DYNC1I1 Antibody

How do researchers validate DYNC1I1 antibody specificity in disease models?

Antibody validation requires multi-modal approaches to confirm target specificity. For DYNC1I1, three primary methods are employed:

• Genetic knockdown/knockout validation: siRNA-mediated DYNC1I1 silencing in cell lines (e.g., gastric cancer models) followed by Western blot (WB) analysis showing reduced signal intensity compared to controls . Proteintech’s protocol recommends using U-87 MG glioblastoma cells with >70% knockdown efficiency as a negative control .

• Immunohistochemical (IHC) concordance: Comparison of staining patterns across normal vs. cancerous tissues (e.g., high DYNC1I1 expression in gastric adenocarcinoma vs. low in normal epithelium) .

• Mass spectrometry verification: Immunoprecipitated DYNC1I1 complexes analyzed via LC-MS/MS to confirm peptide matches to DYNC1I1 (UniProt ID: O14576) .

Table 1: Validation benchmarks for DYNC1I1 antibodies

What criteria determine antibody selection for DYNC1I1 functional studies?

Selection depends on four experimentally validated parameters:

• Epitope location: Antibodies targeting the N-terminal (aa 1-220) show superior performance in co-immunoprecipitation (co-IP) assays of DYNC1I1/NF-κB complexes compared to C-terminal binders .

• Species cross-reactivity: Rabbit polyclonal antibodies (e.g., Proteintech 13808-1-AP) demonstrate consistent reactivity across human, mouse, and rat models , critical for translational studies.

• Application-specific validation: Antibodies with published WB data using 8% SDS-PAGE (30-50 μg protein/lane) and IHC protocols with TE buffer pH 9.0 antigen retrieval yield reproducible results in gastric cancer migration assays .

• Lot-to-lot consistency: Suppliers providing ≥3 independent validation datasets (e.g., siRNA knockdown in HGC-27 cells + xenograft IHC) minimize batch variability risks .

How to resolve contradictory DYNC1I1 expression data across cancer types?

Context-dependent DYNC1I1 roles necessitate careful experimental design:

Case 1: Gastric cancer vs. glioblastoma

• Pro-tumorigenic role: DYNC1I1 promotes IL-6/STAT3 signaling in gastric cancer via NF-κB nuclear translocation (HR=2.227, p<0.0001) .

• Tumor-suppressive role: Low DYNC1I1 correlates with poor survival in glioblastoma (p=0.003) .

Resolution strategies:

• Perform tissue-specific phosphorylation state analysis (DYNC1I1-Ser421 phosphorylation alters cargo binding in neurons vs. cancer cells) .

• Use isoform-specific primers in qPCR validation (NM_001278421 vs. NM_001377).

• Employ proximity ligation assays to confirm DYNC1I1 interaction partners (e.g., STAT3 in gastric cancer vs. TDP-43 in neurodegenerative models) .

What controls are essential for DYNC1I1 knockdown/rescue experiments?

Robust phenotypic studies require:

A. Negative controls:

• Non-targeting siRNA with matched GC content

• DYNC1I1-/- cell lines (e.g., CRISPR-modified HGC-27 clones)

B. Rescue controls:

• Transfection with DYNC1I1 cDNA containing silent mutations in siRNA target regions

• Co-treatment with lysosomal inhibitors (e.g., chloroquine) to monitor autophagy-mediated antibody degradation

C. Experimental endpoints:

• Migration assays: 48-hour timepoint with 50% serum reduction to isolate DYNC1I1 effects from proliferation

• Nuclear/cytoplasmic fractionation to quantify NF-κB translocation efficiency

How to optimize DYNC1I1 antibody performance in co-IP assays?

Critical protocol modifications based on gastric cancer studies :

Buffer composition:

• Lysis buffer: 50 mM Tris-HCl (pH 7.4), 150 mM NaCl, 1% NP-40, 0.25% sodium deoxycholate

• Protease inhibitor cocktail with 10 mM N-ethylmaleimide (prevents deubiquitination)

Technical parameters:

Post-IP validation requires Silver staining showing intact DYNC1I1 (~71 kDa) and co-precipitated partners (e.g., P65 at 65 kDa) .

What advanced techniques enhance DYNC1I1 pathway analysis?

A. Spatial transcriptomics:

• Combine MERFISH with DYNC1I1 IHC to map IL-6/STAT3 activation hotspots in gastric tumor margins .

B. Live-cell imaging:

• DYNC1I1-GFP constructs + SiR-tubulin to quantify microtubule transport velocities (normal: 1.2 μm/s ± 0.3; Loa mutation: 0.6 μm/s ± 0.2) .

C. Phosphoproteomics:

• TiO2 enrichment of DYNC1I1 phosphopeptides followed by MRM-MS to quantify activation states in drug-resistant vs. naive tumors .

How to address nonspecific bands in DYNC1I1 Western blots?

Systematic troubleshooting approach:

Step 1: Epitope mapping

• Compare bands in DYNC1I1-overexpressing MGC-803 cells vs. parental lines .
  Step 2: Buffer optimization

• Add 2% SDS to transfer buffer to improve 71 kDa band resolution .
  Step 3: Cross-validation

• Confirm findings with independent antibodies (e.g., Novoprolabs 110135 vs. Proteintech 13808-1-AP) .

Common artifacts:

• 55 kDa band: Degradation product (add 10 μM MG132 during lysis)

• 85 kDa band: DYNC1H1 cross-reactivity (use isoform-specific knockout controls)

What emerging roles of DYNC1I1 require updated experimental frameworks?

A. Ciliogenesis regulation:

• DYNC1I1 depletion induces primary cilia defects in RPE-1 cells (35% reduction, p<0.01) . Protocol: Serum starvation + acetylated α-tubulin staining.

B. Neurodegenerative interfaces:

• LoaHom mutations impair autophagosome transport (40% increased p62 aggregates, p<0.001) . Key assay: Co-treatment with bafilomycin A1 and TBK1 inhibitors.

C. Chemoresistance mechanisms:

• DYNC1I1 overexpression reduces cisplatin sensitivity in HGC-27 cells (IC50 increase from 2.1 μM to 5.8 μM) . Validation: ALDH1A1 activity assays + xenograft models.

How to design DYNC1I1 studies controlling for dynein complex heterogeneity?

Strategy 1: Subunit-specific inhibition

• Compare phenotypes from DYNC1I1 siRNA vs. dynapyrazole-A (general dynein inhibitor) .

Strategy 2: Proximity proteomics

• TurboID-tagged DYNC1I1 identifies context-dependent interactomes (e.g., IL-6 promoters vs. centrosomal proteins) .

Strategy 3: Single-molecule tracking

• TIRF microscopy with DYNC1I1-HaloTag reveals two transport populations: processive (65%) and diffusive (35%) .

What multidisciplinary approaches resolve DYNC1I1 pleiotropy?

Integrative analysis pipeline:

• CRISPR screens: Identify synthetic lethal partners (e.g., KIF5B knockout synergizes with DYNC1I1 inhibition) .

• Patient-derived models: Use DYNC1I1D338N fibroblasts to study developmental defects vs. cancer roles .

• Cryo-EM: 3.8 Å structure of DYNC1I1/dynactin complex informs mutation-specific functional assays .

Validation triad:

• Orthogonal antibody validation (≥2 host species)

• Genetic rescue (≥80% expression recovery)

• Clinical correlation (TCGA survival analysis + IHC cohorts)