Log In Sign up
The largest database of trusted experimental protocols
> Anatomy > Cell Component > Vacuole

Vacuole

Vacuole: A membranous sac or cavity within the cytoplasm of a cell, often used for storage or digestion.
Vacuoles play a crucial role in plant and fungal cells, but are also found in some animal cells.
They can contain various substances, such as water, waste products, or digestive enzymes.
Understandingg the structure and function of vacuoles is important for research in cell biology, plant science, and microbiology.

Most cited protocols related to «Vacuole»

1
Standardized Rodent NASH Histological Scoring
Briefly, the two key features of NASH, steatosis and inflammation, were categorized as follows: steatosis was determined by analyzing hepatocellular vesicular steatosis, i.e. macrovesicular steatosis and microvesicular steatosis separately, and by hepatocellular hypertrophy as defined below (Fig. 2). Inflammation was scored by analyzing the amount of inflammatory cell aggregates (Fig. 2). The proposed rodent scoring system is shown in Table 4 and options for its use in diagnosis are shown in S1 Fig. The purpose of this scoring system is however not to derive a single score, but to score the individual features.
Macrovesicular steatosis and microvesicular steatosis were both separately scored and the severity was graded, based on the percentage of the total area affected, into the following categories: 0 (<5%), 1 (5–33%), 2 (34–66%) and 3 (>66%). The difference between macrovesicular and microvesicular steatosis was defined by whether the vacuoles displaced the nucleus to the side (macrovesicular) or not (microvesicular). Similarly, the level of hepatocellular hypertrophy, defined as cellular enlargement more than 1.5 times the normal hepatocyte diameter, was scored, based on the percentage of the total area affected, into the following categories: 0 (<5%), 1 (5–33%), 2 (34–66%) and 3 (>66%). For hepatocellular hypertrophy the evaluation was merely based on abnormal enlargement of the cells, irrespective of rounding of the cells and/or changes in cytoplasm or the number of vacuoles, and is therefore not a substitute of ballooning. The unweight sum of the scores for steatosis (macrovesicular steatosis, microvesicular steatosis and hypertrophy) thus ranged from 0–9. Both steatosis and hypertrophy were evaluated at a 40 to 100× magnification and only the sheets of hepatocytes were taken into account (terminal hepatic venules and portal tracts etc were excluded).
Inflammation was evaluated by counting the number of inflammatory foci per field using a 100 x magnification (view size of 3.1 mm2). A focus was defined a cluster, not a row, of ≥5 inflammatory cells. Five different fields were counted and the average was subsequently scored into the following categories: normal (<0.5 foci), slight (0.5–1.0 foci), moderate (1.0–2.0 foci), severe (>2.0 foci).
Hepatic fibrosis was identified using Sirius Red stained slides at 40 x magnification and evaluated by scoring whether pathologic collagen staining was absent (only in vessels) or collagen staining observed within the liver slide, the latter further defined as mild, moderate or massive. In addition, the percentage of the total area affected was evaluated using using image analysis of surface area on Sirius red stained slides.
Liang W., Menke A.L., Driessen A., Koek G.H., Lindeman J.H., Stoop R., Havekes L.M., Kleemann R, & van den Hoek A.M. (2014). Establishment of a General NAFLD Scoring System for Rodent Models and Comparison to Human Liver Pathology. PLoS ONE, 9(12), e115922.
Full text: Click here
Publication 2014
Blood Vessel Cell-Derived Microparticles Cell Enlargement Cell Nucleus Cells Collagen Cytoplasm Diagnosis Fibrosis, Liver Hepatocyte Hypertrophy Inflammation Liver Nonalcoholic Steatohepatitis Portal System Rodent Steatohepatitis Vacuole Venules
2
Complementing Homology-Ontology Annotations with Subcellular Localization Predictions
To complement incomplete annotations in the background database, a homology-ontology annotation retrieved by BLAST should be accompanied by an accurate subcellular localization prediction for each homologous sequence. CELLO has been shown to be helpful for the prediction of subcellular localizations of the proteins found in a proteomic data. [28] (link) Using multiple, integrated machine-learned classifiers, CELLO predicts which of four subcellular localizations in archaea and in Gram-positive bacteria, five subcellular localizations in Gram-negative bacteria, and twelve subcellular localizations in eukaryotes that the targeted protein might be found in, with the four archaeal and Gram-positive bacterial localizations being the extracellular space, the cell wall, the cytoplasmic membrane, and the cytoplasm; the five Gram-positive bacterial localizations being the extracellular space, the outer membrane, the periplasmic and cytoplasmic (inner) membranes, and the cytoplasm; and the 12 eukaryotic localizations being chloroplasts, the cytoplasm, the cytoskeleton, the endoplasmic reticulum, the extracellular/secretory space, the Golgi, lysosomes, mitochondria, the nucleus, peroxisomes, the plasma membrane, and vacuoles. Due to subcellular data increased exponentially over the years, CELLO has been trained on latest models and denoted as update version wrapping in CELLO2GO. And the resultant datasets used for prediction and evaluation is from PSORTb3.0 [23] (link).
Yu C.S., Cheng C.W., Su W.C., Chang K.C., Huang S.W., Hwang J.K, & Lu C.H. (2014). CELLO2GO: A Web Server for Protein subCELlular LOcalization Prediction with Functional Gene Ontology Annotation. PLoS ONE, 9(6), e99368.
Full text: Click here
Publication 2014
Archaea Cell Nucleus Cell Wall Chloroplasts Cytoplasm Cytoskeleton Endoplasmic Reticulum Eukaryota Eukaryotic Cells Extracellular Space Golgi Apparatus Gram-Positive Bacteria Gram Negative Bacteria Homologous Sequences Lysosomes Mitochondria Periplasm Peroxisome Plasma Membrane Proteins secretion Tissue, Membrane Vacuole
3
Constructing Negative Datasets for PPI Analysis
The PPI data was collected from Saccharomyces cerevisiae core subset of database of interacting proteins (DIP) (27 (link)), version DIP_20070219. The reliability of this core subset has been tested by two methods, expression profile reliability (EPR) and paralogous verification method (PVM) (28 (link)). At the time of doing the experiments, the core subset contained 5966 interaction pairs. The protein pairs that contained a protein with <50 amino acids were removed and the remaining 5943 protein pairs comprised the final positive data set. All proteins in the data set were aligned using the multiple sequence alignment tool, cd-hit program (29 (link)). The aligned result shows that among the 5943 protein pairs, the overwhelming majority of them (5594 PPIs) have <40% pairwise sequence identity to one another. Although there are only 349 pairs with ≥40% identity in the training data set, the classifier will possibly be biased to these homologous sequence pairs.
Since the non-interacting pairs were not readily available, three strategies for constructing negative data set were used in order to compare the effects of different training data sets on the performance of the method. The first strategy has been described by Shen and colleagues (26 (link)) in detail. The non-interacting pairs were generated by randomly pairing proteins that appeared in the positive data set. Here the negative data set based on this method is called Prcp. The second is based on such an assumption that proteins occupying different subcellular localizations do not interact. The subcellular localization information of the proteins in the positive data set was extracted from Swiss-Prot (http://www.expasy.org/sprot/). The proteins without subcellular localization information and those denoted as ‘putative’, ‘hypothetical’ were excluded. The remaining proteins were grouped into eight subsets based on the eight main types of localization—cytoplasm, nucleus, mitochondrion, endoplasmic reticulum, golgi apparatus, peroxisome, vacuole and cytoplasm&nucleus. Each subset contained 10 proteins at least. The non-interacting pairs were generated by pairing proteins from one subset with proteins from the other subset. It must be pointed out that proteins from cytoplasm subset and nucleus subset cannot be paired with those from cytoplasm&nucleus subset. Here the negative data set based on subcellular localization information is called Psub. The two strategies must meet three requirements: (i) the non-interacting pairs cannot appear in the whole DIP yeast interacting pairs, (ii) the number of negative pairs is equal to that of positive pairs and (iii) the contribution of proteins in negative set should be as harmonious as possible (24 (link),26 (link)).
As a comparison, the third strategy was used for creating non-interacting pairs composed of artificial protein sequences. It has been demonstrated that if a sequence of one interacting pair is shuffled, then the two proteins can be deemed not to interact with each other (30 ). Thus, the negative data set was prepared by shuffling the sequences of right-side interacting pairs with k-let (k = 1,2,3) counts using the Shufflet program (31 (link)).
Guo Y., Yu L., Wen Z, & Li M. (2008). Using support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences. Nucleic Acids Research, 36(9), 3025-3030.
Publication 2008
Amino Acids Amino Acid Sequence Cell Nucleus Cytoplasm Endoplasmic Reticulum Golgi Apparatus Homologous Sequences Mitochondria Peroxisome Prepulse Inhibition Proteins Saccharomyces cerevisiae Saccharomyces cerevisiae Proteins Sequence Alignment SET protein, human Staphylococcal Protein A Vacuole
4
Benchmarking Plant Subcellular Localization
We downloaded the set of crop plant proteins (barley, wheat, rice, maize) from the cropPal database24 (link) and chose those that have a subcellular localization of either ‘plastid’ (100 proteins), ‘mitochondrion’ (61 proteins), ‘nucleus’ (165 proteins), ‘peroxisome’ (11 proteins), ‘vacuole’ (18 proteins), ‘plasma membrane’ (84 proteins, ‘endoplasmic reticulum’ (43 proteins) and ‘cytosol’ (48 proteins) determined by GFP-tagging. We only kept those sequences that started with an ‘M’. For the UniProt test set, we downloaded plant proteins (taxonomy:“Viridiplantae [33090]”) that were entered after our training sets were compiled (created:[20160301 TO 20160902]) for several compartments supported by experimental evidence (“Nucleus [SL-0191]”; “Mitochondrion [SL-0173]”, “Chloroplast [SL-0049]”, “Peroxisome [SL-0204]”, “Vacuole”, “Secreted”, “Endoplasmic reticulum”, “Cytoplasm”). We manually removed those entries that localize to multiple compartments, except for the category nucleus for which we also allowed an additional cytoplasmic localization. All plant and effector test sets are available at http://localizer.csiro.au/data.html.
When evaluating performance, the number of true positives (TPs), true negatives (TNs), false positives (FPs) and false negatives (FNs) were used. Sensitivity is defined as the proportion of positives that are correctly identified whereas specificity is the proportion of negatives that are correctly identified. Precision (positive predictive value, PPV, ) is a measure which captures the proportion of positive predictions that are true. Both accuracy and the Matthews Correlation Coefficient can be used to evaluate the overall performance of a method. The MCC ranges from −1 to 1, with scores of −1 corresponding to predictions in total disagreement with the observations, 0.5 to random predictions and 1 to predictions in perfect agreement with the observations. For our classifier, we count LOCALIZER predictions that are ‘chloroplast’, ‘chloroplast and possible mitochondrial’, ‘chloroplast and nucleus’ and ‘chloroplast & possible mitochondrial and nucleus’ as chloroplast predictions (same strategy for mitochondrial predictions). A protein that carries a predicted transit peptide with an additional predicted NLS might have experimental evidence only for one of those locations due to the technical hurdles of recognizing dual targeting20 (link) and should thus not necessarily be counted as a false positive prediction. A protein is counted as a nucleus prediction only if it has the category ‘nucleus’ to avoid assigning a protein to multiple predictions in the evaluation. Many plant subcellular localization methods have been published, however only a small number are available as standalone software or have the option of submitting large batch sequence files to a web server. This makes it prohibitive for researchers to use them routinely for data analysis and thus, our benchmark only includes methods that can be locally installed with ease or have a web server with a batch file submission option (Supplementary Table S2).
Sperschneider J., Catanzariti A.M., DeBoer K., Petre B., Gardiner D.M., Singh K.B., Dodds P.N, & Taylor J.M. (2017). LOCALIZER: subcellular localization prediction of both plant and effector proteins in the plant cell. Scientific Reports, 7, 44598.
Full text: Click here
Publication 2017
Cell Nucleus Chloroplasts Crop, Avian Cytoplasm Cytosol Endoplasmic Reticulum Green Plants Hordeum vulgare Hypersensitivity Maize Mitochondria Nuclear Localization Signals Peroxisome Plant Proteins Plants Plasma Membrane Plastids Proteins Rice Staphylococcal Protein A Triticum aestivum Vacuole
5
Histopathological Assessment of Creutzfeldt-Jakob Disease
Members of the reference group (PP, AG, HK) prepared a draft of the assessment instructions, including a detailed description of the typical histopathological features as well as the major exclusion criteria for each CJD type (Table 1), a diagnostic flow chart to be followed during the assessment (Fig. 1), photographs and definitions of the pathology to be evaluated (Fig. 2; Table 2), and a standardized data sheet (Table 3). The latter aimed to collect information on the dominant vacuole size, on the main pattern of PrP deposition, and on whether or not kuru-type or florid amyloid plaques were seen. In addition, five specific questions concerning the distribution and severity of histopathological lesions in specific neuroanatomical structures were included (Table 3). Finally, an alternative nomenclature, more suitable for a histopathological diagnosis performed in the absence of molecular data, was proposed for each of the sporadic disease variants or subtypes (Table 4).
Assessors were then invited to a joint meeting to discuss the document drafts and simultaneously assess some exemplary cases using a multi-headed microscope.
The documents were then refined based on the most significant suggestions that emerged during the meeting, and a final version of each document (Tables 1, 2, 3, 4; Figs. 1, 2) was prepared by the reference group to be circulated among the participants.
Parchi P., de Boni L., Saverioni D., Cohen M.L., Ferrer I., Gambetti P., Gelpi E., Giaccone G., Hauw J.J., Höftberger R., Ironside J.W., Jansen C., Kovacs G.G., Rozemuller A., Seilhean D., Tagliavini F., Giese A, & Kretzschmar H.A. (2012). Consensus classification of human prion disease histotypes allows reliable identification of molecular subtypes: an inter-rater study among surveillance centres in Europe and USA. Acta neuropathologica, 124(4), 517-529.
Publication 2012
Diagnosis Figs Joints Kuru Microscopy Plaque, Amyloid Vacuole Vision

Most recents protocols related to «Vacuole»

1
Histopathological Analysis of Abdominal Aorta
After the elasticity of the rat abdominal aorta was measured by ultrasound equipment, the animals were euthanized using barbiturate injections before dissection. The bifurcation of the abdominal aorta was searched between the kidneys, and the aorta was separated to the first segment of the aortic arch (Fig. 2). A 1.5–2-cm abdominal aorta was cut 1 cm above the bifurcation of the abdominal aorta, and fixed in 10% paraformaldehyde immediately after the lumen was rinsed with normal saline and phosphate-buffered saline (PBS). After subsequent trimming, dewatering, and paraffin embedding, the 4-μm-thick sections were subjected to Masson staining. The steps were as follows: ① Dewaxing of paraffin sections to water: successively immerse the sections into xylene I 20 min-xylene 20 min-anhydrous ethanol and 10 min-anhydrous ethanol 10 min-95% alcohol 5 min-90% alcohol 5 min-80% alcohol 5 min-70% alcohol 5 min-distilled water for washing. ② Hematoxylin staining for 5 min in the Masson staining kit using Weigert’s ferric hematoxylin, washing with tap water, differentiation with 1% hydrochloric acid and alcohol for a few seconds, and washing with running water for a few minutes to turn blue. ③ Lichun red staining: Lichun red acid fuchsin solution was used in the Masson staining kit for 5–10 min and rinsed with distilled water. ④ Phosphomolybdic acid treatment: The sections were treated with phosphomolybdic acid solution in Masson staining kit for 3–5 min. ⑤ Aniline blue staining: Without washing, the Masson staining kit was directly used for redyeing with aniline blue solution for 5 min. ⑥ Differentiation: 1% acetic acid treatment for 1 min. ⑦ Dehydration sealing: The sections were successively immersed in 95% alcohol I 5 min-95% alcohol II 5 min-anhydrous ethanol I 5 min-anhydrous ethanol 5 min-xylene and 5 min-xylene for 5 min in dehydration and transparency. The sections are taken out of xylene and dried slightly, followed by sealing with neutral gum. Masson stain is specific for compound staining. It can specifically display collagen fibers (blue) and muscle fibers (red). The pathological results showed that the collagen fibrous hyperplasia or the formation of lipid vacuoles is associated with the early arterial wall lesions (Fig. 3).

Anatomical view of abdominal aorta in spontaneously hypertensive rat (inside the blue circle is the abdominal aorta)

Interpretation of histopathological images of Masson staining (the long black arrow shows the muscle fibers, the short black arrow shows collagen fibers, and orange circles indicate lipid vacuoles)

Liu J., Qiu L., Su Y., Zhang H., Shi X., Hu X, & Qian L. (2023). Evaluation of early arterial wall lesions by elastography parameters in spontaneously hypertensive rats. BMC Cardiovascular Disorders, 23, 123.
Full text: Click here
Publication 2023
Absolute Alcohol Acetic Acid acid-fuchsin aniline blue Animals Aorta Aortas, Abdominal Arch of the Aorta Arteries barbiturate Collagen Dehydration Dissection Elasticity Ethanol Fibrosis Hematoxylin Hydrochloric acid Hyperplasia Kidney Lipids Lipogenesis Muscle Tissue Neoplasm Metastasis Normal Saline Paraffin paraform Phosphates phosphomolybdic acid Rats, Inbred SHR Saline Solution Stains Ultrasonography Vacuole Verhoeffs iron hematoxylin Xylene
2
Quantitative Histological Analysis of Splenic and Renal Pathology
PAS staining was used for evaluation of vacuolization of splenic cells. Splenic vacuolization was quantified by counting the number of vacuoles in a grid area (10 x 10 caskets; grid area: 0.0156 mm²) for 20 adjacent areas (magnification 1000x). Quantification and evaluation for staining of KIM-1, F4-80, CD3, Ki67 and CC-3 with a grid area of 0.0977 mm² and 400x magnification was performed as described previously (18 (link)). Staining of BTK positive cells was quantified by counting the number of intersections overlapping the positive brown staining in a grid area (10 x 10 caskets; grid area: 0.0977 mm²) for 20 adjacent cortical areas (magnification 400x). Staining of Ly6g was quantified by counting the number of caskets with positive brown staining in a grid area (10 x 10 caskets; grid area: 0.0977 mm²) for 20 adjacent cortical areas (magnification 400x). Images were taken using KEYENCE BZ-X800 microscope and BZ-X800 viewer after performing white balance and auto exposure at magnification of 400x (1000x for spleen). Quantification of thrombocytes and fibrin deposition was performed as described previously (18 (link), 19 (link)).
Kröller S., Wissuwa B., Dennhardt S., Krieg N., Thiemermann C., Daniel C., Amann K., Gunzer F, & Coldewey S.M. (2023). Bruton’s tyrosine kinase inhibition attenuates disease progression by reducing renal immune cell invasion in mice with hemolytic-uremic syndrome. Frontiers in Immunology, 14, 1105181.
Full text: Click here
Publication 2023
Blood Platelets Cells Cortex, Cerebral Fibrin HAVCR1 protein, human Intersectional Framework Microscopy Spleen Vacuole
3
Chest CT Scans for Lung Nodule Evaluation
Chest CT scans were conducted using the SOMATOM Perspective (Siemens Healthineers, Erlangen, Germany), and SOMATOM Definition Flash (Siemens Healthineers, Erlangen, Germany) CT scanner according to the following protocol parameters: patients were in a supine position, and the range was from the apex to the costophrenic angle. Additional parameters were as follows: 90–130 KVp; 50–140 mAs; rotation speed, 0.5 r/s; pitch, 1; slice thickness and interval for axial images, 5 mm/5 mm; and reconstruction by standard algorithm or medium-sharp algorithm after scanning, 1 mm/1 mm.
All CT morphological characteristics were evaluated in the lung window (level, −600 HU; width, 1,600 HU) by two experienced chest radiologists and two thoracic surgeons who were blinded to the pathological results of the nodules. The CT morphological characteristics, including median nodule diameter of the nodule (measured on the lung window), margin (coarse, smooth), vacuole (present, absent), lobulation (present, absent), spiculation (present, absent), pleural indentation (present, absent), and the location in the lung were evaluated with reference to the Fleischner Society’s glossary of terms for thoracic imaging. All CT images were reviewed in random order.
Liu Z., Ran H., Yu X., Wu Q, & Zhang C. (2023). Immunocyte count combined with CT features for distinguishing pulmonary tuberculoma from malignancy among non-calcified solitary pulmonary solid nodules. Journal of Thoracic Disease, 15(2), 386-398.
Publication 2023
CAT SCANNERS X RAY Chest Lung Patients Pleura Radiologist Reconstructive Surgical Procedures Surgeons Vacuole X-Ray Computed Tomography
4
Isolation and Treatment of Synchronised 3D7 Schizonts
Tightly synchronised 3D7 schizonts expressing endogenously GFP-tagged GAPM2 (glideosome-associated protein with multiple membrane spans 256 (link),57 (link)) were isolated from a 10–20 mL culture (5–10% parasitaemia) using 60% Percoll 55 (link), washed twice in pre-warmed RPMI, and treated with 1 µM of the PKG-inhibitor compound 2 (provided by Dr. Mike Blackman, The Francis Crick Institute, UK)58 (link),59 (link), and incubated for 6 h. Segmented schizonts were washed once and then resuspended in pre-warmed RPMI without Albumax or phenol red but with the addition of 1 µM E64 (Sigma) to allow the parasitophorous vacuole to rupture but prevent rupture of the red blood cell membrane. This helped us identify very late stage schizonts in the SEM. Cells were kept warm and vitrified within an hour.
Ferreira J.L., Pražák V., Vasishtan D., Siggel M., Hentzschel F., Binder A.M., Pietsch E., Kosinski J., Frischknecht F., Gilberger T.W, & Grünewald K. (2023). Variable microtubule architecture in the malaria parasite. Nature Communications, 14, 1216.
Full text: Click here
Publication 2023
Cells Erythrocyte Membrane Membrane Proteins Parasitemia Percoll Schizonts Vacuole
5
Orthotopic Xenograft OSCC Mouse Model with EGFR Variants

Protocol full text hidden due to copyright restrictions

Open the protocol to access the free full text link

Publication 2023
Atrophy Cells Cytoplasm Dilatation Fingers Hemorrhage Heterografts HLA-G Antigen Injections, Intraperitoneal Injuries Kidney Light Microscopy Liver Lung Lymph Node Metastasis Males matrigel Mus Necrosis Neoplasms Neutrophil Infiltration Obstetric Delivery Ovum Implantation Pathologists Safety Spleen Therapies, CAR T-Cell Thomsen-Friedenreich antibodies Tissues Tongue Vacuole Zoletil

Top products related to «Vacuole»

1
Fm4 64 by Thermo Fisher Scientific
Sourced in United States, Germany, Japan, China, United Kingdom, Belgium
FM4-64 is a lipophilic styryl dye that is commonly used as a fluorescent membrane stain in biological research. It selectively labels the plasma membrane and can be used to visualize endocytic processes in live cells.
2
Oil red o by Merck Group
Sourced in United States, Germany, China, Japan, United Kingdom, Sao Tome and Principe, Italy, Macao, Australia, France, Switzerland, Spain, India, Poland, Canada
Oil Red O is a fat-soluble dye used in histology and cell biology for the staining of neutral lipids, such as triglycerides and cholesterol esters. It is a useful tool for the identification and visualization of lipid-rich structures in cells and tissues.
3
Dexamethasone (dex) by Merck Group
Sourced in United States, Germany, United Kingdom, China, Japan, Italy, Sao Tome and Principe, Macao, France, Australia, Switzerland, Canada, Denmark, Spain, Israel, Belgium, Ireland, Morocco, Brazil, Netherlands, Sweden, New Zealand, Austria, Czechia, Senegal, Poland, India, Portugal
Dexamethasone is a synthetic glucocorticoid medication used in a variety of medical applications. It is primarily used as an anti-inflammatory and immunosuppressant agent.
4
Indomethacin by Merck Group
Sourced in United States, Germany, United Kingdom, Brazil, Italy, Sao Tome and Principe, China, India, Japan, Denmark, Australia, Macao, Spain, France, New Zealand, Poland, Singapore, Switzerland, Belgium, Canada, Argentina, Czechia, Hungary
Indomethacin is a laboratory reagent used in various research applications. It is a non-steroidal anti-inflammatory drug (NSAID) that inhibits the production of prostaglandins, which are involved in inflammation and pain. Indomethacin can be used to study the role of prostaglandins in biological processes.
5
Insulin by Merck Group
Sourced in United States, Germany, United Kingdom, China, France, Canada, Italy, Sao Tome and Principe, Japan, Switzerland, Macao, Israel, Australia, Spain, Austria, Sweden, Poland, Denmark, New Zealand, Belgium, Portugal, Ireland, Netherlands, Brazil, Colombia, India, Morocco, Argentina
Insulin is a lab equipment product designed to measure and analyze insulin levels. It provides accurate and reliable results for research and diagnostic purposes.
6
Oil red o solution by Merck Group
Sourced in United States, Germany, China, United Kingdom, France, Canada, Japan, Switzerland
Oil Red O solution is a lipophilic dye used to stain neutral lipids and triglycerides in fixed cells or tissues. It is commonly used in histological and cytological applications. The solution provides a red coloration to indicate the presence of lipids.
7
4 6 diamidino 2 phenylindole (dapi) by Merck Group
Sourced in United States, Germany, Japan, United Kingdom, China, Italy, Sao Tome and Principe, France, Macao, Canada, Switzerland, Spain, Australia, Denmark, India, Poland, Israel, Belgium, Sweden, Ireland, Netherlands, Panama, Brazil, Portugal, Czechia, Puerto Rico, Austria, Hong Kong, Singapore
DAPI is a fluorescent dye that binds strongly to adenine-thymine (A-T) rich regions in DNA. It is commonly used as a nuclear counterstain in fluorescence microscopy to visualize and locate cell nuclei.
8
Alcian blue by Merck Group
Sourced in United States, Germany, United Kingdom, Sao Tome and Principe, Japan, China, Belgium, Italy, France, Australia
Alcian blue is a staining dye used in histology and microscopy to detect the presence of acidic polysaccharides, such as glycosaminoglycans, in biological samples. It has a high affinity for sulfated and carboxylated molecules, enabling the visualization of specific tissue structures and cellular components.
9
Softworx software by Cytiva
Sourced in United States, Germany
SoftWoRx is a software solution developed by Cytiva for microscopy imaging and analysis. It provides a comprehensive platform for managing and processing microscopy data, supporting a wide range of imaging techniques and file formats.
10
Hoechst 33342 by Thermo Fisher Scientific
Sourced in United States, Germany, United Kingdom, Japan, China, France, Canada, Spain, Belgium, Italy, Australia, Austria, Denmark, Netherlands, Switzerland, Ireland, New Zealand, Portugal, Brazil, Argentina, Singapore, Poland, Ukraine, Macao, Thailand, Finland, Lithuania, Sweden
Hoechst 33342 is a fluorescent dye that binds to DNA. It is commonly used in various applications, such as cell staining and flow cytometry, to identify and analyze cell populations.

More about "Vacuole"

Vacuoles are membrane-bound organelles found in the cytoplasm of plant, fungal, and some animal cells.
These specialized compartments play crucial roles in storage, digestion, and waste management within the cell.
Vacuoles are often compared to the lysosomes of animal cells, as they can contain digestive enzymes and serve as sites of cellular recycling and degradation.
In plant cells, vacuoles are particularly important for maintaining cell turgor pressure, storing water, and sequestering toxic compounds.
Researchers studying vacuoles may utilize various staining techniques and imaging methods, such as FM4-64 for visualizing membrane dynamics, Oil Red O for lipid detection, and DAPI or Hoechst 33342 for nuclear staining.
Pharmacological agents like Dexamethasone, Indomethacin, and Insulin can also be used to investigate the regulation and function of vacuoles.
Understanding the structure, function, and regulation of vacuoles is essential for advancements in cell biology, plant science, and microbiology.
Powerful imaging software like SoftWoRx can assist researchers in analyzing and quantifying vacuolar dynamics and properties.
By harnessing the latest tools and techniques, scientists can gain deeper insights into this crucial organelle and its role in cellular homeostasis and function.
With the help of AI-driven platforms like PubCompare.ai, researchers can optimize their vacuole studies by easily locating the most reliable and effective protocols from the literature, preprints, and patents.
This can enhance the reproducibility and accuracy of their research, leading to more impactful discoveries in the field of vacuole biology.