At5g36000 Antibody

What is At5g36000 and why is it significant in Arabidopsis thaliana research?

At5g36000 is a protein-coding gene in Arabidopsis thaliana (Mouse-ear cress), which serves as a key model organism in plant molecular biology. The gene product plays important roles in cellular processes that are still being actively investigated by the research community. Studying At5g36000 contributes to our understanding of plant development, stress responses, and fundamental cellular mechanisms. Antibodies against this protein enable researchers to investigate its expression patterns, subcellular localization, and potential functional roles in various physiological contexts. The protein is part of the expanding resource of Arabidopsis proteins that are being characterized through antibody development initiatives to advance plant science research .

What applications can the At5g36000 antibody be used for in experimental plant research?

The At5g36000 antibody has been validated for multiple research applications including Enzyme-Linked Immunosorbent Assay (ELISA) and Western Blotting (WB). These techniques allow researchers to detect and quantify the presence of At5g36000 protein in various experimental preparations . The antibody enables studies on protein expression levels under different environmental conditions, developmental stages, or genetic backgrounds. When properly validated, such antibodies can potentially be used for immunocytochemistry to visualize subcellular localization patterns. This is particularly valuable for understanding protein function in context, as cellular localization often correlates with specific roles in biological processes. As part of broader antibody resources for Arabidopsis research, these tools facilitate systems biology approaches to understanding protein localization, interaction networks, and functional dynamics .

How is the At5g36000 antibody produced and what validation methods ensure its specificity?

The At5g36000 antibody is produced using a recombinant protein approach rather than synthetic peptides. This method has demonstrated higher success rates for generating effective antibodies against plant proteins . The antibody is raised in rabbits using recombinant Arabidopsis thaliana At5g36000 protein as the immunogen, resulting in a polyclonal IgG antibody that recognizes multiple epitopes of the target protein . The antibody undergoes affinity purification to improve specificity and reduce background signal.

Validation typically includes Western blot analysis where the antibody should detect a band of the expected molecular weight. Ideally, validation involves testing in genetic backgrounds where the target protein is absent or reduced (e.g., knockout or knockdown lines), which provides strong evidence for specificity. For advanced applications like immunocytochemistry, additional controls are necessary to confirm that the observed signals accurately represent the protein's distribution . The antibody development process includes bioinformatic analysis to identify antigenic regions with minimal cross-reactivity to other proteins, using a cutoff of 40% amino acid sequence similarity as a guide for specificity .

What are the optimal storage and handling conditions for maintaining At5g36000 antibody efficacy?

For maximum retention of At5g36000 antibody activity, long-term storage should be at -20°C or preferably -80°C, with the antibody maintained in its recommended storage buffer (containing 50% glycerol, 0.01M PBS, pH 7.4, and 0.03% Proclin 300 as preservative) . Avoid repeated freeze-thaw cycles as these can progressively denature antibody proteins and reduce binding efficiency. When handling the antibody, always use clean pipette tips and sterile technique to prevent contamination.

For working solutions, store at 4°C for short-term use (typically 1-2 weeks), but prepare fresh dilutions for critical experiments. Before each use, gently mix the antibody solution without vortexing to prevent protein denaturation. Always centrifuge briefly before opening to collect any solution that may be in the cap. For long-term studies, consider preparing single-use aliquots upon receipt to minimize freeze-thaw cycles. Monitor antibody performance regularly with positive controls to detect any decline in reactivity, which would necessitate obtaining a fresh lot .

How can researchers troubleshoot non-specific binding issues when using the At5g36000 antibody?

When encountering non-specific binding with the At5g36000 antibody, a systematic troubleshooting approach is recommended. First, increase blocking stringency by extending blocking times or using alternative blocking agents (e.g., 5% BSA, 5% milk, commercial blocking reagents) to reduce background signal. Optimize antibody concentration by performing a dilution series to identify the minimal concentration that produces specific signal while minimizing background.

Include additional washing steps with increased stringency (higher salt concentration or mild detergent) to remove weakly bound antibody. For Western blots, consider using PVDF membranes instead of nitrocellulose if high background persists, as PVDF may offer better signal-to-noise ratios for some antibodies. Perform pre-adsorption tests by incubating the antibody with recombinant target protein prior to the experiment to confirm specificity.

Most critically, include appropriate negative controls in each experiment, such as samples from knockout/knockdown lines when available, or secondary antibody-only controls to distinguish true signal from technical artifacts. If possible, validate results using an alternative antibody targeting a different epitope of the same protein. For persistent issues, consider affinity purification against the specific antigen to enhance specificity, as studies have shown that affinity purification "massively improved the detection rate" for plant protein antibodies .

What considerations should be made when designing co-localization studies using At5g36000 antibody?

When designing co-localization studies with the At5g36000 antibody, careful planning of controls and technical parameters is essential. First, select appropriate subcellular markers with well-established localization patterns as references. The CPIB antibody resource includes validated antibodies against subcellular markers such as BiP (endoplasmic reticulum), γ-cop (Golgi), PM-ATPase (plasma membrane), and MDH (mitochondria) that can be used alongside the At5g36000 antibody .

For dual or multi-labeling experiments, select secondary antibodies with minimal spectral overlap to avoid false co-localization signals. When the At5g36000 antibody and the marker antibody are from the same species, use sequential immunostaining with intermediate blocking steps, or consider direct conjugation of one antibody to eliminate cross-reactivity.

Microscopy settings must be optimized to minimize bleed-through between fluorescence channels, including proper exposure settings and sequential scanning in confocal microscopy. Apply quantitative co-localization analysis using established coefficients (e.g., Pearson's, Manders') rather than relying solely on visual assessment. Include single-labeled controls to set thresholds and confirm channel separation.

To validate biological relevance of co-localization, perform experiments under different physiological conditions that might affect protein trafficking or interactions. Complement imaging studies with biochemical fractionation approaches to independently confirm protein compartmentalization. Finally, interpret results cautiously, recognizing that spatial proximity does not necessarily indicate functional interaction without additional supporting evidence .

How should quantitative data from At5g36000 antibody experiments be analyzed for statistical significance?

Select statistical tests based on data distribution and experimental design. For normally distributed data comparing two groups, use Student's t-test; for multiple group comparisons, employ ANOVA followed by appropriate post-hoc tests (e.g., Tukey's HSD). For non-parametric data, consider Mann-Whitney U test or Kruskal-Wallis test instead. Report both P-values and effect sizes to indicate both statistical significance and biological relevance.

For immunofluorescence quantification, establish consistent imaging parameters across all samples and use automated analysis algorithms to eliminate observer bias. Analyze a sufficient number of cells (typically >50 per condition) to account for cellular heterogeneity. For co-localization studies, apply appropriate coefficients and statistical measures rather than relying on visual assessment alone.

Report all normalization methods, statistical tests, and software used for analysis to ensure reproducibility. Present data with appropriate error bars (standard deviation for data variability, standard error for precision of mean estimation) and clearly indicate sample sizes. Consider performing power analysis before experiments to determine appropriate sample sizes needed to detect expected effect sizes .

How can researchers distinguish between specific signal and background when using At5g36000 antibody in immunolocalization studies?

Distinguishing specific signal from background in immunolocalization studies with the At5g36000 antibody requires implementation of multiple control experiments and optimization strategies. First, include a negative control using tissue from At5g36000 knockout or knockdown plants when available, which should show minimal or significantly reduced signal compared to wild-type samples. Additionally, perform secondary antibody-only controls to identify potential non-specific binding of the secondary detection system.

Pre-absorb the At5g36000 antibody with recombinant antigen prior to staining as a blocking control; this should substantially reduce specific signals while non-specific binding may persist. Compare staining patterns across different fixation and permeabilization protocols, as genuine signals typically show consistent patterns across methods while artifacts may vary.

Perform concentration gradient tests with the primary antibody to identify the optimal dilution where specific signal remains strong while background is minimized. Specific signal typically shows a dose-dependent relationship with antibody concentration, whereas non-specific background may not follow the same pattern.

For fluorescence microscopy, analyze spectral properties of the signal, as true signals typically exhibit characteristic emission profiles while autofluorescence often has broader emission spectra. Incorporate differential interference contrast (DIC) or phase contrast imaging alongside fluorescence to correlate signals with cellular structures. Apply signal deconvolution algorithms and appropriate thresholding during image processing to enhance signal-to-noise ratio.

The quality of the antibody is critical, and studies have shown that "affinity purification of antibodies massively improved the detection rate" for plant protein antibodies , making this an important consideration when working with the At5g36000 antibody.

What approaches can resolve conflicting results when using At5g36000 antibody across different experimental conditions?

When facing conflicting results with the At5g36000 antibody across different experimental conditions, adopt a systematic investigation approach. First, thoroughly document all experimental variables including antibody lot numbers, buffer compositions, incubation times/temperatures, sample preparation methods, and detection systems. This comprehensive record allows identification of potential sources of variation.

Standardize critical parameters by creating detailed standard operating procedures (SOPs) for each technique and adhering to them strictly across experiments. Perform side-by-side comparisons using the same antibody lot on different sample types or with different protocols to directly assess the impact of specific variables.

Consider epitope accessibility issues by testing multiple extraction or fixation methods, as protein conformation or interactions may mask epitopes under certain conditions. The recombinant protein approach used for generating the At5g36000 antibody may recognize specific conformational epitopes that are differentially exposed under various experimental conditions .

Validate key findings with orthogonal approaches that don't rely on antibody binding, such as mass spectrometry, RNA expression analysis, or functional assays. For particularly important or controversial findings, consider requesting independent validation from collaborating laboratories using their own protocols and equipment.

If protein modifications are suspected to affect antibody recognition, perform experiments to specifically test this hypothesis, such as phosphatase treatment if phosphorylation is suspected to alter epitope recognition. Finally, communicate transparently about any inconsistencies when publishing results, describing the specific conditions under which observations were reproducible and acknowledging limitations .

What buffer conditions optimize At5g36000 antibody performance in different applications?

Optimizing buffer conditions for the At5g36000 antibody requires systematic testing across different applications. For Western blotting, start with standard TBST (Tris-buffered saline with 0.1% Tween-20) for washing and antibody dilution, but consider reducing Tween-20 concentration to 0.05% if background is acceptable, as this may enhance specific binding. The antibody is maintained in a storage buffer containing 50% glycerol, 0.01M PBS (pH 7.4), and 0.03% Proclin 300 as preservative , which provides stability during storage.

For immunoprecipitation experiments, use gentler detergents like NP-40 or Triton X-100 at 0.1-0.5% rather than SDS or deoxycholate to maintain native protein conformation. Include protease inhibitors in all extraction buffers to prevent degradation of the target protein. For plant tissue specifically, consider adding polyvinylpyrrolidone (PVP) to extraction buffers at 1-2% to remove interfering phenolic compounds that can reduce antibody effectiveness.

For ELISA applications, optimize blocking buffer composition (BSA vs. milk vs. commercial blockers) through direct comparison. PBS with 0.05% Tween-20 is typically appropriate for ELISA washing steps. For all applications, maintain consistent pH (typically 7.2-7.6) and ionic strength across experiments to ensure reproducibility.

Temperature can significantly impact antibody binding kinetics; while room temperature incubation is standard, overnight incubation at 4°C often improves signal-to-noise ratio, particularly for low-abundance proteins. When troubleshooting suboptimal results, systematically alter one buffer component at a time while keeping others constant to identify optimal conditions for specific experimental systems .

What controls are essential when validating new lots of At5g36000 antibody?

Rigorous validation of new At5g36000 antibody lots is critical for maintaining experimental consistency and reliability. Essential controls include positive reference samples from previous successful experiments using earlier antibody lots, allowing direct comparison of band pattern, intensity, and specificity. Ideally, include samples from At5g36000 knockout or knockdown plants as negative controls, which should show no signal or significantly reduced signal intensity.

Perform concentration gradient tests with the new antibody lot to determine optimal working dilutions, which may differ from previous lots. Compare recognition patterns across multiple sample types where the target protein is expressed at different levels to assess sensitivity and dynamic range.

Include recombinant At5g36000 protein as a standard for quantitative comparisons between lots, enabling normalization of detection efficiency. For Western blotting, perform parallel blots with new and previous antibody lots using identical samples and conditions to directly assess performance differences.

Evaluate cross-reactivity by testing the antibody against samples known to contain proteins with sequence similarity to At5g36000, particularly those identified during the antibody design phase where a "cut off of 40% similarity score (at amino acid level) was used as a guide" . Document all validation results, including images of blots or immunostaining, in a laboratory database for future reference. If significant differences are observed between lots, contact the manufacturer for technical support and consider requesting a replacement if performance falls below acceptable standards .

How can At5g36000 antibody be integrated into advanced protein interaction studies?

Integrating the At5g36000 antibody into protein interaction studies requires careful experimental design to maximize specificity and sensitivity. For co-immunoprecipitation (Co-IP) studies, optimize extraction conditions to maintain native protein complexes while ensuring efficient solubilization of membrane-associated proteins if relevant. Use chemical crosslinking approaches like formaldehyde treatment prior to extraction to stabilize transient interactions when appropriate.

For pull-down assays, consider coupling the antibody to solid supports (e.g., protein A/G beads, magnetic beads, or activated sepharose) using optimized coupling chemistry to maintain binding capacity. Pre-clear lysates with the same support without antibody to reduce non-specific binding. Include appropriate controls, such as IP with non-specific IgG from the same species as the At5g36000 antibody, to distinguish specific from non-specific interactions.

When combined with mass spectrometry for interaction partner identification, implement stringent filtering criteria for identified proteins, using quantitative approaches like SILAC or TMT labeling to differentiate true interactors from background proteins. Cross-validate key interactions using reciprocal IP with antibodies against putative interaction partners when available.

For in situ interaction studies, consider proximity ligation assays (PLA) where the At5g36000 antibody is used alongside antibodies against suspected interaction partners to visualize protein-protein interactions within intact cells with nanometer resolution. For all interaction studies, validate key findings with orthogonal approaches such as yeast two-hybrid, FRET, or split complementation assays to build a comprehensive understanding of At5g36000 protein interaction networks .

How does the recombinant protein approach for At5g36000 antibody production compare to synthetic peptide approaches?

The recombinant protein approach used for At5g36000 antibody production offers several significant advantages over synthetic peptide methods. Comprehensive data from antibody development initiatives revealed that "the success rate with the peptide antibodies was very low" whereas the recombinant protein approach yielded substantially better results . The recombinant approach uses larger protein fragments (typically around 100 amino acids) that better represent the native protein structure compared to short peptides (typically 10-15 amino acids). This leads to recognition of multiple epitopes, including conformational epitopes that may be critical for detecting the native protein.

The failure of peptide approaches often stems from epitope prediction limitations, as "prediction methods identify individual stretches of amino acids (continuous epitopes), whereas epitopes are very often discontinuous, involving distant subsequences brought together by the protein's tertiary structures" . Additionally, synthetic peptides may not fold correctly, failing to generate antibodies that recognize the native protein structure.

Quantitatively, studies have shown that among antibodies raised against recombinant proteins, approximately 55% could detect signals with high confidence, with about 31% being suitable for immunocytochemistry . This success rate represents a significant improvement over peptide-based approaches, which were largely abandoned due to poor performance.

The recombinant protein method allows more thorough bioinformatic analysis to identify antigenic regions with minimal cross-reactivity. Researchers typically use a cutoff of 40% sequence similarity to other proteins when selecting antigenic regions, reducing the likelihood of off-target binding . This careful selection process, combined with affinity purification, results in antibodies with higher specificity and utility across multiple applications.

What experimental modifications are needed when using At5g36000 antibody with different plant tissue types?

Adapting experimental protocols for different plant tissue types requires tissue-specific modifications to optimize At5g36000 antibody performance. For protein extraction from different tissues, adjust mechanical disruption methods based on tissue hardness; use mortar and pestle grinding with liquid nitrogen for leaves and soft tissues, while roots and stems may require more vigorous homogenization with bead beating. The extraction buffer composition should be optimized for each tissue type, with higher detergent concentrations (0.5-1% Triton X-100) for tissues with abundant membranes.

For immunohistochemistry, fixation protocols require tissue-specific adjustment. Soft tissues may be adequately fixed with 4% paraformaldehyde for 1-2 hours, while woody tissues may require longer fixation times or vacuum infiltration to ensure complete penetration. The thickness of sections should be adjusted based on tissue density and antibody penetration efficiency, typically ranging from 5-10 μm for paraffin sections to 50-100 μm for free-hand sections.

Blocking protocols need customization based on tissue autofluorescence and endogenous peroxidase activity. Leaf tissues often require additional treatments to reduce chlorophyll autofluorescence, such as incubation in 0.1% sodium borohydride or extended methanol washes. For tissues with high phenolic content, include polyvinylpyrrolidone (1-2%) or polyvinylpolypyrrolidone (PVPP) in extraction and blocking solutions.

Antigen retrieval methods may be necessary for fixed tissues where protein crosslinking has reduced epitope accessibility. Heat-induced epitope retrieval in citrate buffer (pH 6.0) or enzymatic treatment with proteases may be required, with the specific approach determined empirically for each tissue type.

Finally, detection sensitivity requirements vary among tissues based on target protein abundance. Low-expression tissues may require signal amplification systems such as tyramide signal amplification (TSA) or more sensitive detection methods like quantum dots instead of conventional fluorophores .

How can AI-based tools enhance experimental design and data analysis when working with At5g36000 antibody?

AI-based tools offer significant advantages for enhancing experimental design and data analysis when working with At5g36000 antibody. For epitope prediction and antibody design, AI models like ABodyBuilder2 can predict antibody structures with high accuracy, particularly for challenging regions like Complementary Determining Regions (CDRs) . These models outperform traditional methods, achieving root mean square deviations (RMSD) of approximately 2.81 Å for structure predictions, enabling better understanding of binding mechanisms and potential cross-reactivity.

For experimental image analysis, deep learning approaches can automate and standardize quantification of immunostaining patterns. Convolutional neural networks (CNNs) trained on expert-annotated immunofluorescence images can recognize specific subcellular localization patterns more consistently than human observers, reducing subjective interpretation and increasing throughput. These systems can be particularly valuable for co-localization studies, processing hundreds of images to generate statistically robust quantification.

In Western blot analysis, AI tools can enhance lane detection, band identification, and intensity quantification while compensating for gel irregularities. Machine learning algorithms can also identify subtle pattern differences between experimental conditions that might be missed in manual analysis.

For protein interaction prediction, tools incorporating AI algorithms can predict potential interaction partners for At5g36000 based on sequence features, structural properties, and existing interaction databases. These predictions can guide experimental design by highlighting the most promising candidate interactions to investigate.