{"title":"Metabolism","description":"","products":[{"product_id":"gapdh-antibody-sc-f0003","title":"GAPDH Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eGlyceraldehyde-3-phosphate dehydrogenase (GAPDH), an abundant glycolytic enzyme, serves as a crucial intracellular messenger in apoptotic cell death. While traditionally viewed as a housekeeping enzyme, GAPDH also functions in DNA repair as a uracil DNA glycosylase, acts as an Oct-1 coactivator OCA-S for S-phase-dependent histone H2B transcription, and interacts with transfer RNA and DNA. Depending on the literature source, GAPDH may also be discussed as GAPDH Loading Control and Glyceraldehyde-3-Phosphate Dehydrogenase.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm, cytoskeleton, membrane, and nucleus, which can matter when signal is compared across treatments or changing cell states. Following GAPDH across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eGAPDH is commonly interpreted in the context of metabolism research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm, cytoskeleton, and membrane, 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 cytoplasm, cytoskeleton, and membrane across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\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 GAPDH. 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 GAPDH reflect biology rather than handling. When interpreting GAPDH, 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 GAPDH 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":57577378873689,"sku":"F0003-20UL","price":149.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577378906457,"sku":"F0003-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577378939225,"sku":"F0003-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0003-IF.png?v=1773598065"},{"product_id":"akt1-akt2-akt3-antibody-sc-f0004","title":"Akt (pan) Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eAkt (pan) is a target of interest in many antibody-based workflows. Akt, also known as PKB or Rac, is pivotal in regulating cell survival and apoptosis. It shares homology with the PKA and PKC families of protein kinases. Akt activation is triggered by various stimuli like insulin, platelet-derived growth factor (PDGF), epidermal growth factor (EGF), and basic fibroblast growth factor (bFGF). Depending on the literature source, Akt (pan) may also be discussed as Akt (pan).\u003c\/p\u003e\u003cp\u003eReported cellular context includes cell membrane, cytoplasm, endosome, and membrane, which can matter when signal is compared across treatments or changing cell states. Following Akt (pan) 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\u003eAkt (pan) is commonly interpreted in the context of cancer, neuroscience, and metabolism research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cell membrane, cytoplasm, and endosome, 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 cell membrane, cytoplasm, and endosome across matched conditions\u003c\/li\u003e\n\u003cli\u003echanges associated with proliferative state, oncogenic signaling, or treatment response\u003c\/li\u003e\n\u003cli\u003ecompartment-specific patterns relevant to neuronal polarity, transport, or synaptic context\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\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 Akt (pan). 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 Akt (pan) reflect biology rather than handling. When interpreting Akt (pan), 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 Akt (pan) 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":57577379004761,"sku":"F0004-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577379037529,"sku":"F0004-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577379070297,"sku":"F0004-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0004-IHC1.jpg?v=1773598069"},{"product_id":"rps6kb1-antibody-sc-f0011","title":"p70 S6 Kinase Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eRPS6KB1 is a target of interest in many antibody-based workflows. The p70 Ribosomal protein S6 kinase 1 (S6K1, p70S6K) is part of the AGC subfamily of serine\/threonine protein kinases, which includes cyclic AMP-dependent protein kinase, cyclic GMP-dependent protein kinase, and protein kinase C. The S6K family is located on chromosome 17q23. Through alternative splicing and different translational start sites, the human S6K1 gene produces two isoforms: p70 and p85. Depending on the literature source, RPS6KB1 may also be discussed as p70 S6 Kinase and p70 S6 kinase alpha.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm, membrane, mitochondrion, and mitochondrion outer membrane, which can matter when signal is compared across treatments or changing cell states. Following RPS6KB1 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eRPS6KB1 is commonly interpreted in the context of cancer, metabolism, and cell signaling research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm, membrane, and mitochondrion, 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 cytoplasm, membrane, and mitochondrion across matched conditions\u003c\/li\u003e\n\u003cli\u003echanges associated with proliferative state, oncogenic signaling, or treatment response\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003esignal-dependent shifts after ligand, inhibitor, or growth-factor perturbation\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 RPS6KB1. 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 RPS6KB1 reflect biology rather than handling. When interpreting RPS6KB1, 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 RPS6KB1 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":57577379660121,"sku":"F0011-20UL","price":149.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577379692889,"sku":"F0011-100UL","price":369.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577379725657,"sku":"F0011-2X100UL","price":549.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0011-IHC1.jpg?v=1773598077"},{"product_id":"tp53-antibody-sc-f0020","title":"p53 Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003ep53 is a tumor suppressor protein encoded by the TP53 gene, acting as a transcription factor that regulates cell cycle arrest, apoptosis, and DNA repair to maintain genomic stability. Structurally, it is a 393-amino acid protein with an N-terminal transactivation domain (TAD), a proline-rich domain (PRD), a central DNA-binding domain (DBD), a tetramerization domain (TET), and a C-terminal regulatory domain (CTD). p53 is primarily expressed in the nucleus, where its levels are tightly controlled by negative regulators like MDM2 and MDMX, which promote its degradation. Depending on the literature source, TP53 may also be discussed as p53.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm, cytoskeleton, endoplasmic reticulum, and mitochondrion, which can matter when signal is compared across treatments or changing cell states. Following TP53 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eTP53 is commonly interpreted in the context of cancer, metabolism, and dna damage \/ repair research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm, cytoskeleton, and endoplasmic reticulum, 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 cytoplasm, cytoskeleton, and endoplasmic reticulum across matched conditions\u003c\/li\u003e\n\u003cli\u003echanges associated with proliferative state, oncogenic signaling, or treatment response\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003estress-induced changes after checkpoint activation or genotoxic challenge\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 TP53. 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 TP53 reflect biology rather than handling. When interpreting TP53, 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 TP53 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":57577380151641,"sku":"F0020-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577380184409,"sku":"F0020-100UL","price":319.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577380217177,"sku":"F0020-2X100UL","price":479.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0020-IF.png?v=1773598084"},{"product_id":"eif2ak3-antibody-sc-f0027","title":"PERK Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eEIF2AK3 is a target of interest in many antibody-based workflows. PERK, an ER-resident sensor and eIF2α kinase, is one of three ER stress sensors in mammalian cells, along with IRE1α and ATF6, triggering the unfolded protein response (UPR). It plays a crucial role in mitochondrial and thermogenic gene expression via GABPα activation and UCP1-mediated thermogenesis both in vitro and in vivo. Depending on the literature source, EIF2AK3 may also be discussed as PERK.\u003c\/p\u003e\u003cp\u003eReported cellular context includes endoplasmic reticulum membrane, which can matter when signal is compared across treatments or changing cell states. Following EIF2AK3 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eEIF2AK3 is commonly interpreted in the context of metabolism and cell signaling research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans endoplasmic reticulum membrane, 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\u003esignal enrichment within endoplasmic reticulum membrane relative to the broader cellular background\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003esignal-dependent shifts after ligand, inhibitor, or growth-factor perturbation\u003c\/li\u003e\n\u003cli\u003eco-patterning with orthogonal markers and control conditions that clarify pathway state\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 EIF2AK3. 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 EIF2AK3 reflect biology rather than handling. When interpreting EIF2AK3, 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 EIF2AK3 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":57577381364057,"sku":"F0027-20UL","price":169.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577381396825,"sku":"F0027-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577381429593,"sku":"F0027-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0027-wb.gif?v=1773598090"},{"product_id":"bax-antibody-sc-f0037","title":"Bax Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eThe mechanisms of apoptosis contribute to understanding the pathologies associated with uncontrolled cell growth or death. Apoptosis involves two major pathways: the extrinsic or death receptor pathway and the intrinsic or mitochondrial pathway. The intrinsic pathway is regulated by the Bcl-2 (B-cell lymphoma 2) family of proteins, which can be classified based on their anti-apoptotic (e. g., Bcl-2, Bcl-x, Bcl-w, Mcl-1, and A1\/Bfl-1) or pro-apoptotic (e. g., Bax, Bak, and Bok\/Mtd) actions. Depending on the literature source, BAX may also be discussed as NT.\u003c\/p\u003e\u003cp\u003eReported cellular context includes mitochondrion outer membrane and cytoplasm, which can matter when signal is compared across treatments or changing cell states. Following BAX across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eBAX is commonly interpreted in the context of cancer, metabolism, and apoptosis research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans mitochondrion outer membrane 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 outer membrane and cytoplasm across matched conditions\u003c\/li\u003e\n\u003cli\u003echanges associated with proliferative state, oncogenic signaling, or treatment response\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003eseparation of survival-associated changes from stress or death-associated readouts\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 BAX. 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 BAX reflect biology rather than handling. When interpreting BAX, 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 BAX 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":57577381757273,"sku":"F0037-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577381790041,"sku":"F0037-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577381822809,"sku":"F0037-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0037-IHC1.jpg?v=1773598096"},{"product_id":"hif1a-antibody-sc-f0101","title":"HIF-1α Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eHIF1A is a target of interest in many antibody-based workflows. Hypoxia-inducible factor (HIF) is a transcriptional complex crucial for regulating gene expression in response to oxygen levels. The HIF1 complex is made up of two subunits, HIF-1α and HIF-1β, both of which are basic helix-loop-helix proteins belonging to the PAS (Per, ARNT, Sim) family. HIF1 controls the transcription of a wide range of genes that aid in adapting to hypoxic conditions, including those involved in angiogenesis, erythropoiesis, cell cycle regulation, metabolism, and apoptosis. Depending on the literature source, HIF1A may also be discussed as HIF-1alpha and Hypoxia Inducible Factor 1 alpha.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm and nucleus, which can matter when signal is compared across treatments or changing cell states. Following HIF1A across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eHIF1A is commonly interpreted in the context of cancer, metabolism, and cell signaling research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm and nucleus, 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 cytoplasm and nucleus across matched conditions\u003c\/li\u003e\n\u003cli\u003echanges associated with proliferative state, oncogenic signaling, or treatment response\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003esignal-dependent shifts after ligand, inhibitor, or growth-factor perturbation\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 HIF1A. 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 HIF1A reflect biology rather than handling. When interpreting HIF1A, 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 HIF1A 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":57577383264601,"sku":"F0101-20UL","price":149.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577383297369,"sku":"F0101-100UL","price":359.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577383330137,"sku":"F0101-2X100UL","price":539.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0101-IF.png?v=1773598119"},{"product_id":"sqstm1-antibody-sc-f0106","title":"SQSTM1 \/ p62 Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eSQSTM1\/p62, a 62-kDa protein expressed widely across various tissues, is a key autophagy adaptor, binding ubiquitin and autophagy substrates through its ubiquitin-associated domain and LC3-interacting region, respectively. It regulates mitochondrial turnover via mitophagy and serves as a crucial regulator of mtDNA expression machinery, inducing mtDNA expression in renal tubular epithelial cells through p38-dependent upregulation of mitochondrial ribosomal protein L12 (MRPL12). Depending on the literature source, SQSTM1 may also be discussed as SQSTM1 \/ p62 and p62 (Sequestosome-1).\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm, cytosol, endosome, and lysosome, which can matter when signal is compared across treatments or changing cell states. Following SQSTM1 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eSQSTM1 is commonly interpreted in the context of metabolism, autophagy, and cell signaling research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm, cytosol, and endosome, 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 cytoplasm, cytosol, and endosome across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003einterpretation alongside flux, cargo handling, or lysosomal context\u003c\/li\u003e\n\u003cli\u003esignal-dependent shifts after ligand, inhibitor, or growth-factor perturbation\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 SQSTM1. 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 SQSTM1 reflect biology rather than handling. When interpreting SQSTM1, 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 SQSTM1 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":57577383625049,"sku":"F0106-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577383657817,"sku":"F0106-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577383690585,"sku":"F0106-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0106-IF.png?v=1773598124"},{"product_id":"bcl-2-antibody-sc-f0125","title":"Bcl-2 Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eBCL-2 is a crucial anti-apoptotic protein that, along with its pro-survival homologs, prevents programmed cell death during development and in response to cellular stress. It plays a significant role in regulating apoptosis by interacting with pro-apoptotic BH3-only proteins and BAX-like proteins. Anti-apoptotic BCL-2 proteins, which include BCL-XL, BCL-w, MCL-1, and A1, possess three conserved BCL-2 homology (BH) domains-BH1, BH2, and BH3.\u003c\/p\u003e\u003cp\u003eReported cellular context includes mitochondrion outer membrane, nucleus membrane, and endoplasmic reticulum membrane, which can matter when signal is compared across treatments or changing cell states. Following BCL-2 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eBCL-2 is commonly interpreted in the context of metabolism and apoptosis research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans mitochondrion outer membrane, nucleus membrane, and endoplasmic reticulum membrane, 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 outer membrane, nucleus membrane, and endoplasmic reticulum membrane across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003eseparation of survival-associated changes from stress or death-associated readouts\u003c\/li\u003e\n\u003cli\u003eco-patterning with orthogonal markers and control conditions that clarify pathway state\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 BCL-2. 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 BCL-2 reflect biology rather than handling. When interpreting BCL-2, 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 BCL-2 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":57577384018265,"sku":"F0125-20UL","price":159.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577384051033,"sku":"F0125-100UL","price":319.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577384083801,"sku":"F0125-2X100UL","price":479.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0125-IHC1.jpg?v=1773598130"},{"product_id":"foxo1-antibody-sc-f0141","title":"FoxO1 Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eFOXO1 is a target of interest in many antibody-based workflows. The forkhead box O (FoxO) transcription factors are pivotal players in numerous physiological and pathological processes, spanning apoptosis, cell cycle regulation, stress response, glucose metabolism, cellular differentiation, development, and tumor suppression. The potent functions of FoxOs are intricately regulated through various mechanisms, encompassing posttranslational modifications such as phosphorylation, acetylation, methylation, and ubiquitination, subcellular localization, and direct protein-protein interactions. Depending on the literature source, FOXO1 may also be discussed as FKHR\/FOXO1 and FOXO1A.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm and nucleus, which can matter when signal is compared across treatments or changing cell states. Following FOXO1 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eFOXO1 is commonly interpreted in the context of metabolism and developmental biology research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm and nucleus, 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 cytoplasm and nucleus across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003estage-dependent patterns during differentiation, morphogenesis, or lineage commitment\u003c\/li\u003e\n\u003cli\u003eco-patterning with orthogonal markers and control conditions that clarify pathway state\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 FOXO1. 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 FOXO1 reflect biology rather than handling. When interpreting FOXO1, 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 FOXO1 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":57577384771929,"sku":"F0141-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577384804697,"sku":"F0141-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577384837465,"sku":"F0141-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0141-IHC1.jpg?v=1773598141"},{"product_id":"gsk3b-antibody-sc-f0142","title":"GSK-3β Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eGSK3B is a target of interest in many antibody-based workflows. Glycogen synthase kinase-3 (GSK-3) is a crucial serine\/threonine kinase that regulates various cellular functions, including metabolism, transcription, translation, cell growth, and apoptosis. It is present in two similar isoforms, GSK-3α and GSK-3β, which are highly expressed in the brain. GSK-3α and GSK-3β have distinct N-terminal regions but share approximately 98% homology in the internal kinase domain. Depending on the literature source, GSK3B may also be discussed as GSK-3beta.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cell membrane, cytoplasm, membrane, and nucleus, which can matter when signal is compared across treatments or changing cell states. Following GSK3B across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eGSK3B is commonly interpreted in the context of cancer, neuroscience, and metabolism research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cell membrane, cytoplasm, and membrane, 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 cell membrane, cytoplasm, and membrane across matched conditions\u003c\/li\u003e\n\u003cli\u003echanges associated with proliferative state, oncogenic signaling, or treatment response\u003c\/li\u003e\n\u003cli\u003ecompartment-specific patterns relevant to neuronal polarity, transport, or synaptic context\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\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 GSK3B. 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 GSK3B reflect biology rather than handling. When interpreting GSK3B, 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 GSK3B 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":57577384870233,"sku":"F0142-20UL","price":149.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577384903001,"sku":"F0142-100UL","price":359.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577384935769,"sku":"F0142-2X100UL","price":539.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0142-IF.png?v=1773598143"},{"product_id":"mtor-antibody-sc-f0169","title":"mTOR Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eThe mammalian target of rapamycin (mTOR) is a serine-threonine protein kinase with broad regulatory roles in cell growth, proliferation, metabolism, protein synthesis, and autophagy. In the brain, mTOR is vital for synaptic plasticity, learning, and cortical development. It forms two functional complexes, mTORC1 and mTORC2.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm, cytoplasmic vesicle, endoplasmic reticulum, and golgi apparatus, which can matter when signal is compared across treatments or changing cell states. Following MTOR across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eMTOR is commonly interpreted in the context of neuroscience, metabolism, and cell signaling research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm, cytoplasmic vesicle, and endoplasmic reticulum, 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 cytoplasm, cytoplasmic vesicle, and endoplasmic reticulum across matched conditions\u003c\/li\u003e\n\u003cli\u003ecompartment-specific patterns relevant to neuronal polarity, transport, or synaptic context\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003esignal-dependent shifts after ligand, inhibitor, or growth-factor perturbation\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 MTOR. 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 MTOR reflect biology rather than handling. When interpreting MTOR, 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 MTOR 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":57577410789721,"sku":"F0169-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577410822489,"sku":"F0169-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577410855257,"sku":"F0169-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0169-IF.png?v=1773598171"},{"product_id":"glut4-antibody-sc-f0174","title":"Glut4 Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eGLUT4 (Glucose Transporter Type 4) is a member of the SLC2 family of facilitative glucose transporters, primarily found in insulin-sensitive tissues such as skeletal muscle, adipose tissue, and the heart. It is essential for the regulation of glucose homeostasis, as it facilitates glucose uptake into cells in response to insulin or exercise. Depending on the literature source, GLUT4 may also be discussed as Solute carrier family 2 and facilitated glucose transporter member 4.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cell membrane, cytoplasm, and membrane, which can matter when signal is compared across treatments or changing cell states. Following GLUT4 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eGLUT4 is commonly interpreted in the context of metabolism and cell signaling research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cell membrane, cytoplasm, and membrane, 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 cell membrane, cytoplasm, and membrane across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003esignal-dependent shifts after ligand, inhibitor, or growth-factor perturbation\u003c\/li\u003e\n\u003cli\u003eco-patterning with orthogonal markers and control conditions that clarify pathway state\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 GLUT4. 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 GLUT4 reflect biology rather than handling. When interpreting GLUT4, 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 GLUT4 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":57577423503705,"sku":"F0174-20UL","price":169.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577423536473,"sku":"F0174-100UL","price":319.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577423569241,"sku":"F0174-2X100UL","price":479.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0174-wb.gif?v=1773598181"},{"product_id":"bace-antibody-sc-f0179","title":"BACE Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eBACE1 (β-site amyloid precursor protein cleaving enzyme 1) is a 501-amino acid type 1 transmembrane aspartic protease that initiates Aβ generation by cleaving the amyloid precursor protein (APP). Predominantly expressed in brain neurons, BACE1 is synthesized as a zymogen in the endoplasmic reticulum and contains two critical aspartic acid residues (Asp32 and Asp228) in its active site. Depending on the literature source, BACE may also be discussed as BACE1.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cell membrane, cell projection, cytoplasmic vesicle, and endoplasmic reticulum, which can matter when signal is compared across treatments or changing cell states. Following BACE across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eBACE is commonly interpreted in the context of neuroscience and metabolism research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cell membrane, cell projection, and cytoplasmic vesicle, 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 cell membrane, cell projection, and cytoplasmic vesicle across matched conditions\u003c\/li\u003e\n\u003cli\u003ecompartment-specific patterns relevant to neuronal polarity, transport, or synaptic context\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003eco-patterning with orthogonal markers and control conditions that clarify pathway state\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 BACE. 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 BACE reflect biology rather than handling. When interpreting BACE, 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 BACE 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":57577423798617,"sku":"F0179-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577423831385,"sku":"F0179-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577423864153,"sku":"F0179-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0179-IF.png?v=1773598185"},{"product_id":"rps6-antibody-sc-f0198","title":"Phospho-S6 Ribosomal Protein (S235\/236) Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eRPS6 is a target of interest in many antibody-based workflows. Growth factors and mitogens effectively stimulate sustained cell growth and proliferation by upregulating mRNA translation. Activation of p70 S6 kinase and subsequent phosphorylation of S6 ribosomal protein are key events in this process. Phosphorylation of S6 ribosomal protein leads to enhanced translation of mRNA transcripts containing an oligopyrimidine tract in their 5' untranslated regions. Depending on the literature source, RPS6 may also be discussed as Phospho-S6 Ribosomal Protein (S235\/236) and Phospho-S6 Ribosomal Protein (Ser235\/236).\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm and nucleus, which can matter when signal is compared across treatments or changing cell states. Following RPS6 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eRPS6 is commonly interpreted in the context of metabolism research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm and nucleus, 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 cytoplasm and nucleus across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003edifferences between total target abundance and site-specific regulation when modified forms are compared\u003c\/li\u003e\n\u003cli\u003eco-patterning with orthogonal markers and control conditions that clarify pathway state\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 RPS6. 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 RPS6 reflect biology rather than handling. When interpreting RPS6, 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 RPS6 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":57577424290137,"sku":"F0198-20UL","price":169.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577424322905,"sku":"F0198-100UL","price":419.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577424355673,"sku":"F0198-2X100UL","price":629.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0198-IF.png?v=1773598195"},{"product_id":"akt1-antibody-sc-f0217","title":"Akt1 Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eAKT, a central protein in many cellular pathways, plays crucial roles in cell survival, proliferation, glucose uptake, metabolism, angiogenesis, and responses to radiation and drugs. There are three isoforms of AKT: AKT1, AKT2, and AKT3, each with distinct physiological functions, properties, and expression patterns that vary depending on the cell type.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cell membrane, cytoplasm, membrane, and mitochondrion, which can matter when signal is compared across treatments or changing cell states. Following AKT1 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eAKT1 is commonly interpreted in the context of metabolism and cell signaling research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cell membrane, cytoplasm, and membrane, 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 cell membrane, cytoplasm, and membrane across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003esignal-dependent shifts after ligand, inhibitor, or growth-factor perturbation\u003c\/li\u003e\n\u003cli\u003eco-patterning with orthogonal markers and control conditions that clarify pathway state\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 AKT1. 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 AKT1 reflect biology rather than handling. When interpreting AKT1, 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 AKT1 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":57577432219993,"sku":"F0217-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577432252761,"sku":"F0217-100UL","price":319.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577432285529,"sku":"F0217-2X100UL","price":479.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0217-wb.gif?v=1773598203"},{"product_id":"akt2-antibody-sc-f0218","title":"Akt2 Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eAkt, also known as PKB or Rac, plays a critical role in regulating cell survival and apoptosis. There are three isoforms of AKT (AKT1, AKT2, and AKT3), each with distinct functions, characteristics, and expression patterns depending on the cell type. AKT2 is particularly associated with various aspects of cancer biology, including cell metabolism, proliferation, survival, metastasis, angiogenesis, and resistance to drugs.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cell membrane, cytoplasm, endosome, and membrane, which can matter when signal is compared across treatments or changing cell states. Following AKT2 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eAKT2 is commonly interpreted in the context of metabolism and cell signaling research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cell membrane, cytoplasm, and endosome, 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 cell membrane, cytoplasm, and endosome across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003esignal-dependent shifts after ligand, inhibitor, or growth-factor perturbation\u003c\/li\u003e\n\u003cli\u003eco-patterning with orthogonal markers and control conditions that clarify pathway state\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 AKT2. 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 AKT2 reflect biology rather than handling. When interpreting AKT2, 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 AKT2 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":57577432318297,"sku":"F0218-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577432351065,"sku":"F0218-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577432383833,"sku":"F0218-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0218-wb.gif?v=1773598204"},{"product_id":"bcl2l11-antibody-sc-f0222","title":"Bim Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eBCL2L11 is a target of interest in many antibody-based workflows. Bim\/Bod is a pro-apoptotic protein that belongs to the BH3-only group of Bcl-2 family members and is located on chromosome 2q13 in humans. There are three main Bim isoforms in humans: BimEL (198 aa; 22. 1 kDa), BimL (138 aa; 15. Depending on the literature source, BCL2L11 may also be discussed as Bim.\u003c\/p\u003e\u003cp\u003eReported cellular context includes membrane and mitochondrion, which can matter when signal is compared across treatments or changing cell states. Following BCL2L11 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eBCL2L11 is commonly interpreted in the context of metabolism and apoptosis research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans membrane and mitochondrion, 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 membrane and mitochondrion across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003eseparation of survival-associated changes from stress or death-associated readouts\u003c\/li\u003e\n\u003cli\u003eco-patterning with orthogonal markers and control conditions that clarify pathway state\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 BCL2L11. 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 BCL2L11 reflect biology rather than handling. When interpreting BCL2L11, 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 BCL2L11 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":57577432613209,"sku":"F0222-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577432645977,"sku":"F0222-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577432678745,"sku":"F0222-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0222-IHC1.jpg?v=1773598211"},{"product_id":"cav1-antibody-sc-f0224","title":"Caveolin-1 Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eCAV1 is a target of interest in many antibody-based workflows. Caveolae are small pits, typically measuring 50-100 nm, found in the plasma membrane of most mammalian cells. There are three known members of the caveolin family: caveolin-1, 2, and 3, with distinct tissue distributions. Among these, caveolin-1 is a crucial membrane protein that plays a structural role in caveolae formation. Depending on the literature source, CAV1 may also be discussed as Caveolin-1 and Caveolin 1.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cell membrane, golgi apparatus, and membrane, which can matter when signal is compared across treatments or changing cell states. Following CAV1 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eCAV1 is commonly interpreted in the context of metabolism research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cell membrane, golgi apparatus, and membrane, 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 cell membrane, golgi apparatus, and membrane across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\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 CAV1. 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 CAV1 reflect biology rather than handling. When interpreting CAV1, 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 CAV1 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":57577432711513,"sku":"F0224-20UL","price":149.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577432744281,"sku":"F0224-100UL","price":359.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577432777049,"sku":"F0224-2X100UL","price":539.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0224-IHC1.jpg?v=1773598213"},{"product_id":"fasn-antibody-sc-f0225","title":"Fatty Acid Synthase Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eFatty acid synthetase (FASN) is a multifunctional enzyme responsible for synthesizing long-chain saturated fatty acids from acetyl-CoA and malonyl-CoA, utilizing NADPH. As an active homodimer, FASN possesses seven distinct catalytic activities and is involved in lipid production, primarily in the liver for distribution to metabolically active tissues or storage in adipose tissue. Depending on the literature source, FASN may also be discussed as Fatty Acid Synthase.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm, which can matter when signal is compared across treatments or changing cell states. Following FASN across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eFASN is commonly interpreted in the context of cancer and metabolism research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans 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\u003esignal enrichment within cytoplasm relative to the broader cellular background\u003c\/li\u003e\n\u003cli\u003echanges associated with proliferative state, oncogenic signaling, or treatment response\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003eco-patterning with orthogonal markers and control conditions that clarify pathway state\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 FASN. 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 FASN reflect biology rather than handling. When interpreting FASN, 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 FASN 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":57577432908121,"sku":"F0225-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577432940889,"sku":"F0225-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577432973657,"sku":"F0225-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0225-IHC1.jpg?v=1773598215"},{"product_id":"hk2-antibody-sc-f0228","title":"Hexokinase II Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eHexokinase II (HK2), a key member of the hexokinase family, is an enzyme that catalyzes the phosphorylation of glucose to glucose-6-phosphate, the first step in glycolysis. Structurally, it is a 100 kDa protein with N-terminal and C-terminal domains for ATP binding, catalysis, and interaction with mitochondrial proteins like VDAC on the outer mitochondrial membrane, enabling direct ATP access and apoptosis inhibition. Depending on the literature source, HK2 may also be discussed as Hexokinase II and HXK II.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm, membrane, mitochondrion, and mitochondrion outer membrane, which can matter when signal is compared across treatments or changing cell states. Following HK2 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eHK2 is commonly interpreted in the context of metabolism research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm, membrane, and mitochondrion, 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 cytoplasm, membrane, and mitochondrion across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\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 HK2. 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 HK2 reflect biology rather than handling. When interpreting HK2, 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 HK2 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":57577433137497,"sku":"F0228-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577433170265,"sku":"F0228-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577433203033,"sku":"F0228-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0228-wb.gif?v=1773598217"},{"product_id":"ern1-antibody-sc-f0229","title":"IRE1α Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eERN1 is a target of interest in many antibody-based workflows. IRE1 is a conserved dual endoribonuclease\/protein kinase that is indispensable for directing the endoplasmic reticulum (ER) stress response in yeast, flies, and worms. IRE1 initiaties the ESR, showing IRE1 and XBP-1 could regulate cell proliferation. IRE1α activity controls a subset of the ER stress response and mediates proliferation through tight control of Xbp-1 splicing. Depending on the literature source, ERN1 may also be discussed as IRE1alpha.\u003c\/p\u003e\u003cp\u003eReported cellular context includes endoplasmic reticulum membrane, which can matter when signal is compared across treatments or changing cell states. Following ERN1 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eERN1 is commonly interpreted in the context of metabolism and cell signaling research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans endoplasmic reticulum membrane, 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\u003esignal enrichment within endoplasmic reticulum membrane relative to the broader cellular background\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003esignal-dependent shifts after ligand, inhibitor, or growth-factor perturbation\u003c\/li\u003e\n\u003cli\u003eco-patterning with orthogonal markers and control conditions that clarify pathway state\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 ERN1. 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 ERN1 reflect biology rather than handling. When interpreting ERN1, 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 ERN1 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":57577433268569,"sku":"F0229-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577433301337,"sku":"F0229-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577433334105,"sku":"F0229-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0229-wb.gif?v=1773598218"},{"product_id":"insr-antibody-sc-f0230","title":"Insulin Receptor β Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eINSR is a target of interest in many antibody-based workflows. The Insulin Receptor β is a tyrosine-specific protein kinase with distinctive kinase activity. This tyrosine kinase activity is intrinsic to the β subunit of the insulin receptor and is directly stimulated by insulin binding. Insulin regulates cellular metabolism and growth by stimulating tyrosine phosphorylation of the β subunit of the insulin receptor. Depending on the literature source, INSR may also be discussed as Insulin Receptor beta.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cell membrane, endosome, lysosome, and membrane, which can matter when signal is compared across treatments or changing cell states. Following INSR across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eINSR is commonly interpreted in the context of metabolism, endocrinology, and cell signaling research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cell membrane, endosome, and lysosome, 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 cell membrane, endosome, and lysosome across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003eresponses to hormone-dependent signaling or endocrine feedback context\u003c\/li\u003e\n\u003cli\u003esignal-dependent shifts after ligand, inhibitor, or growth-factor perturbation\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 INSR. 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 INSR reflect biology rather than handling. When interpreting INSR, 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 INSR 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":57577433366873,"sku":"F0230-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577433399641,"sku":"F0230-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577433432409,"sku":"F0230-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0230-wb.gif?v=1773598220"},{"product_id":"stk11-antibody-sc-f0235","title":"LKB1 Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eLKB1 (STK11) is a type of protein known as a serine\/threonine kinase and serves as a crucial tumor suppressor. It plays a significant role in regulating cell structure, apoptosis (cell death), and maintaining energy balance within cells by controlling various downstream kinases. Initially identified as the gene responsible for Peutz-Jeghers syndrome (PJS), a condition characterized by the presence of hamartomatous polyps and distinctive pigmentation of the oral mucosa, LKB1 has since been implicated in a range of malignancies, including lung, gastrointestinal, pancreatic, and breast cancers. Depending on the literature source, STK11 may also be discussed as LKB1.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm, membrane, mitochondrion, and nucleus, which can matter when signal is compared across treatments or changing cell states. Following STK11 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eSTK11 is commonly interpreted in the context of cancer, metabolism, and cell signaling research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm, membrane, and mitochondrion, 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 cytoplasm, membrane, and mitochondrion across matched conditions\u003c\/li\u003e\n\u003cli\u003echanges associated with proliferative state, oncogenic signaling, or treatment response\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003esignal-dependent shifts after ligand, inhibitor, or growth-factor perturbation\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 STK11. 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 STK11 reflect biology rather than handling. When interpreting STK11, 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 STK11 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":57577433760089,"sku":"F0235-20UL","price":169.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577433792857,"sku":"F0235-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577433825625,"sku":"F0235-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0235-IHC1.jpg?v=1773598226"},{"product_id":"igf1r-ir-antibody-sc-f0247","title":"Phospho-IGF-1R (Y1135\/1136)\/IR (Y1150\/1151) Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eIGF1R\/IR is a target of interest in many antibody-based workflows. Phospho-IGF-I Receptor β (Tyr1135\/1136) and Insulin Receptor β (Tyr1150\/1151) are phosphorylated forms of specific tyrosine residues on the β subunits of the IGF-1 receptor (IGF-1R) and insulin receptor (IR), respectively. These receptors are heterotetrameric transmembrane tyrosine kinases composed of two alpha and two beta subunits. Depending on the literature source, IGF1R\/IR may also be discussed as Phospho-IGF-1R (Y1135\/1136)\/IR (Y1150\/1151) and Phospho IGFIR (Tyr1135\/1136).\u003c\/p\u003e\u003cp\u003eReported cellular context includes cell membrane, membrane, endosome, and lysosome, which can matter when signal is compared across treatments or changing cell states. Following IGF1R\/IR 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\u003eIGF1R\/IR is commonly interpreted in the context of immunology, metabolism, and developmental biology research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cell membrane, membrane, and endosome, 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 cell membrane, membrane, and endosome across matched conditions\u003c\/li\u003e\n\u003cli\u003econtext differences tied to immune-cell state, activation, or lineage composition\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003estage-dependent patterns during differentiation, morphogenesis, or lineage commitment\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 IGF1R\/IR. 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 IGF1R\/IR reflect biology rather than handling. When interpreting IGF1R\/IR, 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 IGF1R\/IR 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":57577434939737,"sku":"F0247-20UL","price":159.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577434972505,"sku":"F0247-100UL","price":389.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577435005273,"sku":"F0247-2X100UL","price":579.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0247-IHC1.jpg?v=1773598246"},{"product_id":"eif2alpha-antibody-sc-f0257","title":"Phospho-eIF2α (Ser51) Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eEIF2ALPHA is a target of interest in many antibody-based workflows. Glucose deficiency triggers the unfolded protein response (UPR), an essential pathway evolved to manage protein misfolding stress within the endoplasmic reticulum (ER). One crucial component of the UPR is PERK, which phosphorylates the alpha subunit of eukaryotic translation initiation factor 2 (eIF2) at serine 51 (S51). Depending on the literature source, EIF2ALPHA may also be discussed as Phospho-eIF2alpha (Ser51) and EIF2S1 (phospho S51).\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm and mitochondrion, which can matter when signal is compared across treatments or changing cell states. Following EIF2ALPHA across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eEIF2ALPHA is commonly interpreted in the context of metabolism research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm and mitochondrion, 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 cytoplasm and mitochondrion across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003edifferences between total target abundance and site-specific regulation when modified forms are compared\u003c\/li\u003e\n\u003cli\u003eco-patterning with orthogonal markers and control conditions that clarify pathway state\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 EIF2ALPHA. 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 EIF2ALPHA reflect biology rather than handling. When interpreting EIF2ALPHA, 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 EIF2ALPHA 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":57577435234649,"sku":"F0257-20UL","price":169.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577435267417,"sku":"F0257-100UL","price":419.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577435300185,"sku":"F0257-2X100UL","price":629.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0257-IHC1.jpg?v=1773598249"},{"product_id":"tcf7l2-antibody-sc-f0265","title":"TCF4\/TCF7L2 Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eTCF4 (Transcription Factor 4) and TCF7L2 (T-cell factor 7-like 2) are transcription factors from the TCF\/LEF family, central to the Wnt\/β-catenin signalling pathway, which regulates cell fate, differentiation, and metabolism. Structurally, both proteins contain a high-mobility group (HMG) box domain that allows DNA binding and transcriptional regulation. Depending on the literature source, TCF7L2 may also be discussed as TCF4\/TCF7L2 and TCF-4.\u003c\/p\u003e\u003cp\u003eReported cellular context includes nucleus, which can matter when signal is compared across treatments or changing cell states. Following TCF7L2 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eTCF7L2 is commonly interpreted in the context of neuroscience, metabolism, and developmental biology research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans nucleus, 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\u003esignal enrichment within nucleus relative to the broader cellular background\u003c\/li\u003e\n\u003cli\u003ecompartment-specific patterns relevant to neuronal polarity, transport, or synaptic context\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003estage-dependent patterns during differentiation, morphogenesis, or lineage commitment\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 TCF7L2. 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 TCF7L2 reflect biology rather than handling. When interpreting TCF7L2, 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 TCF7L2 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":57577436021081,"sku":"F0265-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577436053849,"sku":"F0265-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577436086617,"sku":"F0265-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0265-wb.gif?v=1773598260"},{"product_id":"aifm1-antibody-sc-f0268","title":"AIF Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eAIFM1 is a target of interest in many antibody-based workflows. Apoptosis-inducing factor (AIF), also known as programmed cell death 8 (Pdcd8), is a conserved flavoprotein with pyridine nucleotide-disulphide oxidoreductase and DNA binding domains. In vivo, AIF protects against neuronal apoptosis caused by oxidative stress. However, in vitro, AIF has a proapoptotic role where it translocates to the nucleus upon induction of the mitochondrial death pathway, leading to chromatin condensation and DNA fragmentation. Depending on the literature source, AIFM1 may also be discussed as AIF.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm, membrane, mitochondrion, and mitochondrion inner membrane, which can matter when signal is compared across treatments or changing cell states. Following AIFM1 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eAIFM1 is commonly interpreted in the context of neuroscience, cardiovascular, and metabolism research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm, membrane, and mitochondrion, 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 cytoplasm, membrane, and mitochondrion across matched conditions\u003c\/li\u003e\n\u003cli\u003ecompartment-specific patterns relevant to neuronal polarity, transport, or synaptic context\u003c\/li\u003e\n\u003cli\u003echanges linked to vascular, contractile, or hemodynamic cell-state cues\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\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 AIFM1. 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 AIFM1 reflect biology rather than handling. When interpreting AIFM1, 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 AIFM1 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":57577436348761,"sku":"F0268-20UL","price":149.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577436381529,"sku":"F0268-100UL","price":359.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577436414297,"sku":"F0268-2X100UL","price":539.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0268-IF.png?v=1773598265"},{"product_id":"prkaa1-prkaa2-antibody-sc-f0269","title":"AMPKα Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eAMP-activated protein kinase (AMPK) functions as a sensor of cellular energy levels, modulating processes involved in ATP consumption or regeneration to maintain cellular energy balance. AMPK exists as a heterotrimeric complex comprising a catalytic α-subunit and two regulatory subunits, β and γ. Both isoforms of the catalytic subunit (AMPKα1 and AMPKα2) are activated in response to ATP depletion, triggered by events such as exercise and cellular stress, which elevate cellular AMP levels as ATP levels decline due to adenylate kinase activity. Depending on the literature source, AMPKα may also be discussed as AMPKalpha.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm and nucleus, which can matter when signal is compared across treatments or changing cell states. Following AMPKα 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\u003eAMPKα is commonly interpreted in the context of metabolism and cell signaling research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm and nucleus, 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 cytoplasm and nucleus across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003esignal-dependent shifts after ligand, inhibitor, or growth-factor perturbation\u003c\/li\u003e\n\u003cli\u003eco-patterning with orthogonal markers and control conditions that clarify pathway state\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 AMPKα. 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 AMPKα reflect biology rather than handling. When interpreting AMPKα, 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 AMPKα 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":57577436447065,"sku":"F0269-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577436479833,"sku":"F0269-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577436512601,"sku":"F0269-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0269-wb.gif?v=1773598266"},{"product_id":"acaca-acacb-antibody-sc-f0270","title":"Acetyl-CoA Carboxylase Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eAcetyl-CoA carboxylase (ACC) is a biotin-dependent enzyme that catalyzes the irreversible carboxylation of acetyl-CoA to produce malonyl-CoA through its two catalytic activities, biotin carboxylase (BC) and carboxyltransferase (CT). Eukaryotic acetyl-CoA carboxylases are 250-kDa single-chain, multi-domain enzymes that function as dimers and higher oligomers. There are two types: ACC1 and ACC2. Depending on the literature source, Acetyl-CoA Carboxylase may also be discussed as Acetyl-CoA Carboxylase and ACC1.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm, which can matter when signal is compared across treatments or changing cell states. Following Acetyl-CoA Carboxylase 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\u003eAcetyl-CoA Carboxylase is commonly interpreted in the context of metabolism research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans 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\u003esignal enrichment within cytoplasm relative to the broader cellular background\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003edifferences between total target abundance and site-specific regulation when modified forms are compared\u003c\/li\u003e\n\u003cli\u003eco-patterning with orthogonal markers and control conditions that clarify pathway state\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 Acetyl-CoA Carboxylase. 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 Acetyl-CoA Carboxylase reflect biology rather than handling. When interpreting Acetyl-CoA Carboxylase, 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 Acetyl-CoA Carboxylase 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":57577436578137,"sku":"F0270-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577436610905,"sku":"F0270-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577436643673,"sku":"F0270-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0270-IF.png?v=1773598268"},{"product_id":"mitochondrial-antibody-sc-f0274","title":"COX IV Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eCOX IV (Cytochrome c oxidase subunit IV) is a small nuclear-encoded protein that plays a critical regulatory and structural role in mitochondrial complex IV, the terminal enzyme of the electron transport chain. It is synthesized with an N-terminal mitochondrial transit peptide that directs its import into mitochondria, where the mature protein integrates into the cytochrome c oxidase complex near the heme and copper catalytic centers. Depending on the literature source, MITOCHONDRIAL may also be discussed as COX IV and cytochrome c oxidase subunit 4 isoform 1.\u003c\/p\u003e\u003cp\u003eReported cellular context includes membrane, mitochondrion, and mitochondrion inner membrane, which can matter when signal is compared across treatments or changing cell states. Following MITOCHONDRIAL across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eMITOCHONDRIAL is commonly interpreted in the context of metabolism research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans membrane, mitochondrion, and mitochondrion inner membrane, 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 membrane, mitochondrion, and mitochondrion inner membrane across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\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 MITOCHONDRIAL. 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 MITOCHONDRIAL reflect biology rather than handling. When interpreting MITOCHONDRIAL, 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 MITOCHONDRIAL 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":57577436971353,"sku":"F0274-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577437004121,"sku":"F0274-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577437036889,"sku":"F0274-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0274-IF.png?v=1773598277"},{"product_id":"gsk3a-gsk3b-antibody-sc-f0282","title":"GSK-3α\/β Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eGSK-3α\/β is a target of interest in many antibody-based workflows. Glycogen synthase kinase-3 (GSK-3) is a type of serine\/threonine kinase that plays a vital role in regulating various cellular functions, including metabolism, transcription, translation, cell growth, and apoptosis. GSK-3 exists in two similar isoforms, known as GSK-3α and GSK-3β. These isoforms share almost identical central kinase domains but differ substantially in their termini. Depending on the literature source, GSK-3α\/β may also be discussed as GSK-3alpha\/beta and GSK3.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cell membrane, cytoplasm, membrane, and nucleus, which can matter when signal is compared across treatments or changing cell states. Following GSK-3α\/β 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\u003eGSK-3α\/β is commonly interpreted in the context of neuroscience, metabolism, and cell signaling research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cell membrane, cytoplasm, and membrane, 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 cell membrane, cytoplasm, and membrane across matched conditions\u003c\/li\u003e\n\u003cli\u003ecompartment-specific patterns relevant to neuronal polarity, transport, or synaptic context\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003esignal-dependent shifts after ligand, inhibitor, or growth-factor perturbation\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 GSK-3α\/β. 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 GSK-3α\/β reflect biology rather than handling. When interpreting GSK-3α\/β, 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 GSK-3α\/β 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":57577437561177,"sku":"F0282-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577437593945,"sku":"F0282-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577437626713,"sku":"F0282-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0282-wb.gif?v=1773598286"},{"product_id":"pik3ca-antibody-sc-f0293","title":"PI3 Kinase p110α Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003ePI3 Kinase p110α (Phosphoinositide 3-kinase p110α, PI3K p110α), encoded by the PIK3CA gene, is a catalytic subunit of class I PI3Ks that plays a central role in cell signaling downstream of receptor tyrosine kinases (RTKs), small GTPases like Ras, and G-protein βγ subunits. Structurally, p110α consists of multiple domains, including the adaptor-binding domain (ABD), Ras-binding domain (RBD), C2 domain, helical domain, and the kinase domain, and forms a heterodimer with a regulatory subunit (e. g., p85α), which contains SH2 domains critical for recruitment to phosphotyrosine residues. Depending on the literature source, PIK3CA may also be discussed as PI3 Kinase p110alpha and Phosphatidylinositol 4.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm, cytosol, and plasma membrane, which can matter when signal is compared across treatments or changing cell states. Following PIK3CA across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003ePIK3CA is commonly interpreted in the context of cancer, metabolism, and cell signaling research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm, cytosol, and plasma membrane, 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 cytoplasm, cytosol, and plasma membrane across matched conditions\u003c\/li\u003e\n\u003cli\u003echanges associated with proliferative state, oncogenic signaling, or treatment response\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003esignal-dependent shifts after ligand, inhibitor, or growth-factor perturbation\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 PIK3CA. 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 PIK3CA reflect biology rather than handling. When interpreting PIK3CA, 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 PIK3CA 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":57577438347609,"sku":"F0293-20UL","price":199.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577438380377,"sku":"F0293-100UL","price":489.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577438413145,"sku":"F0293-2X100UL","price":729.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0293-wb.gif?v=1773598297"},{"product_id":"pik3r1-antibody-sc-f0294","title":"PI3 Kinase p85 Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003ePIK3R1 is a target of interest in many antibody-based workflows. Middle T antigen (MT) analysis has revealed significant insights into tyrosine phosphorylation and phosphatidylinositol 3-kinase (PI3K). PI3K is activated upon binding to phosphotyrosine residues of growth factor receptors or adaptors, as well as by integrin-dependent cell adhesion and G protein-coupled receptors. Its lipid product, phosphatidylinositol-3,4,5 trisphosphate (PIP3), recruits signaling proteins with pleckstrin homology (PH) domains to the membrane, activating them. Depending on the literature source, PIK3R1 may also be discussed as PI3 Kinase p85 and Phosphatidylinositol 3-kinase regulatory subunit alpha.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm, cytosol, nucleus, and perinuclear region of cytoplasm, which can matter when signal is compared across treatments or changing cell states. Following PIK3R1 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003ePIK3R1 is commonly interpreted in the context of immunology, metabolism, and cell signaling research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm, cytosol, and nucleus, 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 cytoplasm, cytosol, and nucleus across matched conditions\u003c\/li\u003e\n\u003cli\u003econtext differences tied to immune-cell state, activation, or lineage composition\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003esignal-dependent shifts after ligand, inhibitor, or growth-factor perturbation\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 PIK3R1. 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 PIK3R1 reflect biology rather than handling. When interpreting PIK3R1, 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 PIK3R1 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":57577438445913,"sku":"F0294-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577438478681,"sku":"F0294-100UL","price":319.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577438511449,"sku":"F0294-2X100UL","price":479.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0294-wb.gif?v=1773598299"},{"product_id":"park2-antibody-sc-f0296","title":"Parkin Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003ePARK2 is a target of interest in many antibody-based workflows. Parkin, a cytosolic E3 ubiquitin ligase, and PINK1, a mitochondrial protein kinase, play crucial roles in mitochondrial quality control. Parkin contains a ubiquitin-like domain (Ubl) at its N-terminus and four zinc-coordinating RING-like domains (RING0, RING1, IBR, and RING2). The Ubl domain plays a special role in parkin activation and a number of binding partners that bind the Ubl domain are associated with parkin activation. Depending on the literature source, PARK2 may also be discussed as Parkin.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cell projection, cytoplasm, endoplasmic reticulum, and membrane, which can matter when signal is compared across treatments or changing cell states. Following PARK2 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003ePARK2 is commonly interpreted in the context of metabolism research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cell projection, cytoplasm, and endoplasmic reticulum, 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 cell projection, cytoplasm, and endoplasmic reticulum across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\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 PARK2. 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 PARK2 reflect biology rather than handling. When interpreting PARK2, 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 PARK2 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":57577438642521,"sku":"F0296-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577438675289,"sku":"F0296-100UL","price":319.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577438708057,"sku":"F0296-2X100UL","price":479.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0296-wb.gif?v=1773598302"},{"product_id":"ulk1-antibody-sc-f0305","title":"Phospho-ULK1 (Ser555) Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003ePhospho-ULK1 (Ser555) is a crucial phosphorylation site within the ULK1 protein complex, pivotal for regulating autophagy, a cellular process crucial for maintaining metabolic balance. AMP-activated protein kinase (AMPK) phosphorylates Ser555 in response to energy stress like glucose deprivation, enhancing ULK1 activity and promoting autophagy initiation. Depending on the literature source, ULK1 may also be discussed as Phospho-ULK1 (Ser555) and ULK1 (phospho S555).\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm, which can matter when signal is compared across treatments or changing cell states. Following ULK1 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eULK1 is commonly interpreted in the context of metabolism and cell signaling research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans 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\u003esignal enrichment within cytoplasm relative to the broader cellular background\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003esignal-dependent shifts after ligand, inhibitor, or growth-factor perturbation\u003c\/li\u003e\n\u003cli\u003edifferences between total target abundance and site-specific regulation when modified forms are compared\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 ULK1. 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 ULK1 reflect biology rather than handling. When interpreting ULK1, 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 ULK1 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":57577440051545,"sku":"F0305-20UL","price":159.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577440084313,"sku":"F0305-100UL","price":389.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577440117081,"sku":"F0305-2X100UL","price":579.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0305-wb.gif?v=1773598311"},{"product_id":"vdac-antibody-sc-f0314","title":"VDAC Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eThe voltage-dependent anion channel (VDAC) is a type of β-barrel membrane protein found in the outer mitochondrial membrane (OMM). It serves to control the movement of crucial metabolites into and out of the mitochondria using a voltage-sensing mechanism and through ligand binding under physiologically relevant conditions. Depending on the literature source, VDAC may also be discussed as Porin and VDAC1.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cell membrane, membrane, mitochondrion, and mitochondrion outer membrane, which can matter when signal is compared across treatments or changing cell states. Following VDAC across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eVDAC is commonly interpreted in the context of metabolism and apoptosis research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cell membrane, membrane, and mitochondrion, 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 cell membrane, membrane, and mitochondrion across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003eseparation of survival-associated changes from stress or death-associated readouts\u003c\/li\u003e\n\u003cli\u003eco-patterning with orthogonal markers and control conditions that clarify pathway state\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 VDAC. 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 VDAC reflect biology rather than handling. When interpreting VDAC, 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 VDAC 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":57577442312537,"sku":"F0314-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577442345305,"sku":"F0314-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577442378073,"sku":"F0314-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0314-IHC1.jpg?v=1773598324"},{"product_id":"atf4-antibody-sc-f0318","title":"ATF-4 Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eActivating Transcription Factor 4 (ATF4) is a key stress-responsive protein that regulates cellular adaptation to stress conditions, including endoplasmic reticulum (ER) stress, nutrient deprivation, and oxidative damage. It functions through the eIF2α\/ATF4 axis within the integrated stress response (ISR), enabling cells to adjust by upregulating genes involved in autophagy, amino acid metabolism, and redox balance. Depending on the literature source, ATF4 may also be discussed as ATF-4 and CREB-2\/ATF-4.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cell membrane, cytoplasm, cytoskeleton, and membrane, which can matter when signal is compared across treatments or changing cell states. Following ATF4 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eATF4 is commonly interpreted in the context of metabolism and oxidative stress research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cell membrane, cytoplasm, and cytoskeleton, 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 cell membrane, cytoplasm, and cytoskeleton across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\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\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 ATF4. 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 ATF4 reflect biology rather than handling. When interpreting ATF4, 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 ATF4 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":57577442574681,"sku":"F0318-20UL","price":169.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577442607449,"sku":"F0318-100UL","price":319.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577442640217,"sku":"F0318-2X100UL","price":479.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0318-IF.png?v=1773598328"},{"product_id":"bak-antibody-sc-f0321","title":"Bak Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eBak, a proapoptotic member of the Bcl-2 family, shares sequence homology at BH1, BH2, and BH3 regions, exerting pro-apoptotic activity. Oligopeptides corresponding to the BH3 region of Bax and Bak can induce apoptotic cell death. BAK serves as an essential gateway to mitochondrial dysfunction required for cell death in response to various stimuli.\u003c\/p\u003e\u003cp\u003eReported cellular context includes membrane, mitochondrion, and mitochondrion outer membrane, which can matter when signal is compared across treatments or changing cell states. Following BAK across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eBAK is commonly interpreted in the context of metabolism and apoptosis research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans membrane, mitochondrion, and mitochondrion outer membrane, 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 membrane, mitochondrion, and mitochondrion outer membrane across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003eseparation of survival-associated changes from stress or death-associated readouts\u003c\/li\u003e\n\u003cli\u003eco-patterning with orthogonal markers and control conditions that clarify pathway state\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 BAK. 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 BAK reflect biology rather than handling. When interpreting BAK, 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 BAK 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":57577443393881,"sku":"F0321-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577443426649,"sku":"F0321-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577443459417,"sku":"F0321-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0321-IF.png?v=1773598340"},{"product_id":"drp1-antibody-sc-f0328","title":"DRP1 Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eThe mammalian homolog of C. elegans DRP-1 and yeast Dnm1p, alternatively known as Drp1, Dlp1, DVLP, or Dymple, belongs to the dynamin superfamily of GTPases. Members of this family have diverse cellular functions including vesicle scission, organelle fission, viral resistance, and intracellular traffickingDrp1 causes mitochondria to collapse into perinuclear clusters, which indicates that Drp1 specifically affects mitochondrial morphology. Depending on the literature source, DRP1 may also be discussed as DLP1 and DNM1L.\u003c\/p\u003e\u003cp\u003eReported cellular context includes coated pit, cytoplasm, cytoplasmic vesicle, and golgi apparatus, which can matter when signal is compared across treatments or changing cell states. Following DRP1 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eDRP1 is commonly interpreted in the context of metabolism research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans coated pit, cytoplasm, and cytoplasmic vesicle, 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 coated pit, cytoplasm, and cytoplasmic vesicle across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\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 DRP1. 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 DRP1 reflect biology rather than handling. When interpreting DRP1, 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 DRP1 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":57577443787097,"sku":"F0328-20UL","price":169.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577443819865,"sku":"F0328-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577443852633,"sku":"F0328-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0328-IF.png?v=1773598346"},{"product_id":"prkab1-prkab2-antibody-sc-f0371","title":"AMPKβ1\/2 Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eAMPKβ1\/2 is a target of interest in many antibody-based workflows. AMP-activated protein kinase (AMPK) is a well-known energy sensor that is activated to regulate diverse signals and metabolic pathways in response to various stimuli including caloric restriction sustaining energy homeostasis and serving as the guardian of metabolism. AMPK is a heterotrimeric complex composed of a catalytic α subunit and regulatory β and γ subunits, each of which is encoded by two or three distinct genes (α1, 2; β1, 2; γ1, 2, 3). Depending on the literature source, AMPKβ1\/2 may also be discussed as AMPKbeta1\/2 and AMPK.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm, nucleoplasm, and nucleus, which can matter when signal is compared across treatments or changing cell states. Following AMPKβ1\/2 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\u003eAMPKβ1\/2 is commonly interpreted in the context of metabolism and cell signaling research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm, nucleoplasm, and nucleus, 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 cytoplasm, nucleoplasm, and nucleus across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003esignal-dependent shifts after ligand, inhibitor, or growth-factor perturbation\u003c\/li\u003e\n\u003cli\u003eco-patterning with orthogonal markers and control conditions that clarify pathway state\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 AMPKβ1\/2. 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 AMPKβ1\/2 reflect biology rather than handling. When interpreting AMPKβ1\/2, 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 AMPKβ1\/2 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":57577447719257,"sku":"F0371-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577447752025,"sku":"F0371-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577447784793,"sku":"F0371-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0371-IHC1.jpg?v=1773598387"},{"product_id":"irs-1-antibody-sc-f0386","title":"IRS-1 Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eThe Insulin Receptor Substrate (IRS) proteins constitute a family of cytoplasmic adaptor proteins crucial for insulin signaling. IRS-1, the first identified family member, was initially noted as a 185 kD phosphoprotein. Both IRS-1 and IRS-2 are widely expressed and serve as principal mediators of insulin-dependent mitogenesis and the regulation of glucose metabolism across various cell types. Depending on the literature source, IRS-1 may also be discussed as IRS1+IRS2.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm, nucleoplasm, nucleus, and plasma membrane, which can matter when signal is compared across treatments or changing cell states. Following IRS-1 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eIRS-1 is commonly interpreted in the context of cancer, metabolism, and endocrinology research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm, nucleoplasm, and nucleus, 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 cytoplasm, nucleoplasm, and nucleus across matched conditions\u003c\/li\u003e\n\u003cli\u003echanges associated with proliferative state, oncogenic signaling, or treatment response\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003eresponses to hormone-dependent signaling or endocrine feedback context\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 IRS-1. 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 IRS-1 reflect biology rather than handling. When interpreting IRS-1, 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 IRS-1 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":57577448702297,"sku":"F0386-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577448735065,"sku":"F0386-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577448767833,"sku":"F0386-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0386-wb.gif?v=1773598402"},{"product_id":"pdhk1-antibody-sc-f0392","title":"PDHK1 Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003ePDHK1 is a target of interest in many antibody-based workflows. Under normoxic conditions, pyruvate generated from glycolysis undergoes conversion to acetyl-CoA via the action of pyruvate dehydrogenase (PDH), forming a crucial connection between glycolysis and the TCA cycle. PDH activity is tightly regulated through phosphorylation and dephosphorylation mechanisms. Pyruvate dehydrogenase kinase (PDHK) phosphorylates PDH, leading to its inactivation, while pyruvate dehydrogenase phosphatase catalyzes the dephosphorylation of PDH, resulting in its activation.\u003c\/p\u003e\u003cp\u003eReported cellular context includes mitochondrion, which can matter when signal is compared across treatments or changing cell states. Following PDHK1 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003ePDHK1 is commonly interpreted in the context of metabolism and cell signaling research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans mitochondrion, 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\u003esignal enrichment within mitochondrion relative to the broader cellular background\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003esignal-dependent shifts after ligand, inhibitor, or growth-factor perturbation\u003c\/li\u003e\n\u003cli\u003eco-patterning with orthogonal markers and control conditions that clarify pathway state\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 PDHK1. 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 PDHK1 reflect biology rather than handling. When interpreting PDHK1, 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 PDHK1 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":57577449095513,"sku":"F0392-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577449128281,"sku":"F0392-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577449161049,"sku":"F0392-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}]},{"product_id":"pnpla2-antibody-sc-f0419","title":"ATGL Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003ePNPLA2 is a target of interest in many antibody-based workflows. Adipose triglyceride lipase (ATGL) serves as the pivotal enzyme responsible for liberating fatty acids (FAs) from triacylglycerol (TG) stores during intracellular lipolysis, thus generating FAs utilized for energy production. ATGL, by catalyzing the initial step, plays a prominent role in this process, cleaving TG to yield diacylglycerol (DG) and FA. Depending on the literature source, PNPLA2 may also be discussed as ATGL and Adipose Triglyceride Lipase.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cell membrane, cytoplasm, lipid droplet, and membrane, which can matter when signal is compared across treatments or changing cell states. Following PNPLA2 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003ePNPLA2 is commonly interpreted in the context of metabolism research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cell membrane, cytoplasm, and lipid droplet, 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 cell membrane, cytoplasm, and lipid droplet across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\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 PNPLA2. 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 PNPLA2 reflect biology rather than handling. When interpreting PNPLA2, 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 PNPLA2 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":57577451454809,"sku":"F0419-20UL","price":149.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577451487577,"sku":"F0419-100UL","price":319.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577451520345,"sku":"F0419-2X100UL","price":479.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0419-IHC1.jpg?v=1773598445"},{"product_id":"hk1-antibody-sc-f0435","title":"Hexokinase 1 Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eHK1 is a target of interest in many antibody-based workflows. Hexokinase catalyzes the conversion of glucose to glucose-6-phosphate, initiating glycolysis. In mammals, four distinct isoforms of hexokinase have been identified: hexokinase I, II, III, and IV (glucokinase). Hexokinases I, II, and III localize to the outer mitochondrial membrane, playing pivotal roles in sustaining elevated rates of aerobic glycolysis observed in cancer cells. Depending on the literature source, HK1 may also be discussed as Hexokinase 1 and Hexokinase I.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm, membrane, mitochondrion, and mitochondrion outer membrane, which can matter when signal is compared across treatments or changing cell states. Following HK1 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eHK1 is commonly interpreted in the context of metabolism research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm, membrane, and mitochondrion, 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 cytoplasm, membrane, and mitochondrion across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\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 HK1. 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 HK1 reflect biology rather than handling. When interpreting HK1, 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 HK1 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":57577459712345,"sku":"F0435-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577459745113,"sku":"F0435-100UL","price":319.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577459777881,"sku":"F0435-2X100UL","price":479.0,"currency_code":"EUR","in_stock":true},{"title":"Default Title","offer_id":57577459810649,"sku":null,"price":0.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0435-IHC1.jpg?v=1773598462"},{"product_id":"ins-antibody-sc-f0436","title":"Insulin Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eInsulin is a peptide hormone composed of 51 amino acids, secreted by the β-cells of the pancreas. It plays a vital role in maintaining glucose homeostasis by binding to the insulin receptor (IR), a transmembrane receptor tyrosine kinase composed of two extracellular α-subunits responsible for ligand binding and two intracellular β-subunits that mediate signal transduction. Depending on the literature source, INS may also be discussed as Insulin and c-peptide.\u003c\/p\u003e\u003cp\u003eReported cellular context includes secreted, which can matter when signal is compared across treatments or changing cell states. Following INS across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eINS is commonly interpreted in the context of metabolism, endocrinology, and cell signaling 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.\u003c\/p\u003e\u003cp\u003eConsider these angles when interpreting target-level changes:\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003esignal enrichment within secreted relative to the broader cellular background\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003eresponses to hormone-dependent signaling or endocrine feedback context\u003c\/li\u003e\n\u003cli\u003esignal-dependent shifts after ligand, inhibitor, or growth-factor perturbation\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 INS. 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 INS reflect biology rather than handling. When interpreting INS, 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 INS 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":57577459843417,"sku":"F0436-20UL","price":169.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577459876185,"sku":"F0436-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577459908953,"sku":"F0436-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0436-wb.gif?v=1773598462"},{"product_id":"bad-antibody-sc-f0445","title":"Phospho-Bad (Ser112) Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eThe mammalian BAD protein is a member of the BCL-2 family, specifically classified within the BH3-only subgroup. In healthy cells, pro-apoptotic BCL-2 family proteins-including BH3-only proteins like BAD and multidomain proteins such as BAX and BAK-are constitutively expressed. These proteins are typically considered inactive death effectors that require specific activation signals to initiate their pro-apoptotic functions. Depending on the literature source, BAD may also be discussed as Phospho-Bad (Ser112) and Bcl2-associated agonist of cell death.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm, membrane, mitochondrion, and mitochondrion outer membrane, which can matter when signal is compared across treatments or changing cell states. Following BAD across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eBAD is commonly interpreted in the context of metabolism, apoptosis, and cell signaling research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm, membrane, and mitochondrion, 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 cytoplasm, membrane, and mitochondrion across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003eseparation of survival-associated changes from stress or death-associated readouts\u003c\/li\u003e\n\u003cli\u003esignal-dependent shifts after ligand, inhibitor, or growth-factor perturbation\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 BAD. 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 BAD reflect biology rather than handling. When interpreting BAD, 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 BAD 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":57577460597081,"sku":"F0445-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577460629849,"sku":"F0445-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577460662617,"sku":"F0445-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0445-wb.gif?v=1773598475"},{"product_id":"ctsb-antibody-sc-f0474","title":"Cathepsin B Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eCTSB is a target of interest in many antibody-based workflows. Cathepsin B, a member of the papain-like family of cysteine proteases, is synthesized as a preproenzyme consisting of 339 amino acids, with an approximate molecular weight of 38 kDa. It possesses both endopeptidase and exopeptidase activities, functioning as a peptidyldipeptidase by removing dipeptides from the C terminus of polypeptide substrates. Depending on the literature source, CTSB may also be discussed as Cathepsin B and G60.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cell membrane, lysosome, and membrane, which can matter when signal is compared across treatments or changing cell states. Following CTSB across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003eCTSB is commonly interpreted in the context of metabolism research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cell membrane, lysosome, and membrane, 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 cell membrane, lysosome, and membrane across matched conditions\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\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 CTSB. 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 CTSB reflect biology rather than handling. When interpreting CTSB, 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 CTSB 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":57577462694233,"sku":"F0474-20UL","price":179.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577462727001,"sku":"F0474-100UL","price":399.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577462759769,"sku":"F0474-2X100UL","price":599.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0474-IHC1.jpg?v=1773598506"},{"product_id":"pdcd4-antibody-sc-f0489","title":"PDCD4 Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003eProgrammed cell death factor 4 (PDCD4) is a tumor suppressor gene that exerts antineoplastic effects. It's located on chromosome 3q21. 3.\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm and nucleus, which can matter when signal is compared across treatments or changing cell states. Following PDCD4 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003ePDCD4 is commonly interpreted in the context of cancer and metabolism research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm and nucleus, 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 cytoplasm and nucleus across matched conditions\u003c\/li\u003e\n\u003cli\u003echanges associated with proliferative state, oncogenic signaling, or treatment response\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003eco-patterning with orthogonal markers and control conditions that clarify pathway state\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch2\u003eVariant Considerations\u003c\/h2\u003e\u003cp\u003eWhere appropriate, pairing PDCD4 with orthogonal markers can improve confidence in interpretation, especially when experimental questions extend from baseline characterization into comparative or perturbation-driven studies.\u003c\/p\u003e\u003cp\u003eIf your project spans exploratory questions, the regular version offers a balanced option for establishing baseline signal behavior for PDCD4. 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 PDCD4 reflect biology rather than handling. When interpreting PDCD4, 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 PDCD4 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":57577463775577,"sku":"F0489-20UL","price":149.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577463808345,"sku":"F0489-100UL","price":359.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577463841113,"sku":"F0489-2X100UL","price":539.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0489-IHC1.jpg?v=1773598527"},{"product_id":"pink1-antibody-sc-f0490","title":"PINK1 Antibody","description":"\u003ch2\u003eAbout the Target\u003c\/h2\u003e\u003cp\u003ePTEN-induced putative kinase 1 (PINK1) is a mitochondrial serine\/threonine kinase that plays a crucial role in maintaining mitochondrial function and integrity, as well as in minimizing the release of cytochrome c from mitochondria. PINK1 is closely linked to the development of early-onset Parkinson's disease (PD).\u003c\/p\u003e\u003cp\u003eReported cellular context includes cytoplasm, membrane, mitochondrion, and mitochondrion inner membrane, which can matter when signal is compared across treatments or changing cell states. Following PINK1 across matched perturbations can help separate abundance effects from shifts in localization, complex assembly, or pathway state.\u003c\/p\u003e\u003ch2\u003eResearch Context\u003c\/h2\u003e\u003cp\u003ePINK1 is commonly interpreted in the context of immunology, metabolism, and cell signaling research, and readouts are often stronger when a study separates expression changes from compartment-level redistribution. When reported signal spans cytoplasm, membrane, and mitochondrion, 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 cytoplasm, membrane, and mitochondrion across matched conditions\u003c\/li\u003e\n\u003cli\u003econtext differences tied to immune-cell state, activation, or lineage composition\u003c\/li\u003e\n\u003cli\u003eresponses linked to nutrient status, mitochondrial state, or metabolic rewiring\u003c\/li\u003e\n\u003cli\u003esignal-dependent shifts after ligand, inhibitor, or growth-factor perturbation\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 PINK1. 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 PINK1 reflect biology rather than handling. When interpreting PINK1, 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 PINK1 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":57577463873881,"sku":"F0490-20UL","price":139.0,"currency_code":"EUR","in_stock":true},{"title":"100 µl","offer_id":57577463906649,"sku":"F0490-100UL","price":329.0,"currency_code":"EUR","in_stock":true},{"title":"2 × 100 µl","offer_id":57577463939417,"sku":"F0490-2X100UL","price":489.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0923\/1011\/0553\/files\/F0490-wb.gif?v=1773598529"}],"url":"https:\/\/absource-diagnostics.myshopify.com\/collections\/metabolism.oembed?page=197","provider":"Absource Diagnostics","version":"1.0","type":"link"}