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Cell Nucleolus

The cell nucleolus is a distinct nuclear subcompartment responsible for the biogenesis of ribosomes, the cellular organelles that translate genetic information into proteins.
It is the site of rRNA transcription, processing, and assembly of ribosomal subunits.
The nucleolus plays a crucial role in cellular metabolism, growth, and proliferation.
Optimizing research on the cell nucleolus can be facilitated by PubCompare.ai, a leading AI-driven platform that enhances reproducibility and accuracy.
PubCompare.ai's powerful search and comparison tools can help researchers quickly locate the best protocols from literature, pre-prints, and patents, while its AI-driven analysis identifies the most reliable and effective methods to advance cell nucleolus studies.
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Most cited protocols related to «Cell Nucleolus»

Tumor slides from the internal training cohort were reviewed by 2 pathologists (K.K. and W.D.T.) who were blinded to patient clinical outcomes; they used an Olympus BX51 microscope (Olympus Optical Co. Ltd., Tokyo, Japan) with a standard 22-mm diameter eyepiece.
Tumor STAS was defined as tumor cells within air spaces in the lung parenchyma beyond the edge of the main tumor (Figure 1A and 1D) and was composed of 3 morphological patterns: 1) micropapillary structures consisting of papillary structures without central fibrovascular cores (Figure 1A and 1B),15 (link), 16 (link) which occasionally form ring-like structures within air spaces (Figure 1C); 2) solid nests or tumor islands consisting of solid collections of tumor cells filling air spaces (Figure 1D and 1E)17 (link); and 3) single cells consisting of scattered discohesive single cells (Figure 1F). The edge of the main tumor was defined as the smooth surface of the tumor which is easily recognizable at gross or at low-power field examination as highlighted with the dotted line in Figure 1A. Tumor STAS was considered present when tumor STAS, as defined above, was identified beyond the edge of the main tumor even if it existed only in the first alveolar layer from the tumor edge. Lesions of STAS consist of tumor cells which morphologically appear to be situated within air spaces as micropapillary clusters, solid nests or single cells that are detached from alveolar walls. This differs from lepidic growth where tumor cells grow in a linear fashion along the surface of alveolar walls. Extent of air space filling by tumor cells varied from abundant cellular infiltrates to very inconspicuous single cells or micropapillary clusters that were sometimes difficult to distinguish from alveolar macrophages. In addition, distance between tumor surface and farthest STAS from tumor edge was measured by a ruler. Since lung specimens were not consistently inflated during processing, in order to account for artifactual atelectasis, we also measured according to the number of alveolar spaces.
Tumor cells of STAS were distinguished from alveolar macrophages using the following methods. Macrophages in smokers typically have cytoplasm containing faint brown pigment and black carbon granules while in nonsmokers the pigment is lacking and cytoplasm is sometimes foamy. Nuclei are small, uniform, and regular, without atypia. Nuclear folds are frequent and nucleoli are inconspicuous or absent. In contrast, tumor cells of STAS typically lack cytoplasmic pigment or foamy cytoplasm. They often grow in cohesive clusters and nuclei are atypical with hyperchromasia and frequent nucleoli. The distinction of STAS from artifacts was done in the following way. Tumor floaters were favored, by the presence of clusters of cells often randomly scattered over tissue and at the edges of the tissue section. Presence of jagged edges of tumor cell clusters suggested tumor fragmentation or edges of a knife cut during specimen processing rather than STAS. Linear strips of cells that were lifted off of alveolar walls also favored the presence of artifact. Identification of tumor cells distant from the main tumor was regarded as an artifact unless intraalveolar tumor cells could be demonstrated in a continuum of airspaces containing intraalveolar tumor cells back to the tumor edge.
According to the International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society histological classification, the percentage of each histologic pattern—lepidic, acinar, papillary, solid, and micropapillary—was recorded in 5% increments and tumors were classified by their predominant pattern.1 (link) Each histologic pattern was considered present in the tumor when it comprised ≥5% of the overall tumor.7 (link) Presence of visceral pleural, lymphatic, and vascular invasion was also recorded.
Publication 2015
Atelectasis Blood Vessel Carbon Black Cell Nucleolus Cell Nucleus Cells Cytoplasm Cytoplasmic Granules Europeans Lung Lung Cancer Macrophage Macrophages, Alveolar Microscopy Neoplasms Non-Smokers Pathologists Patients Pigmentation Pleura, Visceral Respiratory Rate Snup Syncope Tissues Vision
Four clones at least were picked from each transformation. Validation PCR was performed using a common forward primer from the 3′ end of the SWAT modules (S4 reverse complement), and a gene specific reverse primer from the gene coding sequence. All four clones were imaged via a high-content screening platform in bright-field and GFP channels (see below). Images of all clones were reviewed manually, and up to three localizations were assigned to each clone. Assignments were: Ambiguous; Below threshold; Bud; Bud neck; Cell periphery; Cytosol; ER; Mitochondria; Nuclear periphery; Nucleolus; Nucleus; Punctate; Vacuole; Vacuole membrane. Only strains with a duplicate repeating localization assignment and validated by PCR were chosen to compose the SWAT-GFP and SWAP-SP-GFP collections.
Publication 2016
Cell Nucleolus Cell Nucleus Cells Clone Cells Cytosol Genes, vif Mitochondria Neck Oligonucleotide Primers Open Reading Frames Strains Tissue, Membrane Vacuole
TNM6 (Sobin and Wittekind, 2002 ) was used in staging and World Health Organization (WHO) criteria in grading the differentiation (Hamilton et al, 2010 ). The SACs were detected by the WHO 2010 criteria as described earlier (Hamilton et al, 2010 ; Sajanti et al, 2014 (link)), including saw-toothed epithelial serrations, clear or eosinophilic cytoplasm, vesicular nuclei with distinct nucleoli, well-preserved polarity, and abundant mucin production. Tumour growth pattern at the advancing tumour border was classified using the earlier described criteria (Jass et al, 1996 (link)), briefly diffuse, irregular clusters or small glands or cords of cells infiltrating to surrounding tissue vs expanding, well-circumscribed margins. Lymphatic invasion was defined as tumour cells present in vessels with an endothelial lining but lacking a muscular wall, and blood vessel invasion was evaluated positive if there were tumour cells in vessels with a thick muscular wall or in vessels containing red blood cells. The areal percentage of tumour necrosis was visually estimated by inspecting manually all available tumour slides. The method was otherwise analogous with two previous studies (Pollheimer et al, 2010 (link); Richards et al, 2012 (link)), but no predetermined cutoff scores were utilised in this study. Tumour necrosis in haematoxylin and eosin (H&E)-stained sections was specified as an area with increased eosinophilia and nuclear shrinkage, fragmentation and disappearance, with shadows of tumour cells visible to variable extent (Figure 1). Neutrophilic inflammatory infiltrate at the boundaries of an area was considered to support the classification of that area as necrotic but was not required by definition. Intraluminal necrosis fulfilled the criteria and was included in the evaluation of tumour necrosis percentage. All the histological analyses were performed blinded to the clinical data.
Publication 2016
Blood Vessel Cell Nucleolus Cell Nucleus Cells Cone-Rod Dystrophy 2 Cytoplasm Endothelium Eosin Eosinophil Eosinophilia Erythrocytes Inflammation Mucins Muscle Tissue Necrosis Neoplasms Neoplasms, Vascular Tissue Neutrophil Spindle Assembly Checkpoint Tissues
Nucleus contours were determined on the HP1β (embryos) and DAPI (mammary gland and A. thaliana) images.
Images of rabbit nuclei were denoised with a median filter and a Gaussian filter. They were subsequently segmented through two different pathways.
HP1β images (embryos), on which several nuclei are present, were analyzed with the Insight Toolkit (ITK) library. The robust automatic threshold selection method (RATS) [100] was used to compute a threshold to ‘binarize’ the HP1β images. The threshold is computed as the mean of the intensity values in the HP1β image weighted by their Gaussian gradient magnitude. To avoid the high gradient values in the nucleus caused by non-homogeneous content, the small bright and dark zones were removed with a 3D area opening and a grayscale fill hole transformation before computing the gradient. The joined masks of nuclei were separated using a watershed transform on the distance map. Truncated nuclei at the image border as well as objects smaller than 200 µm3 were removed.
A semi-automated procedure was developed to segment mammary gland nuclei from the DAPI image. DAPI signal was denoised with a median and a Gaussian filter, and manually thresholded to produce partial nuclear masks. The DAPI signal was mostly present on the border of the nuclei. As a result, thresholding this signal results in an incomplete nucleus, in which the center is not filled and the border is not continuous. The nuclear borders were thus closed with a morphological closing transform with a large round kernel, and content of the nuclei was filled with a binary hole filling transform. The masks of the different nuclei were then separated by a watershed transform on the distance map, and the nuclei from the cell types of interest were manually selected.
Confocal image stacks of A. thaliana nuclei were processed and analyzed with programs developed using the Free-D software libraries [67] (link). Each image stack contained a single nucleus. Images were automatically cropped to limit processing to a bounding box surrounding the nucleus. To separate the nucleus from the background, a preliminary intensity threshold was then computed using the isodata algorithm [101] . This algorithm is sensitive to the relative size of the nucleus within the image. As a result, the threshold was generally too high because of the larger background size. To correct for this bias, the intensity average m and standard-deviation s were computed over the nucleus region defined by the preliminary threshold and the actual threshold was set to m-2s. The resulting binary image generally contained holes, corresponding in particular to the nucleolus, and presented boundary irregularities due to noise. In addition, bumps were also observed on some nuclei at their basal and apical faces, because of blur from chromocenters [39] (link). Hole filling, opening and closing binary morphological operators [65] were therefore applied to regularize the binary image. The subsequent processing and analyses were confined to this final nucleus mask. A surface model of the nuclear envelope was generated by applying the marching cubes algorithm [102] to the binary mask.
Publication 2010
Cell Nucleolus Cell Nucleus Cuboid Bone DAPI DNA Library Embryo Face Mammary Gland Nuclear Envelope Nucleus Solitarius Rabbits
Cases of oropharyngeal SCC were identified from a Radiation Oncology head and neck database at Barnes-Jewish Hospital from 1997 to 2004. This is an IRB approved combined retrospective/prospective database with a waiver for retrospective data collection and patient consent for prospective data collection. Radiation was either postoperative for patients managed with an up front surgical approach, or definitive for patients managed without surgery. Patients were treated exclusively with intensity modulated radiation therapy (IMRT) by a single radiation oncologist (WLT). Patients had a minimum of 2 years of clinical follow-up assessed from the end of radiation therapy with the exception of 5 patients who were lost to follow up within 2 years. The mean length of follow-up was 3.3 years (range of 5 months to 8 years). Only cases with primary and pre-treatment surgical pathology material available for review were included. The diagnosis of SCC was confirmed by slide review and all recognized variants such as verrucous, spindle cell, papillary, adenosquamous, undifferentiated, and basaloid squamous cell carcinomas were excluded. Particular care was taken to exclude cases of basaloid squamous cell carcinoma, which is histologically distinct from nonkeratinizing squamous cell carcinoma. Basaloid squamous cell carcinoma, as defined by Wain’s criteria, is intimately associated with keratinizing squamous cell carcinoma, and is composed of a lobular proliferation of small, crowded cells with scant cytoplasm and round, hyperchromatic nuclei [24 (link)]. In addition, it has cystic spaces with mucin-like material, coagulative necrosis and stromal hyalinosis with basement membrane-like material [22 (link)].
The cases were classified independently by three reviewers (RDC, SKEM, JSL), prior to HPV testing and without knowledge of clinical outcome, into the following three categories based upon histologic features: NK SCC, K SCC, and those with overlapping features (referred to as hybrid SCC). NK SCC was defined as forming sheets, nests or trabeculae with pushing borders, little stromal response, and having ovoid to spindled, hyperchromatic cells that lack prominent nucleoli and have indistinct cell borders (Fig. 1). Comedo-type necrosis and brisk mitotic activity were often present but were not considered requisite features. While varying from well to poorly differentiated, K SCC was defined as entirely composed of mature squamous cells without areas with NK SCC morphology (Fig. 2). Hybrid SCC showed nonkeratinizing morphology but with areas of squamous maturation (Fig. 3). Discrepant cases were collectively arbitrated around a multi-headed microscope by all three study pathologists and placed in a single category.
Publication 2009
Cancellous Bone Cell Nucleolus Cell Nucleus Cell Proliferation Cells Coagulation, Blood Cytoplasm Diagnosis Hybrids Membrane, Basement Microscopy Mucocele Neck Necrosis Operative Surgical Procedures Oropharynxs Pathologists Patients Radiation Oncologists Radiotherapy Radiotherapy, Intensity-Modulated Squamous Cell Carcinoma Squamous Epithelial Cells

Most recents protocols related to «Cell Nucleolus»

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Publication 2023
alexa fluor 488 Alexa Fluor 555 anti-IgG Antibodies Cell Nucleolus Cell Nucleus Cells DAPI EPOCH protocol fibrillarin Floods Fluorescence Genotype Goat HeLa Cells Intestinal Atresia, Multiple Light Microscopy Microscopy, Confocal Mus Orlistat paraform Rabbits Serum Submersion Sulfoxide, Dimethyl Technique, Dilution Triton X-100 Tween 20
Confocal image stacks of S10B follicles stained for fibrillarin or EU were genotypically blinded and each follicle was assigned a nucleolar defect severity score according to the number of nurse cell nucleoli with morphological changes; the four posterior nurse cells were excluded from the analysis as their morphology is distinct. The nucleolar morphology categories included: normal (0-1 disrupted nucleoli), mild (2-4 disrupted nucleoli), and severe (5 or more disrupted nucleoli). Morphology defects included nucleoli that are pebbled (several smaller, disconnected portions of nucleolus) or rounded (single, nearly spherical nucleolus). In the context of NLS Actin overexpression (Figure 6), we assessed nucleolar morphology in relation to the level of nuclear actin. The level of nuclear actin was categorized as low which exhibits a nuclear actin haze, medium which exhibits a network of thin nuclear actin filaments, and high which exhibits thick nuclear actin filamentous structures termed rods. Quantification of nucleolar volume was performed using Imaris (Oxford, RRID:SCR_007370) on yw S10B follicles stained for EU. The single anterior-most nurse cell was compared to the average volume of four posterior nurse cells (from the third row of nurse cells).
Publication 2023
Actins Cell Nucleolus Cells fibrillarin Hair Follicle Microfilaments Nurses Ovarian Follicle Rod Photoreceptors
Genotypically de-identified images were analyzed using ImageJ (Abramoff et al., 2004 ) for DNase I, C4 and AC15 for specific stages of oogenesis. Follicle staging was assigned based on morphology and size.
DNase I nucleolar to cytoplasmic ratios were quantified from single confocal slices in S7/8 follicles by measuring the integrated density of fluorescence within a square in the nucleolus, compared to a square in the adjacent cytoplasm; the focal planes chosen had the strongest nucleolar DNase I signal. Three paired measurements were made per cell and the average nucleolar/cytoplasmic ratio was determined. Three cells per follicle were measured. DNase I data were analyzed and statistical analysis performed using Prism (Graphpad, RRID: SCR_002798).
Quantification of C4 nucleolar actin was performed on confocal image stacks of follicles stained with anti-actin C4, WGA, and Phalloidin; as necessary, brightness and contrast were adjusted to score all the C4 nucleolar actin present. Data were collected for S5-6, S7-8, and S9 follicles. For each follicle the number of nurse cells exhibiting structured nucleolar C4 actin was scored.
Quantification of AC15 nuclear actin level and nucleolar puncta presence/size was performed on confocal image stacks of follicles stained with anti-actin AC15, WGA, and DAPI. For AC15 nuclear actin level, data were collected for S5-6, S7-8, and S9 follicles. For each follicle the nurse cells and the follicle cells were scored for their level of AC15 staining on a 5-point scale ranging from background levels typical of what is observed in wild-type S3 follicles (-) to the strongest staining typical of what is observed in wild-type S10 follicles (+++). For AC15 puncta, data were collected for S7/8, S9, and S10 follicles; as necessary, brightness, contrast and zoom were adjusted to score the puncta. Each follicle was scored as having either no AC15 puncta, small puncta, or large/obvious puncta in the nurse cells. C4 and AC15 data were analyzed using Excel (Microsoft, RRID: SCR_016137) and statistical analysis was preformed using R (Vienna, Austria, RRID: 001905).
Publication 2023
Actins Cell Nucleolus Cells Cytoplasm DAPI Deoxyribonuclease I Fluorescence Hair Follicle Nurses Oogenesis Ovarian Follicle Phalloidine prisma
GV oocytes from 12-wk-old females were stained with DAPI and scored according to their maturation stage. Absence of a ring around the nucleolus was counted as “NSN,” a partial ring as “intermediate.” and a full ring “SN.” Between 116 and 247 oocytes collected from several different females were analyzed per genotype (number of mice: control = 5, Ehmt2 cKO = 3, Ehmt1 cKO = 2, Ehmt1/2 cDKO = 3).
Publication 2023
Cell Nucleolus DAPI Females Genotype Mice, House Oocytes
Neuron counts were performed as previously described (9 (link)). In brief, torsos of E16, P0, P30, or P120 mice were fixed in 4%PFA/PBS for 4 h to overnight and cryoprotected in 30% sucrose/PBS for 24 to 48 h. P30 tissues were decalcified in 0.5 M EDTA prior to sucrose treatment. Torsos were then mounted in OCT and serially sectioned (12 μm). Next, every fifth section was stained with solution containing 0.5% cresyl violet (Nissl). Cells in both SCGs with characteristic neuronal morphology and visible nucleoli were counted using ImageJ.
Publication 2023
Cell Nucleolus Cells cresyl violet Cromolyn Sodium Edetic Acid Mus Neurons Sucrose Tissues Torso

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DAPI is a fluorescent dye used in microscopy and flow cytometry to stain cell nuclei. It binds strongly to the minor groove of double-stranded DNA, emitting blue fluorescence when excited by ultraviolet light.
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More about "Cell Nucleolus"

The cell nucleolus is a specialized nuclear subcompartment that plays a crucial role in cellular metabolism, growth, and proliferation.
It is responsible for the biogenesis of ribosomes, the cellular organelles that translate genetic information into proteins.
The nucleolus is the site of rRNA transcription, processing, and assembly of ribosomal subunits, making it an essential component of protein synthesis.
Optimizing research on the cell nucleolus can be facilitated by PubCompare.ai, a leading AI-driven platform that enhances reproducibility and accuracy.
PubCompare.ai's powerful search and comparison tools can help researchers quickly locate the best protocols from literature, pre-prints, and patents, while its AI-driven analysis identifies the most reliable and effective methods to advance cell nucleolus studies.
Researchers can also leverage various microscopy techniques and software tools to study the structure and function of the cell nucleolus.
The BX51 microscope, for example, is a popular choice for high-resolution imaging, while the Stereo Investigator software can be used for quantitative analysis of nucleolar morphology and distribution.
Additionally, the use of fluorescent dyes like DAPI (4',6-diamidino-2-phenylindole) and acridine orange can help visualize the nucleolus and its components.
The TRIzol reagent is commonly used for RNA extraction, while the LSM 710 confocal microscope and FACSCalibur flow cytometer can provide detailed insights into nucleolar dynamics and protein localization.
For comprehensive analysis of cell nucleolus-related processes, researchers may also employ Actinomycin D, a potent inhibitor of RNA synthesis, and utilize the LAS AF software for advanced image processing and analysis.
By combining the power of PubCompare.ai with the versatile tools and techniques available, researchers can take their cell nucleolus studies to the next level, driving advancements in our understanding of this crucial nuclear subcompartment and its role in cellular function.