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生物素标记试剂盒使用说明书

生物素标记试剂盒使用说明书货号: EBLK0002产品介绍：Elabscience生物素标记试剂盒提供了生物素标记所需全部试剂，用于含有氨基（NH2-）抗体的标记。

生物素已经活化，可直接使用，每个试剂盒足以完成3次标记，每次可标记0.2-2mg。

试剂盒中包括6个用于抗体标记脱盐的Filtration tube，不用透析，操作简便，熟练操作90min可完成整个标记过程。

产品特点：试剂全面:本试剂盒提供了生物素标记所需全部试剂。

快速:整个过程仅需90min。

方便:通过Filtration tube即可脱盐，无需透析或者凝胶过滤。

使用灵活:既可用于微量标记又可大量标记，每次可标记0.2-2mg。

理想的标记效果:已经优化确定了最适的标记比例，降低标记不足或由于过度标记而失活的可能性。

产品组成：标记过程需要仪器：1. 10ul，50ul，200ul，1000ul可调高精度移液器2. 恒温箱（37℃）3. 离心机（离心力可达到12,000×g）储存条件：本试剂盒未开封前在2-8℃可稳定保存一年生物素标记反应原理：NH2-Reactive Biotin专一地与伯胺反应（N-末端及赖氨酸残基侧链）形成稳定的酰胺键生物素标记NH2-Reactive Biotin使用量的计算：每个反应中生物素试剂的使用量取决于待标记蛋白质的量和浓度。

通过优化，我们确定了标记2mg/ml的抗体（IgG ，150KD），使用生物素和抗体的分子比为20:1能达到较理想的标记效果。

1、标记2mg/ml的抗体，使用生物素和抗体的分子比为20:1时，应加入生物素量的计算方法：ml蛋白×2mg蛋白ml蛋白×1mmolIgG150,000mgIgG×20mmol生物素mmol蛋白= mmol生物素2、对于10mmol的生物素溶液，应加入反应中该生物素体积的计算方法：mmol生物素×1,000,000μLL ×L10mmol= ul生物素计算示例：对于0.5ml 2mg/ml的IgG（分子量为150,000）溶液，需加入10mM的生物素溶液13.3ul。

ABTS 自由基清除能力检测试剂盒说明书__微量法UPLC-MS-4571

ABTS自由基清除能力检测试剂盒说明书货号：UPLC-MS-4571规格：100T/48S微量法产品内容：使用前请认真核对试剂体积与瓶内体积是否一致，有疑问请及时联系工作人员。

试剂名称规格保存条件提取液液体80mL×1瓶2-8℃保存试剂一液体40mL×1瓶2-8℃保存试剂二粉剂×1支2-8℃保存试剂三液体20μL×1支2-8℃保存试剂四液体 1.5mL×1支-20℃保存试剂五粉剂×1支2-8℃保存溶液的配制：1、试剂二：临用前加入1mL蒸馏水，充分溶解；用不完的试剂可分装后保存，-20℃可保存四周，避免反复冻融；2、试剂三工作液的配制：液体置于试剂瓶内EP管中。

临用前根据样本量按试剂三（μL）：蒸馏水（mL）=1μL:12mL的比例配成试剂三工作液，现用现配，用多少配多少，尽量在4h之内用完；3、试剂四工作液的配制：可先将试剂四-20℃分装保存。

临用前根据样本量按试剂四：试剂一（V:V）=1:9的比例配成试剂四工作液，现配现用，用不完的试剂放于-20℃可保存两周。

4、试剂五：粉剂置于试剂瓶内玻璃瓶中，含有5mg维生素C，临用前加入2.8mL提取液，充分振荡溶解；配成10mmol/L维生素C溶液，用于阳性对照。

2-8℃可保存两周。

5、ABTS工作液的配制：临用前根据试验所需量按试剂一：试剂二：试剂三工作液（V:V:V）=76:5:4的比例配成ABTS工作液，现用现配，室温避光保存，务必在30分钟内使用。

产品说明：ABTS法可用于亲水性和亲脂性物质抗氧化能力测定，是使用最广泛的间接检测方法。

ABTS经氧化后生成稳定的蓝绿色阳离子ABTS自由基，在405nm或734nm处有最大吸收峰。

被测物质加入ABTS自由基溶液后，所含抗氧化成分能与ABTS自由基发生反应而使反应体系褪色，405nm的吸光度下降，在一定范围内其吸光度的变化与自由基被清除的程度成正比。

过氧化氢酶（CAT）活性检测试剂盒说明书

过氧化氢酶（CAT）活性检测试剂盒说明书微量法货号：BC0205规格：100T/96S产品组成：使用前请认真核对试剂体积与瓶内体积是否一致，有疑问请及时联系吉至工作人员。

试剂名称规格保存条件提取液液体110mL×1瓶4℃保存试剂一液体30mL×1瓶4℃保存试剂二液体110μL×1瓶4℃保存溶液的配制：1、试剂二：液体置于试剂瓶内EP管中，使用前需先离心。

2、检测工作液的配制：A、使用96孔UV板，取试剂二25μL中加入5mL试剂一，充分混匀，作为工作液（约26T），现用现配；也可根据样本量按比例配制（提供一个15mL空瓶）。

B、使用微量石英比色皿，取试剂二25μL中加入6.5mL试剂一，充分混匀，作为工作液（约34T），现用现配；也可根据样本量按比例配制（提供一个15mL空瓶）。

产品说明：CAT(EC1.11.1.6)广泛存在于动物、植物、微生物和培养细胞中，是最主要的H2O2清除酶，在活性氧清除系统中具有重要作用。

H2O2在240nm下有特征吸收峰，CAT能够分解H2O2，使反应溶液240nm下的吸光度随反应时间而下降，根据吸光度的变化率可计算出CAT活性。

注意：实验之前建议选择2-3个预期差异大的样本做预实验。

如果样本吸光值不在测量范围内建议稀释或者增加样本量进行检测。

需自备的仪器和用品：紫外分光光度计/酶标仪、台式离心机、可调式移液器、微量石英比色皿/96孔（UV板）、研钵/匀浆器、冰和蒸馏水。

操作步骤：一、样本处理（可适当调整待测样本量，具体比例可以参考文献）1、细菌、细胞或组织样本的制备：a、细菌或培养细胞：收集细菌或细胞到离心管内，离心后弃上清；按照每500万细菌或细胞加入1mL提取液，超声波破碎细菌或细胞（功率200W，超声3s，间隔10s，重复30次）；8000g4℃离心10min，取上清，置冰上待测。

b、组织：按照组织质量（g）：称取约0.1g组织，加入1mL提取液进行冰浴匀浆。

叶酸ELISA试剂盒产品手册说明书

Product ManualFolic Acid ELISA KitCatalog NumberMET-5068 96 assaysMET-5068-5 5 x 96 assays FOR RESEARCH USE ONLYNot for use in diagnostic proceduresIntroductionFolic acid is a B vitamin also known as Vitamin B9. Since humans don’t synthesize folic acid, it is required from the diet and is therefore considered to be an essential vitamin. In cells, folic acid is required for amino acid metabolism as well as to carry one-carbon groups used for methylation reactions and synthesis of nucleic acids (such as thymine and purine bases). Therefore, deficiency in folic acid disrupts DNA synthesis and cell division, affecting mostly hematopoietic cells and abnormal tissue growths because of their higher rate of cell division.Folic acid is used to supplement folic acid deficiency. This deficiency can cause anemia. Symptoms of anemia can include fatigue, heart palpitations, difficulty breathing, open sores observed on the tongue, and color changes of the hair or skin. Deficiency can occur in children after only a month of consuming a folic acid deficient diet. In adults, normal total body folic acid levels are between10,000–30,000 micrograms (µg) with plasma levels of greater than 7 nM (3 ng/mL) (Table 1). Women also take supplemental folic acid during pregnancy to prevent fetal neural tube defects (NTDs). Insufficient levels of dietary folic acid in early pregnancy are thought to be the cause of over half of babies born with neural tube defects. As a result, over 50 countries add folic acid to certain foods as a way to decrease NTD incidents in the population.Concentration (ng/mL) Concentration (nM) Adults 3-20 7-45.3Children 5-21 11.3-47.6Infants 14-51 31.7-115.5Table 1. Reference ranges for folic acid in human plasma.The Folic Acid ELISA Kit is a competitive enzyme immunoassay developed for rapid detection and quantitation of folic acid in serum, cell or tissue samples. The quantity of folic acid in unknown samples is determined by comparing its absorbance with that of a known folic acid standard curve. The kit has detection sensitivity limit of 24 pg/mL folic acid. Each Folic Acid ELISA Kit provides sufficient reagents to perform up to 96 assays, including standard curve and unknown samples. Assay PrincipleThe Folic Acid ELISA kit is a competitive ELISA for the quantitative measurement of folic acid. The unknown folic acid samples or folic acid standards are first added to a Folic Acid Conjugate preabsorbed microplate. After a brief incubation, an Anti-Folic Acid antibody is added, followed by an HRP conjugated secondary antibody. The folic acid content in unknown samples is determined by comparison with a predetermined folic acid standard curve.Related Products1.MET-5054: L-Amino Acid Assay Kit2.MET-5056: Branched Chain Amino Acid Assay Kit3.MET-5151: S-Adenosylhomocysteine (SAH) ELISA Kit4.MET-5152: S-Adenosylmethionine (SAM) ELISA Kit5.STA-674: Glutamate Assay KitKit ComponentsBox 1 (shipped at room temperature)1.96-well Protein Binding Plate (Part No. 231001): One strip well 96-well plate.2.Anti-Folic Acid Antibody (500X) (Part No. 50681C): One 10 µL vial.3.Secondary Antibody, HRP Conjugate (Part No. 231009): One 20 µL vial.4.Assay Diluent (Part No. 310804):One 50 mL bottle.5.10X Wash Buffer (Part No. 310806): One 100 mL bottle.6.Substrate Solution (Part No. 310807): One 12 mL amber bottle.7.Stop Solution (Part. No. 310808): One 12 mL bottle.8.Folic Acid Standard (Part No. 50682C): One 100 µL amber vial of 10 µg/mL Folic Acid inwater.Box 2 (shipped on blue ice packs)1.100X Folic Acid Conjugate (Part No. 50683C): One 100 µL amber vial.Materials Not Supplied1.Folic acid samples such as serum, plasma, or folic acid extracted from cells or tissues2.Tissue Homogenizer3.1X PBS4.10 µL to 1000 µL adjustable single channel micropipettes with disposable tips5.50 µL to 300 µL adjustable multichannel micropipette with disposable tips6.Multichannel micropipette reservoir7.Microplate reader capable of reading at 450 nm (620 nm as optional reference wave length) StorageUpon receipt, aliquot and store 100X Folic Acid Conjugate at -20ºC and avoid multiple freeze/thaw cycles. Store all other components at 4ºC. The 100X Folic Acid Conjugate and Folic Acid Standard are light sensitive and must be stored accordingly.Preparation of Reagents•Folic Acid Conjugate Coated Plate: Dilute the proper amount of 100X Folic Acid Conjugate 1:100 into 1X PBS. Add 100 μL of the diluted 1X Folic Acid Conjugate to each well and incubate at 37ºC for two hours or overnight at 4ºC. Remove the coating solution and wash twice with 200 μL of 1X PBS. Blot plate on paper towels to remove excess fluid. Add 200 μL of Assay Diluent to each well and block for 1 hr at room temperature. Transfer the plate to 4ºC and remove the Assay Diluent immediately before use.Note: The Folic Acid Conjugate-coated wells are not stable and should be used within 24 hrs after coating. Only coat the number of wells to be used immediately.•1X Wash Buffer: Dilute the 10X Wash Buffer to 1X with deionized water. Stir to homogeneity. •Anti-Folic Acid Antibody and Secondary Antibody: Immediately before use dilute the Anti-Folic Acid Antibody 1:500 and Secondary Antibody 1:1000 with Assay Diluent. Do not store diluted solutions.Preparation of Standard Curvee the provided stock Folic Acid Standard 10 µg/mL solution to prepare a series of the remainingstandards according to Table 1 below.Standard Tubes 10 µg/mL Folic AcidStandard (µL)Assay Diluent(µL)Folic Acid(ng/mL)Folic Acid(nM)1 10 990 100 2272 100 of Tube #1 300 25 56.753 100 of Tube #2 300 6.25 14.194 100 of Tube #3 300 1.56 3.555 100 of Tube #4 300 0.391 0.8876 100 of Tube #5 300 0.098 0.2227 100 of Tube #6 300 0.024 0.0558 0 300 0 0 Table 1. Preparation of Folic Acid Standards.Preparation of Samples•Serum: Avoid hemolyzed and lipemic blood samples. Collect blood in a tube with no anticoagulant. Allow the blood to clot at room temperature for 30 minutes. Centrifuge at 2500 x g for 20 minutes. Remove the yellow serum supernatant without disturbing the white buffy layer.Aliquot samples for testing and store at -80ºC. Perform dilutions in Assay Diluent or PBS containing 0.1% BSA as necessary.•Plasma: Avoid hemolyzed and lipemic blood samples. Collect blood with heparin or citrate and centrifuge at 2000 x g and 4ºC for 10 minutes. Remove the plasma layer and store on ice. Avoid disturbing the white buffy layer. Aliquot samples for testing and store at -80ºC. Perform dilutions in Assay Diluent or PBS containing 0.1% BSA as necessary.Note: This assay is not compatible with rabbit serum or plasma due to high levels of rabbit IgG that will cross react with the secondary antibody.•Cells or tissues: Homogenize 50-200 mg of the cell pellet or tissue in 0.5-2 mL of ice-cold PBS using a mortar and pestle or by dounce homogenization. Incubate the homogenate at 4°C for 20 minutes. Transfer the homogenate to a centrifuge tube and centrifuge at 12000 x g for 20 minutes.Recover the supernatant and transfer to a fresh tube. Store resuspended sample at -20°C or colder until ready to test by ELISA. Perform dilutions in Assay Diluent or PBS containing 0.1% BSA as necessary.Assay Protocol1.Prepare and mix all reagents thoroughly before use. Each folic acid sample including unknownand standard should be assayed in duplicate.2.Add 50 µL of unknown sample or Folic Acid standards to the wells of the Folic Acid Conjugatecoated plate. Incubate at room temperature for 10 minutes on an orbital shaker.3.Add 50 µL of the diluted Anti-Folic Acid antibody to each well, incubate at room temperature for 1hour on an orbital shaker.4.Wash microwell strips 3 times with 250 µL 1X Wash Buffer per well with thorough aspirationbetween each wash. After the last wash, empty wells and tap microwell strips on absorbent pad or paper towel to remove excess 1X Wash Buffer.5.Add 100 µL of the diluted Secondary Antibody-HRP Enzyme Conjugate to all wells.6.Incubate at room temperature for 1 hour on an orbital shaker.7.Wash microwell strips 3 times according to step 4 above. Proceed immediately to the next step.8.Warm Substrate Solution to room temperature. Add 100 L of Substrate Solution to each well,including the blank wells. Incubate at room temperature on an orbital shaker. Actual incubation time may vary from 2-30 minutes.Note: Watch plate carefully; if color changes rapidly, the reaction may need to be stopped sooner to prevent saturation.9.Stop the enzyme reaction by adding 100 µL of Stop Solution into each well, including the blankwells. Results should be read immediately (color will fade over time).10.Read absorbance of each microwell on a spectrophotometer using 450 nm as the primary wavelength.Example of ResultsThe following figures demonstrate typical Folic Acid ELISA results. One should use the data below for reference only. This data should not be used to interpret actual results.Figure 1: Folic Acid ELISA Standard Curve.Figure 2: Folic Acid Levels in Human, Mouse or Rat Serum compared to Negative Control (Assay Diluent). Serum samples were diluted 1:5 in Assay Diluent and tested according to the Assay Protocol.References1.Bibbins-Domingo, K; Grossman, DC.; Curry, SJ.; Davidson, KW.; Epling, John W.; García, FAR.;Kemper, AR.; Krist, AH.; Kurth, AE.; Landefeld, CS; Mangione, CM.; Phillips, William R.;Phipps, MG.; Pignone, MP.; Silverstein, M; Tseng, C-W (2017). JAMA. 317: 183.2.Marino, BS.; Fine, KS (2009). Blueprints Pediatrics. Lippincott Williams & Wilkins. p. 131.3.Bailey, LB. (2009). Folate in Health and Disease, Second Edition. CRC Press. p. 198.4.Obeid, R; Herrmann, W (2012). Curr. Drug Metab. 13: 1184–1195.5.Wilson RD, Wilson RD, Audibert F, Brock JA, Carroll J, Cartier L, Gagnon A, Johnson JA,Langlois S, Murphy-Kaulbeck L, Okun N, Pastuck M, Deb-Rinker P, Dodds L, Leon JA, Lowel HL, Luo W, MacFarlane A, McMillan R, Moore A, Mundle W, O'Connor D, Ray J, Van den Hof M (2015). J Obstet Gynaecol Can. 37: 534–52.6.Figueiredo JC1, Grau MV, Haile RW, Sandler RS, Summers RW, Bresalier RS, Burke CA,McKeown-Eyssen GE, Baron JA. J. Natl. Cancer Inst.101: 432–5.Recent Product Citations1.McCarthy, G.A. et al. (2023). A Novel 3DNA® Nanocarrier effectively delivers payloads topancreatic tumors. Transl Oncol. 32:101662. doi: 10.1016/j.tranon.2023.101662.2.Shinagawa, A. et al. (2022). Short-Term Combined Intake of Vitamin B2 and Vitamin E DecreasesPlasma Homocysteine Concentrations in Female Track Athletes. Dietetics. 1(3):216-226. doi:10.3390/dietetics1030019.3.Siervo, M. et al. (2020). Nitrate-Rich Beetroot Juice Reduces Blood Pressure in Tanzanian Adultswith Elevated Blood Pressure: A Double-Blind Randomized Controlled Feasibility Trial. J Nutr.doi: 10.1093/jn/nxaa170.4.Simanjuntak, Y. et al. (2020). Preventive effects of folic acid on Zika virus-associated poorpregnancy outcomes in immunocompromised mice. PLoS Pathog. 16(5):e1008521. doi:10.1371/journal.ppat.1008521.5.Zhu, J. et al. (2018). Effect of maternal folic acid supplementation on prostatitis risk in the ratoffspring. Int Urol Nephrol. 50(11):1963-1973. doi: 10.1007/s11255-018-1969-8.WarrantyThese products are warranted to perform as described in their labeling and in Cell Biolabs literature when used in accordance with their instructions. THERE ARE NO WARRANTIES THAT EXTEND BEYOND THIS EXPRESSED WARRANTY AND CELL BIOLABS DISCLAIMS ANY IMPLIED WARRANTY OF MERCHANTABILITY OR WARRANTY OF FITNESS FOR PARTICULAR PURPOSE. CELL BIOLABS’sole obligation and purchaser’s exclusive remedy for breach of this warranty shall be, at the option of CELL BIOLABS, to repair or replace the products.In no event shall CELL BIOLABS be liable for any proximate, incidental or consequential damages in connection with the products.Contact InformationCell Biolabs, Inc.7758 Arjons DriveSan Diego, CA 92126Worldwide: +1 858-271-6500USA Toll-Free: 1-888-CBL-0505E-mail: ********************©2017-2023: Cell Biolabs, Inc. - All rights reserved. No part of these works may be reproduced in any form without permissions in writing.。

BioMart数据库查询与转换工具说明书

Package‘convertid’November29,2023Type PackageTitle Convert Gene IDs Between Each Other and Fetch Annotations fromBiomartVersion0.1.8Date2023-11-29Author Vidal Fey[aut,cre],Henrik Edgren[aut]Maintainer Vidal Fey<*******************>Description Gene Symbols or Ensembl Gene IDs are converted using the Bimap interface in'Annota-tionDbi'in convertId2()butthat function is only provided as fallback mechanism for the most com-mon use cases in data analysis.The main functionin the package is convert.bm()which queries BioMart using the full capacity of the API pro-vided through the'biomaRt'package.Presets and defaults are provided for convenience but all``marts'',``ﬁl-ters''and``attributes''can be set by the user.Function convert.alias()converts Gene Sym-bols to Aliases and vice versa and function likely_symbol()attempts to determine the most likely current Gene Symbol.Depends AnnotationDbiImports org.Hs.eg.db,org.Mm.eg.db,plyr,stringr,biomaRt,stats,xml2,utils,rappdirs,assertthat,methods,httr,BiocFileCacheLicense GPL-3Encoding UTF-8RoxygenNote7.2.3NeedsCompilation noRepository CRANDate/Publication2023-11-2913:40:02UTC12convert.alias R topics documented:convert.alias (2)convert.bm (3)convertid (4)convertId2 (5)get.bm (6)likely_symbol (7)todisp2 (9)Index10 convert.alias Convert Symbols to Aliases and Vice Versa.Descriptionconvert.alias()attempts toﬁnd all possible symbol-alias combinations for a given gene symbol,i.e.,it assumes the input ID to be either an Alias or a Symbol and performs multiple queries toﬁndall possible counterparts.The input IDs are converted to title and upper case before querying and all possibilities are tested.There are species presets for Human and Mouse annotations.Usageconvert.alias(id,species=c("Human","Mouse"),db=NULL)Argumentsid(character).Vector of gene symbols.species(character).One of"Human"and"Mouse".Defaults to"Human".db(AnnotationDb object).Annotation package object.ValueA data.frame with two columns:’SYMBOL’:The ofﬁcial gene symbol.’ALIAS’:All possible aliases.See AlsoselectExamplesconvert.alias("TRPV4")convert.bm3 convert.bm Retrieve Additional Annotations from BiomartDescriptionconvert.bm()is a wrapper for get.bm()which in turn makes use of getBM()from the biomaRtpackage.It takes a matrix or data frame with the IDs to be converted in one column or as row namesas input and returns a data frame with additional annotations after cleaning the fetched annotationsand merging them with the input data frame.Usageconvert.bm(dat,id="ID",biom.data.set=c("human","mouse"),biom.mart=c("ensembl","mouse","snp","funcgen","plants"),host="https://",biom.filter="ensembl_gene_id",biom.attributes=c("ensembl_gene_id","hgnc_symbol","description"),biom.cache=rappdirs::user_cache_dir("biomaRt"),use.cache=TRUE,sym.col="hgnc_symbol",rm.dups=FALSE,verbose=FALSE)Argumentsdat matrix or data.frame.Matrix or data frame with the ids to be converted in acolumn or as row names.id of the column with the ids to be converted,special name"rownames"will use the row names.biom.data.set character of length one.Biomart data set to use.biom.mart character vector.Biomart to use(uses theﬁrst element of the vector),defaultsto"ensembl".host character of length one.Host URL.biom.filter character of length of biomartﬁlter,i.e.,type of query ids,defaultsto"ensembl_gene_id".biom.attributescharacter vector.Biomart attributes,i.e.,type of desired result(s);make surequery id type is included!biom.cache character.Path name giving the location of the cache getBM()uses if use.cache=TRUE.Defaults to the value in the BIOMART_CACHE environment variable.4convertid use.cache(logical).Should getBM()use the cache?Defaults to TRUE as in the getBM()function and is passed on to that.sym.col of the column in the query result with gene symbols.rm.dups logical.Should duplicated input IDs(‘biom.filter’)be removed from theresult?verbose(logical).Should verbose output be written to the console?Defaults to FALSE.DetailsWrapped around‘get.bm‘.ValueA data frame with the retrieved information.Author(s)Vidal FeySee AlsogetBMExamples##Not run:dat<-data.frame(ID=c("ENSG00000111199","ENSG00000134121","ENSG00000176102","ENSG00000171611")) bm<-convert.bm(dat)bm##End(Not run)convertid Convert Gene IDs Between Each Other and Fetch Annotations fromBiomartDescriptionGene Symbols or Ensembl Gene IDs are converted using the Bimap interface in’AnnotationDbi’in convertId2()but that function is only provided as fallback mechanism for the most common usecases in data analysis.The main function in the package is convert.bm()which queries Biomartusing the full capacity of the API provided through the’biomaRt’package.Presets and defaultsare provided for convenience but all"marts","ﬁlters"and"attributes"can be set by the user.Func-tion convert.alias()converts Gene Symbols to Aliases and vice versa and function likely_symbol()attempts to determine the most likely current Gene Symbol.DetailsconvertId25Package:convertidType:PackageInitial version:0.1-0Created:2021-08-18License:GPL-3LazyLoad:yesAuthor(s)Vidal Fey<*******************>Maintainer:Vidal Fey<*******************>convertId2Convert Gene Symbols to Ensembl Gene IDs or vice versaDescriptionconvertId2()uses the Bimap interface in AnnotationDbi to extract information from annotation packages.The function is limited to Human and Mouse annotations and is provided only as fallback mechanism for the most common use cases in data analysis.Please use the Biomart interface function convert.bm()for moreﬂexibility.UsageconvertId2(id,species=c("Human","Mouse"))Argumentsid(character).Vector of gene symbols.species(character).One of"Human"and"Mouse".Defaults to"Human".ValueA named character vector where the input IDs are the names and the query results the values.See AlsoBimap-envirAPIExamplesconvertId2("ENSG00000111199")convertId2("TRPV4")6get.bm get.bm Make a Query to Biomart.Descriptionget.bm()is a user-friendly wrapper for getBM()from the biomaRt package with default settingsfor Human and Mouse.It sets all needed variables and performs the query.Usageget.bm(values,biom.data.set=c("human","mouse"),biom.mart=c("ensembl","mouse","snp","funcgen","plants"),host="https://",biom.filter="ensembl_gene_id",biom.attributes=c("ensembl_gene_id","hgnc_symbol","description"),biom.cache=rappdirs::user_cache_dir("biomaRt"),use.cache=TRUE,verbose=FALSE)Argumentsvalues character vector of ids to be converted.biom.data.set character of length one.Biomart data set to use.Defaults to’human’(inter-nally translated to"hsapiens_gene_ensembl"if biom.mart="ensembl").biom.mart character vector.Biomart to use(uses theﬁrst element of the vector),defaultsto"ensembl".host character of length one.Host URL.biom.filter character of length of biomartﬁlter,i.e.,type of query ids,defaultsto"ensembl_gene_id".biom.attributescharacter vector.Biomart attributes,i.e.,type of desired result(s);make surequery id type is included!biom.cache character.Path name giving the location of the cache getBM()uses if use.cache=TRUE.Defaults to the value in the BIOMART_CACHE environment variable.use.cache(logical).Should getBM()use the cache?Defaults to TRUE as in the getBM()function and is passed on to that.verbose(logical).Should verbose output be written to the console?Defaults to FALSE.ValueA data frame with the retrieved information.Author(s)Vidal FeySee AlsogetBMExamples##Not run:val<-c("ENSG00000111199","ENSG00000134121","ENSG00000176102","ENSG00000171611") bm<-get.bm(val)bm##End(Not run)likely_symbol Retrieve Symbol Aliases and Previous symbols to determine a likelycurrent symbolDescriptionlikely_symbol()downloads the latest version of the HGNC gene symbol database as a textﬁle and query it to obtain symbol aliases,previous symbols and all symbols currently in use.(Option-ally)assuming the input ID to be either an Alias or a Symbol or a Previous Symbol it performs multiple queries and compares the results of all possible combinations to determine a likely current Symbol.Usagelikely_symbol(syms,alias_sym=TRUE,prev_sym=TRUE,orgnsm="human",hgnc=NULL,hgnc_url=NULL,output=c("likely","symbols","all"),verbose=TRUE)Argumentssyms(character).Vector of Gene Symbols to be tested.alias_sym(logical).Should the input be assumed to be an Alias?Defaults to TRUE.prev_sym(logical).Should the input be assumed to be a Previous Symbol?Defaults to TRUE.orgnsm(character).The organism for which the Symbols are tested.hgnc(data.frame).An optional data frame with the needed HGNC annotations.(Needs to match the format available at hgnc_utl!)hgnc_url(character).URL where to download the HGNC annotation dataset.Defaultsto"ftp:///pub/databases/genenames/new/tsv/hgnc_complete_set.txt".output(character).One of"likely","symbols"and"all".Determines the scope ofthe output data frame.Defaults to"likely"which will return the inout Symboland the determined likely Symbol.verbose(logical).Should messages be written to the console?Defaults to TRUE.DetailsPlease note that the algorithm is very slow for large input vectors.ValueA data.frame with the following columns depending on the output setting.output="likely":’likely_symbol’’input_symbol’output="symbols":’current_symbols’’likely_symbol’’input_symbol’’all_symbols’output="all":’orig_input’’organism’’current_symbols’’likely_symbol’’input_symbol’’all_symbols’NoteOnly fully implemented for Human for now.Examples##Not run:likely_symbol(c("ABCC4","ACPP","KIAA1524"))todisp29 ##End(Not run)todisp2Convenience Function to Convert Ensembl Gene IDs to Gene SymbolsDescriptiontodisp2()uses Biomart by employing get.bm()to retrieve Gene Symbols for a set of Ensembl Gene IDs.It is mainly meant as a fast way to convert IDs in standard gene expression analysis output to Symbols,e.g.,for visualisation,which is why the input ID type is hard coded to ENSG IDs.If Biomart is not available the function can fall back to use convertId2()or a user-provided data frame with corresponding ENSG IDs and Symbols.Usagetodisp2(ensg,lab=NULL,biomart=TRUE,verbose=FALSE)Argumentsensg(character).Vector of Ensemble Gene IDs.Other ID types are not yet sup-ported.lab(data.frame).A data frame with Ensembl Gene IDs as row names and Gene Symbols in the only column.biomart(logical).Should Biomart be used?Defaults to TRUE.verbose(logical).Should verbose output be written to the console?Defaults to FALSE.ValueA character vector of Gene Symbols.See Alsoget.bmExamples##Not run:val<-c("ENSG00000111199","ENSG00000134121","ENSG00000176102","ENSG00000171611") sym<-todisp2(val)sym##End(Not run)Index∗packageconvertid,4∗utilitiesconvert.bm,3get.bm,6todisp2,9convert.alias,2convert.bm,3convertid,4convertId2,5get.bm,6,9getBM,4,7likely_symbol,7select,2todisp2,910。

代谢检测试剂盒指南

检测法带有近红外荧光读数、比色或荧光读数的In-Cell ELISA 蛋白质印迹抗体混合（Cocktail） ICC 抗体混合（Cocktail）流式抗体混合（Cocktail）
检测试剂盒 ab110217, ab110216, ab140359
ab123545 ab170194 ab168540
JC-1：ab113850
酶标仪、显微镜、590/520-570) 在高浓度下形成红色聚集物（未聚 JC-10 （比
检测法
读数
细胞外耗氧量
细胞内氧水平糖酵解活性（细胞外酸化）
备注
检测试剂盒
随着细胞呼吸，氧气浓度降低，染料信号将增强。油层 ab197243
将细胞与空气隔离。
染料扩散到细胞中。染料荧光被胞内氧淬灭。
ab197245
糖酵解过程中产生乳酸，导致胞外基质酸化。染料荧光 ab197244
随酸化而增加。
脂肪酸氧化 (FAO) 乳酸
图 4.使用 In-Cell ELISA 试剂盒 ab110217 评估 COX-1（线粒体 DNA 编码）和 SDH-A（细胞核 DNA 编码）的相对量，进而评估氯霉素对线粒体生物合成的抑制作用。
“我正在使用该试剂盒 [ab110217] 进行 500 多种化合物的高通量筛查。该试剂盒具有很高的可重现性，而且我没有观察到任何批间差异。强烈推荐该试剂盒。” - Analeeb Sajid 博士
abcam
工具指南
代谢检测试剂盒指南
用于酶标仪、显微镜和流式细胞仪的简易、精细检测试剂盒
代谢是一个复杂的过程，也是生物学的核心。代谢异常会引发从癌症到神经退行性病变等一系列后果。我们的简易检测试剂盒可以简化您代谢方面的研究。使用酶标仪、显微镜或流式细胞仪上的读数来分析活细胞、裂解液和生物体液。检查： - 糖、脂、氨基酸、糖酵解和柠檬酸循环过程中的酶等 - 耗氧量、乳酸产生以及 ATP、NADH 和类似分子 - 线粒体与线粒体功能 - 氧化应激、ROS、抗氧化剂和相关细胞损伤

细胞衰老特异性β-半乳糖苷酶检测试剂盒产品说明书(中文版)

细胞衰老特异性β-半乳糖苷酶检测试剂盒产品说明书（中文版）主要用途细胞衰老特异性β-半乳糖苷酶检测试剂是一种旨在以X-Gal为底物，通过细胞内β-半乳糖苷酶在酸性条件下稳定表达，催化生成深蓝色的沉积产物，从而在光学显微镜下观察到蓝色表达的细胞作为细胞复制性衰老（replicative senescence）的分子特征来识别和探测衰老细胞的权威而经典的技术方法。

该技术由大师级科学家精心研制、成功实验证明的。

广泛应用于细胞生物学研究。

其适用于各种人体和动物细胞或活体内（in vivo）检测。

产品即到即用，性能稳定，参数优化，着色敏感，堪称国际上同类产品最佳。

技术背景细胞复制性衰老是细胞控制其生长潜能的保障机制。

衰老细胞停滞在细胞周期的G1期，表现出细胞扁平、胀大、颗粒增多的形态特征，尤其在酸性条件下，细胞内β-半乳糖苷酶活性增加，而成为细胞衰老的生物学标记。

产品内容清理液（Reagent A）毫升固定液（Reagent B）毫升酸性液（Reagent C）毫升稀释液（Reagent D）毫升染色液（Reagent E）毫升产品说明书 1份保存方式保存染色液（Reagent E）在－20℃冰箱里；其余的保存在4℃冰箱里；稀释液（Reagent D）和染色液（Reagent E）用铝箔封裹，避免光照；有效保证6月用户自备2毫升离心管：用于配制染色工作液15毫升锥形离心管：用于配制染色工作液恒温培养箱：用于染色孵育光学显微镜：用于观察染色后的细胞实验步骤操作一、25cm2细胞培养瓶染色实验开始前，将试剂盒里的染色液（Reagent E）从－20℃的冰箱里取出，放进冰槽里等待溶化。

然后移取xx毫升稀释液（Reagent D）到2毫升离心管，加入xx微升染色液（Reagent E），混匀后，放进37℃恒温水槽里预热，标记为染色工作液。

然后进行下列操作：1．小心抽去25cm2细胞培养瓶里的培养液2．小心加入xx毫升清理液（Reagent A），清洗生长中的细胞表面3．小心抽去清理液4．小心加入xx毫升固定液（Reagent B），覆盖整个生长表面5．在室温下孵育5分钟6．小心抽去固定液7．小心加入xx毫升酸性液（Reagent C），清洗细胞表面8．小心抽去酸性液9．重复实验步骤7和8一次10．小心加入xx毫升染色工作液，覆盖整个细胞表面11．放进37℃培养箱，孵育3小时至16小时，或细胞呈现蓝色（注意：避免液体蒸发）12．在光学显微镜下观察和计数：表达衰老特异性β- 半乳糖苷酶的细胞为阳性细胞，呈现蓝色。

基因毒性杂质限度指南(转载中英文)

20060628 EMEA/CHMP/QWP/251344/2006 基因毒性杂质限度指南（转载中英文）London, 28 June 2006CPMP/SWP/5199/02EMEA/CHMP/QWP/251344/2006TABLE OF CONTENTS 目录EXECUTIVE SUMMARY 内容摘要 (3)1. INTRODUCTION 介绍 (3)2. SCOPE 范围 (3)3. LEGAL BASIS法律依据 (3)4. TOXICOLOGICAL BACKGROUND 毒理学背景 (4)5. RECOMMENDATIONS 建议 (4)5.1 Genotoxic Compounds With Sufficient Evidence for a Threshold-Related Mechanism具有充分证据证明其阈值相关机理的基因毒性化合物 (4)5.2 Genotoxic Compounds Without Sufficient Evidence for a Threshold-Related Mechanism不具备充分证据支持其阈值相关机理的基因毒性化合物 (5)5.2.1 Pharmaceutical Assessment 药学评价 (5)5.2.2 Toxicological Assessment 毒理学评价 (5)5.2.3 Application of a Threshold of Toxicological Concern 毒理学担忧阈值应用 (5)5.3 Decision Tree for Assessment of Acceptability of Genotoxic Impurities基因毒性杂质可接受性评价决策树 (7)REFERENCES. 参考文献 (8)EXECUTIVE SUMMARY 内容摘要The toxicological assessment of genotoxic impurities and the determination of acceptable limits for such impurities in active substances is a difficult issue and not addressed in sufficientdetail in the existing ICH Q3X guidances. The data set usually available for genotoxic impurities is quite variable and is the main factor that dictates the process used for the assessment of acceptable limits. In the absence of data usually needed for the application of one of the established risk assessment methods, i.e. data from carcinogenicity long-term studies or data providing evidence for a threshold mechanism of genotoxicity, implementation of a generally applicable approach as defined by the Threshold of Toxicological Concern (TTC) is proposed. A TTC value of 1.5 μg/day intake of a genotoxic impurity is considered to be associated with an acceptable risk (excess cancer risk of <1 in 100,000 over a lifetime) for most pharmaceuticals. From this threshold value, a permitted level in the active substance can be calculated based on the expected daily dose. Higher limits may be justified under certain conditions such as short-term exposure periods.基因毒性杂质的毒理学评估和这些杂质在活性药物中的可接受标准的测定是一件困难的事情，并且在现有的ICH Q3X指南中也没有详细的规定。

BD破膜剂试剂手册中文

破膜剂试剂手册BD Cytofix / Cytoperm 固定/渗透试剂盒手册（目录号554714）BD Cytofix / Cytoperm Plus固定/渗透试剂盒（BD GolgiStop含有莫能菌素的蛋白质转运抑制剂）（目录号554715）BD Cytofix / Cytoperm Plus固定/渗透试剂盒（含布雷菲德菌素A的BDGolgiPlug蛋白质转运抑制剂）（目录号555028）Ficoll是Amersham Biosciences AB的注册商标。

Hypaque是Amersham Health AS的注册商标。

BD流式细胞仪是1流激光产品。

仅供研究使用。

不用于诊断或治疗程序。

2016年Becton，Dickinson 和公司。

本出版物的任何部分不得以电子，机械，磁性，光学，化学，手册或其他方式以任何形式或通过任何方式复制，传播，转录，存储在检索系统中或翻译成任何语言或计算机语言，未经BD Biosciences事先书面许可。

购买不包括或携带任何权利转售或转让本产品作为独立产品或另一产品的组成部分。

未经Becton，Dickinson和Company明确书面许可，严禁使用本产品以外的许可使用。

2016BD，BD徽标和所有其他商标均为Becton，Dickinson和Company的产权。

目录BD Cytofix / Cytoperm固定/渗透试剂盒BD Cytofix / Cytoperm Plus固定/渗透试剂盒（BD GolgiStop蛋白质转运抑制剂）BD Cytofix / Cytoperm Plus固定/渗透试剂盒（BD GolgiPlug蛋白质运输抑制剂）警告和注意事项1.一般程序A.刺激细胞1.使用BD GolgiStop的程序蛋白质转运抑制剂（含有莫能菌素）2。

使用BD GolgiPlug 蛋白质转运抑制剂的程序（含布雷菲德菌素A）B.方案：细胞表面抗原和细胞内细胞因子的多色染色1收集细胞2.阻止Fc受体3.细胞表面抗原染色4.固定和渗透细胞5.备选固定和渗透方案6细胞内细胞因子染色C.流式细胞分析。

硫氧化还原蛋白试剂盒介绍

硫氧化还原蛋白试剂盒介绍本试剂盒用于体外定量检测血清、血浆、组织匀浆及相关液体样本中小鼠硫氧化还原蛋白（Trx）的含量。

有效期：6个月保存条件：2-8℃本试剂盒仅供科研使用，不得用于临床诊断实验原理试剂盒采用双抗体一步夹心法酶联免疫吸附试验（ELISA）。

往预先包被小鼠硫氧化还原蛋白（Trx）捕获抗体的包被微孔中，依次加入标本、标准品、HRP标记的检测抗体，经过温育并彻底洗涤。

用底物TMB显色，TMB在过氧化物酶的催化下转化成蓝色，并在酸的作用下转化成终的黄色。

颜色的深浅和样品中的小鼠硫氧化还原蛋白（Trx）呈正相关。

用酶标仪在450nm 波长下测定吸光度（OD 值），计算样品浓度。

样本处理及要求1.血清：将收集于血清分离管的全血标本在室温放置2小时或4℃过夜，然后1000×g离心20 分钟，取上清即可，或将上清置于-20℃或-80℃保存，但应避免反复冻融。

2.血浆：用EDTA或肝素作为抗凝剂采集标本，并将标本在采集后的30分钟内于2-8℃1000×g离心15分钟，取上清即可检测，或将上清置于-20℃或-80℃保存，但应避免反复冻融。

3. 组织匀浆：用预冷的PBS (0.01M, pH=7.4)冲洗组织，去除残留血液（匀浆中裂解的红细胞会影响测量结果），称重后将组织剪碎。

将剪碎的组织与对应体积的PBS（一般按1:9的重量体积比，比如1g的组织样品对应9mL的PBS，具体体积可根据实验需要适当调整，并做好记录。

推荐在PBS中加入蛋白酶抑制剂）加入玻璃匀浆器中，于冰上充分研磨。

为了进一步裂解组织细胞，可以对匀浆液进行超声破碎，或反复冻融。

后将匀浆液于5000×g离心5~10分钟，取上清检测。

4. 细胞培养物上清或其它生物标本：请1000×g离心20分钟，取上清即可检测，或将上清置于-20℃或-80℃保存，但应避免反复冻融。

注：标本溶血会影响后检测结果，因此溶血标本不宜进行此项检测。

维生素B1含量试剂盒说明书HPLC法50管48样

维生素B1.含量试剂盒说明书HP1.C法50管/48样测定意义：维生素是人和动物为维持正常的生理功能而必须从食物中获得的一类微量有机物质，在人和动物生长、代谢、发育过程中发挥着重要的作用。

维生素大致可分为脂溶性和水溶性两大类，水溶性维生素主要包括维生素C和B族维生素。

维生素B1.又叫硫胺素（Thiamine）,可保持神经机能正常，促进糖代谢，保障脑的正常功能，防止精神疲劳和倦怠，预防心脑疾病，被称为“心脏与神经的维生素二测定原理：维生素BI在210nm下有吸收峰，可以利用高效液相色谱法测定其含量。

需自备的实验用品：高效液相色谱仪、低速离心机、溶剂抽滤装置、氮吹仪、涡旋振荡器、针头式过滤器（有机系，50个，0.22μm）∖滤膜（水系和有机系各1个，0.45μmM耐水C18柱（4.6x250mm）、可调式移液器、样品瓶（50个，2m1.）、内衬管（50个，放置在样品瓶内用于微量样品进样）、乙月青（色谱级，10Om1.）和超纯水。

试剂的组成和配制：试剂一：液体IOOm1.X1.瓶，4℃保存（提取液）；试剂二：维生素BI标准品0.5mgx1.支，-20°C保存。

实验前的准备工作：1、将超纯水IOOOm1.和乙晴IOOm1.用0.45μm的滤膜抽滤，以除去溶剂中的杂质，防止堵塞色谱柱。

（注：蒸储水用水系滤膜抽滤，甲醇用有机系滤膜抽滤）。

2、流动相A：0.05mo1.∕1.磷酸二氢钾缓冲溶液（含0.2%三乙胺，pH6.0）-乙月青（97:3）,即970m1.水+6∙6g磷酸二氧钾+1.94m1.三乙胺+3Om1.三乙胺；流动相B：甲醇。

A和B比例为90:IOo3、将配好的流动相超声30分钟，以脱去溶剂中的气泡，防止堵塞色谱柱。

维生素B1.的提取：称取约0.1g样本，加入Im1.试剂一,冰浴匀浆,冰水浴超声30min。

8000g离心IOmin,取上清液，针头式过滤器过滤后待测。

操作过程注意低温、避光。

Bioconductor的数据包library（biomaRt）简介生信菜鸟团

Bioconductor的数据包library（biomaRt）简介生信菜鸟团这是发布在bioconductor平台上面的一个数据库文件，可以通过R里面下载安装并使用，非常方便。

其实在ensembl数据库里面也有一个biomart，我之前也讲过这个平台，非常好用，可以把任意的数据库之间的ID号进行转换。

为了更好的理解和掌握biomaRt，我们可以先通过在线资源来了解一下它的原型biomart （）。

biomart 是为生物科研提供数据服务的免费软件，它为数据下载提供打包方案。

它有许多成功的应用实例，比如欧洲生物信息学中心（The European Bioinformatics Institute ，EBI）维护的Ensembl 数据库（/）就使用biomart提供数据批量下载服务，还有COSMIC, Uniprot, HGNC, Gramene, Wormbase 以及dbSNP等。

这个就是一个R平台的biomart而已，但是非常好用！> library(biomaRt)> head(listMarts(), 3)biomart version1 ensembl ENSEMBL GENES 79 (SANGER UK)2 snp ENSEMBL VARIATION 79 (SANGER UK)3 regulation ENSEMBL REGULATION 79 (SANGER UK)这是这个biomart最具有代表性的三个数据库，用listMarts()可以查看得知，它总共有58个数据库。

ensembl这是创建了人的ensembl数据库对象> head(listFilters(ensembl), 3)name description1 chromosome_name Chromosome name2 start Gene Start (bp)3 end Gene End (bp)可以看到对人的数据库ensembl来说，有多种字段可以来挑选自己感兴趣的东西，最常用的的当然是染色体号及起始终止坐标啦，用listFilters(ensembl),以查看得知，它总共有284中挑选感兴趣数据的方式。

BioMart

位置数据库描述
找到我们需要目标数据库
脊椎动物真核生物
点击ENSEMBL找到目标数据集
找到所需要的的人类基因数据集
（二）设置过滤器
点击Filters进入界面
基因组位置基因信息
属性
表达产物
（三）查看序列属性
特性结构
同源序列
序列
序列
未剪切的序列
（四）下载序列默认格式
碱基数量
wwwbiomartorg一选择数据库进入主页面生物数据查询入口点击community位置描述数据库找到我们需要目标数据库脊椎动物真核生物点击ensembl找到目标数据集找到所需要的的人类基因数据集二设置过滤器点击filters进入界面基因组位置基因信息表达产物属性三查看序列属性特性同源序列结构序列序列未剪切的序列四下载序列默认格式碱基数量
所以，利用BioMart可以批量获得相关数据，并可以方便地得到一个物种所有的或某个限定范围内的蛋白、核酸序列以及其他信息。下面来看看BioMart检索方法及过程：（BioMart地址：）
（一）选择数据库，进入主页面
生物数据查询入口
点击comቤተ መጻሕፍቲ ባይዱunity
BioMart检索实例
实例分析：下载人类的所有基因序列
09生物技术1班：黄媛20090402701038 李双20090402701043
BioMart简介
BioMart也是比较常用的数据库查询工具之一。它的便捷之处在于将存储于多个数据库的基因、蛋白的序列信息和注释信息全部整合到一起，也就是说它是一个高度集成的数据库。用户可以方便地查询一个基因在不同数据库中得 ID、基因组位置、表达、结构等信息。

植物褪黑素(MT)说明书

植物褪黑素(MT)酶联免疫分析试剂盒使用说明书本试剂盒仅供研究使用。

检测范围：96T0.2ng/L -15 ng/L使用目的：本试剂盒用于测定植物组织，细胞及其它相关样本中褪黑素(MT)含量。

实验原理本试剂盒应用双抗体夹心法测定标本中植物褪黑素(MT)水平。

用纯化的植物褪黑素(MT)抗体包被微孔板，制成固相抗体，往包被单抗的微孔中依次加入植物褪黑素(MT)，再与HRP标记的褪黑素(MT)抗体结合，形成抗体-抗原-酶标抗体复合物，经过彻底洗涤后加底物TMB显色。

TMB在HRP酶的催化下转化成蓝色，并在酸的作用下转化成最终的黄色。

颜色的深浅和样品中的植物褪黑素(MT)呈正相关。

用酶标仪在450nm波长下测定吸光度（OD值），通过标准曲线计算样品中植物褪黑素(MT)浓度。

试剂盒组成130倍浓缩洗涤液20ml×1瓶7终止液6ml×1瓶2酶标试剂6ml×1瓶8标准品（20ng/L）0.5ml×1瓶3酶标包被板12孔×8条9标准品稀释液 1.5ml×1瓶4样品稀释液6ml×1瓶10说明书1份5显色剂A液6ml×1瓶11封板膜2张6显色剂B液6ml×1/瓶12密封袋1个标本要求1．标本采集后尽早进行提取，提取按相关文献进行，提取后应尽快进行实验。

若不能马上进行试验，可将标本放于-20℃保存，但应避免反复冻融2．不能检测含NaN3的样品，因NaN3抑制辣根过氧化物酶的（HRP）活性。

操作步骤1.标准品的稀释：本试剂盒提供原倍标准品一支，用户可按照下列图表在小试管中进行稀释。

10ng/L5号标准品150μl的原倍标准品加入150μl标准品稀释液5ng/L4号标准品150μl的5号标准品加入150μl标准品稀释液2.5ng/L3号标准品150μl的4号标准品加入150μl标准品稀释液1.25ng/L2号标准品150μl的3号标准品加入150μl标准品稀释液0.625ng/L1号标准品150μl的2号标准品加入150μl标准品稀释液2.加样：分别设空白孔（空白对照孔不加样品及酶标试剂，其余各步操作相同）、标准孔、待测样品孔。

大鼠S100蛋白(S100B)酶联免疫分析试剂盒说明书

大鼠S100B蛋白（S100B）酶联免疫分析试剂盒使用说明书本试剂盒仅供研究使用检测范围：312pg/ml-20000pg/ml最低检测限：78pg/ml特异性：本试剂盒可同时检测天然或重组的大鼠S100B，且与其他相关蛋白无交叉反应。

有效期：6个月预期应用：ELISA法定量测定大鼠血清、血浆、细胞培养上清或其它相关生物液体中S100B 含量。

说明1．试剂盒保存：-20℃（较长时间不用时）；2-8℃（频繁使用时）。

2．浓洗涤液低温保存会有盐析出，稀释时可在水浴中加温助溶。

3．中、英文说明书可能会有不一致之处，请以英文说明书为准。

4．刚开启的酶联板孔中可能会含有少许水样物质，此为正常现象，不会对实验结果造成任何影响。

实验原理用纯化的抗体包被微孔板，制成固相载体，往包被抗S100B抗体的微孔中依次加入标本或标准品、生物素化的抗S100B抗体、HRP标记的亲和素，经过彻底洗涤后用底物TMB显色。

TMB在过氧化物酶的催化下转化成蓝色，并在酸的作用下转化成最终的黄色。

颜色的深浅和样品中的S100B呈正相关。

用酶标仪在450nm波长下测定吸光度（OD值），计算样品浓度。

试剂盒组成及试剂配制1.酶联板（Assay plate）：一块（96孔）。

2.标准品（Standard）：2瓶（冻干品）。

3.样品稀释液（Sample Diluent）：1×20ml/瓶。

4.生物素标记抗体稀释液（Biotin-antibody Diluent）：1×10ml/瓶。

5.辣根过氧化物酶标记亲和素稀释液（HRP-avidin Diluent）：1×10ml/瓶。

6.生物素标记抗体（Biotin-antibody）：1×120μl/瓶（1：100）7.辣根过氧化物酶标记亲和素（HRP-avidin）：1×120μl/瓶（1：100）8.底物溶液（TMB Substrate）：1×10ml/瓶。

细胞凋亡试剂盒使用说明书 - 专业提供基因,重组蛋白,抗体

细胞凋亡试剂盒使用说明书目录号：CSB-AP11921h本试剂盒仅供研究使用概述细胞凋亡是细胞的基本特征之一，在机体的胚胎发育、组织修复、内环境的稳定等方面都起到十分重要的作用。

在正常细胞中磷脂酰丝氨酸（PS）分布在细胞膜脂质双层的内侧，而在细胞凋亡早期，细胞膜中的磷脂酰丝氨酸（PS）由脂膜内侧翻向外侧。

此磷脂酰丝氨酸可与Annexin V, 一种分子量为35~36kD的Ca2+依赖性磷脂结合蛋白以高度亲和力相结合。

凋亡后期死亡的细胞与其它因素死亡的细胞，虽都可与A nnexin V结合，但死亡的细胞其细胞膜通透，也可被碘化丙啶（Propidium Iodide, PI），一种核酸染料染成红色。

如将Annexin V标以荧光素（RPE、FITC等)作为荧光探针，结合PI, 利用荧光显微镜或流式细胞仪检测，就可以将处于不同凋亡时期的细胞区分开来。

试剂盒组成及试剂配制1.Annexin V-FITC：保存在Tris-HCl (20mM), NaCl(50mM) 缓冲液中，含1mg/ml BSA和0.09% NaN3。

2.Binding Buffer：Hepes(10 mM) /NaOH (pH 7.4), NaCl (140 mM),CaCl2(2.5 mM). 经0.2μm孔径过滤器除菌。

2–8℃保存。

3.Propidium Iodida(PI)：50 ug/ml 溶于PBS (pH7.4)中。

操作步骤实验开始前，请提前配置好所有试剂，试剂或样品稀释时，均需混匀，混匀时尽量避免起泡。

1.悬浮细胞离心（350g, 3 min）收集；贴壁细胞用不含EDTA的胰酶消化收集（胰酶消化时间不易过长，以防引起假阳性）；2.用PBS洗涤细胞二次（350g, 3 min），计数细胞，每样0.1ml 含1~5×105细胞；3.用400μL Binding Buffer悬浮细胞；4.加入5μL Annexin V-FITC, 混匀，加入5 μL Propidium Iodide，混匀；5.室温避光反应10 min；6.荧光显微镜下观察或用流式细胞仪检测。

细胞沉默调节蛋白1SIRT1活性荧光定量检测试剂盒中文版

细胞沉默调节蛋白1 （SIRT1）活性荧光定量检测试剂盒（中文版）主要用途细胞沉默调节蛋白1 （SIRT1）活性荧光定量检测试剂是一种旨在通过荧光探针氨甲基香豆素标记人工合成的乙酰化p53多肽底物，经过SIRT1脱乙酰基后，被位点特异性氨基肽酶水解，释放黄色对硝基苯胺，即采用荧光法来测定细胞裂解萃取样品中酶活性的权威而经典的技术方法。

该技术经过精心研制、成功实验证明的。

其适用于各种细胞裂解萃取液样品（动物、人体）、核蛋白样品、部分或完全纯化酶样品中SIRT1的活性定量检测，以及抑制剂和激活剂的筛选。

产品严格无菌，即到即用，操作简捷，性能稳定。

技术背景人体组蛋白脱乙酰基酶（histone deacetylase；HDAC）家属分成三类共20多个蛋白：种类I（class I）与酵母Rpd3蛋白同源，包括HDAC1、2、3、8，存在于细胞核中；种类II（class II）与酵母Hda1蛋白同源，包括HDAC4、5、6、7、9a、9b、10和HDRP/MITR，存在于细胞核和细胞浆里；上述两大种类的蛋白分子催化结构域含有锌，且曲古菌素A（trichostatin A；TSA）敏感。

种类III（class III）为NAD+辅助因子必需，又称为sirtuins，与酵母Sir2（Silent Information Regulator 2）同源，包括SIRT1至7等，为曲古菌素A（trichostatin A；TSA）不敏感型赖氨酸脱乙酰基酶（lysyl-deacetylase）。

催化赖氨酸脱乙酰基反应，以及由NAD+和乙酰（acetyl）基团转化产生的烟酰胺（nicotinamide）和O-乙酰ADP核糖（O-acetyl-ADP-ribose）。

SIRT1位于细胞核内，与酵母Sir2同源性最高。

其功能在于调节p53活性和抑制细胞凋亡。

基于人工合成的乙酰化p53（379至382氨基酸位点）多肽底物Ac-Arg—His-Lys-Lys（Ac）具有抗氨基肽酶切离的功能，首先使用荧光染料氨甲基香豆素（aminomethylcoumarin；AMC）来标记乙酰化p53多肽底物，其次进行脱乙酰化，最后底物进一步在氨基肽酶的催化酶解，释放出具有强烈荧光的7-氨-4甲基香豆素（7-amino-4-methylcoumarin；AMC）（激发波长350-360nm；散发波长450-465nm），由此来定量测定沉默调节蛋白1的活性。

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The biomaRt package provides access to on line annotation resourcesprovided by the Biomart Project /.The goalsof the Biomart project are to provide a query-oriented data managementsystem that can be used for’data mining’like searches of complex descriptivedata.Weﬁrst need to create an instance of the Mart class which stores theconnection information to the database.All available BioMart Web servicescan be listed using the function listMarts.The function head reduces theoutput to theﬁrst couple of entries.>library("biomaRt")>head(listMarts())biomart version1ensembl ENSEMBL52GENES(SANGER UK)2snp ENSEMBL52VARIATION(SANGER UK)3vega VEGA33(SANGER UK)4msd MSD PROTOTYPE(EBI UK)5uniprot UNIPROT PROTOTYPE(EBI UK)6htgt HIGH THROUGHPUT GENE TARGETING AND TRAPPING(SANGER UK) We will use Ensembl for our example.>mart=useMart("ensembl")Often BioMart databases contain more than one dataset.We can check foravailable datasets using the function listDatasets.>head(listDatasets(mart))dataset description version 1oanatinus_gene_ensembl Ornithorhynchus anatinus genes(OANA5)OANA5 2cporcellus_gene_ensembl Cavia porcellus genes(cavPor3)cavPor3 3gaculeatus_gene_ensembl Gasterosteus aculeatus genes(BROADS1)BROADS1 4lafricana_gene_ensembl Loxodonta africana genes(BROADE1)BROADE1 5agambiae_gene_ensembl Anopheles gambiae genes(AgamP3)AgamP3 6mlucifugus_gene_ensembl Myotis lucifugus genes(MICROBAT1)MICROBAT1 We will work with the hsapiens gene ensembl set,and update our Martobject accordingly.>ensembl=useDataset("hsapiens_gene_ensembl",mart=mart)1For the Ensembl database biomaRt oﬀers a set of convenience functions forthe most common tasks.The function getGene uses a vector of query IDsto look up names,descriptions,and chromosomal locations of correspondinggenes.getGo can be used to fetch Gene Ontology(GO)annotations andgetSequences retrieves diﬀerent kinds of sequence information.getSNPand getHomolog are useful to query SNP data or to map gene identiﬁersfrom one species to another.Exercise1Fetch the sequences of3 UTRs of our set of diﬀerentially expressed genesusing getSequence.Take a look at its manual page to learn about thefunction’s parameters.Think about which type of gene IDs we have availablefor a set of genes.We arbitrarily choose two EG IDs,(1001and1002).Solutions:>EGs=c("1001","1002")>utr=getSequence(id=EGs,seqType="3utr",mart=ensembl,+type="entrezgene")>utr[1,]1GCGGCCTGCCTGCAGGGCTGGGGACCAAACGTCAGGCCACAGAGCATCTCCAAGGGGTCTCAGTTCCCCCTTCAGCTGAGGACT entrezgene11001biomaRt allows us to retrieve many diﬀerent kinds of data in a veryﬂexible manner.To understand how its generalized query API works,weﬁrst have to learn about the termsﬁlter and attribute.Aﬁlter deﬁnes therestriction on a query,for example,to show results only for a subset of genesselected by a gene identiﬁer.Attributes deﬁne the values we want to retrieve,for instance,the IDs of PFAM domains for these genes.You can get a listof availableﬁlters with listFilters>head(listFilters(ensembl))name description1chromosome_name Chromosome name2start Gene Start(bp)3end Gene End(bp)4band_start Band Start5band_end Band End6marker_start Marker Start2and of available attributes with listAttributes.>head(listAttributes(ensembl))name description1ensembl_gene_id Ensembl Gene ID2ensembl_transcript_id Ensembl Transcript ID3ensembl_peptide_id Ensembl Protein ID4canonical_transcript_stable_id Canonical transcript stable ID(s)5description Description6chromosome_name Chromosome NameFor some BioMart databases,in particular for Ensembl,there are manyattributes andﬁlters available,and you can control the attributes that arelisted by listAttributes with the page parameter.The general-purposequery interface of biomaRt is provided by the function getBM.Exercise2For our set of diﬀerentially expressed genes,ﬁnd associated protein domains.Such domains are stored for instance in the PFAM,Prosite,or InterPro databases.Try toﬁnd domain IDs for one or for all of these sources.Solutions:>domains=getBM(attributes=c("entrezgene","pfam","prosite", +"interpro"),filters="entrezgene",value=EGs,mart=ensembl)>interpro=split(domains$interpro,domains$entrezgene)>interpro[1]$ 1001[1]"IPR002126""IPR009124""IPR015919""IPR000233""IPR002126""IPR009124"[7]"IPR015919""IPR000233"Or we can consider a diﬀerent genome,that of the mouse.>listDatasets(ensembl)[1:10,]dataset description version 1oanatinus_gene_ensembl Ornithorhynchus anatinus genes(OANA5)OANA5 2cporcellus_gene_ensembl Cavia porcellus genes(cavPor3)cavPor3 3gaculeatus_gene_ensembl Gasterosteus aculeatus genes(BROADS1)BROADS134lafricana_gene_ensembl Loxodonta africana genes(BROADE1)BROADE1 5agambiae_gene_ensembl Anopheles gambiae genes(AgamP3)AgamP3 6mlucifugus_gene_ensembl Myotis lucifugus genes(MICROBAT1)MICROBAT1 7hsapiens_gene_ensembl Homo sapiens genes(NCBI36)NCBI36 8aaegypti_gene_ensembl Aedes aegypti genes(AaegL1)AaegL1 9csavignyi_gene_ensembl Ciona savignyi genes(CSAV2.0)CSAV2.0 10fcatus_gene_ensembl Felis catus genes(CAT)CAT >ensembl=useDataset("mmusculus_gene_ensembl",mart=ensembl)>attributes=listAttributes(ensembl)>attributes[1:10,]name description1ensembl_gene_id Ensembl Gene ID2ensembl_transcript_id Ensembl Transcript ID3ensembl_peptide_id Ensembl Protein ID4canonical_transcript_stable_id Canonical transcript stable ID(s)5description Description6chromosome_name Chromosome Name7start_position Gene Start(bp)8end_position Gene End(bp)9strand Strand10band Band>filters=listFilters(ensembl)>filters[1:10,]name description1chromosome_name Chromosome name2start Gene Start(bp)3end Gene End(bp)4band_start Band Start5band_end Band End6marker_start Marker Start7marker_end Marker End8strand Strand9chromosomal_region Chromosome Regions10with_affy_mu11ksuba with Affymetrix Microarray mu11ksuba ID(s)>EGs=c("18392","18414","56513")>getBM(attributes="external_gene_id",filters="entrezgene",+values=EGs,mart=ensembl)4external_gene_id1Orc1l2Osmr3Pard6a>getBM(attributes=c("entrezgene","transcript_start","transcript_end"), +filters="entrezgene",values=EGs,mart=ensembl)entrezgene transcript_start transcript_end11839210825206610828863321841467635906824283356513108225054108227393456513108225571108227393556513108225571108227262You canﬁnd out about diﬀerent types of attributes.The current versionof biomaRt has changed quite a bit.Now it uses the concept of pages todivide up the attributes.>pages=attributePages(ensembl)>listAttributes(ensembl,page="structure")name description103ensembl_gene_id Ensembl Gene ID104ensembl_transcript_id Ensembl Transcript ID105ensembl_peptide_id Ensembl Protein ID106chromosome_name Chromosome Name107start_position Gene Start(bp)108end_position Gene End(bp)109transcript_start Transcript Start(bp)110transcript_end Transcript End(bp)111strand Strand112external_gene_id Associated Gene Name113external_gene_db Associated Gene DB114cds_length CDS Length115transcript_count Transcript count116description Description117ensembl_exon_id Ensembl Exon ID118exon_chrom_start Exon Chr Start(bp)119exon_chrom_end Exon Chr End(bp)120rank Exon Rank in Transcript121phase phase5And now equipped with that information we can use the getBM functionto extract the exon start and end positions for a particular gene.We willuse Pard6a,with EG ID56513.>getBM(attributes=c("ensembl_exon_id","exon_chrom_start","exon_chrom_end"), +filters="entrezgene",values="56513",mart=ensembl)ensembl_exon_id exon_chrom_start exon_chrom_end1ENSMUSE000006799311082250541082252172ENSMUSE000002288211082260741082262963ENSMUSE000005803401082265081082273934ENSMUSE000003438621082255711082257035ENSMUSE000007063381082265111082273936ENSMUSE00000706337108226508108227262We can also search based on GO terms.First we look up a GO term,then we use biomaRt to get the unique EG IDs associated with that term.You could easily compare this with the results form the Bioconductor mouseannotation package.>library("GO.db")>library("org.Mm.eg.db")>GOTERM[["GO:0016564"]]GOID:GO:0016564Term:transcription repressor activityOntology:MFDefinition:Any transcription regulator activity that prevents or downregulates transcription.Synonym:negative transcriptional regulator activitySynonym:transcriptional repressor activity>GOEGs=unique(org.Mm.egGO2EG[["GO:0016564"]])>GOEGs[1]"11614""11770""11906""11910""12029""12053" [7]"12151""12265""12395""13047""13048""13163" [13]"13345""13433""15110""15184""15205""15242" [19]"15404""15412""15426""16468""16600""16969" [25]"17257""17425""17701""17859""17936""17937" [31]"17978""18037""18091""18171""18432""18507"6[37]"19015""19016""19401""19645""19712""19763" [43]"19821""20185""20218""20230""20371""20465" [49]"20473""20602""20893""21385""21386""21833" [55]"21834""21849""21907""22025""22778""22781" [61]"23942""23950""24136""27049""29871""52679" [67]"53975""54427""56218""56233""56381""56461" [73]"57741""58805""59058""66935""67824""71041" [79]"72567""74120""74123""74318""79221""81703" [85]"83925""84653""93759""108655""110521""110805" [91]"114142""114712""140477""208727""216161""231004" [97]"231798""234219""237412""240690""245688""329416" [103]"330627""382867""100009600"Then we can retrieve these from biomaRt like this:>geneLocs<-getBM(c("ensembl_gene_id","transcript_start","transcript_end", +"chromosome_name"),"entrezgene",GOEGs,mart=ensembl)7。