The sections were examined and digital images of stained arteries were recorded using Olympus dotSlide system (Olympus, Tokyo, Japan).
Dotslide system
Manufactured by Olympus
Sourced in Japan, United Kingdom
The DotSlide system is a versatile slide scanning device designed for high-resolution digital imaging of microscope slides. It utilizes a motorized stage and autofocus mechanism to capture detailed images of samples, enabling efficient digitization of slide-based specimens.
Automatically generated - may contain errors
Lab products found in correlation
600d camera, by Zeiss (2 mentions)
Ndp view software, by Hamamatsu Photonics (1 mentions)
Nanozoomer, by Olympus (1 mentions)
Bx61vs microscope, by Olympus (1 mentions)
Uplansapo 20x 0.75 objective, by Olympus (1 mentions)
Image pro plus 7, by Media Cybernetics (1 mentions)
Dpx mountant, by Merck Group (1 mentions)
Illustrator cs6 cc, by Adobe (1 mentions)
Tma software, by Olympus (1 mentions)
Xc10 camera, by Olympus (1 mentions)
Haematoxylin, by Merck Group (1 mentions)
Matlab, by MathWorks (1 mentions)
Olyvia software, by Olympus (1 mentions)
Nanozoomer, by Hamamatsu Photonics (1 mentions)
Rodent block m, by Biocare Medical (1 mentions)
Hematoxylin, by Agilent Technologies (1 mentions)
3 3 diaminobenzidine (dab), by Biocare Medical (1 mentions)
Target retrieval solution, by Agilent Technologies (1 mentions)
16 protocols using dotslide system
1
Arterial Imaging Using Olympus Microscopy
2
Quantification of Immunohistochemical Staining
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Quantification of the stained paraffin sections was performed applying a recently published protocol by Scholl et al.22 In brief, the slides were scanned at 40× magnification using NDPview software (Hamamatsu, NanoZoomer) in Vienna and Olympus dotSlide system (v2.5; Olympus) in Amsterdam. Scans were exported as .TIFF files and transferred to ImageJ (v64, open source). Subsequently, WM and GM regions of interest were selected based on myelin (Nissl–Luxol fast blue), balloon/giant cell (vimentin), and dysmorphic neuron (SMI32, NeuN, Map2) staining. An optimal threshold was chosen for every antibody to distinguish between positive staining and background. The following thresholds were selected: C1q (Vienna cohort: WM, 0–125; GM, 0–100; Amsterdam cohort: WM, 0–170; GM, 0–150), C3c (Vienna cohort: WM, 0–115; GM, 0–140; Amsterdam cohort: WM, 0–150; GM, 0–170), C3d (Vienna and Amsterdam cohorts: WM, 0–160; GM, 0–160), Syn (Vienna cohort: WM, 0–130; GM, 0–100; Amsterdam cohort: WM, 0–200; GM, 0–210), and SMI31 (Vienna cohort: WM, 0–210; Amsterdam cohort: WM, 0–210).
3
Quantification of Purkinje Cells in Cerebellum
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Tissue sections were stained with hematoxylin and eosin (HE). Parasagittal sections of cerebellum were scanned using an Olympus BX61VS microscope with UPlanSApo 20x/0.75 objective and TIFF images were acquired using the Olympus dotSlide system (Olympus, Tokyo, Japan). All those PCs were counted in which a nucleus with a nucleolus was observed. The PC layer was traced and length of the trace was measured using ImageProPlus 7.0 software (Media Cybernetics, Rockville, MD, USA). The number of PCs per unit of PC layer was quantified for the anterior, central, posterior, and flocculonodular transversal zones of the cerebellum [21 (link)].
4
Immunohistochemical Analysis of Tumor Necrosis
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Representative tumor areas were fixed in formalin, embedded in paraffin, cut into 2–3-µm sections and either stained with hematoxylin and eosin or prepared for IHC, which was performed as previously described (23 (link)). Thus, two cell conditioning periods of 8 min at 95°C and 4 min at 100°C on a hot plate using Tris-EDTA, pH=8, buffer were performed on previously dewaxed formalin-fixed paraffin-embedded tissue sections. Sections were incubated for 42 min at 37°C with a 1:50 dilution anti-Ki67 antibody (Master Diagnóstica, Granada, Spain; cat. no. MAD-000310QD), and the staining was performed with the IHC 3,3′-diaminobenzidine system (Ventana Medical Systems, Tuscon, AZ, USA). The results were evaluated by pathologists blinded to the clinicopathological and molecular data. The extension of the necrotic area present in each tissue sample was assessed by measuring the total tumor and necrotic areas. The tumors were scanned and the percentage of the necrotic region was normalized to the total area using the dotSlide system and dotSlide 2.1 software (Olympus Corporation, Tokyo, Japan). These procedures were performed by independent personnel of the pathology unit of the University of Salamanca (Salamanca, Spain).
5
Frozen Section Histological Staining Workflow
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Frozen sections were dried in an S160 incubator (Stuart, UK) at 37 °C for 1 h. The slides were soaked in Mayer's Hematoxylin for 10 min and rinsed in water for 5 min. 0.03% acid alcohol was applied for a further 10 min before being rinsed in tap water for a further 5 min. Slides were then stained in 0.5% Eosin for 5 min and rinsed briefly in ddH2O followed by an ethanol dehydration gradient (50%, 70%, 90%, and 100% ethanol twice). These were submerged in Xylene for 5 min and then mounted on coverslips with DPX mountant (Sigma Aldrich, UK). Slides were initially visualized at ×20 magnification on a conventional Olympus microscope with a built‐in dotSlide system (Olympus, UK), which provided an overview of the entire sample. Chorioretinal tissues were subsequently imaged at a higher (×40) magnification. Images were visualized using the OlyVIA software suite V3.3 (Olympus, UK).
6
Three-dimensional reconstruction of biological samples
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Living specimens were photographed with a Canon 600D Camera mounted on a Zeiss- Stemi 2000. Parafin- and semithin sections were analyzed with an Olympus microscope (BX-51). Sections were photographed with an Olympus camera (Olympus cc12) using the dot slide system (2.2 Olympus, Hamburg) and aligned using imod [58 (link)] and imod align (http://www.q-terra.de/biowelt/3drekon/guides/imod_first_aid.pdf ). 3D reconstructions were performed with Fiji (1.45b) [57 (link)]/trakem [59 (link)] and Amira (5.0). Adobe (San Jose, CA, USA) Photoshop (CC) and Illustrator (CS6/CC) were used to prepare all figures.
7
Histological Staining of Tissue Sections
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Following nuclear staining with Weigert's haematoxylin, sections were stained with Alcian blue 8GX (5 mg/mL in 1% (v/v) glacial acetic acid) and Sirius red F3B (10 mg/mL in saturated picric acid). Sets of three sequential sections were stained with Alcian blue only, Sirius red only, and both stains. Stained samples were imaged with the Olympus dotSlide system (Olympus Microscopy, Southend-on-Sea, UK).
8
Immunostaining Quantification in Glioma
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The TMAs were scanned at magnification × 20 using the Olympus dotSlide system and TMA software (Olympus, UK). Digital image analysis of the immunostaining was performed with Fiji/ImageJ (Wayne Rasband, National Institutes of Health, Bethesda, MD, USA, version 1.49u44 (link)) to obtain protein load. For each antibody, a threshold was determined to quantify the percentage image area immunostained by the antibody and expressed as protein load (%). The use of protein load as the standard quantification measure for all immunostaining in human glioma samples provided consistency within the measurement allowing fair comparisons between regions.
9
Ovarian Follicle Quantification Protocol
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To estimate ovarian follicle numbers, paraffin-embedded ovaries were exhaustively sectioned at 5 μm and stained with periodic acid-Schiff and haematoxylin (n = 5/group) (Sigma-Aldrich). Whole tissue section images were captured on the DotSlide system at ×20 objective using an XC10 camera (Olympus). The total number of primordial, transitional, primary follicles was quantified in every ninth section of each ovary and the total number of secondary and antral follicles were counted in every 36th section using a similar strategy as previously described (Tilly 2003 (link), Hutt et al. 2006) (link). Follicles were counted if the oocyte nucleus was present. Total follicle numbers were obtained by multiplying the raw counts of oocytes sampled (Q-) by nine to correct for the sections not counted. The number of corpora lutea was determined by direct counting of every 36th section encompassing the entire ovary. Adjacent sections were evaluated to ensure each corpora lutea was only counted once.
10
Automated Histological Tissue Segmentation
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The section or sections of the chosen slice and of one adjacent slice were digitally scanned with 103 magnification by using a slide scanner (DotSlide system; Olympus, Tokyo, Japan). For slices that consisted of multiple sections, the digital sections were copied to Photoshop (Adobe Systems, San Jose, Calif) and combined to reconstruct one whole slice to enable matching with MR.
The digitized sections were segmented into lumen, nuclei, and stroma and epithelial cytoplasm by color deconvolution (17) by using software (Matlab; Mathworks, Natick, Mass). The difference in staining of stroma and epithelial cytoplasm was too small to allow differentiation and they were therefore combined into one feature. Individual lumens were defined by adjoining pixels with a low optical density and a circular area larger than 1.6 3 10 23 mm 2 . Training pixels were used to determine the contribution of nuclei and stroma and cytoplasm to every pixel based on the optical densities. For each section, these pixels were assigned to nuclei or stroma and cytoplasm based on cutoff values. The cutoff values were optimized (T.K., with 6 years of experience) by visual inspection of the segmentation results (Fig 2).
The digitized sections were segmented into lumen, nuclei, and stroma and epithelial cytoplasm by color deconvolution (17) by using software (Matlab; Mathworks, Natick, Mass). The difference in staining of stroma and epithelial cytoplasm was too small to allow differentiation and they were therefore combined into one feature. Individual lumens were defined by adjoining pixels with a low optical density and a circular area larger than 1.6 3 10 23 mm 2 . Training pixels were used to determine the contribution of nuclei and stroma and cytoplasm to every pixel based on the optical densities. For each section, these pixels were assigned to nuclei or stroma and cytoplasm based on cutoff values. The cutoff values were optimized (T.K., with 6 years of experience) by visual inspection of the segmentation results (Fig 2).
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