SPAC821.13c Antibody

What is SPAC821.13c and why is it significant in S. pombe research?

SPAC821.13c is a gene locus in Schizosaccharomyces pombe (strain 972 / ATCC 24843), commonly known as fission yeast. The protein encoded by this gene (UniProt accession: Q9UT43) has drawn research interest due to its potential roles in cellular processes. Antibodies against this protein enable researchers to investigate its expression, localization, and function in various experimental conditions. The significance lies in understanding fundamental cellular mechanisms in this model organism, which often has conserved pathways relevant to human biology and disease. Unlike general antibody approaches, SPAC821.13c-specific investigations require specialized knowledge of yeast genetics and cell biology to properly interpret experimental outcomes and avoid methodological pitfalls .

How does research using SPAC821.13c Antibody differ from studies in other model organisms?

Research utilizing SPAC821.13c Antibody fundamentally differs from antibody-based studies in other model organisms due to S. pombe's unique cellular architecture, gene expression patterns, and cell cycle regulation. The thick cell wall of fission yeast necessitates modified sample preparation protocols when compared to mammalian systems. Additionally, interpreting results requires contextualizing within the specialized genetic nomenclature and biology of S. pombe. Unlike mammalian systems where multiple antibodies against various epitopes might be available, research with specialized yeast antibodies often involves more extensive validation and optimization since fewer commercial options exist. This necessitates more rigorous controls and potentially developing complementary genetic approaches to confirm findings .

What are the recommended positive and negative controls when using SPAC821.13c Antibody?

When designing experiments with SPAC821.13c Antibody, proper controls are essential for result validation. The recommended positive control is wild-type S. pombe strain 972 lysate, which expresses the native protein at endogenous levels. For negative controls, researchers should consider:

• A deletion strain (SPAC821.13c∆) if available

• Pre-immune serum controls for immunoprecipitation experiments

• Competitive blocking with recombinant SPAC821.13c protein

• Non-specific IgG from the same species as the primary antibody

The implementation of both types of controls is critical for distinguishing specific signals from background, particularly in techniques like immunofluorescence where S. pombe autofluorescence can interfere with signal detection. Advanced applications may benefit from epitope-tagged versions of the protein as additional specificity controls .

What are the optimal fixation and permeabilization methods for immunofluorescence with SPAC821.13c Antibody?

For successful immunofluorescence using SPAC821.13c Antibody in S. pombe, fixation and permeabilization protocols must address the yeast's rigid cell wall while preserving epitope accessibility. The optimal fixation method involves 3.7% formaldehyde for 30 minutes at room temperature, followed by enzymatic cell wall digestion using a combination of zymolyase (1mg/ml) and lysing enzymes (0.5mg/ml) for 15-30 minutes at 37°C. This two-step approach balances structural preservation with antibody penetration. Alternative methods include methanol fixation (-20°C for 6 minutes) for certain applications, though this may compromise some epitopes. For permeabilization, 0.1% Triton X-100 for 5 minutes is typically effective after cell wall digestion. Critical parameters include maintaining pH stability during fixation (pH 7.5) and careful monitoring of spheroplast formation during digestion to prevent cell lysis while ensuring adequate permeabilization. Researchers should validate these conditions empirically for their specific experimental system, as protein localization and abundance may influence optimal protocol parameters .

How should researchers optimize protein extraction from S. pombe for Western blotting with SPAC821.13c Antibody?

Optimizing protein extraction from S. pombe for Western blotting with SPAC821.13c Antibody requires addressing several critical factors. The recommended procedure employs mechanical disruption of cells using acid-washed glass beads in a lysis buffer containing 50mM Tris-HCl (pH 7.5), 150mM NaCl, 5mM EDTA, 10% glycerol, 1mM PMSF, and protease inhibitor cocktail. For optimal extraction:

• Harvest cells in mid-logarithmic phase (OD600 0.5-0.8) to ensure consistent protein expression

• Perform cell disruption at 4°C with 6-8 cycles of 30-second vortexing followed by 30-second cooling

• Include 1% Triton X-100 or 0.1% SDS in the lysis buffer to improve solubilization

• Centrifuge lysates at 13,000g for 15 minutes to remove cell debris

Critical methodological considerations include preventing protein degradation through rapid processing and thorough protease inhibition. For membrane-associated proteins, consider using specialized detergent combinations like CHAPS (0.5%) or digitonin (1%). Protein quantification using Bradford or BCA assays should be performed prior to SDS-PAGE, with loading 20-40μg of total protein per lane for optimal detection. Researchers should empirically determine the optimal primary antibody dilution (typically starting at 1:1000) and incubation conditions (overnight at 4°C versus 2 hours at room temperature) .

What are the methodological differences between using SPAC821.13c Antibody for co-immunoprecipitation versus chromatin immunoprecipitation?

The methodological approaches for co-immunoprecipitation (Co-IP) versus chromatin immunoprecipitation (ChIP) using SPAC821.13c Antibody differ significantly in sample preparation, buffer compositions, and analytical endpoints.

For Co-IP of protein complexes:

• Utilize gentle lysis conditions (0.5% NP-40 or 0.1% Triton X-100) to preserve native protein-protein interactions

• Pre-clear lysates with Protein A/G beads for 1 hour at 4°C to reduce non-specific binding

• Incubate cleared lysates with 2-5μg SPAC821.13c Antibody overnight at 4°C with gentle rotation

• Capture immunocomplexes with fresh Protein A/G beads for 2 hours at 4°C

• Wash extensively (4-5 times) with decreasing salt concentrations to remove non-specific interactions

For ChIP applications:

• Crosslink cells with 1% formaldehyde for 15 minutes at room temperature

• Quench with 125mM glycine for 5 minutes

• Lyse cells and sonicate chromatin to 200-500bp fragments

• Immunoprecipitate with 5-10μg SPAC821.13c Antibody overnight

• Reverse crosslinks at 65°C for 4-6 hours before DNA purification

The critical difference lies in preserving protein-protein interactions for Co-IP versus protein-DNA interactions for ChIP. Additionally, buffer compositions differ significantly, with ChIP requiring more stringent washing conditions to reduce background. Both techniques benefit from optimization of antibody concentration, incubation time, and washing stringency to balance signal strength with specificity .

How can researchers address high background or non-specific binding when using SPAC821.13c Antibody?

High background or non-specific binding when using SPAC821.13c Antibody can arise from multiple sources and requires systematic troubleshooting. To address these issues, researchers should implement a tiered approach:

First, optimize blocking conditions by testing different blocking agents:

• 5% non-fat dry milk in TBST (standard, economical approach)

• 3-5% BSA in TBST (preferred for phospho-specific antibodies)

• Commercial blocking reagents specifically formulated for yeast applications

Second, modify antibody incubation parameters:

• Reduce primary antibody concentration (test serial dilutions from 1:500 to 1:5000)

• Shorten incubation time or switch from room temperature to 4°C

• Add 0.1-0.5% Tween-20 or 0.05% Triton X-100 to reduce hydrophobic interactions

Third, increase washing stringency:

• Extend wash duration (5 washes of 10 minutes each)

• Increase salt concentration in wash buffers (up to 500mM NaCl)

• Add 0.1% SDS to wash buffers for particularly problematic applications

For persistent non-specific binding, pre-adsorption of the antibody with S. pombe lysate from a strain lacking the target protein can dramatically improve specificity. Additionally, cross-reactivity can be assessed using peptide competition assays to confirm binding specificity. Remember that yeast cell walls contain polysaccharides that may bind certain antibodies non-specifically, so appropriate cell wall removal or disruption is crucial for reducing this source of background .

What factors contribute to variability in SPAC821.13c Antibody performance across different experimental batches?

Variability in SPAC821.13c Antibody performance across experimental batches can significantly impact research reproducibility and should be systematically addressed. Several key factors contribute to this variability:

• Antibody production and storage conditions:

  • Lot-to-lot variations in antibody production

  • Freeze-thaw cycles causing IgG degradation

  • Improper storage temperature or buffer conditions

• Sample preparation inconsistencies:

  • Variations in cell growth phase and metabolic state

  • Differences in lysis efficiency between batches

  • Inconsistent protein extraction yields

• Experimental parameter fluctuations:

  • Variations in blocking efficiency

  • Inconsistent washing stringency

  • Temperature fluctuations during incubation steps

To minimize these variations, researchers should implement standardized protocols with detailed documentation of all parameters, including cell density at harvest, lysis conditions, protein quantification methods, and antibody dilution calculations. Creating a reference standard (a well-characterized lysate aliquoted and stored at -80°C) allows for comparative analysis across experiments. Additionally, maintaining a consistent source of antibody (ideally the same lot number) throughout a study, and validating each new lot against previous results, can significantly reduce variability. For critical experiments, running technical replicates with different antibody aliquots can help identify and account for antibody-specific variation .

How should researchers interpret conflicting results between SPAC821.13c Antibody immunodetection and genetic expression data?

When faced with discrepancies between SPAC821.13c Antibody immunodetection and genetic expression data (e.g., RNA-seq or RT-PCR), researchers must systematically analyze potential sources of conflict while considering the biological implications. This incongruity may result from:

• Post-transcriptional regulation: Differences between mRNA and protein levels due to translation efficiency, protein stability, or degradation pathways

• Post-translational modifications: Modifications that alter epitope recognition by the antibody

• Protein localization changes: Subcellular compartmentalization affecting extraction efficiency

• Technical limitations: Antibody specificity issues or RNA quantification biases

To resolve these conflicts, implement a multi-faceted approach:

• Confirm antibody specificity using knockout/knockdown controls

• Employ alternative antibodies targeting different epitopes of the same protein

• Use epitope-tagged versions of the protein for orthogonal detection

• Validate with quantitative mass spectrometry for absolute protein quantification

• Examine temporal dynamics, as mRNA and protein levels may be offset temporally

The discrepancy itself may reveal important biological insights about gene regulation. For instance, stable proteins may persist despite reduced transcription, or rapid protein degradation might occur despite high mRNA levels. Document experimental conditions meticulously, as stress responses in S. pombe can dramatically alter the relationship between transcription and translation. Consider cell cycle effects, as SPAC821.13c expression may be cell cycle-dependent, causing population-average measurements to mask important regulatory patterns .

How can SPAC821.13c Antibody be utilized for studying protein-protein interactions in S. pombe?

SPAC821.13c Antibody offers sophisticated approaches for studying protein-protein interactions in S. pombe, beyond standard co-immunoprecipitation techniques. Researchers can implement advanced methodologies including:

• Proximity-dependent biotin identification (BioID): By fusing a promiscuous biotin ligase to SPAC821.13c, researchers can identify proximal proteins when the antibody is used to isolate the bait protein after biotinylation.

• Förster Resonance Energy Transfer (FRET) microscopy: When combined with fluorescently-tagged potential interaction partners, SPAC821.13c Antibody can be used to verify interactions through acceptor photobleaching or sensitized emission FRET.

• Tandem affinity purification (TAP): By creating a TAP-tagged version of SPAC821.13c and using the antibody as part of a two-step purification process, researchers can isolate native protein complexes under near-physiological conditions.

• Chemical crosslinking coupled with mass spectrometry (XL-MS): The antibody can immunoprecipitate crosslinked complexes, which are then analyzed by mass spectrometry to identify not only interaction partners but also structural information about the complex.

For all these approaches, critical methodological considerations include maintaining native cellular conditions during sample preparation, optimizing crosslinking conditions (if applicable), and implementing appropriate controls to distinguish specific from non-specific interactions. Quantitative analysis using SILAC or TMT labeling can further enhance the ability to distinguish genuine interactions from background. When integrated with genetic approaches such as synthetic genetic arrays or suppressor screens, these antibody-based methods provide powerful insights into functional protein networks in S. pombe .

What approaches exist for combining SPAC821.13c Antibody with advanced imaging techniques?

Combining SPAC821.13c Antibody with advanced imaging techniques enables researchers to gain unprecedented insights into protein dynamics and localization in S. pombe. Several sophisticated approaches warrant consideration:

• Super-resolution microscopy:

  • Structured illumination microscopy (SIM) provides 2x resolution improvement with minimal sample preparation modifications

  • Stochastic optical reconstruction microscopy (STORM) offers 10-20nm resolution by using photoswitchable fluorophores conjugated to secondary antibodies

  • Stimulated emission depletion (STED) microscopy can achieve 30-80nm resolution by employing specialized secondary antibodies with appropriate fluorophores

• Live-cell imaging strategies:

  • Microfluidic devices combined with antibody fragments or nanobodies for real-time protein tracking

  • Integration with optogenetic tools for simultaneous visualization and manipulation

• Correlative light and electron microscopy (CLEM):

  • Pre-embedding immunogold labeling with SPAC821.13c Antibody followed by electron microscopy

  • On-section immunolabeling of cryosections for preserved ultrastructural context

• Expansion microscopy:

  • Physical expansion of immunolabeled samples using hydrogel embedding and polymer expansion

  • Compatible with standard confocal microscopy while achieving effective super-resolution

For optimal results, sample preparation must be specifically adapted to each technique. For example, super-resolution methods require careful attention to fixation that preserves nanoscale structures, while specialized fixatives like glutaraldehyde/paraformaldehyde mixtures are necessary for CLEM approaches. Signal amplification strategies such as tyramide signal amplification (TSA) or rolling circle amplification can enhance detection of low-abundance proteins. Quantitative image analysis should implement appropriate algorithms for co-localization analysis, intensity measurement, and statistical validation .

How can isotopically labeled antibodies improve quantitative analysis of SPAC821.13c expression?

Isotopic labeling of antibodies represents an advanced approach to enhance quantitative analysis of SPAC821.13c expression in S. pombe. This methodology offers several distinct advantages over conventional antibody applications:

• Absolute quantification capability:

  • 13C or 15N-labeled antibodies provide internal standards for mass spectrometry-based absolute quantification

  • Enable precise determination of protein copy numbers per cell rather than relative expression levels

• Enhanced signal-to-noise ratio:

  • Isotope-coded antibodies combined with mass spectrometry detection eliminate autofluorescence issues common in yeast cells

  • Allow multiplexed detection of multiple proteins simultaneously without spectral overlap concerns

• Improved reproducibility:

  • Isotope-labeled antibody standards can normalize for technical variations in sample processing

  • Support batch correction for long-term studies or meta-analyses

Implementation requires sophisticated technical approaches, including expression of the antibody in an E. coli fermentation system using 13C-glucose and 13C-celtone for near-complete (>99%) isotopic incorporation. The labeled antibodies can be utilized in targeted proteomics workflows like SISCAPA (Stable Isotope Standards and Capture by Anti-Peptide Antibodies) or as internal standards in traditional immunoassays with mass spectrometric detection.

Critical methodological considerations include maintaining the binding characteristics of the antibody during the labeling process, validating equivalent affinity between labeled and unlabeled versions, and developing appropriate calibration curves for absolute quantification. For maximum benefit, researchers should consider combining this approach with genetic strategies like gene tagging to distinguish between different isoforms or post-translationally modified versions of SPAC821.13c .

How does research data from SPAC821.13c studies compare with other S. pombe protein analyses?

When comparing research data from SPAC821.13c studies with other S. pombe protein analyses, researchers should consider several key dimensions that influence data interpretation and integration:

Integration of SPAC821.13c data with broader S. pombe proteomics requires normalization strategies to account for technical variations in antibody performance and experimental conditions. When conducting comparative studies, researchers should implement consistent sample preparation protocols, utilize the same antibody lots when possible, and include appropriate internal controls for normalization.

For meta-analyses across multiple studies, statistical approaches like random-effects models can help account for inter-study variability. Additionally, researchers should consider the genomic context of SPAC821.13c, as neighboring genes may exhibit coordinated expression patterns that provide functional insights. Integration with publicly available datasets from repositories like PomBase can further contextualize experimental findings within the broader knowledge of S. pombe biology .

What methodological considerations are critical when comparing SPAC821.13c antibody results across different research laboratories?

Comparing SPAC821.13c antibody results across different research laboratories requires careful attention to methodological variations that may impact data interpretation. Critical factors include:

• Antibody source and validation:

  • Different suppliers or custom-produced antibodies may target different epitopes

  • Validation criteria vary between laboratories (Western blot, IP efficiency, immunofluorescence patterns)

  • Lot-to-lot variations even from the same supplier

• S. pombe strain backgrounds:

  • Genetic differences between laboratory strains can affect protein expression

  • Auxotrophic markers or integration of tags may influence results

  • Mutation accumulation in laboratory strains over time

• Experimental protocols:

  • Cell lysis methods (mechanical, enzymatic, detergent-based)

  • Buffer compositions and pH conditions

  • Incubation times and temperatures

  • Detection systems (chemiluminescence, fluorescence, colorimetric)

To facilitate meaningful cross-laboratory comparisons, researchers should implement standardized reporting of key methodological parameters, including detailed antibody information (catalog number, lot number, validation data), complete strain genotypes, growth conditions (media composition, temperature, harvest OD), and comprehensive experimental protocols. When possible, direct exchange of key reagents (particularly antibody aliquots and strain stocks) between laboratories can identify whether discrepancies arise from reagent or methodological differences.

For collaborative projects, implementing a round-robin testing approach where identical samples are processed in different laboratories can quantify inter-laboratory variation. Statistical methods like Z-score normalization or quantile normalization may help harmonize data across studies for meta-analyses. Additionally, reporting raw data alongside processed results enables other researchers to apply consistent analytical methods across datasets .

How can researchers integrate SPAC821.13c expression data with broader -omics datasets in fission yeast research?

Integrating SPAC821.13c expression data with broader -omics datasets requires sophisticated bioinformatic approaches to synthesize information across multiple molecular levels. Researchers should consider the following comprehensive strategy:

• Multi-omics data correlation:

  • Cross-reference antibody-based protein quantification with transcriptomics data to identify post-transcriptional regulation

  • Integrate with phosphoproteomics or other PTM datasets to assess regulatory mechanisms

  • Correlate with metabolomics data to identify functional consequences of protein expression changes

• Network analysis approaches:

  • Construct protein-protein interaction networks using co-immunoprecipitation data

  • Develop gene regulatory networks by combining ChIP-seq and expression data

  • Implement network propagation algorithms to predict functional relationships

• Temporal dynamics integration:

  • Align time-course experiments across different -omics platforms

  • Apply time-delay correlation analysis to identify cause-effect relationships

  • Develop mathematical models incorporating different molecular levels

• Visualization and analytical tools:

  • Utilize specialized platforms like Cytoscape for network visualization

  • Implement dimensionality reduction techniques (PCA, t-SNE, UMAP) to identify patterns

  • Apply machine learning approaches to classify experimental conditions or predict outcomes

For maximum value, researchers should deposit their data in standardized formats in public repositories like PomBase, GEO, or ProteomeXchange with detailed metadata. To address the challenges of data heterogeneity, normalization approaches specific to each data type should be implemented before integration. For instance, antibody-based Western blot quantification may require different normalization strategies than mass spectrometry-based proteomics data.

Advanced integrative approaches like multi-block partial least squares can identify latent variables that explain coordinated changes across different molecular levels. When properly implemented, these integrative approaches can reveal emergent properties not apparent from any single data type, providing comprehensive insights into SPAC821.13c function within the broader cellular context .