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9 results for “stacking microscopy”
◂Fig. 3 Gynoecium of C. crenata %yellow frames), C. cf. grandicalyx %blue frames) and C. sinensis %pink frames; A, B stack shot images; C–K light microscopy; G polarised light; TS in horizontal orientation). A, B Anthetic female flower, calyx and corolla partly removed. B LS of gynoecium. C LS of functionally female flower %note strongly stained peripheral tissue of corolla, anther and gynoecium). D LS of gynoecium. E, F TS of functionally female flower %note strongly stained, peripheral tissue). G TS of functionally female flower %note crystal deposition). H LS of ovule %note stalked embryo sac). J TS of functionally male flower with non-functional ovules. K LS of functionally male flower %style lacking, original position indicated by an asterisk) %LS, longisection; TS, transverse section; a,anther; bs, basal septum; c, calyx; car, carpel; co, corolla; db, dorsal bundles; es, embryo sac; fs, false septum; lb, lateral bundles; o, ovule; stg, stigma; sty, style; t, trichomes; tt, transmission tissue; ut, peripheral, strongly stained tissue; vb, ventral bundles; vs, ventral slit) in Observations on flower and fruit anatomy in dioecious species of Cordia (Cordiaceae, Boraginales) with evolutionary interpretations
◂Fig. 3 Gynoecium of C. crenata %yellow frames), C. cf. grandicalyx %blue frames) and C. sinensis %pink frames; A, B stack shot images; C–K light microscopy; G polarised light; TS in horizontal orientation). A, B Anthetic female flower, calyx and corolla partly removed. B LS of gynoecium. C LS of functionally female flower %note strongly stained peripheral tissue of corolla, anther and gynoecium). D LS of gynoecium. E, F TS of functionally female flower %note strongly stained, peripheral tissue). G TS of functionally female flower %note crystal deposition). H LS of ovule %note stalked embryo sac). J TS of functionally male flower with non-functional ovules. K LS of functionally male flower %style lacking, original position indicated by an asterisk) %LS, longisection; TS, transverse section; a,anther; bs, basal septum; c, calyx; car, carpel; co, corolla; db, dorsal bundles; es, embryo sac; fs, false septum; lb, lateral bundles; o, ovule; stg, stigma; sty, style; t, trichomes; tt, transmission tissue; ut, peripheral, strongly stained tissue; vb, ventral bundles; vs, ventral slit)
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>
Two-photon fluorescence microscopy image stacks of human brain sections (grey and white matter)
<p>Two-photon fluorescence microscopy (TPFM) image stacks of human brain sections including grey matter (N<sub>g</sub>=10) and white matter (N<sub>w</sub>=10), considered in the validation of the 3D fiber orientation analysis pipeline proposed in: "<em>Fiber enhancement and 3D orientation analysis in label-free two-photon fluorescence microscopy</em>". <br> Human brain tissue was preliminarily treated for TPFM following the label-free MAGIC preparation technique, presented in (Costantini et al., <em>Scientific Reports</em> 2021).</p> <p>The PSF of the TPFM system has a FWHM of (0.692, 0.692, 2.612) μm along the x, y, and z axes, respectively, whereas the adopted voxel size is 0.88 μm x 0.88 μm x 1 μm.</p>
Example of Fluorescence Lifetime Imaging Microscopy (FLIM) image stack in .ptu format
<p>The dataset is a 3D stack of fluorescence lifetime imaging microscopy (FLIM) images in ptu format to be used as test and training data. It contains the original .lif file (1) with the stack and a single plane image (to be opened using LAS X and LAS X SMD FLIM), exported raw FLIM data in .ptu format of the stack (3) and the single plane (2a) (to be opened in software capable of reading .ptu files) as well as an intensity image in .tif format (2b) of the single plane for a quick sample overview.</p> <p>The sample is a cross-section of hazel (<em>Corylus avellana</em>) 'diclinous male flower t.s.' with Etzold staining provided by the company Zeiss (CZ 01/05). The dataset was generated using a Leica Stellaris 8 upright confocal laser scanning microscope using a 93x/1.4 glycerol immersion objective. Each image of the 65 slice stack with z step size of 0.287 µm contains 512 x 512 pixels with a pixel size of 0.078 µm x 0.078 µm. Excitation was done with a white-light laser at 491 nm and a laser pulse rate of 40 MHz and a pixel dwell time of 2.0875 µs. Images were acquired using a HyD X detector in counting mode in the spectral range of 496 to 739 nm using Leica Application Suite X (LAS X) version 4.4.0.24861 and LAS X SMD FLIM version 4.5.0 for FLIM image acquisition. 10 frames were accumulated per image. Metadata is available as text file (4a) and as metadata files from LAS X (4b).</p>
Stacking microscopy images of the pteropod Limacina bulimoides
<p>Pteropods, a group of holoplanktonic gastropods, are regarded as bioindicators of the effects of ocean acidification on open ocean ecosystems, because their thin aragonitic shells are susceptible to dissolution. While there have been recent efforts to address their capacity for physiological acclimation, it is also important to gain predictive understanding of their ability to adapt to future ocean conditions. However, little is known about the levels of genetic variation and large scale population structuring of pteropods, key characteristics enabling local adaptation. We examined the spatial distribution of genetic diversity in the mitochondrial cytochrome <i>c</i> oxidase I (COI) and nuclear 28S gene fragments, as well as shell shape variation, across a latitudinal transect in the Atlantic Ocean (35°N-36°S) for the pteropod <i>Limacina bulimoides</i>. We observed high levels of genetic variability (COI π = 0.034, 28S π = 0.0021) and strong spatial structuring (COI Φ<sub>ST </sub>= 0.230, 28S Φ<sub>ST </sub>= 0.255) across this transect. Based on the congruence of mitochondrial and nuclear differentiation, as well as differences in shell shape, we identified a primary dispersal barrier in the southern Atlantic subtropical gyre (15-18°S). This barrier is maintained despite the presence of expatriates, a gyral current system, and in the absence of any distinct oceanographic gradients in this region, suggesting that reproductive isolation between these populations must be strong. A secondary dispersal barrier supported only by 28S pairwise Φ<sub>ST </sub>comparisons was identified in the equatorial upwelling region (between 15°N-4°S), which is concordant with barriers observed in other zooplankton species. Both oceanic dispersal barriers were congruent with regions of low abundance reported for a similar basin-scale transect that was sampled two years later. Our finding supports the hypothesis that low abundance indicates areas of suboptimal habitat that result in barriers to gene flow in widely-distributed zooplankton species. Such species may in fact consist of several populations or (sub)species that are adapted to local environmental conditions, limiting their potential for adaptive responses to ocean changes. Future analyses of genome-wide diversity in pteropods could provide further insight into the strength, formation and maintenance of oceanic dispersal barriers.</p>
Stacking microscopy images of the pteropod Limacina bulimoides
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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>
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: Automated segmentation of skin strata in reflectance confocal microscopy depth stacks
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.