SPACA4 is a protein encoded by the SPACA4 gene, predominantly expressed in the testis and involved in sperm maturation. It localizes to the sperm acrosome, a structure critical for fertilization. While its exact mechanistic role remains under investigation, SPACA4 is hypothesized to participate in acrosomal biogenesis or sperm-egg interaction .
The SPACA4 antibody has been validated for detecting endogenous SPACA4 protein in human and rodent tissues. In Western blot assays, it recognizes a band at approximately 25–30 kDa, consistent with the predicted molecular weight of SPACA4 .
While not explicitly cited in the provided sources, antibodies targeting acrosomal proteins like SPACA4 are typically used in reproductive biology to study sperm morphology and acrosome reactions.
Although unrelated to SPACA4, search results highlight SPA14 liposomes—a TLR4 agonist-based adjuvant used in vaccine development. Notably:
SPA14 enhances IgG1/IgG2c antibody responses and neutralizing titers against cytomegalovirus (CMV) antigens in mice .
In non-human primates, SPA14 demonstrated adjuvant efficacy comparable to AS01B (a licensed adjuvant in Shingrix®), inducing durable B-cell responses .
Nomenclature Ambiguity: The term "SPAC4G8.04" does not align with standard gene or protein naming conventions. Potential typographical errors or outdated identifiers should be clarified.
Functional Data Gap: While the SPACA4 antibody is commercially available, mechanistic insights into SPACA4’s role in fertility or disease remain limited.
Species Cross-Reactivity: Despite broad predicted reactivity, experimental validation in non-human models (e.g., pig, dog) is warranted .
Investigate SPACA4’s role in male infertility using knockout models.
Explore therapeutic applications in contraceptive development or fertility treatments.
SPAC4G8.04 is a TBC domain-containing protein found in Schizosaccharomyces pombe (fission yeast). TBC domain proteins typically function as GTPase-activating proteins (GAPs) for Rab GTPases, playing crucial roles in membrane trafficking pathways. Studying this protein helps elucidate fundamental cellular processes in eukaryotic cells. Researchers investigate SPAC4G8.04 to understand conserved mechanisms of vesicular transport, which have implications for understanding similar processes in higher eukaryotes including humans.
The SPAC4G8.04 antibody has been validated primarily for detecting the endogenous protein in fission yeast samples. Common applications include:
Western blotting
Immunocytochemistry (ICC)
Immunofluorescence (IF)
Immunoprecipitation (IP)
When using this antibody in novel applications, thorough validation steps should be conducted including appropriate controls to ensure specificity and sensitivity in your experimental system .
| Feature | Monoclonal Anti-SPAC4G8.04 | Polyclonal Anti-SPAC4G8.04 |
|---|---|---|
| Source | Single B-cell clone | Multiple B-cells |
| Epitope recognition | Single epitope | Multiple epitopes |
| Batch-to-batch variation | Minimal | Significant |
| Applications | Highly specific detection | Broader detection capability |
| Signal intensity | Generally lower | Generally higher |
| Cross-reactivity | Minimal | Potentially higher |
| Recommended for proteins with high homology | Yes | Caution advised |
| Cost | Higher | Lower |
For highly conserved domains like the TBC domain in SPAC4G8.04, monoclonal antibodies generally offer better specificity but may be more sensitive to epitope masking or denaturation depending on experimental conditions .
For optimal Western blot detection of SPAC4G8.04:
Sample preparation:
Extract proteins under non-denaturing conditions if targeting conformational epitopes
Include protease inhibitors to prevent degradation
Gel electrophoresis:
Use 10-12% polyacrylamide gels for optimal separation
Expected molecular weight: Approximately 30-35 kDa
Transfer and blocking:
PVDF membranes typically yield better results than nitrocellulose
Block with 5% non-fat dry milk in TBST (Tris-buffered saline with 0.1% Tween-20)
Antibody incubation:
Primary antibody dilution: 1:1000 to 1:2000
Incubate overnight at 4°C for optimal binding
Secondary antibody: Anti-species IgG HRP-conjugate at 1:5000 dilution
Detection:
Enhanced chemiluminescence (ECL) substrate
Exposure time: Start with 30 seconds, adjust as needed
Validation through knockout or knockdown controls is essential to confirm specificity .
Optimizing immunofluorescence for SPAC4G8.04 in S. pombe requires specific considerations:
Cell fixation:
4% paraformaldehyde (15 minutes at room temperature) preserves cellular architecture
Alternatively, cold methanol fixation (6 minutes at -20°C) may better preserve certain epitopes
Cell wall digestion:
Critical for antibody penetration
Use zymolyase (1 mg/ml for 30 minutes at 37°C)
Monitor digestion microscopically to prevent over-digestion
Permeabilization:
0.1% Triton X-100 (5 minutes) after fixation
Over-permeabilization can lead to signal loss and morphological artifacts
Antibody dilutions:
Primary: 1:100 to 1:500 in blocking buffer
Secondary: 1:500 fluorophore-conjugated antibody
Mounting:
Use antifade mounting medium containing DAPI for nuclear counterstaining
Allow 24 hours for hardening before imaging
Controls:
Include wild-type and SPAC4G8.04 deletion strains in parallel
Pre-immune serum control to assess background
Thorough washing between each step is crucial for reducing background fluorescence .
Cross-reactivity is a significant concern when using S. pombe SPAC4G8.04 antibodies in other yeast species:
Sequence homology:
SPAC4G8.04 shares TBC domain conservation with proteins in:
Saccharomyces cerevisiae (budding yeast): ~35-40% homology
Candida albicans: ~30% homology
Cryptococcus neoformans: ~25% homology
Epitope conservation:
Higher conservation in catalytic regions
Lower conservation in regulatory domains
Epitope mapping is recommended before cross-species applications
Validation strategies for cross-species applications:
Western blot with recombinant proteins from target species
Preabsorption controls with recombinant SPAC4G8.04
Peptide competition assays
Parallel evaluation with species-specific antibodies when available
Alternative approaches:
Consider epitope-tagging approaches in heterologous systems
Use of conserved domain antibodies that target highly preserved regions
Researchers should conduct preliminary validation experiments to determine antibody utility in non-S. pombe species .
For investigating SPAC4G8.04 protein-protein interactions in membrane trafficking:
Co-immunoprecipitation (Co-IP):
Lyse cells in non-denaturing buffer (50 mM Tris-HCl pH 7.5, 150 mM NaCl, 1% NP-40, protease inhibitors)
Pre-clear lysate with Protein A/G beads
Immunoprecipitate with anti-SPAC4G8.04 antibody (5 μg per 1 mg protein)
Analyze precipitates for interacting partners by Western blot or mass spectrometry
Proximity labeling techniques:
BioID or TurboID fusion with SPAC4G8.04
Expression in S. pombe followed by streptavidin pulldown
Mass spectrometry identification of proximal proteins
Validation of interactions using SPAC4G8.04 antibody
Fluorescence microscopy:
Dual immunofluorescence with SPAC4G8.04 antibody and antibodies against putative interacting proteins
Quantify colocalization using Pearson's or Mander's coefficient
Super-resolution microscopy for precise localization
Förster Resonance Energy Transfer (FRET):
Fluorophore-conjugated antibodies against SPAC4G8.04 and potential partners
Live cell imaging to detect energy transfer indicating protein proximity
Split-GFP complementation:
Tag SPAC4G8.04 with GFP fragment
Tag potential interactors with complementary GFP fragment
Fluorescence indicates protein-protein interaction
Validate with SPAC4G8.04 antibody in parallel experiments
These approaches reveal functional protein networks involving SPAC4G8.04 in vesicular trafficking pathways .
Investigating post-translational modifications (PTMs) of SPAC4G8.04 requires specialized approaches:
Phosphorylation analysis:
Immunoprecipitate SPAC4G8.04 using specific antibody
Western blot with phospho-specific antibodies (Ser/Thr/Tyr)
Phos-tag SDS-PAGE for mobility shift detection
Mass spectrometry for precise phosphorylation site mapping
Compare samples with and without phosphatase treatment
Ubiquitination assessment:
Immunoprecipitate under denaturing conditions to preserve ubiquitin linkages
Western blot with anti-ubiquitin antibodies
Use deubiquitinase inhibitors during sample preparation
Consider using tagged ubiquitin (His6-Ub) for pulldown experiments
SUMOylation detection:
Similar to ubiquitination but using SUMO-specific antibodies
Include SUMO protease inhibitors (e.g., N-ethylmaleimide)
Analyze with anti-SUMO antibodies following SPAC4G8.04 immunoprecipitation
Glycosylation analysis:
Use lectins (e.g., ConA, WGA) for glycoprotein enrichment
Treat samples with glycosidases to confirm modifications
PNGase F treatment for N-linked glycans
O-glycosidase for O-linked glycans
Acetylation detection:
Immunoprecipitate SPAC4G8.04
Probe with anti-acetyl-lysine antibodies
Include histone deacetylase inhibitors during preparation
Data integration:
Correlate PTM status with cellular conditions (stress, cell cycle phase)
Map modifications to protein domains to infer functional significance
Compare PTM patterns across growth conditions
These methodologies provide insights into the regulatory mechanisms controlling SPAC4G8.04 function .
| Screening Approach | Methodology | Key Considerations | Data Analysis |
|---|---|---|---|
| Protein microarray | Spot lysates from mutant libraries on nitrocellulose; probe with fluorescently-labeled SPAC4G8.04 antibody | Signal normalization; positive/negative controls; replicate spots | Cluster analysis; hit validation by secondary assays |
| High-content imaging | Immunofluorescence in 96/384-well format; automated image acquisition | Cell segmentation parameters; multi-parametric phenotype scoring | Machine learning for phenotype classification |
| Flow cytometry screening | Label cells with fluorescent SPAC4G8.04 antibody; sort based on signal intensity | Permeabilization optimization; gating strategy; fluorophore selection | Population distribution analysis; SPADE or viSNE analysis |
| Reverse phase protein array | Immobilize cellular lysates on slides; probe with SPAC4G8.04 antibody | Lysate dilution series; antibody validation; signal amplification | Quantitative comparison across conditions |
| ELISA-based screening | Capture SPAC4G8.04 on antibody-coated plates; detect with secondary system | Assay miniaturization; signal window optimization | Z'-factor calculation for assay quality |
For all high-throughput approaches, rigorous antibody validation and optimization of detection parameters are essential for reliable results. Appropriate statistical methods should be applied to account for plate-to-plate variation and identify true biological effects versus technical artifacts .
| Issue | Possible Causes | Solutions |
|---|---|---|
| No signal in Western blot | Protein degradation; Improper transfer; Inefficient extraction | Add fresh protease inhibitors; Optimize transfer conditions; Try alternative lysis buffers (RIPA, NP-40, etc.) |
| High background | Insufficient blocking; Excessive antibody concentration; Non-specific binding | Increase blocking time (overnight at 4°C); Titrate antibody; Add 0.1-0.5% Tween-20 to washing buffer |
| Multiple bands | Cross-reactivity; Protein degradation; Post-translational modifications | Preabsorb antibody; Include protease inhibitors; Compare with knockout control |
| Inconsistent results between experiments | Batch-to-batch antibody variation; Sample preparation differences; Detection system variability | Use same antibody lot; Standardize protocols; Include loading controls and normalization standards |
| Weak signal | Insufficient antigen; Low antibody affinity; Suboptimal detection system | Increase protein loading; Optimize antibody concentration; Try signal enhancement systems (amplification reagents) |
| Poor reproducibility in immunostaining | Fixation variations; Antibody access issues; Cell heterogeneity | Standardize fixation protocol; Optimize permeabilization; Increase cell numbers for analysis |
| Loss of reactivity over time | Antibody degradation; Epitope masking during storage | Aliquot antibody; Avoid freeze-thaw cycles; Store at recommended temperature (-20°C or -80°C) |
For persistent issues, epitope retrieval methods (such as heat-induced or enzymatic retrieval) may improve antibody access to target epitopes. Additionally, switching from polyclonal to monoclonal antibodies (or vice versa) can resolve specificity issues .
Comprehensive validation of SPAC4G8.04 antibodies should include:
Genetic validation:
Test antibody against wild-type and SPAC4G8.04Δ (knockout) strains
Expected: Signal present in wild-type, absent in knockout
If knockout is lethal, use conditional depletion systems
Expression validation:
Test against overexpression systems
Correlation between expression level and signal intensity
Test in heterologous systems (e.g., E. coli expressing recombinant protein)
Epitope mapping:
Use peptide arrays to identify binding epitopes
Confirm epitope conservation if using across species
Predict potential cross-reactivity based on epitope sequence
Specificity assays:
Peptide competition experiments
Pre-absorption tests with recombinant protein
Mass spectrometry confirmation of immunoprecipitated proteins
Application-specific validation:
For Western blotting: Single band at expected molecular weight
For immunofluorescence: Subcellular localization consistent with known biology
For flow cytometry: Clear separation between positive and negative populations
Cross-reactivity assessment:
Test against related proteins (other TBC domain proteins)
Test in multiple cell types/species if intended for broad use
Documentation:
Record all validation experiments with detailed protocols
Include representative images/blots in publications
Report antibody catalog number, lot, and dilution used
For quantitative experiments using SPAC4G8.04 antibodies, implement these quality control measures:
Standard curve development:
Use purified recombinant SPAC4G8.04 protein at known concentrations
Generate standard curves for each experiment
Determine linear detection range and limits of detection/quantification
Controls for each experiment:
Positive control: Wild-type S. pombe extract
Negative control: SPAC4G8.04Δ extract
Technical replicates: Minimum of 3 per sample
Biological replicates: Minimum of 3 independent cultures/preparations
Normalization strategy:
Include invariant loading controls (e.g., actin, GAPDH)
For immunofluorescence: Normalize to cell size or nuclear signal
For flow cytometry: Use fluorescence calibration beads
Signal saturation prevention:
Perform dilution series for each new lot of antibody
Ensure signal falls within linear range of detection
Avoid overexposure in imaging applications
Batch effects minimization:
Process all comparative samples simultaneously
Include inter-experimental calibrators
Use randomization strategies for sample processing
Statistical validation:
Calculate coefficient of variation (CV) between replicates (aim for <15%)
Determine signal-to-noise ratio (S/N >5 for quantitative work)
Apply appropriate statistical tests based on data distribution
Documentation:
Record antibody lot, dilution, incubation conditions
Document image acquisition parameters
Maintain raw data alongside processed results
Quality indicators table:
| Quality Parameter | Acceptable Range | Warning Threshold | Action if Threshold Exceeded |
|---|---|---|---|
| Coefficient of Variation | <10% | >15% | Repeat experiment |
| Signal-to-Background Ratio | >5:1 | <3:1 | Optimize blocking/washing |
| Standard Curve R² | >0.98 | <0.95 | Prepare fresh standards |
| Negative Control Signal | <5% of positive | >10% of positive | Check antibody specificity |
| Replicate Correlation | r>0.90 | r<0.85 | Investigate technical variables |
These measures ensure reliable quantitative data for publication-quality research using SPAC4G8.04 antibodies .
Interpreting SPAC4G8.04 expression changes requires careful consideration of multiple factors:
Baseline expression context:
Establish normal expression across growth phases in wild-type cells
Document subcellular localization patterns under standard conditions
Consider cell cycle variation (G1, S, G2, M phases)
Quantification methodology:
Western blot: Densitometry normalized to loading controls
qPCR: ΔΔCt method with validated reference genes
Immunofluorescence: Integrated signal intensity normalized to cell volume
Flow cytometry: Mean fluorescence intensity shifts
Statistical analysis framework:
Determine appropriate statistical tests based on data distribution
Apply multiple comparison corrections for large datasets
Calculate effect sizes in addition to p-values
Consider biological significance thresholds (e.g., >1.5-fold change)
Biological context interpretation:
Correlate with membrane trafficking alterations
Consider interactions with Rab GTPases
Relate to cellular stress responses
Evaluate in context of cell growth/division phenotypes
Comparative analysis:
Examine co-regulation with other membrane trafficking genes
Compare with related TBC domain proteins
Consider parallels with mammalian orthologues
Validation approaches:
Confirm protein level changes with transcript analysis
Verify with alternative methodologies
Assess functional consequences of observed changes
Confounding factors to consider:
Cell density effects on expression
Media composition influences
Temperature sensitivity
Sample preparation artifacts
A comprehensive interpretation relates expression changes to functional outcomes in membrane trafficking pathways rather than viewing them in isolation .
For robust analysis of SPAC4G8.04 subcellular localization:
Image acquisition optimization:
Use confocal microscopy for optical sectioning
Acquire z-stacks to capture complete 3D distribution
Select appropriate objective (63x or 100x) for resolution
Minimize photobleaching with optimized laser power/exposure
Co-localization approach:
Use established compartment markers:
Golgi: Anp1-RFP
Endosomes: Vps27-RFP
ER: Ero1-RFP
Vacuole: FM4-64 staining
Calculate quantitative co-localization metrics:
Pearson's correlation coefficient (values from -1 to +1)
Mander's overlap coefficient (values from 0 to 1)
Intensity correlation analysis (ICA)
Quantitative analysis methods:
Line scan analysis across cellular compartments
Intensity distribution profiling
Object-based co-localization
Distance-based metrics between objects
Dynamic localization assessment:
Time-lapse imaging during cellular processes
Photobleaching techniques (FRAP/FLIP) to measure mobility
Conditional expression systems to monitor transport kinetics
Software tools for analysis:
ImageJ/Fiji with JACoP plugin for co-localization analysis
CellProfiler for automated compartment segmentation
Imaris for 3D reconstruction and analysis
Custom MATLAB scripts for specialized analysis
Reporting standards:
Include scale bars in all images
Present representative images alongside quantification
Report number of cells analyzed (minimum 30-50 cells per condition)
Include raw values for co-localization metrics with statistical analysis
Functional correlation:
Relate localization changes to functional outcomes
Test localization under genetic or pharmacological perturbations
Connect localization patterns to protein interaction networks
These practices ensure reliable interpretation of SPAC4G8.04 localization data, avoiding common pitfalls such as overinterpretation of partial co-localization or artifacts from sample preparation .
Emerging research directions for SPAC4G8.04 in membrane trafficking include:
Advanced imaging applications:
Super-resolution microscopy (STORM, PALM) for precise localization
Lattice light-sheet microscopy for dynamic trafficking events
Correlative light-electron microscopy (CLEM) to connect function with ultrastructure
Live-cell tracking of SPAC4G8.04 with split-fluorescent protein technologies
Disease model applications:
Investigation of SPAC4G8.04 orthologs in neurodegenerative disorders
Cancer cell trafficking alterations linked to TBC domain proteins
Trafficking dynamics in models of secretory/lysosomal storage diseases
Therapeutic targeting of dysfunctional membrane trafficking pathways
Synthetic biology approaches:
Engineered SPAC4G8.04 variants with modified regulatory domains
Optogenetic control of SPAC4G8.04 function
Biosensor development for real-time trafficking visualization
Synthetic organelle creation with engineered trafficking components
Systems biology integration:
Comprehensive mapping of SPAC4G8.04 interaction network
Mathematical modeling of trafficking dynamics
Multi-scale modeling from molecular to cellular levels
Machine learning applications for trafficking phenotype prediction
Evolutionary studies:
Comparative analysis of TBC domain proteins across eukaryotic lineages
Functional conservation testing in diverse species
Evolutionary pressures on membrane trafficking systems
Ancient origin and specialization of trafficking machinery
These research directions build upon fundamental knowledge of SPAC4G8.04 function while extending into translational applications and theoretical understanding of cellular organization principles .
Emerging technologies with potential to transform SPAC4G8.04 research:
Next-generation antibody technologies:
Single-domain nanobodies with improved penetration
Recombinant antibody fragments for super-resolution imaging
Site-specific conjugation for precise labeling
Intrabodies for live-cell detection
Advanced proteomics approaches:
Proximity-dependent biotinylation (BioID, TurboID)
Crosslinking mass spectrometry (XL-MS)
Thermal proximity coaggregation (TPCA)
Limited proteolysis-coupled mass spectrometry
Genome engineering advances:
Prime editing for precise genetic modifications
CRISPR interference/activation for endogenous regulation
Base editing for specific amino acid modifications
Scarless tagging methods for endogenous visualization
Single-cell technologies:
Single-cell proteomics for heterogeneity assessment
Spatial transcriptomics to relate location to function
Live-cell lineage tracing with protein dynamics
Single-cell western blotting for protein analysis
Microfluidic and organ-on-chip platforms:
High-throughput phenotypic screening
Real-time monitoring of trafficking dynamics
Reconstituted trafficking systems in vitro
Gradient generation for directional trafficking studies
Computational advancements:
Deep learning for image analysis and phenotype classification
Molecular dynamics simulations of protein interactions
Predictive modeling of trafficking network responses
Integration of multi-omics data for systems-level understanding
Technology integration table:
| Technology | Application to SPAC4G8.04 Research | Anticipated Timeline | Technical Challenges |
|---|---|---|---|
| Cryo-electron tomography | Visualize SPAC4G8.04 in native cellular context | Current-3 years | Sample preparation; Resolution limits |
| Alphamers | Ultra-specific detection without cross-reactivity | 2-5 years | Validation; Commercialization barriers |
| DNA-PAINT super-resolution | Nanoscale localization of trafficking components | Current-2 years | Multi-color implementation; Speed |
| Deep learning image analysis | Automated trafficking pathway classification | Current | Training data requirements; Validation |
| Protein structure prediction | SPAC4G8.04 structure and interaction modeling | Current | Experimental validation; Dynamic regions |
| Optical tweezers | Single-molecule force measurements during trafficking | 3-5 years | Technical complexity; Low throughput |
| In situ sequencing | Spatial mapping of trafficking complexes | 2-4 years | Sensitivity; Multiplexing capacity |
These technological advances will enable increasingly sophisticated analyses of SPAC4G8.04's role in cellular trafficking networks .
SPAC4G8.04 research offers valuable insights into evolutionarily conserved trafficking mechanisms:
Evolutionary conservation analysis:
TBC domain proteins represent ancient GTPase regulatory mechanisms
Functional conservation despite sequence divergence
Core trafficking machinery predates eukaryotic radiation
Model for studying essentiality versus adaptatability in trafficking
Translational research opportunities:
Human ortholog identification and functional comparison
Conservation of regulatory mechanisms
Identification of trafficking vulnerabilities in disease states
Therapeutic targeting strategies based on conserved domains
Comparative genomics framework:
Multi-species alignment of trafficking components
Gain/loss patterns across evolutionary history
Correlation between trafficking complexity and organismal complexity
Identification of lineage-specific adaptations
Structural biology insights:
Conservation of catalytic mechanisms
Diversification of regulatory domains
Evolution of protein-protein interaction interfaces
Structure-function relationships across species
Developmental biology connections:
Role of trafficking in cellular differentiation
Conservation of trafficking dynamics during development
Species-specific adaptations in specialized cell types
Evolutionary innovations in trafficking regulation
Quantitative evolutionary study approaches:
Evolutionary rate analysis of trafficking components
Positive selection signatures in trafficking pathways
Co-evolution networks identifying functional relationships
Ancient duplication events leading to specialization
Phylogenetic distribution of trafficking features:
| Trafficking Feature | S. pombe | S. cerevisiae | C. elegans | D. melanogaster | Vertebrates |
|---|---|---|---|---|---|
| TBC domain diversity | Limited | Moderate | Expanded | Expanded | Highly expanded |
| Rab GTPase specificity | Broad | Narrow | Mixed | Mixed | Highly specific |
| Regulatory phosphorylation | Present | Present | Complex | Complex | Multiple layers |
| Tissue-specific isoforms | None | None | Limited | Present | Abundant |
| Interaction with cytoskeleton | Basic | Basic | Elaborate | Elaborate | Highly complex |
| Disease associations | N/A | N/A | Several | Many | Numerous |