Journal: Frontiers in Neuroanatomy

Article Title: 3D Digital Anatomic Angioarchitecture of the Rat Spinal Cord: A Synchrotron Radiation Micro-CT Study

doi: 10.3389/fnana.2020.00041

Figure Lengend Snippet: Overview and quantification of the 3D morphology of the rat spinal cord microvasculature. (A–C) The raw image of the spinal cord microvasculature among cervical, thoracic, and lumbar regions. (D–F) Representative images of the segmented vasculature and the color-coded labeled vessels of the spinal cord among the three regions. The major arteries and veins including the posterior spinal artery (PSA), central sulcus artery (CSA), anterior spinal artery (ASA), and the posterior spinal vein (PSV). (G–I) The pseudocolored image of the spinal cord microvasculature among three regions. The color bar on the left of the panel indicates the associated vessel diameters. The smallest capillaries are depicted in red and the largest vessels in blue. (J–N) The mean vessel volume fraction, vessel thickness, segment density, bifurcation density, and segment length were relatively lower in the thoracic region than the cervical and lumbar spinal cord; * p < 0.05. (O) Vessel distribution histogram revealed the vessel thickness in the thoracic region with a diameter of less than 25 μm was slightly lower than that of the cervical and lumbar regions. C, cervical spinal cord; T, thoracic spinal cord; L, lumbar spinal cord. * p < 0.05. Scale bar = 200 μm.

Article Snippet: After the vasculature was segmented from the spinal cord parenchyma with Image Pro Analyser 3D software (Version 7.0, Media Cybernetics Inc., USA) based on the iterative gray-level threshold algorithm (Reichold et al., ), we performed the following protocol for the calculation of the 3D vascular morphological parameters (Kim et al., ). (1) Vectorization, skeletonization based on the voxel erosion (Lang et al., ) plugin in Image Pro Analyser 3D software, was used to automatically extract the centerline of the vessel tree ( ). (2) Once the centerline was extracted, numerous vascular quantification parameters such as vessel segment numbers, vessel segment density, vessel segment length, etc could be systematically analyzed and robustly quantified for complex vessel structure. defined the schema of vessel tree parameters.

Techniques: Labeling

Journal: Frontiers in Neuroanatomy

Article Title: 3D Digital Anatomic Angioarchitecture of the Rat Spinal Cord: A Synchrotron Radiation Micro-CT Study

doi: 10.3389/fnana.2020.00041

Figure Lengend Snippet: Representative 3D printed model of the rat spinal cord microvasculature. (A,C) The spinal cord vasculature template obtained from SRμCT for different perspectives. (B,D) The corresponding 3D printed spinal cord vasculature model shows the complex interweaving of vessel branches. ASA, anterior spinal arteries; CSA, central sulcus arteries. (A,C) Scale bar = 200 μm; (B,D) scale bar = 5 cm.

Article Snippet: After the vasculature was segmented from the spinal cord parenchyma with Image Pro Analyser 3D software (Version 7.0, Media Cybernetics Inc., USA) based on the iterative gray-level threshold algorithm (Reichold et al., ), we performed the following protocol for the calculation of the 3D vascular morphological parameters (Kim et al., ). (1) Vectorization, skeletonization based on the voxel erosion (Lang et al., ) plugin in Image Pro Analyser 3D software, was used to automatically extract the centerline of the vessel tree ( ). (2) Once the centerline was extracted, numerous vascular quantification parameters such as vessel segment numbers, vessel segment density, vessel segment length, etc could be systematically analyzed and robustly quantified for complex vessel structure. defined the schema of vessel tree parameters.

Techniques:

Journal: Cells

Article Title: Proteins and Molecular Pathways Relevant for the Malignant Properties of Tumor-Initiating Pancreatic Cancer Cells

doi: 10.3390/cells9061397

Figure Lengend Snippet: Inhibition of S100A8, S100A9, and LGALS3BP expression impairs migration of pancreatic cancer cell line L3.6sl. ( A ) Representative images of the specific knockdown cells (shS100A8, shS100A9, and shLGALS3BP) compared to non-target control shRNA cells (sh ctrl) shown are after 0.0, 6.0, 12.0, and 24.0 h. Black lines enclose areas free of migratory cancer cells. Quantification of migration ability over time ( B ): 0.0 h control shRNA area was set to 0%; all other values were normalized accordingly. Measurements were done with the ProgRes Software. Error bars represent standard deviations of six measurements. Statistical significances were assessed by a two-way ANOVA followed by a Dunnett’s post-hoc test (* for p ≤ 0.05, ** for p ≤ 0.01. and *** for p ≤ 0.001, n = 6).

Article Snippet: Cell movement was assessed after 0.0, 6.0, 12.0, and 24.0 h and documented using a transmission light microscope with ProgRes capture system (Jenoptik GmbH, Jena, Germany).

Techniques: Inhibition, Expressing, Migration, Knockdown, Control, shRNA, Software