At5g33355 Antibody

What is At5g33355 and why is it studied in plant research?

At5g33355 encodes a defensin-like (DEFL) family protein in Arabidopsis thaliana, commonly known as mouse-ear cress . Defensin-like proteins generally play crucial roles in plant immune responses, particularly against fungal pathogens, and can also function in plant development and reproduction. Research interest in At5g33355 stems from its differential expression patterns observed during stress responses and developmental stages. This protein has been identified as being downregulated in myb36 mutants, suggesting a potential role in processes regulated by the MYB36 transcription factor, which is known to orchestrate Casparian strip formation in plant roots . Understanding At5g33355 function contributes to our knowledge of plant defense mechanisms and developmental processes.

What sample types are suitable for At5g33355 antibody applications?

At5g33355 antibodies are primarily designed for use with Arabidopsis thaliana tissues and cell cultures. Suitable sample types include:

• Root tissue sections - particularly relevant due to At5g33355's downregulation in myb36 mutants which affect Casparian strip formation

• Leaf tissue samples

• Cell culture extracts derived from Arabidopsis

• Protein extracts from whole plants or specific tissues

When preparing samples, it's important to consider the extracellular localization of the target protein . Standard protein extraction protocols should include steps to effectively isolate extracellular and membrane-associated proteins. For fixed tissue sections, ensure proper antigen retrieval techniques to expose the epitope while maintaining tissue morphology.

What are the basic validation steps required before using At5g33355 antibody?

Before using the At5g33355 antibody in experimental applications, several validation steps are recommended:

• Expression verification: Confirm At5g33355 expression in your sample type using RT-qPCR or existing -omics data. This is particularly important as expression varies across tissues and developmental stages .

• Specificity testing: Run Western blots using positive and negative controls. Wild-type Arabidopsis tissue can serve as a positive control, while tissues from knockout mutants (if available) can serve as negative controls .

• Cross-reactivity assessment: Test the antibody against related defensin-like family proteins to ensure specificity, particularly important given the sequence similarities within this family.

• Titration experiments: Determine optimal antibody concentration for your specific application by testing a range of dilutions.

Implementation of these validation steps ensures reliable and reproducible experimental results while minimizing the risk of false positives or negatives due to antibody non-specificity .

How should I design experiments to study At5g33355 expression patterns in different tissues?

When designing experiments to study At5g33355 expression patterns across different tissues, consider the following comprehensive approach:

• Multi-technique verification: Combine immunohistochemistry (IHC) using the At5g33355 antibody with transcript analysis methods such as RT-qPCR and in situ hybridization. This triangulation approach provides stronger evidence of expression patterns than any single method.

• Developmental staging: Sample tissues at multiple developmental stages, as defensin-like proteins often show stage-specific expression patterns. Based on data from myb36 mutant studies, focus particularly on root developmental stages when Casparian strips are forming .

• Reference gene selection: When performing RT-qPCR, carefully select stable reference genes specific to each tissue type for accurate normalization.

• Tissue fixation optimization: For immunohistochemistry applications, optimize fixation protocols to preserve antigen integrity while maintaining tissue architecture. This is particularly important for extracellular proteins like At5g33355 .

• Microscopy controls: Include fluorescence minus one (FMO) controls and isotype controls in microscopy experiments to account for autofluorescence and non-specific binding.

• Stress treatments: Design experiments to include various stress conditions (biotic and abiotic) to assess whether At5g33355 expression changes in response to environmental challenges.

This methodological framework ensures comprehensive characterization of At5g33355 expression across tissues and conditions, providing insight into its biological function.

What are the optimal protocols for detecting At5g33355 protein using immunoblotting?

For optimal detection of At5g33355 protein using immunoblotting techniques, follow this specialized protocol:

• Sample preparation:

  • Extract proteins using buffers optimized for extracellular proteins (containing 1% Triton X-100 or NP-40)

  • Include protease inhibitors to prevent degradation

  • Enrich for extracellular proteins using subcellular fractionation if low abundance is an issue

• Gel selection and transfer:

  • Use 12-15% polyacrylamide gels (the defensin-like proteins are typically small)

  • Transfer to PVDF membranes (preferred over nitrocellulose for small proteins)

  • Use semi-dry transfer systems with 10-15% methanol in transfer buffer

• Blocking and antibody incubation:

  • Block with 5% BSA (preferred over milk for plant samples)

  • Incubate with primary At5g33355 antibody (CSB-PA668190XA01DOA) at 1:500-1:2000 dilution overnight at 4°C

  • Use fluorescent-labeled secondary antibodies for more quantitative results

• Controls:

  • Include wild-type Arabidopsis extracts as positive control

  • Include samples from knockdown/knockout lines if available

  • Consider running parallel blots with antibodies targeting known co-expressed genes identified in myb36 mutant studies

• Validation approach:

  • Verify band specificity through competition assays with recombinant At5g33355 protein

  • Confirm signal absence in relevant knockout lines or RNAi lines

  • Consider orthogonal approaches for verification as outlined in antibody validation best practices

This protocol accounts for the specific challenges of detecting extracellular plant defensin-like proteins while maximizing specificity and sensitivity.

How can I optimize At5g33355 antibody for flow cytometry applications?

Optimizing At5g33355 antibody for flow cytometry requires specialized approaches for plant cells:

• Protoplast preparation:

  • Start with fresh Arabidopsis tissue and prepare protoplasts using established enzymatic methods

  • Filter cell suspensions through 40μm mesh to remove aggregates

  • Confirm protoplast viability before antibody staining

• Fixation and permeabilization:

  • Use gentle fixation (0.5-2% paraformaldehyde) to preserve cellular architecture

  • For At5g33355 detection, apply mild permeabilization (0.1% saponin) to maintain extracellular epitope accessibility

  • Wash thoroughly between steps to remove residual fixatives

• Antibody titration and validation:

  • Perform antibody titration experiments (1:100 to 1:2000) to determine optimal concentration

  • Include fluorescence minus one (FMO) controls to set accurate gates

  • Validate antibody specificity using approaches described in Figure 3 of search result :

    • Compare staining between wild-type and At5g33355 knockout/knockdown lines

    • Use orthogonal approaches, including transgenic lines expressing fluorescent-tagged At5g33355

    • Validate with cell lines expressing different levels of the target

• Multi-parameter analysis:

  • Include cell-type specific markers to identify relevant populations

  • Use cell tracker dyes to distinguish different genotypes when mixed in the same tube

  • Consider viability dyes to exclude dead cells

• Data interpretation:

  • Use appropriate compensation controls for multi-color panels

  • Apply consistent gating strategies across experiments

  • Analyze expression levels as median fluorescence intensity rather than percent positive

This systematic approach addresses the challenges specific to plant cell flow cytometry while ensuring reliable detection of the At5g33355 protein .

How can I use At5g33355 antibody to investigate its role in Casparian strip formation?

To investigate At5g33355's potential role in Casparian strip formation using its specific antibody, implement this advanced experimental design:

• Co-localization studies with Casparian strip markers:

  • Perform dual immunofluorescence using At5g33355 antibody alongside established Casparian strip markers such as CASP1, CASP2, and CASP3

  • Apply high-resolution confocal microscopy with z-stack acquisition to precisely determine spatial relationships between At5g33355 and Casparian strip components

  • Quantify co-localization using Pearson's or Mander's coefficients

• Developmental time-course analysis:

  • Examine At5g33355 protein localization during sequential stages of root development

  • Correlate At5g33355 expression patterns with the initiation and maturation of Casparian strips

  • Compare timing of At5g33355 expression relative to other genes downregulated in myb36 mutants (e.g., CASP family proteins)

• Functional perturbation experiments:

  • Use RNAi or CRISPR approaches to knockdown/knockout At5g33355 expression

  • Apply lignin-specific stains (e.g., basic fuchsin) to visualize Casparian strip integrity

  • Conduct apoplastic tracer assays using propidium iodide to assess barrier function

  • Measure ion content in shoots to determine if At5g33355 disruption affects nutrient homeostasis

• Protein interaction studies:

  • Perform co-immunoprecipitation experiments using At5g33355 antibody to identify interacting partners

  • Validate interactions through yeast two-hybrid or bimolecular fluorescence complementation assays

  • Look specifically for interactions with known Casparian strip components (CASPs, ESBs) and MYB36-regulated proteins

• Transcriptomic correlation analysis:

  • Compare expression profiles of At5g33355 with other genes affected in myb36 mutants using published microarray data

  • Identify potential co-regulated genes that might function in the same pathway

This comprehensive approach leverages the specific antibody to elucidate both the localization and function of At5g33355 in relation to Casparian strip development and function.

What approaches should I use to troubleshoot non-specific binding of At5g33355 antibody?

When encountering non-specific binding issues with the At5g33355 antibody, implement this systematic troubleshooting approach:

• Cross-reactivity analysis:

  • Perform BLAST searches to identify proteins with sequence similarity to At5g33355

  • Pay special attention to other defensin-like family proteins that share structural motifs

  • Test antibody reactivity against recombinant versions of potential cross-reactive proteins

  • Use epitope mapping to determine if shared domains are causing cross-reactivity

• Blocking optimization:

  • Test alternative blocking agents (BSA, normal serum, commercial blockers)

  • Increase blocking time and concentration

  • Add 0.1% Tween-20 to washing buffers to reduce hydrophobic interactions

  • Consider pre-adsorption of antibody with plant extracts from At5g33355 knockout lines

• Sample preparation refinement:

  • Optimize protein extraction methods specifically for extracellular proteins

  • Include additional washing steps when working with plant tissues that contain high phenolic compounds

  • Test different fixation methods for immunohistochemistry (paraformaldehyde vs. methanol)

  • Adjust antigen retrieval protocols to optimize epitope exposure while minimizing non-specific binding

• Advanced validation strategies:

  • Apply the validation framework outlined in Figure 3 of search result

  • Use genetic approaches (knockdown/knockout) to confirm specificity

  • Compare multiple antibody clones targeting different epitopes of At5g33355

  • Implement orthogonal detection methods to verify antibody specificity

• Titration and incubation optimization:

  • Conduct systematic dilution series to identify optimal antibody concentration

  • Test different incubation temperatures and times

  • Evaluate the effect of adding detergents or carrier proteins to the antibody diluent

  • Consider using Fab fragments instead of whole IgG for reduced background

This comprehensive troubleshooting strategy addresses multiple potential sources of non-specific binding while maintaining detection sensitivity for the target protein .

How can I integrate At5g33355 antibody data with transcriptomic and proteomic approaches?

To effectively integrate At5g33355 antibody data with transcriptomic and proteomic approaches, implement this multi-omics strategy:

• Correlation analysis between protein and transcript levels:

  • Perform parallel quantitative western blots using At5g33355 antibody and RT-qPCR across multiple conditions

  • Calculate Pearson correlation coefficients between protein and mRNA levels

  • Investigate potential post-transcriptional regulation mechanisms if discrepancies exist

  • Create integrative visualizations comparing protein vs. transcript expression across tissues or treatments

• Spatial-temporal mapping of expression:

  • Combine immunohistochemistry data with in situ hybridization or single-cell RNA-seq

  • Create computational models that integrate protein localization with transcript distribution

  • Identify potential regulatory elements by correlating protein localization with transcription factor binding sites

• Network analysis integration:

  • Construct protein-protein interaction networks using co-immunoprecipitation with At5g33355 antibody followed by mass spectrometry

  • Integrate interaction data with co-expression networks derived from transcriptomic studies

  • Focus on connections with other proteins downregulated in myb36 mutants (CASPs, ESBs)

  • Use network visualization tools to identify functional modules and key regulatory hubs

• Multi-omics data integration framework:

  • Apply dimensionality reduction techniques (PCA, t-SNE) to integrated datasets

  • Implement machine learning approaches to identify patterns across multiple data types

  • Develop predictive models for At5g33355 function based on integrated datasets

  • Validate model predictions with targeted experiments using the antibody

• Perturbation response profiling:

  • Compare changes in At5g33355 protein levels (antibody-based detection) with transcriptomic responses under various stresses

  • Create comprehensive tables documenting concordant and discordant responses across omics layers

  • Identify potential mechanisms of regulation by examining the dynamics of response timing

This integrated approach leverages the specificity of antibody-based detection while providing a systems-level understanding of At5g33355 function in the context of broader cellular networks .

How should I analyze flow cytometry data using At5g33355 antibody for plant cell populations?

Flow cytometry data analysis using At5g33355 antibody requires specialized approaches for plant cell populations:

• Gating strategy optimization:

  • Begin with forward/side scatter to identify intact protoplasts

  • Apply stringent doublet discrimination to ensure single-cell analysis

  • Use viability dyes to exclude dead or damaged cells

  • Implement hierarchical gating to identify specific cell populations (e.g., root endodermal cells) using established markers

  • Set positive gates using fluorescence minus one (FMO) controls

• Expression quantification methods:

  • Report median fluorescence intensity (MFI) rather than percentage positive cells

  • Calculate signal-to-noise ratio between specific staining and background

  • Normalize expression to account for autofluorescence common in plant cells

  • When comparing multiple samples, use standardized beads for instrument calibration

  • Consider analyzing data using density plots rather than histograms for better visualization

• Comparative analysis framework:

  • When comparing wild-type and mutant populations:

    • Use cell tracker dyes to analyze mixed populations in the same tube

    • Apply appropriate statistical tests (t-test for two conditions, ANOVA for multiple conditions)

    • Generate quantitative data tables showing MFI values with statistical significance indicators

  • For developmental time-course studies, use curve-fitting approaches to model expression changes

• Cell heterogeneity assessment:

  • Apply computational approaches to identify subpopulations based on At5g33355 expression

  • Consider viSNE or UMAP dimensionality reduction for visualizing heterogeneity

  • Use FlowSOM or similar algorithms for automated population identification

  • Correlate At5g33355 expression with other measured parameters

• Data visualization standards:

  • Present raw data as dot plots with clear indication of gating boundaries

  • Include representative histograms showing expression distribution

  • Generate heatmaps for comparing expression across multiple conditions

  • Include all necessary controls in supplementary materials

This specialized analytical framework addresses the unique challenges of plant cell flow cytometry while maximizing the information obtained from At5g33355 antibody staining .

What strategies can resolve contradictory results between antibody detection and transcript levels of At5g33355?

When faced with contradictory results between At5g33355 antibody detection and transcript levels, implement this systematic resolution strategy:

• Temporal dynamics investigation:

  • Conduct time-course experiments to identify potential delays between transcription and translation

  • Sample at multiple timepoints to capture the dynamic relationship between mRNA and protein

  • Create comprehensive data tables showing transcript vs. protein levels at each timepoint

  • Consider protein half-life determination using cycloheximide chase experiments

• Post-transcriptional regulation assessment:

  • Examine potential microRNA targeting of At5g33355 using computational prediction tools

  • Investigate RNA-binding protein interactions that might affect translation efficiency

  • Consider analyzing polysome association to determine translation rates

  • Evaluate protein degradation rates in different conditions using chase experiments

• Technical validation approach:

  • Verify antibody specificity using the approaches outlined in Figure 3 of search result :

    • Test in knockout/knockdown systems

    • Use orthogonal detection methods

    • Consider epitope availability in different sample preparation methods

  • Validate RT-qPCR results with multiple primer pairs and reference genes

  • Sequence the target region to confirm absence of mutations affecting antibody binding

• Alternative splicing and protein processing analysis:

  • Investigate potential alternative splicing events affecting the antibody epitope

  • Consider post-translational modifications that might affect antibody recognition

  • Examine protein processing events (e.g., signal peptide cleavage) in defensin-like proteins

  • Use mass spectrometry to identify actual protein forms present in samples

• Biological context interpretation:

  • Consider subcellular localization effects - antibody may detect only certain pools of protein

  • Evaluate tissue-specific factors that might affect translation efficiency

  • Examine potential regulatory elements in the 5' and 3' UTRs that might influence translation

  • Consider environmental factors that might differentially affect transcription vs. translation

This comprehensive approach systematically addresses potential sources of discrepancy, recognizing that differences between transcript and protein levels can reflect important biological regulation rather than technical artifacts .

How can I quantitatively analyze At5g33355 protein expression in relation to Casparian strip development?

For quantitative analysis of At5g33355 protein expression in relation to Casparian strip development, implement this advanced analytical framework:

• Multi-parameter image analysis pipeline:

  • Acquire high-resolution confocal z-stacks of root sections co-stained for At5g33355 and Casparian strip markers

  • Develop automated image segmentation protocols to identify cell boundaries, nuclei, and Casparian strip domains

  • Quantify At5g33355 signal intensity within defined subcellular compartments

  • Measure co-localization coefficients with established Casparian strip proteins (CASPs, ESBs)

  • Create distance maps to quantify At5g33355 proximity to developing Casparian strips

• Developmental stage correlation analysis:

  • Define quantitative metrics for Casparian strip developmental stages:

    • Measure lignin deposition using fluorescent indicators

    • Quantify barrier function using apoplastic tracer penetration assays

    • Assess suberin deposition timing and extent

  • Correlate At5g33355 protein levels with these developmental markers

  • Apply regression analysis to determine predictive relationships

  • Generate comprehensive data tables showing protein expression vs. developmental metrics

• Comparative expression analysis in genetic backgrounds:

  • Quantify At5g33355 protein expression in wild-type, myb36 mutants, and other relevant genotypes

  • Measure expression relative to other proteins downregulated in myb36 mutants

  • Apply cluster analysis to identify proteins with similar expression patterns

  • Create expression heat maps across genotypes and developmental stages

• Functional correlation metrics:

  • Correlate At5g33355 protein levels with:

    • Barrier function measurements using fluorescent tracers

    • Ion transport efficiency measured by ICP-MS of shoot material

    • Susceptibility to stresses that challenge barrier function

  • Develop multivariate models predicting functional outcomes based on protein expression patterns

• Visualization and statistical analysis:

  • Generate standardized plots showing protein expression across root zones

  • Apply appropriate statistical tests for comparing expression between conditions

  • Use bootstrapping approaches for robust confidence interval determination

  • Implement dimension reduction techniques to visualize complex relationships

This analytical framework provides quantitative insights into the relationship between At5g33355 expression and Casparian strip development, potentially revealing mechanistic connections between this defensin-like protein and barrier formation .

How can At5g33355 antibody be used in chromatin immunoprecipitation (ChIP) studies?

While At5g33355 is a defensin-like protein rather than a transcription factor, its antibody can still be valuable in chromatin studies through the following advanced approaches:

• Protein-DNA interaction studies via ChIP:

  • Use At5g33355 antibody in modified ChIP protocols to identify potential non-canonical associations with chromatin

  • Implement stringent controls including IgG controls and At5g33355 knockout samples

  • Perform sequential ChIP (ChIP-reChIP) to examine potential co-localization with known transcription factors involved in defensin regulation

  • Apply both ChIP-qPCR for targeted analysis and ChIP-seq for genome-wide binding profiles

  • Focus analysis on promoter regions of genes co-regulated with At5g33355 in myb36 mutants

• Chromatin architecture studies:

  • Combine At5g33355 antibody staining with fluorescence in situ hybridization (FISH) to visualize spatial relationships between the protein and specific genomic loci

  • Use proximity ligation assays (PLA) to detect interactions between At5g33355 and chromatin-associated proteins

  • Apply microscopy-based approaches to examine nuclear localization during specific developmental or stress conditions

  • Quantify changes in nuclear localization patterns across different cell types and conditions

• Proteomics approaches:

  • Perform antibody-based pulldowns followed by mass spectrometry to identify potential nuclear interaction partners

  • Use cellular fractionation to quantify distribution between nuclear and cytoplasmic compartments

  • Apply APEX2 proximity labeling with At5g33355 fusion proteins to identify proteins in close proximity within nuclear compartments

  • Validate interactions using reciprocal co-immunoprecipitation with antibodies against identified partners

• Integration with epigenomic data:

  • Correlate At5g33355 binding patterns with histone modifications and DNA methylation profiles

  • Generate integrated genomic views showing protein binding in relation to chromatin states

  • Develop computational models predicting At5g33355 association based on chromatin features

  • Create comprehensive tables showing correlations between binding events and epigenetic marks

This multifaceted approach extends the utility of At5g33355 antibody beyond conventional applications, potentially revealing unexpected roles in nuclear processes despite its primary classification as an extracellular defensin-like protein .

What are the emerging techniques for studying At5g33355 protein interactions in planta?

Emerging techniques for studying At5g33355 protein interactions in planta using specific antibodies include:

• Advanced proximity labeling approaches:

  • Implement BioID or APEX2 fusion systems with At5g33355 to identify proximal interacting partners in their native cellular context

  • Apply TurboID variants optimized for plant systems to achieve rapid labeling under physiological conditions

  • Combine with cell-type specific promoters to map interactomes in relevant tissues

  • Use quantitative proteomics to compare interaction networks across developmental stages or stress conditions

  • Create comprehensive interaction maps comparing At5g33355 with other defensin-like proteins

• Single-molecule imaging techniques:

  • Apply direct stochastic optical reconstruction microscopy (dSTORM) using At5g33355 antibody for super-resolution imaging

  • Implement single-particle tracking to monitor protein dynamics in living cells

  • Use fluorescence correlation spectroscopy (FCS) to measure diffusion characteristics and complex formation

  • Apply fluorescence resonance energy transfer (FRET) to verify direct protein-protein interactions

  • Develop quantitative analysis pipelines for extracting interaction parameters from imaging data

• In situ structural biology approaches:

  • Utilize proximity-dependent protein correlation profiling to map protein complex topologies

  • Apply chemical crosslinking followed by mass spectrometry (XL-MS) to identify interaction interfaces

  • Implement hydrogen-deuterium exchange mass spectrometry (HDX-MS) to identify structural changes upon interaction

  • Use cryo-electron tomography on immunogold-labeled samples to visualize complexes in their native context

  • Generate structural models of protein complexes based on integrated data

• Advanced functional genomics integration:

  • Combine CRISPR-based gene editing with antibody-based detection to study interaction dynamics

  • Implement synthetic biology approaches to rewire interaction networks

  • Use optogenetic tools to control protein associations with temporal precision

  • Apply multiplexed reporter systems to monitor multiple interactions simultaneously

  • Integrate interaction data with phenotypic outcomes through machine learning approaches

• Multi-dimensional interaction mapping:

  • Create protein-protein, protein-lipid, and protein-metabolite interaction networks

  • Map changes in interaction landscape across developmental gradients in roots

  • Identify conditional interactions that occur only under specific stresses

  • Generate comprehensive interaction tables documenting affinity constants, interaction domains, and biological outcomes

These cutting-edge approaches extend beyond conventional co-immunoprecipitation methods, providing spatial, temporal, and functional dimensions to our understanding of At5g33355 interactions in the context of Casparian strip development and plant defense responses .

How can computational approaches enhance the utility of At5g33355 antibody in research?

Computational approaches can significantly enhance the utility of At5g33355 antibody in research through these advanced strategies:

• Epitope prediction and antibody engineering:

  • Apply machine learning algorithms to identify optimal epitopes specific to At5g33355

  • Use structural prediction tools to assess epitope accessibility in native protein conformations

  • Implement computational design of synthetic antibodies with enhanced specificity

  • Generate in silico models of antibody-antigen complexes to guide experimental optimization

  • Create computational workflows for predicting cross-reactivity with related defensin-like proteins

• Image analysis automation:

  • Develop deep learning algorithms for automated detection of At5g33355 signal in microscopy images

  • Implement instance segmentation approaches for single-molecule detection and counting

  • Create computational pipelines for co-localization analysis with Casparian strip markers

  • Apply tracking algorithms to monitor protein dynamics in time-lapse imaging

  • Generate quantitative reports including spatial distribution metrics and statistical analyses

• Multi-omics data integration frameworks:

  • Develop computational models integrating antibody-based protein detection with transcriptomic data

  • Create network visualization tools highlighting At5g33355 interactions within cellular pathways

  • Implement Bayesian approaches to infer causal relationships between At5g33355 and other components

  • Apply tensor factorization methods to integrate multiple data types across experimental conditions

  • Generate comprehensive data tables showing protein-transcript correlations across conditions

• Digital tissue atlases and spatial modeling:

  • Create computational frameworks for mapping At5g33355 expression onto canonical root anatomical models

  • Implement virtual reality visualization of protein localization data across developmental stages

  • Develop predictive models of protein diffusion and transport within tissue contexts

  • Apply agent-based modeling to simulate protein behavior at the Casparian strip

  • Generate interactive data repositories for comparing expression patterns across experiments

• Experimental design optimization:

  • Use power analysis algorithms to determine optimal sample sizes for antibody-based experiments

  • Implement experimental design tools that maximize information gain while minimizing resources

  • Apply machine learning for quality control in antibody-based detection systems

  • Develop automated pipelines for antibody validation according to best practices

  • Create decision support systems for troubleshooting antibody experiments

These computational approaches transform antibody-based research from primarily descriptive to predictive and integrative, maximizing the scientific value derived from At5g33355 antibody applications while ensuring reproducibility and robustness .