HAO2 Polyclonal antibody

HAO2 Polyclonal Antibody for WB, IF/ICC, ELISA
Cat No. 16563-1-AP

产品说明书

宿主/亚型

Rabbit / IgG

种属反应性

human, mouse, rat

应用

WB, IF/ICC, ELISA and More (1)

(S)-2-hydroxy-acid oxidase, peroxisomal, 2-Hydroxyacid oxidase 2, Cell growth-inhibiting gene 16 protein, EC:1.1.3.15, GIG16

缓冲液配方:  PBS and Azide
PBS and Azide
偶联物:  Unconjugated
Unconjugated
规格: 

-/ -


经过测试的应用

Positive WB detected inMCF-7 cells, HEK-293 cells, mouse kidney tissue, HepG2 cells, rat kidney tissue
Positive IF/ICC detected inHepG2 cells

推荐稀释比

应用推荐稀释比
Western Blot (WB)WB : 1:1000-1:8000
Immunofluorescence (IF)/ICCIF/ICC : 1:200-1:800
It is recommended that this reagent should be titrated in each testing system to obtain optimal results.
Sample-dependent, Check data in validation data gallery.

发表文章中的应用

IHCSee 1 publications below

产品信息

16563-1-AP targets HAO2 in WB, IHC, IF/ICC, ELISA applications and shows reactivity with human, mouse, rat samples.

经测试应用 WB, IF/ICC, ELISA Application Description
文献引用应用IHC
经测试反应性 human, mouse, rat
文献引用反应性human
免疫原 HAO2 fusion protein Ag9838 种属同源性预测
宿主/亚型 Rabbit / IgG
抗体类别 Polyclonal
产品类型 Antibody
全称 hydroxyacid oxidase 2 (long chain)
别名 (S)-2-hydroxy-acid oxidase, peroxisomal, 2-Hydroxyacid oxidase 2, Cell growth-inhibiting gene 16 protein, EC:1.1.3.15, GIG16
计算分子量 351 aa, 39 kDa
观测分子量 35-40 kDa
GenBank蛋白编号BC020863
基因名称 HAO2
Gene ID (NCBI) 51179
RRIDAB_3669250
偶联类型 Unconjugated
形式 Liquid
纯化方式Antigen affinity purification
UNIPROT IDQ9NYQ3
储存缓冲液 PBS with 0.02% sodium azide and 50% glycerol , pH 7.3
储存条件Store at -20°C. Stable for one year after shipment. Aliquoting is unnecessary for -20oC storage.

实验方案

Product Specific Protocols
WB protocol for HAO2 antibody 16563-1-APDownload protocol
IF protocol for HAO2 antibody 16563-1-APDownload protocol
Standard Protocols
Click here to view our Standard Protocols

发表文章

SpeciesApplicationTitle
humanIHC

Arthritis Res Ther

Investigation of ferroptosis-associated molecular subtypes and immunological characteristics in lupus nephritis based on artificial neural network learning

Authors - Li Zhang
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