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Cytosol

Q: What are the common challenges in cytosol research?

One of the key challenges in cytosol research is ensuring reproducibility and accuracy of results. Cytosol studies often involve complex protocols and methodologies, making it difficult to compare findings across different studies. Additionally, identifying the most effective products and techniques for cytosol analysis can be time-consuming and labor-intensive.

Cytosol, the fluid-filled region within a cell that is outside the cell nucleus and organelles.
It is the site of numerous metabolic and synthetic activities essential for cell function.
Cytosol research is critical for understanding cellular processes and developing effective therapeutics.
Optimize your cytosol studies with PubCompare.ai, an AI-driven platform that enhances reproducibility and research accuarcy.
Easily locate the best protocols from literature, pre-prints, and patents using our comparison tools.
Identify the most effective products and methodologies to streamline your cytosol research.

Most cited protocols related to «Cytosol»

Benchmarking Cell Deconvolution Algorithms

We performed two in silico experiments to assess the detection limits of different deconvolution algorithms. In the first experiment (Supplementary Fig. 6), we used the same cell line GEPs described above to compare CIBERSORT and RLR with five other GEP deconvolution methods^{4 (link)–8 (link)}. We evaluated detection limit using Jurkat cells (spike-in concentrations of 0.5%, 1%, 2.5%, 5%, 7.5%, and 10%), whose reference GEP (median of three replicates in GSE11103) was added into randomly created background mixtures of the other three blood cell lines. Five mixtures were created for each spike-in concentration. Predicted Jurkat fractions were assessed in the presence of differential tumor content, which we simulated by adding HCT116 (described above) in ten even increments, from 0% to 90%. Of note, we also used the same marker or signature genes described for simulated tumors (above). In a second experiment (Supplementary Fig. 7a), we compared CIBERSORT with QP^{5 (link)}, LLSR^{4 (link)}, PERT^{6 (link)}, and RLR. We spiked naïve B cell GEPs from the leukocyte signature matrix into four random background mixtures of the remaining 21 leukocyte subsets in the signature matrix. The same background mixtures were used for each spike-in. We also tested the addition of unknown content by adding defined proportions (0 to 90%) of randomly permuted expression values from a naïve B cell reference transcriptome (median expression profile from samples used to build LM22, Supplementary Table 1). We then repeated this analysis for each of the remaining leukocyte subsets in LM22 (Supplementary Fig. 7b).

Newman A.M., Liu C.L., Green M.R., Gentles A.J., Feng W., Xu Y., Hoang C.D., Diehn M, & Alizadeh A.A. (2015). Robust enumeration of cell subsets from tissue expression profiles. Nature methods, 12(5), 453-457.

Publication 2015

B-Lymphocytes BLOOD Cell Lines Cytosol Genes Jurkat Cells Leukocyte Count Leukocytes Neoplasms Transcriptome

UpSetR: Visualizing Set Intersections

Here we present an R package named ‘UpSetR’ based on the ‘UpSet’ technique (Lex et al., 2014 (link); Lex and Gehlenborg, 2014 ) that employs a matrix-based layout to show intersections of sets and their sizes. It is implemented using ggplot2 (Wickham, 2009 ) and allows data analysts to easily generate generate UpSet plots for their own data. UpSetR support three input formats: (i) a table in which the rows represent elements and columns include set assignments and additional attributes; (ii) sets of elements names; and (iii) an expression describing the size of the set intersections as introduced by the venneuler package (Wilkinson, 2012 (link)). UpSetR provides support for the visualization of attributes associated with the elements contained in the sets, enabling researchers to explore and characterize the intersections. UpSetR differs from the original UpSet technique as it is optimized for static plots and for integration into typical bioinformatics workflows. We also provide a Shiny app that allows researchers to create publication-quality UpSet plots directly in a web browser.
UpSetR visualizes intersections of sets as a matrix in which the rows represent the sets and the columns represent their intersections (Fig. 1 and Supplementary Figs. S1 and S2 for comparisons of Venn and Euler diagrams with UpSetR plots). For each set that is part of a given intersection, a black filled circle is placed in the corresponding matrix cell. If a set is not part of the intersection, a light gray circle is shown. A vertical black line connects the topmost black circle with the bottommost black circle in each column to emphasize the column-based relationships. The size of the intersections is shown as a bar chart placed on top of the matrix so that each column lines up with exactly one bar. A second bar chart showing the size of the each set is shown to the left of the matrix.

Conway J.R., Lex A, & Gehlenborg N. (2017). UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics, 33(18), 2938-2940.

Publication 2017

Cytosol Figs Intersectional Framework Light

CIBERSORTx: Bulk RNA Deconvolution Tool

Similar to its predecessor, CIBERSORTx was developed within a web framework with its back-end based on R and PHP and hosted at http://cibersortx.stanford.edu. This web framework minimizes inherent dependencies on specific hardware, software packages and libraries, and file-system attributes. Users are presented with a detailed guide employing several step-by-step Tutorials, and allowing the recreation of key figures in this work, including for each step depicted in Figure 1. Through this interface, CIBERSORTx allows users to process gene expression data representing a bulk admixture of different cell types, along with (1) a signature gene file that enumerates the genes defining the expression profile for each cell type of interest. For the latter, users can either use existing/curated signature matrices for reference cell types, or can create custom signature gene files by providing the reference gene expression profiles of pure cell populations. Specifically, to create a custom signature gene matrix, users can provide single-cell RNA sequencing data or data from bulk sorted samples, along with the phenotypic identities of single cell types or cell populations of interest.
Given these input files, CIBERSORTx allows (2) imputation of the fractional representations of each cell type present in the mixture, similar to its predecessor. However, unlike CIBERSORT, CIBERSORTx now supports deconvolution from bulk RNA-seq data by implementing the critical batch correction methods described above. CIBERSORTx also allows imputation of GEPs for individual in silico purified cell-types in two distinct modes as described above (i.e., (3) group-mode and (4) high-resolution). The resulting imputed cell fractions and imputed cell type-specific GEPs are then rendered as heat maps, tables, stacked bar plots for visualization and downloading. In addition, customizable t-SNE plots are automatically generated for high-resolution purification results.
The interactive CIBERSORTx user interface is powered by the jQuery JavaScript library and various open source libraries (including phpMailer, idiorm, blueimp jQuery-File-Upload, DataTables, phpExcel and mPDF), with the graphical user interface of the website powered by Twitter Bootstrap 2.3.2 and R Shiny. The site runs on an Apache server on a virtual machine and stores user and job data in a MySQL database. However, users have complete control over their data and can delete them at will. Each user’s environment includes example datasets used for benchmarking, tutorials for the use of CIBERSORTx and preparation of input data, and other example files.

Newman A.M., Steen C.B., Liu C.L., Gentles A.J., Chaudhuri A.A., Scherer F., Khodadoust M.S., Esfahani M.S., Luca B.A., Steiner D., Diehn M, & Alizadeh A.A. (2019). Determining cell-type abundance and expression from bulk tissues with digital cytometry. Nature biotechnology, 37(7), 773-782.

Publication 2019

cDNA Library Cell Microarray Analysis Cells Cytosol Dietary Fiber Gene Expression Genes Microtubule-Associated Proteins Phenotype RNA-Seq

PhenoGraph: Single-cell Clustering

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Open the protocol to access the free full text link

Publication 2015

Cytosol Phenotype Population Group Python

Deconvolving Cell Type Fractions from Bulk Transcriptomics

A number of computational methods have been proposed to infer cell type abundance, cell type-specific GEPs, or both from bulk tissue expression profiles^{2 (link)–8 (link)}. These methods generally assume that biological mixture samples can be modeled as a system of linear equations, where a single mixture transcriptome m with n genes is represented as the product of H and f, where H represents an n × c cell type expression matrix consisting of expression profiles for the same n genes across c distinct cell types, and f represents a vector of size c, consisting of cell type mixing proportions.
To infer cell type abundance using this linear model within CIBERSORTx, let M be an n × k matrix with n genes and k mixture GEPs, let matrix B be a subset of H containing discriminatory marker genes for each of the c cell subsets (i.e., signature or basis matrix^{15 (link),74 (link),75 (link)}), and let M’ be the subset of M that contains the same marker genes as B. Given M’ and B, the following equation can then be used to impute F, a c × k fractional abundance matrix with columns [f₁,f₂,…,f_k]:

B \times F_{•, j} = M_{•, j}^{'}, 1 \leq j \leq k

where F_i,j≥ 0 for all i, j, the system is overdetermined (i.e., n > c), and expression data in M’ and B are represented in non-log linear space⁷⁶. (Note that M_i,• and M_•,j denote row i and column j of matrix M, respectively). Many methods either normalize F or impose an additional constraint on F such that for each mixture sample, the inferred mixing coefficients sum to one, allowing F to be directly interpreted as cell type proportions (with respect to the cell subsets in B)^{3 (link)}. We previously introduced CIBERSORT as a method to estimate F using an implementation of ν-support vector regression, a machine learning technique that is robust to noise, unknown mixture content, and collinearity among cell type reference profiles^{15 (link)}. CIBERSORT was used to impute F in this work, and within this imputation workflow, the batch correction scheme described below was used for all cross-platform analyses, unless stated otherwise (Supplementary Table 1).

Publication 2019

Biological Models Biopharmaceuticals Cells Cloning Vectors Cytosol Dietary Fiber Genes Genes, vif Genetic Markers Matrix-M Tissues Transcriptome

Most recents protocols related to «Cytosol»

Potentiation of Gluconeogenesis by LbNOX

Not available on PMC !

Example 6

As a result of its ability to elevate the intracellular ratio of NAD⁺ to NADH, LbNOX is also capable of potentiating gluconeogenesis in mammalian cells (e.g., human cells). The first step of gluconeogenesis from lactate is the conversion of lactate to pyruvate, which requires cytosolic NAD⁺. Gluconeogenesis from lactate was significantly increased when primary hepatocytes were transduced with either LbNOX or mitoLbNOX-containing adenovirus (FIG. 3D). The effect of LbNOX and mitoLbNOX on gluconeogenesis was commensurate to their effect on lactate/pyruvate ratio (FIG. 3B), suggesting that cytoplasmic and not mitochondrial NAD⁺/NADH is important for regulation of gluconeogenesis rate from lactate. These examples demonstrate the ability of water-forming NADH oxidases to control the rate of gluconeogenesis upon introducing these enzymes to mammalian cells.

US11866736B2. Protein prostheses for mitochondrial diseases or conditions (2024-01-09). The General Hospital Corporation [US]. Inventors: Vamsi Mootha [US], Denis Titov [US], Valentin Cracan [US], Zenon Grabarek [US].

Patent 2024

Adenovirus Vaccine Cells Cytoplasm Cytosol Enzymes Gluconeogenesis Hepatocyte Homo sapiens Lactates Mammals Mitochondria NADH Protoplasm Pyruvates Water-Splitting Enzyme of Photosynthesis

Targeting ADAR1 in High Interferon Tumors

Not available on PMC !

Example 17

Since interferon signaling is spontaneously activated in a subset of cancer cells and exposes potential therapeutic vulnerabilities, it was tested whether there is evidence for similar endogenous interferon activation in primary human tumors. An IFN-GES threshold was computed to predict ADAR dependency across the CCLE cell lines and was determined to be a z-score above 2.26 (FIG. 66, panel A). This threshold was applied to The Cancer Genome Atlas (TCGA) tumors, to identify primary cancers with similarly high interferon activation. Restricting the analysis to the 4,072 samples analyzed by TCGA with at least 70% tumor purity as estimated by the ABSOLUTE algorithm (Carter et al. (2012) Nat. Biotechnol. 30:413-421), 2.7% of TCGA tumors displayed IFN-GESs above this threshold (FIG. 66, panel B and. GSEA of amplified genes in these high purity, high interferon tumors revealed the top pathway as “Type I Interferon Receptor Binding”, comprising 17 genes that all encode type I interferons and are clustered on chromosome 9p21.3 (FIG. 67).

Furthermore, analysis of TCGA copy number data showed that the interferon gene cluster including IFN-β (IFNβI), IFN-ε (IFNE), IFN-ω (IFNWI), and all 13 subtypes of IFN-α on chromosome 9p21.3, proximal to the CDKN2A/CDKN2B tumor suppressor locus, is one of the most frequently homozygously deleted regions in the cancer genome. The interferon genes comprise 16 of the 26 most frequently deleted coding genes across 9,853 TCGA cancer specimens for which ABSOLUTE copy number data are available (FIG. 66, panels C and D). Interferon signaling and activation, both in tumors with high IFN-GESs or deletions in chromosome 9p, therefore represent a biomarker to stratify patients who benefit from interferon modulating therapies.

In summary, specific cancer cell lines have been identified with elevated IFN-β signaling triggered by an activated cytosolic DNA sensing pathway, conferring dependence on the RNA editing enzyme, ADAR1. In cells with low, basal interferon signaling, the cGAS-STING pathway is inactive and PKR levels are reduced (FIG. 68, panel A). Upon cGAS-STING activation, interferon signaling and PKR protein levels are elevated but ADAR1 is still able to suppress PKR activation (FIG. 68, panel B). However, once ADAR1 is deleted, the abundant PKR becomes activated and leads to downstream signaling and cell death (FIG. 68, panel C). This is also shown in normal cells lines (e.g. A549 and NCI-H1437) once exogenous interferon is introduced (FIG. 68, panel D). ADAR1 deficiency in cell lines with high interferon levels, whether from endogenous or exogenous sources, led to phosphorylation and activation of PKR, ATF4-mediated gene expression, and apoptosis. Recent studies have shown that cGAS activation and innate interferon signaling, induced by cytosolic DNA released from the nucleus by DNA damage and genome instability (Mackenzie et al. (2017) Nature 548:461-465; Harding et al. (2017) Nature 548:466-470), led to elevated interferon-related gene expression signatures, which have been linked to resistance to DNA damage, chemotherapy, and radiation in cancer cells (Weichselbaum et al. (2008) Proc. Natl. Acad. Sci. USA 105:18490-18495). In high-interferon tumors, blocking ADAR1 might be effective to induce PKR-mediated apoptotic pathways while upregulating type I interferon signaling, which could contribute to anti-tumor immune responses (Parker et al. (2016) Nature 16:131-144). Alternatively, in tumors without activated interferon signaling, ADAR1 inhibition can be combined with localized interferon inducers, such as STING agonists, chemotherapy, or radiation. Generation of specific small molecule inhibitors targeting ADAR1 exploits this novel vulnerability in lung and other cancers and serves to enhance innate immunity in combination with immune checkpoint inhibitors.

US11873486B2. Modulating dsRNA editing, sensing, and metabolism to increase tumor immunity and improve the efficacy of cancer immunotherapy and/or modulators of intratumoral interferon (2024-01-16). Dana-Farber Cancer Institute, Inc. [US]. Inventors: Matthew Meyerson [US], William N. Haining [US], Jeffrey Ishizuka [US], Robert Manguso [US], Hugh Gannon [US].

Patent 2024

agonists Apoptosis ATF4 protein, human Biological Markers CDKN2A Gene Cell Death Cell Lines Cell Nucleus Cells Chromogranin A Chromosome Deletion Chromosomes, Human, Pair 3 Cytosol DNA Damage Electromagnetic Radiation Enzymes Gene, Cancer Gene Clusters Gene Expression Genes Genome Genomic Instability Homo sapiens IFNAR2 protein, human Immune Checkpoint Inhibitors Immunity, Innate inhibitors Interferon-alpha Interferon Inducers interferon omega 1 Interferons Interferon Type I Lung Malignant Neoplasms Neoplasms Oncogenes Patients Pharmacotherapy Phosphorylation Proteins Psychological Inhibition Response, Immune Tumor Suppressor Genes

Water-Forming NADH Oxidases Restore Proliferation

Not available on PMC !

Example 8

GiNOX, a water-forming NADH oxidase derived from Giardia intestinalis, and mitoGiNOX are capable of restoring the proliferation of mammalian cells cultured in pyruvate-depleted media and in the presence of antimycin, a complex III inhibitor. HeLa Tet3G cells cultured in the presence of varying concentrations of pyruvate demonstrated a diminished pyruvate-dependency in the presence of antimycin when GiNOX and mitoGiNOX were expressed in these cells (FIG. 13). Notably, both GiNOX and mitoGiNOX were capable of alleviating the pyruvate auxotrophy, which further illustrates that cytosolic water-forming NADH oxidases can ameliorate the effects of a defective respiratory chain, as these enzymes need not be targeted to the mitochondria in order to restore redox balance.

Patent 2024

antimycin Antimycin A Cell Proliferation Cells Culture Media Cytosol Electron Transport Complex III Enzymes Eukaryotic Cells Giardia lamblia HeLa Cells Mammals Mitochondria NADH Oxidation-Reduction Pyruvate Respiratory Chain Water-Splitting Enzyme of Photosynthesis

Quantifying Voltage-Dependent Ca2+ Release Dynamics

Detection of fluorescence from the Ca²⁺-sensitive dye dialyzed into the fiber’s cytosol was achieved with a Zeiss LSM 800 microscope equipped with a 63× oil immersion objective (numerical aperture 1.4). Standard green and red configurations were used for detection of the fluorescence of fluo-4 and rhod-2, respectively. Voltage-activated fluorescence changes were imaged using the line-scan mode (x,t) of the system. They were expressed as F/F₀ with F₀ as the baseline fluorescence. In experiments designed to follow the effect of probenecid after acute application of the drug, the changes in resting fluorescence F₀ were normalized to F₀ in the control condition at the beginning of the experiment; this value is referred to as _intialF₀. In graphs presenting fluorescence transients, the y-scale bar corresponds to the indicated multiple of F₀. Quantification of the Ca²⁺ release flux (dCa_Tot/dt) underlying the rhod-2 Ca²⁺ transients was performed as previously described (Lefebvre et al., 2011 (link); Kutchukian et al., 2016 (link)). The resting Ca²⁺ concentration was assumed to be 100 nM in all conditions, except for results shown in Figs. 8 and 9, for which the change in resting Ca²⁺ concentration induced by probenecid was implemented in the Ca²⁺ release flux calculation. For this, the resting Ca²⁺ level in the presence of probenecid was calculated from the increase in resting fluorescence, assuming an initial resting [Ca²⁺] level of 0.1 µM, and F_max/F_min and K_D values for rhod-2 of 30 and 1.63 µM, respectively (Sanchez et al., 2021 (link)). The voltage-dependence of the peak Ca²⁺ release flux from each muscle fiber was fitted with a Boltzmann function:

\frac{d C a_{T o t}}{d t} = \frac{M a x \frac{d C a_{T o t}}{d t}}{\{1 + \exp [(V_{0.5} - V) / k]\}},

with Max dCa_Tot/dt the maximum Ca²⁺ release flux, V_0.5 the mid-activation voltage, and k the steepness factor.

Jaque-Fernandez F., Allard B., Monteiro L., Lafoux A., Huchet C., Jaimovich E., Berthier C, & Jacquemond V. (2023). Probenecid affects muscle Ca2+ homeostasis and contraction independently from pannexin channel block. The Journal of General Physiology, 155(4), e202213203.

Publication 2023

Cytosol Figs Fluo 4 Fluorescence Microscopy Muscle Tissue Pharmaceutical Preparations Probenecid Radionuclide Imaging rhod-2 Submersion Transients

Quantification of cGAS Localization in Cells

4–5 d following transduction, 500,000 cells were plated on 0.1% poly-L-lysine in water-coated (#8920; Sigma-Aldrich) coverslips and left for 30 min to attach. Cells were fixed in PBS 4% paraformaldehyde for 20 min at RT. Coverslips were washed twice in PBS and quenched with 500 μl of freshly prepared PBS glycine (375 mg glycine in 50 ml of PBS) for 10 min at RT. Cells were then permeabilized and simultaneously blocked with PBS, 0.2 % BSA, 0.05% saponin, and 1% goat serum (#G9023; Sigma-Aldrich) for 30 min at RT. Coverslips were incubated with primary antibody against cGAS (clone D1D3G) or isotype control (clone DA1E) at a concentration of 0.085 μg/ml in PBS, 0.2% BSA, and 0.05% saponin, and left overnight at 4°C in a moist chamber. Coverslips were washed five times with PBS, 0.2% BSA, and 0.05% saponin, and incubated in secondary antibody goat anti-rabbit IgG at 1:400 (#A21246; Invitrogen) for 45 min at RT. Coverslips were washed five times with PBS, 0.2% BSA, and 0.05% saponin, rinsed in water, and mounted on a glass slide using 10 µl fluoromount-G with DAPI (#00-4959-520; Invitrogen). Glass slides were allowed to dry in a 37°C chamber for 30 min and stored at 4°C. Images were acquired on a Leica DmI8 inverted microscope equipped with an SP8 confocal unit using a 40× (1.3 NA) oil objective. Image analysis was performed using Fiji software (Schindelin et al., 2012 (link)). Homemade scripts were used to analyze cGAS localization. All images were smoothened using a filtering of mean radius 1 pixel. For each Z-stacks, an optical section in the middle of the nuclei was chosen. Then, for each condition, a binary mask of the nuclei and a mask of the cell were obtained by applying a threshold respectively on the DAPI signal or the cGAS signal to define nuclear and cytosolic regions. To avoid out-of-focus cells and to be sure that measurement will be done inside nuclei, a filter was applied to keep only cells with a section larger than 20 µm² containing a nucleus section larger than 16 μm². Finally, average cGAS and GFP intensities were measured in the whole cell, the nuclei, and the cytosol (defined by the whole cell excluding the nuclear region).

Jeremiah N., Ferran H., Antoniadou K., De Azevedo K., Nikolic J., Maurin M., Benaroch P, & Manel N. (2023). RELA tunes innate-like interferon I/III responses in human T cells. The Journal of Experimental Medicine, 220(5), e20220666.

Publication 2023

anti-IgG Cell Nucleus Cells Chromogranin A Clone Cells Cytosol DAPI Glycine Goat Immunoglobulin Isotypes Immunoglobulins Lysine Microscopy paraform Poly A Rabbits Radius Saponin Serum Strains Vision

Top products related to «Cytosol»

Nuclear cytosol fractionation kit by Abcam

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The Nuclear/Cytosol Fractionation Kit is a laboratory product designed for the extraction and separation of nuclear and cytosolic cellular components. It provides a standardized procedure for the isolation of these subcellular fractions for further analysis.

Ne per nuclear and cytoplasmic extraction reagent by Thermo Fisher Scientific

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The NE-PER Nuclear and Cytoplasmic Extraction Reagents are a set of buffers designed to facilitate the isolation of nuclear and cytoplasmic protein fractions from eukaryotic cells. The reagents enable the separation of these cellular compartments, allowing for further analysis or study of the extracted proteins.

Paris kit by Thermo Fisher Scientific

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The PARIS™ Kit is a nucleic acid isolation kit designed to purify RNA and DNA from a variety of sample types. The kit utilizes a spin column-based format to efficiently capture and elute nucleic acids, providing a simple and reliable method for sample preparation.

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Protease inhibitor cocktail is a laboratory reagent used to inhibit the activity of proteases, which are enzymes that break down proteins. It is commonly used in protein extraction and purification procedures to prevent protein degradation.

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Fura-2 AM is a fluorescent calcium indicator used for measuring intracellular calcium levels. It is a cell-permeable derivative of the parent compound Fura-2. Fura-2 AM can be loaded into cells, where intracellular esterases cleave off the acetoxymethyl (AM) ester group, trapping the Fura-2 indicator inside the cell.

Mitochondria cytosol fractionation kit by Abcam

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The Mitochondria/Cytosol Fractionation Kit is a laboratory tool designed to separate and isolate mitochondria and cytosol fractions from cells. It provides a simple and reliable method to obtain these cellular components for further analysis.

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The NE-PER Nuclear and Cytoplasmic Extraction Kit is a laboratory product designed to separate and extract nuclear and cytoplasmic cellular components. It provides a reliable and consistent method for the isolation of these cellular fractions.

What is the importance of cytosol research?

Cytosol research is critical for understanding cellular processes and developing effective therapeutics. The cytosol, the fluid-filled region within a cell outside the nucleus and organelles, is the site of numerous metabolic and synthetic activities essential for cell function. Studying the cytosol helps researchers elucidate the intricate workings of cells and unlock new opportunities for therapeutic interventions.

What are the common challenges in cytosol research?

How can PubCompare.ai help with cytosol research?

PubCompare.ai is an AI-driven platform that can enhance the efficiency and accuracy of cytosol research. The platform allows researchers to screen protocol literature more efficiently, leveraging AI to pinpoint critical insights. PubCompare.ai can help researchers identify the most effective protocols related to cytosol for their specific research goals, highlighting key differences in protocol effectiveness and enabling them to choose the best option for reproducibility and accuarcy.

Are there different types or variations of cytosol?

Yes, there are different types or variations of cytosol that can be studied. For example, the cytosol can be further divided into the aqueous and particulate fractions, each with its own unique composition and functions. Additionally, the cytosol can be examined in different cell types or under various physiological or pathological conditions, providing insights into the dynamic nature of this cellular compartment.

What are some specific applications of cytosol research?

Cytosol research has a wide range of applications, from understanding basic cellular processes to developing new theraputic approaches. Researchers may study the cytosol to investigate metabolic pathways, signal transduction mechanisms, protein folding and trafficking, or the role of cytosolic components in disease pathogenesis. Cytosol studies can also inform the development of targeted therapies, such as those that modulate cytosolic enzymes or signaling cascades.

More about "Cytosol"

Cytosol, the intracellular fluid-filled region outside the cell nucleus and organelles, is a critical component of cellular function.
This aqueous matrix hosts numerous metabolic and synthetic processes essential for cell survival and proliferation.
Cytoplasm, the cytosolic compartment, is the site of protein synthesis, energy production, signal transduction, and various other vital cellular activities.
Researchers studying the cytosol utilize a range of techniques to isolate and analyze this cellular fraction, including Nuclear/Cytosol Fractionation Kits, NE-PER Nuclear and Cytoplasmic Extraction Reagents, and PARIS™ Kits.
These tools enable the separation of nuclear and cytosolic components, facilitating the study of specific cellular processes and signaling pathways.
Protease inhibitor cocktails are often employed to preserve the integrity of cytosolic proteins during extraction and analysis.
PVDF membranes are commonly used in Western blotting techniques to detect and quantify cytosolic proteins of interest.
Calcium-sensitive dyes, such as Fura-2 AM, allow researchers to monitor cytosolic calcium levels, which are crucial for numerous cellular functions, including signaling, metabolism, and organelle dynamics.
Fractionation techniques, like the Mitochondria/Cytosol Fractionation Kit and Membrane and Cytosol Protein Extraction Kit, enable the isolation of specific subcellular compartments, enabling the study of organelle-cytosol interactions and the role of the cytosol in various cellular processes.
Optimizing cytosol research with cutting-edge tools and methodologies is essential for understanding cellular mechanics and developing effective therapeutic strategies.
PubCompare.ai, an AI-driven platform, can enhance the reproducibility and accuracy of cytosol studies by providing access to the best protocols from literature, preprints, and patents, as well as identifying the most effective products and methodologies to streamline your research.