Nav1.8/SCN10A Antibody

About the Target

Nav1. 8/SCN10A, encoded by the SCN10A gene, is a voltage-gated sodium channel α-subunit belonging to the TTX-resistant class, characterized by a cysteine substitution in its pore region that confers resistance to nanomolar tetrodotoxin inhibition. Structurally, it is a large transmembrane protein with four homologous domains (I-IV), each containing six membrane-spanning segments (S1-S6) that form the voltage-sensing and pore-forming regions, and is often associated with β-subunits that modulate gating and trafficking. Depending on the literature source, SCN10A may also be discussed as Nav1.8/SCN10A and Sodium channel protein type 10 subunit alpha.

Reported cellular context includes cell membrane and membrane, which can matter when signal is compared across treatments or changing cell states. Following SCN10A across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.

Research Context

SCN10A is commonly interpreted in the context of neuroscience and cardiovascular research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cell membrane and membrane, a defined reference condition can make comparisons more interpretable across perturbations, passages, or replicate sets.

Consider these angles when interpreting target-level changes:

• apparent redistribution between cell membrane and membrane across matched conditions
• compartment-specific patterns relevant to neuronal polarity, transport, or synaptic context
• changes linked to vascular, contractile, or hemodynamic cell-state cues
• co-patterning with orthogonal markers and control conditions that clarify pathway state

Variant Considerations

If your project spans exploratory questions, the regular version offers a balanced option for establishing baseline signal behavior for SCN10A. This can help when protocols evolve over time and the goal is to compare experiments using a stable reference workflow.

Standardize sampling time, control choice, and downstream analysis thresholds so apparent differences in SCN10A reflect biology rather than handling. When interpreting SCN10A, it is often useful to decide early whether the main question is overall abundance, compartmental enrichment, or context-dependent redistribution.

For multi-run studies, a shared reference condition can keep SCN10A trends easier to compare across datasets. That kind of consistency is especially helpful when follow-up work expands to new perturbations, model systems, or longitudinal collections.