Anti-PLIN1 autoantibodies are biomarkers strongly associated with AGL pathogenesis. In a cohort of 40 AGL patients, 50% tested positive for anti-PLIN1 antibodies, predominantly of the IgG subclass (35% IgG alone, 12.5% IgG + IgM) .
AGL Subtype | Anti-PLIN1 Positivity Rate |
---|---|
Autoimmune | 45.5% (10/22) |
Panniculitis | 66.7% (10/15) |
Idiopathic | 0% (0/3) |
Anti-PLIN1 antibodies correlate with autoimmune or inflammatory AGL subtypes .
These autoantibodies are absent in healthy controls, obese individuals, and patients with other lipodystrophy forms (e.g., APL) .
Anti-PLIN1 antibodies exhibit distinct subclass distributions and epitope specificity:
Patient Code | Isotype | IgG Subclasses | Light Chains | IgG Titer (AU/mL) |
---|---|---|---|---|
AGL14 | IgG | IgG1, IgG3 | κ + λ | 33,640 |
Primary Epitope: Residues 383–403 of PLIN1 (100% reactivity in IgG/IgM+ patients).
Anti-PLIN1 autoantibodies are linked to severe metabolic derangements:
Anti-PLIN1 antibodies disrupt lipid metabolism by targeting PLIN1, a protein essential for lipid droplet stability. This autoimmune response leads to:
Accelerated lipolysis and fat loss.
Systemic metabolic complications (e.g., insulin resistance, hypertriglyceridemia) .
AGL14 Antibody is a polyclonal antibody raised against the AGAMOUS-LIKE 14 (AGL14) protein, a MADS-box transcription factor from Arabidopsis thaliana (Mouse-ear cress). The antibody specifically recognizes the recombinant Arabidopsis thaliana AGL14 protein and is produced in rabbits through immunization with purified recombinant protein . This antibody is designed for detecting endogenous AGL14 protein in plant tissue samples and is primarily reactive with Arabidopsis thaliana samples. The fundamental role of this antibody is to enable researchers to study AGL14 protein expression, localization, and function in plant developmental biology.
AGL14 Antibody has been validated for several research applications including:
Enzyme-Linked Immunosorbent Assay (ELISA) - For quantitative measurement of AGL14 protein levels
Western Blotting (WB) - For detection of AGL14 protein in complex mixtures and assessment of protein expression levels
These applications have been validated to ensure specific identification of the antigen . While these represent the confirmed applications, researchers should conduct preliminary validation tests when adapting the antibody for other immunological techniques such as immunohistochemistry or immunoprecipitation.
To maintain optimal activity of AGL14 Antibody, the following storage conditions are recommended:
Store at -20°C or -80°C upon receipt
Avoid repeated freeze-thaw cycles that can lead to protein denaturation and loss of activity
The antibody is supplied in a storage buffer containing 0.03% Proclin 300 as a preservative, 50% Glycerol, and 0.01M PBS at pH 7.4
For long-term storage, -80°C is preferable, while -20°C is suitable for antibodies that will be used within several months. When working with the antibody, aliquoting into smaller volumes before freezing is recommended to avoid multiple freeze-thaw cycles.
When optimizing Western blotting protocols with AGL14 Antibody, consider the following methodological approaches:
Sample Preparation:
Use fresh plant tissue samples whenever possible
Include protease inhibitors during extraction to prevent protein degradation
Optimize protein extraction buffers for nuclear proteins (as AGL14 is a transcription factor)
Blotting Parameters:
Test dilution ranges between 1:500 to 1:2000 for primary antibody
Optimize blocking conditions (5% non-fat milk or BSA)
Consider longer incubation times (overnight at 4°C) for improved sensitivity
Detection System:
For low abundance proteins, chemiluminescent detection systems offer higher sensitivity
Consider signal enhancement systems when working with tissues where AGL14 expression is limited
Controls:
Include positive controls (Arabidopsis thaliana wild-type tissue)
Use negative controls (AGL14 knockout lines if available)
Include loading controls (anti-actin or anti-histone antibodies)
As this antibody is affinity-purified , it typically provides good specificity, but background optimization may still be necessary depending on your specific tissue type and extraction method.
Validating antibody specificity is crucial for generating reliable data. For AGL14 Antibody, consider these validation approaches:
Genetic Validation:
Compare antibody detection in wild-type plants versus AGL14 knockout/knockdown lines
Use overexpression lines to confirm increased signal intensity
Molecular Validation:
Perform peptide competition assays by pre-incubating the antibody with excess recombinant AGL14 protein
Conduct immunoprecipitation followed by mass spectrometry to confirm the identity of pulled-down proteins
Functional Validation:
Correlate protein detection with known expression patterns of AGL14 mRNA
Compare results with alternative antibodies against the same target (if available)
Technical Replication:
Test specificity across multiple technical platforms (ELISA, WB, etc.)
Validate across different tissue types and developmental stages
Document all validation steps thoroughly in your methodology to strengthen the reliability of your research findings.
While AGL14 Antibody has not been explicitly validated for ChIP applications , researchers interested in using it for chromatin studies should consider:
Protocol Adaptation:
Optimize crosslinking conditions (1-2% formaldehyde for 10-15 minutes)
Test sonication parameters to achieve DNA fragments of 200-500 bp
Use higher antibody concentrations (2-5 µg per reaction) for initial optimization
Controls for ChIP Validation:
Include input chromatin control
Use IgG from rabbit serum as a negative control
Target known AGL14 binding sites from literature for positive control regions
Antibody-Specific Considerations:
Pre-clear chromatin with protein A/G beads to reduce background
Extend incubation time (overnight at 4°C with rotation)
Consider dual crosslinking (formaldehyde plus protein-specific crosslinkers)
Validation Approach:
Start with qPCR of predicted binding regions
Confirm enrichment at known MADS-box binding motifs (CArG boxes)
Proceed to ChIP-seq only after successful validation by qPCR
As a polyclonal antibody, batch-to-batch variation might affect ChIP efficiency, so preliminary testing with each new lot is recommended.
When faced with discrepancies between protein levels detected with AGL14 Antibody and corresponding mRNA expression data, consider the following analytical framework:
Possible Explanations for Contradictions:
Observation | Potential Biological Explanation | Methodological Consideration |
---|---|---|
High mRNA, Low protein | Post-transcriptional regulation | Sample preparation method affecting protein recovery |
Low mRNA, High protein | Increased protein stability | Antibody cross-reactivity with related MADS-box proteins |
Spatial discrepancy | Cell-type specific translation | Tissue sampling differences between RNA and protein studies |
Temporal discrepancy | Time lag between transcription and translation | Different time points for RNA vs. protein sampling |
Resolution Approaches:
Perform time-course experiments to capture temporal dynamics
Use cell-type specific markers alongside AGL14 detection
Employ protein degradation inhibitors to assess protein turnover rates
Confirm results with alternative detection methods (e.g., mass spectrometry)
Analyze regulation by miRNAs or other post-transcriptional regulators
When reporting such contradictions, present both datasets transparently and discuss possible biological mechanisms rather than dismissing either result as experimental error.
Quantitative analysis of AGL14 protein expression requires careful experimental design and data normalization:
Experimental Design Considerations:
Sample developmental stages systematically (establish clear morphological markers)
Include biological replicates (minimum n=3) for each developmental stage
Process all samples simultaneously to minimize technical variation
Normalization Strategies:
Use constitutively expressed proteins (actin, tubulin) as loading controls
Consider developmental stage-specific reference proteins when appropriate
Employ total protein normalization methods (Stain-Free technology, Ponceau S)
Quantification Methods:
Use densitometry software with background subtraction
Establish linear range of detection for antibody
Report relative expression ratios rather than absolute values
Include statistical analysis (ANOVA with post-hoc tests) when comparing stages
Data Presentation:
Present normalized data with error bars
Include representative Western blot images
Document image acquisition parameters and software used for quantification
This methodical approach helps ensure reproducibility and reliable interpretation of developmental expression patterns.
When encountering weak or absent signals with AGL14 Antibody, employ this systematic troubleshooting approach:
Sample Preparation Issues:
Verify protein extraction efficiency with Bradford/BCA assays
Ensure adequate protein loading (20-50 μg total protein)
Test alternative extraction buffers optimized for nuclear proteins
Include protease inhibitor cocktails to prevent degradation
Antibody-Related Factors:
Verify antibody viability (avoid expired antibody)
Increase antibody concentration (try 1:250 - 1:500 dilutions)
Extend primary antibody incubation (overnight at 4°C)
Test different antibody lots if available
Detection System Adjustments:
Use high-sensitivity ECL substrates for chemiluminescence
Extend film exposure time or increase imaging duration
Try signal enhancement systems (biotin-streptavidin amplification)
Consider switching to fluorescent secondary antibodies for greater linearity
Biological Considerations:
Confirm developmental stage/tissue known to express AGL14
Consider environmental conditions that might affect expression
Verify if your plant genotype has altered AGL14 expression
If the protein remains undetectable despite optimization, consider enrichment steps such as immunoprecipitation before Western blotting or using overexpression systems as positive controls.
High background with AGL14 Antibody can significantly impact data quality. Address this issue with these targeted approaches:
Blocking Optimization:
Test different blocking agents (5% milk, 3-5% BSA, commercial blockers)
Extend blocking time (2-3 hours at room temperature)
Include 0.1-0.3% Tween-20 in washing and antibody dilution buffers
Washing Improvements:
Increase washing duration (5-10 minutes per wash)
Add additional washing steps (5-6 washes)
Use higher salt concentration in wash buffers (up to 500 mM NaCl)
Antibody Adjustments:
Further dilute primary antibody (1:1000 - 1:5000)
Pre-absorb antibody with plant extract from non-expressing tissue
Reduce secondary antibody concentration
Use highly cross-adsorbed secondary antibodies
Membrane Handling:
Minimize membrane drying during procedure
Consider using PVDF instead of nitrocellulose (or vice versa)
Test freshly prepared buffers
Technical Considerations:
Ensure complete protein transfer to membrane
Verify blocking agent compatibility with detection system
Check for contamination in reagents
Document successful optimization conditions for future experiments to ensure reproducibility.
AGL14 Antibody can be adapted for spatial expression analysis through these methodological approaches:
Immunohistochemistry (IHC) Protocol Development:
Optimize tissue fixation (4% paraformaldehyde or 3:1 ethanol:acetic acid)
Test different antigen retrieval methods (citrate buffer, pH 6.0)
Use decreasing ethanol series for rehydration
Block with 3-5% BSA or normal serum in PBS with 0.1% Triton X-100
Apply primary antibody (1:100 - 1:500) overnight at 4°C
Detect with fluorescent or enzyme-conjugated secondary antibodies
Whole-Mount Immunofluorescence:
Modify sample clearing protocols (ClearSee or modified Pseudo-Schiff reagents)
Extend antibody incubation times (24-48 hours)
Use confocal microscopy for 3D visualization
Correlative Approaches:
Combine with in situ hybridization for mRNA-protein correlation
Use with cell-type specific markers to identify precise expression domains
Implement with EdU labeling to correlate with cell division patterns
These approaches can provide valuable insights into AGL14 protein localization during key developmental processes in Arabidopsis thaliana, particularly in root and floral development where MADS-box transcription factors play critical roles.
Co-immunoprecipitation (Co-IP) with AGL14 Antibody can reveal protein interaction networks but requires careful optimization:
Extraction Buffer Composition:
Include mild detergents (0.5-1% NP-40 or Triton X-100)
Add protease and phosphatase inhibitors
Test varying salt concentrations (100-300 mM NaCl)
Include stabilizing agents (5-10% glycerol)
Protocol Optimization:
Pre-clear lysates with Protein A/G beads to reduce background
Test different antibody-to-lysate ratios (2-5 μg antibody per mg protein)
Optimize incubation time (2 hours to overnight at 4°C)
Use gentle washing conditions to preserve weak interactions
Controls:
Input control (5-10% of starting material)
IgG control (rabbit IgG at equivalent concentration)
Reverse Co-IP with antibodies against suspected interacting partners
Negative control using tissue without AGL14 expression
Validation Methods:
Mass spectrometry identification of co-immunoprecipitated proteins
Western blot verification of known or suspected interaction partners
Functional validation through genetic studies
Since AGL14 is a transcription factor, expect interactions with other MADS-box proteins, chromatin modifiers, and transcriptional co-regulators. Consider crosslinking approaches for capturing transient nuclear interactions.
Integrating AGL14 Antibody with proteomics creates powerful research opportunities:
Sample Preparation Strategies:
Use antibody-based enrichment prior to mass spectrometry
Implement BioID or TurboID proximity labeling with AGL14 fusion proteins
Compare results with conventional Co-IP to identify stable vs. transient interactors
Analysis Workflow:
Perform immunoprecipitation with AGL14 Antibody
Process samples for LC-MS/MS analysis
Use label-free quantification or isotope labeling for comparative studies
Apply appropriate statistical filtering (fold-change ≥2, p-value ≤0.05)
Validate top candidates with reciprocal Co-IP or in vivo techniques
Data Integration Framework:
Data Type | Analysis Method | Integration Approach |
---|---|---|
Proteomics | Protein interaction networks | Map to known MADS-box complexes |
ChIP-seq | DNA binding profiles | Correlate protein interactions with genomic targets |
RNA-seq | Transcriptional outputs | Connect interaction partners to regulated genes |
Phenomics | Mutant phenotypes | Link protein complexes to developmental outcomes |
This integrative approach can reveal functional protein modules involved in AGL14-mediated developmental processes, providing insight beyond simple binary interactions.
When applying AGL14 Antibody across different plant species, consider these methodological approaches:
Sequence Homology Assessment:
Perform alignment of AGL14 orthologs across target species
Identify conservation in the immunogenic region used to generate the antibody
Calculate percent identity and similarity in epitope regions
Validation Strategy for Cross-Species Application:
Begin with Western blot to confirm band at expected molecular weight
Include positive control (Arabidopsis thaliana extract)
Perform peptide competition assays with heterologous proteins
Validate with genetic approaches where possible (knockdowns in target species)
Optimization for Different Species:
Test increased antibody concentration for distantly related species
Modify extraction buffers based on tissue-specific compounds
Adjust incubation conditions (time, temperature)
Consider species-specific blockers to reduce background
Expected Cross-Reactivity:
High probability: Brassicaceae family members
Moderate probability: Other eudicots with conserved MADS-box domains
Lower probability: Monocots and gymnosperms
Document both successful and unsuccessful cross-species applications to contribute to the broader understanding of antibody specificity across plant lineages.