ARPC1A Antibody

What is ARPC1A and what is its primary cellular function?

ARPC1A is a 41kDa subunit of the Actin Related Protein 2/3 (ARP2/3) complex, which consists of seven proteins originally identified in Acanthamoeba. This complex plays an essential role in the formation of branched actin filament networks, particularly in lamellipodia-mediated cell migration. ARPC1A contributes to cytoskeletal remodeling by facilitating the generation of actin filament networks that are crucial for cellular movement and invasion. In cancer contexts, ARPC1A's function in orchestrating cytoskeletal dynamics appears to enhance the metastatic potential of tumor cells, as evidenced by its effects on prostate cancer migration and invasion capabilities.

What are the most reliable detection methods for ARPC1A in research settings?

For reliable ARPC1A detection, researchers commonly employ:

• Western Blotting (WB): Effective for quantifying ARPC1A expression levels in cell lysates, typically using antibodies targeting specific regions like the central domain (amino acids 157-184).

• Immunohistochemistry (IHC) on paraffin-embedded sections: Particularly valuable for visualizing ARPC1A distribution in tissue samples.

• Flow Cytometry (FACS): Useful for analyzing ARPC1A expression at the cellular level.

• Immunofluorescence (IF): Ideal for observing subcellular localization, especially when studying cytoskeletal structures.

Selection of the appropriate method depends on research objectives, with antibody specificity being crucial for reliable results. Polyclonal antibodies generated against synthetic peptides from specific ARPC1A regions (such as amino acids 157-184) have demonstrated good performance across multiple applications.

How should researchers evaluate ARPC1A antibody specificity before experimental use?

Evaluating ARPC1A antibody specificity requires a multi-faceted approach:

• Positive and negative control samples: Use cell lines or tissues with known ARPC1A expression patterns.

• Knockdown validation: Employ siRNA-mediated ARPC1A knockdown (as demonstrated in prostate cancer studies) to confirm signal reduction corresponds with protein depletion.

• Peptide competition assay: Pre-incubate antibody with immunizing peptide to verify signal elimination.

• Cross-reactivity assessment: Test antibody against related proteins, particularly ARPC1B, which shares structural similarities.

• Multiple antibody validation: Use antibodies targeting different ARPC1A epitopes to confirm consistent localization and expression patterns.

Researchers should document antibody clone information, source, dilution factors, and incubation conditions to ensure reproducibility. For ARPC1A, antibodies targeting the central region (amino acids 157-184) have demonstrated reliable specificity in human samples.

How can researchers effectively use ARPC1A antibodies to study cytoskeletal dynamics in cancer cells?

For studying cytoskeletal dynamics using ARPC1A antibodies, researchers should:

• Employ phalloidin co-staining: Phalloidin binds F-actin, allowing visualization of the relationship between ARPC1A localization and actin filament organization. This approach effectively reveals how ARPC1A knockdown alters cytoskeletal architecture in prostate cancer cells.

• Use live-cell imaging: Combine GFP-tagged ARPC1A with fluorescent actin markers to monitor real-time cytoskeletal remodeling during cell migration or invasion.

• Implement super-resolution microscopy: Techniques such as STORM or PALM provide nanoscale resolution of ARPC1A's spatial relationship with actin filaments and other cytoskeletal components.

• Conduct proximity ligation assays: These reveal ARPC1A's interactions with other components of the ARP2/3 complex and potential regulatory proteins.

• Correlate with functional assays: Connect cytoskeletal changes to functional outcomes using migration assays, invasion assays, and pseudopodia formation analysis.

In prostate cancer research, ARPC1A antibody staining has revealed that ARPC1A silencing substantially reduces actin filament abundance, consistent with the observed reduction in cell migration and invasion capabilities.

What experimental design is recommended for investigating ARPC1A's role in cancer metastasis using antibody-based techniques?

A comprehensive experimental design for investigating ARPC1A's role in metastasis should include:

This integrated approach has successfully demonstrated ARPC1A's involvement in prostate cancer metastasis, where ARPC1A overexpression significantly promoted lung metastasis without affecting primary tumor growth.

How should researchers interpret contradictory ARPC1A antibody staining patterns between tissue types or experimental conditions?

When encountering contradictory ARPC1A staining patterns, researchers should systematically:

• Validate antibody performance:

  • Ensure the antibody recognizes the correct isoform (ARPC1A vs ARPC1B)

  • Verify antibody specificity through western blotting

  • Test multiple antibodies targeting different epitopes

• Consider biological variables:

  • Examine tissue-specific post-translational modifications that might affect epitope accessibility

  • Investigate potential splicing variants with altered antibody binding sites

  • Assess subcellular redistribution of ARPC1A under different conditions

• Evaluate technical factors:

  • Standardize fixation protocols, as overfixation may mask epitopes

  • Optimize antigen retrieval methods (e.g., sodium citrate buffer at pH 6.0)

  • Test different blocking solutions to reduce background

  • Standardize detection systems

• Conduct functional correlations:

  • Relate staining patterns to functional outcomes in the same model systems

  • Use genetic manipulation to validate antibody specificity

• Consider context-dependent regulation:

  • Investigate whether glutamine metabolism (an upstream regulator of ARPC1A) differs between conditions

  • Assess microenvironmental factors that might influence ARPC1A expression or localization

In prostate cancer research, standardized protocols using a semi-quantitative analysis based on staining intensity and proportion of positive cells have helped resolve interpretation challenges.

What prognostic significance does ARPC1A expression have in prostate cancer, and how should researchers quantify this?

ARPC1A expression has significant prognostic value in prostate cancer, as demonstrated by rigorous analysis of clinical cohorts. Researchers should quantify this relationship using:

This methodological approach has established ARPC1A as an independent prognostic factor, with a 1.581-fold greater risk of BCR after radical prostatectomy in patients with elevated ARPC1A expression compared to those without.

How do ARPC1A antibodies help elucidate the relationship between glutamine metabolism and cytoskeletal changes in cancer cells?

ARPC1A antibodies have been instrumental in revealing the molecular connection between glutamine metabolism and cytoskeletal dynamics in cancer progression through:

• Co-immunoprecipitation studies:

  • Identify protein-protein interactions between ARPC1A and metabolic regulators

  • Validate these interactions through reciprocal pulldowns using specific antibodies

• Sequential immunofluorescence:

  • Visualize co-localization of ARPC1A with glutamine metabolic enzymes

  • Track cytoskeletal reorganization following glutamine pathway modulation

• Metabolic intervention studies:

  • Use ARPC1A antibodies to monitor protein expression after glutamine deprivation or metabolism inhibition

  • Correlate changes in ARPC1A levels with alterations in cell morphology and motility

• Signaling pathway analysis:

  • Employ ARPC1A antibodies alongside phospho-specific antibodies to map signaling cascades connecting glutamine metabolism to cytoskeletal regulation

  • Perform western blot analysis after pathway inhibition to establish causality

• Rescue experiments:

  • Evaluate whether ARPC1A overexpression can rescue migration and invasion defects caused by glutamine metabolism inhibition

This methodological approach has established glutamine metabolism as an upstream regulator of ARPC1A in prostate cancer, demonstrating that glutamine promotes migration, invasion, and cytoskeletal changes through ARPC1A-dependent mechanisms.

What are the optimal conditions for using ARPC1A antibodies in immunohistochemistry of paraffin-embedded tissues?

For optimal ARPC1A immunohistochemistry on paraffin-embedded tissues, researchers should follow this validated protocol:

• Sample preparation:

  • Fix tissues in 10% neutral buffered formalin for 24-48 hours

  • Process and embed in paraffin following standard protocols

  • Section at 4-5 μm thickness onto adhesive slides

• Deparaffinization and rehydration:

  • Xylene: 3 changes of 5 minutes each

  • 100% ethanol: 2 changes of 3 minutes each

  • 95%, 80%, 70% ethanol: 3 minutes each

  • Distilled water: 5 minutes

• Antigen retrieval:

  • Method: Heat-induced epitope retrieval

  • Buffer: Sodium citrate buffer (10 mmol/L, pH 6.0)

  • Condition: Pressure cooker for 7 minutes

  • Cooling: 20 minutes at room temperature

• Endogenous peroxide blocking:

  • 0.3% H₂O₂ for 30 minutes at room temperature

• Protein blocking:

  • 5% normal goat serum for 1 hour at room temperature

• Primary antibody incubation:

  • Dilution: 1:200 of anti-ARPC1A antibody

  • Incubation: Overnight at 4°C in a humidified chamber

  • Antibody selection: Use antibodies targeting central region (amino acids 157-184) for reliable results

• Secondary antibody and detection:

  • Apply anti-rabbit secondary antibody for 1 hour at room temperature

  • Visualization: Diaminobenzidine as chromogen

  • Counterstain: Hematoxylin

  • Dehydration and mounting using standard protocols

This protocol has been successfully employed in prostate cancer tissue microarray analysis, revealing significant associations between ARPC1A expression and clinical outcomes.

What controls and validation steps are necessary when using ARPC1A antibodies for research?

A comprehensive validation framework for ARPC1A antibody-based research should include:

• Essential controls:

  • Positive tissue control: Use tissues with known ARPC1A expression (e.g., prostate cancer tissues for high expression)

  • Negative tissue control: Use tissues with minimal ARPC1A expression

  • Isotype control: Apply matched isotype antibody at the same concentration

  • No primary antibody control: Assess secondary antibody specificity

  • Absorption control: Pre-incubate primary antibody with immunizing peptide

• Technical validation:

  • Western blot: Confirm antibody detects a single band at expected molecular weight (41 kDa)

  • Multiple antibody comparison: Use antibodies targeting different ARPC1A epitopes (N-terminal, central, C-terminal)

  • Cross-platform verification: Compare results across IHC, IF, and western blotting

• Biological validation:

  • siRNA knockdown: Confirm reduced staining in ARPC1A-depleted samples

  • Overexpression system: Verify increased signal in ARPC1A-overexpressing cells

  • Known biological effect confirmation: Validate cytoskeletal changes after ARPC1A manipulation

• Reporting standards:

  • Document antibody catalog number, clone information, and lot number

  • Report detailed protocols including dilutions, incubation times, and buffers

  • Specify scoring system for immunohistochemistry (e.g., H-score = I × P)

This validation approach ensures reliable and reproducible results when investigating ARPC1A's role in biological processes, particularly in cancer research contexts.

What are the most effective siRNA sequences for ARPC1A knockdown studies when validating antibody specificity?

For effective ARPC1A knockdown studies that validate antibody specificity, researchers should consider:

• Validated siRNA sequences:

  • siRNA-1: 5′-ACGGACACAUCACAGGUAUUGTT-3′

  • siRNA-2: 5′-GCGAUUUCAUUCCAUUCUUGATT-3′ (high efficacy)

  • siRNA-3: 5′-GCCUAUGUCUGGAGUCAGAAATT-3′ (high efficacy)

• Transfection optimization:

  • Cell density: 3 × 10⁵ cells per well in 6-well plates

  • Confluence: Transfect at 70% confluence

  • Transfection reagent: Lipofectamine 3000 reagent following manufacturer's protocol

  • siRNA concentration: 20-50 nM (optimize for each cell line)

  • Incubation time: 48-72 hours for maximum knockdown

• Knockdown verification methods:

  • Western blot: Quantify protein reduction using validated ARPC1A antibodies

  • qRT-PCR: Measure mRNA depletion in parallel

  • Immunofluorescence: Visualize reduction in cellular ARPC1A levels

• Experimental controls:

  • Non-targeting siRNA control

  • Mock transfection control

  • Untreated control

  • Positive control siRNA targeting a housekeeping gene

• Functional validation:

  • Phenotypic assays to confirm biological effects of ARPC1A depletion

  • In prostate cancer models, confirm migration and invasion inhibition

Among the three sequences tested, siRNA-2 and siRNA-3 demonstrated high silencing efficiency and were successfully used in subsequent functional experiments to demonstrate ARPC1A's role in prostate cancer cell migration and invasion.

How should researchers interpret ARPC1A expression patterns in relation to cellular invasion mechanisms?

When interpreting ARPC1A expression patterns in relation to invasion mechanisms, researchers should:

• Correlate subcellular localization with invasion structures:

  • Leading edge/lamellipodia: ARPC1A enrichment suggests active involvement in directed migration

  • Invadopodia: Co-localization with cortactin and other invadopodia markers indicates role in matrix degradation

  • Cell-cell junctions: ARPC1A presence may indicate involvement in collective migration

• Analyze expression in relation to invasion assays:

  • Transwell invasion: Higher ARPC1A expression correlates with increased invasive capacity

  • 3D matrix invasion: ARPC1A localization at invasive protrusions indicates functional involvement

  • Matrix degradation: ARPC1A-positive structures often correlate with zones of matrix proteolysis

• Integrate with cytoskeletal dynamics:

  • F-actin co-localization: Strong co-localization indicates direct involvement in actin network formation

  • Branched vs. bundled actin: ARPC1A associates primarily with branched actin networks in invasive structures

  • Phenotypic consequences: ARPC1A knockdown reduces pseudopodia formation and impairs directed movement

• Contextualize with molecular pathways:

  • Upstream regulators: Consider glutamine metabolism status when interpreting ARPC1A effects

  • Downstream effectors: Examine relationship with matrix metalloproteinases and other invasion mediators

Research in prostate cancer cells has demonstrated that ARPC1A knockdown significantly reduces invasive capacity in Matrigel-based transwell assays and alters cytoskeletal architecture, suggesting its critical role in invasion mechanisms.

What statistical approaches are most appropriate for analyzing ARPC1A immunohistochemistry data in cancer prognostic studies?

For rigorous statistical analysis of ARPC1A immunohistochemistry data in cancer prognostic studies, researchers should implement:

• Scoring system standardization:

  • Use semi-quantitative H-score (intensity × proportion) approach

  • Categorize scores into defined expression levels (e.g., "-", "+", "++", "+++")

  • Ensure inter-observer concordance through multiple independent pathologists

• Correlation with clinicopathological features:

  • Chi-square or Fisher's exact test for categorical variables

  • Student's t-test or ANOVA for continuous variables

  • Spearman's rank correlation for ordinal relationships

• Survival analysis approaches:

  • Kaplan-Meier curves with log-rank test for univariate analysis

  • Cox proportional hazards regression for multivariate analysis

  • Competing risk analysis when multiple outcome events are possible

• Predictive performance metrics:

  • ROC curve analysis to determine optimal cutoff values

  • Area under the curve (AUC) calculation for predictive accuracy

  • Sensitivity and specificity determination at optimal thresholds

  • Positive and negative predictive values calculation

• Validation strategies:

  • Internal validation: Bootstrap or cross-validation

  • External validation: Independent patient cohorts

  • Comparison with established prognostic markers

What are the emerging applications of ARPC1A antibodies in understanding treatment resistance mechanisms?

Emerging applications of ARPC1A antibodies for investigating treatment resistance mechanisms include:

• Therapy-induced cytoskeletal remodeling:

  • Use ARPC1A antibodies to monitor changes in cytoskeletal architecture following chemotherapy or targeted therapy

  • Correlate ARPC1A expression/localization with development of resistant phenotypes

  • Investigate whether cytoskeletal reorganization through ARPC1A contributes to therapy evasion

• Metabolic adaptation assessment:

  • Given ARPC1A's relationship with glutamine metabolism, use antibodies to evaluate whether metabolic reprogramming during treatment involves ARPC1A-dependent mechanisms

  • Test combination approaches targeting both glutamine metabolism and ARPC1A pathways

• Cell plasticity and phenotypic transitions:

  • Apply ARPC1A antibodies to track epithelial-mesenchymal transition (EMT) in response to therapy

  • Determine whether ARPC1A expression changes correlate with acquisition of stem-like properties in resistant cells

• Dormancy and metastatic recurrence:

  • Employ ARPC1A antibodies to examine cytoskeletal changes in dormant tumor cells

  • Investigate whether ARPC1A reactivation coincides with emergence from dormancy and metastatic outgrowth

• Biomarker development:

  • Validate ARPC1A as a predictive biomarker for therapy response

  • Develop quantitative image analysis platforms for ARPC1A immunohistochemistry to standardize assessment

These approaches could significantly advance our understanding of how cytoskeletal dynamics contribute to treatment resistance and potentially identify new therapeutic vulnerabilities in treatment-refractory disease.

How might multi-omics approaches complement ARPC1A antibody studies to provide comprehensive insights into cytoskeletal regulation in cancer?

Integrating multi-omics approaches with ARPC1A antibody studies creates powerful opportunities for comprehensively understanding cytoskeletal regulation:

• Proteomics integration:

  • Combine ARPC1A immunoprecipitation with mass spectrometry to identify interaction partners in different cancer contexts

  • Use phosphoproteomics to map signaling networks regulating ARPC1A function

  • Apply proximity labeling with ARPC1A-BioID fusion proteins to identify proximal proteins in living cells

• Transcriptomics correlation:

  • Correlate ARPC1A protein expression (detected by antibodies) with transcriptomic profiles

  • Identify gene expression signatures associated with high ARPC1A expression

  • The analysis could expand on Gene Set Enrichment Analysis findings that link ARPC1A to 'focal adhesion', 'adherens junction', and 'oxidative phosphorylation' pathways

• Metabolomics connections:

  • Relate ARPC1A expression levels to metabolomic profiles, particularly focusing on glutamine metabolism intermediates

  • Investigate metabolic dependencies in ARPC1A-high versus ARPC1A-low tumors

• Spatial multi-omics:

  • Combine ARPC1A immunofluorescence with spatial transcriptomics to map cytoskeletal regulation in the tumor microenvironment

  • Analyze tumor-stroma interactions influenced by ARPC1A expression

• Single-cell analysis:

  • Use ARPC1A antibodies for single-cell protein analysis alongside single-cell RNA-seq

  • Identify cellular subpopulations with distinct ARPC1A expression and functional properties

This integrated approach would provide unprecedented insights into how ARPC1A orchestrates cytoskeletal dynamics in response to metabolic and microenvironmental cues, potentially revealing new therapeutic approaches targeting cytoskeletal vulnerabilities in cancer.