DOF1.4 Antibody

What is the DOF1.4 transcription factor and why is it significant in plant research?

DOF1.4 belongs to the DOF (DNA-binding One Finger) family of plant-specific transcription factors characterized by a unique C2-C2 zinc finger DNA binding domain. These proteins play crucial roles in various biological processes in plants, particularly in Arabidopsis thaliana. The significance of DOF1.4 stems from its involvement in regulating metabolic pathways, particularly nitrogen assimilation and carbon metabolism .

Research findings indicate that DOF transcription factors bind to specific DNA sequences with an AAAG core motif, and DOF1 in particular has been shown to enhance light-dependent activation of target genes through binding to specific promoter regions . Metabolic engineering studies with DOF1 have demonstrated its potential to improve nitrogen assimilation, which is essential for primary plant metabolism including ammonia assimilation .

What applications are most suitable for DOF1.4 antibody in plant research?

Based on technical specifications from multiple manufacturers, DOF1.4 antibody has been validated for the following applications:

The antibody is particularly valuable for studying DOF1.4 expression patterns, protein-protein interactions, and transcriptional regulation mechanisms in plant tissues . When designing experiments, researchers should consider the specific application and optimize antibody concentrations accordingly, as the actual working concentration may vary based on experimental conditions.

How should DOF1.4 antibody be stored and handled to maintain its efficacy?

Proper storage and handling of DOF1.4 antibody is crucial for maintaining its specificity and sensitivity over time. Based on manufacturer recommendations :

• Store at -20°C for long-term storage (up to one year)

• For frequent use and short-term storage (up to one month), store at 4°C

• Avoid repeated freeze-thaw cycles which can degrade antibody performance

• Most preparations contain preservatives such as sodium azide (0.02-0.03%) and stabilizers like glycerol (50%) and BSA (0.5%)

• Upon receipt, aliquot the antibody into smaller volumes if frequent use is anticipated

• For lyophilized formulations, reconstitute according to manufacturer's instructions and store reconstituted antibody as directed

Implementing these storage protocols will help ensure consistent experimental results and extend the useful life of the antibody.

How can I validate the specificity of a DOF1.4 antibody for my experimental system?

Validating antibody specificity is critical before conducting comprehensive experiments. For DOF1.4 antibody, implement the following validation approach:

• Positive control testing: Use recombinant DOF1.4 protein or plant tissues known to express DOF1.4 (Arabidopsis thaliana samples)

• Western blot validation: Verify a single band at the expected molecular weight (~72 kDa observed vs. calculated ~131 kDa for some DOF family members)

• Knockdown/knockout controls: Compare antibody reactivity in wild-type vs. DOF1.4 knockdown/knockout samples

• Pre-absorption test: Pre-incubate the antibody with purified antigen before immunostaining to confirm signal elimination

• Cross-reactivity assessment: Test against other DOF family members to ensure specificity. Research has demonstrated that properly validated antibodies can distinguish between closely related DOF transcription factors

As shown in experimental validation studies, proper antibody screening should include multiple parallel approaches. One study demonstrated that monoclonal anti-TCP1 antibody and anti-MYB6 and anti-DOF11 sera bound specifically to their respective antigens without cross-reacting with other related transcription factors, including other DOF and MYB factors, when properly validated .

What are the optimal experimental conditions for using DOF1.4 antibody in immunolocalization studies?

For successful immunolocalization of DOF1.4 in plant tissues:

• Fixation: Use 4% paraformaldehyde for 2-4 hours at room temperature for tissue preservation without epitope masking

• Antigen retrieval: Apply citrate buffer (pH 6.0) heat treatment if working with paraffin-embedded tissues

• Blocking: Use 5-10% normal serum (from the species of the secondary antibody) with 1-3% BSA in PBS or TBS to reduce background

• Primary antibody incubation:

  • Dilution: 1:50-1:200 (optimize for your specific tissue)

  • Incubation time: Overnight at 4°C

  • Include proper controls (secondary antibody only, isotype control)

• Visualization method: For fluorescence detection, use appropriate secondary antibodies conjugated to fluorophores. For colorimetric detection, HRP-conjugated secondary antibodies with DAB substrate work effectively

• Counterstaining: DAPI for nuclear visualization can help contextualize DOF1.4 localization, as it should show nuclear enrichment consistent with its role as a transcription factor

Optimization may be required for specific plant tissues, as different tissues may require adjusted fixation times or antibody concentrations.

How can I quantify DOF1.4 protein levels accurately across different plant tissues?

For accurate quantification of DOF1.4 protein across plant tissues:

• Sample preparation consistency:

  • Harvest tissues at the same developmental stage and time of day

  • Use standardized extraction buffer (e.g., 50mM Tris-HCl pH 7.5, 150mM NaCl, 1% Triton X-100, 0.5% sodium deoxycholate, plus protease inhibitors)

  • Normalize protein amounts via Bradford or BCA assay before analysis

• Western blot quantification:

  • Use recommended dilutions (1:500-1:2000)

  • Include recombinant DOF1.4 protein standard curve

  • Use housekeeping proteins (e.g., actin, tubulin) as loading controls

  • Employ digital image analysis software for densitometry

• ELISA quantification:

  • Develop a sandwich ELISA using DOF1.4 antibody (1:20000 dilution recommended)

  • Include standard curves with recombinant protein

  • Account for tissue-specific matrix effects

• Data normalization strategies:

  • Normalize to total protein loaded

  • Compare to consistent reference genes/proteins

  • Consider tissue-specific extraction efficiency

Research indicates that comparing immunoblot signal intensities across diverse tissue types requires careful normalization, as extraction efficiency may vary between different plant tissues due to differences in cell wall composition and secondary metabolites.

How can I adapt DOF1.4 antibody for chromatin immunoprecipitation (ChIP) to identify its genomic binding sites?

Adapting DOF1.4 antibody for ChIP requires specific optimization:

• Crosslinking optimization:

  • Test both formaldehyde (1-1.5%, 10-15 min) and dual crosslinking with DSG followed by formaldehyde for transcription factors

  • Consider tissue-specific cell wall barriers in plants for efficient crosslinking

• Chromatin sonication:

  • Aim for 200-500bp fragments

  • Develop tissue-specific sonication protocols (power, cycle, duration)

  • Verify fragmentation by agarose gel electrophoresis

• Immunoprecipitation:

  • Pre-clear chromatin with protein A/G beads

  • Use 3-5μg antibody per 25μg chromatin

  • Include IgG control and input samples

  • Extended incubation (overnight at 4°C) with gentle rotation

• Washing and elution:

  • Use stringent washing buffers with increasing salt concentrations

  • Confirm enrichment by qPCR of known target sites before sequencing

• Data analysis considerations:

  • Look for AAAG core motif enrichment, which is the known DOF binding site

  • Integrate with transcriptomic data to associate binding with gene expression

Based on research with DOF transcription factors, potential target genes include those involved in carbon metabolism and nitrogen assimilation pathways. Studies have identified that DOF1 can bind specifically to promoter regions of genes like C4PEPC through AAAG-containing sequence motifs, with five specific binding sites (a, b, f, g, and h) identified through electrophoretic mobility shift assay (EMSA) .

What strategies can overcome cross-reactivity issues when studying DOF1.4 in the presence of other DOF family members?

DOF transcription factors share highly conserved DNA binding domains, making specificity challenging. Advanced strategies include:

• Epitope mapping and antibody selection:

  • Choose antibodies raised against unique regions outside the conserved DOF domain

  • Verify epitope sequence uniqueness through multiple sequence alignment of all DOF family members

  • Consider using antibodies raised against peptides from the C-terminal region, which shows greater variability among DOF proteins

• Validation through protein arrays:

  • Test antibody against multiple DOF proteins simultaneously

  • Research has shown successful discrimination between DOF family members using protein arrays. In one study, anti-DOF11 antibody specifically recognized DOF11 and did not cross-react with other proteins, including other DOF transcription factors

• Competitive binding assays:

  • Pre-incubate with recombinant proteins of closely related DOF members

  • Quantify signal reduction to assess cross-reactivity

• Combined approaches:

  • Complement antibody-based detection with mass spectrometry for definitive identification

  • Use genetic approaches (knockouts/knockdowns) alongside antibody detection

• Signal verification:

  • Verify subcellular localization patterns (DOF1.4 should be primarily nuclear)

  • Confirm molecular weight differences between DOF family members

Research with anti-DOF11 and anti-DOF1 antibodies has demonstrated that properly validated antibodies can achieve specificity even among closely related transcription factor family members .

How can protein interaction studies with DOF1.4 be optimized using this antibody?

To investigate DOF1.4 protein-protein interactions:

• Co-immunoprecipitation (Co-IP):

  • Use 2-5μg DOF1.4 antibody per 500μg protein lysate

  • Pre-clear lysates with protein A/G beads

  • Include appropriate controls (IgG, no-antibody controls)

  • Consider using crosslinking agents (DSP, formaldehyde) for transient interactions

  • Verify pulled-down proteins by mass spectrometry

• Proximity ligation assay (PLA):

  • Use DOF1.4 antibody (1:100) alongside antibodies against suspected interaction partners

  • Optimize fixation to preserve protein complexes while maintaining epitope accessibility

  • Include single-antibody controls to verify specificity of PLA signal

• FRET-based approaches:

  • Use fluorophore-conjugated DOF1.4 antibody pairs or antibody fragments

  • Consider using antibody fragments (Fab) to reduce steric hindrance

• Bimolecular fluorescence complementation (BiFC) validation:

  • Use antibody detection to confirm expression of fusion proteins

  • Verify localization patterns match antibody staining

Based on research with DOF transcription factors, potential interaction partners include proteins involved in transcriptional regulation. Research has shown that DOF proteins can interact with each other, as demonstrated with Dof1 and Dof2, which can bind to identical DNA sequences yet have opposite effects on transcription activation .

What are the most common causes of false positive or false negative results when using DOF1.4 antibody, and how can they be addressed?

Common issues and solutions when working with DOF1.4 antibody:

Research with plant transcription factor antibodies has shown that fixation protocols significantly impact epitope accessibility. The success observed with DOF, MYB, and TCP antibodies in protein array studies demonstrates that with proper optimization, these issues can be overcome .

How can I modify protocols for DOF1.4 antibody use in different plant species beyond Arabidopsis thaliana?

When adapting DOF1.4 antibody for use in non-Arabidopsis species:

• Sequence homology assessment:

  • Perform BLAST analysis of the immunogen sequence against the target species

  • Predict cross-reactivity based on epitope conservation (>70% identity suggests potential reactivity)

  • Consider evolutionary distance between species

• Tissue preparation modifications:

  • Adjust fixation times based on tissue density and composition

  • For tissues with high phenolic content, include polyvinylpyrrolidone (PVP) in extraction buffers

  • Optimize cell wall digestion for immunocytochemistry in species with differing cell wall composition

• Antibody dilution optimization:

  • Perform dilution series (typically starting 2-5× more concentrated than for Arabidopsis)

  • Include appropriate positive controls (e.g., Arabidopsis samples processed in parallel)

• Validation in the new species:

  • Verify molecular weight of detected protein matches predicted size

  • Confirm subcellular localization pattern consistent with transcription factor function

  • If possible, verify with genetic approaches (RNAi, CRISPR) in the target species

How does phosphorylation or other post-translational modifications affect DOF1.4 antibody recognition?

Post-translational modifications (PTMs) can significantly impact antibody recognition of DOF1.4:

• Effect of phosphorylation:

  • Phosphorylation can alter protein conformation, potentially masking or exposing epitopes

  • If the antibody was raised against a non-phosphorylated peptide, phosphorylation near the epitope may reduce binding

  • Consider using phosphatase treatment of samples as a control if phosphorylation is suspected

• Strategies for detecting modified forms:

  • Use phospho-specific antibodies in combination with total DOF1.4 antibody

  • Perform lambda phosphatase treatment on parallel samples to assess phosphorylation impact

  • Use Phos-tag™ gels to separate phosphorylated forms before Western blotting

• Other relevant PTMs:

  • SUMOylation or ubiquitination may alter apparent molecular weight

  • PTMs may affect subcellular localization, altering detection patterns in immunolocalization

  • For comprehensive studies, combine antibody-based detection with mass spectrometry

• Experimental design considerations:

  • Include samples from different physiological conditions or treatments known to affect PTM status

  • Consider time-course experiments to capture dynamic changes in modification

  • Compare nuclear and cytoplasmic fractions to assess impact of PTMs on localization

Research on transcription factors suggests that their activity and localization are often regulated by phosphorylation. While specific PTM data for DOF1.4 is limited, these modifications likely play important roles in regulating its function and should be considered when interpreting antibody-based detection results.

How can AlphaFold and other computational structural approaches be used alongside DOF1.4 antibody studies?

Integrating computational structural biology with DOF1.4 antibody research:

• Epitope prediction and antibody design:

  • Use AlphaFold to predict DOF1.4 structure and identify surface-exposed regions

  • Design antibodies against predicted surface epitopes for higher success rates

  • Evaluate potential cross-reactivity with other DOF family members based on structural similarities

• Interpreting interaction studies:

  • Model DOF1.4 interactions with DNA and protein partners

  • Recent studies show AlphaFold can achieve >30% accuracy in modeling protein-antibody complexes

  • Predict how antibody binding might affect protein function or interactions

• PTM effect prediction:

  • Model how phosphorylation or other modifications might alter protein conformation

  • Predict impact on antibody accessibility to epitopes

• Experiment planning and validation:

  • Use structural predictions to design targeted mutations for functional studies

  • Validate computational models with antibody accessibility experiments

  • Identify potential conformational epitopes that might be missed in peptide-based approaches

Recent research demonstrates that AlphaFold's interface pLDDT (I-pLDDT) score provides excellent discrimination between incorrect and accurate antibody-antigen models (AUC=0.93) . This suggests computational approaches can provide valuable guidance for antibody-based experiments with DOF1.4.

What are the latest methodologies for combining DOF1.4 antibody with single-cell techniques?

Advanced single-cell approaches with DOF1.4 antibody:

• Single-cell Western blotting:

  • Apply DOF1.4 antibody (1:250-1:500) in microfluidic single-cell Western platforms

  • Allows quantification of protein levels in individual cells

  • Compare expression levels across different cell types within plant tissues

• Mass cytometry (CyTOF) adaptation:

  • Conjugate DOF1.4 antibody with rare earth metals

  • Enables multiplexed protein detection in single cells

  • Can be combined with cell type-specific markers

• Proximity ligation assays in single cells:

  • Combine DOF1.4 antibody with antibodies against potential interaction partners

  • Visualize protein-protein interactions at single-cell resolution

  • Quantify interaction frequencies across cell populations

• Single-cell ChIP approaches:

  • Develop DOF1.4 antibody-based CUT&RUN or CUT&Tag protocols

  • Map genomic binding sites in specific cell types

  • Integrate with single-cell transcriptomics data

• Spatial transcriptomics integration:

  • Combine immunolocalization of DOF1.4 with spatial transcriptomics

  • Correlate protein localization with target gene expression in tissue context

Recent developments in antibody-based techniques for plants can be applied to DOF1.4 research. For example, approaches similar to those used in developing customizable methods for cattle antibody sequencing and annotation could potentially be adapted for plant systems .

How can active learning approaches improve antibody-antigen binding prediction for DOF transcription factors?

Applying active learning to DOF antibody research:

• Improving prediction accuracy:

  • Apply library-on-library screening approaches where multiple DOF antigens are tested against antibody libraries

  • Active learning algorithms can reduce required antigen mutant variants by up to 35% compared to random testing

  • Accelerate learning process by selecting the most informative experiments

• Out-of-distribution prediction challenges:

  • Address challenges when predicting interactions for test antibodies and antigens not represented in training data

  • Develop strategies for handling the many-to-many relationships between antibodies and antigens

• Experimental design optimization:

  • Use active learning to guide epitope mapping experiments

  • Prioritize most informative tests to reduce experimental burden

  • Apply Bayesian optimization techniques to identify optimal conditions

• Implementation methodology:

  • Start with small labeled dataset of DOF-antibody interactions

  • Iteratively expand dataset using most informative new experiments

  • Integrate structural data from AlphaFold predictions

Recent research demonstrates that active learning algorithms specifically designed for antibody-antigen binding prediction can significantly outperform random sampling approaches, suggesting potential applications for optimizing DOF1.4 antibody studies .

How might DOF1.4 antibody applications evolve with advances in plant synthetic biology and genome editing?

Emerging applications at the intersection of DOF1.4 antibody research and cutting-edge plant biotechnology:

• CRISPR-engineered epitope tagging:

  • Use CRISPR to insert epitope tags in endogenous DOF1.4 gene

  • Enable more specific antibody detection while maintaining native regulation

  • Compare tag-specific antibody with traditional DOF1.4 antibody for validation

• Monitoring synthetic transcription factor circuits:

  • Utilize DOF1.4 antibody to track engineered DOF-based transcription factors

  • Monitor protein levels and localization in synthetic biology applications

  • Assess stability and activity of chimeric DOF constructs

• Post-translational regulation studies:

  • Combine CRISPR-modified DOF1.4 (with altered PTM sites) with antibody detection

  • Track how engineered variants affect metabolism in plants

  • Build on previous metabolic engineering studies that showed DOF1 can improve nitrogen assimilation

• Optogenetic applications:

  • Use antibodies to validate light-responsive DOF fusion proteins

  • Monitor subcellular localization changes upon light activation

  • Quantify protein levels and complex formation in optogenetically controlled systems

Recent research demonstrating the use of DOF1 for improving nitrogen assimilation and other essential metabolic pathways suggests expanding applications in plant biotechnology that will require advanced antibody-based monitoring approaches .

What are the prospects for developing more specific monoclonal antibodies against DOF1.4?

Future directions for improving DOF1.4 antibody specificity:

• Next-generation monoclonal development:

  • Target unique regions identified through comprehensive sequence alignment of all DOF family members

  • Use phage display technology to select high-affinity antibodies with minimal cross-reactivity

  • Develop recombinant antibodies with engineered specificity

• Single B cell sequencing approaches:

  • Adapt methods from mammalian antibody research to plant antigens

  • Use high-throughput screening to identify B cells producing high-specificity antibodies

  • Recent advances in antibody sequencing technologies now allow fast and comprehensive analysis of antibody repertoires that could be applied to developing improved anti-DOF1.4 antibodies

• Nanobody development:

  • Generate smaller single-domain antibodies with potential for improved epitope access

  • Engineer DOF1.4-specific nanobodies for application in living plant cells

  • Develop intrabodies that can track DOF1.4 in vivo

• Machine learning optimization:

  • Use computational approaches to predict optimal epitopes for specificity

  • Design antibodies in silico before validation in vitro

  • Apply active learning strategies to prioritize the most informative experiments

The growing importance of DOF transcription factors in metabolic engineering applications will likely drive development of more specific antibody tools, building on techniques established in other research fields.

How will integrating DOF1.4 antibody studies with multi-omics approaches advance plant systems biology?

Integrative strategies combining antibody-based DOF1.4 detection with multi-omics:

• ChIP-seq + transcriptomics integration:

  • Map DOF1.4 binding sites genome-wide using ChIP-seq

  • Correlate binding with gene expression changes under various conditions

  • Identify direct vs. indirect regulatory targets

• Proteomics + interactomics:

  • Use DOF1.4 antibody for immunoprecipitation followed by mass spectrometry

  • Identify condition-specific protein interaction partners

  • Map the dynamic DOF1.4 interactome under different environmental conditions

• Metabolomics correlation:

  • Relate DOF1.4 protein levels/activity to metabolic profiles

  • Track changes in carbon and nitrogen metabolism pathways known to be influenced by DOF transcription factors

  • Identify novel metabolic roles beyond established functions

• Multi-scale integration approaches:

  • Develop computational frameworks that integrate antibody-based protein data with transcriptomics, metabolomics, and phenomics

  • Build predictive models of DOF1.4 function in plant development and stress response

  • Apply network biology approaches to position DOF1.4 in larger regulatory networks