GABARAPL2/GATE-16 Antibody

About the Target

GATE-16 (GABARAPL2) is a crucial protein for autophagy and plays a significant role in regulating membrane transport. Along with other family members like GABARAP and LC3, GATE-16 integrates into autophagosomal membranes in response to autophagic stimuli such as starvation. It undergoes cleavage at its carboxyl terminus, leading to conjugation with phosphatidylethanolamine or phosphatidylserine, converting it into a type II membrane-bound form essential for autophagosome formation. Depending on the literature source, GABARAPL2 may also be discussed as GABARAPL2/GATE-16 and GATE-16.

Reported cellular context includes cytoplasmic vesicle, endoplasmic reticulum, golgi apparatus, and membrane, which can matter when signal is compared across treatments or changing cell states. Following GABARAPL2 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.

Research Context

GABARAPL2 is commonly interpreted in the context of autophagy research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasmic vesicle, endoplasmic reticulum, and golgi apparatus, 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 cytoplasmic vesicle, endoplasmic reticulum, and golgi apparatus across matched conditions
• interpretation alongside flux, cargo handling, or lysosomal context
• co-patterning with orthogonal markers and control conditions that clarify pathway state
• time-matched comparisons so changes reflect biology rather than handling or sampling drift

Variant Considerations

If your project spans exploratory questions, the regular version offers a balanced option for establishing baseline signal behavior for GABARAPL2. 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 GABARAPL2 reflect biology rather than handling. When interpreting GABARAPL2, 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 GABARAPL2 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.