Normalization of tumor vasculature is an emerging strategy to improve cytotoxic therapies. Here we show that eliminating nitric oxide (NO) production from tumor cells via neuronal NO synthase silencing or inhibition establishes perivascular gradients of NO in human glioma xenografts in mice and normalizes the tumor vasculature, resulting in improved tumor oxygenation and response to radiation treatment. Creation of perivascular NO gradients may be an effective strategy for normalizing abnormal vasculature.

Uncontrolled growth in a confined space generates mechanical compressive stress within tumors, but little is known about how such stress affects tumor cell behavior. Here we show that compressive stress stimulates migration of mammary carcinoma cells. The enhanced migration is accomplished by a subset of “leader cells” that extend filopodia at the leading edge of the cell sheet. Formation of these leader cells is dependent on cell microorganization and is enhanced by compressive stress. Accompanied by fibronectin deposition and stronger cell–matrix adhesion, the transition to leader-cell phenotype results in stabilization of persistent actomyosin-independent cell extensions and coordinated migration. Our results suggest that compressive stress accumulated during tumor growth can enable coordinated migration of cancer cells by stimulating formation of leader cells and enhancing cell–substrate adhesion. This novel mechanism represents a potential target for the prevention of cancer cell migration and invasion.

Summary In vivo imaging technologies drive the development of improved cancer therapies by revealing critical aspects of the complex pathophysiology of solid tumors in small animal models[1]. The abnormal vascular function, which predicts tumor malignant potential and presents broad barriers to effective treatment, has been studied at the subcellular size scale using multiphoton (MP) microscopy [2], and at significantly larger size scales using ultrasound, µCT and µMRI[3–5]. However, limited in vivo imaging approaches exist to study the vascular function at the network level, i.e., with sufficient resolution to discern smaller vessels while maintaining a field of view and penetration depth large enough to reveal interconnectivity and inhomogeneities across the tumor and surrounding tissue. One promising technology operating at this size scale is optical frequency domain imaging (OFDI) using Doppler-methods to detect blood flow.

Not all tumor vessels are equal. Tumor-associated vasculature includes immature vessels, regressing vessels, transport vessels undergoing arteriogenesis and peritumor vessels influenced by tumor growth factors. Current techniques for analyzing tumor blood flow do not discriminate between vessel subtypes and only measure average changes from a population of dissimilar vessels. We developed methodologies for simultaneously quantifying blood flow (velocity, flux, hematocrit and shear rate) in extended networks at single-capillary resolution in vivo. Our approach relies on deconvolution of signals produced by labeled red blood cells as they move relative to the scanning laser of a confocal or multiphoton microscope and provides fully resolved three-dimensional flow profiles within vessel networks. Using this methodology, we show that blood velocity profiles are asymmetric near intussusceptive tissue structures in tumors in mice. Furthermore, we show that subpopulations of vessels, classified by functional parameters, exist in and around a tumor and in normal brain tissue.

Background: Recent clinical trials of VEGF inhibitors have shown promise in the treatment of recurrent glioblastomas (GBM). However, the survival benefit is usually short-lived as tumors escape anti-VEGF therapies. Here we tested the hypothesis that Platelet Derived Growth Factor-C (PDGF-C), an isoform of the PDGF family, affects GBM progression independent of VEGF pathway and hinders anti-VEGF therapy.Principal Findings: We first showed that PDGF-C is present in human GBMs. Then, we overexpressed or downregulated PDGF-C in a human GBM cell line, U87MG, and grew them in cranial windows in nude mice to assess vessel structure and function using intravital microscopy. PDGF-C overexpressing tumors had smaller vessel diameters and lower vascular permeability compared to the parental or siRNA-transfected tumors. Furthermore, vessels in PDGF-C overexpressing tumors had more extensive coverage with NG2 positive perivascular cells and a thicker collagen IV basement membrane than the controls. Treatment with DC101, an anti-VEGFR-2 antibody, induced decreases in vessel density in the parental tumors, but had no effect on the PDGF-C overexpressing tumors.Conclusion: These results suggest that PDGF-C plays an important role in glioma vessel maturation and stabilization, and that it can attenuate the response to anti-VEGF therapy, potentially contributing to escape from vascular normalization.

Intravital multiphoton microscopy has provided powerful mechanistic insights into health and disease and has become a common instrument in the modern biological laboratory. The requisite high numerical aperture and exogenous contrast agents that enable multiphoton microscopy, however, limit the ability to investigate substantial tissue volumes or to probe dynamic changes repeatedly over prolonged periods. Here we introduce optical frequency domain imaging (OFDI) as an intravital microscopy that circumvents the technical limitations of multiphoton microscopy and, as a result, provides unprecedented access to previously unexplored, crucial aspects of tissue biology. Using unique OFDI-based approaches and entirely intrinsic mechanisms of contrast, we present rapid and repeated measurements of tumor angiogenesis, lymphangiogenesis, tissue viability and both vascular and cellular responses to therapy, thereby demonstrating the potential of OFDI to facilitate the exploration of physiological and pathological processes and the evaluation of treatment strategies.

Tissue engineering requires formation of a de novo stable vascular network. Because of their ability to proliferate, differentiate into endothelial cells, and form new vessels, blood-derived endothelial progenitor cells (EPCs) are attractive source of cells for use in engineering blood vessels. However, the durability and function of EPC-derived vessels implanted in vivo are unclear. To this end, we directly compared formation and functions of tissue-engineered blood vessels generated by peripheral blood- and umbilical cord blood-derived EPCs in a model of in vivo vasculogenesis. We found that adult peripheral blood EPCs form blood vessels that are unstable and regress within 3 weeks. In contrast, umbilical cord blood EPCs form normal-functioning blood vessels that last for more than 4 months. These vessels exhibit normal blood flow, perm-selectivity to macromolecules, and induction of leukocyte-endothelial interactions in response to cytokine activation similar to normal vessels. Thus, umbilical cord blood EPCs hold great therapeutic potential, and their use should be pursued for vascular engineering.

Brain tissue is the most complex tissue in the mammalian anatomy. It is structurally complex, containing diverse cell types and vasculature with complex spatial relationships. It is also complex in terms of molecular constituents, and their relationship to the structural constituents. There is a need for technologies to map normal and injured brain tissue and compute appropriate quantitative measurements in support of a large number of investigations. We present a systematic methodology named FARSIGHT (fluorescence association rules for image-based insight), combining multi-parameter immuno-histochemical labeling, three-dimensional confocal microscopy, linear unmixing, segmentation, and associative morphometry to map and quantify diverse aspects of brain tissue. In addition to morphometric measurements of individual structures from individual imaging channels, we propose the use of distance maps, distance-weighted integrals, and distributions as an efficient basis for computing a versatile set of such associative measurements. These features enable classification of all the major cell types, and computation of their inter-relationships.

This paper presents automated methods for robust modeling and analysis of 3-D tumor microvasculature. Our methodology uses a cylindroidal superellipsoid to model localized segments of vasculature. The proposed vessel model has an explicit, low-order parameterization, allowing for joint estimation of boundary and centerline information, thereby approximating the medial axis. Further, this explicit parameterization provides a geometric framework for traversing vessels in a directed manner. Topological information like branch point location and connectivity is provided as a side effect. The proposed methodology behaves quite well across scalespace, shows a high degree of insensitivity to adjacent structures and implicitly handles branching. Exemplar results are presented from a pre-clinical study of tumor microvasculature in mice

Tumor vasculature has a high degree of irregularity as compared to normal vasculature. The quantification of the morphometric complexity in tumor images can be useful in diagnosis. Also, it is desirable in several other medical applications to have an automated complexity analysis to aid in diagnosis and prognosis under treatment. e.g. in diabetic retinopathy and in arteriosclerosis. In addition, prior efforts at segmentation of the tumor vasculature using matched filtering, template matching and splines have been hampered by the irregularity of these vessels. We try to solve both problems by introducing a novel technique for vessel detection, followed by a tracing-independent complexity analysis based on a combination of ideas. First, the vessel cross-sectional profile is modeled using a continuous and everywhere differentiable family of super-Gaussian curves. This family generates rectangular profiles that can accurately localize the vessel boundaries in microvasculature images. Second, a robust non-linear regression algorithm based on M-estimators is used to estimate the parameters that optimally characterize the vessel’s shape. A framework for the quantitative analysis of the complexity of the vasculature based on the vessel detection is presented. A set of measures that quantify the complexity are proposed viz. Squared Error, Entropy-based and Minimum Description Length-based Shape Complexities. They are completely automatic and can deal with complexities of the entire vessel unlike existing tortuousity measures which deal only with vessel centerlines. The results are validated using carefully constructed phantom and real image data with ground truth information from an expert observer.

This paper presents model-based information-theoretic methods to quantify the complexity of tumor microvasculature, taking into account shape, textural, and structural irregularities. The proposed techniques are completely automated, and are applicable to optical slices (3-D) or projection images (2-D). Improvements upon the prior literature include: (i) measuring local (vessel segment) as well as global (entire image) vascular complexity without requiring explicit segmentation or tracing; (ii) focusing on the vessel boundaries in the complexity estimate; and (iii) added robustness to image artifacts common to tumor microvasculature images. Vessels are modeled using a family of super-Gaussian functions that are based on the superquadric modeling primitive common in computer vision. The superquadric generalizes a simple ellipsoid by including shape parameters that allow it to approximate a cylinder with elliptical cross-sections (generalized cylinder). The super-Gaussian is obtained by composing a superquadric with an exponential function giving a form that is similar to a standard Gaussian function but with the ability to produce level sets that approximate generalized cylinders. Importantly, the super-Gaussian is continuous and differentiable so it can be fit to image data using robust non-linear regression. This fitting enables quantification of the intrinsic complexity of vessel data vis-a-vis the super-Gaussian model within a minimum message length (MML) framework. The resulting measures are expressed in units of information (bits). Synthetic and real-data examples are provided to illustrate the proposed measures.