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Endocytic Vesicles

Endocytic Vesicles: Membrane-bound organelles involved in the internalization of substances from the extracellular environment.
They play a crucial role in processes like nutrient uptake, signal transduction, and immune response.
Endocytic vesicles form through the invagination of the plasma membrane, capturing materials that are then transported into the cell for processing or degradation.
Studying the mechanisms and regulation of endocytic vesicle formation and trafficking is essential for understanding fundamental cellular functions and developing therapeutics for related disorders.
Leverge the power of PubCompare.ai to optimize your endocytic vesicle research protocols and take your expereinces to new heights.

Most cited protocols related to «Endocytic Vesicles»

Modeling Alzheimer's Disease in iPSC-derived Neurons

Primary fibroblast cultures were established from dermal punch biopsies taken from individuals following informed consent and Institutional Review Board approval. To generate iPSCs, fibroblasts were transduced with MMLV vectors containing the complementary DNAs for OCT4, SOX2, KLF4, c-MYC and ± EGFP. IPSC-derived NPCs were differentiated for 3 weeks, neurons were purified by FACS, and amyloid-β, p-tau/total tau and aGSK-3β were measured on purified control and mutant neurons from multiple lines cultured in parallel for an additional 5 days by multi-spot electrochemiluminescence assays (Meso Scale Diagnostics). Early endosomes were analysed by confocal microscopy on purified neurons co-cultured with human astrocytes (Lonza) for 12 days. To ensure reproducible and consistent data, we found that it is important to differentiate and evaluate neurons from full sets of mutant and control iPSC lines together.

Israel M.A., Yuan S.H., Bardy C., Reyna S.M., Mu Y., Herrera C., Hefferan M.P., Van Gorp S., Nazor K.L., Boscolo F.S., Carson C.T., Laurent L.C., Marsala M., Gage F.H., Remes A.M., Koo E.H, & Goldstein L.S. (2012). Probing sporadic and familial Alzheimer’s disease using induced pluripotent stem cells. Nature, 482(7384), 216-220.

Publication 2012

Amyloid Astrocytes Biological Assay Biopsy Cloning Vectors Diagnosis DNA, Complementary Endocytic Vesicles Ethics Committees, Research Fibroblasts Homo sapiens Induced Pluripotent Stem Cells KLF4 protein, human Microscopy, Confocal Neurons Oncogenes, myc POU5F1 protein, human SOX2 protein, human

Modeling Alzheimer's Disease in iPSC-derived Neurons

Publication 2012

Electromagnetic Stimulation of Neuronal Tissue

A model of a coil with metal terminals was positioned inside a homogeneous volume conductor representing the saline solution or the retinal tissue. The model was implemented using FEM simulations to determine whether such small coils could generate electric fields strong enough to evoke action potentials in neuronal tissue, provided the coils were positioned close to excitable cells. All of the electromagnetic quantities introduced in this work are summarized in Table 1. An inductor is the ideal magnetic field generator, and it stores the magnetic field energy W generated by the supplied electric current i. Simulations were performed by considering low frequencies (that is, where l is the maximum dimension of the object and f₀ is the maximum current frequency) and ignoring the contribution of the displacement currents (that is, ). The optimal μMS coil is an inductor with magnetic energy W: where A is the magnetic potential, and the curl is the magnetic flux density (that is, B = × A). The portion of energy W lost reduces the Q-factor or efficiency and the inductance of the coil. Part of the magnetic energy W in equation (1) is therefore available to elicit neuronal activity. The electric fields and magnetic flux densities were found by solving numerically the following magnetostatics equation39 : where σ is the electrical conductivity expressed in S/m.
The induced currents and electric fields in the tissue are expressed by Faraday's law: where Φ is the scalar potential. Let us now consider the following cylindrical coordinates (r, z, φ), where the coil is in the rz plane (that is, (r, z₀, φ)) and each turn of the coil has coordinates r_i and φ [0;2π]. We assumed that Φ = 0 (ref. 40 (link)), because in an unbounded medium, Φ is only due to free charges and no such sources are present. Furthermore, we consider the frequency domain by assuming time harmonic fields with angular frequency ω and we will perform simulations with the maximum frequency (70 kHz) of the class D amplifier used in the experiments, as the pulse can be represented as 1/2 of a sinusoidal/cosinusoidal function. The induced electric fields E = −jωA (or equation (3) transformed in frequency domain) were found by solving the following quasistatic equation39 : where J^e is the external current, u_φ is the unit vector in the φ-direction and each turn of the coil, approximated by a circle with radius r and potential V_r, has an electric current amplitude derived from:
FEM numerical simulations were conducted to study the microscopic magnetic flux density generated by the MEMS microinductor (Fig. 6). The FEM simulations were performed in Multiphysics 4.2a with the AC/DC module (COMSOL, Burlington MA, USA) using the emqa model or the electromagnetic quasi-static approximation.
The model of induced electric fields was solved for the magnetic vector potential A in equation (4). There were no weak constraints, and all constraints were ideal. The overall geometry consisted of a cylinder of 3.0 mm in radius and 3.0 mm in height and was chosen to study the induced electric field around the coil which contained four different types of objects: a solenoid coil, two terminals, a quartz core and physiological solution. The solenoid consisted of 21 turns (rings) and was 500 μm in height and 500 μm in internal diameter as well as a 5μm×10 μm trace section. The terminals were two cylinders of 200 μm in radius and 200 μm in height. The quartz core consisted of 500 μm diameter and 500 μm in height on top and on the bottom copper terminals. The physiological solution was a cylinder of 3.0 mm in radius and 3.0 mm in height. Table 1 describes the material properties of the coil and the surrounding physiological solution/tissue and the constant values used in the simulations. Further details are provided in Supplementary Methods.

Bonmassar G., Lee S.W., Freeman D.K., Polasek M., Fried S.I, & Gale J.T. (2012). Microscopic magnetic stimulation of neural tissue. Nature Communications, 3, 921-.

Publication 2012

Action Potentials As-A 2 Cells Cloning Vectors Copper Debility Electric Conductivity Electricity Electromagnetics Endocytic Vesicles Magnetic Fields Metals Microscopy Neurons physiology Pulse Rate Quartz Radius Retina Saline Solution Sinusoidal Beds Tissues

Dissecting Substance P-Mediated Signaling

The study was designed to examine the contribution of SP-induced endocytosis of the NK₁R to signal transduction in subcellular compartments, excitation of spinal neurons, and nociception. Endocytosis of the NK₁R was examined in HEK293 cells by using BRET to assess the proximity between the NK₁R and proteins resident in the plasma membrane and early endosomes and by localizing fluorescent SP by confocal microscopy. BRET was also used to examine the assembly of signaling complexes, which were localized in endosomes by immunofluorescence and super-resolution microscopy. Signaling in subcellular compartments of HEK293 cells was studied by expressing genetically encoded FRET biosensors, which allowed analysis of signaling with high spatial and temporal fidelity. NK₁R endocytosis was studied in spinal neurons in slice preparations and in vivo by immunofluorescence and confocal microscopy. To examine the excitation of pain-transmitting neurons, cell-attached patch clamp recordings were made from second-order neurons in slices of rat spinal cord. Nociceptive behavior was evaluated in conscious mice after intraplantar administration of capsaicin, formalin, or CFA. To examine the contribution of NK₁R endocytosis to signaling, neuronal excitation, and nociception, HEK293 cells, rat spinal cord slices, or mice were treated with pharmacological or genetic inhibitors of clathrin, dynamin, or βARRs, or with peptide inhibitors of NK₁R/βARR interactions. Peptidic and small-molecule antagonists of the NK₁R were conjugated to the lipid cholestanol, which facilitated endosomal targeting and retention of antagonists. Cholestanol-conjugated antagonists were used to directly evaluate the contribution of NK₁R signaling in endosomes to SP-induced compartmentalized signaling in HEK293 cells, excitation of spinal neurons, and nociception. Institutional Animal Care and Use Committees approved all studies.

Jensen D.D., Lieu T., Halls M.L., Veldhuis N.A., Imlach W.L., Mai Q.N., Poole D.P., Quach T., Aurelio L., Conner J., Herenbrink C.K., Barlow N., Simpson J.S., Scanlon M.J., Graham B., McCluskey A., Robinson P.J., Escriou V., Nassini R., Materazzi S., Geppetti P., Hicks G.A., Christie M.J., Porter C.J., Canals M, & Bunnett N.W. (2017). Neurokinin 1 receptor signaling in endosomes mediates sustained nociception and is a viable therapeutic target for prolonged pain relief. Science translational medicine, 9(392), eaal3447.

Publication 2017

Aftercare antagonists Biosensors Capsaicin Cell Membrane Proteins Cells Cholestanol Clathrin Consciousness Dynamins Endocytic Vesicles Endocytosis Endosomes Fluorescence Resonance Energy Transfer Formalin HEK293 Cells Immunofluorescence inhibitors Institutional Animal Care and Use Committees Lipids Microscopy Microscopy, Confocal Mus Neurons Nociception Pain Peptides Reproduction Retention (Psychology) Spinal Cord

Stiffness Calculation from Shear Wave Images

A number of methods exist for calculating stiffness from the acquired wave images. The approach we use in the brain can be summarized by three steps. First, we calculate the first temporal harmonic of the vector curl of the acquired wave images. Applying the inversion algorithm to the first temporal harmonic ensures that the shear waves only contain displacements at the frequency of interest, while calculating the curl removes the effects of longitudinal waves that would otherwise produce artifacts in the inversion results [12 ]. For traditional postprocessing techniques, the partial derivatives for the curl calculation are estimated by central differences (i.e., convolution with a [-1, 0, 1] kernel in the direction of interest). Although the curl was not necessary for accurate inversion of the simulated shear waves, it was applied to maintain a consistent postprocessing point spread function between the simulation and in vivo data. Second, the curl images are smoothed to make the stiffness estimate more resistant to noise. We use a filter of the form (1-x²)²(1-y²)²(1-z²)² where x, y and z are linearly spaced from -1 to 1 over the chosen window size [13 (link)]. Typically we use a 5x5x5 window, which is effectively a 3x3x3 filter since the filter will be 0 at the edges of the window. In the third and final step, three elastograms (one for each component of the curl) are calculated from the smoothed curl images using a direct inversion of the Helmholtz equation that is assumed to model the shear wave propagation [14 (link)]. These elastograms are combined into a single estimate using a weighted average based on the amplitude of shear wave motion in each of the components of the curl. Direct inversion calculates a complex shear modulus with the storage modulus as the real part and the loss modulus as the imaginary part. We convert the complex modulus to shear stiffness (the product of wave speed squared and density where density is assumed to be that of water) since shear stiffness is more resistant to noise and all three of these quantities (shear stiffness, storage modulus and loss modulus) are highly correlated with each other in these data. The stiffness is then calculated as the median value within the region of interest (ROI). Using this approach, stiffness in a given voxel is calculated using information from a 7x7x7 neighborhood (a 3x3x3 kernel for each of the curl calculation, smoothing and Laplacian calculation), which would be expected to introduce an edge artifact that is 3 voxels wide. For display purposes only, elastograms were smoothed with a 3x3x3 median filter, while quantitative measures were extracted from the unsmoothed elastograms.

Murphy M.C., Huston J I.I.I., Jack CR J.r., Glaser K.J., Senjem M.L., Chen J., Manduca A., Felmlee J.P, & Ehman R.L. (2013). Measuring the Characteristic Topography of Brain Stiffness with Magnetic Resonance Elastography. PLoS ONE, 8(12), e81668.

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Publication 2013

Brain Cloning Vectors derivatives Displacement, Psychology Elastograms Endocytic Vesicles Indium Inversion, Chromosome

Most recents protocols related to «Endocytic Vesicles»

Endurance and Resistance Exercise Protocols for Aerosol Assessment

For an endurance exercise, we use a spinning class protocol with an average intensity of 63% of the maximum power determined in a graded exercise test until exhaustion. The protocol consisted of a 5 min rest period at the beginning of the measurement as a reference and a 1 h exercise period with 2 × 3 min short drinking breaks. At the beginning and toward the end of the exercise period, the intensity was increased and decreased in steps. Each exercise level was maintained at a constant intensity for 3 min. Fig. 5A shows the intensity profile of the spinning class.
The graded exercise test was performed with each subject at least 2 d apart to the spinning session protocol. The test started with 50 W, and the power was increased by 25 W every 4 min until exhaustion.
All endurance exercises were programmed and run on a cycle ergometer (Ergoline Ergoselect 200, Lode B.V., Netherlands). Four women and four men performed this cycling session during which aerosol particle concentration measurement and respiratory ventilation parameters were assessed.
Both, the graded exercise test and the spinning class session were performed with the aerosol particle measurement system running. By using the same setting, it is possible to check the transferability of the results of the graded exercise test to the practical spinning session.
For this purpose, the respective ventilation and aerosol particle concentration values of the graded exercise test are used according to the power profile of the spinning session, so that a chronological progression can be created.
For the infection risk assessment in a typical gym, also a resistance training study with four women and four men and three different exercises was performed. As in the endurance study, the protocol started with a 5-min resting period as reference. The exercises were leg extensions, biceps curls, and overhead presses. These exercises were selected because they could be performed in the clean air tent, and also the subjects could keep their heads steady and connected to the measurement equipment. For each exercise, three sets with 8 to 10 repetitions were performed. The resistance was adjusted for each exercise and subject to approximately 80% of the 1 RM. Subjects started the sets every 150 s. The individual sets had a duration of approximately 30 s (19 to 42 s). After the third set of each exercise, the break was 4 min long. Fig. 5B shows the exercise protocol.
Due to the short measurement time for these exercise phases (19 to 42 s), the sometimes-low aerosol particle concentration (<80 particles/L), and the counting efficiency of the optical particle counter, it is possible that no particle at all is measured during the exercise phase (19 to 42 s). In these cases, the aerosol particle concentration was set to the resting value.

Schumm B., Heiber M., Grätz F., Stabile L., Buonanno G., Schönfelder M., Hain R., Kähler C.J, & Wackerhage H. (2023). Respiratory aerosol particle emission and simulated infection risk is greater during indoor endurance than resistance exercise. Proceedings of the National Academy of Sciences of the United States of America, 120(9), e2220882120.

Publication 2023

Disease Progression Endocytic Vesicles Ergoline Exercise Tests Head Health Risk Assessment Infection Respiratory Rate Woman

Deciphering the Emergence of Context Selectivity

Can we derive a more intuitive picture of why and how context selectivity increases over learning? We have seen in the previous paragraphs that context selectivity increases because the difference within the first set of curly parenthesis of Equation 141 is positive (while the difference within the second set of curly parenthesis vanishes). To simplify the math, we assume that

c^{A} = - c^{B}

; this condition thus reads:

⟨ Δ k_{i}^{11} Δ k_{i}^{22} ⟩_{i} - ⟨ Δ k_{i}^{11} Δ k_{i}^{(\hat{Q} + 1) (\hat{Q} + 1)} ⟩_{i} > 0.

(With respect to Equation 142, we could get rid of pairs of trials with

s = ((\hat{Q} + 1) 1)

because, when

c^{A} = - c^{B}

, they give identical results to

s = (11)

.)
Equation 160 indicates that, over learning, activity from trial

s = (11)

becomes closer (i.e., more correlated) to activity from trials with the same category and context, such as

s^{'} = (22)

, than trials with the same category but different context, such as

s^{'} = ((\hat{Q} + 1) (\hat{Q} + 1))

. On the contrary, activity from trial

s = (11)

becomes equally close to activity from trials with different category and same context, such as

s^{'} = ((\hat{Q} + 1) 2)

, and trials with different category and different context, such as

s^{'} = (2 (\hat{Q} + 1))

. This can be seen from Equation 159, from which

⟨ Δ k_{i}^{11} Δ k_{i}^{(\hat{Q} + 1) 2} ⟩_{i} - ⟨ Δ k_{i}^{11} Δ k_{i}^{2 (\hat{Q} + 1)} ⟩_{i} = 0

follows.
The geometrical relationships implied by both Equation 160 and Equation 161 can be easily verified in Figure 6—figure supplement 1C, which shows the synaptic drive from simulated circuits; the middle panel shows a circuit for which we have exactly

c^{A} = - c^{B}

. Taken together, Equation 160 and Equation 161 indicate that the increase in context selectivity comes from activity clustering by context over learning; such clustering is, however, category dependent. This leads to the emergence of four statistically distinguishable clouds, one for each combination of category and context. This is visible in simulated activity from Figure 6—figure supplement 1C, and is illustrated in Figure 7A–C.

Mastrogiuseppe F., Hiratani N, & Latham P. (2023). Evolution of neural activity in circuits bridging sensory and abstract knowledge. eLife, 12, e79908.

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Publication 2023

Dietary Supplements Endocytic Vesicles Genetic Selection Vision

Identifying Cuproptosis-Related Long Ncornas

Tsvetkov et al. recently discovered several genes essential for cuproptosis (4 (link)). Those genes identified in either Cu-DDC or elesclomol-Cu group were also included. In addition, the copper importer SLC31A1, copper exporters ATP7A/ATP7B, and lipoylated proteins were considered cuproptosis-related genes. Although experiments have demonstrated that Fe-S cluster proteins were downregulated after elesclomol treatment, the particular mechanism was unclear. Thus, Fe-S cluster proteins were not included in our analysis. Following that, Pearson correlation analysis was performed between cuproptosis-related genes and lncRNAs. Those lncRNAs with |correlation coefficient| >0.3 and P<0.001 were identified as CuRLs.

Wang X., Jing H, & Li H. (2023). A novel cuproptosis-related lncRNA signature to predict prognosis and immune landscape of lung adenocarcinoma. Translational Lung Cancer Research, 12(2), 230-246.

Publication 2023

ATP7A protein, human ATP7B protein, human Copper elesclomol Endocytic Vesicles Genes Multiple Birth Offspring Proteins RNA, Long Untranslated

TCGA-LUAD patients were divided into low- and high-risk groups using the median risk score as the cut-off. Kaplan-Meier survival was applied to evaluate the differences in overall survival (OS) between the low- and high-risk patients. Univariate and multivariate Cox regression analyses were used to investigate whether the risk model based on identified cuproptosis-related lncRNAs was an independent risk factor considering other clinical signatures (age, gender, and stage) in LUAD patients. A predictive nomogram was developed using the R package ‘rms’. The calibration curve and time-dependent receiver operating characteristic curve (ROC) were used to evaluate the predictive ability of the model. Principal component analysis (PCA) was performed to evaluate the grouping ability of the whole genome, cuproptosis genes, all CuRLs, and the identified 10-CuRLs signature.

Wang X., Jing H, & Li H. (2023). A novel cuproptosis-related lncRNA signature to predict prognosis and immune landscape of lung adenocarcinoma. Translational Lung Cancer Research, 12(2), 230-246.

Publication 2023

Endocytic Vesicles Gender Genes Genome Patients Population at Risk RNA, Long Untranslated

Exploring lncRNA-driven Pathways in Cancer

GSVA was applied to explore differences in enrichment scores between low-risk and high-risk groups, using pathways from the Molecular Signatures Database (MSigDB) hallmark set as a reference. GSEA was also performed for each lncRNA of the 10 CuRLs. The correlation between the expression of each lncRNA and other genes was computed. Gene lists were sorted by the value of correlation and then GSEA was performed based on KEGG pathway database with P value cutoff set to 0.05. Protein-coding genes co-expressed with the 10 CuRLs were identified using |Pearson correlation coefficient| >0.5 and P<0.001 as the cut-off. GO and KEGG functional enrichment analyses were carried out to understand the functions of these correlated mRNAs. These analyses were conducted mainly based on R packages ‘GSVA’, ‘msigdbr’, ‘clusterProfiler’ (22 (link)), ‘org.Hs.eg.db’, and ‘enrichplot’.

Wang X., Jing H, & Li H. (2023). A novel cuproptosis-related lncRNA signature to predict prognosis and immune landscape of lung adenocarcinoma. Translational Lung Cancer Research, 12(2), 230-246.

Publication 2023

Endocytic Vesicles Gene Products, Protein Genes Population at Risk RNA, Long Untranslated RNA, Messenger

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More about "Endocytic Vesicles"

Endocytic vesicles, also known as clathrin-coated vesicles or endosomes, are specialized membrane-bound organelles involved in the internalization of materials from the extracellular environment.
These vesicles play a crucial role in a variety of cellular processes, including nutrient uptake, signal transduction, and immune response.
The formation of endocytic vesicles occurs through the invagination of the plasma membrane, capturing materials that are then transported into the cell for processing or degradation.
Understanding the mechanisms and regulation of endocytic vesicle formation and trafficking is essential for understanding fundamental cellular functions and developing therapeutics for related disorders.
Researchers can leverage powerful tools and techniques to optimize their endocytic vesicle research protocols.
The RNeasy FFPE Kit, for example, allows for the extraction of high-quality RNA from formalin-fixed, paraffin-embedded (FFPE) samples, which can be useful for studying the gene expression patterns associated with endocytic vesicle-related processes.
Fluorescent dyes like Hoechst 33342 and LysoTracker Red DND-99 can be used to visualize and track the dynamics of endocytic vesicles and related organelles within cells.
The BacMam 2.0 system, on the other hand, enables the efficient delivery of genetic materials into mammalian cells, facilitating the study of endocytic vesicle-associated proteins and their functions.
The CellLight Early Endosomes-GFP reagent can be used to label early endosomes, providing a valuable tool for monitoring the early stages of endocytic vesicle formation and trafficking.
ZEN software, a powerful image analysis platform, can be utilized to quantify and analyze the data collected from these experiments.
Additionally, the use of common cell culture reagents, such as DAPI for nuclear staining and FBS for cell growth, can be integrated into endocytic vesicle research protocols.
LysoTracker dyes can also be employed to visualize and study the dynamics of lysosomes, which are closely linked to the endocytic pathway.
By leveraging the insights gained from the MeSH term description and the power of PubCompare.ai, researchers can optimze their endocytic vesicle research protocols, leading to enhanced reproducibility, accuracy, and ultimately, a deeper understanding of this crucial cellular process.
The Ab2900 antibody, for instance, can be used to detect and study the localization of endocytic vesicle-associated proteins, such as clathrin, within cellular samples.