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> Anatomy > Cell Component > Cell Wall

Cell Wall

The cell wall is a rigid, semi-permeable layer surrounding the cell membrane of certain organisms, including plants, fungi, and some bacteria.
It provides structural support, protects the cell from mechanical stress, and regulates the passage of molecules in and out of the cell.
The cell wall is composed of various polysaccharides, such as cellulose, chitin, and peptidoglycan, depending on the organism.
It plays a crucial role in cell shape, growth, and division, as well as in plant adaptations to the environment.
Understaning the structure and function of the cell wall is essential for research in areas like plant biology, microbiology, and biotechnology.

Most cited protocols related to «Cell Wall»

1
Tomato Genome Assembly and Annotation
A total of 21 Gb of Roche/454 Titanium shotgun and matepair reads and 3.3 Gb of Sanger paired-end reads, including ~200,000 BAC and fosmid end sequence pairs, were generated from the ‘Heinz 1706’ inbred line (Supplementary Sections 1.1-1.7), assembled using both Newbler and CABOG and integrated into a single assembly (Supplementary Sections 1.17-1.18). The scaffolds were anchored using two BAC-based physical maps, one high density genetic map, overgo hybridization and genome-wide BAC FISH (Supplementary Sections 1.8-1.16 and 1.19). Over 99.9% of BAC/fosmid end pairs mapped consistently on the assembly and over 98% of EST sequences could be aligned to the assembly (Supplementary Section 1.20). Chloroplast genome insertions in the nuclear genome were validated using a matepair method and the flanking regions were identified (Supplementary Sections 1.22-1.24). Annotation was carried out using a pipeline based on EuGene that integrates de novo gene prediction, RNA-Seq alignment and rich function annotation (Supplementary Section 2). To facilitate interspecies comparison, the potato genome was re-annotated using the same pipeline. LTR retrotransposons were detected de novo with the LTR-STRUC program and dated by the sequence divergence between left and right solo LTR (Supplementary Section 2.10). The genome of S. pimpinellifolium was sequenced to 40x depth using Illumina paired end reads and assembled using ABySS (Supplementary Section 3). The tomato and potato genomes were aligned using LASTZ (Supplementary Section 4.1). Identification of triplicated regions was done using BLASTP, in-house generated scripts and three way comparisons between tomato, potato and S. pimpinellifolium using MCscan (Supplementary Sections 4.2-4.4). Specific gene families/groups (genes for ascorbate, carotenoid and jasmonate biosynthesis, cytochrome P450s, genes controlling cell wall architecture, hormonal and transcriptional regulators, resistance genes) were subjected to expert curation/analysis, (Supplementary Section 5). PHYML and MEGA were used to reconstruct phylogenetic trees and MCSCAN was used to infer gene collinearity (Supplementary Section 5.2).
The tomato genome sequence provides insights into fleshy fruit evolution. (2012). Nature, 485(7400), 635-641.
Publication 2012
Anabolism Carotenoids Cell Wall Chromosome Mapping Crossbreeding Cytochrome P450 Fishes Genes Genome Genome, Chloroplast Insertion Mutation jasmonate Lycopersicon esculentum Microtubule-Associated Proteins Physical Examination Retrotransposons RNA-Seq Solanum tuberosum Titanium Transcription, Genetic
2
Benchmark Dataset for Plant Protein Localization
Protein sequences were collected from the Swiss-Prot database at http://www.ebi.ac.uk/swissprot/. The detailed procedures are basically
the same as those elaborated in [13] (link); the only differences are as follows.
(1) To get the updated benchmark dataset, instead of version 49.3 of the
Swiss-Prot database, the version 55.3 released on 29-Apr-2008 was adopted.
(2) In order to make the new predictor also able to deal with proteins
having two or more location sites, the multiplex proteins are no longer excluded in this
study. Actually, according to a statistical analysis on the current database, about
8% of plant proteins were found located in more than one location.
After strictly following the aforementioned procedures, we finally obtained a benchmark
dataset containing 978 different protein sequences, which are distributed
among 12 subcellular locations (Fig. 1); i.e., where represents the subset for the subcellular location of cell membrane, for cell wall, for chloroplast, and so forth; while represents the symbol for “union” in the set
theory. A breakdown of the 978 plant proteins in the benchmark dataset according to their 12 location sites is given in Table 1. To avoid redundancy and homology bias, none of the proteins in has pairwise sequence identity to any other in a same subset. The
corresponding accession numbers and protein sequences are given in Table S1.
Since some proteins in may occur in two or more locations, it is instructive to introduce the
concept of “locative protein” [23] (link), as briefed as follows. A
protein coexisting at two different location sites will be counted as 2 locative
proteins even though the two are with completely the same sequence; if
coexisting at three sites, 3 locative proteins; and so forth. Thus, it follows where is the number of total locative proteins, the number of total different protein sequences, the number of proteins with one location, the number of proteins with two locations, and so forth;
while is the number of total subcellular location sites concerned (for the
current case, as shown in Fig. 1).
For the current 978 different protein sequences, 904 occur in one subcellular location,
71 in two locations, 3 in three locations, and none in four or more locations.
Substituting these data into Eq.2, we have which is fully consistent with the figures in Table 1 and the data in Table S1.
To develop a powerful method for predicting protein subcellular localization, it is very
important to formulate the sample of a protein in terms of the core features that are
intrinsically correlated with its localization in a cell. To realize this, the strategy
by integrating the GO representation and PseAAC representation was adopted in the
original Plant-PLoc [13] (link). In this study, the essence of such a strategy will be
still kept. However, in order to overcome the four shortcomings as mentioned in Introduction for Plant-PLoc [13] (link), a completely different
combination approach has been developed, as described below.
Chou K.C, & Shen H.B. (2010). Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization. PLoS ONE, 5(6), e11335.
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Publication 2010
Amino Acid Sequence Catabolism Cells Cell Wall Chloroplasts Plant Proteins Plants Proteins Staphylococcal Protein A Teaching
3
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.
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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
4
Staphylococcal DNA Isolation and Manipulation
The bacterial strains and plasmids used in this study are described in Table 2. Oligonucleotides were purchased from Integrated DNA Technologies and are listed in Table 3. Genomic DNA was isolated using the Qiagen 100/G genomic tip (Qiagen). Weakening of the staphylococcal cell wall required the addition of 100 µg of lysostaphin (Ambi) into the lysis buffer and incubation at 37°C for 30 min. Plasmids and PCR products were isolated using the Wizard plus kits (Promega), with T4 DNA ligase also purchased from Promega. Plasmids were isolated from staphylococci as described previously (3 (link)). Restriction enzymes, T4 DNA polymerase, and Phusion DNA polymerase were purchased from New England Biolabs. Phire Hotstart DNA polymerase was purchased from Thermofisher. Sanger sequencing was supplied by Eurofins. Routine manipulation of S. aureus and E. coli was performed as described by Monk et al. (3 (link)). X-Gal (5-bromo-4-chloro-3-indolyl-β-d-galactopyranoside; Melford) was used at 50 µg/ml in E. coli and 100 µg/ml in S. aureus. Antibiotics were purchased from Sigma Aldrich and used at the following concentrations: carbenicillin (Car), 100 µg/ml; chloramphenicol (Cm), 10 µg/ml; and kanamycin (Kan), 50 µg/ml (E. coli) and 100 µg/ml (S. aureus).
Monk I.R., Tree J.J., Howden B.P., Stinear T.P, & Foster T.J. (2015). Complete Bypass of Restriction Systems for Major Staphylococcus aureus Lineages. mBio, 6(3), e00308-15.
Publication 2015
5-bromo-4-chloro-3-indolyl beta-galactoside Antibiotics Bacteria Buffers Carbenicillin Cell Wall Chloramphenicol DNA-Directed DNA Polymerase DNA Restriction Enzymes Escherichia coli Galactose Genome Kanamycin Lysostaphin Monks Oligonucleotides Plasmids Promega Staphylococcus Staphylococcus aureus Strains T4 DNA Ligase
5
Quantitative Lineage Mapping and Growth Analysis
Once the cells have been segmented from two different time points, the cells and their progeny can be identified manually. Each mesh is loaded in a separate channel and roughly aligned manually so that the cells outlines match. For each cell in the second time point, the user identify a mother cell with a mouse click (Video 4). The lineage information is then used to compare cell size (areal growth) or the projected signal intensity in the original cells and their daughters.
A segmented mesh contains information about the cells neighborhood, that is, which are the cell walls shared by two cells and where do the cell walls intersect. The mesh can be simplified to contain only vertices necessary to describe each cell contour and the connections between neighbor cells (Figure 4—figure supplement 1). Plant cells do not slide with respect to each other, therefore the junction between cell walls can be used as landmarks to track tissue deformation over time series (Green et al., 1991 (link)). Combined with the cell lineage information, the simplified cellular mesh (Figure 4—figure supplement 1) is used to find the correspondence between cell junctions in meshes extracted from different time points (Figure 4—figure supplement 2). After identifying pairs of junctions conserved in both meshes using the lineage information, we project for each cell the junctions on the average cell plane and compute a best fit of the 2D transformation (translation, rotation, anisotropic scaling) that will minimize the squared distance between pairs of junctions (Goodall and Green, 1986 (link); Routier-Kierzkowska and Kwiatkowska, 2008 (link)). Decomposing the transformation into singular vectors and values gives the PDGs and associated scaling values (PDGmax, PDGmin), that describe anisotropic growth. Anisotropy values used in (Figure 4 and Figure 4—figure supplements 2, 3) were computed according to the following definition: anisotropy = PDGmax/PDGmin.
The cellular mesh can also be used to compute other quantities, such as the tissue curvature (Figure 3—figure supplement 1 and Figure 4—figure supplement 3). In that case the vertices belonging to the cell outline are used to compute the principal curvatures for each cell center, within a given periphery. Color maps resulting from the computation of growth, curvature, signal quantification, etc. can be written to a spreadsheet giving easy access for further processing. Similarly, cell axis vectors can also be exported to be either re-rendered in MorphoGraphX or loaded for further analysis using other software, such as Matlab or Python.
Barbier de Reuille P., Routier-Kierzkowska A.L., Kierzkowski D., Bassel G.W., Schüpbach T., Tauriello G., Bajpai N., Strauss S., Weber A., Kiss A., Burian A., Hofhuis H., Sapala A., Lipowczan M., Heimlicher M.B., Robinson S., Bayer E.M., Basler K., Koumoutsakos P., Roeder A.H., Aegerter-Wilmsen T., Nakayama N., Tsiantis M., Hay A., Kwiatkowska D., Xenarios I., Kuhlemeier C, & Smith R.S. (2015). MorphoGraphX: A platform for quantifying morphogenesis in 4D. eLife, 4, e05864.
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Publication 2015
Anisotropy Cells Cell Wall Cloning Vectors Daughter Dietary Supplements Epistropheus Intercellular Junctions Maritally Unattached Microtubule-Associated Proteins Mus Plant Cells Python Signal Transduction Stem Cells Tissues

Most recents protocols related to «Cell Wall»

1
Truncated Lys68 Endolysin Variants Exhibit Activity
Not available on PMC !

Example 1

Lys68 is a globular endolysin, i.e. does not exhibit an apparent domain structure with an enzymatic domain and a cell wall binding domain, as encountered for various other endolysins. The inventor hypothesized, that Lys68 endolysin may nonetheless exhibit a core region responsible for enzymatic activity and tested this hypothesis with truncated versions of Lys68, namely Lys68(1-132) (SEQ ID NO:32), Lys68(1-148) (SEQ ID NO:33) and Lys68(7-162) (SEQ ID NO:34).

Briefly, the following experiment was carried out: Exponentially growing P. aeruginosa cells were harvested by centrifugation and subsequently resuspended in 0.05 M Tris/HCl pH 7.7 buffer saturated with chloroform. This cell suspension was incubated for 45 minutes at room temperature. Afterwards, cells were washed with 20 mM HEPES pH 7.4 and finally adjusted to an OD600 of ca. 1.5 with 20 mM HEPES pH 7.4. In order to test the muralytic activity, 270 μl of chloroform treated cells were mixed with 30 μl of purified variants of Lys68 in a 96 well plate and the OD600 was monitored in a microplate reader.

The result is shown in FIG. 1. All constructs showed activity.

US11866750B2. Endolysin variant (2024-01-09). SASINAPAS CO., LTD. [TH]. Inventors: Martin Griessl [DE].
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Patent 2024
Cells Cell Wall Centrifugation Chloroform endolysin enzyme activity Enzymes Eye HEPES Inventors Pseudomonas aeruginosa Tromethamine
2
Enzymatic Characterization of Marmoricola sp.
To determine hydrolysis of starch, urea, Tween 20, 40, 60, and 80, the isolate was cultured on TSA at 30°C for a week, as described by Cowan and Steel [37 ]. The enzyme activity was evaluated using API ZYM kit, API 20NE kit (bioMérieux, France), and acid production was tested API 50CH test (bioMérieux, France) according to the manufacturer’s instructions at 30°C for 2 days. The type strains, M. bovistercoris NEAU-LLET and M. pseudoresistence CC-5209T, which are related to KUDC0405T, were analyzed under the same conditions. The cell wall peptidoglycan was analyzed using an amino acid analyzer (L-8900; Hitachi, Japan). To analyze the polar lipids, two-dimensional thin layer chromatography (TLC) analysis were used according to Minnikin et al. [38 (link)]. The fatty acid composition analysis was performed using the Microbial Identification System from cells of the strain KUDC0405T, and reference strains were incubated on TSA at 30°C for 7 days. To determine siderophore production by strain KUDC0405T, chrome azurol S (CAS) media were used as previously described [39 (link), 40 (link)].
Hwang Y.J., Lee S.Y., Son J.S., Youn J.S., Lee W., Shin J.H., Lee M.H, & Ghim S.Y. (2023). Microbacterium elymi sp. nov., Isolated from the Rhizospheric Soil of Elymus tsukushiensis, a Plant Native to the Dokdo Islands, Republic of Korea. Journal of Microbiology and Biotechnology, 33(2), 188-194.
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Publication 2023
Acids Amino Acids Cell Wall chrome azurol S enzyme activity Fatty Acids Hydrolysis Lipids Peptidoglycan Siderophores Starch Steel Strains Thin Layer Chromatography Tween 20 Urea
3
Histological Analysis of Tree Wood Samples
We selected two trees from our best crossdated individuals and extracted a rectangular section of wood of 1 cm width and 1 cm height covering the entire sapwood section for each sample. We next divided the aforementioned pieces into 3–5 cm long segments which were boiled for one hour to soften in a 1:3 solution of water and glycerin. Using a slide microtome (Gärtner and Nievergelt, 2010 (link)) we cut transverse histological wood sections (15–20 µm thick) for image analysis. The microsections were stained using a mix of safranin (1%) and astra blue (0.5%) solutions in order to dye the cell walls containing lignin (red) or only cellulose (blue). The sections were then fixed and permanently mounted onto glass microscope slides using a synthetic resin [EukittTM, Quick-hardening mounting medium (Sigma-Aldrich)]. Digital images were captured using an AmScope 12 MP Color CMOS Digital Eyepiece Microscope Camera installed in a light transmission microscope (Leitz, Laborlux 11, Type 020-435.028) using magnifications of 40X, 100X and 200X. We created panoramic photographs stitching together multiple overlapping images using PtGui Pro (v. 8.3.3) software. This process allowed us to compare the anatomical and sanded scanned images used previously for measuring ring-width side-by-side.
Pacheco-Solana A., Oelkers R., D’Arrigo R., Santos G.M., Rodriguez-Caton M., Tejedor E., Ferrero E., Fuentes A.F., Maldonado C, & Andreu-Hayles L. (2023). Radiocarbon and wood anatomy as complementary tools for generating tree-ring records in Bolivia. Frontiers in Plant Science, 14, 1135480.
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Publication 2023
Astra Blue Cellulose Cell Wall Chronic multifocal osteomyelitis Fingers Glycerin Light Microscopy Lignin Microscopy Microtomy Resins, Synthetic safranine T Transmission, Communicable Disease Trees
4
Cell Proliferation Dynamics in Lizard Telencephalon
To assess the distribution of proliferating cells in the VZ, lizards injected with [3H]-thymidine with a survival time of 1.5 h were used (n = 5). The VZ was divided in six different regions, including three sulcal zones (sulcus medalis, sulcus lateralis and sulcus ventralis/terminalis) and three intersulcal zones (intersulcus corticalis, intersulcus lateralis, and intersulcus septalis) (Figure 1B). The number of [3H]-thymidine labeled cells was counted in all these regions relative to the total number of cells. This quantification was performed in two telencephalic levels: one pre-commissural (anterior) and one post-commissural (posterior), analyzing for each level a total of 7 semithin sections which were 9 μm apart to avoid counting the same cell twice. Different types of counts were performed by quantifying the total number of labeled cells/1000 cells considering sulci vs. intersulcal regions, comparing between the different sulci and intersulcal regions and differentiating between the pre- and post-commissural levels for each animal.
To characterize the ultrastructure of VZ proliferative cells and their derivatives, the brains of specimens with 1.5, 6, 12, 24, and 72 h survival times were examined. Between 50 and 150 [3H]-thymidine-positive ([3H]-thy+) cells were analyzed for each survival time, including at least two different antero-posterior levels per lizard. These cells were studied by transmission electron microscopy (TEM) to determine their ultrastructural characteristics. Counts were also made of the number of cells in mitosis (M phase) labeled relative to the total number of [3H]-thy+ cells.
The analysis of specimens with long survival times (1, 3, 6, and 12 months) focused mainly on the cell layer of the MC, although we also investigated whether there were labeled cells in the walls of the LVs. Within the MC we analyzed the ultrastructure of 25–50 [3H]-thy+ cells from each survival time to see to which neuronal type they corresponded.
González-Granero S., Font E., Desfilis E., Herranz-Pérez V, & García-Verdugo J.M. (2023). Adult neurogenesis in the telencephalon of the lizard Podarcis liolepis. Frontiers in Neuroscience, 17, 1125999.
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Publication 2023
Animals Brain Cells Cell Survival Cell Wall derivatives Division Phase, Cell Lizards Mitosis Neurons Telencephalon Thymidine Transmission Electron Microscopy
5
Quantifying Meristematic Characteristics in Lateral Roots
The lateral roots were fixed in formalin–acetic acid–methanol (FAA) according to the method of Livingston et al.62 (link). Blocks were sectioned with a Leica RM2016 rotary microtome at 5 μm. Fast green FCF and safranin O were used to stain the sections. The lignified or corkified cell wall and vessel element will be dyed red and other tissues will be dyed green. Images were captured with a Nikon Eclipse E100. Subsequently, the length of the meristematic zone and the number of meristematic cells were analyzed using ImageJ 1.53.
Wang S., Feng Y., Lou Y., Niu J., Yin C., Zhao J., Du W, & Yue A. (2023). 3-Hydroxy-3-methylglutaryl coenzyme A reductase genes from Glycine max regulate plant growth and isoprenoid biosynthesis. Scientific Reports, 13, 3902.
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Publication 2023
Acetic Acid Blood Vessel Cell Wall Fast Green FCF Formalin Meristem Methanol Microtomy Plant Roots safranine T Stains Tissues

Top products related to «Cell Wall»

1
Calcofluor white by Merck Group
Sourced in United States, Germany, United Kingdom, Japan, Italy, China, France, India
Calcofluor white is a fluorescent brightening agent used in microscopy and staining applications. It binds to cellulose and chitin in cell walls, allowing for the visualization of fungal and other microbial structures.
2
Propidium iodide by Merck Group
Sourced in United States, Germany, United Kingdom, China, Italy, Japan, France, Sao Tome and Principe, Macao, Canada, Spain, India, Belgium, Australia, Israel, Switzerland, Poland, Ireland, Argentina, Austria, Brazil, Sweden, Portugal, New Zealand, Netherlands, Slovakia, Norway, Hungary, Czechia, Denmark
Propidium iodide is a fluorescent dye commonly used in molecular biology and flow cytometry applications. It binds to DNA and is used to stain cell nuclei, allowing for the identification and quantification of cells in various stages of the cell cycle.
3
Mutanolysin by Merck Group
Sourced in United States, Germany, United Kingdom, Australia, Poland, Canada, Japan, Israel, Macao, Ireland, Switzerland
Mutanolysin is a muralytic enzyme that cleaves the peptidoglycan layer of bacterial cell walls. It is commonly used in laboratory settings for the preparation and analysis of bacterial samples.
4
Congo red by Merck Group
Sourced in United States, Germany, India, France, Italy, Sao Tome and Principe, China, Poland, United Kingdom
Congo red is a synthetic dye used as a laboratory reagent. It is a dark red crystalline powder that is soluble in water and certain organic solvents. Congo red is commonly used as an indicator in various analytical and diagnostic applications, particularly in the identification of amyloid proteins.
5
Lysozyme by Merck Group
Sourced in United States, Germany, United Kingdom, Japan, China, Poland, Ireland, Switzerland, Italy, Sao Tome and Principe, India, Canada, France, Belgium, Australia, Netherlands, Singapore, Denmark, Macao, Portugal
Lysozyme is an enzyme that catalyzes the hydrolysis of 1,4-beta-linkages between N-acetylmuramic acid and N-acetyl-D-glucosamine residues in peptidoglycan, which is a major component of the cell walls of gram-positive bacteria. This function makes lysozyme an effective antimicrobial agent.
6
Lyticase by Merck Group
Sourced in United States, Germany, China, France
Lyticase is a laboratory enzyme used for the digestion of bacterial cell walls. It is derived from the fungus Arthrobacter luteus and is commonly used in molecular biology and microbiology experiments to facilitate the extraction of cellular contents, including DNA and RNA.
7
Sorbitol by Merck Group
Sourced in United States, Germany, United Kingdom, China, Denmark, France, Italy, New Zealand
Sorbitol is a sugar alcohol that is commonly used in various laboratory applications. It serves as a humectant, sweetener, and cryoprotectant in various formulations and processes. Sorbitol is a crystalline powder with a mild, sweet taste.
8
Lsm 880 by Zeiss
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The LSM 880 is a laser scanning confocal microscope designed by Zeiss. It is a versatile instrument that provides high-resolution imaging capabilities for a wide range of applications in life science research.
9
Calcofluor white cfw by Merck Group
Sourced in United States, Germany
Calcofluor white (CFW) is a fluorescent dye that binds to cellulose and chitin, which are structural components in the cell walls of fungi, plants, and some bacteria. CFW fluoresces under ultraviolet (UV) or blue light, making it a useful tool for microscopic visualization and detection of these materials.
10
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.

More about "Cell Wall"

The cell wall is a crucial component of many organisms, including plants, fungi, and bacteria.
It is a rigid, semi-permeable layer that surrounds the cell membrane, providing structural support, protection from mechanical stress, and regulating the passage of molecules in and out of the cell.
The cell wall is composed of various polysaccharides, such as cellulose, chitin, and peptidoglycan, depending on the organism.
Understanding the structure and function of the cell wall is essential for research in areas like plant biology, microbiology, and biotechnology.
Techniques like Calcofluor white, Propidium iodide, Mutanolysin, Congo red, Lysozyme, Liticase, and Sorbitol can be used to study the cell wall.
Imaging tools like the LSM 880 confocal microscope can also be used to visualize and analyze the cell wall.
The cell wall plays a key role in cell shape, growth, and division, as well as in plant adaptations to the environment.
It is a fasinating and complex structure that continues to be an important area of study for researchers.
By utilizing the latest tools and techniques, such as those provided by PubCompare.ai, scientists can optimize their cell wall research and make data-driven decisions to advance our understanding of this critical cellular component.