PAI1 Antibody

About the Target

PAI1 is a target of interest in many antibody-based workflows. Plasminogen Activator Inhibitor-1 (PAI-1), also known as serpin E1, is a major serine protease inhibitor encoded by the SERPINE1 gene on chromosome 7 and serves as the main physiological blocker of tissue-type (tPA) and urokinase-type (uPA) plasminogen activators, which convert plasminogen to plasmin in fibrinolysis. This 379‑amino‑acid glycoprotein (~48 kDa) has a conserved serpin fold of three β‑sheets, nine α‑helices, and a reactive center loop (RCL) with residues Arg346 and Met347 essential for protease recognition and irreversible inhibition. Depending on the literature source, PAI1 may also be discussed as PLANH1 and SERPINE1.

Reported cellular context includes secreted, which can matter when signal is compared across treatments or changing cell states. Following PAI1 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.

Research Context

PAI1 is commonly interpreted in the context of cancer, neuroscience, and cardiovascular research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans secreted, a defined reference condition can make comparisons more interpretable across perturbations, passages, or replicate sets.

Consider these angles when interpreting target-level changes:

• signal enrichment within secreted relative to the broader cellular background
• changes associated with proliferative state, oncogenic signaling, or treatment response
• compartment-specific patterns relevant to neuronal polarity, transport, or synaptic context
• changes linked to vascular, contractile, or hemodynamic cell-state cues

Variant Considerations

If your project spans exploratory questions, the regular version offers a balanced option for establishing baseline signal behavior for PAI1. 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 PAI1 reflect biology rather than handling. When interpreting PAI1, 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 PAI1 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.