PLCG1 Antibody, FITC conjugated

What is the biological significance of PLCG1 in cellular signaling?

PLCG1 (Phospholipase C gamma 1) plays a critical role in cellular signaling as it mediates the production of second messenger molecules diacylglycerol (DAG) and inositol 1,4,5-trisphosphate (IP3). This enzyme is essential for regulating intracellular signaling cascades and becomes activated in response to ligand-mediated activation of receptor-type tyrosine kinases, including PDGFRA, PDGFRB, EGFR, and FGFR1-4. Additionally, PLCG1 functions as a guanine nucleotide exchange factor that binds to the GTPase DNM1, catalyzing GDP dissociation and allowing GTP binding, which enhances DNM1-dependent endocytosis. The protein also plays a significant role in actin reorganization and cell migration processes, making it a critical component in multiple cellular functions .

Why use a FITC-conjugated PLCG1 phospho Y783 antibody in research?

A FITC-conjugated PLCG1 phospho Y783 antibody provides direct fluorescent detection capabilities without requiring secondary antibodies, streamlining experimental workflows. This specific antibody targets the phosphorylated tyrosine 783 residue of PLCG1, which is a critical phosphorylation site that serves as a recognized marker for PLCG1 activation . The FITC conjugation makes this antibody particularly suitable for flow cytometry applications where direct detection of activated PLCG1 can be quantitatively measured in various cell populations. This antibody allows researchers to monitor PLCG1 activation status in response to different treatments or genetic manipulations, providing insights into signaling pathway dynamics in real-time without additional staining steps .

What sample types are compatible with PLCG1 phospho Y783 antibody?

The PLCG1 phospho Y783 antibody has been validated for detecting the phosphorylated form of PLCG1 in human samples. While the primary validated application is flow cytometry, the antibody may potentially be used for other immunodetection methods based on sequence homology predictions. The antibody is generated using a synthetic peptide immunogen corresponding to the human PLCG1 phospho Y783 region, ensuring specificity for the phosphorylated form. For experimental planning, researchers should note that while human reactivity has been confirmed, applications to other species require validation before proceeding with full-scale experiments .

How should the FITC-conjugated PLCG1 antibody be stored for optimal performance?

For maximum stability and antibody performance, store the FITC-conjugated PLCG1 antibody at -20°C or -80°C immediately upon receipt. The antibody is supplied in a buffer containing 50% glycerol, 0.01M PBS at pH 7.4, with 0.03% Proclin 300 as a preservative. It is critical to avoid repeated freeze-thaw cycles as these can degrade both the antibody protein and the FITC fluorophore, resulting in decreased signal intensity and increased background in experiments. When working with the antibody, quickly aliquot into single-use volumes before returning to storage. FITC conjugates should also be protected from prolonged light exposure during storage and handling to prevent photobleaching of the fluorophore, which would compromise detection sensitivity .

How can PLCG1 antibodies be used to investigate cancer progression mechanisms?

Research utilizing PLCG1 antibodies has revealed that PLCG1 overexpression correlates with tumor progression and poor survival outcomes in low-grade glioma (LGG) patients, as verified through The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. To investigate this mechanism, researchers can use PLCG1 antibodies in immunohistochemistry to quantify expression levels across tumor grades and correlate findings with patient survival data. For functional studies, combining PLCG1 antibody detection with PLCG1-targeted siRNA knockdown experiments has demonstrated dramatic effects on growth, migration, and invasiveness of IDH wild-type LGG cell lines. Flow cytometry using FITC-conjugated PLCG1 phospho Y783 antibodies allows researchers to quantitatively track changes in PLCG1 activation status in response to various treatments, providing insights into the molecular mechanisms underlying its role in cancer progression .

What methodologies can be employed to study PLCG1's role in BCR-ABL1 and FLT3-ITD signaling pathways?

To investigate PLCG1's role in BCR-ABL1 and FLT3-ITD signaling pathways, researchers can implement multiple complementary approaches. Co-immunoprecipitation assays have successfully identified a physical association between BCR-ABL1 and PLCG1 in K562 cells, revealing that this interaction occurs in both the presence and absence of BCR-ABL1 kinase activity. Western blotting with phospho-specific antibodies targeting PLCG1 Y783 can be used to monitor PLCG1 activation status following treatment with kinase inhibitors like imatinib, dasatinib, or AC220 (quizartinib). For functional studies, CRISPR-Cas9 mediated PLCG1 knockout in CML and AML cell lines enables assessment of proliferation effects through cell growth competition assays, where parental and knockout cells are mixed in a 1:1 ratio and monitored over time. Flow cytometry with FITC-conjugated phospho-PLCG1 antibodies provides a direct measurement of PLCG1 activation in response to different stimuli or inhibitors, allowing correlation between PLCG1 phosphorylation status and downstream pathway activation .

What experimental controls should be included when using PLCG1 phospho Y783 antibodies in flow cytometry?

When designing flow cytometry experiments using FITC-conjugated PLCG1 phospho Y783 antibodies, several critical controls must be included for reliable data interpretation:

Additionally, perform titration experiments to determine optimal antibody concentration, as both under and over-staining can compromise data quality. When analyzing data, establish gating strategies based on the controls listed above to accurately distinguish positive from negative populations and quantify shifts in phosphorylation status .

How can PLCG1 antibodies help elucidate mechanisms of resistance to tyrosine kinase inhibitors?

PLCG1 antibodies, particularly those detecting the phosphorylated Y783 residue, can be instrumental in investigating mechanisms of tyrosine kinase inhibitor (TKI) resistance. Recent research has demonstrated that PLCG1 knockout increases sensitivity to BCR-ABL1 TKIs like imatinib and dasatinib in CML cells, suggesting PLCG1 involvement in resistance pathways. To study this mechanism, researchers can analyze PLCG1 phosphorylation status in TKI-resistant versus sensitive cell lines using flow cytometry with FITC-conjugated phospho-PLCG1 antibodies. Combining PLC inhibitors (such as U73122) with TKIs provides insight into potential synergistic effects that could overcome resistance. Cell viability assays following drug treatment in PLCG1-manipulated cells can quantify the contribution of this pathway to survival. Additionally, monitoring downstream RAS activation in relation to PLCG1 status helps map the signaling network involved in resistance development, as PLCG1 has been shown to activate RAS through novel mechanisms that may serve as alternative survival pathways when primary kinase targets are inhibited .

What are optimal fixation and permeabilization protocols for PLCG1 phospho-epitope detection?

Detecting phosphorylated PLCG1 requires careful consideration of fixation and permeabilization conditions to preserve phospho-epitopes while allowing antibody access. For optimal results with FITC-conjugated PLCG1 phospho Y783 antibodies, implement the following protocol:

• Harvest cells and wash twice with ice-cold PBS to remove media components

• Fix cells with 4% paraformaldehyde for 10-15 minutes at room temperature

• Wash twice with PBS containing 2% FBS to remove excess fixative

• Permeabilize cells using either:
  a. 90% ice-cold methanol for 30 minutes on ice (preferred for phospho-epitopes)
  b. 0.1% Triton X-100 in PBS for 15 minutes at room temperature

• Wash twice with PBS containing 2% FBS

• Block with 5% normal serum (matching host species of secondary antibody) for 30 minutes

• Incubate with FITC-conjugated PLCG1 phospho Y783 antibody diluted in blocking buffer for 1 hour at room temperature or overnight at 4°C

• Wash three times with PBS containing 2% FBS

• Analyze by flow cytometry, protecting samples from light

For troubleshooting, if phospho-signal is weak, try phosphatase inhibitors (50mM NaF, 10mM Na3VO4) in all buffers. If background is high, increase blocking time or add 0.1% BSA to antibody dilution buffer .

How can phospho-PLCG1 signal be optimized when studying tyrosine kinase inhibitor effects?

Optimizing phospho-PLCG1 signal when studying tyrosine kinase inhibitor effects requires careful experimental design to capture the dynamic nature of phosphorylation events. Implement a time-course analysis following inhibitor treatment to determine the optimal timepoint for detecting changes in PLCG1 phosphorylation status. Since inhibition of BCR-ABL1 kinase activity with imatinib or dasatinib abolishes phosphorylation of PLCG1 at tyrosine 783, establishing a baseline before treatment is essential. Similarly, FLT3-ITD inhibition with AC220 (quizartinib) eliminates PLCG1 phosphorylation in FLT3-ITD-dependent cell lines. To maximize signal detection:

• Include phosphatase inhibitors in all buffers (50mM NaF, 10mM Na3VO4, 1mM β-glycerophosphate)

• Maintain cold chain throughout sample processing to prevent phosphate hydrolysis

• Use gentle cell dissociation methods to prevent signaling artifacts

• Optimize antibody concentration through titration experiments

• Consider cell synchronization to reduce variation in signaling status

• Use appropriate positive controls (e.g., growth factor stimulated cells) alongside inhibitor-treated samples

When analyzing data, calculate the percentage of cells with active phospho-PLCG1 signal and the mean fluorescence intensity to quantify both the proportion of responding cells and the degree of PLCG1 activation .

What strategies can address non-specific binding when using FITC-conjugated antibodies?

Non-specific binding is a common challenge when working with FITC-conjugated antibodies like the PLCG1 phospho Y783 antibody. To minimize this issue and improve signal-to-noise ratio in your experiments, implement the following strategies:

• Optimize blocking conditions by testing different blocking agents:

  • 5-10% normal serum (from the same species as secondary antibody)

  • 1-5% BSA in PBS

  • Commercial blocking buffers specifically designed for flow cytometry

• Implement stringent washing protocols:

  • Increase wash volume (use at least 10-15× cell pellet volume)

  • Add 0.1% Tween-20 to wash buffers to reduce hydrophobic interactions

  • Perform at least 3-4 wash steps after antibody incubation

• Perform antibody titration experiments to determine the optimal concentration that provides maximum specific signal with minimal background

• Include an Fc receptor blocking step if working with cell types that express Fc receptors

• Reduce autofluorescence through:

  • Fresh sample preparation

  • Using red blood cell lysis buffers that preserve white cell morphology

  • Including a quenching step (e.g., 0.1% Sudan Black B for 20 minutes)

• Centrifuge antibody solutions at 14,000g for 10 minutes before use to remove aggregates that could cause non-specific binding

• For intracellular staining, compare different permeabilization reagents to identify the optimal protocol for your specific cell type .

How do you design controls to validate PLCG1 pathway activation in leukemia models?

Designing appropriate controls to validate PLCG1 pathway activation in leukemia models requires a multi-faceted approach that addresses both technical and biological variables. Based on research showing PLCG1's role in BCR-ABL1 and FLT3-ITD signaling pathways, implement the following control strategy:

• Positive control for PLCG1 activation:

  • Stimulate cells with platelet-derived growth factor (PDGF) or epidermal growth factor (EGF) to activate receptor tyrosine kinases known to phosphorylate PLCG1

  • Include a cell line with constitutively active PLCG1 signaling (e.g., K562 for BCR-ABL1 or Molm14 for FLT3-ITD)

• Negative controls for PLCG1 pathway:

  • Generate PLCG1 knockout cell lines using CRISPR-Cas9 as true negative controls

  • Treat cells with specific tyrosine kinase inhibitors (imatinib/dasatinib for BCR-ABL1 or AC220 for FLT3-ITD) to abolish upstream activation

  • Include PLC inhibitor U73122 (using empirically determined IC50 of 5μM in K562 cells with 48hrs treatment)

• Pathway validation controls:

  • Monitor both PLCG1 Y783 phosphorylation and downstream effectors (DAG, IP3, calcium flux)

  • Assess RAS activation status, as PLCG1 has been shown to activate RAS in these models

  • Perform pathway reconstruction experiments by sequentially adding back components in knockout backgrounds

• Experimental validation through functional readouts:

  • Cell proliferation assays (using cell growth competition assays between parental and knockout cells)

  • Apoptosis measurements following TKI treatment (comparing control and PLCG1 knockout cells)

  • Colony formation assays to assess long-term growth potential

This comprehensive control strategy ensures that observed effects are specifically attributable to PLCG1 pathway activation rather than experimental artifacts or alternative signaling mechanisms .

How should flow cytometry data from PLCG1 phospho Y783 antibody staining be analyzed and interpreted?

• Quality assessment and preprocessing:

  • Verify forward/side scatter profiles to identify intact cells and exclude debris

  • Apply doublet discrimination to ensure single-cell analysis

  • Establish viability gating if a viability dye was included

• Phospho-PLCG1 signal analysis:

  • Set positive/negative boundaries using isotype and phosphatase-treated controls

  • Calculate both percentage of positive cells and median fluorescence intensity (MFI)

  • For treatment studies, compute fold-change in MFI relative to baseline

• Statistical analysis:

  • Apply appropriate statistical tests based on experimental design (t-test for two conditions, ANOVA for multiple conditions)

  • Present data as box plots showing distribution across replicates

  • Include individual data points to demonstrate variability

• Biological interpretation:

  • Correlate PLCG1 phosphorylation with concurrent activation of upstream kinases

  • Evaluate relationship between PLCG1 activation and downstream effects

  • Compare results across different cell lines or patient samples to identify patterns

• Data visualization:

  • Present overlay histograms for direct comparison between conditions

  • Use heat maps for time-course or dose-response studies

  • Consider dimensional reduction techniques (tSNE/UMAP) for complex datasets

When interpreting results, remember that phospho-PLCG1 (Y783) signals correlate with activation of receptor tyrosine kinases like BCR-ABL1 and FLT3-ITD, and inhibition of these upstream kinases abolishes PLCG1 phosphorylation .

How can results from PLCG1 studies be integrated with other cancer signaling pathway data?

Integrating PLCG1 signaling data with broader cancer pathway information requires a multi-omics approach that contextualizes PLCG1 function within the complex network of cellular signaling. To effectively integrate these datasets:

This integrative approach has revealed that PLCG1 overexpression correlates with tumor progression and poor survival in low-grade glioma patients, and plays a critical role in RAS activation by BCR-ABL1 and FLT3-ITD in leukemias .

What are the implications of PLCG1's role in RAS activation for therapeutic targeting strategies?

The discovery of PLCG1's critical role in RAS activation downstream of oncogenic tyrosine kinases has significant implications for developing novel therapeutic targeting strategies:

• Alternative pathway targeting: Since PLCG1 partly activates RAS through a novel mechanism distinct from the classical GRB2-SOS pathway, inhibiting PLCG1 may overcome resistance mechanisms that develop against direct tyrosine kinase inhibitors. PLCG1 knockout decreases RAS nucleotide exchange factor activity, suggesting PLCG1 inhibition could suppress RAS activation even when upstream mutations are present .

• Combination therapy rationale: Experimental data shows that combining the PLC inhibitor U73122 with imatinib leads to enhanced reduction in cell viability in K562 CML cells. This suggests a therapeutic strategy where dual targeting of the primary oncogenic kinase and PLCG1 could improve treatment efficacy and potentially overcome resistance. The IC50 for U73122 in K562 cells was determined to be 5μM with 48 hours treatment, providing a baseline for dosing considerations .

• Biomarker development: PLCG1 phosphorylation status at Y783 could serve as a biomarker for:

  • Predicting sensitivity to tyrosine kinase inhibitors

  • Monitoring treatment response

  • Early detection of developing resistance

• Precision medicine approaches: The observation that PLCG1 knockout increases sensitivity to BCR-ABL1 TKIs but not to FLT3 inhibitors indicates leukemia-specific dependencies that could inform personalized treatment strategies. Patients could potentially be stratified based on PLCG1 expression/activation patterns to determine optimal therapeutic approaches .

• Novel drug development opportunities: The understanding that PLCG1 contributes to RAS activation and subsequent proliferation of leukemia cells provides rationale for developing specific PLCG1 inhibitors as a new class of targeted therapies. These could be particularly valuable in contexts where direct RAS inhibition remains challenging .

What novel applications of PLCG1 antibodies are emerging in cancer research?

Emerging applications of PLCG1 antibodies in cancer research are expanding our understanding of signaling networks and creating new diagnostic and therapeutic opportunities:

• Single-cell phospho-signaling analysis: FITC-conjugated PLCG1 phospho Y783 antibodies are enabling researchers to perform high-dimensional, single-cell analysis of signaling heterogeneity within tumor populations. This approach reveals distinct cell subpopulations with differential pathway activation and potential treatment responsiveness that would be masked in bulk analyses.

• Patient-derived xenograft (PDX) model monitoring: PLCG1 antibodies are being utilized to track signaling dynamics in PDX models, providing insight into how patient-specific tumors respond to targeted therapies. This application bridges preclinical and clinical research, potentially accelerating translation of findings.

• Liquid biopsy development: Detection of phosphorylated PLCG1 in circulating tumor cells using highly sensitive flow cytometry approaches may serve as a minimally invasive biomarker for monitoring treatment response and early detection of resistance development.

• Spatial signaling analysis: Advanced microscopy combined with phospho-PLCG1 antibodies enables visualization of signaling microdomains within cells and spatial heterogeneity within tumors, providing context to pathway activation that may influence therapeutic responses.

• Combinatorial targeting strategy development: High-throughput screening approaches utilizing phospho-PLCG1 detection are identifying novel drug combinations that synergistically inhibit cancer cell growth by simultaneously targeting PLCG1 and complementary pathways.

Based on current research showing PLCG1's roles in tumor progression and links to poor survival outcomes, these emerging applications hold promise for developing more effective personalized treatment approaches for cancers with aberrant PLCG1 signaling .

How might understanding PLCG1 phosphorylation improve patient stratification for targeted therapies?

Understanding PLCG1 phosphorylation patterns could significantly enhance patient stratification for targeted therapies through several mechanisms:

• Predictive biomarker development: PLCG1 phosphorylation status may serve as a predictive biomarker for response to tyrosine kinase inhibitors. Research has demonstrated that PLCG1 knockout increases sensitivity to BCR-ABL1 TKIs, suggesting that patients with high PLCG1 activation may require combination approaches or higher drug doses to achieve optimal responses .

• Resistance mechanism identification: Monitoring changes in PLCG1 phosphorylation during treatment could identify emerging resistance mechanisms. Persistent PLCG1 phosphorylation despite BCR-ABL1 or FLT3 inhibition might indicate alternative pathway activation requiring adjustment of therapeutic strategy.

• Cancer subtype classification: Differential patterns of PLCG1 activation across patient samples may reflect distinct molecular subtypes with varying dependency on specific signaling pathways. This could refine current classification systems and guide subtype-specific treatment approaches.

• Novel therapeutic target identification: Analysis of PLCG1 co-activation patterns with other signaling molecules could reveal patient-specific vulnerabilities for targeted intervention. For example, patients with dual activation of PLCG1 and parallel signaling pathways might benefit from combination approaches targeting both mechanisms.

• Multi-parameter stratification models: Integrating PLCG1 phosphorylation data with other molecular features (genetic mutations, gene expression profiles) could generate comprehensive stratification models with improved predictive power for treatment outcomes.

This approach is supported by research showing that elevated PLCG1 expression correlates with tumor progression and poor survival in low-grade glioma patients, and that PLCG1 plays a critical role in RAS activation by oncogenic kinases in leukemia models .

What technological advances might enhance detection of phosphorylated PLCG1 in research and clinical settings?

Technological advances that could enhance detection of phosphorylated PLCG1 in both research and clinical applications include:

• Mass cytometry (CyTOF) integration: Adapting PLCG1 phospho-antibodies for mass cytometry would enable simultaneous detection of dozens of phosphorylation events alongside PLCG1 activation, providing comprehensive signaling network analysis at single-cell resolution. This technology uses metal-tagged antibodies instead of fluorophores, eliminating spectral overlap issues.

• Proximity ligation assays (PLA): This emerging technique can detect protein-protein interactions and post-translational modifications with exceptional sensitivity. For PLCG1, PLA could reveal not only phosphorylation status but also interactions with binding partners like BCR-ABL1, providing mechanistic insights with spatial resolution in intact cells or tissues.

• Automated image cytometry: Combining high-content imaging with machine learning algorithms could enable automated quantification of phospho-PLCG1 levels in tissue sections or complex cellular models, increasing throughput and reducing subjective interpretation.

• Microfluidic phospho-flow technologies: Miniaturized flow cytometry platforms requiring minimal sample volume could enable phospho-PLCG1 analysis from limited patient material, such as fine needle aspirates or pediatric samples, expanding clinical applicability.

• Digital spatial profiling: This technology allows multiplex analysis of proteins and phospho-proteins with spatial context in tissue sections, enabling correlation of PLCG1 activation with microenvironmental features and other signaling events at specific locations within tumors.

• Enhanced fluorophore chemistry: Development of brighter, more photostable fluorophores conjugated to PLCG1 antibodies would improve signal-to-noise ratios, enabling detection of lower abundance phosphorylation events and expanding the dynamic range of measurement.

• Aptamer-based detection systems: DNA/RNA aptamers specifically recognizing phosphorylated PLCG1 could provide alternatives to antibody-based detection with potentially improved reproducibility and production scalability for clinical applications.

These technological advances would facilitate more precise monitoring of PLCG1 activation in research settings and potentially enable translation to clinical applications for patient stratification and treatment monitoring .