DOI: 10.5061/dryad.6c44q
title: Data from: D quantification of tumor vasculature in lymphoma xenografts in NOD/SCID mice allows to detect differences among vascular-targeted therapies

description: Quantitative characterization of the in vivo effects of vascular-targeted
 therapies on tumor vessels is hampered by the absence of useful 3D
 vascular network descriptors aside from microvessel density. In this
 study, we extended the quantification of planar vessel distribution to the
 analysis of vascular volumes by studying the effects of antiangiogenic
 (sorafenib and sunitinib) or antivascular (combretastatin A4 phosphate)
 treatments on the quantity and spatial distributions of thin microvessels.
 These observations were restricted to perinecrotic areas of treated human
 multiple myeloma tumors xenografted in immunodeficient mice and to
 microvessels with an approximate cross-sectional area lower than 75 µm2.
 Finally, vessel skeletonization minimized artifacts due to possible
 differential wall staining and allowed a comparison of the various
 treatment effects. Antiangiogenic drug treatment reduced the number of
 vessels of every caliber (at least 2-fold fewer vessels vs. controls;
 p&lt;0.001, n = 8) and caused a heterogeneous distribution of the
 remaining vessels. In contrast, the effects of combretastatin A4 phosphate
 mainly appeared to be restricted to a homogeneous reduction in the number
 of thin microvessels (not more than 2-fold less vs. controls;
 p&lt;0.001, n = 8) with marginal effects on spatial distribution.
 Unexpectedly, these results also highlighted a strict relationship between
 microvessel quantity, distribution and cross-sectional area.
 Treatment-specific changes in the curves describing this relationship were
 consistent with the effects ascribed to the different drugs. This finding
 suggests that our results can highlight differences among
 vascular-targeted therapies, providing hints on the processes underlying
 sample vascularization together with the detailed characterization of a
 pathological vascular tree.
description: 3DFilterText of an ImageJ macro used to remove small unconnected particles from samples before voxel classification.S8-3DFilter.txtMacro_VesselCalibrometryext of an ImageJ macro used to classify voxels in a binary stack according to the minimal Cartesian surface to which they belong (i.e., the approximated vessel cross-section).S9_Macro_VesselCalibrometry.txtSpatial_Dispersion pluginSource code for the ImageJ plug-in used in the reference paper to calculate the 3D spatial dispersion of vessels in binary stacks. Expansion was performed by rhombicuboctahedral dilation after volume normalization according to the total number of vessel voxels.S10_Spatial_Dispersion.javaFilteredStacksThe filteredStacks.zip file carries a compressed folder containing 32 binary stacks representative of vascular trees ready to be splitted in substacks according to vessel caliber. The stacks have already been filtered in order to remove particles less than 1 µm3 but their voxels should still be classified by vessel masking as reported in the paper. BEWARE ! upon expansion this file will occupy 1.45 GB (gigabytes) of space.OriSect_x_PaperThe OriSect.zip file carries a compressed folder containing 32 subfolders each containing 6 stacks representative of vascular maps of vessels showing specific projected cross-sections (values in Table I of the paper). In order to correctly normalize all of the stacks against a common reference, the stack with the highest percent Volume of signal (black pixels) should be added to the other 31 folders. BEWARE ! upon expansion this file will occupy 9.3 GB (gigabytes) of space.DataForFigure_4DataForFigures_5&6Graph_1Graph obtained using data archived in file DataForFigures_5&6.txt. Explanation on the meaning of the graph can be found in the readMe associated to that file.Graph_2Graph obtained using data archived in file DataForFigures_5&6.txt. Explanation on the meaning of the graph can be found in the readMe associated to that file.Graph_3Graph obtained using data archived in file DataForFigures_5&6.txt. Explanation on the meaning of the graph can be found in the readMe associated to that file.Graph_4Graph obtained using data archived in file DataForFigures_5&6.txt. Explanation on the meaning of the graph can be found in the readMe associated to that file.Graph_5Graph obtained using data archived in file DataForFigures_5&6.txt. Explanation on the meaning of the graph can be found in the readMe associated to that file.Graph_6Graph obtained using data archived in file DataForFigures_5&6.txt. Explanation on the meaning of the graph can be found in the readMe associated to that file.Graph_7Graph obtained using data archived in file DataForFigures_5&6.txt. Explanation on the meaning of the graph can be found in the readMe associated to that file.Graph_8Graph obtained using data archived in file DataForFigures_5&6.txt. Explanation on the meaning of the graph can be found in the readMe associated to that file.Graph_9Graph obtained using data archived in file DataForFigures_5&6.txt. Explanation on the meaning of the graph can be found in the readMe associated to that file.Graph_10Graph obtained using data archived in file DataForFigures_5&6.txt. Explanation on the meaning of the graph can be found in the readMe associated to that file.Graph_11Graph obtained using data archived in file DataForFigures_5&6.txt. Explanation on the meaning of the graph can be found in the readMe associated to that file.Graph_12Graph obtained using data archived in file DataForFigures_5&6.txt. Explanation on the meaning of the graph can be found in the readMe associated to that file.Graph_13Graph obtained using data archived in file DataForFigures_5&6.txt. Explanation on the meaning of the graph can be found in the readMe associated to that file.Graph_14Graph obtained using data archived in file DataForFigures_5&6.txt. Explanation on the meaning of the graph can be found in the readMe associated to that file.Graph_15Graph obtained using data archived in file DataForFigures_5&6.txt. Explanation on the meaning of the graph can be found in the readMe associated to that file.Macro_Spatial_DispersionGiven a set of isotropic, equidimensional binary stacks of squared images, this ImageJ macro calculates the number of cycles of rhombicubocthedric dilation necessary to fill 90%, 95% and 99% of each volume on the basis of the distribution of the black pixels. The macro asks for a source folder containing a set of subfolders (mandatory) containing binary image stacks (.tif files) with equal dimensions. The procedure is iterated over the nested folders for all the images present in every folder. The results are saved in a target location, choosed by the operator, as a number of .txt result files - one for each subfolder - named after the source subfolders. Results are expressed as raw values (Hv) which are normalized, subfolder after subfolder, according to the highest percentual volume observed in each subfolder to give nHv results. To normalize all subfolders in a single run, the image presenting the highest percentual volume should be present in all subfolders. This macro has been validated against simple images manually dilated. This version analyzes all .tif stacks present in nested folders. It behaves alike the accompanying ImageJ plugin but it is at least an order of magnitude slower. Its function was essentially that of validating the results obtained with the plugin.S11_Macro_Spatial_Dispersion.txt