ESF1.3 Antibody

What is ESF1.3 protein and why is it significant in plant research?

ESF1.3 is a protein found in Arabidopsis thaliana (Mouse-ear cress), an important model organism in plant molecular biology. The protein belongs to the ESF (EMBRYO SURROUNDING FACTOR) family which plays crucial roles in early embryonic development and potentially in pre-rRNA processing pathways in plants. Understanding ESF1.3 function provides insights into fundamental cellular processes in plant development. Research methodologies typically involve genetic knockout studies, localization experiments, and protein-protein interaction analyses to elucidate its function in plant developmental biology .

What are the key specifications of commercially available ESF1.3 antibodies?

ESF1.3 antibodies are typically polyclonal antibodies raised in rabbits against recombinant Arabidopsis thaliana ESF1.3 protein. These antibodies are generally supplied in liquid form, containing preservatives like 0.03% Proclin 300 and storage components such as 50% glycerol in PBS buffer (pH 7.4). They are purified using antigen affinity methods and are non-conjugated. The target UniProt accession number for Arabidopsis thaliana ESF1.3 is A8MQA5. For optimal performance, these antibodies should be stored at -20°C or -80°C, avoiding repeated freeze-thaw cycles that could compromise their activity .

What are the validated applications for ESF1.3 antibodies in plant research?

ESF1.3 antibodies have been validated for Enzyme-Linked Immunosorbent Assay (ELISA) and Western Blotting (WB) applications in plant research. For Western Blotting protocols, researchers should optimize protein extraction methods specific to plant tissues, which typically require additional steps to remove polyphenols and polysaccharides that can interfere with protein separation. Methodologically, sample preparation often involves grinding plant tissue in liquid nitrogen followed by extraction with a buffer containing protease inhibitors and reducing agents. For ELISA applications, serial dilutions should be performed to determine optimal antibody concentration, typically starting with a 1:1000 dilution and adjusting based on signal strength .

How do ESF1.3 antibodies compare with antibodies for related proteins like ESF1?

While ESF1.3 antibodies specifically target the ESF1.3 isoform in Arabidopsis thaliana, antibodies against related proteins like ESF1 may recognize different subcellular compartments or show different expression patterns. Based on research with similar proteins, ESF1 has been associated with pre-ribosomal particles and plays roles in rRNA processing . When designing experiments requiring multiple antibodies, researchers should verify the specificity of each antibody through appropriate controls, including knockout/knockdown validation, to ensure accurate interpretation of results. Methodologically, when studying multiple related proteins, sequential immunoprecipitation techniques can be employed to distinguish complex interactions between family members .

What are the recommended protocols for optimizing Western blot conditions specifically for ESF1.3 antibody in plant samples?

For optimal Western blot results with ESF1.3 antibody in plant samples, researchers should implement a multi-step optimization strategy. First, extract proteins using a plant-specific buffer containing 100 mM Tris-HCl (pH 8.0), 150 mM NaCl, 5 mM EDTA, 10% glycerol, 1% Triton X-100, 1 mM DTT, and plant protease inhibitor cocktail. For Arabidopsis samples, grinding 100 mg tissue in liquid nitrogen followed by extraction in 300 μl buffer yields optimal results. Second, during electrophoresis, use freshly prepared 12% SDS-PAGE gels and limit sample heating to 70°C for 5 minutes to prevent protein aggregation. Third, for transfer, a semi-dry system (15V for 30 minutes) with PVDF membranes pre-activated in methanol shows superior results compared to nitrocellulose membranes. Fourth, block membranes with 5% non-fat dry milk in TBST for 2 hours at room temperature. Fifth, incubate with primary ESF1.3 antibody at 1:500 dilution overnight at 4°C in 1% BSA/TBST solution. Sixth, wash membranes 5 times (5 minutes each) with TBST before secondary antibody incubation. Finally, optimize chemiluminescent detection by testing exposure times between 30 seconds and 5 minutes .

How can immunoprecipitation with ESF1.3 antibody be optimized to investigate protein-protein interactions in plant developmental biology?

For effective immunoprecipitation (IP) with ESF1.3 antibody, researchers should implement a comprehensive protocol specifically adapted for plant tissues. Begin by harvesting 1-2 g of fresh plant tissue and flash-freezing in liquid nitrogen. Homogenize tissue thoroughly using a pre-chilled mortar and pestle, then extract proteins with a specialized plant IP buffer containing 50 mM HEPES (pH 7.5), 150 mM NaCl, 10% glycerol, 1 mM EDTA, 0.5% Triton X-100, 1 mM PMSF, and plant protease inhibitor cocktail. Pre-clear lysates by incubating with Protein A/G agarose beads for 1 hour at 4°C with gentle rotation. For antibody coupling, pre-incubate 5 μg of ESF1.3 antibody with 50 μl Protein A/G beads in IP buffer for 2 hours at 4°C. Wash coupled beads three times with IP buffer before adding 1-2 mg of pre-cleared protein extract. Incubate overnight at 4°C with gentle rotation. Implement stringent washing steps (5 washes with IP buffer containing increasing NaCl concentrations from 150 mM to 300 mM) to minimize non-specific interactions. Elute proteins using a gentle acidic elution buffer (100 mM glycine, pH 2.5) followed by immediate neutralization with 1M Tris-HCl (pH 8.0). To validate interactions, perform reciprocal IPs and analyze results using both Western blotting and mass spectrometry .

What approaches should be used to validate ESF1.3 antibody specificity when contradictory localization data emerges?

When faced with contradictory localization data using ESF1.3 antibody, researchers should implement a multi-faceted validation strategy. First, perform side-by-side immunofluorescence using multiple fixation methods (4% paraformaldehyde, cold methanol, and acetone fixation at -20°C for 10 minutes) to rule out fixation artifacts. Second, validate antibody specificity using esf1.3 knockout/knockdown plant lines as negative controls, which should show significantly reduced or absent signal. Third, complement immunolocalization with orthogonal approaches such as fluorescent protein tagging (N-terminal and C-terminal GFP fusions) expressed under native promoters. Fourth, perform subcellular fractionation followed by Western blotting to biochemically confirm localization patterns. Fifth, use epitope competition assays with recombinant ESF1.3 protein to confirm signal specificity. Sixth, employ super-resolution microscopy techniques like Structured Illumination Microscopy (SIM) or Stochastic Optical Reconstruction Microscopy (STORM) to resolve potential colocalization with nuclear markers like nucleophosmin or SURF6 at higher resolution. Finally, validate findings using multiple antibody lots and comparing monoclonal versus polyclonal antibodies when available .

How can quantitative imaging approaches be combined with ESF1.3 immunostaining to analyze developmental changes in protein expression?

Implementing quantitative imaging with ESF1.3 immunostaining requires a systematic methodological approach. Begin with consistent sample preparation: fix plant tissues at precisely timed developmental stages using 4% paraformaldehyde for 30 minutes under vacuum infiltration, followed by standard embedding and sectioning protocols. For immunostaining, use automated staining platforms when available to ensure consistent incubation times and temperatures. Implement a tiered dilution series (1:100, 1:500, 1:1000) of ESF1.3 antibody to establish the linear detection range. Capture images using confocal microscopy with standardized acquisition parameters: constant laser power, detector gain, pixel dwell time, and optical section thickness. Include calibration standards in each imaging session, such as fluorescent beads with known intensity values. For quantification, establish an automated analysis pipeline using open-source software like CellProfiler or ImageJ/Fiji with custom macros that: (1) segment individual cells or subcellular compartments, (2) measure background-corrected integrated intensity values, (3) normalize signals to cell volume or nuclear area, and (4) extract morphometric parameters. Apply statistical approaches that account for cell-to-cell variability, such as hierarchical mixed models. To validate expression changes, correlate imaging data with orthogonal quantification methods such as RT-qPCR for transcript levels and quantitative Western blotting for protein levels .

What strategies can address cross-reactivity concerns when using ESF1.3 antibody in species other than Arabidopsis thaliana?

When extending ESF1.3 antibody applications to non-Arabidopsis species, researchers must implement systematic cross-reactivity validation. Begin with in silico analysis: perform sequence alignment of ESF1.3 across target species, focusing on the immunogen region (typically 15-20 amino acids) to predict potential cross-reactivity. Calculate sequence identity and similarity percentages, with >70% identity suggesting higher probability of cross-reactivity. Before experimental application, validate using a multi-tier approach: First, perform Western blot analysis on protein extracts from multiple species simultaneously under identical conditions, looking for bands at the predicted molecular weight (calculated based on amino acid composition of each ortholog). Second, implement peptide competition assays using synthetic peptides derived from the ESF1.3 sequences of target species to determine epitope specificity. Third, employ heterologous expression systems to express tagged versions of ESF1.3 orthologs from different species in a neutral background (e.g., E. coli or yeast) to test antibody recognition in a controlled context. Fourth, validate immunostaining patterns by comparing with fluorescent protein fusions in transgenic plants of target species. Finally, for quantitative applications across species, develop calibration curves using recombinant proteins from each species to account for potential differences in antibody affinity. When cross-reactivity is confirmed but signal strength varies, optimize primary antibody concentration independently for each species through systematic titration experiments .

How can researchers address high background signals when using ESF1.3 antibody in immunohistochemistry of plant tissues?

High background signals in plant immunohistochemistry with ESF1.3 antibody can be systematically addressed through a comprehensive troubleshooting workflow. First, optimize fixation conditions: test 4% paraformaldehyde fixation with variable durations (15, 30, 60 minutes) and compare with alternative fixatives like cold acetone (-20°C for 10 minutes). Second, implement enhanced blocking protocols: increase blocking time to 2 hours and test different blocking agents (5% BSA, 5% normal goat serum, 5% non-fat dry milk) in combination with 0.3% Triton X-100 to determine optimal background reduction. Third, add a peroxidase quenching step (3% hydrogen peroxide for 10 minutes) when using HRP-based detection systems to eliminate endogenous peroxidase activity common in plant tissues. Fourth, incorporate additional washing steps with higher stringency: use PBST with 0.1% to 0.3% Tween-20 and extend wash times to 15 minutes per wash with at least 5 wash cycles. Fifth, reduce primary antibody concentration through systematic titration (test 1:100, 1:250, 1:500, 1:1000) while extending incubation time to 48 hours at 4°C to maintain sensitivity. Sixth, add 5% non-fat dry milk to the antibody diluent to further reduce non-specific binding. Seventh, implement a pre-adsorption step by incubating the diluted antibody with acetone powder prepared from wild-type plant tissue (5 mg/ml) for 1 hour before application. Finally, consider secondary antibody optimization by comparing different vendors and testing highly cross-adsorbed secondary antibodies specifically designed to minimize cross-reactivity in plant tissues .

What methodological approaches can resolve contradictory Western blot data when using ESF1.3 antibody?

When confronted with contradictory Western blot results using ESF1.3 antibody, researchers should implement a systematic technical validation framework. First, evaluate protein extraction methods: compare at least three different extraction buffers (RIPA, urea-based, and plant-specific extraction buffers) to ensure complete solubilization of the target protein. Second, validate sample integrity by performing total protein staining (Ponceau S or SYPRO Ruby) on membranes before immunoblotting. Third, implement a comprehensive gel percentage gradient analysis (8%, 10%, 12%, and 15% acrylamide) to optimize separation of the target protein based on its molecular weight. Fourth, conduct transfer efficiency validation using reversible protein stains immediately after transfer and before blocking. Fifth, perform detailed antibody validation including lot-to-lot comparison, testing multiple dilutions (1:250, 1:500, 1:1000, 1:2000) with extended incubation times (1 hour at room temperature versus overnight at 4°C), and comparing different detection systems (chemiluminescence versus fluorescence-based detection). Sixth, implement positive and negative controls including recombinant ESF1.3 protein as a positive control and knockout/knockdown plant material as a negative control. Seventh, evaluate post-translational modifications by incorporating phosphatase treatment and other enzymatic processes that might affect protein migration. Finally, consider protein degradation issues by adding increased concentrations of protease inhibitors (2X standard concentration) and performing time-course studies of sample stability under various storage conditions (fresh, -20°C, -80°C for 24 hours, 1 week, and 1 month) .

What are the optimal conditions for long-term storage and maintaining activity of ESF1.3 antibody in a research laboratory?

For optimal long-term storage and maintained activity of ESF1.3 antibody, researchers should implement a comprehensive preservation strategy. Begin by preparing working aliquots immediately upon receipt: divide the stock antibody into 10-20 μl single-use aliquots in screw-cap microcentrifuge tubes with O-rings to prevent evaporation. For primary storage, maintain antibody aliquots at -80°C in a designated non-frost-free freezer with temperature monitoring and alarm systems. For working aliquots in active use, store at -20°C for up to 3 months, avoiding more than 5 freeze-thaw cycles per aliquot. Implement a quality control system by designating one aliquot as a reference standard to be tested at 6-month intervals against fresh aliquots using a standardized Western blot protocol with consistent positive control samples. Document the signal-to-noise ratio and EC50 values to track antibody performance over time. For antibodies showing reduced activity, optimize stabilization through addition of cryoprotectants: supplement with an additional 10% glycerol (final concentration of 60%) and 0.5% BSA to prevent protein adsorption to tube walls. For laboratories without reliable -80°C storage, lyophilization offers an alternative: freeze antibody solutions containing 1% BSA and 5% trehalose as cryoprotectants, followed by lyophilization in a controlled environment. Store lyophilized antibodies with desiccant at -20°C and reconstitute only the needed amount in the original buffer system. Finally, implement an antibody tracking database recording receipt date, lot number, aliquot preparation, storage conditions, and validation test results over time to ensure experimental reproducibility and facilitate troubleshooting if performance issues arise .

How can ESF1.3 antibody be used in chromatin immunoprecipitation (ChIP) experiments to investigate potential DNA-binding activities?

For adapting ESF1.3 antibody for chromatin immunoprecipitation (ChIP) experiments, researchers should implement a plant-specific protocol with several critical modifications. Begin with 1-3 grams of fresh Arabidopsis tissue and perform crosslinking using 1% formaldehyde under vacuum infiltration for exactly 10 minutes, followed by quenching with 125 mM glycine. Extract nuclei using a plant-specific nuclear isolation buffer containing 0.25 M sucrose, 10 mM Tris-HCl (pH 8.0), 10 mM MgCl₂, 1% Triton X-100, 5 mM β-mercaptoethanol, 0.1 mM PMSF, and protease inhibitor cocktail. Sonicate chromatin using a Bioruptor or similar device with optimized conditions (30 seconds ON/30 seconds OFF for 15-20 cycles) to achieve DNA fragments between 200-500 bp, verified by agarose gel electrophoresis. Before immunoprecipitation, pre-clear chromatin with protein A/G beads for 2 hours at 4°C. For the immunoprecipitation step, incubate 10 μg of ESF1.3 antibody with 25 μl of protein A/G magnetic beads for 4 hours at 4°C, then add pre-cleared chromatin and incubate overnight with gentle rotation. Include appropriate controls: (1) a technical negative control using non-specific IgG, (2) a biological negative control using esf1.3 mutant tissue, and (3) a positive control targeting histone H3. Implement high-stringency washing steps with buffers containing increasing salt concentrations (150 mM to 500 mM NaCl). After elution and reverse crosslinking (65°C for 6 hours), purify DNA using phenol-chloroform extraction followed by ethanol precipitation with glycogen as a carrier. Validate ChIP efficiency using qPCR against predicted binding regions based on motif analysis or previously identified targets of related proteins. For genome-wide binding site identification, prepare ChIP-seq libraries using a low-input library preparation kit and perform sequencing with at least 20 million reads per sample. During bioinformatic analysis, implement peak calling algorithms specifically optimized for plant ChIP-seq data, such as MACS2 with plant-specific parameters .

What considerations are important when designing fluorescence resonance energy transfer (FRET) experiments using ESF1.3 antibody and fluorescently-tagged interaction partners?

Designing FRET experiments with ESF1.3 antibody requires meticulous planning to generate meaningful interaction data. First, select appropriate fluorophore pairs: for indirect immunofluorescence with ESF1.3 antibody, use Alexa Fluor 488 (donor) conjugated secondary antibody combined with potential interaction partners tagged with Alexa Fluor 555 or 568 (acceptor), ensuring spectral overlap for efficient energy transfer while minimizing bleed-through. Calculate Förster distance (R₀) for the selected pair, typically 5-6 nm for Alexa Fluor combinations. Second, validate antibody labeling specificity through single-label controls and spectral unmixing. Third, implement expression level controls: use inducible promoters for tagged interaction partners to maintain physiologically relevant expression levels, as FRET efficiency is concentration-dependent. Fourth, create appropriate controls including: (1) donor-only and acceptor-only samples for bleed-through correction, (2) a positive FRET control using dual-labeled antibodies with known separation distances, and (3) negative controls using mutated versions of interaction partners with disrupted binding interfaces. Fifth, optimize fixation and permeabilization: test paraformaldehyde fixation with variable durations (10, 20, 30 minutes) followed by gradient permeabilization with Triton X-100 (0.1%, 0.3%, 0.5%) to preserve spatial relationships while allowing antibody access. Sixth, measure FRET using multiple complementary techniques: (a) acceptor photobleaching to calculate FRET efficiency directly from donor dequenching, (b) fluorescence lifetime imaging microscopy (FLIM) to measure donor lifetime changes independent of concentration, and (c) sensitized emission with careful correction for bleed-through and cross-excitation. Finally, perform statistical validation by analyzing multiple cells (>30) across at least three independent experiments, applying appropriate statistical tests for significance, and reporting FRET efficiency distributions rather than simple averages .

How can ESF1.3 antibody be utilized in proximity ligation assays (PLA) to investigate protein-protein interactions in plant cellular contexts?

Implementing proximity ligation assays (PLA) with ESF1.3 antibody requires a specialized protocol adapted for plant tissues. Begin sample preparation with fresh plant material fixed in 4% paraformaldehyde using vacuum infiltration (15 minutes), followed by dehydration and embedding in either paraffin for sectioning or clearing with ClearSee solution for whole-mount preparations. Perform antigen retrieval using citrate buffer (pH 6.0) at 95°C for 20 minutes to unmask epitopes while preserving tissue architecture. For antibody compatibility, ESF1.3 rabbit polyclonal antibody must be paired with a mouse monoclonal antibody against the potential interaction partner; validate both antibodies independently using immunofluorescence before attempting PLA. Optimize primary antibody concentrations through titration experiments (typically 1:100 to 1:500 dilutions) to minimize background while maintaining specific signal. Implement the PLA protocol with plant-specific modifications: (1) extend blocking time to 3 hours using 5% BSA with 0.3% Triton X-100 in PBS, (2) incubate primary antibodies overnight at 4°C, (3) use PLA probes with 2-hour incubation at 37°C in a humidity chamber, (4) extend ligation step to 1 hour, and (5) optimize amplification time between 100-140 minutes based on empirical testing. Include critical controls: (1) technical negative controls omitting one primary antibody, (2) biological negative controls using tissues from knockout/knockdown plants, (3) spatial controls with proteins known to localize to different subcellular compartments, and (4) positive controls with established interaction partners. For quantification, collect z-stack images with consistent parameters across all samples and use 3D analysis software to count PLA puncta per cell or nuclear volume. Apply spatial statistics to analyze distribution patterns of interaction sites relative to nuclear landmarks or specific cellular compartments. Finally, validate key interactions using orthogonal methods such as co-immunoprecipitation or bimolecular fluorescence complementation .

What statistical approaches are recommended for analyzing variability in ESF1.3 immunolocalization patterns across different plant tissues and developmental stages?

For robust statistical analysis of ESF1.3 immunolocalization patterns across diverse plant tissues and developmental stages, researchers should implement a multi-level quantitative framework. Begin with experimental design optimization: use a minimum of 5 biological replicates per condition, with technical triplicates for each sample, and implement randomized processing to minimize batch effects. For image acquisition, collect minimum 10 fields of view per sample with standardized parameters, capturing at least 50-100 cells per condition. Implement quantitative image analysis using specialized plant cell segmentation algorithms in CellProfiler or custom ImageJ macros that account for plant-specific cellular architecture. Extract multi-parameter datasets including: (1) nuclear/cytoplasmic intensity ratios, (2) correlation coefficients with known subcellular markers, (3) texture features capturing distribution patterns, and (4) morphometric parameters of labeled structures. For statistical testing, apply nested ANOVA models to account for hierarchical data structure (cells within tissues within plants), or implement linear mixed-effects models with tissue type and developmental stage as fixed effects and biological replicate as random effect. For temporal developmental analyses, apply functional data analysis (FDA) to characterize continuous changes in expression patterns. Implement dimensionality reduction techniques (PCA, t-SNE, or UMAP) to visualize multi-dimensional relationships between different tissues and stages. For pattern classification, train supervised machine learning algorithms (Random Forest or Support Vector Machines) to automatically categorize ESF1.3 distribution patterns, with cross-validation to ensure model robustness. Finally, integrate immunolocalization data with transcriptomic datasets using correlation networks to place observed protein distribution patterns in broader developmental contexts. Report effect sizes alongside p-values, with appropriate corrections for multiple testing (Benjamini-Hochberg procedure with FDR < 0.05) .

How should researchers interpret contradictory results between ESF1.3 protein localization and its predicted subcellular targeting based on sequence analysis?

When confronted with contradictions between observed ESF1.3 localization and sequence-based predictions, researchers should implement a systematic reconciliation approach. First, reevaluate bioinformatic predictions using multiple complementary algorithms (SignalP, TargetP, PSORT, DeepLoc) with plant-specific parameters, calculating consensus prediction scores across methods. Second, examine the protein sequence for cryptic localization signals including non-canonical nuclear localization sequences, organelle targeting peptides, or membrane association domains that might be overlooked by standard prediction tools. Third, investigate potential post-translational modifications that could alter localization: perform phosphorylation prediction using PlantPhos and analyze ubiquitination sites using UbPred, then validate experimentally using phosphatase treatments or ubiquitination inhibitors before immunostaining. Fourth, evaluate developmental or condition-specific localization changes: perform time-course experiments across developmental stages and under various environmental stresses (heat, cold, drought, pathogen exposure) to capture dynamic relocalization events. Fifth, assess protein isoforms: design isoform-specific antibodies or primers to determine if alternative splicing or alternative promoter usage generates protein variants with different localization patterns. Sixth, implement proximity-dependent labeling techniques (BioID or TurboID fused to ESF1.3) to map the protein's molecular neighborhood independently of antibody-based detection. Seventh, investigate protein-protein interactions that might mediate piggyback transport: identify interaction partners using IP-MS and test whether knockdown of these partners affects ESF1.3 localization. Finally, reconcile contradictions by developing a comprehensive model that integrates experimental data with prediction algorithms, potentially revealing novel regulatory mechanisms governing protein localization or identifying previously uncharacterized functions in unexpected cellular compartments .

What approaches can differentiate between specific and non-specific signals when using ESF1.3 antibody in complex plant tissues with high autofluorescence?

To differentiate specific from non-specific signals when using ESF1.3 antibody in autofluorescent plant tissues, researchers should implement a comprehensive technical strategy. First, characterize tissue autofluorescence profiles by acquiring spectral scans (400-700 nm in 10 nm increments) of unstained tissues under multiple excitation wavelengths to identify optimal imaging channels that minimize overlap with autofluorescent compounds like chlorophyll, phenolics, and cell wall components. Second, implement chemical autofluorescence quenching: pretreat sections with 0.1% sodium borohydride for 10 minutes to reduce aldehyde-induced fluorescence, 0.1 M glycine to quench free aldehyde groups from fixatives, and 0.1% Sudan Black B in 70% ethanol for lipofuscin quenching. Third, apply computational correction algorithms: collect multispectal images and perform linear unmixing based on reference spectra of pure autofluorescence and fluorophore signals. Fourth, implement optical approaches: use fluorophores with emission maxima above 580 nm (far-red region) where plant autofluorescence is minimized, or employ time-gated detection to separate antibody fluorescence (longer lifetime) from autofluorescence (shorter lifetime). Fifth, validate specificity through controlled experiments: (1) perform peptide competition assays using gradient concentrations of blocking peptide, (2) implement gradient fixation protocols to distinguish protocol-induced artifacts from genuine signals, (3) compare patterns in wild-type versus esf1.3 knockout/knockdown tissues, and (4) correlate immunofluorescence with fluorescent protein fusions expressed under native promoters. Sixth, apply signal-to-noise ratio quantification: calculate SNR values across different regions of interest and establish threshold criteria for positive detection (typically SNR > 5). Finally, implement machine learning-based signal classification: train convolutional neural networks on annotated image datasets to automatically distinguish specific antibody signals from autofluorescence patterns based on morphological and intensity features .