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133 protocols using amira 6

1

Visualize Ribosomes in 3D TEM

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To enhance the contrast of TEM insets, the sum projections of 5 slices each around a central slice are shown. For 3D visualization, membranes were manually segmented in AMIRA 6.2 (FEI) and subtomogram averages of ribosomes (2x binned) were placed at positions derived from template-matching and cleaned by classification using custom MATLAB (Mathworks) scripts. Static scenes and videos were rendered in Chimera (Pettersen et al., 2004 (link)) at 13.68 Å pixel size (bin 2x).
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2

Quantitative Analysis of Neuronal Dendrites

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Segmentation and volume rendering of neurons and neuropils were carried out in AMIRA 6.2 (FEI, Hillsboro, OL). To quantify the dendritic volume, we counted the number of voxels with segmentation data of individual neurons. We excluded soma and axon-like processes for subsequent analysis. The volume distribution of dendrites (Fig. 3D,E) was calculated using a custom-made program written in Matlab and an image processing toolbox (MathWorks, Natick, MA). Maximum intensity projection images were prepared with ImageJ45 . Figures were prepared in Adobe Illustrator CS (Adobe Systems, San Jose, CA). The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
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3

Micro-CT Analysis of Porous Structure

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This was performed on a Skyscan 1171 MCT (Skyscan, Kontich, Belgium) which was fitted to an 11 megapixel detector. The X-ray tube ran at 80 kV and 100 µA with an Al 0.5 mm filter removing low-energy X-rays. The sample scanning conditions were: rotation step = 0.25° over 360°; exposure time = 1765 ms per slice; random movement = 10; a resolution resulted of 4.00–4.47 µm per pixel. Each sinogram was reconstructed with the tomographic software N Recon Client and Server 1.6.9 with GPU support (Skyscan, Kontich, Belgium). The channel/pore network was built based on the reconstructed two-dimensional tomography slices using a skeletonization algorithm with the software Amira 6.2 (FEI, Berlin, Germany) [23 (link)].
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4

Foreleg Bone Microstructure Analysis

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For the anatomical studies scanning of the dry foreleg was carried out by a SkyScan 1172 (Bruker Corp., Billerica, USA.) at 40 kV and 250 µA, with a camera pixel size of 8.8 µm, image pixel size 2.0 µm; 1573 projections were recorded over the 180° rotation. For 3D reconstruction, the graphic segmentation tool software Amira® 6.2 (FEI Company, Visage Imaging, Germany) has been used. For the experiments, the scanning has been done in a transfusion mode with the same parameters and without rotation.
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5

Tracking Neuronal Nuclear Migration in Cerebellum

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The nuclei of neurons were tracked manually over the course of the movie by using Slidebook 6 (Intelligent Imaging Innovations) and a Marianas Spinning Disk confocal microscope. The distance from the edge (DFE) of the slice was computed using Amira 6.2 (FEI, Thermo Fisher Scientific) and added as an intensity channel to the respective movie. Only movies in which the overall XY drift was limited to 5 μm were considered in the analysis. Nuclear coordinates (X, Y, DFE) tracked at each time-point were exported to Excel (Microsoft) in order to plot the distance distribution for each time-point and calculate the instant speed average and average distance variation. Angles were calculated from the distance variation and instant speed vectors at each time-point. Data were divided into layers based on extrapolated measurements from histologic sections at P7, i.e., oEGL: 0–30 μm; iEGL: 30–50 μm; ML: 50–100 μm; and IGL: >100 μm, based on their DFE at a given time-point.
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6

Segmentation and Visualization of Neuron Morphology

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The segmentation and volume rendering of neurons and neuropils were performed with AMIRA 6.2 (FEI, Hillsboro, OL, USA). For images of single neuron morphology, masked images were used for visualization. We performed segmentation of individual neurons in confocal stacks. We first detected the signal with the Amira “Interactive Thresholding” function. We subsequently corrected any false detection by manual tracing. Using this image as a mask, we obtained the final masked images shown in the figures using a custom-made program written in MATLAB and the image processing toolbox (MathWorks, Natick, MA, USA). Maximum intensity projection images were prepared with ImageJ (National Institutes of Health, Bethesda, MD, USA)51 . The contrast and brightness of images were modified in Image J. Figures were prepared in Adobe Illustrator CS (Adobe Systems, San Jose, CA, USA).
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7

Visualizing Microscopic Structures

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MIB software (Microscopy image browser software) (University of Helsinki, Finland) was used to stack the images produced by TEM from ultrathin sections. They are aligned and cropped, then stored with the *. Am file. Thermo Fisher Scientific’s Amira 6.5 software (Amira 3D2021.2, Thermo Fisher Scientific, Waltham, MA, USA) generates the surfaces and volume rendering for these microscopic images [19 ]. The generated files are edited using Adobe Photoshop CS6 and CorelDraw Graphics suite 2021.
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8

Quantitative Analysis of Tumor Vasculature

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Animals were anesthetized by isoflurane (Forene, Abbott France, Rungis, France) inhalation (3–5% for induction and 2–3% for maintenance) and then after placed in a warm imaging bed (Minerve, Esternay, France) allowing the maintenance of isoflurane anesthesia. CT images were acquired on a dedicated small animal μCT scanner (Skyscan1076, Bruker, Kontich, Belgium) while continuously rotating the camera by 180° with the following parameters: 80 kV, 0.5 mm Al filter, 120 μA source current, 35 μm isotropic resolution, 230 ms exposure time, 2 projection images per 0.5° rotation step, and a prospective respiratory synchronization. The projections were reconstructed using a filtered backprojection algorithm using Skyscan software (NRecon, Skyscan). For tumor angiography analysis, an alkaline earth-based nanoparticulate contrast agent (Viscover ExiTron nano 12000, Miltenyi Biotec, Paris, France) was injected in the mouse tail vein. Mice were imaged during the next 30 min following injection, a period during which no reduction in contrast was observed (31 (link)). Analysis of 3D reconstructed images and quantification of the vascular network were performed using Amira 6.5 software (Thermofisher, USA).
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9

Quantifying Fluorescence Dynamics in Calcium Imaging

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Data processing, analysis and image visualization were done using Matlab 2019b (Mathworks, Natick, MA), ImageJ, Fiji, Amira 6 (Thermo Fisher Scientific, St Louis, MO) and Photoshop 21 (Adobe, San Jose, CA).44 (link),45 (link) A piecewise rigid motion correction algorithm for calcium imaging data (NoRMCorre) was used for image registration.46 (link) All plots of data show fluorescence amplitude in time without baseline correction to demonstrate rate of photobleaching. The signal-to-noise ratio (SNR) was quantified as the peak height above baseline over the standard deviation of the baseline in regions without apparent action potentials. The ΔF/F0 was quantified as the difference between peak height and the averaged baseline in regions without apparent action potential divided by the baseline.
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10

3D Cervical Spine Kinematics Analysis

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The CT images were segmented using Amira 6 (Thermo Fisher Scientific, Waltham, MA) to create three-dimensional (3D) anatomical vertebral bone models of the cervical spine (C2-T1), as shown in Fig. 2a. These 3D vertebral bones were imported into Rhinoceros 5.0 (Robert McNeel & Associates, Seattle, WA) to establish local coordinate systems (CSs) and disc location landmarks on the vertebrae. To measure the intervertebral relative rotations and translations, two local endplate CSs were created at the centers of the proximal and distal endplate profiles in the sagittal plane, respectively (Fig. 2b); the proximal axis of each endplate CS was perpendicular to a flat plane fitting to the corresponding disc footprint surface (Fig. 2c) [2 (link),16 (link)]. In this study, the overall cervical spine motions were defined as the motions of C2 relative to T1 measured using their CSs (marked as red in Fig. 2a). To measure regional disc deformation, five points (i.e., anterior, center, posterior, left, and right) were selected on the proximal disc footprint surface (Fig. 2c); their shortest distances to the distal disc footprint surface were defined as the local disc heights corresponding to the respective five anatomic sites [18 (link),19 (link)].
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