Recombinant Chicken GTPase-activating Rap/Ran-GAP domain-like protein 3 (GARNL3), partial

What is GARNL3 and what cellular functions does it perform?

GARNL3 (GTPase Activating Rap/RanGAP Domain-Like 3) is a protein involved in GTPase regulation pathways. It functions primarily through its GTPase-activating domain, which modulates the activity of small GTPases in the Rap and Ran families. These GTPases serve as molecular switches that regulate various cellular processes including cell proliferation, cytoskeletal organization, and nucleocytoplasmic transport. Recent evidence suggests that GARNL3 may play significant roles in cellular processes related to development and potentially in disease conditions such as cancer, particularly in the context of temozolomide resistance in glioblastoma . The protein's activity appears to be linked to signaling pathways that influence cell growth and differentiation across multiple species, with conserved functions observed in vertebrates from zebrafish to humans .

How is GARNL3 structurally characterized across species?

GARNL3 contains a characteristic GTPase-activating Rap/RanGAP domain that is highly conserved across species. Structural studies indicate that chicken GARNL3 shares significant homology with human and zebrafish orthologs. The human GARNL3 protein has a molecular mass of approximately 110.6 kDa , while the zebrafish variant is available in recombinant form with high purity (>80%) . The protein typically contains multiple functional domains, including the catalytic GAP domain responsible for stimulating the intrinsic GTPase activity of target proteins. Comparative analysis across species reveals conserved motifs that are essential for interaction with GTPases, suggesting evolutionary preservation of critical functional regions. While specific structural data on chicken GARNL3 is limited in the provided search results, researchers can infer structural characteristics based on homology with better-characterized orthologs from other species.

What genetic elements regulate GARNL3 expression in avian models?

The regulation of GARNL3 expression in avian models appears to involve multiple genetic elements, though specific data on chicken GARNL3 regulation is limited. Based on insights from related research, GARNL3 expression may be influenced by transcription factors such as Epiregulin (EREG), which has been identified as a putative upstream regulator in mammalian systems . In the context of chicken reproduction, GARNL3 likely shares regulatory mechanisms with the related GARNL1 gene, which has been associated with reproductive traits in chickens . Research approaches should consider examining promoter regions, enhancer elements, and transcription factor binding sites to fully characterize the regulatory landscape of chicken GARNL3. When designing expression studies, researchers should account for tissue-specific regulatory elements that may influence GARNL3 expression patterns across different developmental stages and physiological conditions in avian models.

What expression systems are optimal for producing recombinant chicken GARNL3?

For optimal expression of recombinant chicken GARNL3, mammalian expression systems have demonstrated superior results compared to bacterial or insect cell systems. Based on recombinant protein production strategies for zebrafish and human GARNL3, mammalian cells provide appropriate post-translational modifications and protein folding environments . Specifically, HEK293 cells have been successfully used for human GARNL3 expression with C-terminal MYC/DDK tags . For chicken GARNL3, researchers should consider:

• Vector selection: Vectors with strong CMV promoters and appropriate signal sequences for secretion

• Codon optimization: Adjust codons to enhance expression in the chosen host system

• Fusion tags: Consider His-tags for purification or fusion partners like MYC/DDK for detection

• Inducible systems: Tetracycline-inducible systems may help if GARNL3 expression affects cell viability

When designing expression constructs, researchers should ensure inclusion of complete functional domains based on sequence analysis and comparative studies with other species' GARNL3 proteins. The expression system should be selected based on downstream applications, with mammalian cells providing more native-like protein structure and modifications compared to prokaryotic systems .

What purification strategies yield high-purity chicken GARNL3 protein?

Effective purification of recombinant chicken GARNL3 requires multi-step approaches to achieve research-grade purity. Based on established methods for related proteins, the following purification strategy is recommended:

• Affinity chromatography: For His-tagged constructs, immobilized metal affinity chromatography (IMAC) using Ni-NTA or Co2+ resins serves as an effective initial capture step

• Ion exchange chromatography: As a secondary purification step to remove contaminants with different charge properties

• Size exclusion chromatography: For final polishing and buffer exchange

The purification protocol should be optimized to achieve >80% purity as determined by SDS-PAGE and Western blot analysis, similar to the standards reported for zebrafish GARNL3 . For proteins expressed in mammalian systems like HEK293 cells, researchers should consider incorporating the following buffer conditions:

• Lysis buffer: 25 mM Tris-HCl pH 7.3-7.5, 150 mM NaCl, 1% detergent (such as NP-40), protease inhibitors

• Purification buffers: 25 mM Tris-HCl, pH 7.3, with decreasing concentrations of imidazole for IMAC

• Final storage buffer: 25 mM Tris-HCl, pH 7.3, 100 mM glycine, 10% glycerol

Endotoxin levels should be maintained below 1.0 EU per μg of protein as determined by the LAL method to ensure suitability for cell-based assays .

What are the recommended methods for detecting GARNL3 expression in chicken tissues?

For comprehensive analysis of GARNL3 expression in chicken tissues, researchers should employ multiple complementary techniques:

• RT-qPCR: Design chicken-specific primers targeting conserved regions of GARNL3 mRNA. Based on primers used for human GARNL3 (Forward: 5'-AACAATCAACGTGTCCCTCAAT-3'; Reverse: 5'-TTTGTCCAGATTCATGGCACTT-3') , researchers can design similar primers for chicken GARNL3 through sequence alignment and analysis.

• Western blotting: Use commercially available antibodies against conserved epitopes or custom antibodies against chicken-specific sequences. For tagged recombinant proteins, anti-tag antibodies (e.g., anti-His, anti-MYC, anti-DDK) provide reliable detection.

• Immunohistochemistry: For tissue localization studies, optimize fixation conditions (4% paraformaldehyde is typically effective) and perform antigen retrieval if necessary.

• Mass spectrometry: For protein identification and characterization of post-translational modifications.

Expression analysis should include appropriate controls:

• Positive control: Tissues known to express GARNL3 (based on RNA-seq databases)

• Negative control: Tissues with minimal GARNL3 expression

• Housekeeping gene controls: GAPDH for RT-qPCR (Forward: 5'-GGAGCGAGATCCCTCCAAAAT-3'; Reverse: 5'-GGCTGTTGTCATACTTCTCATGG-3')

Quantification should employ the 2^(-ΔΔCq) method for RT-qPCR data, normalizing GARNL3 expression to housekeeping genes .

How does GARNL3 contribute to developmental processes in avian models?

• Cell signaling regulation: As a GTPase-activating protein, GARNL3 modulates GTPase activity, potentially influencing pathways critical for embryonic development.

• Reproductive development: The related GARNL1 gene has been associated with reproductive traits in chickens , suggesting GARNL3 may play parallel roles in reproductive system development.

• Potential involvement in neurogenesis: Based on its expression patterns in other vertebrates and its association with EGFRvIII signaling , GARNL3 may contribute to neural development.

To investigate GARNL3's developmental roles in avian models, researchers should consider:

• Temporal expression analysis across embryonic stages

• Spatial expression mapping in developing tissues

• Loss-of-function studies using approaches such as CRISPR-Cas9 genome editing or morpholino knockdown

• Gain-of-function studies through overexpression of wild-type or mutant GARNL3

• Protein interaction studies to identify developmental signaling partners

Developmental phenotypes resulting from GARNL3 modulation should be carefully documented and compared with expression patterns to establish causative relationships between GARNL3 activity and specific developmental processes.

What approaches can elucidate GARNL3's role in cancer signaling pathways?

Recent findings have implicated GARNL3 in cancer signaling pathways, particularly in the context of temozolomide (TMZ) resistance in glioblastoma . To investigate GARNL3's role in cancer signaling in avian or other model systems, researchers should consider:

• Expression correlation analysis: Assess GARNL3 expression in relation to known oncogenes or tumor suppressors. Recent studies identified correlations between reduced GARNL3 expression and TMZ resistance in glioblastoma, with EREG identified as a putative upstream regulator .

• Pathway analysis: Determine how GARNL3 interacts with established cancer signaling pathways such as EGFR signaling. The connection between EGFRvIII and GARNL3 downregulation provides a starting point for investigating these interactions .

• Loss-of-function and gain-of-function studies: Modulate GARNL3 expression in cancer cell models to assess effects on proliferation, apoptosis, migration, and drug sensitivity.

• Protein interaction mapping: Identify GARNL3 binding partners in cancer-relevant contexts using techniques such as co-immunoprecipitation followed by mass spectrometry.

• Immune infiltration analysis: Recent studies have linked GARNL3 expression to alterations in immune cell profiles , suggesting immunological assessment should be included in GARNL3 cancer research.

Experimental design should incorporate multidimensional approaches:

• In vitro cell culture models

• Ex vivo tissue explants

• In vivo animal models

• Computational analysis of cancer genomics databases

When investigating GARNL3 in cancer contexts, researchers should establish clear connections between expression changes, signaling pathway alterations, and functional outcomes relevant to cancer progression or therapy response.

How can researchers investigate GARNL3 mutations in relation to disease phenotypes?

To investigate the relationship between GARNL3 mutations and disease phenotypes, researchers should implement a comprehensive strategy combining genomic, functional, and clinical approaches:

• Mutation identification and characterization:

  • Utilize the COSMIC database to identify known somatic mutations in GARNL3

  • Perform targeted sequencing of GARNL3 in relevant disease samples

  • Analyze whole-exome or whole-genome sequencing data to identify novel GARNL3 variants

• Structural-functional analysis:

  • Map mutations onto protein domain structures to predict functional impacts

  • Use computational tools to assess conservation and potential pathogenicity

  • Generate recombinant proteins with specific mutations for biochemical characterization

• Functional validation:

  • Develop cell-based assays to assess mutant GARNL3 function

  • Implement CRISPR-Cas9 knock-in strategies to introduce specific mutations

  • Assess effects on GTPase regulation, protein interactions, and downstream signaling

• Phenotypic correlation:

  • Establish genotype-phenotype correlations through statistical analysis

  • Develop animal models expressing GARNL3 mutations

  • Investigate tissue-specific effects of mutations

For avian-specific research, consider:

• Analyzing naturally occurring GARNL3 variants in different chicken breeds

• Correlating variants with traits of interest using genome-wide association studies

• Developing chicken models with engineered GARNL3 mutations

Data analysis should incorporate both variant frequency information and functional impact predictions to prioritize mutations for in-depth characterization.

What statistical approaches should be used when analyzing GARNL3 expression data?

Analyzing GARNL3 expression data requires rigorous statistical approaches tailored to the experimental design and data type. Researchers should consider:

• For RT-qPCR data:

  • Utilize the 2^(-ΔΔCq) method for relative quantification

  • Apply appropriate normalization using multiple stable reference genes

  • Implement statistical tests based on data distribution:

    • Parametric: t-test (two groups) or ANOVA (multiple groups) for normally distributed data

    • Non-parametric: Mann-Whitney U test or Kruskal-Wallis test for non-normally distributed data

• For RNA-seq data:

  • Apply appropriate normalization methods (TPM, FPKM, or DESeq2 normalization)

  • Utilize differential expression analysis tools like DESeq2, edgeR, or limma-voom

  • Implement multiple testing correction (Benjamini-Hochberg procedure) to control false discovery rate

• For protein expression data:

  • Normalize to appropriate loading controls

  • Apply densitometric analysis for Western blot quantification

  • Consider ANOVA for multiple sample comparisons

• For correlation analyses:

  • Use Pearson correlation for normally distributed data

  • Apply Spearman correlation for non-parametric relationships

  • Implement multivariate analyses for complex datasets

When analyzing GARNL3 expression in the context of disease studies, statistical approaches should also include:

• Survival analysis using Kaplan-Meier curves and Cox proportional hazards models

• Chi-square tests for genotype frequency analysis (as used in studies of related genes)

• Multivariate analyses to control for confounding factors

Statistical power calculations should be performed prior to experiments to ensure adequate sample sizes for detecting biologically meaningful differences in GARNL3 expression or function.

How should researchers interpret conflicting data regarding GARNL3 function?

When faced with conflicting data regarding GARNL3 function, researchers should implement a systematic approach to interpretation:

• Context-dependent analysis:

  • Evaluate differences in experimental systems (cell types, species, developmental stages)

  • Consider tissue-specific functions that may explain apparently contradictory results

  • Assess whether conflicting results reflect different aspects of multifunctional proteins

• Methodological evaluation:

  • Compare assay sensitivities and specificities

  • Assess whether different detection methods (antibodies, tagged constructs) may influence results

  • Evaluate experimental controls and validation approaches

• Biological validation strategies:

  • Implement multiple complementary techniques to study the same function

  • Perform rescue experiments to confirm specificity of observed phenotypes

  • Utilize different genetic manipulation approaches (siRNA, CRISPR, overexpression)

• Reconciliation approaches:

  • Develop unified models that incorporate seemingly contradictory findings

  • Design experiments specifically to test hypotheses that would reconcile conflicting data

  • Consider temporal dynamics that might explain different observations

For GARNL3 specifically, researchers should be attentive to:

• Potential species-specific differences in function between zebrafish, chicken, and human orthologs

• Context-dependent functions in different tissues or disease states

• Potential functional differences between full-length GARNL3 and partial constructs or splice variants

When publishing findings on GARNL3, researchers should explicitly address contradictions with existing literature and provide detailed methodological information to facilitate reproduction and validation.

What approaches can validate protein-protein interactions involving GARNL3?

To rigorously validate protein-protein interactions involving GARNL3, researchers should implement multiple complementary approaches:

• In vitro interaction studies:

  • Pull-down assays using recombinant GARNL3 (>80% purity) and potential interaction partners

  • Surface plasmon resonance (SPR) to determine binding kinetics

  • Isothermal titration calorimetry (ITC) for thermodynamic characterization

• Cell-based interaction validation:

  • Co-immunoprecipitation of endogenous proteins

  • Proximity ligation assays to detect interactions in situ

  • FRET or BRET assays to detect interactions in living cells

• Domain mapping approaches:

  • Truncation constructs to identify interaction domains

  • Mutation of key residues to disrupt specific interactions

  • Peptide competition assays to confirm specificity

• Functional validation of interactions:

  • Assess effects of interaction disruption on downstream signaling

  • Evaluate co-localization under different cellular conditions

  • Implement genetic approaches (double knockouts, synthetic lethality) to assess functional relevance

• Computational approaches:

  • Molecular docking to predict interaction interfaces

  • Evolutionary analysis to identify co-evolving residues

  • Network analysis to place interactions in broader signaling contexts

For GARNL3 specifically, researchers should focus on:

• Interactions with small GTPases, particularly in the Rap and Ran families

• Potential interactions with upstream regulators like EREG

• Interactions with components of the EGFR signaling pathway, given the connection to EGFRvIII in cancer contexts

When reporting interaction data, researchers should provide quantitative measurements (Kd values, enrichment ratios) and clearly distinguish direct from indirect interactions.

What are common challenges in recombinant GARNL3 expression and how can they be addressed?

Recombinant expression of GARNL3 presents several challenges that researchers should anticipate and address:

• Low expression levels:

  • Challenge: GARNL3's large size (110.6 kDa for human variant) may result in low expression yields

  • Solutions:

    • Optimize codon usage for the expression system

    • Test different promoters (CMV, EF1α) to identify optimal expression levels

    • Consider inducible expression systems to minimize toxicity

    • Implement chaperone co-expression to improve folding

• Protein solubility issues:

  • Challenge: Recombinant GARNL3 may form inclusion bodies or aggregate

  • Solutions:

    • Optimize buffer conditions (25 mM Tris-HCl, pH 7.3, 100 mM glycine, 10% glycerol has proven effective for human GARNL3)

    • Express fusion proteins with solubility enhancers (MBP, SUMO, GST)

    • Reduce expression temperature (28-30°C) to improve folding

    • Implement gradual refolding protocols if inclusion bodies form

• Proteolytic degradation:

  • Challenge: Full-length GARNL3 may be susceptible to proteolysis

  • Solutions:

    • Include protease inhibitors in all buffers

    • Express protein with stabilizing tags at both N- and C-termini

    • Optimize purification protocols to minimize handling time

    • Consider expressing stable domains separately for functional studies

• Inconsistent activity:

  • Challenge: Variable GTPase-activating function in recombinant preparations

  • Solutions:

    • Implement rigorous quality control with activity assays

    • Ensure proper post-translational modifications by choosing appropriate expression systems

    • Store protein in optimal conditions (-20°C to -80°C for long-term)

    • Add stabilizing agents like glycerol (10%) to storage buffers

When troubleshooting GARNL3 expression, maintain detailed records of conditions tested and outcomes observed. Implement iterative optimization focusing on key variables (temperature, pH, salt concentration, additives) and consider structural information to guide troubleshooting efforts.

What controls are essential for validating GARNL3 functional assays?

Robust GARNL3 functional assays require comprehensive controls to ensure valid interpretation of results:

• For GTPase activity assays:

  • Positive controls:

    • Known GTPase-activating proteins with similar substrate specificity

    • Constitutively active GTPase mutants (e.g., Q61L in Ras-family GTPases)

  • Negative controls:

    • Catalytically inactive GARNL3 mutants (mutations in the GAP domain)

    • Buffer-only reactions to establish baseline GTPase activity

  • Specificity controls:

    • Test multiple GTPases to confirm substrate specificity

    • Competition assays with known substrates

• For expression analysis:

  • Positive controls:

    • Tissues known to express GARNL3

    • Spike-in controls of known GARNL3 quantities

  • Negative controls:

    • Tissues with confirmed absence of GARNL3 expression

    • No-template controls for RT-qPCR

  • Reference controls:

    • Multiple housekeeping genes for normalization (e.g., GAPDH)

    • Consistent reference samples across experiments

• For functional phenotype assays:

  • Genetic controls:

    • Multiple independent GARNL3 knockdown or knockout lines

    • Rescue experiments with wild-type GARNL3 to confirm specificity

    • Dose-response experiments with varying GARNL3 levels

  • Pathway controls:

    • Parallel manipulation of known pathway components

    • Pharmacological modulators of related pathways

• For interaction studies:

  • Specificity controls:

    • Unrelated proteins of similar size and charge

    • Competitive binding with peptides or known ligands

  • Technical controls:

    • Input samples to confirm protein presence

    • IgG or other irrelevant antibody controls for immunoprecipitation

When developing new GARNL3 assays, researchers should validate assay performance metrics including:

• Specificity (ability to distinguish GARNL3 from related proteins)

• Sensitivity (limit of detection for GARNL3 activity or expression)

• Reproducibility (inter- and intra-assay variation)

• Dynamic range (ability to detect variations in GARNL3 levels or activity)

How can researchers address cross-reactivity issues in GARNL3 immunodetection?

Cross-reactivity represents a significant challenge in GARNL3 immunodetection due to sequence similarities with related proteins. Researchers can address this issue through several strategies:

• Antibody selection and validation:

  • Target unique epitopes specific to GARNL3 not present in related proteins

  • Validate antibodies using multiple approaches:

    • Testing on samples with confirmed GARNL3 knockdown/knockout

    • Parallel testing with multiple antibodies targeting different epitopes

    • Pre-absorption tests with recombinant protein to confirm specificity

  • Consider generating chicken-specific antibodies if studying avian GARNL3

• Detection strategy optimization:

  • Implement more stringent washing conditions in immunoblotting

  • Optimize antibody concentrations to minimize non-specific binding

  • Use monoclonal antibodies for highest specificity when available

  • Consider detection of tagged recombinant proteins (His, MYC/DDK) for clearer interpretation

• Alternative detection approaches:

  • Mass spectrometry-based detection for highest specificity

  • RNA-based detection methods (RT-qPCR, RNA-seq, RNA-FISH)

  • Genetic tagging approaches (CRISPR knock-in of epitope tags)

• Comprehensive controls:

  • Include samples lacking GARNL3 expression

  • Test antibodies on related proteins to assess cross-reactivity

  • Include peptide competition controls to confirm binding specificity

  • Use recombinant GARNL3 with known concentration as positive control

When troubleshooting cross-reactivity issues:

• Systematically vary blocking agents (BSA, non-fat milk, commercial blockers)

• Test different detection methods (chemiluminescence, fluorescence)

• Consider species-specific secondary antibodies with minimal cross-reactivity

• Document and report all validation steps in publications to improve reproducibility

For chicken GARNL3 specifically, researchers should be particularly attentive to potential cross-reactivity with GARNL1, which has been associated with reproductive traits in chickens and likely shares structural similarities with GARNL3.

Comparison of Expression Systems for Recombinant GARNL3 Production

Primer Design Guide for GARNL3 Detection by RT-qPCR

*Primers for non-human species are designed based on sequence alignments and homology with validated human primers. Researchers should validate these primers in their specific experimental systems.

GARNL3 Storage and Handling Recommendations

What are the most promising research directions for GARNL3 in avian models?

Based on current knowledge about GARNL3 and related proteins, several promising research directions emerge for GARNL3 in avian models:

• Developmental biology: Investigating GARNL3's role in embryonic development and organogenesis in chickens, particularly in neural development and reproductive system formation. This builds upon the known association of related proteins with reproductive traits .

• Comparative genomics: Analyzing GARNL3 conservation and divergence across avian species to understand evolutionary adaptations and functional specialization. This approach can leverage the extensive genomic data available for diverse bird species.

• Disease modeling: Using avian models to study GARNL3's role in disease processes, particularly those related to developmental disorders and cancer. The connection between GARNL3 and temozolomide resistance in glioblastoma suggests potential roles in therapy response that could be explored in avian cancer models.

• Agricultural applications: Exploring GARNL3's potential role in traits relevant to poultry production, building on findings linking related genes to reproductive traits . This could lead to genetic markers for breeding programs focused on improved reproductive performance.

• Fundamental GTPase biology: Using chicken models to elucidate GARNL3's role in regulating specific GTPases and corresponding cellular processes, potentially revealing conserved mechanisms across vertebrates.

Future research should implement cutting-edge methodologies including CRISPR-Cas9 genome editing, single-cell transcriptomics, and advanced imaging techniques to comprehensively characterize GARNL3 function in avian systems. Integration of findings across species will be crucial for building a complete understanding of this protein's biological significance.

How can researchers effectively collaborate across different model systems studying GARNL3?

Effective collaboration across different model systems (zebrafish, chicken, human, etc.) studying GARNL3 requires structured approaches to data sharing, methodology standardization, and comparative analysis:

• Standardization initiatives:

  • Establish common nomenclature for GARNL3 variants and domains

  • Develop standardized assays for GARNL3 activity applicable across species

  • Create shared repositories for validated reagents (antibodies, expression constructs)

• Collaborative platforms:

  • Implement shared databases for GARNL3 expression and interaction data

  • Develop model organism-specific working groups with regular communication

  • Establish consortia focused on specific GARNL3 functions across species

• Integrative analysis approaches:

  • Apply computational tools for cross-species functional annotation

  • Conduct parallel experiments in multiple models using standardized protocols

  • Implement meta-analysis frameworks to integrate findings across studies

• Technology sharing:

  • Exchange optimized protocols for GARNL3 expression and purification

  • Share specialized reagents such as recombinant proteins with >80% purity

  • Develop and distribute genetic tools (CRISPR guides, expression vectors)

• Coordinated research priorities:

  • Identify conserved vs. species-specific functions through systematic comparison

  • Prioritize investigation of contradictory findings across models

  • Coordinate investigation of disease relevance in appropriate model systems

Researchers should leverage existing collaborative frameworks while establishing GARNL3-specific resources. Multi-institutional projects with complementary expertise in different model systems can accelerate progress in understanding this protein's complex biology across vertebrate evolution.

What are the critical quality control parameters for GARNL3 research reproducibility?

To ensure research reproducibility when working with GARNL3, researchers must implement rigorous quality control parameters at multiple experimental stages:

• Recombinant protein quality:

  • Purity: Maintain consistent standards (>80% as determined by SDS-PAGE)

  • Activity: Implement quantitative GTPase activation assays

  • Endotoxin levels: Maintain <1.0 EU per μg protein

  • Storage consistency: Use standardized buffers (25 mM Tris-HCl, pH 7.3, 100 mM glycine, 10% glycerol)

• Antibody validation:

  • Specificity: Confirm using knockout/knockdown controls

  • Lot-to-lot consistency: Test new lots against reference samples

  • Cross-reactivity: Profile against related proteins

  • Application optimization: Validate for each specific application (WB, IP, IHC)

• Expression analysis:

  • PCR efficiency: Maintain 90-110% efficiency for qPCR primers

  • Reference gene stability: Validate stability across experimental conditions

  • Technical replication: Implement triplicate measurements

  • Inter-laboratory validation: Confirm key findings in independent laboratories

• Functional assays:

  • Positive and negative controls: Include in every experiment

  • Dose-response relationships: Establish for GARNL3-dependent effects

  • Time-course analyses: Document temporal dynamics of GARNL3 activity

  • Multiple methodological approaches: Confirm key findings with complementary techniques

• Data reporting:

  • Detailed methodology: Provide complete protocols including buffer compositions

  • Raw data availability: Share through appropriate repositories

  • Statistical analysis transparency: Clearly describe all analyses performed

  • Reagent authentication: Provide catalog numbers and validation data