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STAT3 Protein

STAT3 protein, a member of the STAT (signal transducers and activators of transcription) family, is a critical regulator of gene expression involved in cellular processes such as cell growth, differentiation, and survival.
It is activated by various cytokines, growth factors, and other stimuli, and plays a key role in the regulation of immune and inflammatory responses.
The STAT3 protein has been implicated in the pathogenesis of various diseases, including cancer, inflammatory disorders, and neurodegenerative conditions.
Understanding the mechanisms of STAT3 regulation and developing targeted therapies that modulate its activity is an area of active research in the biomedical field.

Most cited protocols related to «STAT3 Protein»

1
Two-Step Chromatin Immunoprecipitation Protocol
Cited 26 times
An overview of the two-step ChIP protocol is shown in Fig. 1. The selection of the cross-linker should be undertaken with some understanding of the chemistry and effective cross-linking length. We have had success with DSG, an irreversible cross-linking agent that cross-links NHS esters with an effective radius of approximately 7Å. This agent has been useful for highly efficient cross-linking of NF-κB, STAT3, p300/CBP, RNA Pol II and CDK9 where stimulus – inducible chromatin interactions can be seen (4 (link);5 (link)). A summary of the types of useful cross-linkers, their chemistries, spacing arms, and methods for reversal is shown in Table III. Some demonstrations of their application have been previously reported (2 (link);8 ).
Selection of the appropriate negative control for the immunoprecipitation is an important consideration. We typically include a tube of chromatin immunoprecipitated using pre-immune IgG. This control is important in some approaches for quantification using quantitative real-time genomic PCR (Q-RT-gPCR).
Another critical parameter in the design of the experimental protocol is to decide which type of target identification assay will be employed (Schematically diagrammed in Fig. 1). For downstream analysis using qualitative- or quantitative real-time genomic PCR (Q-RT-gPCR), fragmentation of the chromatin into 500–1000 bp fragments is optimal. However, for downstream analysis using next generation sequencing, fragmentation into smaller 300–500 bp fragments is preferred. These fragmentation methods are described as alternate protocols (Section 3.5). Similarly, the method of decross-linking is different for ChIP-Seq applications, and requires selection of one of the alternate protocols (Section 3.9).
Tian B., Yang J, & Brasier A.R. (2012). Two-step Crosslinking for Analysis of Protein-Chromatin Interactions. Methods in molecular biology (Clifton, N.J.), 809, 105-120.
Publication 2012
Arm, Upper Biological Assay CDK9 protein, human Chromatin Chromatin Immunoprecipitation Sequencing DNA Chips EP300 protein, human Esters Genome Immunoprecipitation Radius Real-Time Polymerase Chain Reaction RELA protein, human RNA Polymerase II STAT3 Protein Vision
2
Comprehensive Germline Variant Calling from TCGA Sequencing Data
Cited 24 times

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Publication 2018
Alleles Arhinia, choanal atresia, and microphthalmia Centromere Diploid Cell Exons FANCL protein, human Fanconi Anemia, Complementation Group I Gene, Cancer Genes Genes, vif Genome Germ-Line Mutation Haplotypes INDEL Mutation Malignant Neoplasms MAP2K2 protein, human Mutation Neoplasms NOTCH2 protein, human Nucleotides Oncogenes Pathogenicity POLD1 protein, human POLH protein, human Rad50 protein, human RAD51C protein, human RAD54L protein, human Splice Donor Site STAT3 Protein Strains Susceptibility, Disease TERT protein, human Tumor Suppressor Genes
3
Identifying Conserved Regulatory Modules
Cited 23 times
The pCRMs contained in PReMod were computed using the method described by Blanchette et al. (18 (link)). We only provide a short overview of the method, and refer the interested reader to that article for more details. At the base of PReMod is a set of individual binding site predictions for TFs whose binding preferences are described by PWMs from the Transfac 7.2 database (27 (link)). Putative human binding sites are scored based on how well the human site and its orthologs in mouse and rat match the matrix [orthology is based on Multiz genome-wide alignments (28 (link))]. Putative mouse sites are computed based on an alignment to the human and dog genomes. More precisely, a binding site's score is a weighted sum of the log-likelihood ratio scores in the three species. The score of the modules reported in PReMod reflect the presence, in a region of 100–1000 bp, of a surprisingly large number of binding sites (or, more precisely, a surprisingly large sum of their individual scores), for a few different PWMs. Specifically, to assign a score to a given genomic region, each PWM is first assigned a ‘matrixScore’, which reflects the surprise associated with the density and quality of predicted sites in that region. This surprise (P-value) depends, among other things, on the length and GC-content of the region and the genome-wide number and scores of predicted sites for the same PWM. The PWM with the highest matrixScore is chosen as first ‘tag’ for the region. Its occurrences are then masked, and the process is repeated, selecting a second tag. Up to five tags can be selected for a given module. In the end, the region is assigned a ‘moduleScore’, which reflects the surprise associated with the combined scores of the tags. Depending on which number of tags gives the most significant result, the lower-scoring tags may be rejected. It is important to mention here that although PWMs chosen as tags for a module are likely to be of interest, other PWMs that were not selected could also correspond to factors binding the module. This is particularly true in the case where two or more different PWMs represent binding sites for factors of the same family (e.g. STAT1 and STAT3). Because factors from the same family tend to have similar PWMs, it is very difficult to distinguish between their binding sites. Since their predicted sites will heavily overlap, only one member of the family will be reported as tag. However, this should not be interpreted as an indication that this member is significantly more likely than its homologs to bind the module. Instead, the user should refer to the ‘matrixScore’ to assess the binding potential of a particular TF.
Genomic regions obtaining significant moduleScores (P-value below e−10) are reported in PReMod. We should however emphasize that the prediction algorithm is not very good at identifying the correct boundaries of the CRMs, and that one pCRM may sometimes actually contain two functionally distinct modules, or one module may be split between two CRMs. We encourage the user to consider all types of evidence, (e.g. regions of inter-species conservation) to decide on the correct CRM boundaries.
Ferretti V., Poitras C., Bergeron D., Coulombe B., Robert F, & Blanchette M. (2006). PReMod: a database of genome-wide mammalian cis-regulatory module predictions. Nucleic Acids Research, 35(Database issue), D122-D126.
Publication 2006
Binding Sites BP 100 Family Member Genome Homo sapiens Mice, House Pokeweed Mitogens STAT1 protein, human STAT3 Protein
4
Experimentally Verified TFBS Detection
Cited 19 times
Experimentally verified transcription factor binding site regions were obtained from publicly available ChIP-seq experiments: a CTCF dataset (ENCSR000DLG) from ENCODE [20 (link)] and STAT3 dataset (GSM288353 [21 (link)]) from GEO [22 (link)]. These were selected because they were available in narrow peak format with peak max values, to give the highest probability of focusing on the true binding site, and because matching high-quality TRANSFAC TF models were available.
Sequences corresponding to 50 bases either side of the maximum signal of each ChIP-seq peak were obtained. This length was chosen to allow sufficient sequence to identify TFBSs, while minimising extraneous sequence. Backgrounds were produced by using 101 base genomic sequences 10,000 bases away from the peak, ensuring that none of the background sequences overlapped with surrounding peaks. CiiiDER scans were performed using deficits of 0.2. TRANSFAC scans were performed with equivalent core and matrix similarity score cut-offs of 0.8. Clover analyses were performed with default values. Prism 7 was used to generate ROC curves and associated area under curve (AUC) values for each program.
Gearing L.J., Cumming H.E., Chapman R., Finkel A.M., Woodhouse I.B., Luu K., Gould J.A., Forster S.C, & Hertzog P.J. (2019). CiiiDER: A tool for predicting and analysing transcription factor binding sites. PLoS ONE, 14(9), e0215495.
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Publication 2019
Base Sequence Binding Sites Chromatin Immunoprecipitation Sequencing Clover CTCF protein, human Genome prisma Radionuclide Imaging STAT3 Protein Transcription Factor
5
Molecular Docking Analysis of CDK and STAT3 Inhibitors
Cited 16 times
The three-dimensional (3D) structure of palbociclib (CID: 5330286) and SH-4-54 (CID: 72188643) were retrieved in SDF file format from the PubChem database, while the 3D structures of NSC765690 and NSC765599 were drawn out in sybyl mol2 format using the Avogadro molecular builder and visualization tool vers. 1.XX [46 (link)] and were subsequently transformed into the protein data bank (PDB) format using the PyMOL Molecular Graphics System, vers. 1.2r3pre (Schrödinger, LLC). The PDB file of the 3D structure of the receptors and crystal structures of apo CDK2 (PDB; 4EK3), CDK4/cyclin D3 (PDB; 3G33), CDK6/cyclin (PDB; 1JOW), and STAT3 (PDB; 4ZIA), were retrieved from the Protein Data Bank. The PDB file formats of the ligands (NSC765690, NSC765599, and palbociclib) and the receptors (STAT3; CDK2, 4, and 6) were subsequently converted into the Auto Dock Pdbqt format using AutoDock Vina (vers. 0.8, The Scripps Research Institute, La Jolla, CA, USA) [47 (link)]. Pre-docking preparation of the receptors followed the removal of water molecules, while hydrogen atoms and Kolmman charges were added accordingly. Molecular docking studies were performed using Autodock VINA software and by following the protocols described in our previous study [3 (link)]. The docking results based on hydrogen bonds and electrostatic and hydrophobic interactions of the best pose of the ligand–receptor complexes were expressed as binding energy values (kcal/mol). PyMOL software was used to visualize H-bond interactions, binding affinities, interacting amino acid residues, binding atoms on the ligands and receptors, and 3D graphical representations of ligand-receptor complexes, while 2D graphical illustrations of ligand-binding interactions were further visualized using Discovery studio visualizer vers. 19.1.0.18287 (BIOVIA, San Diego, CA, USA) [48 ].
Lawal B., Liu Y.L., Mokgautsi N., Khedkar H., Sumitra M.R., Wu A.T, & Huang H.S. (2021). Pharmacoinformatics and Preclinical Studies of NSC765690 and NSC765599, Potential STAT3/CDK2/4/6 Inhibitors with Antitumor Activities against NCI60 Human Tumor Cell Lines. Biomedicines, 9(1), 92.
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Publication 2021
Amino Acids CDK2 protein, human CDK6 protein, human Cyclin D3 Cyclins Electrostatics Hydrogen Hydrogen Bonds Hydrophobic Interactions Ligands palbociclib Proteins STAT3 Protein Versed

Most recents protocols related to «STAT3 Protein»

1
Inhibition of STAT3 Signaling in Multiple Myeloma
Not available on PMC !

Example 3

Human multiple myeloma cancer cells are known to undergo increased cell division through IL-6-triggered STAT3 signaling. Numerous studies have shown that let7a-3p miRNA (SEQ ID NO:1), let7a-5p miRNA (SEQ ID NO:2), miR17-3p miRNA (SEQ ID NO:3), miR17-5p miRNA (SEQ ID NO:4), or miR218-5p miRNA (SEQ ID NO:5) inhibits the activity of transcription factor Signal Transducer and Activator of Transcription 3 (STAT3). Human multiple myeloma cells MM.1S were incubated for 48 hrs daily with 10 μg/ml modified miRNA as indicated and expression of the STAT3 target genes was analyzed by RT-PCR. As shown in FIGS. 2C, 4C, 6C, 8C and 10C, incubation with PS-modified let7a-3p miRNA (SEQ ID NO:1), let7a-5p miRNA (SEQ ID NO:2), miR17-3p miRNA (SEQ ID NO:3), miR17-5p miRNA (SEQ ID NO:4), or miR218-5p miRNA (SEQ ID NO:5) inhibited expression of STAT3 target genes, for example, oncogenic Bcl-xL and/or IL-6 genes.

US11857633B2. Phosphorothioate-conjugated miRNAs and methods of using the same (2024-01-02). City of Hope [US]. Inventors: Andreas Herrmann [US], Hua Yu [US].
Full text: Click here
Patent 2024
Cells Division, Cell DNA, Single-Stranded Figs Gene Expression Genes Homo sapiens Malignant Neoplasms MicroRNAs Multiple Myeloma Oligonucleotides Oncogenes Reverse Transcriptase Polymerase Chain Reaction STAT3 Protein Transcription Factor
2
Let7a-5p miRNA Inhibits STAT3 Signaling in Multiple Myeloma
Not available on PMC !

Example 6

Human multiple myeloma cancer cells are known to undergo increased cell division through IL-6-triggered STAT3 signaling. Numerous studies have shown that let7a-5p miRNA (SEQ ID NO:2) inhibits the activity of Signal Transducer and Activator of Transcription 3 (STAT3). Human multiple myeloma cells MM.1S were incubated for 48 hrs daily with 10 μg/ml polymer-modified let7a-5p miRNA as indicated and expression of the STAT3 target gene, oncogenic Bcl-xL gene, was analyzed by RT-PCR. As shown in FIG. 12B, incubation with PS polymer-modified let7a-5p miRNA inhibited expression of Bcl-xL gene.

US11857633B2. Phosphorothioate-conjugated miRNAs and methods of using the same (2024-01-02). City of Hope [US]. Inventors: Andreas Herrmann [US], Hua Yu [US].
Full text: Click here
Patent 2024
Cells Division, Cell Gene Expression Genes Homo sapiens Malignant Neoplasms MicroRNAs Multiple Myeloma Oncogenes Polymers Reverse Transcriptase Polymerase Chain Reaction STAT3 Protein Sugar Phosphates Vertebral Column
3
Immunohistochemical Profiling of Tumor Markers
Immunohistochemical staining for p-STAT3, p-AKT, MET, E-cadherin, and BIM was performed using the streptavidin-peroxidase method. Briefly, 4-μm-thick sections were deparaffinized, rehydrated, and treated with 0.3% H2O2 to block endogenous peroxidase activity. Following rehydration through graded concentrations of ethanol and autoclave, nonspecific binding sites were blocked with 10% normal goat serum. The sections were then incubated at 4 °C overnight with the following antibodies: rabbit antibodies against human p-STAT3 (Cell Signaling Technology, #4060, dilution 1:100), p-AKT (Cell Signaling Technology, catalog #9145, dilution 1:100), MET (Abcam, catalog #Ab227637, dilution 1:30), E-cadherin (Cell Signaling Technology, catalog #3195, dilution 1:50), and BIM (Cell Signaling Technology, catalog #2933, dilution 1:100). The sections were then incubated with biotinylated peroxidase-labeled anti-rabbit antibody (DakoCytomation, catalog #K4003) for 30 min, followed by incubation with streptavidin–biotin peroxidase complex solution. The chromogen 3,3′-diaminobenzidine tetrahydrochloride was used. The expression of p-STAT3, p-AKT, MET, and BIM was defined in samples as any intensity of antibody staining and ≥ 1% of the tumor33 (link)–36 (link). Samples with no E-cadherin membranous staining in any percentage of the tumor were categorized as a loss of expression, while those with E-cadherin membranous staining were categorized as having preserved expression37 (link). Finally, the sections were lightly counterstained with hematoxylin. The stained tissue sections were independently scored by Dr. Chang-Yao Chu at Chi-Mei Medical Center and other pathologists at NCKUH who were blinded to the patients’ clinical characteristics and outcomes.
Chu C.Y., Lin C.Y., Lin C.C., Li C.F., Wu S.Y., Tsai J.S., Yang S.C., Chen C.W., Lin C.Y., Chang C.C., Yen Y.T., Tseng Y.L., Su P.L, & Su W.C. (2023). Effect of BIM expression on the prognostic value of PD-L1 in advanced non-small cell lung cancer patients treated with EGFR-TKIs. Scientific Reports, 13, 3943.
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Publication 2023
Antibodies Antibodies, Anti-Idiotypic azo rubin S Binding Sites Biotin Cadherins E-Cadherin Ethanol Goat Hematoxylin Homo sapiens Immunoglobulins Neoplasms Pathologists Patients Peroxidase Peroxide, Hydrogen Rabbits Rehydration Serum STAT3 Protein Streptavidin Technique, Dilution Tissue, Membrane Tissue Stains
4
Western Blot Analysis of Signaling Pathways
A549 cells cultured in 2D monolayer or 3D aggregates were harvested and lysed in cell signaling lysis buffer (Merck Millipore) containing protease/phosphatase inhibitor cocktail (Cell Signaling Technology, Inc.). Protein quantification was performed using a Bradford assay kit (Bio-Rad Laboratories, Inc.). A total of 20 µg protein per lane was separated by 10% SDS-PAGE, electroblotted onto Immobilon-P Transfer Membranes (Merck Millipore) and blocked for 1 h at room temperature with 3% BSA (Sigma-Aldrich; Merck KGaA) in TBS-T (0.1% Tween-20). The membranes were then probed with the indicated primary antibodies: phosphorylated (p)-STAT3 (Y705; 1:250; rabbit, monoclonal; cat. no. 9145), STAT3 (1:3,000; rabbit, polyclonal; cat. no. 4904), p-Akt (1:250; rabbit, polyclonal; cat. no. 9271), Akt (1:1,000; rabbit, polyclonal; cat. no 9272), p-ERK (1:10,000; rabbit, polyclonal; cat. no. 9101), ERK (1:4,000; rabbit, polyclonal; cat. no. 9102), p-p38 (T180/Y182; 1:250; rabbit, polyclonal; cat. no. 9211), p38 (1:1,000; rabbit, polyclonal; cat. no. 9212), p-FAK (1:200; rabbit, monoclonal; cat. no. 8556), FAK (1:1,000; rabbit, polyclonal; cat. no. 3285), Mcl-1 (1:250; rabbit, polyclonal; cat. no. 4572), survivin (1:200; rabbit, polyclonal; cat. no. 2803), puma (1:500; rabbit, polyclonal; cat. no. 4976), cyclin D1 (1:250; rabbit, polyclonal; cat. no. 2922), cyclin D3 (1:1,000; mouse, monoclonal; cat. no. 2936), CDK2 (1:1,000; rabbit, monoclonal; cat. no. 2546) (all from Cell Signaling Technology, Inc.), MYLK (1:200; mouse, monoclonal; cat. no. sc-365352; Santa Cruz Biotechnology, Inc.) and GAPDH (1:50,000; rabbit, monoclonal; cat. no. ab190480; Abcam) at 4°C overnight. The membranes were washed and incubated for 1 h at room temperature in a 1:5,000 dilution of HRP-conjugated goat anti-rabbit (monoclonal; cat. no. 7074; Cell Signaling Technology, Inc.) or rabbit anti-mouse (polyclonal; cat. no. P0260; Dako; Agilent Technologies, Inc.) secondary antibodies. Visualization of the protein bands was performed with the SuperSignal™ West Pico PLUS Chemiluminescent Substrate (cat. no. 34580; Thermo Fisher Scientific) or SuperSignal™ West Femto Maximum Sensitivity Substrate (cat. no. 34096; Thermo Fisher Scientific, Inc.). Quantification of the bands was carried out by densitometry using ImageJ version 1.53k software (National Institutes of Health).
Keeratichamroen S., Sornprachum T., Ngiwsara L., Ornnork N, & Svasti J. (2023). p‑STAT3 influences doxorubicin and etoposide resistance of A549 cells grown in an in vitro 3D culture model. Oncology Reports, 49(4), 71.
Publication 2023
A549 Cells Antibodies Biological Assay Buffers CDK2 protein, human Cyclin D1 Cyclin D3 Densitometry GAPDH protein, human Goat Hypersensitivity Immobilon P Mice, House Mitogen-Activated Protein Kinase 3 MYLK protein, human Phosphoric Monoester Hydrolases Protease Inhibitors Proteins Puma Rabbits SDS-PAGE Staphylococcal Protein A STAT3 Protein Survivin Technique, Dilution Tissue, Membrane Tween 20
5
Antiviral Activity of Selenium Nanoparticles
Na2SeO3, vitamin C, chitosan, propidium iodide, and 6-coumarin were purchased
from Sigma. Dulbecco’s modified eagle medium (DMEM) and fetal
bovine serum (FBS) were gained from Gibco. Thiazolyl blue tetrazolium
bromide (MTT) and lyso tracker were used from Sigma in the study.
JNK, p53, caspase-3, BAD, JAK, p-STAT3, Akt, and β-actin monoclonal
antibodies were obtained from Cell Signaling Technology (CST). Madin-Darby
canine kidney cells (MDCK) were obtained from American Type Culture
Collection (ATCC CCL-34). H3N2 influenza virus was provided by Virus
laboratory, Guangzhou Institute of Pediatrics, Guangzhou Women and
Children’s Medical Center, Guangzhou Medical University.
Xu T., Lai J., Su J., Chen D., Zhao M., Li Y, & Zhu B. (2023). Inhibition of H3N2 Influenza Virus Induced Apoptosis by Selenium Nanoparticles with Chitosan through ROS-Mediated Signaling Pathways. ACS Omega, 8(9), 8473-8480.
Publication 2023
Actins Ascorbic Acid Caspase 3 CCL 34 Cells Chitosan coumarin Eagle Influenza A Virus, H3N2 Subtype Kidney LysoTracker Orthomyxoviridae Propidium Iodide Serum STAT3 Protein thiazolyl blue Virus Vaccine, Influenza Woman

Top products related to «STAT3 Protein»

1
Stat3 by Cell Signaling Technology
Sourced in United States, United Kingdom, China, Germany, Japan, Morocco
STAT3 is a protein that plays a key role in cell signaling pathways. It functions as a transcription factor, regulating the expression of genes involved in various cellular processes such as cell growth, differentiation, and survival.
2
P stat3 by Cell Signaling Technology
Sourced in United States, United Kingdom, China, Germany, Macao
P-STAT3 is an antibody that specifically detects phosphorylated STAT3 protein. STAT3 (Signal Transducer and Activator of Transcription 3) is a transcription factor that plays a key role in cellular signaling pathways. Phosphorylation of STAT3 is a crucial step in its activation and regulation of target gene expression.
3
Anti stat3 by Cell Signaling Technology
Sourced in United States, United Kingdom, Germany, China
Anti-STAT3 is a primary antibody product from Cell Signaling Technology. It is designed to detect STAT3 (signal transducer and activator of transcription 3), a transcription factor involved in various cellular processes. This antibody can be used for applications such as Western blotting, immunoprecipitation, and immunohistochemistry.
4
Phospho stat3 by Cell Signaling Technology
Sourced in United States, United Kingdom, China, Morocco, Germany
Phospho-STAT3 is a laboratory reagent that detects the phosphorylation of Signal Transducer and Activator of Transcription 3 (STAT3), a transcription factor involved in various cellular processes. This product allows researchers to study the activation and regulation of STAT3 signaling pathways.
5
β actin by Cell Signaling Technology
Sourced in United States, United Kingdom, China, Germany, Japan, Canada, Morocco, Sweden, Netherlands, Switzerland, Italy, Belgium, Australia, France, India, Ireland
β-actin is a cytoskeletal protein that is ubiquitously expressed in eukaryotic cells. It is an important component of the microfilament system and is involved in various cellular processes such as cell motility, structure, and integrity.
6
Gapdh by Cell Signaling Technology
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GAPDH is a protein that functions as an enzyme involved in the glycolysis process, catalyzing the conversion of glyceraldehyde 3-phosphate to 1,3-bisphosphoglycerate. It is a common reference or housekeeping protein used in various assays and analyses.
7
P akt by Cell Signaling Technology
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P-AKT is a phosphorylated form of the AKT protein, a key signaling molecule involved in various cellular processes. The product is used for the detection and quantification of phosphorylated AKT in biological samples.
8
Pvdf membrane by Merck Group
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PVDF membranes are a type of laboratory equipment used for a variety of applications. They are made from polyvinylidene fluoride (PVDF), a durable and chemically resistant material. PVDF membranes are known for their high mechanical strength, thermal stability, and resistance to a wide range of chemicals. They are commonly used in various filtration, separation, and analysis processes in scientific and research settings.
9
β actin by Merck Group
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β-actin is a protein that is found in all eukaryotic cells and is involved in the structure and function of the cytoskeleton. It is a key component of the actin filaments that make up the cytoskeleton and plays a critical role in cell motility, cell division, and other cellular processes.
10
Anti p stat3 by Cell Signaling Technology
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Anti-p-STAT3 is a laboratory reagent that detects the phosphorylated form of the transcription factor STAT3. It is used to monitor the activation state of STAT3 in cellular signaling pathways.

More about "STAT3 Protein"

The STAT3 (signal transducers and activators of transcription 3) protein is a crucial regulator of gene expression, involved in essential cellular processes such as growth, differentiation, and survival.
This transcription factor is activated by a variety of signaling molecules, including cytokines, growth factors, and other stimuli.
STAT3 plays a key role in the regulation of immune and inflammatory responses, and has been implicated in the pathogenesis of various diseases, including cancer, inflammatory disorders, and neurodegenerative conditions.
Understanding the mechanisms of STAT3 regulation and developing targeted therapies that modulate its activity is an area of active research in the biomedical field.
Researchers often study STAT3 activation by examining the phosphorylation of the protein, using techniques like Western blotting with anti-phospho-STAT3 antibodies.
P-STAT3 (phosphorylated STAT3) is a commonly used marker for STAT3 activation.
Additionally, β-actin and GAPDH are often used as loading controls in these experiments.
To advance STAT3 protein research, scientists may utilize AI-driven platforms like PubCompare.ai to optimize their studies.
These platforms can help researchers locate the best protocols from literature, preprints, and patents, and identify the most reliable and effective methods to conduct their STAT3 protein studies.
By leveraging data-driven decision making, researchers can discover new insights and accelerate their STAT3-related investigations.