ALDOC Antibody

What types of ALDOC antibodies are available for research applications?

Various types of ALDOC antibodies are available for research applications, with the most common being:

• Mouse monoclonal antibodies: These offer high specificity and reproducibility, such as the mouse anti-human monoclonal antibody (clone PAT2E11AT) derived from hybridization of mouse F0 myeloma cells with spleen cells from BALB/c mice immunized with recombinant human ALDOC protein .

• Rabbit polyclonal antibodies: These recognize multiple epitopes and are useful for detecting proteins with low expression levels, as exemplified by the rabbit anti-ALDOC antibody (A05296) suitable for IHC and WB applications .
  Many commercially available antibodies target the N-terminal region of ALDOC (amino acids 1-50), which contains unique sequences that differentiate it from other aldolase isoforms . These antibodies typically undergo validation for various applications including Western blotting (WB), immunocytochemistry/immunofluorescence (ICC/IF), immunohistochemistry (IHC), and flow cytometry, with demonstrated reactivity across multiple species including human, mouse, rat, pig, and cow .

How should ALDOC antibodies be stored and handled to maintain optimal activity?

Proper storage and handling of ALDOC antibodies are crucial for maintaining their specificity and activity over time. Most manufacturers recommend storing ALDOC antibodies at -20°C for long-term preservation (up to 12 months) and at 4°C for short-term storage and frequent use (up to one month) . The typical formulation includes PBS buffer (pH 7.4), with additives such as glycerol (10%) to prevent freezing damage and sodium azide (0.02%) as a preservative . When working with these antibodies, it's essential to avoid repeated freeze-thaw cycles as this can lead to antibody degradation and loss of activity . For optimal results, aliquoting the antibody into smaller volumes upon first thaw is recommended to minimize freeze-thaw cycles. Before use, allow the antibody to equilibrate to room temperature and mix gently (avoid vortexing, which can cause protein denaturation). Always follow manufacturer-specific recommendations, as formulations may vary between suppliers, and centrifuge briefly before opening to collect all liquid at the bottom of the vial .

What are the optimal protocols for using ALDOC antibodies in Western blotting?

For optimal Western blotting results with ALDOC antibodies, researchers should implement a protocol that addresses the specific characteristics of this 39 kDa glycolytic enzyme. Begin by preparing protein lysates from tissues or cell lines of interest; based on published research, brain tissue lysates (rat, mouse), cerebellum lysates (cow), hippocampus lysates (pig), or NSCLC cell lines like A549 and NCI-H1299 are appropriate positive controls . Separate 20-40 μg of protein via SDS-PAGE using 10-12% gels, which provide optimal resolution for proteins in ALDOC's molecular weight range.
For the transfer step, use PVDF membranes (preferred over nitrocellulose for their protein retention capabilities) and transfer at 100V for 60-90 minutes in cold transfer buffer. Block membranes using 5% non-fat dry milk or BSA in TBST for 1 hour at room temperature. For primary antibody incubation, dilute anti-ALDOC antibody according to manufacturer specifications; for example, ab190368 has been successfully used at 1/2000 dilution . Incubate membranes with primary antibody overnight at 4°C with gentle agitation.
Following primary antibody incubation, wash membranes thoroughly (3-5 times for 5-10 minutes each) with TBST before adding appropriate HRP-conjugated secondary antibody (anti-mouse or anti-rabbit depending on your primary antibody) at 1:5000-1:10000 dilution for 1 hour at room temperature. After washing, develop using ECL substrate and image. When analyzing results, expect a band at approximately 39 kDa, which is the predicted molecular weight for ALDOC .

How can I optimize immunofluorescence staining with ALDOC antibodies in neural and cancer tissues?

Optimizing immunofluorescence staining with ALDOC antibodies requires different approaches for neural tissues versus cancer samples due to their distinct expression patterns and cellular architecture. For neural tissues (brain sections, cerebellum, hippocampus), begin with paraformaldehyde fixation (4% PFA for 15-20 minutes) followed by permeabilization with 0.2% Triton X-100 for 10 minutes . For cancer tissues, particularly NSCLC samples, slightly stronger permeabilization (0.3% Triton X-100 for 15 minutes) may improve antibody penetration due to altered tissue architecture.
Blocking is critical to reduce background: use 5-10% normal serum (from the species in which the secondary antibody was raised) with 1% BSA in PBS for 1-2 hours at room temperature. For primary antibody incubation, dilute ALDOC antibodies optimally (ab190368 has been successfully used at 1/500-1/1000 dilution) . Incubate sections overnight at 4°C in a humidified chamber to prevent drying.
To enhance detection specificity, consider counterstaining with cell-type specific markers: NeuN for neurons or GFAP for astrocytes in brain tissue , or epithelial markers in cancer samples. For cancer tissue, double-staining with proliferation markers (Ki-67) can provide additional context for ALDOC's role in cell proliferation. Include appropriate negative controls (secondary antibody only) and positive controls (cerebellum sections for ALDOC) . Visualization is best achieved using confocal microscopy, which allows for precise localization of ALDOC within specific cellular compartments.

What validation methods should be employed to confirm ALDOC antibody specificity?

Confirming the specificity of ALDOC antibodies is essential for generating reliable research data, particularly given ALDOC's similarity to other aldolase isoforms. A comprehensive validation approach should include multiple complementary methods. First, perform Western blot analysis using positive control samples (brain tissue lysates, particularly cerebellum) and negative control samples (tissues known to have minimal ALDOC expression) . The antibody should detect a single band at the expected molecular weight of 39 kDa, with higher intensity in positive controls.
For genetic validation, implement knockdown or knockout experiments: use siRNA or shRNA targeting ALDOC in cell lines expressing the protein (such as the NSCLC cell lines A549 or NCI-H1299) . The antibody signal should significantly decrease in Western blot, immunofluorescence, or immunohistochemistry analyses of these knockdown samples compared to controls. This approach has been successfully demonstrated in recent NSCLC research, where shALDOC infection significantly reduced ALDOC protein levels that were detectable by antibody-based methods .
Another powerful validation method is peptide competition assay: pre-incubate the antibody with excess purified ALDOC protein or the immunizing peptide before application to samples. This should abolish or significantly reduce specific staining. Additionally, cross-reactivity testing against other aldolase isoforms (ALDOA and ALDOB) should be conducted to ensure specificity. Finally, compare staining patterns with multiple ALDOC antibodies targeting different epitopes; concordant results from antibodies recognizing distinct regions of the protein provide strong evidence of specificity .

How does ALDOC expression in NSCLC compare to normal lung tissue, and what methodologies best demonstrate this difference?

ALDOC expression is significantly upregulated in NSCLC tissues compared to normal lung tissue, as demonstrated by multiple experimental approaches. Immunohistochemistry analysis of 79 NSCLC patient samples revealed that 48.1% of tumor tissues exhibited high ALDOC expression, compared to only 7.9% of adjacent non-malignant tissues (p < 0.001) . This stark difference in expression patterns makes ALDOC a potential biomarker for NSCLC. Quantitative RT-PCR and Western blot analyses of cell lines further support this finding, showing elevated ALDOC mRNA and protein levels in NSCLC cell lines (A549 and NCI-H1299) compared to the normal bronchial epithelial cell line BEAS-2B .
The following table summarizes the expression patterns observed in clinical samples:

What is the relationship between ALDOC expression and clinical parameters in NSCLC patients?

Analysis of ALDOC expression in NSCLC patients reveals significant correlations with multiple clinical parameters, positioning it as a potential prognostic marker. ALDOC expression demonstrates a strong association with lymph node metastasis (p = 0.031), with 59.5% (25/42) of patients with lymph node involvement exhibiting high ALDOC expression compared to only 35.1% (13/37) of patients without lymph node metastasis . Furthermore, ALDOC expression correlates significantly with advanced pathological stage (p = 0.008), with high ALDOC expression found in 25% of stage I patients, 62.5% of stage II patients, 46.2% of stage III patients, and 83.3% of stage IV patients .
The relationship between ALDOC expression and clinical parameters is summarized in the following table:

What molecular mechanisms link ALDOC to cancer progression in NSCLC?

Research has revealed that ALDOC contributes to NSCLC progression through multiple molecular mechanisms beyond its canonical role in glycolysis. A key mechanism involves ALDOC's influence on MYC-mediated UBE2N transcription . Gene expression analysis following ALDOC knockdown identified 4182 differentially expressed genes, with UBE2N emerging as a significant downstream target . The reduction in UBE2N mRNA and protein levels upon ALDOC knockdown suggests a regulatory relationship between these proteins in NSCLC pathogenesis .
Additionally, ALDOC modulates the Wnt/β-catenin signaling pathway, which plays a crucial role in cancer development and progression . The study demonstrated that ALDOC knockdown led to alterations in the expression of Wnt pathway components, suggesting that ALDOC may promote NSCLC by regulating this key oncogenic pathway . This represents a non-canonical function of ALDOC, as traditional understanding limited its role to glycolytic metabolism.
Recent evidence indicates that metabolic enzymes like ALDOC can have multifaceted roles beyond metabolism, including regulation of gene expression and signaling pathways . In NSCLC, ALDOC interacts with various signaling molecules and transcription factors, suggesting its involvement in cellular processes beyond glycolysis . The identification of these non-metabolic functions provides new insights into how metabolic enzymes can contribute to cancer progression and offers potential targets for therapeutic intervention. These findings align with emerging research demonstrating that metabolic reprogramming in cancer extends beyond altered energy production to include regulatory functions that promote tumor growth and survival .

How can researchers resolve non-specific binding issues when using ALDOC antibodies?

Non-specific binding is a common challenge when using ALDOC antibodies, particularly in tissues with high background or when studying tissues not traditionally associated with ALDOC expression. To resolve these issues, implement a systematic optimization approach focusing on several key parameters. First, evaluate blocking conditions: increase blocking agent concentration (5-10% normal serum or BSA) and extend blocking time (2-3 hours at room temperature or overnight at 4°C) . Consider using commercial blocking reagents specifically designed to reduce background in the application you're using.
For antibody dilution optimization, perform a titration series with your ALDOC antibody to identify the optimal concentration that maximizes specific signal while minimizing background. For Western blotting, dilutions ranging from 1:1000 to 1:5000 have been successful with commercially available antibodies, while for immunofluorescence, dilutions between 1:500 and 1:1000 have shown good results . Extend washing steps between antibody incubations (5-6 washes of 10 minutes each) using buffers containing 0.1-0.3% Tween-20 to remove unbound antibodies effectively.
For tissues with particularly high background, consider antigen retrieval method optimization: compare heat-induced epitope retrieval using citrate buffer (pH 6.0) versus Tris-EDTA buffer (pH 9.0) to determine which provides better specific staining with lower background. Additionally, pre-adsorption of the antibody with tissues known to lack ALDOC expression can reduce non-specific interactions. Finally, when studying ALDOC in non-traditional tissues (like lung cancer), always include appropriate positive controls (brain tissue sections) and negative controls (secondary antibody only) to accurately assess specificity .

What approaches can differentiate between ALDOC's metabolic functions and its non-canonical roles?

Distinguishing between ALDOC's traditional glycolytic function and its emerging non-canonical roles requires sophisticated experimental approaches that selectively target specific aspects of the protein's activity. A domain-specific mutagenesis approach is highly effective: introduce point mutations in the catalytic domain of ALDOC that impair its enzymatic activity without affecting protein stability or expression levels . By comparing cells expressing wild-type ALDOC versus catalytically inactive mutants, researchers can determine which cellular functions depend on ALDOC's enzymatic activity versus its potential protein-protein interactions or other non-enzymatic roles.
Subcellular localization studies provide another avenue for investigation. While ALDOC's glycolytic function occurs primarily in the cytoplasm, non-canonical roles might involve translocation to other cellular compartments. Using fractionation techniques followed by Western blotting or high-resolution immunofluorescence microscopy with organelle-specific markers can reveal compartment-specific localization patterns that suggest non-metabolic functions . For instance, nuclear localization of ALDOC would suggest potential roles in transcriptional regulation, as implied by its effect on MYC-mediated UBE2N transcription .
Interactome analysis through co-immunoprecipitation coupled with mass spectrometry can identify ALDOC's protein binding partners beyond glycolytic pathway components. Recent research has shown ALDOC interacts with components of the Wnt/β-catenin pathway, suggesting mechanisms independent of its enzymatic activity . For functional validation, selective pathway inhibitors can help determine whether ALDOC's effects on cellular phenotypes (proliferation, migration) persist when specific signaling pathways are blocked, thereby linking ALDOC to these pathways independently of its metabolic function .

What controls are essential when interpreting ALDOC antibody-based experimental results?

Proper controls are critical for ensuring valid interpretation of ALDOC antibody-based experiments, particularly given the emerging non-traditional expression patterns and functions of this protein. For technical validation, include antibody specificity controls: a primary antibody omission control to assess non-specific binding of the secondary antibody, and ideally, a peptide competition assay where the ALDOC antibody is pre-incubated with excess purified ALDOC protein before application to samples . This should abolish specific staining if the antibody is truly selective.
Biological controls are equally important: include positive control samples known to express high levels of ALDOC (cerebellum or Purkinje cells) alongside your experimental samples . This verifies that your experimental conditions support ALDOC detection. For negative controls, use tissues or cell lines with minimal ALDOC expression, or better yet, ALDOC knockout or knockdown samples generated through genetic approaches like CRISPR-Cas9 or shRNA . The dramatic reduction in signal in these genetic controls provides compelling evidence of antibody specificity.
When studying ALDOC in cancer contexts, include matched tumor/normal tissue pairs from the same patient to control for individual variability . Additionally, use isoform controls to ensure your antibody doesn't cross-react with other aldolase isoforms (ALDOA, ALDOB) by testing the antibody against purified recombinant proteins of each isoform. For functional studies claiming non-metabolic roles of ALDOC, include metabolic activity controls that measure glycolytic function (e.g., lactate production, oxygen consumption) to determine whether observed phenotypes correlate with ALDOC's enzymatic activity or suggest independent functions . These comprehensive controls enable confident interpretation of results and substantiate claims about ALDOC's expression patterns and functions.

How should immunohistochemical ALDOC expression data be quantified and analyzed?

Quantification of ALDOC immunohistochemical staining requires standardized, reproducible methods to generate reliable data, particularly when evaluating its potential as a biomarker. For semi-quantitative analysis, implement a scoring system that accounts for both staining intensity and percentage of positive cells, similar to the approach used in NSCLC studies . A typical system might classify intensity as: 0 (negative), 1 (weak), 2 (moderate), and 3 (strong), while the percentage of positive cells could be scored as: 0 (<5%), 1 (5-25%), 2 (26-50%), 3 (51-75%), and 4 (>75%). The final score (ranging from 0-12) would be calculated by multiplying intensity by percentage, with scores above a predetermined threshold (typically 4-6) classified as "high expression."
For more objective quantification, utilize digital image analysis software (ImageJ, QuPath, or commercial platforms) to measure parameters such as optical density, H-score, or positive pixel count. This approach reduces observer bias and generates continuous data suitable for statistical analysis. When analyzing multiple tissue samples (such as tissue microarrays), ensure consistency by processing all samples simultaneously under identical conditions and having multiple trained observers score independently.
Statistical analysis should include appropriate tests based on data distribution: chi-square or Fisher's exact test for categorical comparisons (high vs. low ALDOC expression in different tumor stages), and Student's t-test or ANOVA for continuous data. For survival analysis, utilize Kaplan-Meier curves with log-rank tests to compare outcomes between high and low ALDOC expression groups . Additionally, multivariate Cox regression analysis should be performed to determine whether ALDOC expression remains an independent prognostic factor after adjusting for established clinicopathological parameters such as stage, grade, and lymph node status .

What strategies can determine if ALDOC is a suitable biomarker or therapeutic target in cancer research?

Establishing ALDOC as a viable biomarker or therapeutic target in cancer requires a multi-faceted approach that bridges basic science and clinical research. First, conduct comprehensive expression profiling across diverse tumor types and matched normal tissues using techniques such as immunohistochemistry, Western blotting, and qRT-PCR to identify cancer types with significant ALDOC upregulation. Current evidence suggests NSCLC as a promising candidate, with 48.1% of tumor samples showing high ALDOC expression compared to just 7.9% of adjacent normal tissues .
Evaluate ALDOC's association with clinical parameters and outcomes through retrospective analysis of patient cohorts. Research has demonstrated significant correlations between ALDOC expression and lymph node metastasis (p=0.031), pathological stage (p=0.008), and poor survival in NSCLC patients . Validation of these findings in larger, independent cohorts from databases like TCGA and GEO strengthens the case for ALDOC as a prognostic biomarker .
For therapeutic target assessment, conduct functional studies using genetic approaches (siRNA, shRNA, CRISPR-Cas9) to manipulate ALDOC expression in cancer cell lines and xenograft models. Successful ALDOC knockdown studies in NSCLC have already demonstrated effects on cancer-related phenotypes and molecular pathways (particularly UBE2N expression and Wnt signaling) . Determine mechanism of action through pathway analysis and identification of downstream effectors, as evidenced by the discovery that ALDOC affects MYC-mediated UBE2N transcription .
Finally, develop therapeutic strategies targeting ALDOC, potentially including small molecule inhibitors, antibody-drug conjugates, or novel technologies like proteolysis targeting chimeras (PROTACs). Evaluate these strategies in preclinical models before advancing to early-phase clinical trials that incorporate biomarker analysis to identify patient subsets most likely to benefit from ALDOC-targeted therapies.

How can researchers integrate ALDOC findings with broader cancer metabolism research?

Integrating ALDOC research into the broader context of cancer metabolism requires connecting glycolytic alterations with signaling pathways and therapeutic vulnerabilities. Researchers should first position ALDOC within the metabolic reprogramming landscape by conducting comparative metabolomic analyses of cancer cells with varying ALDOC expression levels. This can reveal whether ALDOC overexpression leads to specific metabolic signatures beyond glycolysis, potentially identifying novel metabolic dependencies. Recent findings highlighting ALDOC's non-canonical functions beyond its enzymatic role suggest it may serve as a metabolic switch connecting glycolysis to other cellular processes .
Systems biology approaches can situate ALDOC within the cancer metabolic network. Construct pathway models integrating transcriptomic, proteomic, and metabolomic data to visualize how ALDOC connects to other metabolic and signaling pathways. The discovered link between ALDOC and the Wnt/β-catenin pathway exemplifies such cross-pathway interactions . These models can identify potential synthetic lethal interactions where ALDOC inhibition might be particularly effective when combined with targeting other metabolic enzymes or oncogenic drivers.
Translational applications should explore how ALDOC-related metabolic vulnerabilities can be exploited therapeutically. For instance, investigate whether ALDOC overexpression sensitizes cancer cells to specific metabolic inhibitors or creates dependencies on particular nutrients or metabolic pathways. Additionally, examine whether ALDOC status affects response to standard cancer therapies, potentially serving as a predictive biomarker for treatment selection.
Finally, consider ALDOC in the context of tumor microenvironment and metabolic symbiosis. Investigate whether ALDOC-overexpressing cancer cells interact differently with stromal and immune cells through metabolic cross-talk. Understanding these interactions could reveal opportunities for combination therapies targeting both cancer cell metabolism and the supporting microenvironment. This comprehensive integration approach positions ALDOC research within the conceptual framework of cancer metabolism while highlighting its unique contributions to cancer biology beyond traditional metabolic roles .