ERF035 Antibody

What is ERF and what is its significance in cellular regulation?

ERF (Ets2 repressor factor) belongs to the ETS family of transcription factors that play crucial roles in regulating cell proliferation and differentiation processes. As a transcription factor, ERF contains a highly conserved DNA-binding domain known as the ETS domain, which specifically recognizes and binds to the GGAA/T sequence motif . ERF functions primarily as a transcriptional repressor that modulates cellular responses to growth factor stimulation and helps maintain cellular homeostasis. The protein has several aliases including ETS domain-containing transcription factor ERF and PE-2, and is encoded by the human gene with Entrez ID 2077 . Understanding ERF function is essential for research into cellular growth control mechanisms and developmental processes, as aberrant ERF activity has been implicated in various pathological conditions.

What are the key considerations for ERF antibody validation in experimental workflows?

Proper validation of ERF antibodies is critical for ensuring experimental reliability. Researchers should first verify antibody specificity using recommended positive controls such as A431 cell lysates or ERF-transfected 293T cells . Cross-reactivity assessment is also important, with predicted reactivity extending to mouse, rat, and bovine samples (each with 99% predicted reactivity) . Before beginning experiments, the antibody should be properly prepared by centrifuging briefly prior to opening the vial and storing as a concentrated solution .

Validation protocols should include:

• Western blot analysis with positive and negative control lysates

• Immunoprecipitation assays to confirm target binding

• Immunofluorescence to verify subcellular localization patterns

• Comparison with orthogonal methods such as gene expression data

• Knockout or knockdown controls to demonstrate specificity

This comprehensive validation approach helps minimize experimental artifacts and ensures that observed results genuinely reflect ERF biology.

How should ERF antibodies be handled and stored to maintain optimal activity?

For maximum stability and performance, ERF antibodies should be stored as concentrated solutions and centrifuged briefly prior to opening the vial to ensure that all material is collected at the bottom . Aliquoting the antibody into smaller volumes upon first use prevents repeated freeze-thaw cycles that can degrade antibody function. When preparing working dilutions, researchers should use appropriate buffers that maintain protein stability, typically containing carrier proteins such as BSA and preservatives to prevent microbial contamination.

Temperature control is critical:

• Long-term storage: -20°C to -80°C in small aliquots

• Short-term storage (less than one week): 2-8°C

• Avoid repeated freeze-thaw cycles

Additionally, researchers should maintain sterile conditions when handling antibodies and include appropriate preservatives when diluting stock solutions for extended use. Documentation of lot numbers, dilution factors, and storage conditions is essential for experimental reproducibility.

What methodological approaches provide optimal results when using ERF antibodies in chromatin immunoprecipitation (ChIP) assays?

When employing ERF antibodies in ChIP assays, several methodological considerations can significantly enhance experimental outcomes. The ETS domain of ERF recognizes the GGAA/T sequence motif , so optimization of chromatin fragmentation is critical to preserve these binding sites while generating fragments of appropriate size (typically 200-500bp). Based on approaches used for similar transcription factors, the following protocol optimizations are recommended:

• Crosslinking optimization: Titrate formaldehyde concentration (0.5-1.5%) and incubation time (5-15 minutes) to maintain protein-DNA interactions without overfixing.

• Sonication parameters: Adjust sonication conditions to generate appropriately sized chromatin fragments while preserving epitope integrity. For ERF, moderate sonication conditions often yield better results than aggressive protocols.

• Antibody concentration: Titrate antibody concentration based on preliminary ChIP-qPCR of known ERF targets. Typically, 2-5 μg of antibody per reaction provides optimal signal-to-noise ratio.

• Controls implementation: Include both technical controls (IgG, input) and biological controls (ERF-depleted samples) to validate specificity .

• Sequential ChIP: For investigating ERF interactions with other transcription factors, sequential ChIP (re-ChIP) provides insights into co-occupancy at specific genomic loci.

The efficiency of ChIP protocols can be verified using qPCR analysis of known ERF binding regions prior to proceeding with genome-wide approaches such as ChIP-seq.

How can computational approaches enhance the design and selection of highly specific ERF antibodies?

Recent advances in computational modeling have significantly improved antibody design capabilities, enabling researchers to develop ERF antibodies with customized specificity profiles. Biophysically informed computational models can now disentangle different binding modes and predict antibody-epitope interactions . These approaches offer several advantages for ERF antibody development:

• Binding mode identification: Computational models can distinguish between different binding modes associated with specific ligands, allowing for the discrimination between very similar epitopes . For ERF antibodies, this enables the development of reagents that specifically recognize ERF without cross-reactivity to other ETS family members.

• Sequence-function predictions: Machine learning algorithms can predict antibody properties beyond experimentally observed sequences, expanding the potential antibody design space . This approach has successfully been applied to predict thermal stability from binding affinity measurements.

• Specificity engineering: Shallow dense neural networks can be trained to parametrize binding energies for different modes, enabling the design of antibodies that either specifically bind a single target or exhibit controlled cross-reactivity to multiple targets .

• Validation through phage display: Computational predictions can be validated through phage display experiments, where antibody libraries are selected against various combinations of ligands to assess specificity profiles .

This integration of computational approaches with experimental validation provides a powerful framework for developing next-generation ERF antibodies with precisely tailored binding properties.

What strategies can address common technical challenges in ERF detection and quantification?

Researchers frequently encounter technical challenges when detecting and quantifying ERF in experimental systems. These challenges can be systematically addressed through targeted methodological refinements:

• Low abundance detection: ERF may be expressed at low levels in some cell types, requiring signal amplification strategies:

  • Use high-sensitivity detection systems such as tyramide signal amplification

  • Employ protein concentration techniques prior to detection

  • Optimize cell lysis buffers to improve protein extraction efficiency

• Cross-reactivity management: Given the conserved ETS domain across family members, cross-reactivity must be carefully controlled:

  • Select antibodies targeting non-conserved regions when possible

  • Validate with knockout/knockdown controls

  • Implement competitive binding assays with recombinant ETS proteins

• Post-translational modification detection: ERF function is regulated by phosphorylation and other modifications:

  • Utilize phospho-specific antibodies when studying ERF regulation

  • Employ phosphatase inhibitors during sample preparation

  • Consider specialized extraction buffers that preserve labile modifications

• Quantification standardization: For accurate quantification:

  • Develop standard curves using recombinant ERF protein

  • Implement spike-in controls for normalization

  • Use multiple antibodies targeting different epitopes to confirm results

• Nuclear localization assessment: Since ERF shuttles between cytoplasm and nucleus:

  • Perform careful subcellular fractionation

  • Use immunofluorescence to monitor localization changes

  • Consider live-cell imaging with fluorescently tagged ERF constructs

By systematically addressing these technical challenges, researchers can significantly improve the reliability and sensitivity of ERF detection and quantification in diverse experimental contexts.

How should researchers design experiments to investigate ERF interactions with other transcription factors?

Investigating ERF interactions with other transcription factors requires careful experimental design that accounts for the dynamic nature of these interactions and their context-dependency. Based on methodologies applied to similar transcription factors, the following multi-faceted approach is recommended:

• Co-immunoprecipitation studies:

  • Use ERF antibodies to pull down protein complexes under native conditions

  • Apply a dual-tagging approach with orthogonal purification

  • Include RNase treatment controls to distinguish RNA-mediated from direct protein interactions

  • Verify interactions bidirectionally through reciprocal co-IP

• Proximal labeling approaches:

  • Employ BioID or APEX2 fusion proteins to identify proximal proteins in living cells

  • Express ERF-fusion proteins at near-endogenous levels to prevent artificial interactions

  • Use spatially-restricted labeling to focus on nuclear interactions

• Chromatin occupancy analysis:

  • Implement sequential ChIP (re-ChIP) to identify co-occupancy at specific genomic loci

  • Perform ChIP-seq with antibodies against ERF and potential interacting partners

  • Analyze binding site overlap using bioinformatic approaches

• Functional validation strategies:

  • Conduct reporter assays with mutated binding sites to assess cooperative effects

  • Implement CRISPR-based approaches to modify endogenous binding sites

  • Analyze transcriptional outcomes using RNA-seq after modulating interaction partners

• Structural analysis considerations:

  • When possible, perform structural studies (X-ray crystallography, cryo-EM) of complexes

  • Use peptide arrays to map interaction interfaces

  • Validate structural predictions with targeted mutagenesis

What are the critical parameters for optimizing western blot protocols with ERF antibodies?

Western blot analysis with ERF antibodies requires careful optimization to achieve clean, specific detection with minimal background. Based on experiences with similar transcription factors, the following parameters should be systematically optimized:

Additionally, researchers should implement appropriate controls:

• Positive control: A431 or ERF-transfected 293T lysates

• Negative control: ERF-depleted cell lysate

• Loading control: Housekeeping protein detection on the same membrane

These optimizations ensure reliable detection of ERF while minimizing common technical artifacts.

How can researchers effectively use ERF antibodies in multiplexed immunofluorescence studies?

Multiplexed immunofluorescence studies with ERF antibodies require careful consideration of antibody compatibility, spectral overlap, and signal preservation. The following methodological approach ensures optimal results:

• Antibody panel design:

  • Select ERF antibodies from different host species than other target antibodies

  • Verify that epitope retrieval conditions are compatible across all targets

  • Confirm that all primary antibodies maintain specificity under identical fixation conditions

  • Consider using directly conjugated antibodies to reduce cross-reactivity issues

• Signal separation strategies:

  • Implement spectral unmixing for fluorophores with overlapping emission spectra

  • Use sequential detection methods when antibodies have species conflicts

  • Consider tyramide signal amplification for low-abundance targets

  • Employ zenon labeling technology for same-species antibodies

• Validation approach:

  • Perform single-marker controls alongside multiplexed experiments

  • Include absorption controls to verify antibody specificity

  • Use spectral controls to establish proper compensation parameters

  • Apply computational approaches to correct for autofluorescence

• Image acquisition optimization:

  • Adjust exposure times to accommodate differences in signal intensity

  • Implement linear unmixing algorithms for closely overlapping fluorophores

  • Use confocal microscopy to improve signal-to-noise ratio and colocalization assessment

  • Consider super-resolution approaches for detailed colocalization studies

• Quantification methods:

  • Apply automated segmentation algorithms for unbiased analysis

  • Implement colocalization coefficients (Pearson's, Mander's) for interaction assessment

  • Use machine learning approaches for complex pattern recognition

  • Validate findings with orthogonal methods

This systematic approach enables researchers to effectively visualize ERF in relation to other proteins of interest, providing spatial information that complements biochemical interaction studies.

What approaches can address inconsistent results when using ERF antibodies across different experimental systems?

Inconsistent results with ERF antibodies across different experimental systems often stem from technical variables and biological differences. A systematic troubleshooting approach includes:

• Antibody validation across systems:

  • Verify antibody specificity in each cell line/tissue using knockdown/knockout controls

  • Assess cross-reactivity with related ETS family members in each system

  • Confirm epitope conservation in different species if working with non-human models

  • Consider using multiple antibodies targeting different ERF epitopes

• Sample preparation harmonization:

  • Standardize fixation protocols (fixative type, concentration, duration)

  • Optimize epitope retrieval methods for each experimental system

  • Implement consistent blocking protocols to minimize non-specific binding

  • Use freshly prepared reagents and standardized buffer compositions

• System-specific expression analysis:

  • Quantify ERF expression levels across experimental systems

  • Determine ERF post-translational modification patterns in each system

  • Assess nuclear/cytoplasmic distribution, which may vary by cell type

  • Evaluate potential isoform expression differences between systems

• Technical variations management:

  • Implement internal standards for quantitative comparisons

  • Use automated systems where possible to reduce operator variability

  • Document all experimental parameters thoroughly for systematic analysis

  • Perform technical replicates to assess method reproducibility

• Collaborative validation approach:

  • Share antibody lots and detailed protocols between laboratories

  • Implement blinded analysis of samples to reduce confirmation bias

  • Establish consensus positive and negative controls for community use

This comprehensive approach helps identify the sources of variability and establish reliable protocols for consistent ERF detection across diverse experimental systems.

How can computational methods enhance specificity prediction for new ERF antibody development?

The development of highly specific ERF antibodies can be significantly enhanced through computational approaches that predict antibody-antigen interactions. Recent advances in biophysically informed modeling provide powerful tools for antibody engineering:

• Binding mode identification and separation:

  • Computational models can distinguish between different binding modes, enabling the discrimination of very similar epitopes

  • These models can disentangle contributions from multiple epitopes even when experimental separation is not feasible

  • Machine learning algorithms can predict specificity profiles based on antibody sequence features

• Sequence-structure-function relationships:

  • Energy functions parametrized by shallow dense neural networks can predict antibody binding properties

  • Structural models can identify critical residues that determine specificity between ERF and other ETS family members

  • Computational alanine scanning can predict the impact of mutations on binding specificity

• Library design strategy:

  • Computational approaches can guide the design of phage display libraries with enhanced diversity in key binding residues

  • In silico screening can prioritize variants for experimental testing, expanding the effective library size

  • Models trained on existing selection data can predict outcomes for new combinations of ligands

• Cross-reactivity prediction:

  • Models can simultaneously minimize binding energy for target epitopes while maximizing it for off-target epitopes

  • These approaches can generate antibodies with customized specificity profiles, either highly specific or intentionally cross-reactive

  • Experimental validation through phage display confirms computational predictions

• Implementation platform:

  • Deep mutational scanning data can train models to predict the impact of all possible mutations

  • Feedback loops between computational prediction and experimental validation refine model accuracy

  • Integration with structural information enhances predictive power

These computational approaches significantly accelerate the development of ERF antibodies with precisely defined specificity profiles, reducing the need for extensive experimental screening.

What are the most effective strategies for using ERF antibodies in examining transcriptional regulation mechanisms?

Investigating transcriptional regulation mechanisms involving ERF requires integrating multiple experimental approaches with ERF antibodies. The following comprehensive strategy leverages the binding specificity of the ETS domain to the GGAA/T sequence motif :

• Genome-wide occupancy mapping:

  • Perform ChIP-seq with optimized ERF antibodies to identify binding sites across the genome

  • Implement CUT&RUN or CUT&Tag for higher resolution and lower background

  • Use spike-in controls for quantitative comparisons between conditions

  • Analyze binding sites in context of chromatin accessibility data (ATAC-seq, DNase-seq)

• Transcriptional complex characterization:

  • Conduct ChIP-MS to identify proteins associated with ERF at chromatin

  • Implement proximity labeling approaches (BioID, APEX) to capture transient interactions

  • Perform sequential ChIP to identify co-occupancy with other transcription factors

  • Use protein-protein interaction assays to validate direct interactions

• Functional outcome assessment:

  • Correlate ERF binding with gene expression changes using RNA-seq

  • Implement nascent RNA sequencing (GRO-seq, PRO-seq) to capture direct transcriptional effects

  • Use reporter assays with wild-type and mutated binding sites to assess functional importance

  • Apply CRISPR activation/inhibition targeted to ERF binding sites

• Dynamics and kinetics analysis:

  • Study ERF recruitment kinetics using time-resolved ChIP

  • Implement live-cell imaging with fluorescently tagged ERF

  • Assess binding site turnover rates using competition ChIP approaches

  • Analyze stimulus-dependent changes in ERF occupancy

• Mechanistic dissection:

  • Map functional domains through domain-specific antibodies

  • Assess post-translational modifications at specific genomic loci

  • Investigate nucleosome positioning relative to ERF binding sites

  • Examine 3D chromatin organization changes associated with ERF binding

This integrated approach provides a comprehensive understanding of how ERF contributes to transcriptional regulation networks and helps distinguish its unique functions from those of other ETS family members.

How can ERF antibodies be effectively utilized in single-cell protein analysis techniques?

Single-cell protein analysis represents a frontier in understanding cellular heterogeneity, and ERF antibodies can be effectively deployed in this context through several cutting-edge approaches:

• Mass cytometry (CyTOF) implementation:

  • Metal-conjugated ERF antibodies enable simultaneous detection of dozens of proteins

  • Antibody titration is critical to determine optimal signal-to-noise ratio

  • Careful panel design prevents signal spillover between channels

  • Validation with positive control populations (e.g., A431 cells) establishes detection thresholds

• Single-cell western blot applications:

  • Microfluidic platforms enable western blot analysis at single-cell resolution

  • ERF antibody concentration must be optimized for reduced volumes

  • Multiplexing with lineage markers helps identify cell-type specific ERF expression patterns

  • Protein ladders specific to single-cell applications improve quantification accuracy

• Imaging mass cytometry optimization:

  • Metal-tagged ERF antibodies provide spatial information at subcellular resolution

  • Optimized tissue preparation preserves both antigenicity and morphology

  • Multiplexed with chromatin markers to assess nuclear localization patterns

  • Computational analysis correlates ERF expression with tissue architecture

• Microfluidic proteomics approaches:

  • Droplet-based assays enable high-throughput single-cell protein measurements

  • Proximity ligation assays detect ERF interactions at single-molecule resolution

  • Barcoding strategies allow pooling of different experimental conditions

  • Internal standards enable absolute quantification of ERF protein copies per cell

• Integration with multi-omics data:

  • CITE-seq combines antibody detection with transcriptomics in single cells

  • Computational approaches correlate ERF protein levels with mRNA expression

  • Trajectory analysis reveals dynamics of ERF regulation during cellular processes

  • Spatial transcriptomics integration provides tissue context for ERF function