SCRL11 Antibody

What is SCRN1 and why is it a target of interest for antibody development?

SCRN1 (Secernin 1) is a 50-kDa cytosolic protein that appears to be involved in the regulation of exocytosis from peritoneal mast cells. It belongs to the secernin family, which includes Secernin 1, Secernin 2, and Secernin 3. While the functions of Secernin 2 and Secernin 3 are not well understood, Secernin 1 has emerged as a novel tumor-associated antigen (TAA) and may serve as a universal marker for different cancer types, including gastric cancer . The protein has a calculated molecular weight of 46 kDa but is typically observed at approximately 50 kDa in experimental analyses .

What are the primary applications for SCRN1 antibodies in research?

SCRN1 antibodies are primarily used in the following applications:

Researchers should titrate the antibody in each testing system to obtain optimal results as performance may be sample-dependent .

How should I design a multicolor flow cytometry experiment using antibodies like SCRN1?

When designing a multicolor flow cytometry experiment with antibodies like SCRN1, carefully consider the following aspects:

• Fluorochrome selection: Match fluorochrome brightness with antigen density:

  • High-density antigens: Use low brightness index fluorophores

  • Mid-range density antigens: Use bright/moderate index fluorophores

  • Low-density antigens: Use bright/very bright index fluorophores

• Panel design strategy:

  • For simple 3-4 color panels: Choose fluorochromes requiring minimal compensation (e.g., FITC, APC, Pacific Blue)

  • For 5-8 color panels: Consider spectral overlap and required compensation

  • Include appropriate controls: fluorescence minus one (FMO), single-color compensation controls

• Validation: Use single-color compensation beads for antibody fluorophores to verify that the antibody-fluorochrome combination is functional under your experimental conditions .

What are the key considerations for using SCRN1 antibodies in Western blotting?

For optimal Western blotting with SCRN1 antibodies, consider these methodological aspects:

• Lysate preparation: Effective detection has been demonstrated in lysates from U-251 MG cell line, A549 cells, HeLa cells, and mouse brain tissue .

• Dilution optimization: Start with the recommended dilution range of 1:200-1:1000, but optimize based on your specific sample and detection system .

• Secondary antibody selection: Anti-rabbit IgG H&L(HRP) at 1:5000 dilution has been successfully used as a secondary antibody for rabbit anti-SCRN1 antibodies .

• Protein loading: Approximately 35μg of protein per lane has proven effective for detection .

• Expected molecular weight: Look for bands at approximately 50 kDa, which is the observed molecular weight for SCRN1 (the calculated molecular weight is 46 kDa) .

• Blocking optimization: Standard blocking protocols with BSA or milk proteins are typically effective, but may require optimization for your specific application.

How can I implement active learning strategies to optimize antibody-antigen binding prediction in my research?

Recent research has shown that active learning (AL) techniques can significantly improve experimental efficiency in antibody-antigen research:

• Model-based strategies:

  • Implement Query-by-Committee (QBC) approaches, where multiple models are trained as committee members, and data instances generating the greatest disagreement are selected for labeling

  • Consider Gradient-Based Uncertainty methods, where the model's gradient is used as an indicator of uncertainty to prioritize uncertain antigens for additional experimental measurements

• Performance benefits:

  • Active learning strategies can reduce the number of required antigen mutant variants by up to 35% compared to random data selection

  • Leading algorithms can accelerate the learning process by approximately 28 steps compared to random baselines

• Implementation approach:

  • Frame the task as a binary classification problem

  • Evaluate model performance using receiver operating characteristic area under the curve (ROC AUC)

  • Compare active learning curves (ALC) to determine the most effective strategy for your specific antibody-antigen system

This approach is particularly valuable when working with library-on-library datasets, where many-to-many antibody-antigen interactions are systematically tested.

How can I validate RNA-based predictions of cell surface markers using antibodies?

To validate RNA-based predictions of cell surface markers, consider implementing the following innovative approach:

• Antibody screening strategy:

  • Label arrayed cells (e.g., PBMCs) with unique cell surface barcodes

  • Add experimental surface markers of interest

  • Pool samples and stain with lineage markers and secondary antibodies targeting experimental antibodies

  • Analyze using high-dimensional cytometry (e.g., CyTOF)

• Data validation approach:

  • Compare cell type-specific mRNA expression from scRNA-seq with protein expression data from the antibody screen

  • Discretize markers into high/low expression for each assay

  • Assess the predictive power of scRNA to identify highly expressed markers in the cytometry data

  • Expected outcomes: High positive predictive value and sensitivity with moderate specificity

• Optimization techniques:

  • Incorporate orthogonal data from paired proteomic and RNA datasets

  • Remove genes with low RNA-protein correlation to improve specificity

  • Rank genes by their ability to discriminate scRNA-seq populations and contrast with variable proteins from the screen

This method has been shown to effectively validate both broad immune population markers and granular sub-population markers identified through scRNA-seq.

What are common issues with SCRN1 antibody staining in immunohistochemistry and how can they be resolved?

When troubleshooting immunohistochemistry with SCRN1 antibodies, consider these common issues and solutions:

• Weak or no signal:

  • Optimize antigen retrieval: For SCRN1, suggested methods include TE buffer pH 9.0 or citrate buffer pH 6.0

  • Increase antibody concentration: Try higher concentrations within the recommended range (1:50-1:500)

  • Extend incubation time: Consider overnight incubation at 4°C

  • Enhance detection system: Use polymer-based detection systems for increased sensitivity

• High background:

  • Optimize blocking: Increase blocking time or use alternative blocking agents

  • Dilute antibody further: If background persists with good signal, increase dilution

  • Reduce secondary antibody concentration: Optimize secondary antibody dilution

  • Include additional washing steps: Increase number and duration of washes

• Non-specific binding:

  • Validate antibody specificity: Use appropriate positive controls (e.g., human stomach cancer tissue has shown positive results)

  • Include appropriate negative controls: Omit primary antibody or use isotype controls

  • Pre-absorb antibody: Use antibody pre-absorption with immunizing peptide to confirm specificity

How should I analyze and interpret contradictory results when using SCRN1 antibodies in different experimental systems?

When faced with contradictory results using SCRN1 antibodies across different experimental systems:

• Systematic validation approach:

  • Verify antibody specificity using multiple techniques (WB, IHC, IF)

  • Confirm results with alternative antibody clones targeting different epitopes of SCRN1

  • Use genetic approaches (siRNA knockdown, CRISPR/Cas9) to validate antibody specificity

• Technical considerations:

  • Evaluate buffer compatibility: Different buffer systems may affect epitope accessibility

  • Consider fixation effects: Paraformaldehyde vs. methanol fixation can significantly impact epitope recognition

  • Assess protein modifications: Post-translational modifications may affect antibody recognition in different cell types

• Data integration strategies:

  • Implement orthogonal validation approaches combining RNA and protein data

  • Consider single-cell approaches to identify cell-type-specific expression patterns

  • Use computational methods to reconcile contradictory results from different experimental systems

How can I incorporate SCRN1 antibodies into high-throughput single-cell sequencing applications?

To effectively incorporate antibodies like SCRN1 into high-throughput single-cell sequencing:

• Antibody-based cell tagging approaches:

  • Use antibody-derived tags for both cell surface protein capture and sample multiplexing (cell hashing)

  • Prepare antibody FASTQ files following platform-specific requirements

  • For dual-purpose antibody libraries (protein capture + cell hashing), create appropriately formatted copies of antibody FASTQ files

• Data analysis strategies:

  • Use analysis pipelines like Cell Ranger 9.0+ for cell-hashed data

  • Implement tag calling algorithms appropriate for your specific system

  • For custom tags (like antibody-based hashtag oligos), follow platform-specific guidelines for data processing

• Optimization for single-cell applications:

  • Validate antibody specificity and titrate concentration for optimal signal-to-noise ratio

  • Consider potential epitope masking issues in multiparameter analyses

  • Implement appropriate controls to account for antibody batch effects

What strategies can be used to identify and validate potent antibodies using high-throughput sequencing?

Modern high-throughput approaches for antibody identification and validation include:

• Microfluidic-based single-cell sequencing:

  • Obtain auto-paired heavy- and light-chain sequences from tens of thousands of single B cells in one run

  • Examine B cell clonotype enrichment prior to in vitro antibody expression

  • Group B cells sharing identical CDR3 regions for both heavy and light chains into clonotypes to identify enriched candidates

• Antigen-binding B cell selection strategies:

  • Use biotinylated antigens (like RBD or S protein) to select antigen-binding B cells through magnetic bead separation

  • Combine PBMCs from different sources for sufficient sample loading when needed

  • Filter for B cells with productive V-J spanning heavy-light chain pairs to improve efficiency

• Validation pipeline:

  • Implement scRNA-seq data for cell typing and memory B cell identification

  • Prioritize candidates based on clonotype enrichment and memory B cell characteristics

  • Express selected candidates in vitro and perform functional validation assays

This approach has been successfully used to identify potent neutralizing antibodies from convalescent patients and shows significantly higher efficiency compared to traditional methods .

What controls should be included when using SCRN1 antibodies in research applications?

For rigorous validation of SCRN1 antibody experiments, include these essential controls:

• Antibody validation controls:

  • Positive tissue/cell controls: Use tissues/cells known to express SCRN1 (A549 cells, HeLa cells, mouse brain tissue)

  • Negative controls: Use tissues/cells with low or no SCRN1 expression

  • Peptide competition: Use SCRN1 immunizing peptide to confirm specificity

  • Isotype controls: Include appropriate isotype-matched control antibodies

• Technique-specific controls:

  • For Western blot: Include molecular weight markers and loading controls

  • For IHC/IF: Include primary antibody omission controls and isotype controls

  • For flow cytometry: Include fluorescence minus one (FMO) controls and single-stained compensation controls

• Experimental validation approaches:

  • Genetic knockdown/knockout: Validate antibody specificity using siRNA or CRISPR/Cas9-mediated knockdown/knockout of SCRN1

  • Overexpression models: Confirm antibody recognition in SCRN1 overexpression systems

  • Multi-antibody validation: Use multiple antibodies targeting different SCRN1 epitopes

How can I validate the specificity of my SCRN1 antibody across different species?

To validate SCRN1 antibody specificity across species:

• Cross-reactivity assessment:

  • Review documented reactivity: SCRN1 antibodies have shown reactivity with human, mouse, and rat samples

  • Perform Western blot validation: Test antibody against lysates from multiple species

  • Compare observed molecular weights: Confirm that observed band sizes match predicted molecular weights for each species

• Epitope conservation analysis:

  • Compare the amino acid sequence of the immunizing peptide across species

  • Assess sequence homology in the antibody recognition region (e.g., N-terminal region between amino acids 10-37 for some SCRN1 antibodies)

  • Predict potential cross-reactivity based on epitope conservation

• Experimental validation approaches:

  • Use species-specific positive and negative controls

  • Implement peptide competition assays across species

  • Consider tissue-specific expression patterns that may differ between species

This methodical validation ensures robust, reproducible results when using SCRN1 antibodies in cross-species research applications.

How can I integrate antibody-based protein data with transcriptomic data for comprehensive understanding of SCRN1 biology?

To effectively integrate antibody-based protein data with transcriptomic data:

• Correlation analysis approaches:

  • Compare cell type-specific mRNA expression from scRNA-seq with protein expression from antibody-based assays

  • Expect correlation coefficients ranging from 0.38 to 0.58 for cell surface markers

  • Discretize expression data into high/low categories for categorical analysis

• Predictive modeling strategies:

  • Use RNA expression to predict protein expression patterns

  • Evaluate performance using positive predictive value, sensitivity, and specificity

  • Incorporate RNA-protein correlation data from orthogonal sources to improve prediction accuracy

• Optimization techniques:

  • Remove genes with low RNA-protein correlation to improve specificity

  • Consider cell type-specific correlation patterns

  • Integrate multiple protein detection methods (flow cytometry, mass spectrometry, antibody arrays) for robust validation

Be aware that RNA-protein correlations vary across genes and tissues, which can limit the utility of RNA-based assays for predicting protein expression. Future approaches may incorporate explicit information on these correlations derived from paired RNA and proteomic assays .

What computational approaches can help optimize antibody-antigen binding prediction in SCRN1 research?

Advanced computational approaches for antibody-antigen binding prediction include:

• Active learning algorithms:

  • Model-based strategies: Implement Query-by-Committee (QBC) with multiple trained models to identify instances with high disagreement

  • Gradient-based uncertainty: Use model's gradient as an indicator of uncertainty for prioritizing experiments

  • Diversity-based approaches: Select data points based on sequence diversity alone, without relying on trained models

• Performance evaluation frameworks:

  • Frame tasks as binary classification problems (binding vs. non-binding)

  • Evaluate using receiver operating characteristic area under the curve (ROC AUC)

  • Calculate area under active learning curve (ALC) to assess strategy effectiveness

• Implementation in experimental design:

  • Start with a small labeled dataset and iteratively expand it

  • Prioritize experiments based on computational predictions of informativeness

  • Compare against random selection baselines to quantify improvement

This computational approach has been shown to reduce the number of required experiments by up to 35% and accelerate the learning process by approximately 28 steps compared to random selection strategies .

How might emerging technologies enhance the development and application of antibodies like SCRN1 in research?

Emerging technologies poised to transform antibody research include:

• Advanced single-cell technologies:

  • Integration of protein and RNA measurements at single-cell resolution

  • Spatial transcriptomics combined with antibody-based detection for tissue context

  • High-throughput microfluidic platforms for rapid antibody discovery from thousands of single B cells

• AI-driven antibody engineering:

  • Machine learning models for predicting antibody-antigen interactions

  • Active learning frameworks to minimize experimental iterations

  • Deep learning approaches for optimizing antibody design and functionality

• Novel validation approaches:

  • Multiplex antibody screening strategies for simultaneous validation of hundreds of markers

  • CRISPR-based validation platforms to confirm antibody specificity

  • Orthogonal approaches combining genomic, transcriptomic, and proteomic data

These technologies will likely enable more efficient antibody development, improved specificity validation, and expanded applications in both basic research and clinical settings.

What are the most promising applications of SCRN1 antibodies in translational research?

Promising translational applications for SCRN1 antibodies include:

• Cancer diagnostics and biomarker development:

  • SCRN1 has been identified as a novel tumor-associated antigen (TAA)

  • Potential utility as a universal marker for different cancer types, including gastric cancer

  • Applications in diagnostic immunohistochemistry and liquid biopsy development

• Therapeutic target identification:

  • Investigation of SCRN1's role in exocytosis and cell signaling

  • Potential implications in mast cell-related disorders

  • Exploration of SCRN1 as a novel therapeutic target in cancer

• Model system development:

  • Creation of reporter systems for studying SCRN1 biology

  • Development of in vitro and in vivo models for investigating SCRN1 function

  • Application in drug discovery pipelines targeting SCRN1-associated pathways

These applications highlight the potential of SCRN1 antibodies to bridge basic research with clinical applications, ultimately contributing to improved diagnostic and therapeutic strategies.