AKR1A1 Antibody

What is AKR1A1 and why is it studied in research?

AKR1A1 (aldo-keto reductase family 1, member A1), also known as aldehyde reductase, is a 37 kDa protein that plays important roles in detoxification pathways and metabolic processes. This enzyme catalyzes the reduction of various aldehydes and ketones to their corresponding alcohols, making it significant in multiple physiological and pathological contexts. Research on AKR1A1 spans cancer biology, metabolic disorders, and toxicology studies, where antibodies against this protein serve as crucial tools for detection and quantification .

The protein's involvement in detoxification pathways makes it particularly relevant for understanding cellular responses to oxidative stress and xenobiotic metabolism. Research applications range from basic expression analysis to complex functional studies examining AKR1A1's role in specific disease models.

Which applications are AKR1A1 antibodies most commonly validated for?

AKR1A1 antibodies have been extensively validated for multiple experimental applications, with varying levels of optimization across techniques. The most robustly validated applications include:

The optimal application should be selected based on research objectives, with Western blotting providing the most consistent results across different antibody preparations and sample types .

How do monoclonal and polyclonal AKR1A1 antibodies differ in their research applications?

Monoclonal and polyclonal AKR1A1 antibodies exhibit distinct performance characteristics that can significantly impact experimental outcomes:

Monoclonal AKR1A1 antibodies (e.g., clone 1A11-2A4):

• Offer superior specificity by recognizing a single epitope

• Provide more consistent lot-to-lot reproducibility

• Show excellent performance in applications requiring high specificity such as detection of recombinant AKR1A1 in overexpression systems

• May have lower sensitivity for detecting endogenous protein in some contexts

• Have been validated against full-length recombinant AKR1A1 (aa 1-325)

Polyclonal AKR1A1 antibodies:

• Recognize multiple epitopes, potentially enhancing sensitivity

• Often target specific regions (e.g., C-terminal peptides like DAGHPLYPFNDPY)

• Demonstrate broader cross-reactivity across species (cow, dog, human, mouse, pig, rat)

• May show greater batch-to-batch variation

• Often perform better in applications like IHC where antigen retrieval might expose multiple epitopes

The choice between monoclonal and polyclonal antibodies should be guided by specific experimental requirements, with monoclonal antibodies preferred for applications demanding high specificity, and polyclonal antibodies advantageous for detection of low-abundance targets or applications involving antigen retrieval protocols .

What are the critical controls necessary when using AKR1A1 antibodies for protein detection?

Implementing rigorous controls is essential for generating reliable data with AKR1A1 antibodies. A comprehensive experimental design should include:

Positive controls:

• Cell lines with confirmed AKR1A1 expression (HeLa, L02 cells)

• Recombinant AKR1A1 protein

• Mouse lung tissue for cross-species validation

Negative controls:

• Knockout/knockdown validation using AKR1A1-deficient samples

• Multiple published studies have utilized AKR1A1 KD/KO systems for antibody validation

• Non-transfected cell lysates when working with overexpression systems

Antibody controls:

• Isotype controls (rabbit IgG for polyclonal, mouse IgG1 Kappa for monoclonal antibodies)

• Peptide competition assays, particularly for C-terminal targeted antibodies

• Secondary-only controls to assess background signal

Technical controls:

• Loading controls for Western blot (housekeeping proteins)

• Tissue-specific internal controls for IHC

• Preabsorption with immunizing peptide for antibodies raised against synthetic peptides

Researchers should prioritize validation in their specific experimental system, as antibody performance can vary across different tissue types, fixation methods, and detection systems .

How should researchers address discrepancies in AKR1A1 antibody detection between different experimental approaches?

When faced with conflicting results between different detection methods (e.g., IHC vs. Western blot), researchers should implement a systematic troubleshooting approach:

• Technical factors assessment:

  • Compare protein denaturation states between methods (native vs. denatured)

  • Evaluate epitope accessibility issues in fixed tissues vs. lysates

  • Consider post-translational modifications that might impact antibody recognition

  • Assess sensitivity thresholds of different detection methods

• Antibody validation strategy:

  • Employ orthogonal techniques (e.g., mass spectrometry) to confirm protein identity

  • Test multiple antibodies targeting different epitopes of AKR1A1

  • Compare monoclonal (1A11-2A4) versus polyclonal antibody results

  • Consider RNA-level validation (qPCR) to complement protein detection

• Sample-specific considerations:

  • Examine tissue-specific expression patterns that might explain discrepancies

  • Account for differential expression of AKR1A1 variants

  • Note that both reported protein variants (NP_006057.1 and NP_697021.1) are identical

  • Consider cellular localization that might affect detection in intact tissues

• Resolution approach:

  • Implement a multi-antibody, multi-technique strategy

  • Perform careful titration of antibody concentrations (1:1000-1:8000 for WB, 1:500-1:2000 for IHC)

  • Optimize sample preparation methods for each technique

  • Consider protein enrichment through IP prior to detection for low-abundance samples

Research has demonstrated that varied staining patterns can occur in tissues with different AKR antibodies, highlighting the importance of comprehensive validation when interpreting results across detection platforms .

What are the key considerations for using AKR1A1 antibodies in comparative studies across species?

Cross-species analysis requires careful consideration of epitope conservation and antibody specificity:

Epitope conservation analysis:

• AKR1A1 shows high sequence conservation across mammals

• Mouse and rat homologs share 93% and 94% sequence identity with human, respectively

• C-terminal epitopes (e.g., DAGHPLYPFNDPY) show particularly high conservation

Species validation approaches:

• Western blot analysis of tissues from target species prior to other applications

• Comparison with species-specific positive controls

• Consideration of antibody binding region (N-terminal, internal, or C-terminal)

Application-specific adjustments:

• Species-dependent optimization of antibody dilutions

• Modification of antigen retrieval protocols for IHC in different species

• Adjustment of blocking conditions to minimize background in cross-species studies

Documented cross-reactivity:

• Rabbit polyclonal antibodies typically show broader cross-reactivity

• Mouse monoclonal antibodies may require more extensive validation

• Some polyclonal preparations have confirmed reactivity with cow, dog, human, mouse, pig, and rat samples

When selecting antibodies for multi-species studies, researchers should prioritize reagents with documented cross-reactivity and consider using region-specific antibodies targeting highly conserved epitopes to ensure consistent recognition across species .

What are the optimal antigen retrieval methods for AKR1A1 immunohistochemistry in different tissue types?

Antigen retrieval is critical for successful AKR1A1 detection in fixed tissues, with optimal protocols varying by tissue type:

Heat-induced epitope retrieval (HIER):

• Primary recommendation: TE buffer pH 9.0 for most tissue types

• Alternative approach: Citrate buffer pH 6.0 for tissues with high adipose content

• Optimization required for specific tissue types to balance epitope exposure with morphological preservation

Tissue-specific considerations:

• Thyroid cancer tissue: TE buffer pH 9.0 yields optimal results

• Tonsil tissue: Validated for formalin-fixed paraffin-embedded sections

• HCC and non-HCC samples: May exhibit varied staining patterns requiring optimization

Protocol optimization strategies:

• Systematic comparison of retrieval buffers (citrate, EDTA, Tris-based)

• Titration of retrieval duration (10-30 minutes)

• Temperature adjustment (95-125°C)

• Pressure settings evaluation for pressure cooker-based retrieval

Impact on antibody performance:

• Polyclonal antibodies often tolerate more aggressive retrieval conditions

• Monoclonal antibodies may require more precisely optimized retrieval

• For clone 1A11-2A4, validated protocols exist for formalin-fixed paraffin-embedded human tonsil sections

Researchers should note that different AKR antibodies demonstrate varied staining patterns in tissues, necessitating careful optimization of antigen retrieval protocols for each specific antibody-tissue combination .

How can researchers optimize Western blot protocols for detecting endogenous AKR1A1 in complex samples?

Detecting endogenous AKR1A1 (observed MW: 36 kDa) in complex samples requires careful optimization:

Sample preparation optimization:

• Cell lysis buffer selection: PBS with protease inhibitors recommended

• Protein extraction method: Different extraction methods may expose distinct protein pools

• Subcellular fractionation: Consider if analyzing compartment-specific expression

• Sample loading: 20-50 μg total protein typically sufficient for endogenous detection

Electrophoresis and transfer parameters:

• Gel percentage: 10-12% gels provide optimal resolution around 36 kDa

• Transfer conditions: Semi-dry or wet transfer at 100V for 60-90 minutes

• Membrane selection: PVDF membranes recommended for higher protein retention

Detection optimization:

• Primary antibody dilution: Start with 1:1000 and titrate to 1:8000 as needed

• Incubation conditions: Overnight at 4°C typically yields best results

• Secondary antibody: HRP-conjugated secondary at 1:5000-1:10000 dilution

• Signal development: ECL substrates with moderate sensitivity generally sufficient

Validation approaches:

• Compare detection in positive control lysates (HeLa, L02 cells)

• Include mouse lung tissue as additional positive control

• Run HL-60 cell lysate in parallel as reference

• Position verification: Observed molecular weight (36 kDa) vs. calculated (37 kDa)

For challenging samples, researchers may need to implement signal amplification strategies or consider immunoprecipitation (using 0.5-4.0 μg antibody per 1.0-3.0 mg protein lysate) to enrich AKR1A1 prior to Western blot detection .

What strategies can address non-specific binding when using AKR1A1 antibodies in immunofluorescence applications?

Non-specific binding in immunofluorescence can compromise data quality and interpretation. Systematic optimization includes:

Blocking optimization:

• Extended blocking duration (1-2 hours at room temperature)

• Evaluation of different blocking agents (BSA, normal serum, commercial blockers)

• Matching blocking serum to secondary antibody host species

• Addition of 0.1-0.3% Triton X-100 for improved penetration

Antibody optimization:

• Careful titration starting at 1:50 and extending to 1:500

• Reduced primary antibody concentration if background persists

• Longer incubation at lower concentrations (overnight at 4°C)

• Extended washing steps (5-6 washes of 5-10 minutes each)

Technical considerations:

• Autofluorescence reduction treatments

• Secondary antibody-only controls to assess non-specific binding

• Peptide competition controls for polyclonal antibodies

• Pre-adsorption of antibody with related proteins for enhanced specificity

Cell-specific optimization:

• HeLa cells serve as validated positive control for IF/ICC

• Cell fixation method evaluation (4% PFA, methanol, acetone)

• Permeabilization optimization

• Nuclear counterstain selection to avoid spectral overlap

For problematic samples, researchers should consider signal amplification systems (tyramide signal amplification) or super-resolution microscopy techniques to improve signal-to-noise ratios while maintaining specificity. The recommended dilution range of 1:50-1:500 provides a starting point, but systematic titration is essential for each experimental system .

How do different AKR1A1 antibodies perform in cancer tissue analysis compared to normal tissues?

The performance of AKR1A1 antibodies in cancer versus normal tissues reveals important technical and biological considerations:

Expression pattern analysis:

• Variable staining patterns observed with different AKR antibodies in cancer tissues

• Comparison of 55 HCC (hepatocellular carcinoma) and 55 non-HCC samples revealed distinct expression profiles

• Potential differential expression between cancer and normal tissues requires careful antibody selection and validation

Antibody-specific observations:

• Polyclonal antibodies may detect subtle expression differences between normal and cancer tissues

• 15054-1-AP antibody validated for human thyroid cancer tissue

• Monoclonal antibody (1A11-2A4) validated in formalin-fixed paraffin-embedded human tonsil

• Complete evaluation requires multiple antibodies targeting different epitopes

Technical considerations for cancer tissue analysis:

• Antigen retrieval optimization critical for cancer tissues (TE buffer pH 9.0 recommended)

• Background minimization strategies more important in cancer tissues

• Careful validation with positive and negative controls essential

• Comparative quantification requires standardized protocols

Comparative analysis framework:

• Parallel processing of normal and cancer tissues

• Inclusion of positive control tissues with known expression

• Consistent antibody lots between normal and cancer tissue analysis

• Multiple antibody approach to confirm patterns

Research findings suggest that analyzing AKR expression patterns may have diagnostic or prognostic value, highlighting the importance of validated antibodies and optimized protocols when comparing cancer and normal tissues .

How should researchers interpret contradictory findings about AKR1A1 expression across different studies?

When faced with contradictory findings about AKR1A1 expression patterns, researchers should consider multiple factors that might explain discrepancies:

Methodological differences analysis:

• Antibody source and specificity variations

• Detection system sensitivity differences

• Sample preparation methods (fixation, antigen retrieval)

• Quantification approaches and thresholds

Biological factors consideration:

• Tissue heterogeneity and cellular composition

• Disease stage and progression differences

• Patient population variations

• Environmental and treatment factors

Study design comparison:

• Sample size differences (e.g., studies examining 24 paired samples vs. larger cohorts)

• Matched vs. unmatched control tissues

• Prospective vs. retrospective designs

• Definition of positivity thresholds

Resolution strategies:

• Meta-analysis of existing studies with methodological quality assessment

• Replication studies using standardized protocols

• Multi-antibody validation approach

• Integration of protein and mRNA expression data

• Consideration of tissue microenvironment effects

Research has documented opposing findings regarding expression patterns even within the same AKR family. While some studies reported selective loss of certain AKR members (AKR1C1, AKR1C2) in cancer tissues, others found different patterns, highlighting the complexity of expression analysis and the need for comprehensive technical and biological validation .

What are the critical considerations when selecting AKR1A1 antibodies for quantitative expression analysis?

Quantitative expression analysis demands careful antibody selection and validation:

Specificity verification:

• Cross-reactivity assessment with related AKR family members

• Validation in knockout/knockdown systems

• Epitope mapping and potential interference from post-translational modifications

• Western blot confirmation of single band at expected molecular weight (36 kDa)

Dynamic range evaluation:

• Linear detection range determination

• Standard curve generation with recombinant protein

• Sensitivity threshold assessment

• Upper limit of quantification

Quantification method selection:

• For Western blot: Densitometry with appropriate normalization controls

• For IHC: H-score, Allred score, or digital image analysis

• For ELISA: Standard curve-based absolute quantification

• For IF: Mean fluorescence intensity with background correction

Standardization approaches:

• Internal reference standards inclusion

• Batch correction methods

• Technical replicate concordance assessment

• Inter-observer variability evaluation for subjective scoring methods

Antibody-specific considerations:

• For monoclonal antibody (1A11-2A4): Detection limit for recombinant GST-tagged AKR1A1 is approximately 0.1 ng/ml as capture antibody

• For polyclonal antibodies: Batch-to-batch consistency verification required

• Application-specific optimization of antibody concentration essential

Researchers should implement a validation protocol that includes linearity assessment, reproducibility testing, and system-specific optimization before proceeding with quantitative expression analysis using AKR1A1 antibodies .

How can AKR1A1 antibodies be effectively utilized in multi-protein co-localization studies?

Multi-protein co-localization studies require specialized optimization of AKR1A1 antibodies:

Antibody compatibility assessment:

• Host species selection to avoid cross-reactivity (e.g., rabbit polyclonal AKR1A1 paired with mouse antibodies for other targets)

• Fluorophore selection to minimize spectral overlap

• Sequential staining protocols for same-species primary antibodies

• Validation of each antibody individually before multiplexing

Technical optimizations:

• Confocal microscopy settings optimization for multiple channels

• Signal-to-noise ratio improvement through deconvolution

• Z-stack acquisition for three-dimensional co-localization analysis

• Super-resolution microscopy for sub-cellular co-localization studies

Controls for co-localization:

• Single-stained controls for spectral bleed-through assessment

• Secondary-only controls for each channel

• Biological negative controls where proteins are known not to co-localize

• Positive controls with established co-localization patterns

Quantitative co-localization analysis:

• Pearson's correlation coefficient calculation

• Manders' overlap coefficient assessment

• Object-based co-localization analysis

• Distance-based measurements between protein clusters

Researchers should note that AKR1A1 antibodies have been validated for immunofluorescence in HeLa cells, providing a starting point for optimization in co-localization studies. The recommended dilution range (1:50-1:500) offers flexibility for balancing signal intensity with specificity in multi-protein imaging systems .

What considerations are important when using AKR1A1 antibodies for chromatin immunoprecipitation (ChIP) or protein-protein interaction studies?

While not among the commonly validated applications, adapting AKR1A1 antibodies for specialized techniques requires careful consideration:

For protein-protein interaction studies:

• IP validation: AKR1A1 antibody 15054-1-AP validated for immunoprecipitation in HeLa cells

• Recommended antibody amount: 0.5-4.0 μg for 1.0-3.0 mg total protein lysate

• Crosslinking optimization if studying transient interactions

• Non-denaturing conditions preservation during extraction and purification

For potential ChIP applications:

• DNA-protein crosslinking optimization

• Sonication parameters adjustment for optimal chromatin fragmentation

• Antibody specificity verification in nuclear extracts

• Positive and negative control regions for qPCR validation

• Consideration of epitope accessibility in chromatin context

Technical adaptations:

• Buffer compatibility assessment

• Incubation time extension for efficient precipitation

• Washing stringency optimization

• Elution conditions adjustment

Controls and validation:

• IgG negative control precipitation

• Input sample normalization

• Reciprocal co-IP confirmation

• Mass spectrometry validation of precipitated complexes

While standard IP protocols have been established for AKR1A1 antibody 15054-1-AP, researchers adapting these antibodies for specialized applications such as ChIP should implement extensive validation and optimization protocols to ensure specificity and efficiency in these challenging technical contexts .

How can researchers effectively use AKR1A1 antibodies in tissue microarray (TMA) analysis for high-throughput expression studies?

High-throughput TMA studies present unique challenges for AKR1A1 antibody applications:

Protocol adaptation for TMA:

• Antigen retrieval standardization across the entire array

• Reduced primary antibody concentration (starting at 1:1000) to minimize background

• Extended washing steps to ensure thorough reagent removal

• Automated staining systems calibration for consistent results

Quality control measures:

• Inclusion of positive control cores (HeLa cell pellets, thyroid cancer tissue)

• Negative control cores for background assessment

• Technical replicate cores for reproducibility evaluation

• Internal control tissues within each TMA

Scoring and quantification approaches:

• Digital image analysis standardization

• Algorithm validation for AKR1A1 detection

• Scoring system selection (H-score, percentage positivity, intensity scoring)

• Inter-observer and intra-observer variability assessment

Statistical considerations:

• Appropriate sample size calculations

• Batch effect correction methods

• Missing data handling strategies

• Multiple testing correction for correlation analyses

Research has demonstrated varied staining patterns with different AKR antibodies across multiple tissue samples, highlighting the importance of extensive validation when implementing high-throughput analysis. The observed differential expression patterns between HCC and non-HCC samples suggest potential diagnostic applications for optimized AKR1A1 antibody protocols in TMA-based studies .