GGPPS9 Antibody

What is GGPPS9 and why is it important in plant biological research?

GGPPS9 (Geranylgeranyl pyrophosphate synthase 9) is a critical enzyme in the isoprenoid biosynthetic pathway in plants, particularly in Arabidopsis thaliana. This enzyme catalyzes the condensation of isopentenyl diphosphate (IPP) with allylic diphosphates, which is an essential and major biosynthetic step in the metabolism of all isoprenoids . The importance of GGPPS9 lies in its role in producing geranylgeranyl diphosphate (GGPP), a precursor for various isoprenoid compounds including chlorophylls, carotenoids, gibberellins, and plastoquinones—all vital for plant growth, development, and stress responses.

What applications are GGPPS9 antibodies typically used for in plant molecular biology?

GGPPS9 antibodies are primarily utilized for:

• Western blotting (WB): To detect and quantify GGPPS9 protein expression levels in plant tissues under various experimental conditions .

• Enzyme-linked immunosorbent assay (ELISA): For quantitative measurement of GGPPS9 protein in complex biological samples .

• Investigating constitutive expression patterns: As demonstrated with other enzymes in similar pathways, antibodies can help determine expression profiles across different developmental stages of plants .

• Protein localization studies: To determine subcellular localization of GGPPS9 through immunocytochemistry techniques.

• Protein interaction studies: To identify potential protein partners that interact with GGPPS9 through co-immunoprecipitation followed by mass spectrometry analysis.

How does the GGPPS9 antibody differ from antibodies against other prenyltransferases?

GGPPS9 antibody specifically targets the Arabidopsis thaliana GGPPS9 protein (UniProt ID: Q9LUE1) . It differs from antibodies against other prenyltransferases in several key aspects:

• Epitope specificity: GGPPS9 antibody recognizes specific epitopes on the GGPPS9 protein that are not present in other prenyltransferases like FPPS (farnesyl pyrophosphate synthase) or OPPS (octaprenyl pyrophosphate synthase) .

• Cross-reactivity profile: While some structural similarities exist among prenyltransferases, a properly validated GGPPS9 antibody exhibits minimal cross-reactivity with other members of this enzyme family.

• Applications in comparative studies: GGPPS9 antibody can be used alongside antibodies against other prenyltransferases to investigate the differential expression and regulation of these enzymes in response to various stimuli or developmental stages.

• Species reactivity: The GGPPS9 antibody is specifically raised against and tested for reactivity with Arabidopsis thaliana, whereas antibodies against other prenyltransferases may target homologs from different plant species or even other organisms .

What are the optimal conditions for using GGPPS9 antibody in Western blot applications for detecting low abundance protein in plant extracts?

For detecting low abundance GGPPS9 protein in plant extracts by Western blot, researchers should optimize several parameters:

• Sample preparation:

  • Use a buffer containing protease inhibitors to prevent degradation

  • Enrich target protein through subcellular fractionation if GGPPS9 is known to localize in specific organelles

  • Consider using plant-specific protein extraction buffers containing 50mM Tris-HCl (pH 7.5), 150mM NaCl, 1% Triton X-100, and 0.2% SDS

• Protein loading and separation:

  • Load 50-100μg of total protein per lane

  • Use 10-12% polyacrylamide gels for optimal resolution

  • Include a gradient gel if protein molecular weight is uncertain

• Transfer conditions:

  • Use PVDF membranes rather than nitrocellulose for enhanced protein binding

  • Perform wet transfer at 30V overnight at 4°C for better transfer efficiency of low abundance proteins

• Blocking and antibody incubation:

  • Block with 5% non-fat dry milk in TBST (TBS + 0.1% Tween-20) for 2 hours at room temperature

  • Dilute primary GGPPS9 antibody at 1:1000 to 1:2000 in blocking buffer

  • Incubate with primary antibody overnight at 4°C with gentle rocking

  • For detection, use high-sensitivity ECL substrates or consider fluorescent secondary antibodies for quantitative analysis

• Signal enhancement strategies:

  • Consider using signal enhancing systems or tyramide signal amplification for extremely low abundance targets

  • Optimize exposure times with incremental exposures (30 seconds, 1 minute, 5 minutes, etc.)

This methodology should significantly improve detection limits for GGPPS9 protein even when expressed at low levels .

How can researchers validate the specificity of GGPPS9 antibody to ensure reliable experimental results?

Thorough validation of GGPPS9 antibody specificity is crucial for trustworthy experimental results. Consider implementing the following comprehensive validation protocol:

• Positive and negative controls:

  • Positive control: Recombinant Arabidopsis thaliana GGPPS9 protein

  • Negative control: Pre-immune serum from the same animal species used to generate the antibody

  • Tissue-specific controls: Samples from tissues known to express high or low levels of GGPPS9

• Genetic validation:

  • Compare signal between wild-type plants and GGPPS9 knockout/knockdown mutants

  • Use GGPPS9 overexpression lines to confirm signal enhancement

  • Complementation analysis to verify specificity

• Peptide competition assay:

  • Pre-incubate the antibody with excess immunizing peptide/recombinant protein

  • Observe elimination or significant reduction of specific signal

  • Include non-competing peptide control

• Cross-reactivity assessment:

  • Test reactivity against closely related GGPPS isoforms (GGPPS1-8, 10-12)

  • Evaluate against other prenyltransferases (FPPS, GGPPS) to confirm specificity

  • Test across different plant species to determine species cross-reactivity

• Technical validation:

  • Compare results from multiple detection methods (Western blot, ELISA, immunofluorescence)

  • Verify via orthogonal methods (mass spectrometry, RNA expression data)

  • Confirm single band of expected molecular weight (approximately 37-40 kDa for GGPPS9)

This comprehensive validation approach ensures that experimental findings are specifically attributable to GGPPS9 and not to cross-reactivity with other proteins .

How can researchers interpret contradictory results between antibody-based detection of GGPPS9 and gene expression data?

When faced with contradictions between GGPPS9 protein levels (detected via antibody) and gene expression data, consider these systematic approaches:

• Temporal regulation analysis:

  • Protein expression often lags behind mRNA expression

  • Conduct time-course experiments with sampling at multiple timepoints

  • Compare half-lives of GGPPS9 mRNA versus protein using actinomycin D (transcription inhibitor) and cycloheximide (translation inhibitor)

• Post-transcriptional regulation assessment:

  • Investigate microRNA regulation of GGPPS9 mRNA

  • Examine alternative splicing events using RT-PCR with isoform-specific primers

  • Assess mRNA stability through actinomycin D chase experiments

• Post-translational modification and regulation:

  • Check for protein modifications (phosphorylation, ubiquitination) that might affect antibody recognition

  • Analyze protein stability and turnover rates using cycloheximide chase experiments

  • Investigate compartmentalization or sequestration of GGPPS9 protein that might affect extraction efficiency

• Technical considerations:

  • Verify primer specificity for qRT-PCR experiments

  • Compare antibody detection across multiple antibody clones or lots

  • Evaluate extraction methods for both RNA and protein for efficiency and bias

• Biological interpretation:

  • Calculate protein-to-mRNA ratios across conditions to identify regulatory patterns

  • Consider functional redundancy among GGPPS family members

  • Evaluate translational efficiency using polysome profiling

This systematic approach aids in resolving apparent contradictions and often leads to discovery of novel regulatory mechanisms controlling GGPPS9 expression .

What factors should be considered when designing experiments to investigate GGPPS9 protein-protein interactions using antibody-based techniques?

When investigating GGPPS9 protein-protein interactions, consider these important experimental design factors:

• Interaction preservation strategies:

  • Use mild lysis conditions (150-300mM NaCl, 0.5-1% NP-40 or Triton X-100)

  • Add protein cross-linkers (DSP, formaldehyde) to capture transient interactions

  • Include phosphatase inhibitors (sodium orthovanadate, sodium fluoride) and protease inhibitors

  • Consider native extraction conditions to maintain protein complexes

• Co-immunoprecipitation optimization:

  • Compare direct antibody coupling to beads versus protein A/G approaches

  • Test different antibody concentrations (1-5μg per immunoprecipitation)

  • Optimize wash stringency to balance between specificity and sensitivity

  • Include appropriate controls (pre-immune serum, IgG control, input sample)

• Confirmation techniques:

  • Validate interactions using reciprocal co-immunoprecipitation

  • Implement proximity ligation assay (PLA) for in situ interaction verification

  • Consider bimolecular fluorescence complementation (BiFC) for in vivo confirmation

  • Use mass spectrometry to identify novel interaction partners

• Functional validation:

  • Design experiments to test biological significance of identified interactions

  • Create and test interaction-deficient mutants

  • Analyze phenotypic consequences when disrupting specific interactions

  • Correlate interaction patterns with enzymatic activity measurements

• Data analysis considerations:

  • Quantify relative binding strengths across different conditions

  • Apply statistical analysis to replicate experiments (minimum n=3)

  • Consider stoichiometry of interactions when interpreting results

  • Account for potential binding interference from the antibody itself

This comprehensive approach will enhance the reliability and biological relevance of GGPPS9 protein interaction studies .

How can deep learning approaches be applied to improve antibody-antigen recognition prediction for GGPPS9-related research?

Deep learning approaches offer powerful tools to enhance GGPPS9 antibody research through improved prediction of antibody-antigen interactions:

• Sequence-based prediction models:

  • Implement transformer-based protein language models (like ESM-1b) to predict epitope regions on GGPPS9

  • Apply algorithms similar to DyAb for sequence-based antibody design targeting specific GGPPS9 epitopes

  • Utilize LSTM-based networks to capture local sequence patterns that influence antibody binding

• Structure-based prediction frameworks:

  • Apply AlphaFold2-based approaches to model GGPPS9 antibody-antigen complexes

  • Implement Graph Convolutional Networks (GCNs) to extract local features around target residues

  • Use Attention-Based Bidirectional Long Short-Term Memory (Att-BLSTM) networks to capture global features of GGPPS9

• Integrated experimental-computational pipelines:

  • Design deep mutational scanning (DMS) experiments to comprehensively map GGPPS9 epitopes

  • Feed experimental binding data back into models for iterative improvement

  • Combine multiple prediction tools (similar to AF2Complex) for enhanced accuracy

• Applications in GGPPS9 research:

  • Predict cross-reactivity with other GGPPS isoforms

  • Design higher-affinity antibody variants for improved detection sensitivity

  • Model the effect of post-translational modifications on antibody recognition

• Performance evaluation metrics:

  • Area Under the Receiver Operating Characteristic curve (AUROC)

  • Precision-recall curves for imbalanced datasets

  • Binding affinity prediction error (compared to experimental values)

  • Cross-validation across multiple GGPPS protein variants

This integration of deep learning with GGPPS9 antibody research can significantly accelerate discovery while reducing experimental costs and time .

What strategies can address poor signal-to-noise ratio when using GGPPS9 antibody in immunoblotting experiments?

When encountering poor signal-to-noise ratios with GGPPS9 antibody in immunoblotting, implement these systematic troubleshooting strategies:

• Antibody optimization:

  • Titrate antibody concentration (test dilutions from 1:500 to 1:5000)

  • Reduce primary antibody incubation time or temperature

  • Try different antibody diluents (TBST with 1-5% BSA, casein, or non-fat dry milk)

  • Include 0.1-0.5% Tween-20 or 0.1% Triton X-100 in antibody diluents to reduce non-specific binding

• Blocking optimization:

  • Test different blocking agents (5% BSA, 5% non-fat dry milk, commercial blocking buffers)

  • Increase blocking time (2-16 hours) or temperature

  • Add 0.1-0.5% Tween-20 to blocking buffer

  • Consider adding 5% normal serum from the secondary antibody host species

• Washing protocol enhancement:

  • Increase wash volume (use at least 10× membrane volume)

  • Extend washing time and number of washes (5× 10 minutes)

  • Use more stringent wash buffers (increase Tween-20 to 0.1-0.5%)

  • Implement progressive washing (start with higher salt or detergent and decrease)

• Sample preparation improvements:

  • Include additional protease inhibitors in lysis buffer

  • Perform subcellular fractionation to enrich for GGPPS9-containing compartments

  • Pre-clear lysates with Protein A/G beads to remove components that bind non-specifically

  • Precipitate proteins with TCA/acetone to remove interfering compounds

• Detection system considerations:

  • Switch between different detection methods (chemiluminescence, fluorescence)

  • Try HRP-conjugated protein A/G instead of species-specific secondary antibodies

  • Use signal enhancing systems for weak signals

  • Optimize exposure times or detector sensitivity settings

This systematic approach allows identification of the specific factors affecting signal-to-noise ratio in GGPPS9 antibody applications .

How can researchers troubleshoot inconsistent GGPPS9 antibody performance across different experimental batches?

To address batch-to-batch inconsistency in GGPPS9 antibody performance:

• Antibody storage and handling:

  • Aliquot antibodies into single-use volumes to avoid freeze-thaw cycles

  • Store according to manufacturer recommendations (-20°C or -80°C)

  • Monitor storage conditions (temperature logs, avoid temperature fluctuations)

  • Check for visible precipitates or contamination before use

• Standardization protocols:

  • Implement internal control samples in every experiment

  • Create standard curves using recombinant GGPPS9 protein

  • Normalize signals to housekeeping proteins in the same samples

  • Maintain detailed records of antibody lot numbers and performance characteristics

• Antibody validation per batch:

  • Test each new antibody lot against a reference lot

  • Verify specificity using peptide competition assays

  • Confirm detection limit and linear range for quantification

  • Document specific optimal conditions for each lot

• Technical considerations:

  • Standardize protein extraction and sample preparation protocols

  • Use automated systems where possible to reduce operator variability

  • Implement quality control checkpoints throughout experiments

  • Consider multiple technical replicates for critical experiments

• Advanced solutions:

  • Consider pooling antibody lots for critical long-term studies

  • Develop monoclonal alternatives if polyclonal variability is problematic

  • Implement epitope tagging strategies as alternative approach

  • Consider antibody purification against the immunizing antigen

This systematic approach helps identify and mitigate sources of batch-to-batch variability in antibody performance .

What experimental controls are essential when using GGPPS9 antibody for quantitative protein expression analysis?

A robust experimental design for quantitative GGPPS9 protein expression analysis must include these essential controls:

• Technical validation controls:

  • Positive control: Recombinant GGPPS9 protein or extract from tissue known to express GGPPS9

  • Negative control: Extract from GGPPS9 knockout tissue or cells

  • Loading control: Detection of housekeeping proteins (e.g., actin, GAPDH, tubulin)

  • Dilution series: Serial dilutions of samples to confirm linear detection range

• Antibody validation controls:

  • Primary antibody omission: To assess non-specific binding of secondary antibody

  • Isotype control: Non-specific IgG from same species as GGPPS9 antibody

  • Pre-immune serum control: Serum collected before immunization

  • Peptide competition: Pre-incubation of antibody with immunizing peptide/protein

• Quantification controls:

  • Standard curve: Purified recombinant GGPPS9 at known concentrations

  • Internal reference: Spiked-in control protein at known concentration

  • Normalization controls: Multiple housekeeping proteins to verify consistent loading

  • Technical replicates: Minimum of three per biological sample

• Signal detection controls:

  • Dynamic range verification: Multiple exposure times to ensure signal is within linear range

  • Background subtraction validation: Multiple background regions to ensure accurate subtraction

  • System suitability test: Regular calibration of imaging equipment

  • Inter-assay calibrator: Common sample run across multiple experiments for normalization

• Data analysis controls:

  • Replicate variability assessment: Statistical analysis of technical and biological replicates

  • Outlier identification criteria: Predetermined statistical methods for identifying outliers

  • Blinded quantification: Analyst unaware of sample identity during quantification

  • Alternative method verification: Correlation with orthogonal method (e.g., mass spectrometry)

This comprehensive control framework ensures reliable and reproducible quantitative analysis of GGPPS9 protein expression .

How might next-generation antibody engineering techniques be applied to develop enhanced GGPPS9 antibodies?

Advanced antibody engineering technologies offer promising avenues for developing next-generation GGPPS9 antibodies with superior properties:

• Phage display optimization:

  • Selection against multiple combinations of GGPPS9 ligand conformations

  • Implementation of customized specificity profiles through machine learning-guided selection parameters

  • Negative selection strategies against other GGPPS isoforms to enhance specificity

  • Deep sequencing of phage populations to identify rare high-affinity binders

• Structure-guided engineering:

  • Implement computational design tools like AF2Complex to predict antibody-GGPPS9 interactions

  • Apply deep learning technologies similar to DyAb for sequence-based antibody design

  • Engineer complementarity-determining regions (CDRs) for enhanced affinity and specificity

  • Develop structure-stabilizing modifications to improve antibody shelf-life

• Novel antibody formats:

  • Single-domain antibodies (nanobodies) for improved tissue penetration

  • Bispecific antibodies targeting GGPPS9 and interaction partners simultaneously

  • Intrabodies optimized for intracellular expression and recognition

  • Fragment-based approaches (Fab, scFv) for applications requiring smaller probes

• Recombinant optimization strategies:

  • Humanize antibodies for potential therapeutic applications

  • Remove potential post-translational modification sites that affect consistency

  • Modify constant regions to enhance detection or purification properties

  • Engineer increased solubility and thermal stability for harsh condition applications

• Conjugation technologies:

  • Site-specific conjugation methods for consistent signal-to-antibody ratios

  • Development of oligonucleotide-conjugated antibodies for single-cell analysis

  • Proximity-based labeling for identifying transient interaction partners

  • Cleavable linkers for controlled release applications

These advanced engineering approaches could significantly enhance GGPPS9 antibody performance across research applications .

What role might GGPPS9 antibodies play in understanding plant responses to environmental stresses?

GGPPS9 antibodies offer powerful tools for investigating plant stress responses through these research approaches:

• Abiotic stress response profiling:

  • Quantify GGPPS9 protein expression changes under drought, salinity, temperature, and light stress

  • Correlate GGPPS9 levels with isoprenoid-derived protective compound production

  • Track subcellular relocalization of GGPPS9 during stress responses

  • Investigate post-translational modifications triggered by stress signals

• Stress response pathway analysis:

  • Use co-immunoprecipitation with GGPPS9 antibodies to identify stress-induced protein interactions

  • Implement chromatin immunoprecipitation (ChIP) to study transcription factors regulating GGPPS9

  • Analyze GGPPS9 complex formation under normal versus stress conditions

  • Investigate differential phosphorylation states using phospho-specific antibodies

• Developmental adaptation studies:

  • Examine tissue-specific GGPPS9 expression patterns during stress adaptation

  • Compare wild-type versus stress-tolerant plant varieties for GGPPS9 regulation

  • Track GGPPS9 protein dynamics during recovery phases post-stress

  • Correlate GGPPS9 levels with developmental stage-specific stress responses

• Applied research implications:

  • Screen germplasm collections for favorable GGPPS9 expression patterns

  • Evaluate transgenic lines with modified GGPPS9 expression for stress tolerance

  • Develop GGPPS9-based biomarkers for early stress detection

  • Assess impact of climate change-relevant stresses on GGPPS9-dependent pathways

• Multi-omics integration:

  • Correlate GGPPS9 protein levels with metabolomic profiles under stress

  • Integrate transcriptomic, proteomic, and metabolomic data using GGPPS9 as a focal point

  • Model isoprenoid flux changes during stress responses

  • Identify regulatory nodes controlling GGPPS9 expression and activity

This multifaceted approach using GGPPS9 antibodies can provide crucial insights into plant stress response mechanisms .

How can researchers optimize GGPPS9 antibody-based detection in complex plant tissues with high levels of interfering compounds?

To overcome detection challenges in complex plant tissues:

• Sample preparation optimization:

  • Implement tissue-specific extraction buffers with appropriate detergents and ionic strength

  • Add polyvinylpolypyrrolidone (PVPP) or polyvinylpyrrolidone (PVP) to remove phenolic compounds

  • Utilize TCA/acetone precipitation to eliminate interfering metabolites

  • Include specific additives to neutralize known interferents:

    • 2-mercaptoethanol for disulfide reduction

    • EDTA for chelating metal ions

    • Protease inhibitor cocktails optimized for plant tissues

• Chromatographic pre-fractionation:

  • Implement size exclusion chromatography to separate GGPPS9 from small molecule interferents

  • Apply ion exchange fractionation to reduce sample complexity

  • Consider hydroxyapatite chromatography for partial purification

  • Use affinity-based enrichment with recombinant protein A/G or substrate analogs

• Detection method adaptation:

  • Compare sandwich ELISA versus direct ELISA formats for optimal signal-to-noise ratio

  • Implement proximity ligation assay for increased specificity in tissue sections

  • Consider fluorescence-based detection with spectral unmixing to distinguish from autofluorescence

  • Apply MALDI imaging mass spectrometry for spatial verification of antibody-based detection

• Signal enhancement and interference reduction:

  • Implement signal amplification systems (tyramide signal amplification, rolling circle amplification)

  • Apply computational background correction algorithms specific to plant tissue autofluorescence

  • Use reference wavelength measurements to correct for non-specific absorbance

  • Consider ratiometric approaches with dual-labeled detection systems

• Validation strategies:

  • Spike-in experiments with recombinant GGPPS9 to assess recovery efficiency

  • Compare multiple antibody clones targeting different GGPPS9 epitopes

  • Correlate protein measurements with enzymatic activity assays

  • Implement isotope dilution mass spectrometry as orthogonal verification method

This comprehensive approach addresses the specific challenges of plant tissue analysis .

How can GGPPS9 antibody be effectively used in comparative studies across different plant species and evolutionary lineages?

For effective cross-species GGPPS9 antibody applications:

• Epitope conservation analysis:

  • Conduct multiple sequence alignment of GGPPS9 homologs across target plant species

  • Identify highly conserved regions as potential universal epitopes

  • Determine species-specific variations that might affect antibody recognition

  • Generate phylogenetic trees of GGPPS proteins to predict antibody cross-reactivity

• Validation across species:

  • Test antibody recognition using recombinant GGPPS9 proteins from multiple species

  • Perform Western blot analysis on tissue extracts from diverse plant lineages

  • Include positive controls from Arabidopsis thaliana (the immunogen species)

  • Quantify relative affinity across species using surface plasmon resonance or ELISA titration

• Optimization for cross-species applications:

  • Develop customized extraction protocols for different plant tissues and species

  • Adjust antibody concentration based on empirically determined species-specific affinities

  • Modify blocking and washing conditions for problematic species

  • Consider using cocktails of antibodies targeting different conserved epitopes

• Experimental design for evolutionary studies:

  • Include representatives from major plant lineages (bryophytes, lycophytes, gymnosperms, angiosperms)

  • Standardize sampling by tissue type, developmental stage, and environmental conditions

  • Implement internal normalization standards for cross-species comparisons

  • Design sampling to address specific evolutionary hypotheses about GGPPS function

• Data analysis and interpretation:

  • Apply normalization methods to account for interspecific variations in protein extraction efficiency

  • Develop correction factors based on epitope conservation percentages

  • Consider relative rather than absolute quantification for cross-species comparisons

  • Integrate with genomic, transcriptomic, and metabolomic data for comprehensive evolutionary analysis

This approach enables meaningful comparative studies of GGPPS9 across plant evolutionary lineages .

What potential exists for developing GGPPS9 antibody-based biosensors for real-time monitoring of isoprenoid pathway activity in plant systems?

The development of GGPPS9 antibody-based biosensors presents exciting opportunities for real-time monitoring of isoprenoid pathway dynamics:

• FRET-based biosensor design:

  • Engineer split fluorescent protein constructs fused to GGPPS9-binding antibody fragments

  • Develop FRET pairs with GGPPS9 antibody fragments and substrate analogs

  • Design conformation-sensitive sensors that respond to GGPPS9 activation states

  • Create ratiometric sensors to normalize for expression level variations

• Cellular implementation strategies:

  • Optimize antibody fragment expression for plant cell compatibility

  • Develop targeting sequences for appropriate subcellular compartmentalization

  • Create stable transgenic lines expressing calibrated biosensor systems

  • Design inducible expression systems for temporal control of biosensor deployment

• Detection system development:

  • Implement microfluidic platforms for continuous monitoring in plant tissue extracts

  • Develop plant-compatible imaging systems for whole-tissue visualization

  • Create field-deployable detection systems for agricultural applications

  • Design multiplexed detection for simultaneous monitoring of multiple pathway components

• Validation and calibration:

  • Correlate biosensor signals with mass spectrometry-based metabolite quantification

  • Verify response dynamics using controlled perturbation of isoprenoid pathways

  • Establish dose-response curves using inhibitors and activators

  • Implement machine learning for signal interpretation and pathway state prediction

• Advanced applications:

  • Real-time screening of environmental stress responses

  • High-throughput phenotyping of genetic variants affecting isoprenoid metabolism

  • Dynamic visualization of metabolic flux through branched pathways

  • Integration with optogenetic tools for pathway interrogation

This innovative approach would transform our ability to study isoprenoid metabolism dynamics in living plant systems .

Comparative analysis of GGPPS9 antibody performance in different applications

This comprehensive performance analysis is based on experimental data and can guide researchers in selecting optimal conditions for their specific applications .

GGPPS isoform cross-reactivity profile of GGPPS9 antibody

This cross-reactivity profile helps researchers interpret results when working with plant samples expressing multiple GGPPS isoforms .

Effect of extraction conditions on GGPPS9 antibody detection in plant samples

This data provides guidance for optimizing sample preparation based on specific tissue types and experimental goals .

GGPPS9 expression profile in Arabidopsis thaliana development as detected by antibody-based methods

This expression profile provides a baseline for comparative studies and helps researchers select appropriate developmental stages for GGPPS9 analysis .

Optimization parameters for GGPPS9 antibody-based immunoprecipitation