ARL4C Antibody

What applications can ARL4C antibody (10202-1-AP) be used for?

The ARL4C antibody (10202-1-AP) has been validated for multiple applications:

The antibody has demonstrated reactivity with human samples, with cited reactivity in both human and mouse tissues .

What is the appropriate sample preparation method for ARL4C immunohistochemistry?

For optimal results in immunohistochemistry using ARL4C antibody (10202-1-AP), antigen retrieval with TE buffer at pH 9.0 is suggested. Alternatively, antigen retrieval may be performed with citrate buffer at pH 6.0. For paraffin-embedded tissue sections, the recommended antibody dilution is 1:50-1:500. The calculation of IHC density should involve determining the ratio between the intensity of BCA and the size of the area . Specific protocols are available from the manufacturer for optimal staining conditions. It is important to note that studies involving human tissues should be conducted in accordance with institutional ethics committee approval and with proper informed consent .

How should I optimize ARL4C antibody dilutions for different experimental applications?

Optimization of ARL4C antibody dilutions requires systematic titration in each testing system to obtain optimal results. Start with the recommended dilution ranges:

• For Western Blot: Begin with 1:500 dilution and adjust based on signal-to-noise ratio

• For IHC: Start at 1:100 and adjust based on staining intensity and background

• For IF/ICC: Initial testing at 1:100 is recommended, with adjustments for optimal visualization

The optimal dilution is sample-dependent, and researchers should check validation data galleries provided by manufacturers. When working with new sample types or tissues, a dilution series experiment is recommended to determine optimal conditions. For quantitative applications, standard curves with positive controls at known dilutions should be established .

What are the critical steps for successful Western blot detection of ARL4C?

For successful Western blot detection of ARL4C:

• Sample preparation: Use appropriate lysis buffers that preserve protein integrity (HUVEC cells and human brain tissue are validated positive controls)

• Protein loading: Load 20-40 μg of total protein per lane

• Resolution: Use 12-15% SDS-PAGE gels to properly resolve the 21-25 kDa ARL4C protein

• Transfer: Optimize transfer conditions for proteins in this molecular weight range

• Blocking: Use 5% non-fat milk or BSA in TBST for 1-2 hours at room temperature

• Primary antibody: Incubate with ARL4C antibody (1:500-1:1000 dilution) overnight at 4°C

• Detection: Use HRP-conjugated secondary antibodies and ECL detection systems

When interpreting results, note that the calculated molecular weight of ARL4C is 21 kDa, but the observed molecular weight ranges from 21-25 kDa, possibly due to post-translational modifications .

What controls should be included when working with ARL4C antibody in cancer research?

When designing experiments with ARL4C antibody in cancer research, include the following controls:

• Positive tissue controls: HUVEC cells for WB/IP/IF; human colon cancer and pancreas tissues for IHC

• Negative controls: Tissues or cells known to have low or no ARL4C expression

• Knockdown/knockout controls: ARL4C KD/KO samples to validate antibody specificity

• Isotype controls: Rabbit IgG at equivalent concentrations to assess non-specific binding

• Loading controls: For WB, include appropriate housekeeping proteins

• Technical replicates: Minimum of three to account for technical variability

• Biological replicates: Samples from different patients/sources to account for biological variation

Published literature indicates successful application of ARL4C knockdown approaches in several studies, which can serve as methodological references .

How is ARL4C expression associated with cancer prognosis across different tumor types?

ARL4C expression demonstrates significant prognostic value across multiple cancer types. High ARL4C expression is associated with poor prognosis in several cancers:

• Bladder urothelial carcinoma (BLCA)

• Colon adenocarcinoma (COAD)

• Kidney renal papillary cell carcinoma (KIRP)

• Brain lower grade glioma (LGG)

• Uterine corpus endometrial carcinoma (UCEC)

What methodologies can be used to study ARL4C alterations in cancer tissues?

To comprehensively study ARL4C alterations in cancer tissues, researchers can employ multiple complementary approaches:

• Expression analysis:

  • RNA-seq or microarray analysis (using log2 TPM+1 transformation)

  • qRT-PCR for mRNA quantification

  • Western blot and IHC for protein expression

• Genetic alterations:

  • Whole exome/genome sequencing to identify mutations (particularly relevant in sarcoma, brain lower grade glioma, and esophageal adenocarcinoma)

  • Copy number variation analysis

• Epigenetic regulation:

  • Methylation analysis of the ARL4C promoter, especially in BRCA, COAD, KIRP, and LIHC (positive correlation)

  • Chromatin immunoprecipitation to study transcription factor binding

• Functional studies:

  • Knockdown/knockout experiments using siRNA, shRNA, or CRISPR-Cas9

  • Pathway analysis focusing on Wnt/β-Catenin signaling

• Single-cell analysis:

  • scRNA-seq to study heterogeneity of ARL4C expression within tumors

How can researchers validate the functional role of ARL4C in tumor progression?

To validate the functional role of ARL4C in tumor progression, researchers should implement a multi-faceted experimental approach:

• In vitro functional assays:

  • Proliferation assays after ARL4C knockdown/overexpression

  • Migration and invasion assays (particularly relevant as ARL4C has been shown to influence EMT phenomena)

  • Colony formation and spheroid assays to assess stem cell-like properties

  • Cell cycle and apoptosis analysis

• Molecular mechanism investigation:

  • Co-immunoprecipitation to identify binding partners

  • Pathway analysis focusing on Wnt/β-Catenin signaling, which has been implicated in ARL4C-mediated effects in kidney cancer

  • Downstream target gene expression analysis

• In vivo validation:

  • Xenograft models with ARL4C-modulated cancer cells

  • Patient-derived xenografts (PDXs) with varying ARL4C expression levels

  • Analysis of tumor growth, metastasis, and survival outcomes

• Clinical correlation:

  • Correlation of experimental findings with patient data from TCGA or local cohorts

  • Integration of ARL4C expression with clinical parameters and outcomes

Relevant experimental precedents can be found in studies investigating ARL4C in kidney cancer cell lines, where knockdown of ARL4C inhibited proliferation, migration, and invasion through Wnt/β-Catenin pathway regulation .

How does ARL4C expression correlate with immune cell infiltration in the tumor microenvironment?

ARL4C expression shows significant correlations with immune cell infiltration in the tumor microenvironment (TME), providing important insights for cancer immunology research:

• ARL4C is expressed in various immune cells, including:

  • CD4+ T lymphocytes

  • CD8+ T lymphocytes

  • Natural killer (NK) cells

  • Monocytes

  • Macrophages

• Analysis methods for studying this relationship include:

  • ESTIMATE algorithm to calculate stromal and immune cell scores

  • TIMER2.0 database and CIBERSORT algorithm for immune cell infiltration correlation analysis

  • Single-sample Gene Set Enrichment Analysis (ssGSEA) for classifying high and low ARL4C expression groups and assessing immune cell infiltration

• High ARL4C expression correlates with cancer immune activation, highlighting its potential role in regulating the immune microenvironment of tumors

• Immunofluorescence (IF) can be used to validate co-localization of ARL4C with immune cell markers in tissue samples, with the IF density determined by the ratio between the fluorescence intensity of positive cells and that of DAPI .

What methods can be used to investigate ARL4C's role in tumor immune escape mechanisms?

To investigate ARL4C's role in tumor immune escape mechanisms, researchers can employ the following approaches:

• Analysis of immune checkpoint correlation:

  • Evaluate correlation between ARL4C expression and known immune checkpoint molecules (PD-1, PD-L1, CTLA-4, etc.)

  • Use flow cytometry or multiplex IHC to quantify checkpoint expression in ARL4C-high vs. ARL4C-low tumors

• Immune dysfunction and exclusion analysis:

  • Utilize the TIDE (Tumor Immune Dysfunction and Exclusion) database to analyze ARL4C-associated immune escape in tumors

  • Assess T cell exclusion and dysfunction signatures in relation to ARL4C expression

• Tumor mutation burden (TMB) and microsatellite instability (MSI) analysis:

  • Calculate TMB scores from somatic mutation data

  • Use Spearman's correlation coefficient to examine associations between ARL4C expression and either TMB or MSI

• Functional validation:

  • Co-culture experiments with tumor cells and immune cells under ARL4C manipulation

  • In vivo models with immune-competent mice to assess ARL4C's effect on anti-tumor immunity

  • Analysis of cytokine/chemokine profiles in ARL4C-high versus ARL4C-low tumors

• Single-cell RNA sequencing:

  • Analyze differential ARL4C expression in tumor and immune cell populations

  • Apply Mann-Whitney U test to contrast distribution of ARL4C gene expression across pre- and post-treatment cell groups .

How can ARL4C expression data be integrated with immunotherapy response prediction?

Integration of ARL4C expression data with immunotherapy response prediction involves several sophisticated analytical approaches:

• Predictive analysis frameworks:

  • TISMO database for ARL4C-related immunotherapy prediction in mouse samples

  • TIDE algorithm to predict clinical response to immune checkpoint blockade

• Gene expression correlation analysis:

  • Correlation of ARL4C expression with established immunotherapy response biomarkers

  • GSEA enrichment analysis using Hallmark gene sets from MSigDB to compute normalized enrichment scores (NES) and false discovery rates (FDR) for differentially expressed genes

• Drug sensitivity correlation:

  • Analysis of ARL4C expression in relation to sensitivity to specific immunotherapy drugs

  • Studies have shown positive correlation between ARL4C expression and heightened sensitivity to Staurosporine, Midostaurin, and Nelarabine

• Clinical data integration:

  • Stratification of patient cohorts by ARL4C expression levels

  • Analysis of differential treatment outcomes in immunotherapy clinical trials

• Multi-omics integration:

  • Combine ARL4C expression data with other molecular features (mutation, methylation, etc.)

  • Develop predictive models incorporating multiple biomarkers for enhanced accuracy

These approaches collectively provide a framework for exploring ARL4C as a potential predictive biomarker for immunotherapy response, particularly in cancers where ARL4C demonstrates significant prognostic value .

How do I troubleshoot inconsistent ARL4C staining patterns in immunohistochemistry?

Troubleshooting inconsistent ARL4C staining patterns in immunohistochemistry requires systematic analysis of several factors:

• Fixation issues:

  • Ensure consistent fixation time (recommended: 24-48 hours in 10% neutral buffered formalin)

  • Avoid over-fixation which can mask epitopes

  • Consider testing different fixatives if working with specialized tissues

• Antigen retrieval optimization:

  • Primary recommendation: TE buffer at pH 9.0

  • Alternative: Citrate buffer at pH 6.0

  • Test different retrieval times (15, 20, and 30 minutes)

  • Compare heat-induced versus enzymatic retrieval methods

• Antibody-related factors:

  • Titrate antibody concentration (1:50, 1:100, 1:200, 1:500)

  • Extend primary antibody incubation time (overnight at 4°C may yield more consistent results)

  • Test different detection systems (ABC, polymer-based)

  • Consider lot-to-lot variability; validate each new lot against known positive controls

• Tissue-specific considerations:

  • Different cancer types may require adjusted protocols

  • Note that ARL4C staining has been validated in human colon cancer and pancreas tissues

  • Consider tissue-specific background reduction strategies

• Quantification approach:

  • For consistent density measurement, calculate the ratio between BCA intensity and area size

  • Use digital image analysis software for objective quantification

What are the best practices for studying ARL4C in both normal and cancer tissues comparatively?

When studying ARL4C in both normal and cancer tissues comparatively, employ these best practices:

• Sample selection and preparation:

  • Use matched pairs of tumor and adjacent normal tissue from the same patient whenever possible

  • Ensure consistent processing of both normal and tumor samples

  • Consider tissue microarrays (TMAs) for high-throughput analysis

• Multi-modal analysis:

  • Combine IHC/IF with mRNA expression analysis

  • Validate protein expression changes with transcript levels

  • Use laser capture microdissection to isolate specific cell populations

• Controls and standardization:

  • Include known positive controls (e.g., HUVEC cells, human brain tissue)

  • Use the same antibody lot, dilutions, and protocols for all comparative analyses

  • Implement blinded scoring by multiple observers

• Quantitative assessment:

  • Use digital pathology tools for objective quantification

  • Analyze subcellular localization patterns in addition to expression levels

  • Correct for cell density differences between normal and tumor tissues

• Data integration:

  • Correlate tissue findings with public datasets (TCGA, GTEx)

  • Compare with cancer cell line data from CCLE database

  • Control for potential confounding factors (age, gender, treatment history)

Research has shown significantly higher ARL4C expression in 23 out of 33 cancer types compared to normal tissues, with particularly elevated levels in thymic epithelial tumors (THYM), cholangiocarcinoma (CHOL), and ovarian cancer (OV) .

How can single-cell techniques be applied to study ARL4C expression heterogeneity in tumors?

Single-cell techniques offer powerful approaches to study ARL4C expression heterogeneity in tumors:

• Single-cell RNA sequencing (scRNA-seq):

  • Dissociate fresh tumor samples into single-cell suspensions

  • Sort cells using FACS to enrich for specific populations if needed

  • Process through 10x Genomics or similar platforms

  • Apply computational analysis to:

    • Identify cell clusters with differential ARL4C expression

    • Correlate with cell type-specific markers

    • Analyze expression in pre- and post-treatment samples using Mann-Whitney U test

• Spatial transcriptomics:

  • Apply techniques like Visium (10x Genomics) or GeoMx DSP (NanoString)

  • Map ARL4C expression patterns within the spatial context of the tumor

  • Correlate with histological features and tumor regions

• Single-cell protein analysis:

  • Use CyTOF (mass cytometry) to simultaneously measure ARL4C with other markers

  • Apply multiplex immunofluorescence to visualize ARL4C in different cell populations

  • Calculate IF density as the ratio between fluorescence intensity of positive cells and DAPI

• Integrated analysis:

  • Combine scRNA-seq with surface protein measurements (CITE-seq)

  • Correlate ARL4C expression with functional states of cells

  • Track clonal evolution and ARL4C expression changes during tumor progression

• Data analysis considerations:

  • Apply appropriate normalization methods for single-cell data

  • Use dimensional reduction techniques (tSNE, UMAP) for visualization

  • Implement trajectory analysis to identify developmental relationships between cell populations

These approaches can reveal heterogeneous expression patterns of ARL4C in both tumor and immune cells, providing insights into its role in different cellular compartments within the tumor microenvironment .

How can ARL4C expression analysis be incorporated into precision oncology approaches?

Incorporating ARL4C expression analysis into precision oncology approaches can enhance patient stratification and treatment selection:

• Biomarker development:

  • Establish standardized IHC protocols for clinical implementation

  • Develop RNA-based tests for ARL4C expression quantification

  • Validate cutoff values for "high" versus "low" expression across different cancer types

• Patient stratification:

  • Use ARL4C expression levels to identify high-risk patients in cancer types where it has prognostic value (BLCA, COAD, KIRP, LGG, UCEC)

  • Incorporate ARL4C with other established biomarkers in multi-parameter prognostic models

  • Adjust risk assessment based on cancer-specific ARL4C roles

• Treatment selection:

  • Correlate ARL4C expression with drug sensitivity profiles

  • Patients with high ARL4C expression may show increased sensitivity to Staurosporine, Midostaurin, and Nelarabine

  • Develop companion diagnostics for ARL4C-targeted therapies

• Monitoring response:

  • Track ARL4C expression changes during treatment

  • Evaluate ARL4C expression in circulating tumor cells or cell-free DNA

  • Correlate expression changes with treatment outcomes

• Implementation considerations:

  • Establish quality control parameters for clinical testing

  • Integrate with existing molecular pathology workflows

  • Develop standardized reporting formats for clinicians

Given ARL4C's correlation with immunotherapy response and drug sensitivity, its assessment could become a valuable component of comprehensive molecular tumor profiling .

What is the relationship between ARL4C genetic alterations and cancer development?

The relationship between ARL4C genetic alterations and cancer development reveals important mechanistic insights:

• Mutation patterns:

  • High frequency of ARL4C alterations in sarcoma, brain lower grade glioma, and esophageal adenocarcinoma

  • Mutations play a critical role in the progression of tumorigenesis across these cancer types

• Epigenetic regulation:

  • Positive correlation between ARL4C expression and promoter methylation in breast cancer (BRCA), colon adenocarcinoma (COAD), kidney renal papillary cell carcinoma (KIRP), and liver hepatocellular carcinoma (LIHC)

  • Negative correlation in testicular germ cell tumors (TGCT), uterine corpus endometrial carcinoma (UCEC), lung squamous cell carcinoma (LUSC), and kidney renal clear cell carcinoma (KIRC)

• Pathway interactions:

  • ARL4C functions through the Wnt/β-Catenin pathway in kidney cancer

  • Knockdown of ARL4C inhibits various EMT phenomena including proliferation, migration, and invasion in kidney cancer cell lines

• Dual roles in cancer:

  • Pro-tumorigenic effects in multiple cancer types

  • Potential tumor suppressor role in ovarian cancer, where high expression may impede cell migration

• Research methods:

  • Access mutational data through the UCSC Xena database

  • Analyze promoter methylation patterns in relation to expression

  • Perform pathway analysis to understand the functional consequences of genetic alterations

This complex relationship highlights the context-dependent nature of ARL4C function across different cancer types and suggests the need for cancer-specific approaches to targeting ARL4C .