RFX2 Antibody, FITC conjugated

What is RFX2 and what are its primary biological functions?

RFX2 (Regulatory Factor X 2) is a transcription factor belonging to the RFX family of proteins that bind to the X-box motif in promoter regions. RFX2 functions as a transcriptional activator with significant roles in several biological processes including:

• Transcriptional regulation of target genes through direct binding to promoter regions

• Modulation of the Hippo signaling pathway through regulation of RASSF1 expression

• Involvement in immune surveillance mechanisms, particularly affecting CD8+ T cell function

• Potential tumor suppressor function in certain cancers, notably lung adenocarcinoma (LUAD)

Recent research has demonstrated that RFX2 is significantly downregulated in LUAD tissues compared to adjacent normal tissues. This downregulation correlates with decreased infiltration of CD8+ T cells in the tumor microenvironment, suggesting RFX2 plays a crucial role in immune surveillance . Furthermore, RFX2 has been shown to activate RASSF1 transcription by binding directly to its promoter, which subsequently affects YAP phosphorylation in the Hippo pathway .

What experimental applications are appropriate for FITC-conjugated RFX2 antibodies?

FITC-conjugated RFX2 antibodies are particularly valuable for the following research applications:

• Immunofluorescence microscopy for cellular localization studies

• Flow cytometry for quantitative analysis of RFX2 expression in cell populations

• Immunohistochemistry with fluorescence detection for tissue sections

• Live cell imaging applications where direct detection without secondary antibodies is advantageous

The specific RFX2 antibody (ABIN7150382) referenced in the literature targets amino acids 1-130 of human RFX2 and has been validated for human samples . When selecting application contexts, researchers should consider that this particular antibody preparation has undergone Protein G purification with >95% purity . The FITC conjugation eliminates the need for secondary antibody incubation steps, reducing background and potential cross-reactivity issues in multi-color staining protocols.

How should researchers interpret RFX2 expression patterns in different cell and tissue types?

When analyzing RFX2 expression across various samples, researchers should consider:

• Baseline expression varies significantly between tissue types, with notably lower expression in LUAD cell lines (A-549, NCI-H358, Calu-3, and H1975) compared to normal lung cells like BEAS-2B

• Expression is heterogeneous within tumor samples, requiring careful quantification

• Subcellular localization is critical for functional assessment, as nuclear localization correlates with transcriptional activity

• Correlation with clinical parameters should be evaluated in patient-derived samples

Immunohistochemical analysis of 36 LUAD patient specimens and adjacent normal tissues revealed significantly attenuated RFX2 expression in tumor tissues . RT-qPCR confirmation demonstrated corresponding reduction at the mRNA level. These findings should inform researchers' expectations when analyzing new sample types or conducting comparative studies.

What controls are essential when using FITC-conjugated RFX2 antibodies in immunofluorescence studies?

Proper experimental design for immunofluorescence with FITC-conjugated RFX2 antibodies requires the following controls:

Positive Controls:

• Cell lines with confirmed RFX2 expression (e.g., BEAS-2B normal lung cells)

• Tissues with known RFX2 expression patterns

• Recombinant RFX2 protein for antibody validation

Negative Controls:

• Isotype control: FITC-conjugated rabbit IgG at equivalent concentration

• Cells with RFX2 knockdown through siRNA or shRNA approaches

• Unstained samples for autofluorescence assessment

Additional Controls:

• Competitive binding with unlabeled RFX2 antibody to demonstrate specificity

• Signal quantification standards to ensure consistent measurement across experiments

• Counterstaining with DAPI for nuclear localization reference

When conducting multi-color staining, spectral overlap controls and single-stain controls are essential for accurate compensation during analysis. Document fluorophore imaging settings carefully to ensure reproducibility across experiments.

How can researchers optimize ChIP protocols when studying RFX2 binding to target gene promoters?

Chromatin immunoprecipitation (ChIP) optimization for RFX2 binding analysis should include:

Protocol Optimization Steps:

• Cross-linking: 1% paraformaldehyde fixation is appropriate for most RFX2 binding studies

• Sonication conditions: Optimize to achieve chromatin fragments of 200-500 bp

• Antibody selection: Use highly specific anti-RFX2 antibodies (validated in IP applications)

• Control immunoprecipitations: Include IgG controls to assess non-specific binding

• Input normalization: Reserve 5% of sonicated chromatin as input control prior to immunoprecipitation

Quantification Method:
Calculate enrichment using the formula: ΔCt [normalized ChIP] = Ct [ChIP]–[Ct (Input)–log2(Input Dilution Factor)], where % Input = 2^(−ΔCt[normalized ChIP]) and Input Dilution Factor = 0.05^(−1) = 20 .

Primer Design for Target Regions:
When studying RFX2 binding to the RASSF1 promoter, design primers flanking potential RFX binding motifs. Analysis of ChIP samples paired with luciferase reporter assays can provide functional validation of binding significance .

What cell models are most appropriate for studying RFX2 function in cancer research?

Based on published research, the following cell models have been validated for RFX2 functional studies:

A-549 and NCI-H358 cell lines are particularly valuable for overexpression studies due to their naturally low RFX2 expression levels . These models are well-suited for:

• Co-culture experiments with activated CD8+ T cells to study immune interactions

• Transwell invasion assays to assess metastatic potential

• Apoptosis studies using TUNEL assays to evaluate RFX2's effect on cell death

• Luciferase reporter assays to study transcriptional regulation of target genes

How can dual-luciferase assays be optimized to study RFX2's transcriptional regulatory function?

Dual-luciferase assays provide quantitative assessment of RFX2's ability to regulate promoter activity of target genes such as RASSF1. Optimize these assays using the following approach:

Experimental Setup:

• Construct preparation: Clone the promoter region of interest (e.g., RASSF1) into a pGL3-based luciferase reporter vector

• Co-transfection: Transfect reporter constructs alongside a Renilla luciferase plasmid for normalization

• RFX2 expression: Use stable RFX2-overexpressing cell lines or co-transfect with RFX2 expression vectors

• Controls: Include promoterless vectors and mutated binding site constructs

Optimized Protocol:

• Transfect A-549 and NCI-H358 cells with Lipofectamine 3000 for highest efficiency

• Harvest cells 48 hours post-transfection for optimal protein expression

• Normalize firefly luciferase activity to Renilla activity to control for transfection efficiency

• Analyze results as fold-change relative to control conditions

Validation Approach:
Complement luciferase assays with ChIP experiments to confirm direct binding of RFX2 to the promoter region in question. This combined approach provides both functional and physical evidence of regulatory relationships .

What considerations are important when designing co-culture experiments to study RFX2's role in immune interactions?

When investigating RFX2's influence on immune cell function through co-culture experiments:

Experimental Design:

• Cell preparation: Generate stable RFX2-overexpressing cancer cell lines (e.g., A-549, NCI-H358) via lentiviral infection

• T cell isolation: Obtain CD8+ T cells and activate them prior to co-culture

• Co-culture setup: Establish appropriate ratios of cancer cells to immune cells (typically 1:5 to 1:10)

• Controls: Include vector control cancer cells and non-activated T cells

Analysis Parameters:

• Measure immune activation markers: IFN-γ, GZMB, and PRF1 release by CD8+ T cells

• Assess cancer cell immune evasion: PD-L1 expression levels

• Evaluate cancer cell responses: Viability, invasion capacity, and apoptosis rates

Technical Considerations:

• Ensure stable RFX2 expression through routine verification by RT-qPCR and Western blot

• Standardize T cell activation protocols to ensure consistency across experiments

• Use appropriate transwells or direct co-culture systems depending on whether contact-dependent interactions are being studied

How should researchers approach the analysis of RFX2-dependent regulation of the Hippo signaling pathway?

Investigation of RFX2's effect on Hippo signaling requires multi-level analysis of pathway components:

Key Targets to Measure:

• RASSF1 expression (direct transcriptional target of RFX2)

• YAP phosphorylation status (primary indicator of Hippo pathway activation)

• Downstream effectors of YAP signaling (e.g., CTGF, CYR61)

• Nuclear vs. cytoplasmic YAP localization (reflects pathway activity)

Methodological Approach:

• Gene expression analysis: RT-qPCR to quantify mRNA levels of pathway components

• Protein analysis: Western blotting with phospho-specific antibodies to assess YAP phosphorylation

• Functional studies: Rescue experiments using RASSF1 knockdown in RFX2-overexpressing cells

• Pharmacological validation: Use of Hippo pathway modulators (e.g., PY-60) to confirm pathway involvement

Research has demonstrated that RFX2 depletion downregulates RASSF1, which reduces YAP phosphorylation and affects Hippo pathway signaling. This mechanism promotes immune escape in LUAD . Understanding this signaling cascade is critical for interpreting RFX2's broader functional significance.

What approaches can resolve inconsistent staining patterns when using FITC-conjugated RFX2 antibodies?

When encountering variable staining results with FITC-conjugated RFX2 antibodies:

Systematic Troubleshooting:

• Antibody validation: Confirm specificity with Western blot or ELISA using recombinant RFX2 protein

• Fixation optimization: Test multiple fixation protocols (4% paraformaldehyde, methanol, acetone)

• Permeabilization assessment: Optimize detergent concentration and incubation time

• Antigen retrieval: Evaluate need for epitope unmasking (citrate buffer, EDTA buffer)

• Blocking optimization: Test different blocking agents (BSA, serum, commercial blockers)

Technical Refinements:

• Store antibody properly at 4°C protected from light to prevent photobleaching

• Prepare fresh dilutions for each experiment

• Maintain consistent incubation times and temperatures

• Document imaging parameters meticulously for reproducible quantification

Additional Considerations:
If the buffer contains 50% glycerol and ProClin preservative , be aware that these components can affect staining outcomes at very high or low antibody concentrations. Titrate carefully to determine optimal working concentration.

How can researchers address potential spectral overlap when combining FITC-conjugated RFX2 antibodies with other fluorophores?

Multiplexed fluorescence imaging with FITC-conjugated RFX2 antibodies requires careful consideration of spectral properties:

Fluorophore Selection Strategy:

• Choose fluorophores with minimal spectral overlap with FITC (excitation ~495 nm, emission ~520 nm)

• Recommended combinations: FITC + Cy5, FITC + Texas Red, FITC + DAPI

• Avoid: FITC + GFP, FITC + YFP, FITC + Alexa Fluor 488

Acquisition Optimization:

• Sequential scanning: Capture each fluorophore channel separately rather than simultaneously

• Narrow bandpass filters: Use restrictive emission filters to minimize bleed-through

• Spectral unmixing: Apply computational algorithms to separate overlapping signals

• Signal calibration: Use single-stained samples to establish compensation matrices

Validation Methods:

• Include fluorescence minus one (FMO) controls for each fluorophore

• Verify staining patterns with alternative antibody combinations

• Confirm localization patterns with orthogonal techniques (e.g., cell fractionation)

What strategies can help maximize ChIP-seq sensitivity when studying RFX2 binding genome-wide?

For researchers advancing to genome-wide analysis of RFX2 binding sites:

Protocol Enhancements:

• Cross-linking optimization: Test dual cross-linking with DSG followed by formaldehyde

• Sonication refinement: Verify fragment size distribution using Bioanalyzer or gel electrophoresis

• Antibody screening: Compare multiple anti-RFX2 antibodies for enrichment efficiency

• Sequential ChIP: Consider when studying co-binding with other transcription factors

• Input normalization: Process input samples alongside IP samples through all steps

Bioinformatic Analysis Approach:

• Peak calling: Use specialized algorithms optimized for transcription factor binding (MACS2)

• Motif analysis: Identify RFX binding motifs within enriched regions

• Integration with RNA-seq: Correlate binding events with transcriptional outcomes

• Comparison with published datasets: Validate findings against RFX2 binding in other systems

Validation Strategies:

• qPCR confirmation of selected binding sites before sequencing

• Luciferase reporter assays to verify functional significance of identified binding sites

• CRISPR-Cas9 editing of binding motifs to assess functional consequences

When analyzing RFX2 binding to the RASSF1 promoter specifically, design primers to amplify the regions containing predicted X-box motifs for targeted validation of genome-wide findings.

How should researchers interpret variations in RFX2 expression between different disease states?

When analyzing RFX2 expression across normal and disease conditions:

Interpretation Framework:

• Establish baseline expression in relevant normal tissues

• Compare expression patterns across different disease stages

• Correlate with clinical parameters and patient outcomes

• Assess subcellular localization alongside total expression levels

In lung adenocarcinoma, RFX2 has been found significantly downregulated compared to normal lung tissue through both IHC staining and RT-qPCR analysis . This pattern should be contextualized within the broader molecular profile of each sample, including assessment of RASSF1 expression and YAP phosphorylation status, which function downstream of RFX2.

Methodological Considerations:

• Use multiple detection methods (IHC, RT-qPCR, Western blot) for robust validation

• Include sufficient sample sizes to account for biological variability

• Consider the influence of tumor heterogeneity on expression patterns

• Normalize appropriately using validated reference genes or proteins

What experimental approaches can define the functional significance of RFX2 binding to novel target genes?

To establish the biological relevance of newly identified RFX2 target genes:

Functional Validation Pipeline:

• Binding confirmation: ChIP-qPCR to verify RFX2 occupancy at the promoter region

• Transcriptional impact: Measure target gene expression after RFX2 overexpression or knockdown

• Promoter analysis: Dual-luciferase reporter assays with wild-type and mutated binding sites

• Functional consequences: Assess cellular phenotypes after modulating the target gene

• Pathway integration: Determine how the target gene contributes to RFX2-regulated biological processes

Case Study - RASSF1 as RFX2 Target:
Research has demonstrated that RFX2 activates RASSF1 transcription by binding directly to its promoter. This was established through ChIP assays showing RFX2 enrichment at the RASSF1 promoter and dual-luciferase assays confirming functional activation . Knockdown of RASSF1 reversed the effects of RFX2 overexpression on immune escape, establishing the functional significance of this regulatory relationship.

How can researchers integrate RFX2 findings with broader transcriptomic and proteomic datasets?

For comprehensive understanding of RFX2 function in biological systems:

Data Integration Approaches:

• Transcriptome correlation: Analyze gene expression datasets (e.g., GSE32863, GSE43458, GSE21933) to identify genes co-regulated with RFX2

• Pathway enrichment: Use tools like GEPIA and Jvenn to identify biological processes enriched among RFX2-correlated genes

• Protein interaction networks: Map RFX2 and its targets within signaling cascades

• Clinical correlation: Analyze survival outcomes associated with RFX2 expression patterns

Bioinformatic Resources:

• TIMER 2.0 for analyzing immune infiltration correlation with RFX2 expression

• hTFtarget for predicting potential RFX2 target genes

• UALCAN Proteomics database for analyzing RFX2 protein expression in cancer samples

• Kaplan-Meier Plotter for survival analysis based on RFX2 expression

Integrative analysis has revealed that RFX2 expression positively correlates with CD8+ T cell infiltration specifically in LUAD but not in lung squamous cell carcinoma (LUSC), highlighting the context-specific nature of RFX2 function .