PLCG1 Antibody

What is PLCG1 and what cellular functions does it regulate?

PLCG1 (phospholipase C, gamma 1) is a 149 kDa enzyme that catalyzes the hydrolysis of 1-phosphatidyl-1D-myo-inositol 4,5-bisphosphate to produce 1D-myo-inositol 1,4,5-trisphosphate and diacylglycerol, two important second messengers in signal transduction pathways . PLCG1 functions as a major substrate for heparin-binding growth factor 1 (acidic fibroblast growth factor)-activated tyrosine kinase and plays critical roles in the regulation of cell growth, differentiation, migration, invasion, cell spreading, and immune responses .

The protein is found in various subcellular compartments, including the cytosol and plasma membrane, allowing it to participate in multiple signaling pathways . PLCG1 is particularly important in the intracellular transduction of receptor-mediated tyrosine kinase activators, forming complexes with receptors such as EGF and PDGF, which leads to phosphorylation of PLCG1 at specific tyrosine residues (Tyr771, 783, and 1248) . Dysregulation of PLCG1 has been implicated in several pathological conditions, including cancer, autoimmune disorders, and neurological conditions, making it an important research target .

What types of PLCG1 antibodies are available for research applications?

Several types of PLCG1 antibodies are available for research purposes, each with specific characteristics and applications:

When selecting an antibody, researchers should consider the specific phosphorylation site they wish to detect (if applicable), the host species, the experimental application, and the species reactivity required for their research model . For detection of phosphorylation-dependent activation, phospho-specific antibodies like the Phospho-PLCγ1 (Ser1248) are essential to distinguish between inactive and active forms of the protein .

How can I verify the specificity of a PLCG1 antibody?

Verifying antibody specificity is critical for generating reliable research data. For PLCG1 antibodies, several validation approaches are recommended:

• Western blot analysis: Confirm a single specific band at approximately 149 kDa (the expected molecular weight of PLCG1) . Multiple bands may indicate non-specific binding or protein degradation. Compare results using various cell lines known to express PLCG1, such as Jurkat, MCF-7, HepG2, SH-SY5Y (human) or C6, RH35 (rat) .

• Positive and negative controls: Include a positive control (e.g., cell lines known to express PLCG1) and a negative control (e.g., PLCG1 knockdown cells using siRNA) .

• Phospho-specificity validation: For phospho-specific antibodies, treat samples with and without phosphatase to confirm the antibody's specificity for the phosphorylated form of the protein .

• Cross-reactivity testing: If the antibody is intended for use across multiple species, verify reactivity in each species separately before conducting comparative studies .

• Immunoprecipitation followed by mass spectrometry: This approach can provide definitive evidence of antibody specificity by identifying the precise proteins being recognized.

The antibody datasheet often provides validation data, but researchers should perform their own validation in the specific experimental context to ensure reliable results .

What are the optimal conditions for using PLCG1 antibodies in Western blotting?

Successful Western blotting with PLCG1 antibodies requires careful optimization of several parameters:

Sample preparation and electrophoresis conditions:

• Use 5-20% SDS-PAGE gels for optimal separation, as PLCG1 is a large protein (149 kDa)

• Run at 70V (stacking gel) and 90V (resolving gel) for 2-3 hours for proper separation

• Load approximately 30 μg of protein per lane under reducing conditions

Transfer and detection protocol:

• Transfer proteins to a nitrocellulose membrane at 150 mA for 50-90 minutes

• Block the membrane with 5% non-fat milk in TBS for 1.5 hours at room temperature

• Incubate with primary antibody (e.g., anti-PLCG1) at a dilution of 1:1000 overnight at 4°C

• Wash with TBS-0.1% Tween three times for 5 minutes each

• Probe with an appropriate secondary antibody (e.g., goat anti-rabbit IgG-HRP)

Phospho-specific considerations:

• For phospho-specific antibodies (e.g., Phospho-PLCγ1 Ser1248), maintain phosphorylation status by including phosphatase inhibitors in lysis buffers

• Consider using both phospho-specific and total PLCG1 antibodies to calculate the phosphorylation ratio, which often provides more meaningful data than absolute phosphorylation levels

The expected band size for PLCG1 is approximately 149 kDa, and researchers should be vigilant about verifying this molecular weight to ensure specificity .

How should I design immunohistochemistry experiments to detect PLCG1 in tissue samples?

Immunohistochemistry (IHC) requires specific optimization for PLCG1 detection in tissue samples:

Tissue preparation and antigen retrieval:

• Fix tissue samples with 4% paraformaldehyde to preserve protein structure

• Perform heat-induced epitope retrieval (HIER) using citrate buffer (pH 6.0) or EDTA buffer (pH 9.0) to expose antigenic sites that may be masked during fixation

Staining protocol and optimization:

• Incubate sections with rabbit polyclonal anti-PLCG1 antibody at a 1:100 dilution in 1% bovine serum albumin (BSA)/phosphate-buffered saline (PBS) at room temperature for 1 hour

• Counterstain with hematoxylin and mount with appropriate mounting medium

• Include positive control tissues known to express PLCG1 (e.g., certain cancer tissues) and negative controls (primary antibody omitted)

Scoring and quantification:

• Implement a semi-quantitative scoring system that considers both percentage of PLCG1-positive cells and staining intensity

• Score percentage of stained cells as: 0 (0%), 1 (1–10%), 2 (11–50%), 3 (51–80%), 4 (>80%)

• Evaluate staining intensity as: 0 (negative), 1+ (weak), 2+ (moderate), 3+ (strong)

• Calculate final scores by multiplying the percentage score with the intensity score

This scoring system allows for quantitative comparison between different samples and experimental conditions, facilitating statistical analysis of results .

What approaches can be used to study PLCG1 gene amplification in tumor samples?

Two primary approaches are used to study PLCG1 gene amplification in tumor samples:

Fluorescence in situ hybridization (FISH):

• Use fluorescent PLCG1 FISH probe alongside a control probe (e.g., CEP2/CDC42EP2)

• Fix samples with 4% paraformaldehyde and immobilize on slides

• Co-incubate overnight with FISH probes (50 nmol/L) at 37°C

• Visualize using a fluorescence microscope and digitally capture images

• Calculate the ratio of PLCG1 signals to control probe signals to determine amplification status

TCGA copy number variation (CNV) analysis:

• Extract CNV data from cancer genomics databases like TCGA and cBioportal

• Transform CNV values into segment means using the formula: Segment mean = log2(CNV/2)

• Calculate copy number from segment means: 2 * (2^segment mean)

• Interpret results: diploid regions have segment mean = 0, amplified regions have positive values, and deletions have negative values

• Correlate PLCG1 copy number with mRNA expression levels using Pearson correlation analysis

These complementary approaches provide robust data on PLCG1 amplification, with FISH offering direct visualization in individual samples and TCGA analysis enabling large-scale genomic investigations across patient populations .

How is PLCG1 activity regulated through phosphorylation?

PLCG1 activity is tightly regulated through a complex phosphorylation pattern involving multiple kinases and phosphorylation sites:

Key phosphorylation sites and responsible kinases:

• Tyrosine phosphorylation: PLCγ forms complexes with growth factor receptors (e.g., EGF and PDGF receptors), leading to phosphorylation at Tyr771, Tyr783, and Tyr1248

• Syk kinase phosphorylates PLCγ1 at Tyr783, which is crucial for activating its enzymatic activity

• EGFR phosphorylates PLCγ1 at Tyr783, causing a conformational change that enables interaction between the PLCγ1 SH3 domain and Akt proline-rich motifs

• Akt subsequently phosphorylates PLCγ1 at Ser1248, further modulating its activity

Functional consequences of phosphorylation:

• Phosphorylation at Tyr783 is particularly critical for activating PLCG1's catalytic function

• The conformational change induced by phosphorylation exposes the catalytic domain and facilitates substrate binding

• Sequential phosphorylation at multiple sites creates a regulatory cascade with precise temporal control

Inhibitory phosphorylation:

• While not specific to PLCG1, studies on related phospholipase C isoforms (e.g., PLCβ3) show that phosphorylation by PKA or PKC at certain sites (e.g., Ser1105 in PLCβ3) can inhibit activity

• This bidirectional regulation through activating and inhibitory phosphorylation provides fine-tuned control over PLCG1 signaling

The phosphorylation status of PLCG1 can be studied using phospho-specific antibodies, which recognize PLCG1 only when phosphorylated at specific residues (e.g., Phospho-PLCγ1 (Ser1248) Antibody) .

What role does PLCG1 play in cancer progression and how can this be studied?

PLCG1 has emerged as an important factor in cancer progression, with multiple mechanisms that can be investigated using various experimental approaches:

PLCG1's oncogenic mechanisms:

• Elevated PLCG1 expression is associated with tumor growth and worse prognosis in IDH wild-type lower-grade gliomas (LGGs)

• PLCG1 regulates cancer cell migration, invasion, and spreading, contributing to metastatic potential

• Recurrent PLCG1 mutations causing constitutive activation have been described in various cancers

• PLCG1 mediates intracellular signaling cascades downstream of tyrosine kinase receptors that drive proliferation

Experimental approaches to study PLCG1 in cancer:

• Expression analysis:

  • Analyze PLCG1 mRNA and protein expression in tumor vs. normal tissues using qPCR and Western blotting

  • Correlate expression levels with clinical parameters and patient outcomes using databases like TCGA and CGGA

• Functional studies:

  • Conduct PLCG1 knockdown using targeted siRNA (e.g., si1303) in cancer cell lines

  • Assess effects on proliferation, migration, and invasion in vitro

  • Evaluate tumor growth in mouse models following PLCG1 depletion

• Mechanistic investigations:

  • Study PLCG1 amplification using FISH and CNV analysis

  • Investigate the relationship between PLCG1 amplification and overexpression using correlation analysis

  • Examine PLCG1 phosphorylation status in tumor samples using phospho-specific antibodies

• Therapeutic targeting:

  • Test PLC inhibitors (e.g., U73122) on cancer cell lines in vitro and in mouse models

  • Measure tumor growth suppression and pathway inhibition as indicators of therapeutic efficacy

Research has demonstrated that higher PLCG1 expression correlates with poor clinical outcomes in certain cancers, suggesting its potential as both a prognostic biomarker and therapeutic target .

How can I overcome common challenges when using PLCG1 antibodies in Western blotting?

Researchers frequently encounter technical challenges when working with PLCG1 antibodies in Western blotting. Here are solutions to common problems:

High molecular weight protein detection issues:

• Problem: Poor transfer of large proteins like PLCG1 (149 kDa)

• Solutions:

  • Use a wet transfer system rather than semi-dry

  • Extend transfer time to 90 minutes at 150 mA

  • Reduce gel percentage to 5-20% to improve protein migration

  • Add SDS (0.1%) to transfer buffer to facilitate large protein transfer

Weak or absent signal:

• Problem: Insufficient antibody binding or protein degradation

• Solutions:

  • Optimize antibody concentration (1:1000 is typically effective)

  • Extend primary antibody incubation to overnight at 4°C

  • Include protease inhibitors in lysis buffer to prevent degradation

  • Store samples at -20°C with glycerol (50%) and sodium azide (0.02%) for stability

Multiple or non-specific bands:

• Problem: Non-specific antibody binding

• Solutions:

  • Increase blocking time to 1.5 hours using 5% non-fat milk

  • Wash thoroughly with TBS-0.1% Tween (3 times, 5 minutes each)

  • Use antigen affinity-purified antibodies to improve specificity

  • Validate with positive controls (e.g., Jurkat, MCF-7, HepG2, SH-SY5Y cells for human samples)

Phospho-specific detection issues:

• Problem: Loss of phosphorylation signal

• Solutions:

  • Include phosphatase inhibitors in all buffers

  • Avoid repeated freeze-thaw cycles

  • Prepare fresh lysates whenever possible

  • Use phosphorylation-promoting treatments in positive controls (e.g., EGF stimulation)

Recommended sample preparation:

• Optimal cell lysis buffer: PBS with protease inhibitors, phosphatase inhibitors

• Sample storage: -20°C in buffer containing 0.02% sodium azide and 50% glycerol (pH 7.3)

• Protein loading: 30 μg per lane under reducing conditions

Following these troubleshooting strategies should significantly improve PLCG1 detection in Western blotting experiments.

What considerations are important when selecting and validating PLCG1 siRNAs for knockdown experiments?

Successful PLCG1 knockdown experiments require careful consideration of siRNA design, validation, and experimental controls:

siRNA design and selection:

• Test multiple siRNA sequences targeting different regions of PLCG1 mRNA (e.g., si336, si1303, si1384)

• Select siRNAs that avoid regions with secondary structure that might impede binding

• Verify that siRNA sequences do not have significant homology with other genes to prevent off-target effects

• Note that not all designed siRNAs will be effective (e.g., in published research, si336 and si1384 showed no obvious knockdown effect, while si1303 was effective)

Transfection optimization:

• Use appropriate transfection reagents (e.g., Rfect infection reagent)

• Replace transfection medium with fresh medium approximately 8 hours after transfection

• Optimize cell density to ensure efficient transfection without toxicity

Validation of knockdown efficiency:

• Assess PLCG1 mRNA levels by qRT-PCR at 24-48 hours post-transfection

• Confirm protein knockdown by Western blot, using validated PLCG1 antibodies

• Quantify knockdown efficiency by densitometry, normalizing to housekeeping genes/proteins

• Monitor cell viability to ensure observed effects are due to PLCG1 knockdown rather than cytotoxicity

Essential controls:

• Include a non-targeting siRNA control (siCtrl) with similar length and GC content

• Use untransfected cells as an additional control

• Consider rescue experiments (re-expression of siRNA-resistant PLCG1) to confirm specificity

• For phosphorylation studies, include appropriate stimulation controls (e.g., growth factor treatment)

Downstream functional assays:

• After confirming knockdown, assess effects on cell growth, migration, and invasiveness

• Design time-course experiments to distinguish between immediate and secondary effects

• Consider in vivo validation in appropriate animal models for cancer studies

These considerations are essential for generating reliable data on PLCG1 function through knockdown experiments while minimizing experimental artifacts and misinterpretation.

How can I design experiments to investigate PLCG1 phosphorylation dynamics in response to stimuli?

Investigating PLCG1 phosphorylation dynamics requires careful experimental design and time-resolved analysis:

Stimulus selection and optimization:

• Choose appropriate stimuli based on cellular context (e.g., EGF, PDGF for cells expressing corresponding receptors)

• Determine optimal stimulus concentration through dose-response experiments

• Consider using multiple stimuli to compare pathway-specific responses

Time-course design:

• Capture early phosphorylation events with closely spaced time points (e.g., 0, 1, 2, 5, 10, 30 minutes)

• Include longer time points (1, 2, 6, 24 hours) to assess sustained phosphorylation

• Prepare multiple plates simultaneously and stop reactions at specific time points to ensure consistency

Detection methods:

• Western blotting with phospho-specific antibodies:

  • Use Phospho-PLCγ1 (Ser1248) Antibody (1:1000 dilution) for Western blotting

  • Always probe for both phospho-PLCG1 and total PLCG1 on separate blots or after stripping

  • Calculate phospho-to-total PLCG1 ratio to normalize for expression differences

• Phospho-flow cytometry:

  • Fix cells with formaldehyde and permeabilize with methanol

  • Stain with fluorophore-conjugated phospho-specific antibodies

  • Enables single-cell analysis and detection of population heterogeneity

• Proximity ligation assay (PLA):

  • Visualize and quantify protein interactions and phosphorylation events in situ

  • Provides spatial information about phosphorylation in different cellular compartments

Inhibitor studies to dissect pathways:

• Use specific kinase inhibitors to block upstream regulators (e.g., EGFR inhibitors, PI3K inhibitors)

• Include the PLC inhibitor U73122 to block downstream signaling and assess feedback mechanisms

• Compare phosphorylation at different sites (Tyr771, Tyr783, Tyr1248) to establish phosphorylation sequence

Data analysis and presentation:

• Plot phosphorylation kinetics as phospho/total PLCG1 ratio versus time

• Use statistical analysis to determine significance of changes

• Consider computational modeling to integrate complex phosphorylation dynamics

These approaches allow for comprehensive characterization of PLCG1 phosphorylation dynamics in response to various stimuli, providing insights into the temporal regulation of PLCG1 signaling.

What is the evidence for PLCG1 as a potential therapeutic target in cancer and other diseases?

Emerging research has highlighted PLCG1 as a promising therapeutic target across multiple disease contexts:

Evidence in cancer:

• Higher PLCG1 expression correlates with tumor growth and worse prognosis in IDH wild-type lower-grade gliomas (LGGs)

• PLCG1-targeted siRNA dramatically affects growth, migration, and invasiveness of IDH wild-type LGG cell lines

• The PLC inhibitor U73122 significantly suppresses tumor growth of IDH wild-type LGG cell lines in vitro and in mouse models

• PLCG1 amplification serves as a key mechanism of PLCG1 upregulation in certain cancers, suggesting genomic alteration as a driver event

Validation through multiple approaches:

• Both in vitro and in vivo experiments demonstrate anti-tumor effects of PLCG1 inhibition

• Genomic and transcriptomic data from large patient cohorts (TCGA and CGGA databases) verify the association between elevated PLCG1 expression and poor clinical outcomes

• Multiple cancer cell lines show sensitivity to PLCG1 inhibition, suggesting broad relevance across cancer types

Beyond cancer:

• PLCG1 dysregulation has been implicated in autoimmune disorders and neurological conditions

• The enzyme's central role in signaling cascades downstream of receptor tyrosine kinases makes it relevant to diseases involving aberrant growth factor signaling

Therapeutic strategies under investigation:

• Direct PLCG1 inhibition: PLC inhibitors like U73122 show promise in preclinical models

• Targeting PLCG1 phosphorylation: Inhibitors of upstream kinases (e.g., Syk inhibitors) could prevent PLCG1 activation

• Combination approaches: PLCG1 inhibition may sensitize tumors to existing therapies

• Mutation-specific strategies: For cancers with activating PLCG1 mutations, mutation-specific inhibitors might offer precision targeting

The converging evidence from cellular studies, animal models, and patient data strongly supports PLCG1 as a viable therapeutic target, particularly in cancers where its overexpression drives disease progression .

How can multi-omics approaches be integrated to better understand PLCG1 function in different cellular contexts?

Integration of multi-omics approaches provides a comprehensive understanding of PLCG1 function across diverse cellular contexts:

Genomic approaches:

• Analyze PLCG1 copy number variations using FISH and computational methods from databases like TCGA

• Assess PLCG1 mutations through targeted sequencing or whole-exome sequencing

• Examine regulatory elements through ChIP-seq and ATAC-seq to understand transcriptional control

• Calculate segment means from CNV data using the formula: Segment mean = log2(CNV/2)

Transcriptomic analyses:

• Quantify PLCG1 mRNA expression levels across different tissue types and disease states

• Perform RNA-seq after PLCG1 knockdown to identify downstream gene expression changes

• Use single-cell RNA-seq to reveal cell-type-specific PLCG1 expression patterns

• Correlate PLCG1 expression with patient outcomes using databases like TCGA and CGGA

Proteomic integration:

• Map the PLCG1 interactome through immunoprecipitation coupled with mass spectrometry

• Characterize post-translational modifications, especially phosphorylation at key residues (Tyr771, Tyr783, Tyr1248)

• Employ reverse-phase protein arrays (RPPA) to quantify PLCG1 and related proteins across large sample sets

• Use phospho-specific antibodies to track activation states in different contexts

Metabolomic connections:

• Measure changes in phospholipid metabolism and second messengers (IP3, DAG) following PLCG1 modulation

• Trace calcium flux dynamics as a downstream consequence of PLCG1 activity

• Assess metabolic pathway alterations in PLCG1-overexpressing vs. PLCG1-depleted cells

Computational integration:

• Develop pathway models incorporating PLCG1 signaling nodes

• Use machine learning to identify biomarkers that predict sensitivity to PLCG1 inhibition

• Integrate multi-omics data to reveal context-dependent PLCG1 functions

Examples of successful multi-omics applications:

• Combined genomic (CNV) and transcriptomic analyses revealed a strong correlation between PLCG1 amplification and overexpression in LGGs

• Integration of PLCG1 expression data with patient survival information identified PLCG1 as a prognostic marker in IDH wild-type LGGs

• Proteomic and genomic integration helped establish PLCG1 as a potential therapeutic target, validated through functional studies

This multi-layered approach allows researchers to move beyond correlative observations to mechanistic understanding, facilitating the development of context-specific interventions targeting PLCG1.

What methodological advances are needed to better study PLCG1 in complex tissue microenvironments?

Studying PLCG1 in complex tissue microenvironments presents unique challenges requiring methodological innovations:

Advanced spatial analysis technologies:

• Multiplexed immunofluorescence/immunohistochemistry: Simultaneously detect PLCG1, its phosphorylated forms, and contextual markers (cell type, activation state) within tissue sections

• Imaging mass cytometry (IMC)/Multiplexed ion beam imaging (MIBI): Analyze dozens of proteins simultaneously at subcellular resolution in tissues

• Spatial transcriptomics: Map PLCG1 mRNA expression patterns while preserving spatial context

• In situ proximity ligation assay (PLA): Visualize protein-protein interactions involving PLCG1 within intact tissues

Improved tissue models:

• 3D organoids: Develop patient-derived organoids that better recapitulate tissue architecture

• Tissue-on-chip: Create microfluidic systems that mimic organ-specific environments for PLCG1 functional studies

• Co-culture systems: Establish models incorporating multiple cell types to study PLCG1 in heterotypic cellular interactions

• Ex vivo tissue slice cultures: Maintain native tissue architecture while enabling experimental manipulation

Single-cell analysis approaches:

• Single-cell phospho-proteomics: Measure PLCG1 phosphorylation at the single-cell level to capture heterogeneity

• Single-cell Western blotting: Detect PLCG1 protein in individual cells within complex populations

• Mass cytometry (CyTOF): Simultaneously measure multiple parameters including PLCG1 expression and phosphorylation in thousands of individual cells

In vivo imaging methods:

• Intravital microscopy with fluorescent reporters: Monitor PLCG1 activity in live tissues

• FRET/BRET sensors: Develop biosensors to track PLCG1 activation dynamics in real-time

• PET tracers for PLCG1: Design tracers that bind to PLCG1 or its substrates for non-invasive imaging

Computational tools for integration:

• Spatial statistics: Develop methods to quantify PLCG1 expression patterns and correlations with microenvironmental features

• Deep learning image analysis: Train neural networks to recognize complex patterns in PLCG1 distribution and activation

• Agent-based modeling: Simulate PLCG1 signaling in heterogeneous cellular environments to generate testable hypotheses

Validation strategies:

• Spatial validation of knockdown effects: Use in situ approaches to verify PLCG1 knockdown in specific regions within complex tissues

• Cell type-specific genetic manipulation: Employ conditional knockout models to study PLCG1 function in specific cell types within intact tissues

• Correlative light and electron microscopy (CLEM): Connect PLCG1 localization with ultrastructural features

These methodological advances would transform our understanding of PLCG1 biology by revealing context-dependent functions and regulatory mechanisms within complex tissue microenvironments, potentially leading to more effective therapeutic strategies.