Despite advances in imaging, image-based vascular systems biology has remained challenging because blood vessel data are often available only from a single modality or at a given spatial scale, and cross-modality data are difficult to integrate. Therefore, there is an exigent need for a multimodality pipeline that enables ex vivo vascular imaging with magnetic resonance imaging, computed tomography and optical microscopy of the same sample, while permitting imaging with complementary contrast mechanisms from the whole-organ to endothelial cell spatial scales. To achieve this, we developed ‘VascuViz’—an easy-to-use method for simultaneous three-dimensional imaging and visualization of the vascular microenvironment using magnetic resonance imaging, computed tomography and optical microscopy in the same intact, unsectioned tissue. The VascuViz workflow permits multimodal imaging with a single labeling step using commercial reagents and is compatible with diverse tissue types and protocols. VascuViz’s interdisciplinary utility in conjunction with new data visualization approaches opens up new vistas in image-based vascular systems biology.
This is a preview of subscription content, access via your institution
Subscribe to Nature+
Get immediate online access to the entire Nature family of 50+ journals
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
The authors declare that all the data supporting the findings of this study are included in the paper and its Extended Data and supplementary information files. This includes the availability of imaging data for: (1) the murine brain in Figs. 4 and 5, Extended Data Figs. 5 and 7, Supplementary Tables 2 and 3 and Supplementary Video 1, (2) the breast tumor xenograft in Fig. 3 and Extended Data Figs. 2 and 3 and (3) the kidney and hind limb in Fig. 6. Finally, IHC and H&E data are available in Fig. 2 and Extended Data Fig. 1. The mouse hippocampal data that were used for the labeling of the cornu ammonis and dentate gyrus layers can be accessed freely from the Australian Mouse Brain Mapping Consortium (AMBMC) weblink: www.imaging.org.au/AMBMC.
MATLAB code used in the paper will be made available upon reasonable request from the corresponding author.
Menshykau, D. & Tanaka, S. Mechanistic image-based modelling: concepts and applications. Handb. Exp. Pharmacol. 260, 231–261 (2019).
Sbalzarini, I. F. Modeling and simulation of biological systems from image data. Bioessays 35, 482–490 (2013).
Gómez, H., Georgieva, L., Michos, O. & Iber, D. in Systems Biology (eds Nielsen, J. & Hohmann, S.) 319–340 (Wiley, 2017).
Krucker, T., Lang, A. & Meyer, E. P. New polyurethane-based material for vascular corrosion casting with improved physical and imaging characteristics. Microsc. Res. Tech. 69, 138–147 (2006).
Schaad, L. et al. Correlative imaging of the murine hind limb vasculature and muscle tissue by microCT and light microscopy. Sci. Rep. 7, 41842 (2017).
Kim, E., Zhang, J., Hong, K., Benoit, N. E. & Pathak, A. P. Vascular phenotyping of brain tumors using magnetic resonance microscopy (µMRI). J. Cereb. Blood Flow. Metab. 31, 1623–1636 (2011).
Xue, S. et al. Indian-ink perfusion based method for reconstructing continuous vascular networks in whole mouse brain. PLoS ONE 9, e88067 (2014).
Di Giovanna, A. P. et al. Whole-brain vasculature reconstruction at the single capillary level. Sci. Rep. 8, 12573 (2018).
Mayerich, D., Abbott, L. & McCormick, B. Knife-edge scanning microscopy for imaging and reconstruction of three-dimensional anatomical structures of the mouse brain. J. Microsc. 231, 134–143 (2008).
Erturk, A. et al. Three-dimensional imaging of solvent-cleared organs using 3DISCO. Nat. Protoc. 7, 1983–1995 (2012).
Li, Y. et al. Direct labeling and visualization of blood vessels with lipophilic carbocyanine dye DiI. Nat. Protoc. 3, 1703–1708 (2008).
Todorov, M. I. et al. Machine learning analysis of whole mouse brain vasculature. Nat. Methods 17, 442–449 (2020).
Mayerich, D. et al. Fast macro-scale transmission imaging of microvascular networks using KESM. Biomed. Opt. Express 2, 2888–2896 (2011).
Jing, D. et al. Tissue clearing of both hard and soft tissue organs with the PEGASOS method. Cell Res. 28, 803–818 (2018).
Pries, A. R., Secomb, T. W., Gaehtgens, P. & Gross, J. F. Blood flow in microvascular networks. experiments and simulation. Circ. Res. 67, 826–834 (1990).
Plouraboue, F. et al. X-ray high-resolution vascular network imaging. J. Microsc. 215, 139–148 (2004).
Dyer, E. L. et al. Quantifying mesoscale neuroanatomy using X-ray microtomography. eNeuro https://doi.org/10.1523/ENEURO.0195-17.2017 (2017).
Kirst, C. et al. Mapping the fine-scale organization and plasticity of the brain vasculature. Cell 180, e725 (2020).
Hossler, F. E. & Douglas, J. E. Vascular corrosion casting: review of advantages and limitations in the application of some simple quantitative methods. Microsc. Microanal. 7, 253–264 (2001).
Duvall, C. L., Taylor, W. R., Weiss, D. & Guldberg, R. E. Quantitative microcomputed tomography analysis of collateral vessel development after ischemic injury. Am. J. Physiol. Heart Circ. Physiol. 287, H302–H310 (2004).
Puelles, V. G., Moeller, M. J. & Bertram, J. F. We can see clearly now: optical clearing and kidney morphometrics. Curr. Opin. Nephrol. Hypertens. 26, 179–186 (2017).
Williams, M. P. I. et al. A novel optical tissue clearing protocol for mouse skeletal muscle to visualize endplates in their tissue context. Front Cell Neurosci. 13, 49 (2019).
Partridge, S. C. et al. Diffusion tensor MRI: preliminary anisotropy measures and mapping of breast tumors. J. Magn. Reson. Imaging 31, 339–347 (2010).
Jiang, R. et al. Diffusion tensor imaging of breast lesions: evaluation of apparent diffusion coefficient and fractional anisotropy and tissue cellularity. Br. J. Radiol. 89, 20160076 (2016).
Kim, E. et al. Vasculature-specific MRI reveals differential anti-angiogenic effects of a biomimetic peptide in an orthotopic breast cancer model. Angiogenesis 18, 125–136 (2015).
Kakkad, S. et al. Collagen fibers mediate MRI-detected water diffusion and anisotropy in breast cancers. Neoplasia 18, 585–593 (2016).
Cebulla, J., Kim, E., Rhie, K., Zhang, J. & Pathak, A. P. Multiscale and multi-modality visualization of angiogenesis in a human breast cancer model. Angiogenesis 17, 695–709 (2014).
Kim, E. et al. Multiscale imaging and computational modeling of blood flow in the tumor vasculature. Ann. Biomed. Eng. 40, 2425–2441 (2012).
Raman, V. et al. Characterizing vascular parameters in hypoxic regions: a combined magnetic resonance and optical imaging study of a human prostate cancer model. Cancer Res. 66, 9929–9936 (2006).
Riegler, J. et al. Tumor elastography and its association with collagen and the tumor microenvironment. Clin. Cancer Res. 24, 4455–4467 (2018).
Kakkad, S. M. et al. Hypoxic tumor microenvironments reduce collagen I fiber density. Neoplasia 12, 608–617 (2010).
Dewhirst, M. W. & Secomb, T. W. Transport of drugs from blood vessels to tumour tissue. Nat. Rev. Cancer 17, 738–750 (2017).
Chauhan, V. P. et al. Angiotensin inhibition enhances drug delivery and potentiates chemotherapy by decompressing tumour blood vessels. Nat. Commun. 4, 2516 (2013).
Jain, R. K. Normalization of tumor vasculature: an emerging concept in antiangiogenic therapy. Science 307, 58–62 (2005).
Wang, H. et al. Elastography can map the local inverse relationship between shear modulus and drug delivery within the pancreatic ductal adenocarcinoma microenvironment. Clin. Cancer Res. 25, 2136–2143 (2019).
Stamatelos, S. K., Bhargava, A., Kim, E., Popel, A. S. & Pathak, A. P. Tumor ensemble-based modeling and visualization of emergent angiogenic heterogeneity in breast cancer. Sci. Rep. 9, 5276 (2019).
Ito, H. et al. Arterial fraction of cerebral blood volume in humans measured by positron emission tomography. Ann. Nucl. Med. 15, 111–116 (2001).
Welsh, A. W. et al. Three-dimensional US fractional moving blood volume: validation of renal perfusion quantification. Radiology 293, 460–468 (2019).
Chugh, B. P. et al. Measurement of cerebral blood volume in mouse brain regions using micro-computed tomography. Neuroimage 47, 1312–1318 (2009).
Xiong, B. et al. Precise cerebral vascular atlas in stereotaxic coordinates of whole mouse brain. Front Neuroanat. 11, 128 (2017).
Stolp, H. B. et al. Voxel-wise comparisons of cellular microstructure and diffusion-MRI in mouse hippocampus using 3D bridging of optically-clear histology with neuroimaging data (3D-BOND). Sci. Rep. 8, 4011 (2018).
Pathak, A. P., Ward, B. D. & Schmainda, K. M. A novel technique for modeling susceptibility-based contrast mechanisms for arbitrary microvascular geometries: the finite perturber method. Neuroimage 40, 1130–1143 (2008).
Meyer, E. P., Ulmann-Schuler, A., Staufenbiel, M. & Krucker, T. Altered morphology and 3D architecture of brain vasculature in a mouse model for Alzheimer’s disease. Proc. Natl Acad. Sci. USA 105, 3587–3592 (2008).
Senarathna, J. et al. HemoSYS: a toolkit for image-based systems biology of tumor hemodynamics. Sci. Rep. 10, 2372 (2020).
Pathak, A. P. et al. In vivo ‘MRI phenotyping’ reveals changes in extracellular matrix transport and vascularization that mediate VEGF-driven increase in breast cancer metastasis. PLoS ONE 8, e63146 (2013).
Rege, A., Thakor, N. V., Rhie, K. & Pathak, A. P. In vivo laser speckle imaging reveals microvascular remodeling and hemodynamic changes during wound healing angiogenesis. Angiogenesis 15, 87–98 (2012).
Maeda, K., Mies, G., Olah, L. & Hossmann, K. A. Quantitative measurement of local cerebral blood flow in the anesthetized mouse using intraperitoneal [14C]iodoantipyrine injection and final arterial heart blood sampling. J. Cereb. Blood Flow. Metab. 20, 10–14 (2000).
Gertz, K. et al. Physical activity improves long-term stroke outcome via endothelial nitric oxide synthase-dependent augmentation of neovascularization and cerebral blood flow. Circ. Res. 99, 1132–1140 (2006).
Lorthois, S., Cassot, F. & Lauwers, F. Simulation study of brain blood flow regulation by intra-cortical arterioles in an anatomically accurate large human vascular network: part I: methodology and baseline flow. Neuroimage 54, 1031–1042 (2011).
Lipowsky, H. H. & Zweifach, B. W. Network analysis of microcirculation of cat mesentery. Microvasc. Res. 7, 73–83 (1974).
Gagnon, L. et al. Multimodal reconstruction of microvascular-flow distributions using combined two-photon microscopy and doppler optical coherence tomography. Neurophotonics 2, 015008 (2015).
Hillman, E. M. Optical brain imaging in vivo: techniques and applications from animal to man. J. Biomed. Opt. 12, 051402 (2007).
Fry, B. C., Lee, J., Smith, N. P. & Secomb, T. W. Estimation of blood flow rates in large microvascular networks. Microcirculation 19, 530–538 (2012).
Petzold, G. C. & Murthy, V. N. Role of astrocytes in neurovascular coupling. Neuron 71, 782–797 (2011).
Aggarwal, M., Mori, S., Shimogori, T., Blackshaw, S. & Zhang, J. Three-dimensional diffusion tensor microimaging for anatomical characterization of the mouse brain. Magn. Reson. Med. 64, 249–261 (2010).
Yu, H. et al. Effect of cranial window type on monitoring neurovasculature using laser speckle contrast imaging. Proc. SPIE 9690, 969009 (2016).
Mendez, A. et al. Phenotyping the microvasculature in critical-sized calvarial defects via multimodal optical imaging. Tissue Eng. Part C 24, 430–440 (2018).
Berg, S. et al. Ilastik: interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).
Jiang, H., van Zijl, P. C., Kim, J., Pearlson, G. D. & Mori, S. DtiStudio: resource program for diffusion tensor computation and fiber bundle tracking. Comput. Methods Prog. Biomed. 81, 106–116 (2006).
Reichold, J. et al. Vascular graph model to simulate the cerebral blood flow in realistic vascular networks. J. Cereb. Blood Flow. Metab. 29, 1429–1443 (2009).
Weber, B., Keller, A. L., Reichold, J. & Logothetis, N. K. The microvascular system of the striate and extrastriate visual cortex of the macaque. Cereb. Cortex 18, 2318–2330 (2008).
Pries, A. R. et al. Resistance to blood flow in microvessels in vivo. Circ. Res. 75, 904–915 (1994).
Bhargava, A., Monteagudo, B., Aggarwal, M. & Pathak, A. A novel vascular fiducials-based approach (VASFID) for co-registering multiscale imaging data for microcirculation systems biology. FASEB J. 34, 1 (2020).
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).
Aggarwal, M., Zhang, J., Miller, M. I., Sidman, R. L. & Mori, S. Magnetic resonance imaging and micro-computed tomography combined atlas of developing and adult mouse brains for stereotaxic surgery. Neuroscience 162, 1339–1350 (2009).
Charles, J. P., Cappellari, O., Spence, A. J., Hutchinson, J. R. & Wells, D. J. Musculoskeletal geometry, muscle architecture and functional specialisations of the mouse hindlimb. PLoS ONE 11, e0147669 (2016).
Norton, C. R. et al. Absence of a major role for the Snai1 and Snai3 genes in regulating skeletal muscle regeneration in mice. PLoS Curr. https://doi.org/10.1371/currents.md.e495b27ee347fd3870a8316d4786fc17 (2013).
Senarathna, J. et al. A miniature multi-contrast microscope for functional imaging in freely behaving animals. Nat. Commun. 10, 99 (2019).
This work was supported by NIH/NCI grant nos. 51R01CA196701-05, 1R01CA237597-01A1 and 5R01DE027957-02 (A.P.P.); NIH Instrumentation grant no. S10OD012287 (Cornell University) and a Sidney Kimmel Comprehensive Cancer Center, Quantitative Sciences Pilot Project grant (A.B.). We thank D. Yang for assistance with Supplementary Video 1; Q. Wang for assistance with perfusion experiments; Z. Hou for assistance with image coregistration; A.S. Popel for providing the 4T1 tumor model and R. Pathak for assistance with data segmentation. This work is dedicated to the memories of S. Bhargava and P.I. Pathak.
The authors declare no competing interests.
Peer review information
Nature Methods thanks Shayne Peirce-Cottler and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The GalRh-BVu polymer was compatible with H&E staining of paraffin-embedded (PE) tissues as shown for a 4T1 tumor xenograft sample (a-b), and a kidney sample (c-d). The intravascular polymer appeared dark brown on H&E images as seen in (b) the tumor rim vasculature from (a), and (d) renal cortex vasculature from (c). Black arrows point to a perfused glomerulus in (c-d). The polymer also did not interfere with H&E staining of frozen tissues as shown for the murine hippocampus (e) and cortex (f-g). In H&E labeled images, the cytoarchitecture of the hippocampus and cortex could also be complemented with the vascular visibility of the GalRh-BVu polymer (e-g). The GalRh-BVu polymer bearing blood vessels in (g) could also be imaged using fluorescence microscopy as shown in (h). Similarly, tissue cytoarchitectural alterations seen in a H&E stained 4T1 tumor xenograft sample (i, k) could be complemented with the vascular visibility of the GalRh-BVu polymer in bright field (i, k) and fluorescence microscopy images (j, l). N.B. The brightness and contrast of H&E images were adjusted for visualization purposes without any changes to the original data.
Extended Data Fig. 2 Combining Euclidean distance maps derived from CT with ADC maps derived from DW-MRI in a human breast cancer model.
a-c, A 2D section from the 3D Euclidian distance map (EDM) is shown with an overlay of white contour lines to highlight regions within intervessel distance ranges 0-50 μm (a), 51-150 μm (b) and 151-350 μm (c), respectively. Soft tissue contrast from T1W-MRI data of the same region was employed as the underlay image in (a-c). The contour lines corresponding to the inter-vessel distance ranges shown in (a-c) were mapped on to co-registered apparent diffusion coefficient (ADC) maps derived from DW-MRI (d-f), respectively. Rim and central tumor sub-regions from (d) and (f) were selected and visualized with volume rendered tumor blood vessels (red) derived from CT (g-h). Low ADC regions (blue-green) co-localized with regions with a high density of tumor vasculature (g), while high ADC regions (yellow-red) co-localized with regions exhibiting low vessel density (h). i, Box and whisker plots of whole-tumor ADC distributions showed that 151-350 μm EDM regions were significantly different (p « 0.001) from those for 51-150 μm EDM regions and 0-50 μm EDM regions using a two-tailed Mann-Whitney U-test test at alpha = 0.05. The box and whisker plots corresponding to 0–50 μm, 50-150 μm and 150-350 μm EDM regions show the median, interquartile range (IQR) and the data within the Q1 − 1.5IQR and Q3 + 1.5IQR range. The upper and lower bounds of the displayed intensity range for the merged images shown in (a-f) were adjusted for visualization purposes without any changes to the original data.
Extended Data Fig. 3 Correlation between tumor boundary-to-center profiles for MRI-, CT- and optical imaging-derived vascular microenvironmental (VME) parameters in a human breast cancer model.
a-b, For each VME parameter map (e.g. ADC), we overlaid a 2D grid of points along azimuthal (white points) and radial directions (dashed red lines). An enlarged view of the grid is shown in (b) wherein black arrows point to white contours that are located at the tumor boundary and at 1 mm and 2 mm normal to it. Next, boundary-to-center profiles were calculated along each dashed red line and an average radial profile generated for each VME parameter map. c, ADC correlated with EDM (R2 = 0.43, p = 0.0084). d, Collagen (Col) fractional area correlated inversely with EDM (R2 = 0.35, p = 0.0183). e, FA correlated inversely with ADC (R2 = 0.68, p < 0.0001). f, FA correlated with Col fractional area (R2 = 0.7, p = 0.0001). The Pearson correlation coefficients between each variable pair shown in (a-d) was significant.
a, The sample was embedded in an agarose block prior to CT imaging and directional annotations made. Before sectioning the sample for MPM, the location of the cutting plane was determined relative to the tumor center by matching the embedded sample with its T1W-MRI image, as shown in (b). Red hatched line indicate where the sample was cut. This orientation was then preserved with the help of the directional annotations shown in (c) during cutting, and (d) during cryosectioning.
Extended Data Fig. 5 Creating a 4D (that is 3D + time) visualization by mapping the temporal dynamics of the in vivo functional hyperemic response to 3D ex vivo neurovascular and anatomical data.
a, A thinned-skull preparation for in vivo LSC and IOS imaging. b, The animal was made to breathe room air (AIR), carbogen (95% oxygen/5% carbon dioxide) gas (CARB), and room air (AIR) for 3, 2 and 5 minutes, respectively during which dynamic CBF data was acquired. Black arrows in (b) correspond to the time points (that is 0.4 and 2.5 min, AIR; 3.3 and 5 min CARB; and 7.5 and 10 min, AIR) for which the corresponding CBF maps are shown in (f-k). Black, dark gray and light gray squares (d-e) indicate large (i.e. 115 μm < diameter < 120 μm), medium (i.e. 40 μm < diameter < 60 μm) and small (i.e. 20 μm < diameter < 40 μm) blood vessels that were identified using the IOS image (c) and from which the mean in vivo CBF traces shown in (e) computed. e, Medium (dark gray trace) and large vessels (black trace) showed a larger peak increase in %ΔCBF w.r.t the global baseline than that exhibited by small vessels (light gray trace). f-k, Spatio-temporal evolution of CBF corresponding to the experimental paradigm in (b) illustrating the significant response to carbogen inhalation (h, i). l, The same sample prepared for ex vivo imaging in which the GalFITC-BVu perfused neurovasculature was visible (white contrast) in the intact skull. m-o, Concurrent visualization of the 3D skull anatomy (gray) and underlying neurovasculature (red) using ex vivo CT imaging (7.5 μm) (m-o). p, Fluorescent MIP images of perfused skull vessels from a tiled scan acquired at 10× with confocal microscopy (1.3 μm), and a 25× scan of the same sample acquired with MPM (0.6 μm) (q). The positive vascular contrast in CT data (m-o, r-w) was due to BVu while the GalFITC component provided fluorescence contrast in microscopy data (p-q). r-w, Integrated 4D volume created by co-registering the 2D in vivo CBF maps (f-k) to 3D ex vivo neurovascular and skull anatomy data (m-o) illustrating the time evolution of the functional hyperemic response. The displayed intensity range for (f-k) was adjusted for visualization purposes without altering the original data. A 0.25 minute moving average filter was applied to (e) to reduce noise. Scale bars: 1 mm unless stated otherwise.
To validate our blood flow modeling approach, we employed a 546-segment vascular network of the rat mesentery (a) that was derived from high resolution intravital microscopy imaging data by Pries et al15. To simulate pressure and blood flow values in all segments of the network, experimentally obtained blood flow rates were prescribed at 35 boundary segments while one boundary node was subjected to a constant pressure boundary condition. Blood flow and discharge hematocrit distributions in all 546 segments simulated using our approach (black dots) are compared against the solution of the fully determined system as obtained by Pries et al15. We achieved an R2 = 0.99 for blood flow rates (nl/min) and R2 = 0.93 for discharge hematocrit distributions observing excellent agreement between these data. Moreover, the distribution of the ratio of discharge to tube hematocrit vs. vessel diameter satisfied the well-known Fahraeous effect and showed an R2 = 0.94 against the simulated values reported by Pries et al15 (d). Finally, fractional erythrocyte flow (FQE) vs. fractional blood flow (FQB) distributions obtained using our approach satisfied the phase separation effect (e) in agreement with the distributions reported by Pries et al15 (f). Open and filled circles correspond to data points for daughter vessels α and β at diverging bifurcations. Collectively, these plots demonstrate the validity of our image-based hemodynamic modeling approach and its utility for predicting functional properties of micro-vascular networks. Panel (f) reproduced with permission from15.
Extended Data Fig. 7 The GalRh-BVu polymer remained stable for at least 11 months after sample preparation.
a, LSM image of the thalamic vasculature in a murine brain acquired at 0.33 μm spatial resolution at 6 months after sample preparation. b, LSM of the same field of view acquired at 0.56 μm spatial resolution at 11 months after sample preparation. Here, vascular contrast was enhanced by normalizing the image intensity to 0.1% of the dynamic range followed by 3D median filtering (radius = 2 voxels).
Supplementary Protocols and Tables 1–3.
Video 1. Dynamic functional hyperemic response acquired using in vivo laser speckle imaging (blue-red color map) was integrated to the 3D skull anatomy (gray) and the underlying neurovasculature (red) data acquired ex vivo using CT imaging. The animal was made to breathe room air (AIR), carbogen (95% oxygen/5% carbon dioxide) gas (CARB) and room air (AIR) for 3, 2 and 5 min, respectively, during which dynamic CBF data was acquired. Large (ROI 1) and medium blood vessels (ROI 2) showed a larger peak increase in % ΔCBF with respect to the global baseline than that exhibited by small vessels (ROI 3).
About this article
Cite this article
Bhargava, A., Monteagudo, B., Kushwaha, P. et al. VascuViz: a multimodality and multiscale imaging and visualization pipeline for vascular systems biology. Nat Methods 19, 242–254 (2022). https://doi.org/10.1038/s41592-021-01363-5
This article is cited by
Nature Reviews Physics (2022)