PPP1R13L Antibody, Biotin conjugated

What is PPP1R13L and why is it significant in research?

PPP1R13L, also known as inhibitor of ASPP protein (iASPP), NFkB-interacting protein 1 (NKIP1), RelA-associated inhibitor (RAI), or PPP1R13B-like protein, functions as a critical regulator of apoptosis and transcription. It achieves this primarily through interactions with NF-kappa-B and p53/TP53 proteins. Its significance stems from its role in inhibiting p53-mediated apoptosis by preventing associations between p53/TP53 and apoptosis-stimulating proteins ASPP1 or ASPP2, thereby suppressing the activation of programmed cell death pathways. Additionally, it blocks HIV-1 virus transcription by inhibiting both NF-kappa-B and SP1 activity . Understanding PPP1R13L is particularly valuable in cancer research and viral pathogenesis studies.

What are the key characteristics of biotin-conjugated PPP1R13L antibodies?

Biotin-conjugated PPP1R13L antibodies are polyclonal antibodies typically raised in rabbits against specific peptide sequences from human RelA-associated inhibitor protein. For instance, one available antibody targets the 83-102 amino acid region of the human protein. These antibodies undergo antigen affinity purification to enhance specificity and are provided in a liquid form containing preservatives like 0.03% Proclin 300 in a buffer consisting of 50% glycerol and 0.01M PBS at pH 7.4. They demonstrate confirmed reactivity to human samples and have been validated for applications such as ELISA. The biotin conjugation makes these antibodies particularly valuable for detection systems utilizing streptavidin complexes, enhancing sensitivity in various immunoassays .

What are the validated applications for biotin-conjugated PPP1R13L antibodies?

While biotin-conjugated PPP1R13L antibodies have been primarily validated for Enzyme-Linked Immunosorbent Assay (ELISA) applications, the biotin conjugation offers versatility for numerous other potential applications. The biotin-streptavidin detection system enhances sensitivity in techniques including immunohistochemistry, immunofluorescence, and flow cytometry. This conjugation also enables multiplexed immunoassays by allowing combination with differently labeled primary antibodies. Although specific validation data for applications beyond ELISA may be limited for commercial biotin-conjugated PPP1R13L antibodies, researchers should conduct preliminary validation studies when adapting these antibodies to alternative techniques . For more comprehensive application coverage, non-conjugated PPP1R13L antibodies have demonstrated success in Western blotting (WB) and immunohistochemistry (IHC) applications.

How should biotin-conjugated PPP1R13L antibodies be stored to maintain optimal activity?

To preserve the functional integrity of biotin-conjugated PPP1R13L antibodies, proper storage conditions are essential. Upon receipt, these antibodies should be stored at either -20°C or -80°C. This temperature range prevents protein denaturation and preserves the biotin conjugation. Critically, repeated freeze-thaw cycles must be avoided as they significantly damage antibody structure and function. When working with these antibodies, it's recommended to aliquot the stock solution into single-use volumes before freezing to minimize freeze-thaw events. Prior to use, allow the antibody to thaw completely at 4°C rather than at room temperature. The antibody formulation typically includes stabilizers like 50% glycerol that helps maintain antibody integrity during freeze-thaw transitions . For long-term storage exceeding six months, -80°C is preferable to -20°C to further limit potential degradation.

What controls should be included when designing experiments with PPP1R13L antibodies?

Robust experimental design with PPP1R13L antibodies requires comprehensive controls to ensure valid interpretation of results. Positive controls should include cell lines or tissues known to express PPP1R13L, such as Lncap or HeLa cells, which have been documented in validation studies. For negative controls, consider utilizing samples where the target protein is absent or using blocking peptides that competitively bind the antibody. When performing knockdown or knockout validation, compare PPP1R13L expression between wild-type and gene-modified samples, similar to the approach demonstrated with p53 antibodies in wild-type versus p53 knockout mouse fibrosarcoma . Additionally, isotype controls matching the primary antibody host species but lacking specificity for the target should be employed to identify potential non-specific binding. For biotin-conjugated antibodies specifically, include streptavidin-only controls to assess potential endogenous biotin interference in your experimental system.

How can cross-reactivity concerns be addressed when working with PPP1R13L antibodies?

When addressing cross-reactivity concerns with PPP1R13L antibodies, several strategies can be implemented. First, validate species reactivity carefully, as commercial PPP1R13L antibodies may have different reactivity profiles—for example, some react with both human and mouse samples while others are human-specific . To minimize cross-reactivity with similar proteins (particularly other PPP1R13 family members), utilize antibodies raised against unique epitopes; the biotin-conjugated antibody targeting amino acids 83-102 of human PPP1R13L provides specificity against a distinct region .

For critical experiments, confirm specificity through multiple detection methods—combining Western blot analysis with immunohistochemistry can reveal discrepancies in binding patterns. Pre-absorption tests using the immunizing peptide can verify that observed signals result from specific target binding rather than cross-reactivity. Additionally, comparing staining patterns with antibodies targeting different epitopes of the same protein provides further validation of specificity. In cases where absolute specificity confirmation is required, corroborate results with gene silencing approaches (siRNA or CRISPR) to demonstrate signal reduction corresponds with decreased protein expression.

What dilution optimizations are recommended for different experimental applications?

Optimal dilution determination for PPP1R13L antibodies varies by application and requires systematic titration to balance signal strength and specificity. For Western blot applications with non-conjugated PPP1R13L antibodies, starting dilutions of 1:350 have been validated using standard protocols with 40μg protein per lane and secondary antibody dilutions of 1:8000 . For immunohistochemistry, dilutions typically range from 1:50 to 1:200 depending on detection system sensitivity and tissue preparation methods.

When working specifically with biotin-conjugated PPP1R13L antibodies in ELISA, begin optimization with a dilution series (e.g., 1:500, 1:1000, 1:2000, 1:5000) to identify the range producing the best signal-to-noise ratio. The optimal dilution will depend on several factors including the detection system (HRP-streptavidin vs. alkaline phosphatase-streptavidin), incubation conditions, and substrate sensitivity. For multiplex applications, more conservative dilutions may be necessary to prevent potential bleed-through between detection channels. Importantly, each new lot of antibody should undergo validation and optimization, as antibody concentration and activity can vary between manufacturing batches.

How can endogenous biotin interference be mitigated when using biotin-conjugated antibodies?

Endogenous biotin presents a significant challenge when using biotin-conjugated antibodies like PPP1R13L, particularly in biotin-rich tissues such as liver, kidney, and brain. To mitigate this interference, several approaches should be considered. First, implement a biotin-blocking step before antibody application using commercial biotin-blocking kits that contain avidin/streptavidin followed by free biotin. This sequential blocking saturates endogenous biotin sites and blocks remaining avidin/streptavidin binding sites.

Alternative detection systems can also be employed—if persistent endogenous biotin issues occur, consider using non-biotinylated primary antibodies with directly labeled secondary antibodies. When working with formalin-fixed, paraffin-embedded tissues, extend the antigen retrieval time as this can reduce endogenous biotin accessibility. Additionally, pretreat samples with 0.3% hydrogen peroxide in methanol to quench endogenous peroxidase activity when using HRP-based detection systems. For particularly problematic samples, tyramide signal amplification can provide enhanced sensitivity without increasing background from endogenous biotin. Always include no-primary-antibody controls to assess the level of endogenous biotin signal in your specific experimental system.

How can PPP1R13L antibodies be utilized to study p53-dependent apoptosis regulation?

PPP1R13L antibodies offer powerful tools for investigating the inhibitory relationship between PPP1R13L (iASPP) and p53-mediated apoptosis. A comprehensive research approach would include co-immunoprecipitation experiments using PPP1R13L antibodies to capture protein complexes, followed by Western blot analysis for p53, ASPP1, and ASPP2 to examine their competitive interactions. Chromatin immunoprecipitation (ChIP) assays using both p53 and PPP1R13L antibodies can identify genomic regions where PPP1R13L modulates p53 binding, particularly at pro-apoptotic gene promoters.

For functional studies, researchers should combine PPP1R13L knockdown/overexpression with apoptosis induction (e.g., DNA damage agents), followed by flow cytometry analysis of annexin V/PI staining to quantify apoptotic populations. Immunofluorescence co-localization studies using labeled PPP1R13L and p53 antibodies can reveal subcellular distribution patterns under various stress conditions. RNA-seq analysis comparing gene expression profiles between control and PPP1R13L-depleted cells after p53 activation would identify the broader transcriptional impact of this regulatory relationship. These approaches collectively provide mechanistic insights into how PPP1R13L manipulates the p53 pathway to suppress apoptosis, with implications for cancer therapy resistance mechanisms .

What advantages does biotin conjugation offer for multiplex imaging of PPP1R13L with other apoptosis regulators?

Biotin conjugation provides significant advantages for multiplex imaging studies investigating PPP1R13L in relation to other apoptosis regulators. The biotin-streptavidin system offers exceptional signal amplification, with each biotin molecule capable of binding multiple streptavidin molecules, each carrying multiple reporter molecules. This amplification is particularly valuable when detecting low-abundance proteins within the apoptotic regulatory network.

In multiplex experimental designs, biotin-conjugated PPP1R13L antibodies can be combined with directly labeled antibodies against apoptosis regulators like p53, Bcl-2 family proteins, or caspases. Using streptavidin conjugates with spectrally distinct fluorophores enables clear discrimination between targets. For tissue-based studies, the signal amplification provided by biotin-streptavidin systems allows for detection of subtle expression level changes in different cellular compartments. Moreover, the sequential detection approach facilitated by biotin conjugation permits stripping and reprobing of membranes or slides, enabling examination of multiple proteins from limited samples.

Advanced applications include proximity ligation assays (PLA) combining biotin-conjugated PPP1R13L antibodies with antibodies against interaction partners to visualize and quantify specific protein-protein interactions within cells with nanometer resolution, providing spatial context to biochemical interaction data .

How can PPP1R13L antibodies contribute to understanding the role of this protein in cancer progression?

PPP1R13L antibodies serve as essential tools for elucidating this protein's complex role in cancer biology. Comprehensive immunohistochemical profiling of cancer tissue microarrays using PPP1R13L antibodies can establish expression patterns across cancer types and correlate levels with clinical outcomes and therapeutic responses. This approach helps identify cancer subtypes where PPP1R13L may serve as a biomarker or therapeutic target. In mechanistic studies, combining PPP1R13L antibody-based detection with proliferation, migration, and invasion assays following genetic manipulation reveals how this protein influences key cancer hallmarks.

For signaling pathway analysis, PPP1R13L antibodies enable examination of not only p53 pathway interactions but also NF-κB pathway modulation, providing insight into inflammation-associated cancer progression. ChIP-seq experiments using PPP1R13L antibodies can map genome-wide binding profiles, identifying direct transcriptional targets relevant to cancer progression. In therapeutic development contexts, these antibodies allow monitoring of PPP1R13L expression changes following drug treatments, potentially identifying compounds that downregulate this anti-apoptotic protein.

Given PPP1R13L's role in apoptosis resistance, combining PPP1R13L antibody-based detection with chemotherapy response assays in patient-derived xenografts or organoids could reveal correlations between expression levels and treatment outcomes, potentially guiding personalized therapy approaches in cancers where p53 pathway manipulation represents a viable therapeutic strategy .

How should researchers interpret discrepancies between PPP1R13L protein detection across different applications?

When encountering discrepancies in PPP1R13L detection across techniques like Western blot, immunohistochemistry, and ELISA, systematic analysis is essential. First, consider epitope accessibility differences—fixation procedures in IHC may mask epitopes that remain accessible in denaturing Western blot conditions. The biotin-conjugated antibody targeting amino acids 83-102 may perform differently across applications depending on this region's structural accessibility in various experimental contexts .

Post-translational modifications may also explain discrepancies, as phosphorylation or other modifications could alter antibody recognition in application-specific ways. For quantitative comparisons, recognize that ELISA typically provides more reliable quantification than semi-quantitative Western blot or IHC. When evaluating conflicting results, examine protein localization patterns—nuclear versus cytoplasmic distribution of PPP1R13L may vary by cell type or condition, potentially explaining staining pattern differences.

The gold standard for resolving discrepancies involves orthogonal validation approaches. Confirm protein identity using mass spectrometry following immunoprecipitation with the PPP1R13L antibody. Additionally, validate findings using genetic approaches like siRNA knockdown or CRISPR knockout, observing signal reduction across all applications. Remember that antibody concentration optimization differs between applications—signals might appear discrepant simply due to suboptimal dilution in one application versus another .

What considerations are important when analyzing PPP1R13L expression in relation to p53 status?

Analyzing PPP1R13L expression in relation to p53 status requires careful consideration of their complex regulatory relationship. First, document p53 mutational status in your experimental system, as PPP1R13L may exhibit different regulatory effects on wild-type versus mutant p53. Wild-type p53 function is inhibited by PPP1R13L through prevention of ASPP1/ASPP2 interactions, while effects on mutant p53 variants may differ substantially .

Expression level correlations between PPP1R13L and p53 should be interpreted within the appropriate cellular context. In some cases, increased PPP1R13L expression represents a compensatory mechanism against elevated wild-type p53, while in others, co-expression might indicate disrupted regulatory balance. For accurate interpretation, analyze additional p53 pathway components, particularly ASPP1 and ASPP2, as the ratio between these proteins and PPP1R13L better reflects functional impact than absolute expression levels.

When examining functional outcomes, assess p53 target gene expression (e.g., p21, BAX, PUMA) alongside PPP1R13L and p53 levels to determine pathway activity. For therapeutic implications, evaluate whether PPP1R13L expression correlates with resistance to p53-activating therapies in your model system. Finally, consider cell-type specific effects—the regulatory relationship between PPP1R13L and p53 may vary significantly between tissue types due to differential expression of cofactors and pathway components .

What statistical approaches are recommended for analyzing PPP1R13L expression data across experimental conditions?

Appropriate statistical analysis of PPP1R13L expression data enhances experimental rigor and facilitates meaningful interpretation. For Western blot densitometry comparing PPP1R13L levels across conditions, normalize band intensities to loading controls (β-actin, GAPDH) before applying Student's t-test (two conditions) or ANOVA with post-hoc tests (multiple conditions). A minimum of three biological replicates is essential for reliable statistical analysis, with data typically presented as mean ± standard deviation or standard error.

For immunohistochemical quantification, consider both staining intensity and percentage of positive cells, potentially using H-score or Allred scoring systems for semi-quantitative analysis. When evaluating PPP1R13L in tissue microarrays or clinical samples, employ non-parametric tests like Mann-Whitney U or Kruskal-Wallis if data doesn't follow normal distribution. Correlation analysis (Pearson's or Spearman's) can reveal relationships between PPP1R13L expression and other markers or clinical parameters.

In complex experiments examining PPP1R13L interactions with multiple pathway components under various conditions, multivariate analysis approaches such as principal component analysis or hierarchical clustering help identify patterns. For survival analysis in relation to PPP1R13L expression in clinical samples, Kaplan-Meier curves with log-rank tests for significance determination are appropriate. In all cases, clearly document sample sizes, normality testing results, and specific statistical tests employed to ensure reproducibility and appropriate interpretation of findings.