nr2f1a Antibody

What is NR2F1a and why is it significant in research?

NR2F1a (nuclear receptor subfamily 2, group F, member 1a) is a protein-coding gene that functions as a transcription factor involved in crucial developmental processes. The protein enables RNA polymerase II cis-regulatory region sequence-specific DNA binding activity and nuclear receptor activity . It plays essential roles in brain segmentation, circulatory system development, and endothelial cell proliferation. NR2F1a is expressed in multiple structures including the basal plate midbrain region, brain, cardiovascular system, head, and mesoderm . The human ortholog (NR2F1) is implicated in Bosch-Boonstra-Schaaf optic atrophy syndrome, making it clinically relevant . Beyond development, NR2F1 expression in postnatal and adult mouse brain has been linked to neuronal activity, and its aberrant expression is associated with various cancers, serving as a prognostic marker . This multifaceted role makes NR2F1a antibodies essential tools for understanding its functions in normal development and disease states.

How can researchers validate the specificity of NR2F1a antibodies?

Validating NR2F1a antibody specificity requires a systematic approach incorporating multiple controls:

• Genetic validation approaches:

  • Test antibodies in NR2F1a knockout/knockdown models

  • Compare staining in cells with known expression levels (e.g., differentiated versus undifferentiated hiPSCs, as undifferentiated cells show negligible NR2F1 expression)

  • Employ overexpression systems like transfected HEK293 cells to confirm specificity

• Multi-antibody comparison strategy:

  • Use multiple antibodies targeting different epitopes of NR2F1a

  • Consistent patterns across different antibodies suggest specific binding

  • Recent systematic examination of seven anti-NR2F1 antibodies demonstrated significant variability in specificity and localization patterns

• Technical validation approaches:

  • Calculate signal-to-noise ratios to quantitatively assess specificity

  • Test multiple antibody concentrations (e.g., 0.4, 1.0, and 2.0 μg/ml) to distinguish specific from non-specific binding

  • Include appropriate negative controls (primary antibody omission, secondary antibody alone, isotype controls)

A comprehensive study revealed that nucleolar localization frequently observed with one commonly used antibody (clone H8132) was not replicated with other antibodies and likely represents a technical artifact rather than true biological localization .

What fundamental controls should be included in NR2F1a antibody experiments?

Essential controls for NR2F1a antibody experiments must address both technical and biological specificity:

• Technical controls:

  • Primary antibody omission: Cells treated with secondary antibody only to detect non-specific secondary binding

  • Secondary antibody omission: Cells treated with primary antibody only to assess autofluorescence

  • Isotype control: Cells stained with an irrelevant primary antibody of the same isotype

  • Autofluorescence control: Unstained cells to assess background fluorescence

• Biological controls:

  • Positive expression controls: Cells/tissues known to express NR2F1a

  • Negative expression controls: Cells with minimal expression (e.g., undifferentiated hiPSCs)

  • Genetic knockdown/knockout models when available

• Antibody concentration series:

  • Testing multiple concentrations reveals concentration-dependent artifacts

  • A recent study demonstrated different staining patterns at 0.4, 1.0, and 2.0 μg/ml of the same antibody

• Co-localization controls:

  • Staining with markers of specific subcellular compartments (e.g., nucleolar markers)

  • Recent studies showed that apparent nucleolar NR2F1 staining did not correlate with established nucleolar proteins

Employing these controls systematically can prevent misinterpretation of artifacts as biologically significant signals, as demonstrated by recent research exposing the artificial nature of nucleolar NR2F1 staining patterns .

How do different fixation and immunostaining protocols affect NR2F1a antibody performance?

Fixation and immunostaining protocols significantly influence NR2F1a antibody performance and can generate artifactual localization patterns:

• Fixation method effects:

  • Recent research indicates that the nucleolar-like staining pattern observed with certain antibodies depends partly on fixation methods

  • Paraformaldehyde (PFA) concentration and fixation duration may impact epitope accessibility

  • Different fixation methods (PFA vs. methanol) can yield significantly different staining patterns

• Cell type-specific considerations:

  • The same antibody and fixation protocol may produce different results across cell types

  • For example, in prostate cancer samples, nucleolar localization was observed in epithelial but not stromal cells, though this may reflect technical variations rather than biological differences

• Permeabilization effects:

  • The choice of permeabilization agent (e.g., Triton X-100, methanol, saponin) and concentration can alter antibody accessibility

  • Gentle permeabilization may preserve certain epitopes but limit access to nuclear antigens

• Protocol optimization recommendations:

  • When establishing NR2F1a immunostaining, compare multiple fixation methods systematically

  • Document exact conditions (fixation time, temperature, buffer composition) for reproducibility

  • Consider dual fixation approaches (e.g., PFA followed by methanol) for challenging epitopes

A comprehensive study highlighted how fixation methods contributed to artifact generation with the monoclonal antibody clone H8132, which showed nucleolar-like staining that could not be confirmed with other antibodies or techniques .

What explains the discrepancy in nucleolar localization observed with different NR2F1a antibodies?

The nucleolar localization discrepancy with NR2F1a antibodies has been systematically investigated, revealing several key explanatory factors:

• Antibody-specific artifacts:

  • Comparative analysis of seven anti-NR2F1 antibodies demonstrated that nucleolar-like staining appeared primarily with one specific monoclonal antibody (clone H8132)

  • This pattern could not be confirmed with other antibodies targeting different epitopes, strongly suggesting it represents an artifact

• Factors contributing to artifactual nucleolar staining:

  • Antibody concentration: Higher concentrations increased non-specific binding

  • Fixation methods: Different protocols affected epitope accessibility and cross-linking

  • Cell type variations: The artifact appeared more pronounced in certain cell types

• Expression level dependency:

  • The nucleolar-like staining pattern varied with NR2F1 expression levels

  • Cells with different expression levels showed different staining patterns with the same antibody

• Evidence against biological nucleolar localization:

  • Proteomics databases of nucleolar proteins do not list NR2F1 as a nucleolar component

  • Testing in wild-type and NR2F1-null models demonstrated persistence of some nucleolar staining even in the absence of the target protein

This discrepancy has significant implications for research, as nucleolar aggregates observed with clone H8132 have been used to quantify NR2F1-positive cells in several studies of tumor cells , potentially leading to misinterpretations of NR2F1 function and localization.

How should researchers quantify and interpret signal intensities from NR2F1a antibody staining?

Quantifying and interpreting NR2F1a antibody signals requires objective approaches to distinguish specific from non-specific staining:

• Recommended quantification metrics:

  • Mean Fluorescence Intensity (MFI): Calculate the average intensity within defined cellular regions

  • Signal-to-noise ratio: Compare intensity in positive cells versus background in negative controls

  • Nuclear-to-cytoplasmic ratio: Quantify the relative distribution between compartments

• Threshold determination approaches:

  • Use genetic controls (knockout/knockdown) to establish baseline for non-specific binding

  • Compare multiple antibodies targeting different epitopes to identify consistent patterns

  • Employ concentration series to determine signal threshold above background

• Pattern analysis considerations:

  • For NR2F1a, a transcription factor, predominant nucleoplasmic localization is expected

  • Unusual patterns (like nucleolar foci) should be confirmed with multiple antibodies

  • Recent research revealed that nucleolar-like staining observed with antibody clone H8132 was not biologically significant

• Software-based analysis approaches:

  • Automated intensity measurement across subcellular compartments

  • Co-localization analysis with known markers (nuclear, nucleolar, cytoplasmic)

  • Population distribution analysis to identify expression heterogeneity

• Interpretation guidelines:

  • Interpret quantitative data in the context of appropriate controls

  • Consider fixation and processing effects on signal intensity

  • Be cautious about interpreting unusual localization patterns observed with only one antibody

A systematic study calculated MFI and signal-to-noise ratios for multiple antibodies to objectively assess their performance and specificity, demonstrating the importance of quantitative approaches in antibody validation .

What are the optimal conditions for using NR2F1a antibodies in co-immunoprecipitation experiments?

Optimizing co-immunoprecipitation (Co-IP) for NR2F1a requires careful consideration of antibody characteristics and experimental conditions:

• Antibody selection criteria for Co-IP:

  • Select antibodies validated for recognizing native (non-denatured) NR2F1a

  • Consider epitope accessibility in protein complexes

  • Choose antibodies targeting regions not involved in protein-protein interactions

  • Recent comparative studies of NR2F1 antibodies provide guidance on application-specific performance

• Buffer optimization strategies:

  • Test different lysis buffers to preserve interactions while effectively solubilizing proteins

  • For nuclear transcription factors like NR2F1a, nuclear extraction protocols improve Co-IP efficiency

  • Consider gentle detergents (0.1-0.5% NP-40 or Triton X-100) to maintain complex integrity

  • Include protease and phosphatase inhibitors to prevent degradation and modification during extraction

• Technical optimization parameters:

  • Antibody amount: Titrate to find optimal concentration for efficient immunoprecipitation

  • Bead type: Compare Protein A/G, magnetic vs. agarose beads for best results

  • Incubation conditions: Optimize temperature (4°C typically preferred) and duration

  • Washing stringency: Balance between removing non-specific binding and preserving interactions

• Essential controls:

  • Input control: Sample of lysate before immunoprecipitation

  • IgG control: Immunoprecipitation with irrelevant antibody of same isotype

  • Genetic controls: Compare results between wild-type and knockout/knockdown systems

  • Reciprocal Co-IP: Confirm interactions by immunoprecipitating with antibodies against interaction partners

• Special considerations for transcription factors:

  • DNA shearing or nuclease treatment may be necessary to release chromatin-bound NR2F1a

  • Consider crosslinking approaches to stabilize transient interactions

  • Be aware that post-translational modifications may affect antibody recognition

Comparative studies of different antibodies suggest that those showing specific nuclear staining without artifacts in immunofluorescence may be better candidates for successful Co-IP experiments with NR2F1a .

What are the considerations for using NR2F1a antibodies in chromatin immunoprecipitation (ChIP) assays?

Using NR2F1a antibodies in ChIP assays requires specialized considerations for this transcription factor:

• Antibody selection criteria for ChIP:

  • ChIP-validated antibodies are essential as not all IF or WB-validated antibodies work in ChIP

  • Consider antibodies targeting non-DNA-binding regions of NR2F1a to avoid interference with chromatin interactions

  • Recent research comparing seven different NR2F1 antibodies provides guidance on specificity

• Crosslinking optimization:

  • Formaldehyde concentration (typically 0.75-1%) and time (8-15 minutes) need optimization

  • Dual crosslinking (DSG followed by formaldehyde) may improve capture of protein-protein interactions

  • Over-crosslinking can reduce epitope accessibility and chromatin shearing efficiency

• Chromatin preparation considerations:

  • Sonication conditions must be optimized for fragment size (200-500 bp)

  • Enzymatic fragmentation alternatives may preserve epitopes better for certain antibodies

  • Pre-clearing chromatin reduces non-specific binding

• ChIP-specific controls:

  • Input control: Aliquot of chromatin prior to immunoprecipitation

  • IgG control: ChIP with non-specific IgG matching the antibody species/isotype

  • Positive control regions: Known NR2F1a binding sites from literature

  • Negative control regions: Genomic regions not expected to bind NR2F1a

  • Genetic controls: When possible, cells with NR2F1a knocked down/out

• Technical optimization factors:

  • Antibody amount: Typically 2-5 μg per ChIP reaction, but requires titration

  • Chromatin amount: Usually 25-50 μg per reaction

  • Incubation conditions: Typically overnight at 4°C with rotation

  • Washing stringency: Balance between specificity and sensitivity

Based on recent antibody validation studies, antibodies showing clear nuclear localization without artifacts in immunofluorescence may be more reliable for ChIP applications, while those producing nucleolar-like staining patterns (like clone H8132) should be used cautiously and with appropriate controls .

What are the best practices for using NR2F1a antibodies in flow cytometry?

Optimizing NR2F1a antibody use in flow cytometry requires specific considerations for this nuclear transcription factor:

• Sample preparation for nuclear protein detection:

  • Fixation: Typically 1-4% paraformaldehyde for 10-15 minutes

  • Permeabilization: Requires effective nuclear permeabilization (0.1-0.5% Triton X-100, 70-90% methanol, or commercial permeabilization buffers)

  • Buffer selection: PBS with 1-5% serum or BSA to minimize non-specific binding

• Antibody selection and optimization:

  • Choose fluorophore-conjugated antibodies or optimize primary-secondary combinations

  • Titrate antibody concentration to maximize signal-to-noise ratio

  • Recent comparative studies of NR2F1 antibodies provide guidance on specificity in cellular applications

• Essential controls for flow cytometry:

  • Unstained cells: For autofluorescence assessment

  • Secondary-only control: If using unconjugated primary antibodies

  • Isotype control: Matching the antibody species, isotype and conjugate

  • Biological negative control: Cells with low/no NR2F1a expression (e.g., undifferentiated hiPSCs)

  • Positive control: Cells with known high NR2F1a expression

• Technical considerations for nuclear transcription factors:

  • Extended permeabilization may be necessary for nuclear antigen access

  • Consider fixation impact on fluorophore brightness

  • Use gentle cell dissociation methods to maintain epitope integrity

  • Include viability dye to exclude dead cells which may bind antibodies non-specifically

• Data analysis recommendations:

  • Gating strategy should include forward/side scatter for cell selection

  • Dead cell exclusion is critical for accurate results

  • Set positive/negative thresholds based on controls

  • Consider heterogeneity in expression levels within populations

Based on recent findings regarding antibody specificity, researchers should be cautious about potential artifacts. The study of seven different NR2F1 antibodies revealed significant variability in specificity, with some antibodies producing artificial staining patterns that could impact flow cytometry results .

How should researchers interpret NR2F1a antibody results in cancer research models?

Interpreting NR2F1a antibody results in cancer research requires careful consideration of several critical factors:

• Cancer-specific expression patterns:

  • NR2F1 expression has been linked to various cancers and serves as a prognostic marker

  • Expression varies across cancer types and may correlate with disease progression

  • Aberrant expression has been linked to cancer, making NR2F1 a candidate for therapeutic development

• Antibody selection considerations for cancer tissue:

  • Cancer tissues may require optimized fixation and antigen retrieval

  • Background staining may differ in tumor versus normal tissue

  • Recent research revealed that nucleolar-like staining patterns previously reported in human malignant cells, prostate cancer cells, and breast cancer cells were likely artifacts

• Critical reevaluation of published findings:

  • Nucleolar aggregates observed with antibody clone H8132 have been quantified in several tumor studies

  • Recent evidence suggests this pattern is artificial and does not represent true biological localization

  • Studies using this antibody to quantify NR2F1-positive cells may require reinterpretation

• Recommended validation approaches:

  • Use multiple antibodies targeting different epitopes

  • Include appropriate positive and negative controls

  • Complement antibody-based detection with mRNA analysis

  • Consider genetic manipulation (knockdown/knockout) when feasible

• Alternative localization assessment:

  • Subcellular fractionation followed by Western blotting

  • Co-localization with established compartment markers

  • Proximity ligation assays for interaction studies

Recent comparative analysis demonstrated that the monoclonal antibody clone H8132 produced nucleolar-like staining that was not confirmed with other antibodies, suggesting researchers should exercise caution when interpreting previous cancer studies that relied on this staining pattern .

What are the key considerations for studying NR2F1a in developmental and neurological research?

Studying NR2F1a in developmental and neurological contexts requires specialized considerations due to its critical roles in these processes:

• Developmental expression dynamics:

  • NR2F1a shows complex spatiotemporal expression during development

  • In the developing brain, NR2F1 expression is associated with neurogenesis and regionalization

  • Beyond developmental stages, Nr2f1 expression in postnatal and adult mouse brain has been linked to neuronal activity

• Neurological disease relevance:

  • Human NR2F1 is implicated in Bosch-Boonstra-Schaaf optic atrophy syndrome

  • Appropriate model systems for this disorder require validated antibodies

  • Expression changes in pathological versus normal conditions must be carefully assessed

• Technical considerations for neural tissues:

  • Fixation protocols may need optimization for neural tissue

  • Antigen retrieval methods often require adjustment for brain samples

  • Background autofluorescence can be higher in neural tissue, requiring appropriate controls

• Antibody specificity considerations:

  • Consistent with its function as a transcription factor, NR2F1 is primarily detected in the nucleoplasm

  • Recent research revealed that nucleolar-like staining patterns with certain antibodies are artificial

  • Alternative approaches (RNA scope, genetic tagging) may complement antibody-based detection

• Experimental design recommendations:

  • Include developmental time course analysis when appropriate

  • Use cell type-specific markers for co-localization studies

  • Compare multiple antibodies to confirm localization patterns

  • Consider genetic models (conditional knockouts, reporter lines) when available

How do different anti-NR2F1a antibodies perform across diverse experimental models and species?

Performance of anti-NR2F1a antibodies varies significantly across experimental models and species, requiring systematic validation:

• Species cross-reactivity assessment:

  • Most commercial antibodies are raised against human NR2F1, with variable cross-reactivity

  • Recent studies tested anti-NR2F1 antibodies in both human cell lines and mouse models

  • For zebrafish nr2f1a research, antibody cross-reactivity must be explicitly verified

• Comparison across experimental models:

  • Recent systematic examination of seven anti-NR2F1 antibodies in different human cancer and stem cells as well as in wild type and null mice revealed significant performance variations

  • Some antibodies perform consistently across models, while others show model-specific differences in sensitivity and specificity

• Comparative antibody performance table:

• Application-specific performance variations:

  • Some antibodies work in multiple applications (IF, WB, FC) while others are application-specific

  • Antibodies not recommended for IF by manufacturers sometimes perform adequately in this application

  • Performance in one application doesn't necessarily predict performance in others

• Validation approaches across models:

  • Use genetic controls specific to each model organism when possible

  • Include species-matched positive and negative controls

  • Optimize protocols for each specific model system

  • Cross-validate with alternative detection methods

How can researchers troubleshoot inconsistent staining patterns with NR2F1a antibodies?

Troubleshooting inconsistent NR2F1a antibody staining requires systematic evaluation of multiple factors:

• Antibody-specific factors:

  • Recent comparative study of seven anti-NR2F1 antibodies revealed significant variability in staining patterns

  • Clone H8132 produced nucleolar-like staining patterns not observed with other antibodies

  • Test multiple antibodies targeting different epitopes to identify consistent patterns

• Technical parameters to evaluate:

  • Antibody concentration: Test a range (e.g., 0.4, 1.0, and 2.0 μg/ml) as staining patterns can be concentration-dependent

  • Fixation method: Compare paraformaldehyde versus methanol fixation, as fixation can affect epitope accessibility

  • Permeabilization: Adjust detergent type and concentration

  • Blocking conditions: Optimize blocking buffer composition and duration

  • Antigen retrieval: Test different methods if appropriate

• Cell/tissue-specific considerations:

  • Expression level variations can influence staining patterns

  • Different cell types may require modified protocols

  • Tissue processing methods may affect epitope preservation

• Systematic optimization approach:

  • Change one variable at a time and document results

  • Include positive and negative controls in each experiment

  • Quantify staining intensity and pattern distribution

• Validation strategies for confirming true patterns:

  • Correlate with mRNA expression data

  • Use genetic manipulation (siRNA, CRISPR) to confirm specificity

  • Compare results across multiple detection methods

Recent research demonstrated that nucleolar-like staining observed with antibody clone H8132 was not replicated with other antibodies and may depend on NR2F1 expression level, fixation methods, and tissue type, highlighting the importance of systematic validation .

What quality control metrics should researchers implement for NR2F1a antibody experiments?

Implementing rigorous quality control metrics for NR2F1a antibody experiments ensures reliable and reproducible results:

• Antibody validation metrics:

  • Genetic validation score: Results in knockout/knockdown versus wild-type systems

  • Multi-antibody concordance: Agreement between multiple antibodies targeting different epitopes

  • Signal-to-noise ratio: Quantitative measure of specific signal versus background

  • Lot-to-lot consistency: Testing new antibody lots against reference standards

• Technical quality indicators:

  • Positive and negative control performance in each experiment

  • Technical replicate consistency

  • Background signal levels in negative controls

  • Staining intensity compared to established reference samples

• Quantitative assessment approaches:

  • Mean fluorescence intensity (MFI) in target versus control cells

  • Nuclear-to-cytoplasmic signal ratio for this transcription factor

  • Proportion of cells showing specific staining patterns

  • Co-localization coefficients with compartment markers

• Documentation requirements:

  • Complete antibody information (manufacturer, clone, lot number, concentration)

  • Detailed protocol parameters (fixation time, buffer composition, etc.)

  • Images of positive and negative controls

  • Raw quantification data alongside processed results

• Independent validation methods:

  • Correlation with mRNA expression data

  • Alternative protein detection methods

  • Functional assays to confirm biological relevance

Recent systematic study comparing seven anti-NR2F1 antibodies calculated mean fluorescence intensities and signal-to-noise ratios to objectively assess antibody performance, demonstrating the value of quantitative quality control metrics . This approach revealed that nucleolar-like staining patterns previously used to quantify NR2F1-positive cells in tumor studies were likely artifacts .

How can researchers minimize batch effects and ensure reproducibility in NR2F1a antibody studies?

Minimizing batch effects and ensuring reproducibility in NR2F1a antibody studies requires systematic experimental design and standardized protocols:

• Antibody management practices:

  • Create antibody master stocks with standardized aliquoting

  • Document lot numbers and perform lot-to-lot validation

  • Use the same antibody concentration across experiments

  • Establish quality control samples to test each new antibody batch

• Experimental design strategies:

  • Include biological and technical replicates in each experiment

  • Process samples from different experimental groups simultaneously

  • Randomize sample processing order

  • Include reference standards in each batch

• Protocol standardization:

  • Develop detailed protocols with precise timing, temperatures, and buffer compositions

  • Use automated systems when possible to reduce operator variability

  • Standardize image acquisition settings (exposure, gain, resolution)

  • Implement consistent data analysis workflows

• Reproducibility assessment approaches:

  • Calculate intra- and inter-batch coefficients of variation

  • Perform replicate experiments by different operators

  • Validate key findings using alternative methods

  • Compare results with published data when available

• Documentation and reporting standards:

  • Maintain detailed laboratory notebooks with all experimental conditions

  • Report all antibody details following established guidelines

  • Share raw data and analysis workflows

  • Document all troubleshooting and protocol modifications

Recent research highlighting the artifactual nature of nucleolar staining with one commonly used antibody (clone H8132) demonstrates the importance of rigorous validation across multiple experimental systems . This finding has significant implications for reproducibility in NR2F1 research, as previous studies used this staining pattern to quantify NR2F1-positive cells .