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(FilipovicNKojicMIvanovicMTsudaABlood flow and thrombosis modeling in large arteries. Parallel blood flow simulation PBFS (Editor ed.^eds)Book Blood flow and thrombosis modeling in large arteries. Parallel blood flow simulation PBFS2007City)
FilipovicNKojicMIvanovicMTsudaABlood flow and thrombosis modeling in large arteries. Parallel blood flow simulation PBFS (Editor ed.^eds)Book Blood flow and thrombosis modeling in large arteries. Parallel blood flow simulation PBFS2007CityFilipovicNKojicMIvanovicMTsudaABlood flow and thrombosis modeling in large arteries. Parallel blood flow simulation PBFS (Editor ed.^eds)Book Blood flow and thrombosis modeling in large arteries. Parallel blood flow simulation PBFS2007City, FilipovicNKojicMIvanovicMTsudaABlood flow and thrombosis modeling in large arteries. Parallel blood flow simulation PBFS (Editor ed.^eds)Book Blood flow and thrombosis modeling in large arteries. Parallel blood flow simulation PBFS2007City
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Optimality in the developing vascular system: branching remodeling by means of intussusception as an efficient adaptation mechanismDev Dyn, 224
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JonesEAVle NobleFEichmannAWhat determines blood vessel structure? Genetic prespecification vs. hemodynamicsPhysiology20062138839510.1152/physiol.00020.200617119151JonesEAVle NobleFEichmannAWhat determines blood vessel structure? Genetic prespecification vs. hemodynamicsPhysiology20062138839510.1152/physiol.00020.200617119151, JonesEAVle NobleFEichmannAWhat determines blood vessel structure? Genetic prespecification vs. hemodynamicsPhysiology20062138839510.1152/physiol.00020.200617119151
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The relative contribution of blood flow to vessel structure remains a fundamental question in biology. To define the influence of intravascular flow fields, we studied tissue islands--here defined as intravascular pillars--in the chick chorioallantoic membrane. Pillars comprised 0.02 to 0.5% of the vascular system in 2-dimensional projection and were predominantly observed at vessel bifurcations. The bifurcation angle was generally inversely related to the length of the pillar (R = -0.47, P < .001). The pillar orientation closely mirrored the axis of the dominant vessel with an average variance of 5.62 ± 6.96 degrees (p = .02). In contrast, the variance of pillar orientation relative to nondominant vessels was 36.78 ± 21.33 degrees (p > .05). 3-dimensional computational flow simulations indicated that the intravascular pillars were located in regions of low shear stress. Both wide-angle and acute-angle models mapped the pillars to regions with shear less than 1 dyn/cm . Further, flow modeling indicated that the pillars were spatially constrained by regions of higher wall shear stress. Finally, the shear maps indicated that the development of new pillars was limited to regions of low shear stress. We conclude that mechanical forces produced by blood flow have both a limiting and permissive influence on pillar development in the chick chorioallantoic membrane. Introduction to varying flow patterns in vitro. Flow chamber studies The mechanical influence of blood flow on vessel struc- have demonstrated that mechanical forces, such as wall ture is a fundamental question in developmental [1] and shear stress, have a profound effect on gene transcrip- adaptive [2] biology. In the chick chorioallantoic mem- tional activity [13-15] and endothelial phenotype [16-18]. brane, a common model of microvascular network devel- In vitro umbilical vein endothelial cells exposed to lami- opment, the extra-embryonic area undergoes limited nar shear stress reorganize and elongate their cytoskeletal development in the absence of blood flow [3,4]. In later axes in the direction of flow [19]. The response of cul- embryogenesis, the onset of a heartbeat and active blood tured endothelial cells to in vitro shear stress can also flow is associated with dramatic changes in both embry- include lamellipodial protrusion and mechanotaxis in the onic and extra-embryonic vessels [5-7]. In humans, phys- direction of flow [20,21]. The translation of these in vitro iological conditions such as growth and exercise lead to endothelial cell observations to in vivo structural change adjustments in the structural properties of the vascular is less clear. network [8]. In pathologic conditions such as inflamma- To define the local influence of intravascular flow fields nd rd tion [9,10] and ischemia [11,12], structural adaptations on vessel structure, we have studied 2 and 3 order appear to be essential for tissue repair and regeneration. extra-embryonic microvessels in the chick chorioallant- nd rd Despite these convincing network-level observations, oic membrane (CAM). The 2 and 3 order CAM ves- there is little in vivo data on the local interaction between sels are part of an experimentally accessible planar blood flow and vessel structure. network that, in contrast to the complex gas exchange Attempts to clarify the mechanical influence of flow on and nutrient function of the CAM capillaries, has a sim- nd rd blood vessels have focused on endothelial cell responses ple transport function. More importantly, the 2 and 3 order CAM vessels have a unique morphologic feature; * Correspondence: smentzer@partners.org namely, intravascular tissue islands or "pillars" [22]. Dis- Laboratory of Adaptive and Regenerative Biology, Brigham & Women's Hospital, Harvard Medical School, Boston MA, USA crete structures within the blood stream, pillars have sev- Full list of author information is available at the end of the article © 2010 Lee et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons At- tribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Lee et al. Journal of Angiogenesis Research 2010, 2:11 Page 2 of 9 http://www.jangiogenesis.com/content/2/1/11 eral potential advantages in evaluating the effect of blood wheels were controlled by a MAC5000 controller (Ludl) flow on local vessel development: 1) pillars are lined with and MetaMorph software 7.5 (Molecular Devices, Down- normal-appearing endothelium [23] suggesting a normal ingtown, PA). The 14-bit fluorescent images were digi- responsiveness to intraluminal flow fields, 2) pillars are tally recorded with an electron multiplier CCD discrete structures indicating that local changes in pillar (EMCCD) camera (C9100-02, Hamamatsu, Japan). geometry have a minimal effect on global blood flow, and Images were routinely obtained at frame rates exceeding 3) pillars can be identified by time-series intravital 2D 50 fps with 2 × 2 binning. The images were recorded in imaging providing a simultaneous assessment of pillar image stacks comprising 100 to 500 frames of video geometry and surrounding blood flow. sequences on a Dell Precision workstation (3.06 Ghz dual In this report, we used geometry and blood flow mea- Xeon processors, 15,000 rpm ultra-SCSI hard drives, 4 gb surements derived from intravital microscopy imaging to RAM and an Nvidia Quadro 3450 graphics card with 512 map the mechanical forces within the CAM vessels-- mb memory). The CAMs at EDD13 thru EDD16 were including wall shear stress and blood pressure--using 3D imaged with intermittent time-lapse videos over 6 to 24 computational flow simulations. Pillar geometry sug- hour time period. Selection of vessels was based on an gested the spatial constraint of high wall shear stress. Fur- initial visual survey; identified intravascular pillars were ther, the development of new pillars was limited to then studied in detail. There was no attempt at uniform regions with low shear stress. The result suggests both a or random sampling. limiting and permissive influence of wall shear stress on Fluorescent tracers pillar development in the CAM. The fluorescent plasma marker used for intravital imag- ing was a 5% fluorescein isothiocyanate (FITC)-dextran Methods (2,000,000 MW; Sigma-Aldrich, St. Louis MO) solution Eggs prepared in normal saline immediately prior to injection. Specific pathogen-free, fertilized White Leghorn chicken In some intravital microscopy experiments, green fluo- eggs (G. gallus domesticus) were obtained from Charles rescent (ex 430 nm; em 510), neutrally-charged, polysty- River Laboratories (Franklin, CT). The care of the ani- rene spheres (10 beads/ml) were injected with the mals was consistent with guidelines of the American plasma marker [26]. The 0.5 um microspheres were Association for Accreditation of Laboratory Animal Care labeled with derivatives of the BODIPY fluorochrome (ex (Bethesda, MD). 488 nm, em 510 nm) using organic solvents (Invitrogen, Ex ovo culture Eugene, OR). The plasma marker and intravascular tracer For all experiments, a modified, ex ovo (shell-less) culture solution were injected into the CAM circulation using a method was used [24]. Briefly, the eggs were kept in an R- micro-fine 0.3 ml insulin syringe with a 30G needle (BD, COM 20 digital incubator (GimHae, Korea) at 37.5°C and Franklin Lakes, NJ). 70% humidity with automatic turning for 3 days. On Image analysis embryonic development day (EDD) 3, the eggs were Analysis of video images was performed with Meta- sprayed with 70% ethanol, air-dried in a laminar flow Morph (Molecular Devices). Image stacks were created hood and explanted into a 20 × 100 mm Petri dish (Fal- from the 100 to 500 frame sequences. The image stacks con, BD Biosciences, San Jose, CA). The ex ovo cultures were processed with standard MetaMorph filters. After were maintained in a humidified 2% CO incubator at routine thresholding, the image sequences were mea- nd 37.5°C. To optimize the selective examination of the 2 sured using MetaMorph's integrated morphometry appli- rd and 3 order conducting, as well as facilitate intravital cations. Morphometric measurements such as area, microscopy identification of the intravascular pillars, length, orientation, perimeter, hole area and prolate vol- intravital microscopy was performed on EDD 13-16. ume were routinely obtained. Intravital microscopy system Time-series flow visualization The CAM was imaged using a Nikon Eclipse TE2000 The stream-acquired images were stacked to create a inverted epifluorescence microscope using Nikon Plan time series of 100 or 500 consecutive frames. The stacks Apo 10x and Plan Fluor 20x objectives. The microscope were systematically analyzed to ensure the absence of was custom-fitted with an insulated 37°C convective motion artifact. The stack "maximum" operation selected warming unit with moderate relative humidity [25]. An the highest intensity value for each pixel location X-Cite (EXFO, Vanier, Canada) 120 watt metal halide throughout the time series. Conversely, the stack "mini- light source and a liquid light guide were used to illumi- mum" operation selected the lowest intensity value for nate the CAM. Excitation and emission filters (Chroma, each pixel location. Other filters such as the "median" Rockingham, VT) in separate LEP motorized filter Lee et al. Journal of Angiogenesis Research 2010, 2:11 Page 3 of 9 http://www.jangiogenesis.com/content/2/1/11 operation were similarly applied. The resultant image dominant and nondominant vessels was determined by produced a time series reconstruction of the vessel dur- volumetric flow calculations at the bifurcation. ing the time interval of the image stack. Image stitching Time-series analysis Video mapping was performed by the serial acquisition of Flow patterns were demonstrated using time-series plots 3 × 3 image stacks. The image stacks were acquired using created by measuring the intensity values of a region of MetaMorph to control of an LEP XY motorized stage interest through a time-based series of images. The time- (Ludl); stage accuracy and computer control minimized series plots were constructed using the MetaMorph the time interval between image stacks (<2 sec). After the kymograph application [27]. The application was used to image stacks at the grid positions were acquired, the create a cross-sectional view of grayscale intensity values images were stitched together for a panoramic video, along a region or "transept" drawn on the image stack using the MetaMorph stack montage application. (Figure 1)[28]. The average pixel intensity across the ves- Measurement of vessel-pillar angles sel width was used to track movement in the time-series. Similar to other branch angle studies emphasizing local- The image stack was preprocessed with minimal back- ized geometry [29], the method of branch angle mor- ground subtraction to improve both the signal-to-noise phometry was designed to be sensitive to variation at the ratio and the sensitivity of motion detection. The result- apex of the bifurcation. In layered images, maximally- ing kymograph image was analyzed with a line tool to sized spheres were inscribed in each vessel at the bifurca- define the vessel segment distance, as well as the time and tion. Sequential spheres within each vessel were inscribed velocity data for the region. By convention, the transept so that the surface of the sphere intersected the center- distance was plotted on the X-axis and the descending point of the preceding sphere. The centerline track of the time-series was plotted on the Y-axis. The designation of first two spheres was used to define the vessel coordi- nates. After routine calibration and thresholding, the pil- lar axis was determined by the MetaMorph integrated morphometry application. The bifurcation angle was measured as the angle between the pillar and vessel axis. Finite element mesh (FEM) of vessel bifurcation To develop a finite element mesh of the vessel bifurcation, models were constructed with and without the pillar. The construction of 3D FEM for non-symmetrical, irregularly shaped 3D bifurcating vessel was based on geometry derived from intravital microscopy. Further, the transi- tion at the pillar-wall interface required curved distortion of the mesh to match the morphology of the images from intravital microscopy [30]. Otherwise, geometric mea- surements provided by intravital microscopy and digital image analysis were represented as faithfully as possible in the FEM model. As automation of the FEM generation was not possible at this stage, a customized approach was required for each model. Segmentation was performed Figure 1 The digital images were acquired at a single wavelength (ex 430 nm; em 510 nm). The recorded image stacks were analyzed on the original 2D intravital microscopy image to obtain for flow velocity (B) and recombined into a composite time-series im- 2D polylines of the countours. Subsequently, 2D splines age (C). A,B) A line of selectable orientation and width was drawn along were constructed from the polylines. Finally, a 3D FEM the vessel axis. The distance-time plane (B) provided a longitudinal model made of 6 blocks for smooth mesh continuity was view over the selected length of the vessel. Cells or particles were created using 3D NURBS (Non-Uniform Rational B- tracked through multiple planes of the stack permitting a visual corre- lation in each plane. The white object (arrow) represents a fluorescent Spline) [31]. In addition, two blocks of finer mesh were particle; the slope of the diagonal line represents the velocity of the used around the pillar region to detect subtle changes in particle in the flow stream. Note the different slope of the background wall shear stress. speckle pattern--an observation suggesting the particle is near to, or interacting with, the vessel wall. A,C) The image stack was digitally re- Flow solver combined and pseudocolored for presentation as a time-series image The modeling was performed using a continuum (C). The region within the vessel demonstrating no detectable fluores- cence (arrow) was defined as an intravascular pillar. approach. Governed by the Navier- Stokes equations and Lee et al. Journal of Angiogenesis Research 2010, 2:11 Page 4 of 9 http://www.jangiogenesis.com/content/2/1/11 the continuity equation, the three-dimensional flow of a Computational flow dynamics viscous incompressible fluid was expressed: The numerical model was developed using custom code using the C++ object oriented programming language and OpenGL graphic library [32,34-36]. The brick finite ⎛ ∂v ⎞ (1) rm +⋅vv∇ =−∇p + ∇ v elements with 8 nodes used for our numerical simulation ⎜ ⎟ ∂t ⎝ ⎠ was more efficient than 4 node linear tetrahedral ele- ments [37]. The code was validated using the analytical (2) ∇⋅ v = 0 solution for shear stress and velocities through a straight tube [32,35]. The system of equations (1) was nonlinear where v was the blood velocity in three different direc- due to the convective term and an unsymmetric Gaussian tions v ,v , and v , ρ was the fluid density (1.05 g/cm ), solver was implemented. An eight node finite element x y z was employed with eight unknown velocities and con- and p was pressure, μ was the dynamic viscosity (0.03675 stant pressure over the element which was recovered in g/cm/sec). Equation (1) represented the balance of linear the postprocessing calculation. Mesh independence was momentum, while equation (2) expressed the incom- reached at 70,000 to 80,000 finite elements with error less pressibility condition. The code was validated using the then 0.1% for shear stress distribution. A smooth bound- analytical solution for shear stress and velocities through ary surface was created to maintain a high resolution a straight expanding tube [32]. finite element mesh. For each calculation`, a parallel ver- Boundary conditions sion of the solver [38] required 2 hours on 10 parallel pro- A parabolic velocity profile was prescribed for all calcula- cessors with 2 GB RAM. tions at the inlet of the proximal vessel; the inlet was Statistical analysis more than 10 diameters away from the bifurcation region. Significance estimates were based on multiple compari- A zero free traction boundary condition was kept at the sons of paired data by Student-Newman-Keuls or Mann- outlet sections of distal limbs. This assumption was based Whitney test for non-parametric analysis of variance. on a steady flow condition and the parabolic velocity pro- The values for vessel and pillar orientation for each bifur- file at the inlet and outlet cross-section. Reynolds num- cation were exported from MetaMorph and plotted in ber (Re) was defined as Excel 2007 (Microsoft, Redmond WA). Pearson correla- tions to the unamplified control were determined using VD (3) R = Systat 12 statistical software (Chicago, IL). The signifi- mr / cance level for the sample distribution was defined as P < .05. where V was the mean velocity of the prescribed inlet parabolic velocity profile, and the vessel diameter D was Results measured by intravital microscopy. Resulting Re was Spatial distribution of pillars always less than 1.0, suggesting that the blood flow mod- The spatial distribution of pillars in the maturing CAM, eled here was in a viscous flow regime [33]. EDD 13-16, was assessed by intravital microscopy. Using digitally recombined time-series image stacks (>100 Wall shear stress images), intravascular pillars were defined as intraluminal ∂v t structures with no detectable plasma marker fluores- The wall shear stress is calculated as tm =− , where ∂n cence. Mapping of the CAM microcirculation demon- v denotes the tangential velocity immediately to the strated that the pillars had varied shapes and were distributed over multiple generations of the vascular tree walls, and n is the normal direction at the vessel wall. We (Figure 2). The majority of pillars (83%) were located first calculate the tangential velocity at the integration within one vessel diameter of a bifurcation. Based on spa- points near the wall surface, and then numerically evalu- tial maps of 64.2 mm of CAM in 41 different ex ovo cul- ∂v nd rd ate the velocity gradient ; finally, we obtained 3 com- tures, the pillars comprised 0.02 to 0.5% of the 2 and 3 ∂n order conducting vessels in 2-dimensional projection. ponents (τ , τ , τ ,) of the wall shear stress vector t by x y z multiple the velocity gradient by the viscosity coefficient Blood flow and pillar geometry μ. The effective value of the wall shear stress τ at the The relationship of pillar geometry to intravascular blood eff flow was analyzed by time-series intravital microscopy finite element mesh nodes on the surface was calculated (Figure 3). Visual inspection of the pillar shape suggested as . t =+ttt+ eff x y z that 1) narrow angle bifurcations were associated with elongated pillars, and 2) pillars were consistently oriented Lee et al. Journal of Angiogenesis Research 2010, 2:11 Page 5 of 9 http://www.jangiogenesis.com/content/2/1/11 Figure 2 Spatial distribution of pillars in a region of the CAM. Time-series images of 9 contiguous regions of a CAM, previously injected with FITC- dextran, were digitally reconstructed and stitched into a 3 × 3 montage (A). The vessels were thresholded, binarized and mapped to a 2D grid (B). Morphometric analysis of the binarized image provided a relative measure of both vessel and pillar area. The vessels comprised 31.8% of the total surface area of the CAM in 2D projection; the intravascular pillars comprised 0.4% of the vessel area (inset). parallel to the dominant vessel streamline. To quantify cation was arbitrarily assigned an orientation of 0 the relationship between vessel angle and pillar length, degrees. When the axis of the pillar was determined by morphometric analysis of 84 bifurcations was performed. morphometry, the pillar orientation closely mirrored the The bifurcation angle was generally inversely related to axis of the dominant vessel with an average variance of the length of the pillar suggesting a relationship with 5.62 ± 6.96 degrees (p = .02; Figure 4B). In contrast, the intraluminal blood flow (R = -0.47, P < .001; Figure 4A). variance of the pillar orientation with the nondominant To analyze this relationship in detail, the pillar axis rela- vessels was 36.78 ± 21.33 degrees (p > .05). Further, there tive to the bifurcating vessels was examined by digital was a trend for the longer pillars to more closely reflect morphometry. The axis of the dominant vessel at a bifur- the orientation of the flow stream (Figure 4B). Mechanical forces and pillar shape The mechanical forces potentially shaping pillar geome- try were studied by 3D computational flow modeling; convergent flow models were constructed to reflect the flow conditions observed in 78% (78/101) of bifurcations containing a pillar. In the wide-angle flow models (Figure 5), a region of low shear stress--less than 0.5 dyn/cm -- was observed near the bifurcation (Figure 5B). This region of low shear stress persisted in models without the pillar (not shown). Neighboring these low shear stress regions were areas of higher shear stress indicating that the pillar was constrained, and potentially shaped, by Figure 3 Time-series flow visualization of the CAM intravascular these lateral forces (Figure 5B, inset). In the acute-angle pillars using fluorescence intravital videomicroscopy and intra- flow models (Figure 6), a relatively low shear area--less vascular tracers. A-D) CAM vessels visualized with the plasma marker FITC-dextran and low-density fluorescent particle tracers. E-H) CAM than 0.8 dyn/cm --was demonstrated both upstream and vessels visualized with high-density particle tracers. Pillars (red asterisk) downstream from the bifurcation (Figure 6B). Similar to were identified as intravascular areas with no plasma marker or particle the wide-angle models, the regions of low shear stress tracer throughout the time-series. Bar = 20 um. Lee et al. Journal of Angiogenesis Research 2010, 2:11 Page 6 of 9 http://www.jangiogenesis.com/content/2/1/11 Figure 4 Spatial plots of intravascular pillar anisotropy. A) Morphometry of intravascular pillars at vessels bifurcations demonstrated an inverse correlation between pillar length and vessel angle (R2 = 0.28). B) Comparison of the orientation of the pillar axis and vessel axis. Morphometry deter- mined the longitudinal axis and length of the pillar relative to the vessel axis (arbitrarily assigned 0 degrees). Bifurcations reflecting convergent (open circle) and divergent (closed circle) flow are shown. Regression coefficients reflected an adjusted R2 = 0.05; p = .02. Figure 5 3D computational flow modeling of a wide angle bifur- Figure 6 3D computational flow modeling of an acute angle bi- cation (60 degrees) in the CAM. A 3D finite element model was con- furcation (5 degrees) in the CAM. A 3D finite element model was structed based on geometry obtained from intravital microscopy; constructed based on geometry obtained from intravital microscopy; computational flow dynamics was calculated based on measured in- computational flow dynamics was calculated based on measured in- travascular flow velocity. A) A digital recombination of a 100 consecu- travascular flow velocity. A) A digital recombination of a 100 consecu- tive images demonstrating an intravascular pillar near the bifurcation tive images demonstrating an intravascular pillar near the bifurcation of a converging flow stream; B) wall shear stress, C) blood velocity, and of a converging flow stream; B) wall shear stress, C) blood velocity, and C) blood pressure are also shown. Bar = 50 um C) blood pressure are also shown. Bar = 50 um. Lee et al. Journal of Angiogenesis Research 2010, 2:11 Page 7 of 9 http://www.jangiogenesis.com/content/2/1/11 persisted in models without an intravascular pillar (not lines of the dominant vessel. Mechanical forces poten- shown). In contrast to wall shear stress, the distributions tially influencing pillar geometry were mapped using 3D of flow velocity and pressure were not spatially related to computational simulations. Shear maps demonstrated pillar geometry (Figure 5C, D; Figure 6C, D). that pillars were predictably constrained by regions of high wall shear stress. Further, the development of new Mechanical forces and pillar induction pillars was limited to regions with low shear stress. We The effect of mechanical forces in shaping pillar geome- conclude that mechanical forces have both a limiting and try suggested a potential role in initiating pillar forma- permissive influence on pillar development in the CAM. tion. Most pillars, particularly at vessel bifurcations, Defining the relationship between mechanical forces demonstrated only small changes in pillar geometry dur- and blood flow is experimentally challenging. Focal struc- ing culture. Occasionally, rapid development of intravas- tural changes can be difficult to recognize in 2D intravital cular pillars was observed; these pillars typically formed imaging [33]. Definitive structure can be revealed by downstream of pre-existing pillars (Figure 7A, B). Flow techniques such as corrosion casting and 3D scanning modeling indicated that new pillars formed in regions of electron microscopy [39], but static imaging cannot pro- relatively low shear stress (Figure 7C)--a finding suggest- vide a simultaneous assessment of blood flow. Global ing a permissive relationship between wall shear stress morphologic alterations, more readily identified by intra- and pillar formation. vital microscopy, are confounded by the inter-relation- ship between blood flow and structure. In contrast, Discussion intravascular pillars provide a unique opportunity to In this report, we studied the geometry of intraluminal assess both structure and blood flow. Pillars are discrete tissue islands--here referred to as intravascular pillars-- structures within the blood stream, relatively isolated and their surrounding blood flow in the CAM. Intravital from extravascular soluble tissue factors, that are not only microscopy of vessel bifurcations demonstrated marked identifiable by intravital 2D imaging, but amenable to pillar anisotropy with pillars orienting along the stream- detailed morphometric analysis. The pillars are lined with normal-appearing endothelium [23] suggesting a normal responsiveness to intraluminal flow fields. Computational flow modeling provides important insights into flow-associated mechanical forces. The mechanical forces associated with blood flow include wall shear stress (the frictional force tangential to the ves- sel wall) and circumferential strain (a blood pressure- related force perpendicular to the direction of flow); wall shear stress being the dominant mechanical force in the smooth and continuous flow of the peripheral CAM microcirculation. Although wall shear stress cannot be directly measured, numerical simulations enable not only the calculation of wall shear stress, but the mapping of these mechanical forces to the vessel wall. The spatial relationships revealed by these maps provide important insights into the role of mechanical forces in shaping ves- sel structure and the contribution of mechanical forces in localized disease processes such as atherosclerosis. Despite the value of a numerical analysis, there were also limitations of our computational approach. First, our computational simulation treated the blood as a Newto- Figure 7 Time-series visualization of a developing intravascular nian fluid and discounted the mechanical effects of blood pillar using fluorescence intravital videomicroscopy. Image stacks cells. We have used a continuum approach in this study, (100 images) of the CAM vessels plasma marker FITC-dextran were ob- tained at t = 0 and t = 38 minutes. A) The initial image stack revealed 3 because the chick chorioallantoic membrane has a blood intravascular pillars (arrows). B) At t = 38 minutes, a new intravascular cell concentration that is significantly lower, and more pillar was identified (large arrow) accompanied by notable membrane variable, than adults [40]. This observation suggests that irregularity (small arrow). C) Computational flow analysis of the vessel the structural modification is relatively insensitive to bifurcation without pillars demonstrated an area of low shear stress at blood cell concentration. An alternative to continuum the site of the initial pillar (arrow). D) Morphometry of the initial (A) and subsequent (B) vessels showed a significant increase in pillar area (hole modeling is a discretized approach such as discrete parti- area) and membrane irregularity (perimeter). Bar = 50 um. cle dynamics (DPD) [41-43]. We have successfully applied Lee et al. Journal of Angiogenesis Research 2010, 2:11 Page 8 of 9 http://www.jangiogenesis.com/content/2/1/11 DPD to the analysis of blood components in a parallel- case, the elongation of the pillar likely reflects the domi- plate flow chamber [44]; however, the computational nant streamlines in the flow field. A notable exception is demands of this promising technique currently preclude the observation of pillars in divergent flow streams [46]. its application to the complex geometry of the CAM. In our study, new pillars were commonly observed at Despite the theoretical limitations of a continuum convergent flow streams or in locations "downstream" approach, we suspect that our computational model from pre-existing pillars. One explanation is simply sam- closely approximates the distribution of forces in vivo. pling and/or technical limitations of our study. Another A second limitation of our computational approach is explanation is that pillars are typically formed at conver- that we do not consider extravascular tissue resistance. gent bifurcations but are later observed at divergent flow Since tissue viscoelasticity can neither be derived from streams after spontaneous flow reversal--an infrequent, first principles nor measured in vivo, our simplifying but real, observation in the CAM microcirculation. assumption is that the vessels function as rigid tubes. Our Because new pillars were not predictable from our rationale is that we are modeling smooth and continuous shear maps, we suspect that mechanical forces have a flow in the peripheral extra-embryonic vascular network; permissive role in pillar development; that is, regions of not the pulsatile flow in the peri-embryonic vessels. Fur- low shear stress permit pillar development stimulated by thermore, we are not attempting to define the overall other growth or developmental signals [47]. A diffusible energetics of the extra-embryonic tissues, but rather endothelial activation signal provides one explanation for describe the spatial distribution of the mechanical forces the vessel irregularities observed during pillar develop- within the microvessels. The value of defining force dis- ment. A competing hypothesis is that mechanical forces tribution is that it can provide not only a mechanical stimulate new pillar formation and may even initiate the explanation for structural changes, but a predictive map related process of intussusceptive angiogenesis [48]. of endothelial changes. We anticipate that shear maps These are both intriguing and plausible possibilities that will facilitate metabolic and transcriptional studies both deserve further study. in vitro (e.g. flow chambers) and in vivo (e.g. laser capture microdissection). Authors' contribution The spatial relationship of pillar anisotropy and wall All authors participated in design and conception of the shear stress suggests that blood flow shapes pillar geome- study. GL was lead investigator in the intravital micros- try. The pillar axis was almost uniformly oriented along copy assisted by LM and ML. BG and DS contributed the streamlines of the dominant vessel in a bifurcation. data analysis and study design. Flow dynamics simula- Consistent with these findings, Thoma observed more tions were performed by NF and AK. MK performed the than 100 years ago the importance of the "stromrichtung" corrosion casting and 3D scanning electron microscopy. in determining vessel structure [5]. The relevance of wall SM contributed to study design and manuscript prepara- shear stress in influencing pillar structure is underscored tion. All authors have read and approved the manuscript. by the absence of any comparable spatial relationship: the Abbreviations pillars were oriented randomly with respect to 1) differ- 2D: 2-dimensional; 3D: 3-dimensional; CAM: chorioallantoic membrane; EDD: ent levels of the microcirculation, 2) the longitudinal axis embryonic development day; FEM: finite element model, FITC: fluorescein iso- thiocyanate of the embryo, 3) the vitelline (omphalomesenteric) ves- sels of the yolk sac and 4) the flow streamlines of neigh- Competing interests boring nondominant vessels. The strong spatial The authors declare that they have no competing interests. coincidence of the streamlines and wall shear stress sug- Acknowledgements gests that mechanical forces shape intravascular pillars in The authors wish to thank Mr. Zarko Milosevic for his expert technical assis- the CAM. We speculate that this observation may reflect tance. Supported in part by NIH Grant HL47078, HL75426 and HL94567 a more general influence of mechanical forces on all Author Details microvessel endothelium. Laboratory of Adaptive and Regenerative Biology, Brigham & Women's The influence of blood flow on the lining of CAM Hospital, Harvard Medical School, Boston MA, USA, Molecular and Integrative microvessels is consistent with many observations of Physiological Sciences, Harvard School of Public Health, Boston, MA, USA, Institute of Functional and Clinical Anatomy, University Medical Center of the endothelial cells in vitro and intravascular pillars in vivo. Johannes Gutenberg-University Mainz, Germany and Faculty of Mechanical The orientation and elongation of the intravascular pil- Engineering, University of Kragujevac, Serbia lars observed in our study is consistent with the sensitiv- Received: 30 March 2010 Accepted: 7 July 2010 ity of endothelial cells to the direction of flow. 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Turhan A, Konerding MA, Tsuda A, Ravnic DJ, Hanidizar D, Lin MY, Mentzer Cite this article as: Lee et al., Blood flow shapes intravascular pillar geometry SJ: Bridging mucosal vessels associated with rhythmically oscillating in the chick chorioallantoic membrane Journal of Angiogenesis Research 2010, blood flow in murine colitis. Anat Rec 2007, 291:74-92. 2:11
Journal of Angiogenesis Research – Springer Journals
Published: Jul 7, 2010
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