{"product_id":"kga-antibody-sc-f1632","title":"Glutaminase Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eKGA is a target of interest in many antibody-based workflows. Glutaminase is an enzyme that catalyzes the conversion of glutamine to glutamate, which represents the first and rate-limiting step in the process of glutaminolysis. In mammals, there are two types of glutaminase: kidney-type glutaminase (GLS1) and liver-type glutaminase (GLS2). GLS1 is particularly important for the synthesis of glutathione, which plays a critical role in maintaining redox balance. Depending on the literature source, KGA may also be discussed as Glutaminase and GLS.\u003c\/p\u003e\u003cp\u003eReported cellular context includes mitochondrion and cytoplasm, which can matter when signal is compared across treatments or changing cell states. Following KGA across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state. In practice, this target is often considered at the family or isoform-group level, so experimental interpretation benefits from matched controls and clear comparison logic.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eKGA is commonly interpreted in the context of oxidative stress research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans mitochondrion and cytoplasm, a defined reference condition can make comparisons more interpretable across perturbations, passages, or replicate sets.\u003c\/p\u003e\u003cp\u003eConsider these angles when interpreting target-level changes:\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eapparent redistribution between mitochondrion and cytoplasm across matched conditions\u003c\/li\u003e\n\u003cli\u003eredox-associated shifts that may alter abundance, localization, or pathway coupling\u003c\/li\u003e\n\u003cli\u003eco-patterning with orthogonal markers and control conditions that clarify pathway state\u003c\/li\u003e\n\u003cli\u003etime-matched comparisons so changes reflect biology rather than handling or sampling drift\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch2\u003eVariant Considerations\u003c\/h2\u003e\u003cp\u003eIf your project spans exploratory questions, the regular version offers a balanced option for establishing baseline signal behavior for KGA. This can help when protocols evolve over time and the goal is to compare experiments using a stable reference workflow.\u003c\/p\u003e\u003cp\u003eStandardize sampling time, control choice, and downstream analysis thresholds so apparent differences in KGA reflect biology rather than handling. When interpreting KGA, it is often useful to decide early whether the main question is overall abundance, compartmental enrichment, or context-dependent redistribution.\u003c\/p\u003e\u003cp\u003eFor multi-run studies, a shared reference condition can keep KGA 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.\u003c\/p\u003e","brand":"Selleck Chemicals","offers":[{"title":"20 µl","offer_id":57577800761689,"sku":"F1632-20UL","price":169.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577800794457,"sku":"F1632-100UL","price":399.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577800827225,"sku":"F1632-2X100UL","price":599.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F1632-wb.gif?v=1773599961","url":"https:\/\/absource-diagnostics.myshopify.com\/products\/kga-antibody-sc-f1632","provider":"Absource Diagnostics","version":"1.0","type":"link"}