LN image stacks.zip (119.65 MB)
Images of mouse popliteal lymph node vascular structure derived using phase-contrast synchrotron micro-computed tomography (µCT)
dataset
posted on 2019-10-14, 14:54 authored by Mohammad Jafarnejad, Ahmed Ismail, Delfim Duarte, Cian Vyas, Arsham Ghahramani, David Zawieja, Cristina Lo Celso, Gowsihan Poologasundarampillai, James MooreThis data record consists of a zipped folder LN image stacks.zip. The folder contains four other folders named LN1, LN2, LN3 and LN4 respectively. Each of these folders consists of a collection of images in .tif file format. These images were derived using phase-contrast synchrotron micro-computed tomography (µCT) and show details of lymph node (LN) vascular structure. Folders LN1-LN4 represent lymph node blood vessels of four mouse LNs that varied in overall nominal diameter from 1.0 – 1.4 mm.
The related study aimed to establish a corrosion casting
technique in combination with phase-contrast synchrotron micro-computed tomography (µCT) to quantify the details of lymph node vascular structure with the emphasis on the quantification of surface area distribution. By providing spatially-resolved 3D data on the distribution of blood vessels in lymph nodes, the eventual goal of the study was to understand LN transport phenomena down to the cell-level scale.
All experiments were undertaken with the approval of the Imperial College’s Animal Ethics Committee and were in accordance with its guidelines and the requirements of the United Kingdom Home Office regulations (ASPA 1986).
The blood in LN vasculature was flushed out and replaced with Mercox II resin to cast the lumen of the vasculature. The LN was then surgically excised and placed in a pipette tip before dissolving the tissue with potassium hydroxide. The freeze-dried samples were scanned with high-resolution synchrotron tomography and the radiographs were reconstructed into stack images using a phase-retrieval algorithm. The images were pre-processed by removing the pipette tip image using a cone crop before intensity-based segmentation and manual artefact processing. The binary data were then skeletonized and diameters and length of the vessels as well as the surface area density were calculated. Pressures and velocities of blood flow were estimated in each vessel based on an assumption of Poiseuille flow. The results were visualized with Imaris and further parameters quantified and plotted with Matlab. Please refer to the published article for more details on the methodology.
