DOGL3 Antibody

What is DOGL3 and why is it important in Arabidopsis research?

DOGL3 (DOG1-LIKE 3) belongs to a family of proteins in Arabidopsis thaliana that are involved in critical developmental and regulatory processes. While less studied than some other plant proteins, DOGL3 has emerging importance in plant developmental biology research. The protein's specific cellular functions are still being elucidated through ongoing research, making antibodies against this target particularly valuable for characterizing expression patterns, protein interactions, and localization studies. Researchers typically utilize the DOGL3 antibody in conjunction with other molecular techniques to better understand signaling pathways and protein-protein interactions in Arabidopsis systems .

What are the validated applications for DOGL3 antibody?

The DOGL3 antibody (CSB-PA256069XA01DOA) has been validated for two primary research applications: Enzyme-Linked Immunosorbent Assay (ELISA) and Western Blotting (WB). These techniques allow researchers to detect and semi-quantify DOGL3 protein in various sample preparations. The antibody has been specifically optimized for identifying Arabidopsis thaliana DOGL3 protein. When designing experiments, researchers should note that this polyclonal antibody has been raised in rabbit using recombinant Arabidopsis thaliana DOGL3 protein as the immunogen, which enhances its specificity for plant research applications .

What are the recommended storage conditions for maintaining DOGL3 antibody activity?

To maintain optimal reactivity of the DOGL3 antibody, proper storage is essential. Upon receipt, the antibody should be stored at either -20°C or -80°C, with the latter being preferable for long-term storage. Repeated freeze-thaw cycles significantly degrade antibody performance and should be strictly avoided. To minimize freeze-thaw damage, researchers should consider aliquoting the antibody into smaller working volumes upon receipt. The antibody is supplied in a storage buffer containing 50% glycerol, 0.01M PBS (pH 7.4), and 0.03% Proclin 300 as a preservative, which helps maintain stability during proper storage. For working solutions, antibodies should be kept at 4°C and used within 1-2 weeks to prevent degradation and maintain consistent experimental results .

What is the clonality and isotype of the DOGL3 antibody?

The DOGL3 antibody (CSB-PA256069XA01DOA) is a polyclonal antibody of the IgG isotype. As a polyclonal antibody, it contains a heterogeneous mixture of antibodies that recognize multiple epitopes on the DOGL3 protein. This characteristic can be advantageous for detection applications where signal amplification is desired. The antibody has been raised in rabbits immunized with recombinant Arabidopsis thaliana DOGL3 protein and subsequently purified using antigen affinity purification methods to enhance specificity. This purification process helps reduce background signal in experimental applications by removing non-specific antibodies from the preparation .

How should I optimize Western blot conditions for DOGL3 antibody detection?

When optimizing Western blot conditions for DOGL3 antibody, begin with these methodological considerations:

• Sample preparation: Extract total protein from Arabidopsis tissues using a buffer containing protease inhibitors to prevent degradation. For membrane-associated proteins, include appropriate detergents.

• Gel percentage optimization: Start with 10-12% polyacrylamide gels for proteins in the 30-70 kDa range. Adjust based on the predicted molecular weight of DOGL3.

• Transfer conditions: Use semi-dry or wet transfer methods with optimization for hydrophobic proteins if applicable.

• Blocking optimization: Test both 5% non-fat dry milk and 3-5% BSA in TBST to determine which provides better signal-to-noise ratio.

• Antibody dilution: Begin with a 1:1000 dilution of the primary antibody and adjust based on results. Incubate overnight at 4°C for optimal binding.

• Detection system: Select either chemiluminescent or fluorescent secondary antibodies based on required sensitivity and quantification needs.

• Controls: Always include a positive control (tissue known to express DOGL3) and negative control (tissue from knockdown/knockout lines if available).

Methodically document all optimization steps, as the optimal conditions may vary based on tissue type and protein expression levels .

What are the recommended protocols for immunohistochemistry using DOGL3 antibody?

While the DOGL3 antibody has not been explicitly validated for immunohistochemistry according to the product information, researchers aiming to develop this application should follow this methodical approach:

• Tissue fixation: Fix Arabidopsis tissues in 4% paraformaldehyde for 4-6 hours, followed by paraffin embedding or cryosectioning.

• Antigen retrieval: For paraffin sections, perform heat-induced epitope retrieval using citrate buffer (pH 6.0) at 95°C for 20 minutes, then cool slowly to room temperature.

• Permeabilization: For plant tissues, add a 0.1-0.3% Triton X-100 permeabilization step to improve antibody penetration.

• Blocking: Block with 5% normal goat serum in PBS with 0.1% Tween-20 for 1 hour at room temperature.

• Primary antibody incubation: Begin with a 1:100-1:500 dilution range of DOGL3 antibody and incubate overnight at 4°C. Test multiple dilutions to determine optimal concentration.

• Controls: Include multiple controls: (a) no primary antibody, (b) pre-immune serum, and (c) DOGL3 knockout/knockdown tissue if available.

• Signal detection: Use fluorescently labeled or HRP-conjugated secondary antibodies compatible with rabbit IgG.

• Counterstaining: Consider DAPI for nuclei visualization and appropriate plant cell wall stains for structural context.

How can I quantitatively evaluate DOGL3 expression using ELISA techniques?

For quantitative evaluation of DOGL3 expression using ELISA techniques, researchers should implement this methodical protocol:

• Plate preparation: Coat high-binding 96-well plates with capture antibody (anti-DOGL3) diluted in carbonate-bicarbonate buffer (pH 9.6) at 4°C overnight.

• Sample extraction: Prepare protein extracts from Arabidopsis tissues using a standardized extraction buffer containing protease inhibitors. Centrifuge and collect the supernatant.

• Standard curve generation: Prepare a standard curve using recombinant DOGL3 protein (if available) with concentrations ranging from 0-1000 ng/mL.

• Sample incubation: Add protein extracts to coated wells and incubate for 2 hours at room temperature.

• Detection system:

  • For direct ELISA: Apply HRP-conjugated secondary antibody against rabbit IgG

  • For sandwich ELISA: Use a biotinylated detection antibody followed by streptavidin-HRP

• Signal development: Develop with TMB substrate and stop the reaction with 2N H₂SO₄.

• Quantification: Measure absorbance at 450 nm and calculate protein concentration using the standard curve.

• Data normalization: Normalize results to total protein concentration determined by Bradford or BCA assay.

Include technical replicates (minimum triplicate) and biological replicates across different experimental conditions for statistical validity. This approach provides more reliable quantitative data than semi-quantitative methods like Western blotting .

What are the considerations for using DOGL3 antibody in chromatin immunoprecipitation (ChIP) studies?

For adapting the DOGL3 antibody for chromatin immunoprecipitation studies, consider these methodological guidelines:

• Cross-linking optimization: Test both formaldehyde (1-3%) and dual crosslinking (DSG followed by formaldehyde) approaches for optimal protein-DNA fixation. Plant tissues may require vacuum infiltration for efficient crosslinking.

• Chromatin fragmentation: Optimize sonication conditions specifically for Arabidopsis tissues to achieve DNA fragments of 200-500 bp. Monitor fragmentation using agarose gel electrophoresis.

• Antibody validation: Before full ChIP experiments, perform Western blot on nuclear extracts to confirm the antibody recognizes the native nuclear protein under ChIP conditions.

• Pre-clearing strategy: For polyclonal antibodies like the DOGL3 antibody, implement an extensive pre-clearing step using protein A/G beads to reduce background.

• Antibody concentration: Start with 5-10 μg of antibody per ChIP reaction, adjusting based on preliminary results.

• Controls: Include multiple controls:

  • Input chromatin (pre-immunoprecipitation)

  • IgG control (non-specific rabbit IgG)

  • Positive control region (if known DOGL3 binding sites exist)

  • Negative control region (genomic region not expected to bind DOGL3)

• Elution and reversal: Optimize elution conditions and reversal of crosslinking for plant chromatin, which may differ from standard protocols.

• Downstream analysis: Design qPCR primers for suspected binding regions or prepare samples for ChIP-seq analysis following appropriate library preparation protocols.

How can I determine the specificity of the DOGL3 antibody in complex plant tissue samples?

To rigorously assess the specificity of the DOGL3 antibody in complex plant samples, implement the following methodological approach:

• Peptide competition assay: Pre-incubate the antibody with increasing concentrations of the immunizing peptide/protein before application in Western blot or immunohistochemistry. Specific binding should be progressively inhibited.

• Genetic validation: Test the antibody on:

  • Wild-type Arabidopsis tissues (positive control)

  • DOGL3 knockout/knockdown lines (negative control)

  • DOGL3 overexpression lines (enhanced signal)

• Cross-reactivity assessment: Test the antibody against recombinant proteins from related DOG family members to evaluate potential cross-reactivity within the protein family.

• Immunoprecipitation followed by mass spectrometry: Perform IP using the DOGL3 antibody and analyze precipitated proteins by LC-MS/MS to identify all proteins recognized by the antibody.

• Orthogonal detection methods: Compare results with alternative detection methods such as:

  • RNA expression (RT-qPCR)

  • Fluorescent protein fusion localization

  • In situ hybridization

• Tissue-specific expression analysis: Compare antibody detection patterns across different tissues with known or predicted expression patterns from transcriptomic data.

• Epitope mapping: If possible, determine which epitopes are recognized by different antibody populations within the polyclonal mixture.

What approaches can be used to determine DOGL3 protein-protein interactions in plant cells?

For investigating DOGL3 protein-protein interactions in plant systems, consider these methodological approaches:

• Co-immunoprecipitation (Co-IP):

  • Extract proteins under native conditions using mild detergents

  • Immunoprecipitate DOGL3 using the specific antibody

  • Analyze co-precipitated proteins by mass spectrometry or Western blot for suspected interactors

  • Validate with reciprocal Co-IP using antibodies against identified interactors

• Proximity labeling methods:

  • Generate transgenic plants expressing DOGL3 fused to BioID or TurboID

  • Induce proximity-dependent biotinylation

  • Purify biotinylated proteins and identify by mass spectrometry

  • This approach captures both stable and transient interactions

• Yeast two-hybrid screening:

  • Use DOGL3 as bait against an Arabidopsis cDNA library

  • Validate positive interactions through directed Y2H

  • Further confirm in planta using methods below

• Bimolecular Fluorescence Complementation (BiFC):

  • Generate constructs with DOGL3 and candidate interactors fused to split fluorescent protein fragments

  • Express in Arabidopsis protoplasts or through stable transformation

  • Analyze using confocal microscopy to visualize interaction-dependent fluorescence

• Förster Resonance Energy Transfer (FRET):

  • Create fluorescent protein fusions with DOGL3 and putative interactors

  • Measure energy transfer between fluorophores using acceptor photobleaching or fluorescence lifetime imaging

• Size-exclusion chromatography:

  • Fractionate native protein complexes by size

  • Analyze fractions by Western blot to identify co-eluting proteins

  • Confirm interactions using methodologies above

Each method has strengths and limitations, so combining multiple approaches provides the most reliable characterization of DOGL3's interactome in plant cells .

How can I resolve high background issues when using DOGL3 antibody in Western blots?

When encountering high background in Western blots with DOGL3 antibody, implement this systematic troubleshooting approach:

• Blocking optimization:

  • Test different blocking agents: 5% non-fat dry milk, 3-5% BSA, commercial blocking reagents

  • Extend blocking time to 2 hours at room temperature or overnight at 4°C

  • Add 0.1-0.3% Tween-20 to blocking buffer to reduce hydrophobic interactions

• Antibody dilution and incubation:

  • Increase primary antibody dilution (1:2000-1:5000) to reduce non-specific binding

  • Add 0.05% Tween-20 to antibody dilution buffer

  • Consider adding 5% blocking agent to antibody dilution buffer

  • Ensure thorough washing (5-6 washes, 5-10 minutes each) between primary and secondary antibody incubations

• Membrane handling:

  • Never allow membrane to dry during the procedure

  • Consider using PVDF instead of nitrocellulose for better protein retention and lower background

  • Pre-wet PVDF membrane in methanol before equilibration in transfer buffer

• Sample preparation improvements:

  • Include additional detergents in lysis buffer to better solubilize membrane proteins

  • Add more protease inhibitors to prevent degradation products

  • Centrifuge lysates at higher speed to remove particulates

  • Filter lysates through 0.45 μm filter before loading

• Secondary antibody considerations:

  • Use highly cross-adsorbed secondary antibodies to reduce cross-reactivity

  • Dilute secondary antibody further (1:10,000-1:20,000)

  • Test secondary antibody alone (without primary) to check for direct binding to sample

• Additional techniques:

  • Pre-incubate the antibody with plant extract from non-expressing tissue to absorb non-specific antibodies

  • Consider using protein A/G to purify the IgG fraction before use

Document all changes systematically to identify which modifications most effectively reduce background while maintaining specific signal .

What factors should I consider when comparing data from different experimental batches of DOGL3 antibody?

When comparing results across different experimental batches of DOGL3 antibody, researchers should account for the following methodological considerations:

• Lot-to-lot variation assessment:

  • Perform side-by-side Western blot comparison between old and new antibody lots

  • Document relative signal intensity, background levels, and specific-to-nonspecific signal ratio

  • Calculate adjustment factors if quantitative comparisons are needed

• Standardization protocol:

  • Include identical positive control samples in all experiments

  • Maintain a laboratory reference sample that can be used across experiments

  • Use consistent protein amounts, exposure times, and imaging settings

• Validation parameters:

  • Re-validate each new lot for specificity using knockout/knockdown controls

  • Confirm detection limits with dilution series of recombinant protein or positive control samples

  • Verify epitope recognition is consistent (peptide competition assay)

• Data normalization strategy:

  • Always normalize to appropriate loading controls

  • Consider dual normalization to both a loading control and a reference sample

  • For quantitative applications, generate standard curves with each antibody lot

• Documentation and reporting:

  • Record antibody lot numbers, dilutions, and incubation conditions in laboratory notebooks

  • Include batch information in publications and reports

  • Note any observed differences between batches in experimental records

• Statistical handling:

  • Avoid direct statistical comparisons between data generated with different antibody lots

  • If unavoidable, include appropriate statistical corrections for batch effects

  • Consider using mixed-effects models that can account for batch variation

For long-term studies, purchasing larger amounts of a single lot and storing appropriately in aliquots can minimize batch variation issues .

How can I modify protocols to detect DOGL3 in different plant tissues or developmental stages?

To effectively detect DOGL3 in diverse plant tissues or developmental stages, adapt standard protocols with these methodological refinements:

• Tissue-specific extraction optimization:

  • For high-phenolic tissues (mature leaves, seeds): Add PVPP (2-5%) and increased β-mercaptoethanol (5-10 mM) to extraction buffer

  • For starch-rich tissues (seeds, tubers): Include additional amylase treatment step

  • For tissues with high lipid content: Add 0.5-1% Triton X-100 or NP-40 to extraction buffer

  • For recalcitrant tissues: Consider grinding in liquid nitrogen followed by TCA/acetone precipitation

• Developmental stage considerations:

  • Early developmental stages: Pool larger sample amounts to compensate for potentially lower expression

  • Flowering/reproductive tissues: Add protease inhibitor cocktails at 1.5-2X standard concentration

  • Senescent tissues: Increase reducing agents to counteract oxidation effects

• Loading adjustments:

  • Increase protein loading (30-50 μg) for tissues with expected low expression

  • Use gradient gels (4-20%) to better resolve proteins across different molecular weight ranges

  • Consider using modified Laemmli buffer with urea (2-4 M) for difficult-to-solubilize proteins

• Detection sensitivity enhancement:

  • Switch to more sensitive detection systems (ECL Prime, SuperSignal West Femto)

  • Use signal enhancer solutions before primary antibody incubation

  • For low abundance proteins, consider immunoprecipitation before Western blotting

• Immunohistochemistry adaptation:

  • Optimize fixation times based on tissue density

  • Modify permeabilization conditions (detergent concentration, duration)

  • Adjust antibody concentration based on anticipated expression levels

• Validation strategy:

  • Compare protein detection with tissue-specific transcriptomic data

  • Use reporter gene fusions to validate expression patterns

  • Include appropriate positive control tissues in each experiment

Document optimization parameters for each tissue type to build a comprehensive protocol library for detecting DOGL3 across Arabidopsis development .

How do I interpret contradictory results between DOGL3 antibody detection and transcriptomic data?

When faced with discrepancies between DOGL3 protein detection and transcriptomic data, apply this methodical analytical framework:

• Technical validation first:

  • Repeat protein detection with alternative methods (Western blot, ELISA, immunohistochemistry)

  • Verify transcriptomic data with RT-qPCR targeting different regions of the transcript

  • Check for variant-specific detection issues by designing primers/probes for all known splice variants

• Post-transcriptional regulation assessment:

  • Investigate potential microRNA-mediated translational repression

  • Examine RNA sequencing data for evidence of alternative splicing

  • Assess transcript stability through actinomycin D chase experiments

  • Compare polysome-associated mRNA with total mRNA levels

• Post-translational regulation investigation:

  • Examine protein stability (cycloheximide chase assays)

  • Analyze potential proteolytic processing (use antibodies recognizing different epitopes)

  • Assess compartmentalization effects (compare whole-cell lysates with fractionated samples)

  • Consider conditional protein degradation pathways (proteasome, autophagy)

• Temporal dynamics consideration:

  • Implement time-course studies to capture delays between transcription and translation

  • Analyze circadian or developmental regulation patterns

  • Examine stress-responsive or condition-dependent expression

• Technical limitations acknowledgment:

  • Recognize antibody detection limits versus sensitive RNA detection methods

  • Consider protein extraction efficiency for different cellular compartments

  • Evaluate potential epitope masking due to protein modifications or interactions

• Biological interpretation framework:

  • Develop hypotheses that explain the discrepancy based on biological mechanisms

  • Design targeted experiments to test these hypotheses

  • Consider the broader literature on post-transcriptional regulation in plants

Contradictions often reveal important biological insights about gene regulation when systematically investigated .

What statistical approaches are recommended for analyzing DOGL3 expression data across experimental conditions?

For rigorous statistical analysis of DOGL3 expression data across experimental conditions, implement these methodological approaches:

• Experimental design considerations:

  • Include minimum 3-5 biological replicates per condition

  • Incorporate technical replicates (minimum triplicate) for each biological sample

  • Design balanced experiments with appropriate controls

  • Consider power analysis to determine adequate sample size based on expected effect size

• Data preprocessing:

  • Test for normality using Shapiro-Wilk or Kolmogorov-Smirnov tests

  • Apply appropriate transformations if data is non-normally distributed (log, square root)

  • Identify and address outliers using Grubbs' test or robust statistical methods

  • Normalize to appropriate reference proteins or total protein concentration

• Statistical test selection:

  • For two-group comparisons: Student's t-test (parametric) or Mann-Whitney U test (non-parametric)

  • For multi-group comparisons: One-way ANOVA with appropriate post-hoc tests (Tukey, Bonferroni)

  • For multi-factorial experiments: Two-way or multi-way ANOVA with interaction assessment

  • For time-course experiments: Repeated measures ANOVA or mixed-effects models

• Advanced analytical approaches:

  • For complex experimental designs: Linear mixed-effects models to account for nested variables

  • For high-dimensional data: Principal component analysis (PCA) or partial least squares discriminant analysis (PLS-DA)

  • For correlation studies: Pearson's (parametric) or Spearman's (non-parametric) correlation coefficients

  • For predictive modeling: Multiple regression or machine learning approaches

• Multiple testing correction:

  • Apply appropriate corrections (Bonferroni, Benjamini-Hochberg) when performing multiple comparisons

  • Report both corrected and uncorrected p-values for transparency

  • Consider effect sizes (Cohen's d, fold-change) alongside statistical significance

• Visualization strategies:

  • Create box plots with individual data points visible

  • Use error bars representing standard deviation or standard error as appropriate

  • Include confidence intervals for key comparisons

  • Create heat maps for multi-condition experiments

How should I design controls to validate the specificity of DOGL3 antibody when studying protein localization?

To rigorously validate DOGL3 antibody specificity for protein localization studies, implement this comprehensive control strategy:

• Genetic controls:

  • DOGL3 knockout/knockdown lines (primary negative control)

  • DOGL3 overexpression lines (positive control with enhanced signal)

  • Complemented knockout lines (restored wild-type pattern)

  • Tissue-specific knockouts (differential localization pattern)

• Antibody validation controls:

  • Pre-immune serum application (background assessment)

  • Primary antibody omission (secondary antibody specificity)

  • Peptide competition assay with immunizing antigen (epitope specificity)

  • Absorption controls with recombinant DOGL3 protein (binding specificity)

• Subcellular marker co-localization:

  • Co-stain with established organelle markers

  • Use fractionation followed by Western blotting as orthogonal validation

  • Implement super-resolution microscopy for precise localization

  • Compare with fluorescent protein fusion localization patterns

• Signal verification approaches:

  • Test multiple antibody concentrations to distinguish specific from non-specific signal

  • Apply spectral unmixing to separate true signal from autofluorescence

  • Compare multiple microscopy techniques (confocal, STED, TIRF)

  • Use quantitative co-localization analysis (Pearson's coefficient, Manders' overlap)

• Expression context controls:

  • Include samples from different developmental stages

  • Analyze tissues known to express high versus low DOGL3 levels

  • Examine localization under conditions known to alter expression

• Technical rigor:

  • Conduct parallel immunostaining of all genetic variants simultaneously

  • Maintain identical acquisition parameters across all samples

  • Include internal reference markers in all images

  • Implement blind quantification of localization patterns

Combining these control strategies provides robust validation of antibody specificity and creates a foundation for confident interpretation of DOGL3 localization data .