, bladder, ovarian, and prostate cancers, and is cryptic on normal cells. JAA-F11 has potential, when humanized, for cancer immunotherapy for multiple cancer types. Humanization of JAA-F11, was performed utilizing complementarity determining regions grafting on a homology framework. The objective herein is to test the specificity, affinity and biology efficacy of the humanized JAA-F11 (hJAA-F11). Using a 609 target glycan array, 2 hJAA-F11 constructs were shown to have excellent chemical specificity, binding only to TF-Ag alpha-linked structures and not to TF-Ag beta-linked structures. The relative affinity of these hJAA-F11 constructs for TF-Ag was improved over the mouse antibody, while T20 scoring predicted low clinical immunogenicity. The hJAA-F11 constructs produced antibody-dependent cellular cytotoxicity in breast and lung tumor lines shown to express TF-Ag by flow cytometry. Internalization of hJAA-F11 into cancer cells was also shown using a surface binding ELISA and confirmed by immunofluorescence microscopy. Both the naked hJAA-F11 and a maytansine-conjugated antibody (hJAA-F11-DM1) suppressed in vivo tumor progression in a human breast cancer xenograft model in SCID mice. Together, our results support the conclusion that the humanized antibody to the TF-Ag has potential as an adjunct therapy, either directly or as part of an antibody drug conjugate, to treat breast cancer, including triple negative breast cancer which currently has no targeted therapy, as well as lung cancer.

The tumor-associated Thomsen-Friedenreich glycoantigen (TF-Ag) plays an important role in hematogenous metastasis of multiple cancers. The LTQ Orbitrap LC-MS/MS mass spectrometry analysis of cell surface TF-Ag proteome of metastatic prostate cancer cells reveals that several cell surface glycoproteins expressing this carbohydrate antigen in prostate cancer (CD44, α2 integrin, β1 integrin, CD49f, CD133, CD59, EphA2, CD138, transferrin receptor, profilin) are either known as stem cell markers or control important cancer stem-like cell functions. This outcome points to a potential link between TF-Ag expression and prostate cancer stem-like phenotype. Indeed, selecting prostate cancer cells for TF-Ag expression resulted in the enrichment of cells with stem-like properties such as enhanced clonogenic survival and growth, prostasphere formation under non-differentiating and differentiating conditions, and elevated expression of stem cell markers such as CD44 and CD133. Further, the analysis of the recent literature demonstrates that TF-Ag is a common denominator for multiple prostate cancer stem-like cell populations identified to date and otherwise characterized by distinct molecular signatures. The current paradigm suggests that dissemination of tumor cells with stem-like properties to bone marrow that occurred before surgery and/or radiation therapy is largely responsible for disease recurrence years after radical treatment causing a massive clinical problem in prostate cancer. Thus, developing means for destroying disseminated prostate cancer stem-like cells is an important goal of modern cancer research. The results presented in this study suggest that multiple subpopulation of putative prostate cancer stem-like cells characterized by distinct molecular signatures can be attacked using a single target commonly expressed on these cells, the TF-Ag.

Segmenting microvascular structures is an important requirement in understanding angioadaptation by which vascular networks remodel their morphological structures. Accurate segmentation for separating microvasculature structures is important in quantifying remodeling process. In this work, we utilize a deep convolutional neural network (CNN) framework for obtaining robust segmentations of microvasculature from epifluorescence microscopy imagery of mice dura mater. Due to the inhomogeneous staining of the microvasculature, different binding properties of vessels under fluorescence dye, uneven contrast and low texture content, traditional vessel segmentation approaches obtain sub-optimal accuracy. We consider a deep CNN for the purpose keeping small vessel segments and handle the challenges posed by epifluorescence microscopy imaging modality. Experimental results on ovariectomized - ovary removed (OVX) - mice dura mater epifluorescence microscopy images show that the proposed modified CNN framework obtains an highest accuracy of 99% and better than other vessel segmentation methods.

The purpose of our study was to produce a mechano‐biological model that could characterize and predict adhesive interactions between human breast cancer cells (MB435) and human bone marrow endothelial cells (HBME). Bone is a common site of metastasis for breast cancer and the mechanisms of metastasis are not fully characterized. In this study we have combined experimental and modelling approaches to probe interactions of human bone marrow endothelial cells (HBMEC‐60) with breast cancer cells (MB435). Specifically, atomic force microscopy (AFM) was used to measure adhesion forces between a single MB435 cell (attached to the AFM tip) and a monolayer of HBMEC‐60 cells for differing times of contact. In addition, we developed a stochastic model of the individual adhesive interactions that occur between the two cell‐types to mimic the AFM experimental protocol. This was accomplished by formulating a 2D discrete stochastic‐elastic computational model to study the adhesion binding dynamics that underpin cell‐cell attachment. The model allows detailed quantification in binding dynamics and the location, lifetime, and strength of cell‐cell adhesions. To describe cell‐cell interactions, we considered the following components within a discrete stochastic computational framework: intracellular actin in each cell and adhesion proteins in the cell membranes in 4 possible states: freely diffusing, actin‐bound to the first cell, actin‐bound to the second cell and fully‐bound to cytoskeleton within both cells. Adhesions between the cytoskeleton networks of the two cells were connected to form a network of springs according to the transitions, which obey stochastic binding/unbinding events. Free adhesion molecules diffused via a random walk. The dynamically changing network structure was coupled to the local mechanical environment through force‐dependent binding/unbinding propensities. Experimentally, we found that peak adhesion forces increased with increasing contact time between the MB435 and HBMEC‐60 cells and that total adhesion force increased dramatically within the first 30 sec of contact which then gradually plateaued. These experimental data demonstrate a strong dependence of MB435‐HBME adhesion strength on cell‐cell contact time. Additionally, we found that our stochastic computational model recapitulated these experimental findings, suggesting that important mechanisms have been captured in the model. This validates our experiment vs. model approach and is therefore allowing us to interrogate the model further to derive further insights into the cell‐cell interactions observed here.

Journal Article Deep Learning Segmentation for Epifluorescence Microscopy Images Get access Yasmin M Kassim, Yasmin M Kassim Computational Imaging and VisAnalysis (CIVA) Lab, Department of Electrical Eng. and Computer Science/University of Missouri-Columbia, Columbia, MO, USA Search for other works by this author on: Oxford Academic Google Scholar Olga V Glinskii, Olga V Glinskii Department of Medical Pharmacology and Physiology/University of Missouri-Columbia, Columbia, MO, USAResearch Service, Harry S. Truman Memorial Veterans Hospital, Columbia, MO, USA Search for other works by this author on: Oxford Academic Google Scholar Vladislav V Glinsky, Vladislav V Glinsky Department of Pathology and Anatomical Sciences/University of Missouri-Columbia, Columbia, MO, USAResearch Service, Harry S. Truman Memorial Veterans Hospital, Columbia, MO, USA Search for other works by this author on: Oxford Academic Google Scholar Virginia H Huxley, Virginia H Huxley Department of Medical Pharmacology and Physiology/University of Missouri-Columbia, Columbia, MO, USAResearch Service, Harry S. Truman Memorial Veterans Hospital, Columbia, MO, USA Search for other works by this author on: Oxford Academic Google Scholar Kannappan Palaniappan Kannappan Palaniappan Computational Imaging and VisAnalysis (CIVA) Lab, Department of Electrical Eng. and Computer Science/University of Missouri-Columbia, Columbia, MO, USA Search for other works by this author on: Oxford Academic Google Scholar Microscopy and Microanalysis, Volume 23, Issue S1, 1 July 2017, Pages 140–141, https://doi.org/10.1017/S1431927617001386 Published: 04 August 2017

Automatic segmentation of microvascular structures is a critical step in quantitatively characterizing vessel remodeling and other physiological changes in the dura mater or other tissues. We developed a supervised random forest (RF) classifier for segmenting thin vessel structures using multiscale features based on Hessian, oriented second derivatives, Laplacian of Gaussian and line features. The latter multiscale line detector feature helps in detecting and connecting faint vessel structures that would otherwise be missed. Experimental results on epifluorescence imagery show that the RF approach produces foreground vessel regions that are almost 20 and 25 percent better than Niblack and Otsu threshold-based segmentations respectively.