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13 results for “Confocal stacks”
Confocal stacks from archaeological tools used for harvesting and threshing cereals
<p>Series of confocal stacks obtained with SensoScan, using a S Neox 3D profilometer from Sensofar. Measured surfaces from a series of archaeological tools.</p>
Dataset of confocal microscopy stacks from plant samples - ImageJ SurfCut: a user-friendly, high-throughput pipeline for extracting cell contours from 3D confocal stacks
<p>This data set contains confocal stacks from <em>Arabidopsis thaliana </em><em>35S::GFP-MBD</em> light grown hypocotyl as well as propidium iodide stained cotyledon pavement cells and shoot apical meristem. This is the test dataset for the Fiji macro SurfCut (https://github.com/sverger/SurfCut; 10.5281/zenodo.2635737)</p> <p> </p> <p><strong>Material and methods:</strong></p> <p>Plant material and growth conditions</p> <p><em>Arabidopsis thaliana </em>wild type Col-0 and the microtubule reporter line <em>GFP-MBD</em> (WS-4, (Marc et al. 1998) were used. Seeds were cold treated for 48 hr to synchronize germination. Plants were then grown in a phytotron at 20°C, in a 16 hr light/8 hr dark cycle on solid Murashige and Skoog medium (MS medium, Duchefa, Haarlem, the Netherlands) with 0.8% agar, 1% sucrose, and no vitamin.</p> <p> </p> <p>Confocal microscopy</p> <p>Cell contour staining in the case of PC_PI_Col0_(1-8).tif and SAM_PI_Col-0.tif was performed by staining the cell wall with Propidium Iodide (PI). Plants were immersed in 0.2 mg/ml propidium iodide (PI, Sigma-Aldrich) for 10 min and washed with water prior to imaging. For imaging, samples were either placed on a solid agar medium and immersed in water, or placed between glass slide and coverslip separated by 400 μm spacers to prevent tissue crushing. Images were acquired using a Leica TCS SP8 confocal microscope, equipped with a water immersion objective (HCX IRAPO L 25x/0.95 W). PI excitation was performed using a 552 nm solid-state laser and fluorescence was detected at 600–650 nm. GFP excitation was performed using a 488 nm solid-state laser and fluorescence was detected at 495–535 nm. Stacks of 1024x1024 pixels (pixel size of 0.363 x 0.363 micron) optical section were generated with a Z interval of 0.5 μm.</p> <p> </p> <p><strong>File list:</strong></p> <p>Light grown hypocotyl, <em>GFP-MBD</em> reporter line:</p> <p>- Hypocotyl_GFP-MBD.tif</p> <p>Cotyledon’s pavement cells, PI staining:</p> <p>- PC_PI_Col0_1.tif</p> <p>- PC_PI_Col0_2.tif</p> <p>- PC_PI_Col0_3.tif</p> <p>- PC_PI_Col0_4.tif</p> <p>- PC_PI_Col0_5.tif</p> <p>- PC_PI_Col0_6.tif</p> <p>- PC_PI_Col0_7.tif</p> <p>- PC_PI_Col0_8.tif</p> <p>Shoot apical meristem, PI staining:</p> <p>- SAM_PI_Col-0.tif</p> <p> </p> <p><strong>Reference:</strong></p> <p>Marc, Jan, Cheryl L. Granger, Jennifer Brincat, Deborah D. Fisher, Teh-hui Kao, Andrew G. McCubbin, and Richard J. Cyr. 1998. “A GFP–MAP4 Reporter Gene for Visualizing Cortical Microtubule Rearrangements in Living Epidermal Cells.” <em>The Plant Cell</em> 10 (11): 1927–39. https://doi.org/10.1105/tpc.10.11.1927.</p>
Entire confocal z-stack series as .tif image sequences
<p>The manuscript entitled "Parvalbumin-expressing ependymal cells in rostral lateral ventricle wall adhesions contribute to aging-related ventricle stenosis in mice" shows confocal z-stack maximum intensity projections and thin z-plane reconstructions in the figure plates. The entire confocal z-stack image series are provided here as .tif image sequences, respectively the confocal z-stacks of the negative controls as well. The file names refer to the figure numbers and position in the figure plates. For more information about the immunostaining and image acquisition, see the Materials & Methods and Figure legends in the manuscript.</p>
Phenotypic differences between interfertile Chlamydomonas species- high-resolution confocal z-stacks for visualizing organelle morphology
<p>This repository contains high-resolution confocal z-stacks of two interfertile <i>Chlamydomonas</i> algal species. The protocol to generate this data is described in the associated publication, "Phenotypic differences between interfertile <i>Chlamydomonas</i> species", and briefly summarized here. Cells were collected from agar plates with TAP medium and suspended in 500 µl of liquid TAP medium in a 1.5 ml eppendorf tube overnight. Cells were pelleted using a microcentrifuge at 2000 x g for 2 min and the supernatant removed. For staining mitochondria, PKMito orange was used at a 1:500 concentration and cells were moved to opaque black microcentrifuge tubes and placed on a tube rotator for 45 min. Cells were pelleted again and washed twice with fresh TAP medium. After the final wash and supernatant removal, cells were resuspended in 25 µl of 1.25% low gelling agar in TAP medium (kept at 45 C). Then 1 µl of the cell/agar mixture was mounted on a #1.5 coverslip with a small wax circle drawn to retain the droplet. Coverslips were flipped and placed on a slide and sealed with VALAP. </p><p>Images were collected on a Nikon CSU W-1 SoRA spinning disk confocal microscope equipped with an ORCA-Fusion BT digital scMOS camera. In order to apply deconvolution in the downstream processing, we needed to oversample (sample beyond Nyquist) in z resolution. To do this, we used a 100×/1.45 NA objective in 2.8× SoRa magnification mode, using ROIs of either 670 × 670 × 81 or 850 × 850 × 91. We imaged with a z-step size of 100 nm for sub-Nyquist sampling. We imaged bright-field first, then 640 nm excitation autofluorescence of chloroplasts, and then 561 nm excitation for PKmito orange dye, because the chloroplasts would bleach after 561 nm excitation. We set exposures to 300 ms with 30% and 50% laser power for 640 and 561, respectively.</p><p>We have included a set of demo data (10 images per species) that accompany the pub hosted on the Arcadia Science webpage (3Dmorpho_demo_data). In addition, we included all of the raw data we collected in this experiment (3Dmorpho_raw_data). Please use the point spread functions (PSF) from the zipped folders for each respective dataset (demo or raw). </p>
Confocal stacks of Cii_beta gamma crystalin_hM4D(Gi)_mCherry transgenic Ciona Larvae
<p>Confocal stacks of Cii_beta gamma crystalin_hM4D(Gi)_mCherry transgenic Ciona Larvae used to generate the panels of Hoyer et al.</p>
Confocal image stacks of GFP expression in Drosophila forelegs driven by Gal4 driver expression in foreleg motor neurons
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Confocal microscopy image stacks from "Temporal integration of auxin information for the regulation of patterning"
<p>This dataset contains raw images in CZI format (Zeiss) of shoot apical meristems (SAM) from <em>Arabidopsis thaliana </em>transgenic lines <strong>qDII-pCLV3-pDR5</strong> or <strong>qDII-pCLV3-PIN1</strong>. See <em>(Galvan-Ampudia and Cerutti et al.) </em>for detailed information. This data constitutes the input of the <strong>sam_spaghetti</strong> pipeline (<a href="https://gitlab.inria.fr/mosaic/publications/sam_spaghetti">https://gitlab.inria.fr/mosaic/publications/sam_spaghetti</a>) and can be processed using the scripts and examples provided in the package.</p> <p> </p> <p><strong>File information:</strong></p> <p>File names containing qDII-CLV3-DR5 have the following data:</p> <ul> <li>Channel 1: <em>DII-VENUS-N7</em></li> <li>Channel 2: <em>pDR5:2xmTurquoise2</em></li> <li>Channel 3: <em>pRPS5a:TagBFP-SV40</em></li> <li>Channel 4: <em>pCLV3:mCherry-N7</em></li> </ul> <p>File names containing qDII-CLV3-PIN1-PI have the following data:</p> <ul> <li>Channel 1: <em>DII-VENUS-N7</em></li> <li>Channel 2: <em>pPIN1:PIN1-GFP</em></li> <li>Channel 3: <em>Propidium Iodide (cell walls)</em></li> <li>Channel 4: <em>pRPS5a:TagBFP-SV40</em></li> <li>Channel 5: <em>pCLV3:mCherry-N7</em></li> </ul> <p>Time-lapse sequences are identified as follows:</p> <ul> <li><strong>qDII-CLV3-DR5-E27-LD-SAM7.czi</strong></li> <li><strong>qDII-CLV3-DR5-E27-LD-SAM7-T5.czi</strong></li> <li><strong>qDII-CLV3-DR5-E27-LD-SAM7-T10.czi</strong></li> </ul> <p>where:</p> <ul> <li><strong>qDII-CLV3-DR5</strong> indicates the line</li> <li><strong>E$$-LD</strong> (e.g. E25-LD, E27-LD, etc) indicates independent biological replicas</li> <li><strong>SAM$</strong> is the meristem (technical replica)</li> <li><strong>T$</strong> indicates the time elapsed after the first image (in hours)</li> </ul> <p>For example <strong>qDII-CLV3-DR5-E27-LD-SAM7-T5.czi</strong> is an image of the 7th SAM of the set E27, acquired 5 hours after the first image.</p>
Confocal image stack of aPKC/FoxP co-staining
<p>Confocal image stacks of whole mount preparations of central nervous systems of adult Drosophila.</p><p>Genotype: aPKC-Gal4>CD8::GFP, red - FoxP-LexA>CD8::RFP; D: green - D42-Gal4>CD8::GFP, red - FoxP-LexA>CD8::RFP. Confocal image stacks available at: </p>
Confocal stacks of Cii_PC2_hM4D(Gi)_mCherry transgenic Ciona Larvae
<p>Data related to Supplementary Figure 5 of the manuscript titled: <strong>Polymodal sensory perception drives robust attachment and metamorphosis of a pre-vertebrate zooplanktonic larva. </strong></p>
Data from: Automated segmentation of skin strata in reflectance confocal microscopy depth stacks
Reflectance confocal microscopy (RCM) is a powerful tool for in-vivo examination of a variety of skin diseases. However, current use of RCM depends on qualitative examination by a human expert to look for specific features in the different strata of the skin. Developing approaches to quantify features in RCM imagery requires an automated understanding of what anatomical strata is present in a given en-face section. This work presents an automated approach using a bag of features approach to represent en-face sections and a logistic regression classifier to classify sections into one of four classes (stratum corneum, viable epidermis, dermal-epidermal junction and papillary dermis). This approach was developed and tested using a dataset of 308 depth stacks from 54 volunteers in two age groups (20–30 and 50–70 years of age). The classification accuracy on the test set was 85.6%. The mean absolute error in determining the interface depth for each of the stratum corneum/viable epidermis, viable epidermis/dermal-epidermal junction and dermal-epidermal junction/papillary dermis interfaces were 3.1 μm, 6.0 μm and 5.5 μm respectively. The probabilities predicted by the classifier in the test set showed that the classifier learned an effective model of the anatomy of human skin.
Data from: Ellipsoid segmentation model for analyzing light-attenuated 3D confocal image stacks of fluorescent multi-cellular spheroids
In oncology, two-dimensional in-vitro culture models are the standard test beds for the discovery and development of cancer treatments, but in the last decades, evidence emerged that such models have low predictive value for clinical efficacy. Therefore they are increasingly complemented by more physiologically relevant 3D models, such as spheroid micro-tumor cultures. If suitable fluorescent labels are applied, confocal 3D image stacks can characterize the structure of such volumetric cultures and, for example, cell proliferation. However, several issues hamper accurate analysis. In particular, signal attenuation within the tissue of the spheroids prevents the acquisition of a complete image for spheroids over 100 micrometers in diameter. And quantitative analysis of large 3D image data sets is challenging, creating a need for methods which can be applied to large-scale experiments and account for impeding factors. We present a robust, computationally inexpensive 2.5D method for the segmentation of spheroid cultures and for counting proliferating cells within them. The spheroids are assumed to be approximately ellipsoid in shape. They are identified from information present in the Maximum Intensity Projection (MIP) and the corresponding height view, also known as Z-buffer. It alerts the user when potential bias-introducing factors cannot be compensated for and includes a compensation for signal attenuation.
Data from: Ellipsoid segmentation model for analyzing light-attenuated 3D confocal image stacks of fluorescent multi-cellular spheroids
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Data from: Automated segmentation of skin strata in reflectance confocal microscopy depth stacks
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International Brain Laboratory public data
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OpenNeuro
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