这项工作是由美国国立卫生研究院资助R01-GM-098977和R21-AI-098122到CBK和KEG支持。 RSS是由塞耶创新项目奖学金从工程学院塞耶支持的，部分由卢斯基金会奖学金和部分。

四个主要业务包括肽-MHC II结合试验：1装帧：测试肽标记的对照肽争夺可溶性MHC II类蛋白的溶液相结合。结合测量在很宽的范围内测试肽浓度。 2 - 捕捉：在结合反应接近平衡，肽-MHC II类分子复合物被捕获并与未结合的肽和蛋白的构象依赖性的认可与固定化抗体分离。 3，检测：捕获控制肽是利用时间分辨荧光定量检测。 4分析：光谱数据进行处理，绘制和分析，以确定测试肽与MHC II类蛋白的剂量依赖性的结合特性。 在下面的过程的各个步骤，使用液体处理机器人的建议（如果有）。特别是在384孔格式，自动分配和稀释的液体在多用户最大限度地减少错误，结果一致的油井到良好的卷，并产生更窄95％置信区间为比手动96孔测定（数据未示出）的IC 50值。如果液体处理机器人不可用，可以手工完成注有“（液体处理机器人）”的步骤。类似地，如果可能的话，自动洗板机为与384孔格式兼容的建议。这有效地规范了板洗涤过程。如果洗板机不可用，可以手工完成注有“（洗板机）”的步骤。 1。大衣酶标板（第1天） <ol><li>稀释L243抗体的股票（0.5毫克/毫升）在硼酸盐缓冲液为10微克/毫升的工作浓度。 <ol><li>大衣单384孔板，加入200μl股票抗体的9.8毫升硼酸盐缓冲液中。 （见补充电子表格的计算器替代比例）。 </li></ol></li><li>加入25微升的L243抗体溶液以384瓦特每孔ELL高结合白色酶标板。 （液体处理机器人） </li><li>封板用聚酯薄膜和孵育过夜，4°C。 注意：每个384 - 孔ELISA板可以容纳多达15个测试的肽在8稀释度为1 MHC II类等位基因，以及阳性和阴性对照。这将最终产生一式三份测量。 </li></ol> 2。使测试肽稀释液（第1天） <ol><li>与在DMSO中的每个测试肽的10mM储备开始，进行以下的稀释系列中柠檬酸盐磷酸盐缓冲液使用384孔聚丙烯板（ 表1）。 （液体处理机器人） 注：一个完​​整​​的384孔聚丙烯板需要三个独立的酶标板。聚丙烯板的三分之一将只需要一个ELISA板（ 图1）。 </li></ol><table border="0" cellpadding="0" cellspacing="0"><tbody align="center"><tr><td rowspan="2">稀释号码</td><td>量吨Ø从先前的稀释/盘点</td><td>体积柠檬酸缓冲液中添加</td><td>浓。稀释</td><td>浓。在结合反应</td><td>浓。在酶联免疫吸附中性化</td></tr><tr><td> （微升） </td><td> （微升） </td><td> （μM） </td><td> （μM） </td><td> （μM） </td></tr><tr><td> 1 </td><td> 0.7 </td><td> 35.1 </td><td> 195.531 </td><td> 97.765 </td><td> 48.883 </td></tr><tr><td> 2 </td><td> 14.3 </td><td> 14.3 </td><td> 97.765 </td><td> 48.883 </td><td> 24.441 </td></tr><tr><td> 3 </td><td> 7.1 </td><td> 28.7 </td><td> 19.389 </td><td> 9.695 </td><td> 4.847 </td></tr><tr><td> 4 </td><td> 14.3 </td><td> 14.3 </td><td> 9.695 </td><td> 4.847 </td><td> 2.424 </td></tr><tr><td> 5 </td><td> 7.1 </td><td> 28.7 </td><td> 1.923 </td><td> 0.961 </td><td> 0.481 </td></tr><tr><td> 6 </td><td> 14.3 </td><td> 14.3 </td><td> 0.961 </td><td> 0.481 </td><td> 0.24 </td></tr><tr><td> 7 </td><td> 7.1 </td><td> 28.7 </td><td> 0.191 </td><td> 0.095 </td><td> 0.048 </td></tr><tr><td> 8 </td><td> 14.3 </td><td> 14.3 </td><td> 0.095 </td><td> 0.048 </td><td> 0.024 </td></tr><tr><td colspan="6">取下并丢弃7.1微升的稀释从8号肽溶液。 </td></tr></tbody></table> 表1制备的试验肽的系列稀释 。 <img alt="图1" fo:content-width="5in" fo:src="/files/ftp_upload/51308/51308fig1highres.jpg" src="/files/ftp_upload/51308/51308fig1.jpg" /> 图1。Plate地图肽结合试验（A）地图在聚丙烯384孔板肽稀释。每个聚丙烯板可容纳三组15肽在对唱歌的竞争结合反应<html>乐MHCⅡ类等位基因。 （B）在结合反应接近平衡，每种肽基转移，一式三份，以一个单独的384 -孔ELISA板已预先涂有抗MHC II抗体。 3。准备MHCⅡ类母液（第1天） <ol><li>使反应缓冲液<ol><li>加80微升1mM的PEFA阵营和60mg辛基-β-D-glucopyranaside至3920微升柠檬酸盐磷酸盐缓冲液中。 （见补充电子表格的计算器替代比例）。 </li></ol></li><li>稀选定MHC II类储备溶液至101纳米的反应缓冲液用15毫升锥形管中（ 表2）。 注意：MHC II类分子的浓度最终将50nM的在中和的ELISA测定法的结合反应和25纳米。该MHCⅡ类股票的浓度会因制造商批号不同而不同。 （见补充电子表格的计算器）。 </li></ol>“&gt; <tbody><tr><td> MHCⅡ类等位基因DRB1 *： </td><td> 1501 </td></tr><tr><td> MHCⅡ类股票浓度（毫克/毫升） </td><td> 1.3 </td></tr><tr><td> MHCⅡ类股票浓度（mM） </td><td> 20 </td></tr><tr><td>第一卷。 MHCⅡ类股票添加（毫升） </td><td> 14.65 </td></tr><tr><td>第一卷。反应缓冲液添加量（毫升）： </td><td> 2885.35 </td></tr><tr><td> MHCⅡ类母液浓度（NM） </td><td> 101 </td></tr></tbody></table> 表2制备MHC II类分子的master mix。 4。准备阴性和阳性对照（第1天） <ol><li>加入21.5微升柠檬酸磷酸盐缓冲液从步骤2.1的384孔聚丙烯板（ 图1A）的“阴性对照”好。 </li><li>加入21.5微升柠檬酸磷酸缓冲到384孔聚丙烯板的“阳性对照”好。 注：在“位置略去控制“将在第5节完成，如下。 </li><li>从步骤3.2和吸管取出49.5微升的MHC II类混合液到1.5ml离心管中。 </li><li>加入0.5微升柠檬酸缓冲液的49.5微升的MHCⅡ类主要混合液的步骤4.3。 </li><li>加入21.5微升浓度调节MHCⅡ类预混从步骤4.4的“阴性对照”井384孔聚丙烯板（ 图1A）。 注：此样本代表含有MHC II类蛋白，无法控制的肽，并没有测试肽零信号阴性对照。 </li></ol> 5。添加适当的对照肽的MHCⅡ类母液（第1天） <table border="0" cellpadding="0" cellspacing="0"><tbody><tr><td> MHCⅡ类等位基因DRB1 * </td><td>生物素化的对照肽</td><td> （残基） </td><td>序列</td></tr><tr><td> 0101 </td><td>流感HA-B </td><td> （306-318） </td><td>生物素（AHX）（AHX）PRYVKQNTLKLAT酰胺</td></tr><tr><td> 0301 </td><td>肌红蛋白</td><td> （137-148） </td><td>生物素 - （AHX） - （AHX）LFRKDIAAKYKE-OH </td></tr><tr><td> 0401 </td><td> YAR-B </td><td> （1-14） </td><td>生物素（AHX）（AHX）YARFQSQTTLKQKT-OH </td></tr><tr><td> 0701 </td><td> TetTox-B </td><td> （830-843） </td><td>生物素（AHX）（AHX）QYIKANSKFIGITE-OH </td></tr><tr><td> 1101 </td><td>流感HA-B </td><td> （306-318） </td><td>生物素（AHX）（AHX）PRYVKQNTLKLAT酰胺</td></tr><tr><td> 1501 </td><td> MBP-B </td><td> （84-102） </td><td>生物素（AHX）（AHX）NPVVHFFKNIVTPRTPPPS-OH </td></tr></tbody></table> *我们建议订购10毫克规模的经济。 表3。对照肽的各种MHCⅡ类等位基因。* <ol><li>稀释的对照肽（存储在400μM的在DMSO中）1:20在新鲜的DMSO中，得到稀释的库存在20μM。 <ol><li>加入25微升400微米控制肽475微升的DMSO。注：这是一个大的过剩，但允许粘性DMSO溶液准确的处理。 </li></ol></li><li>进一步从步骤5.1（20毫摩尔）1:100从步骤3.2稀释的对照肽进入MHC II类预混。 <ol><li>加入28.8微升对照肽的稀释步骤5.2其余2,850.5微升的MHCⅡ类主要混合液。 </li></ol></li><li>加入21.5微升的MHC II类预混与对照肽（步骤5.2）的384孔聚丙烯板（步骤4.2）（ 图1A）的阳性对照孔。注：此样本代表含有MHC II类蛋白，对照肽，并没有测试肽高信号阳性对照。 </li></ol> 6。使结合反应（第1天） <ol><li>添加MHCⅡ类预混含对照肽（步骤5.2）以1:1的比例各测试肽稀释液（步骤2.1）创建的结合反应。 <ol><li>加入21.5微升MHCⅡ类母液含有体质TROL肽从步骤5.2至21.5微升从步骤2.1每个测试肽稀释。 （液体处理机器人） </li></ol></li><li>密封结合反应用聚酯薄膜和孵化12-24小时，在37℃的非二氧化碳培养箱控制，无晃动。 </li></ol> 7。消除和转移的结合反应（2天） <ol><li>删除包含从37℃培养箱中培养结合反应（步骤6.2）的384孔聚丙烯板。 </li><li>稀结合反应1:1中和缓冲。 （液体处理机器人） <ol><li>加入43微升的中和缓冲的每个结合反应良好。 </li></ol></li><li>从4℃取出ELISA板（步骤1.3）中，用60的PBS-0.05％吐温20毫升/孔洗3次。 （洗板机） </li><li>转移25微升每中和结合反应步骤从7.2到来自步骤7.3的抗体包被的384 - 孔ELISA板的一式三份孔中。 （ 图1中 </STRONG&gt;）（液体处理机器人） </li><li>用聚酯薄膜并将其放置在37℃培养箱中培养2.5小时或4℃冰箱中过夜覆盖酶标板。 </li></ol> 8。酶联免疫吸附开发（2天或3） <ol><li>从步骤7.5移除ELISA板，洗3次，加入60μl/孔的PBS-0.05％吐温。 （洗板机） </li><li>稀释链霉亲和铕溶液（0.1毫克/毫升的链霉亲和，7的Eu 3 + /链亲和素）1,000倍的DELFIA测定缓冲液。 <ol><li>对1 384 - 孔板，稀释10μl的链霉亲和铕在10毫升分析缓冲液。 </li></ol></li><li>加入25微升稀释的链亲和素 - 铕到384 - 孔ELISA板的各孔中从步骤8.1。 （液体处理机器人） </li><li>在室温下进行1小时的聚酯膜，并置于黑暗覆盖板。注：在1小时培养，从冰箱中取出的增强解决方案，并拨出10毫升/板在黑暗中，在ROOM温度。 </li><li>以下的1小时温育后，从步骤8.3 3×60微升/孔的PBS-0.05％吐温洗涤384孔ELISA板。 （洗板机） </li><li>加25μl的增强溶液到384 - 孔ELISA板的各孔中从步骤8.5。 （液体处理机器人） </li><li>覆盖384 - 孔ELISA板用的聚酯薄膜，并允许板坐在黑暗中在室温下放置10-15分钟。 </li><li> 200，停止诠释：1,000，前340，EM继10-15分钟孵育，用时间分辨荧光酶标仪与铕设置（例如，开始诠释阅读384孔ELISA板的步骤8.7荧光615，截止：无，PMT：自动，读取/好了：50）。 </li></ol> 9。数据分析<ol><li>在并排侧柱3重复值（Y =荧光测量X =测试肽浓度）输入XY格式的图形数据。 </li><li>登录变换的所有数据X值：X =日志（倍） </li><li>符合TRA日志有一个网站竞争性结合模型提取的IC 50值nsformed数据。 </li><li>约束的底部值的平均阴性对照值。 </li><li>全局拟合最佳值，并确保该全局拟合参数低于或等于阳性对照。 </li></ol>

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H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+generation+and+evaluation+of+specific+MHC+binding+predictive+tools:+ARB+matrix+applications.">Automated generation and evaluation of specific MHC binding predictive tools: ARB matrix applications.</a> Immunogenetics. 57, 304-314 (2005).</li><li>Nielsen, M., Lundegaard, C., Lund, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prediction+of+MHC+class+II+binding+affinity+using+SMM-align,+a+novel+stabilization+matrix+alignment+method.">Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method.</a> BMC Bioinformatics. 8, 238 (2007).</li><li>Nielsen, M., Justesen, S., Lund, O., Lundegaard, C., Buus, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NetMHCIIpan-2.0+-+Improved+pan-specific+HLA-DR+predictions+using+a+novel+concurrent+alignment+and+weight+optimization+training+procedure.">NetMHCIIpan-2.0 - Improved pan-specific HLA-DR predictions using a novel concurrent alignment and weight optimization training procedure.</a> Immun. Res. 6, 9 (2010).</li><li>De Groot, A. S., Knopp, P. M., Martin, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=De-immunization+of+therapeutic+proteins+by+T-cell+epitope+modification.">De-immunization of therapeutic proteins by T-cell epitope modification.</a> Dev. Biol. 122, 171-194 (2005).</li><li>Choi, Y., Griswold, K. E., Bailey-Kellogg, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structure-based+redesign+of+proteins+for+minimal+T-cell+epitope+content.">Structure-based redesign of proteins for minimal T-cell epitope content.</a> J. Comput. Chem. 34, 879-891 (2013).</li><li>Parker, A. S., Choi, Y., Griswold, K. E., Bailey-Kellogg, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structure-guided+deimmunization+of+therapeutic+proteins.">Structure-guided deimmunization of therapeutic proteins.</a> J. Comput. Biol. 20, 152-165 (2013).</li><li>Parker, A. S., Griswold, K. E., Bailey-Kellogg, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimization+of+therapeutic+proteins+to+delete+T-cell+epitopes+while+maintaining+beneficial+residue+interactions.">Optimization of therapeutic proteins to delete T-cell epitopes while maintaining beneficial residue interactions.</a> J. Bioinform. Comput. Biol. 9, 207-229 (2011).</li><li>Parker, A. S., Zheng, W., Griswold, K. E., Bailey-Kellogg, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optimization+algorithms+for+functional+deimmunization+of+therapeutic+proteins.">Optimization algorithms for functional deimmunization of therapeutic proteins.</a> BMC Bioinformatics. 11, 180 (2010).</li><li>De Groot, A. S., Martin, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reducing+risk,+improving+outcomes:+Bioengineering+less+immunogenic+protein+therapeutics.">Reducing risk, improving outcomes: Bioengineering less immunogenic protein therapeutics.</a> Clin. Immunol. 131, 189-201 (2009).</li><li>Wang, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+systematic+assessment+of+MHC+class+II+peptide+binding+predictions+and+evaluation+of+a+consensus+approach.">A systematic assessment of MHC class II peptide binding predictions and evaluation of a consensus approach.</a> PLoS Comput. Biol. 4, e1000048 (2008).</li><li>Sette, A., Rappuoli, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reverse+vaccinology:+developing+vaccines+in+the+era+of+genomics.">Reverse vaccinology: developing vaccines in the era of genomics.</a> Immunity. 33, 530-541 (2010).</li><li>Moise, L., Cousens, L., Fueyo, J., De Groot, A. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Harnessing+the+power+of+genomics+and+immunoinformatics+to+produce+improved+vaccines.">Harnessing the power of genomics and immunoinformatics to produce improved vaccines.</a> Expert Opin. Drug Discov. 6, 9-15 (2011).</li><li>Sturniolo, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Generation+of+tissue-specific+and+promiscuous+HLA+ligand+databases+using+DNA+microarrays+and+virtual+HLA+class+II+matrices.">Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices.</a> Nat. Biotechnol. 17, 555-561 (1999).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biologic+drugs+set+to+top+2012+sales.">Biologic drugs set to top 2012 sales.</a> Nat. Med. 18, 636 (2012).</li><li>Seib, K. L., Zhao, X., Rappuoli, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Developing+vaccines+in+the+era+of+genomics:+a+decade+of+reverse+vaccinology.">Developing vaccines in the era of genomics: a decade of reverse vaccinology.</a> Clin. Microbiol. Infect. 18, 109-116 (2012).</li><li>Jiang, W., Boder, E. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+engineering+and+analysis+of+peptide+binding+to+class+II+MHC.">High-throughput engineering and analysis of peptide binding to class II MHC.</a> Proc. Natl. Acad. Sci. U.S.A. 107, 13258-13263 (2010).</li><li>Justesen, S., Harndahl, M., Lamberth, K., Nielsen, L. -. L. B., Buus, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+recombinant+MHC+class+II+molecules+and+high-throughput+peptide-binding+assays.">Functional recombinant MHC class II molecules and high-throughput peptide-binding assays.</a> Immun. 5, 1-20 (2009).</li></ol>

伦纳德·莫伊斯被雇用并持有股票期权EpiVax，公司是一家私人拥有的生物技术公司，总部设在罗德岛普罗维登斯。在此研究报告的工作是没有任何可能与公司的商业目标相关联的偏见。

生物疗法已经确立了自己作为现代医学的基石，相当于2012年32四个五大畅销的药物。在生物制药行业呈现出持续增长几年6，以及新药的不断开发，以及生物仿制药的出现，扩大了生物制药管道。展望未来，评估和减少蛋白质治疗的免疫原性将成为早期生物治疗性发展的组成部分。为了促进这一进程，生物技术可以利用自己的计算方法为表位预测16-22以及旨在减少免疫原性?...

An erratum was issued for: <a href="https://www.jove.com/video/51308/a-high-throughput-mhc-ii-binding-assay-for-quantitative-analysis">A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes</a>.&#160; There were typos in the Protocol section and Table 2.
Step 3.1.1 in the Protocol was updated from:
Add 80 &#181;l of 1 mM Pefa Bloc and 60 mg of octyl-&#946;-D-glucopyranaside to 3,920 &#181;l of Citrate Phosphate Buffer. (See supplemental spreadsheet calculator for alternative scaling).
to:
Add 80 &#181;l of 1 mM Pefa Bloc and 60 &#181;g of octyl-&#946;-D-glucopyranaside to 3,920 &#181;l of Citrate Phosphate Buffer. (See supplemental spreadsheet calculator for alternative scaling).
Step 5.2&#160;in the Protocol was updated from:
Further dilute the Control Peptide from step 5.1 (20 mM) 1:100 into the MHC II Master Mix from step 3.2.
to:
Further dilute the Control Peptide from step 5.1 (20 &#181;M) 1:100 into the MHC II Master Mix from step 3.2.
Table 2 was updated from:
<table border="0" cellpadding="0" cellspacing="0" width="376">
	<tbody>
		<tr>
			<td>MHC II Allele DRB1*:</td>
			<td>1501</td>
		</tr>
		<tr>
			<td>MHC II Stock Concentration (mg/ml)</td>
			<td>1.3</td>
		</tr>
		<tr>
			<td>MHC II Stock Concentration (mM)</td>
			<td>20</td>
		</tr>
		<tr>
			<td>Vol. MHC II Stock to Add (ml)</td>
			<td>14.65</td>
		</tr>
		<tr>
			<td>Vol. Reaction Buffer to Add (ml):</td>
			<td>2885.35</td>
		</tr>
		<tr>
			<td>MHC II Master Mix Concentration (nM)</td>
			<td>101</td>
		</tr>
	</tbody>
</table>
to:
<table border="0" cellpadding="0" cellspacing="0" width="376">
	<tbody>
		<tr>
			<td>MHC II Allele DRB1*:</td>
			<td>1501</td>
		</tr>
		<tr>
			<td>MHC II Stock Concentration (mg/ml)</td>
			<td>1.3</td>
		</tr>
		<tr>
			<td>MHC II Stock Concentration (&#956;M)</td>
			<td>20</td>
		</tr>
		<tr>
			<td>Vol. MHC II Stock to Add (&#956;l)</td>
			<td>14.65</td>
		</tr>
		<tr>
			<td>Vol. Reaction Buffer to Add (&#956;l):</td>
			<td>2885.35</td>
		</tr>
		<tr>
			<td>MHC II Master Mix Concentration (nM)</td>
			<td>101</td>
		</tr>
	</tbody>
</table>

An erratum was issued for: <a href="https://www.jove.com/video/51308/a-high-throughput-mhc-ii-binding-assay-for-quantitative-analysis">A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes</a>.&#160; There were typos in the Protocol section and Table 2.

Erratum: A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

蛋白质是增长最快的类治疗药物6，生物治疗和管道的快速扩张已经越来越注意与开发蛋白质药物和使用相关的挑战。一个独特的代价源于以下事实：在一个健康和功能的免疫系统中，所有的细胞外蛋白质是由抗原呈递细胞（APC）进行采样。一旦被APC内化的蛋白质裂解成小的肽片段，以及推定的免疫原性片段被加载到II类主要组织相容性复合蛋白质（MHC II）的槽。肽-MHC II类复合物，然后显示在APC的表面上，并且真正的免疫原性肽，称为T细胞表位，形成三元MHC II类分子-肽-T细胞受体复合物与同源CD4 + T细胞表面受体7。这个关键的分子识别事件启动一个复杂的信号级联放大，这导致T细胞活化，细胞因子的释放S，CD4 + T细胞介导的​​B细胞成熟，并最终生产流通结合并清除违规的外源蛋白的IgG抗体。因此，免疫原性蛋白可能通过识别组成T细胞表位和变异负责MHC II类分子复合物的形成关键残基进行脱敏。它承担但指出，该T细胞表位可以是很多，大致整个免疫原性蛋白分布，和广大的表位删除突变都可能导致蛋白质功能或稳定性的意外丢失。因此，工程脱敏生物疗法可能是一个复杂和技术上具有挑战性的目标，但是存在成功的T细胞表位缺失项目3,5,8-12的几个例子。不像嫁接型“人性化”，这在很大程度上仅限于抗体疗法，表位缺失可应用于基本上任何蛋白质靶无论序列，结构，功能，或homologo的可用性我们人类的支架。第一步，采取这种方法是识别嵌入靶蛋白序列中的关键肽表位。 使用合成肽和重组人MHCⅡ类分子高通量生化分析可以提供快速的初步见解表位识别和缓解1,3-5。这些类型的ELISA检测可以是一个强有力的补充其它蛋白质/疫苗设计和开发工具。例如，一个公认的实验方法，以表位作图依赖于时间，劳力和资源密集的体外细胞增殖测定15。简言之，将靶蛋白的一级序列首先被分成重叠肽的面板中，常常15 - 聚体与12个残基相邻的肽之间重叠。肽面板化学合成和每种肽的免疫原性是在几个雇用外围布卢不同免疫1测试从人供体13,14隔离数字单核细胞（PBMC）。为了提供更大的信心，结果，肽通常在重复与PBMC从50个或更多不同的捐赠者进行测试。在情况下，去免疫化是最终目标，工作是由需要产生突变肽的附加 ​​板和引入任何deimmunizing突变成全长蛋白为后续功能分析10日前 PBMC中检测测试新的肽面板进一步加剧。虽然这些细胞试验仍然是金标准评估在人类患者的免疫原性潜力，这样的穷举的方法的效率可能通过预过滤推定的免疫原性表位使用快速和高通量MHC II类肽结合试验加以改进。 同样，生化肽-MHC II类分子结合分析可以预测，在硅片的方法相结合，从根本上加速了表位识别的过程。存在各种用于T细胞表位预测的计算工具;例子包括ProPred 16，MHCPred 17，SVRMHC 18，ARB 19，SMM-20对齐，NetMHCIIpan 21以及专有工具，如EpiMatrix由EpiVax 22。同样地，表位预测器最近已结合其他生物信息学和分子建模工具，得到目的是减轻deimmunizing突变可能会破坏蛋白质的结构和功能23-26风险集成蛋白去免疫化的算法。虽然一些抗原表位的预测已被证明是相当准确27,28，计算结果总是需要实验验证。快速，高通量和成本有效的实验方法最适合作为在硅片表位预测的初步过滤。 与此类似，表位的预测可以带动抗原选择反向vaccinoloGY 29,30。例如，在生物信息学的进步已经产生了全基因组的屏幕，迅速地识别在从病原体蛋白质组提取全蛋白质或肽表位的形式的候选疫苗。虽然这种技术能够应用在重塑发现疫苗的保护和发展，它引入了新的挑战中的免疫原性候选疫苗难以控制大型列表的形式。高通量肽-MHC II类分子结合试验可以通过量化肽结合亲和力和结合在多个MHCⅡ类等位基因滥交引导表位的选择。由于与蛋白质脱敏，这样的实验方法最终需要验证的有前途的疫苗导致计算预测。 这里，缩放，以384孔格式的肽-MHC II类分子结合测定法进行说明。该协议是高度并行和相比于以前描述过的96孔板格式1,3-5降低了试剂的成本减少75％。使用单一的液体。ð搬运机器人，这种方法允许一名研究人员能够轻松地分析，一式三份大约90测试肽在一定范围内八个浓度和四个MHCⅡ类等位基因类型，在不到48小时。这篇文章描述了一个384孔ELISA板为7的实验肽对1 MHC II类等位基因的分析的设置，但扩散片计算器被提供作为辅助材料，以便容易地在实验扩展到任何数量的期望的肽和/或MHC的II类分子。

<table border="0" cellpadding="0" cellspacing="0">
	<colgroup>
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="35">
			<td height="35" style="height:35px;width:587px;">Sodium Tetraborate Decahydrate</td>
			<td style="width:357px;">Sigma</td>
			<td style="width:217px;">221732</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Citric Acid</td>
			<td>Sigma</td>
			<td>C1901</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Dibasic Sodium Phosphate</td>
			<td>Sigma</td>
			<td>S7907</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Trizma HCl</td>
			<td>OmniPur</td>
			<td>9310</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Tween-20</td>
			<td>Sigma</td>
			<td>P7949</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">100% DMSO</td>
			<td>Sigma</td>
			<td>D8418</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Octyl-&#946;-D-Glucopyranaside</td>
			<td>Fischer</td>
			<td>29836-26-8</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Pefa Bloc SCF</td>
			<td>Roche</td>
			<td>1158591600</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Dulbecco&#8217;s 10X Phosphate Buffered Saline</td>
			<td>Invitrogen</td>
			<td>14200-166</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">DELFIA Assay Buffer</td>
			<td>Perkin Elmer</td>
			<td>4002-0010</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">DELFIA Enhancement Buffer</td>
			<td>Perkin Elmer</td>
			<td>4001-0010</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Europium Labelled Streptavidin</td>
			<td>Perkin Elmer</td>
			<td>1244-360</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">L243 anti-HLA-DR antibody</td>
			<td>Biolegend</td>
			<td>307602</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Biotinylated tracer peptides</td>
			<td>21st Century Biochemicals</td>
			<td>Custom Order</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Test peptides (1-4mg, 85% purity)</td>
			<td>Genscript</td>
			<td>Custom Order</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Purified HLA-DRB1 monomers (non-biotinylated)</td>
			<td>Benaroya Research Institute*</td>
			<td>Custom Order</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">384-well white EIA/RIA plate</td>
			<td>Thermo</td>
			<td>460372</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Polypropylene 384-well plate</td>
			<td>Costar</td>
			<td>3656</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Film, AxySeal, 80 &#181;m, ELISA Applicator&#160;</td>
			<td>Axygen Asicentific</td>
			<td>PCR-SP</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">MilliQ Water</td>
			<td>N/A</td>
			<td>N/A</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Epimotion Liquid Handler (or similar)</td>
			<td>Eppendorf</td>
			<td>5075</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">Select TS Plate Washer (or similar)</td>
			<td>BioTek</td>
			<td>405</td>
			<td></td>
		</tr>
		<tr height="35">
			<td height="35" style="height:35px;">SpectraMax Gemini Microplate Reader (or similar)</td>
			<td>Molecular Devices</td>
			<td>N/A</td>
			<td></td>
		</tr>
		<tr>
			<td colspan="4" height="98" style="height:99px;width:1161px;">*Recombinant human MHC II molecules can be obtained from the Benaroya Tetramer Core Laboratory. See: https://www.benaroyaresearch.org/our-research/core-resources/tetramer-core-laboratory</td>
		</tr>
	</tbody>
</table>

a high throughput mhc ii binding assay for quantitative analysis of peptide epitopes

生物化学测定重组人MHC II类分子可提供快速，定量分析上市免疫原性表位的识别，缺失或设计1,2。这里，肽-MHC II类分子结合测定法进行缩放，以384孔格式。按比例缩小的协议降低了试剂的成本减少75％，并且比以前描述的96孔方案1,3-5更高的吞吐量。具体而言，实验设计允许多达15个肽对每384孔ELISA板1 MHC II类等位基因的健壮和可重复性分析。使用单一的液体处理机器人，这种方法允许一个研究者来分析，一式三份约90测试肽在一个范围内的8浓度与4 MHC II类等位基因的类型在小于48小时。其他蛋白质去免疫或疫苗的设计和开发领域的工作可能会发现协议是在促进自己的工作非常有用。特别是，一步一步的指示和视觉格式朱庇特应允许其他用户能够快速，方便地建立这种方法在自己的实验室。

阴沟肠杆菌 P99β-内酰胺酶（BLA）（基因ID＃X07274.1）的成熟肽序列与ProPred 16分析假定的肽基料，以MHCⅡ类等位基因DRB1 * 1501（ 表4）。 ProPred确定117九聚肽具有强制性P1锚定残基（ 即一个M，L，I，V，F，Y或在 ​​位置1，这是需要MHC II类分子结合31瓦）。以5％的门槛，只肽分值大于或等于2.6有可能粘合剂。因此，在5％的门槛，只有排名前11肽预测结合MHCⅡ?...

Watch this Scientific Journal Video about A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes at JoVE.com

A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

生物化学测定重组人MHC II类分子可提供快速，定量分析上市免疫原性表位的识别，缺失或设计。这里，缩放到384孔板的肽-MHC II类分子结合测定法进行说明。这种成本效益的格式应该证明在蛋白质去免疫和疫苗的设计和开发领域非常有用。

高通量MHC II类分子结合分析抗原表位肽的定量分析

Biochemical assays with recombinant human MHC II molecules can provide rapid, quantitative insights into immunogenic epitope identification, deletion, or ...

a-high-throughput-mhc-ii-binding-assay-for-quantitative-analysis

Research

JoVE Journal

Biology

14.8K 次观看.  Dartmouth College. 生物化学测定重组人MHC II类分子可提供快速，定量分析上市免疫原性表位的识别，缺失或设计。这里，缩放到384孔板的肽-MHC II类分子结合测定法进行说明。这种成本效益的格式应该证明在蛋白质去免疫和疫苗的设计和开发领域非常有用。 

视频: A High Throughput MHC II Binding Assay for Quantitative Analysis of Peptide Epitopes

Visualizing Antigen Specific CD4+ T Cells using MHC Class II Tetramers

Major histocompatibility complex (MHC) class II tetramers allow the direct visualization of antigen specific CD4+ T cells by flow cytometry.  This method relies on the highly specific interaction between peptide loaded MHC and the corresponding T-cell receptor.  While the affinity of a single MHC/peptide molecule is low, cross-linking MHC/peptide complexes with streptavidin increases the avidity of the interaction, enabling their use as staining reagents.  Because of the relatively low frequencies of CD4+ T cells (~1 in 300,000 for a single specificity) this assay utilizes an in vitro amplification step to increase its threshold of detection.  Mononuclear cells are purified from peripheral blood by Ficoll underlay.  CD4+ cells are then separated by negative selection using biotinylated antibody cocktail and anti-biotin labeled magnetic beads.  Using adherent cells from the CD4- cell fraction as antigen presenting cells, CD4+ T cells are expanded in media by adding an antigenic peptide and IL-2.  The expanded cells are stained with the corresponding class II tetramer by incubating at 37 C for one hour and subsequently stained using surface antibodies such as anti-CD4, anti-CD3, and anti-CD25.  After labeling, the cells can be directly analyzed by flow cytometry.  The tetramer positive cells typically form a distinct population among the expanded CD4+ cells.  Tetramer positive cells are usually CD25+ and often CD4 high.  Because the level of background tetramer staining can vary, positive staining results should always be compared to the staining of the same cells with an irrelevant tetramer.  Multiple variations of this basic assay are possible.  Tetramer positive cells may be sorted for further phenotypic analysis, inclusion in ELISPOT or proliferation assays, or other secondary assays.  Several groups have also demonstrated co-staining using tetramers and either anti-cytokine or anti-FoxP3 antibodies.  

This procedure demonstrates the purification and in vitro expansion of antigen specific CD4+ T cells from whole peripheral blood and their visualization using MHC class II tetramers. Tetramers permit the direct visualization of T cells with a single antigen specificity and defined MHC class II restriction.

可视化抗原特异性CD4 + T细胞的MHC II类四聚四

Major histocompatibility complex (MHC) class II tetramers allow the direct visualization of antigen specific CD4+ T cells by flow cytometry.  This method ...

科研

生物学

A Neuronal and Astrocyte Co-Culture Assay for High Content Analysis of Neurotoxicity

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing measurement of multiple cellular phenotypes within a single assay. In this study, we utilized HCA to develop a novel assay for neurotoxicity. Neurotoxicity assessment represents an important part of drug safety evaluation, as well as being a significant focus of environmental protection efforts. Additionally, neurotoxicity is also a well-accepted in vitro marker of the development of neurodegenerative diseases such as Alzheimer's and Parkinson's diseases. Recently, the application of HCA to neuronal screening has been reported. By labeling neuronal cells with &beta;III-tubulin, HCA assays can provide high-throughput, non-subjective, quantitative measurements of parameters such as neuronal number, neurite count and neurite length, all of which can indicate neurotoxic effects. However, the role of astrocytes remains unexplored in these models. Astrocytes have an integral role in the maintenance of central nervous system (CNS) homeostasis, and are associated with both neuroprotection and neurodegradation when they are activated in response to toxic substances or disease states. GFAP is an intermediate filament protein expressed predominantly in the astrocytes of the CNS. Astrocytic activation (gliosis) leads to the upregulation of GFAP, commonly accompanied by astrocyte proliferation and hypertrophy. This process of reactive gliosis has been proposed as an early marker of damage to the nervous system. The traditional method for GFAP quantitation is by immunoassay. This approach is limited by an inability to provide information on cellular localization, morphology and cell number. We determined that HCA could be used to overcome these limitations and to simultaneously measure multiple features associated with gliosis - changes in GFAP expression, astrocyte hypertrophy, and astrocyte proliferation - within a single assay. In co-culture studies, astrocytes have been shown to protect neurons against several types of toxic insult and to critically influence neuronal survival. Recent studies have suggested that the use of astrocytes in an in vitro neurotoxicity test system may prove more relevant to human CNS structure and function than neuronal cells alone. Accordingly, we have developed an HCA assay for co-culture of neurons and astrocytes, comprised of protocols and validated, target-specific detection reagents for profiling &beta;III-tubulin and glial fibrillary acidic protein (GFAP). This assay enables simultaneous analysis of neurotoxicity, neurite outgrowth, gliosis, neuronal and astrocytic morphology and neuronal and astrocytic development in a wide variety of cellular models, representing a novel, non-subjective, high-throughput assay for neurotoxicity assessment. The assay holds great potential for enhanced detection of neurotoxicity and improved productivity in neuroscience research and drug discovery.

This article describes a novel protocol and reagent set designed for sensitive measurement of neurotoxic effects of compounds and treatments on co-cultures of neurons and astrocytes using high content analysis. Results demonstrate that high content analysis represents an exciting novel technology for neurotoxicity assessment.

一个神经元和星形胶质细胞共培养检测神经毒性的高含量分析

High Content Analysis (HCA) assays combine cells and detection reagents with automated imaging and powerful image analysis algorithms, allowing ...

Mutagenesis and Functional Analysis of Ion Channels Heterologously Expressed in Mammalian Cells

We will demonstrate how to study the functional effects of introducing a point mutation in an ion channel. We study G protein-gated inwardly rectifying potassium (referred to as GIRK) channels, which are important for regulating the excitability of neurons. There are four different mammalian GIRK channel subunits (GIRK1-GIRK4) - we focus on GIRK2 because it forms a homotetramer. Stimulation of different types of G protein-coupled receptors (GPCRs), such as the muscarinic receptor (M2R), leads to activation of GIRK channels. Alcohol also directly activates GIRK channels. We will show how to mutate one amino acid by specifically changing one or more nucleotides in the cDNA for the GIRK channel. This mutated cDNA sequence will be amplified in bacteria, purified, and the presence of the point mutation will be confirmed by DNA sequencing. The cDNAs for the mutated and wild-type GIRK channels will be transfected into human embryonic kidney HEK293T cells cultured in vitro. Lastly, whole-cell patch-clamp electrophysiology will be used to study the macroscopic potassium currents through the ectopically expressed wild-type or mutated GIRK channels. In this experiment, we will examine the effect of a L257W mutation in GIRK2 channels on M2R-dependent and alcohol-dependent activation.

We will demonstrate how to study the effect of a single point mutation on the function of an ion channel.

异源表达在哺乳动物细胞离子通道的突变和功能分析

We will demonstrate how to study the functional effects of introducing a point mutation in an ion channel. We study G protein-gated inwardly rectifying ...

High-throughput Saccharification Assay for Lignocellulosic Materials

Polysaccharides that make up plant lignocellulosic biomass can be broken down to produce a range of sugars that subsequently can be 
used in establishing a biorefinery. These raw materials would constitute a new industrial platform, which is both sustainable and carbon neutral, to replace 
the current dependency on fossil fuel. The recalcitrance to deconstruction observed in lignocellulosic materials is produced by several intrinsic properties 
of plant cell walls. Crystalline cellulose is embedded in matrix polysaccharides such as xylans and arabinoxylans, and the whole structure is encased by the 
phenolic polymer lignin, that is also difficult to digest 1. In order to improve the digestibility of plant materials we need to discover the main bottlenecks 
for the saccharification of cell walls and also screen mutant and breeding populations to evaluate the variability in saccharification 2. These tasks require 
a high throughput approach and here we present an analytical platform that can perform saccharification analysis in a 96-well plate format. This platform has 
been developed to allow the screening of lignocellulose digestibility of large populations from varied plant species. We have scaled down the reaction volumes 
for gentle pretreatment, partial enzymatic hydrolysis and sugar determination, to allow large numbers to be assessed rapidly in an automated system.
This automated platform works with milligram amounts of biomass, performing ball milling under controlled conditions to reduce the plant 
materials to a standardised particle size in a reproducible manner. Once the samples are ground, the automated formatting robot dispenses specified and recorded 
amounts of material into the corresponding wells of 96 deep well plate (Figure 1). Normally, we dispense the same material into 4 wells to have 4 replicates for 
analysis. Once the plates are filled with the plant material in the desired layout, they are manually moved to a liquid handling station (Figure 2). In this 
station the samples are subjected to a mild pretreatment with either dilute acid or alkaline and incubated at temperatures of up to 90&deg;C. The pretreatment solution 
is subsequently removed and the samples are rinsed with buffer to return them to a suitable pH for hydrolysis. The samples are then incubated with an enzyme
mixture for a variable length of time at 50&deg;C. An aliquot is taken from the hydrolyzate and the reducing sugars are automatically determined by the MBTH 
colorimetric method.

A simple, rapid method for determining the saccharification potential of large numbers of plant biomass samples is described. The automated platform for this analysis involves the preparation of the plant biomass for analysis in 96 well plates and the subsequent performance of pretreatment, hydrolysis and quantification of the sugars released.

木质纤维素材料的高通量检测糖化

Polysaccharides that make up plant lignocellulosic biomass can be broken down to produce a range of sugars that subsequently can be 
used in establishing ...

A Quantitative Fitness Analysis Workflow

Quantitative Fitness Analysis (QFA) is an experimental and computational workflow for comparing fitnesses of microbial cultures grown in parallel1,2,3,4. QFA can be applied to focused observations of single cultures but is most useful for genome-wide genetic interaction or drug screens investigating up to thousands of independent cultures. The central experimental method is the inoculation of independent, dilute liquid microbial cultures onto solid agar plates which are incubated and regularly photographed. Photographs from each time-point are analyzed, producing quantitative cell density estimates, which are used to construct growth curves, allowing quantitative fitness measures to be derived. Culture fitnesses can be compared to quantify and rank genetic interaction strengths or drug sensitivities. The effect on culture fitness of any treatments added into substrate agar (e.g. small molecules, antibiotics or nutrients) or applied to plates externally (e.g. UV irradiation, temperature) can be quantified by QFA.
The QFA workflow produces growth rate estimates analogous to those obtained by spectrophotometric measurement of parallel liquid cultures in 96-well or 200-well plate readers. Importantly, QFA has significantly higher throughput compared with such methods. QFA cultures grow on a solid agar surface and are therefore well aerated during growth without the need for stirring or shaking.
QFA throughput is not as high as that of some Synthetic Genetic Array (SGA) screening methods5,6. However, since QFA cultures are heavily diluted before being inoculated onto agar, QFA can capture more complete growth curves, including exponential and saturation phases3. For example, growth curve observations allow culture doubling times to be estimated directly with high precision, as discussed previously1.
Here we present a specific QFA protocol applied to thousands of S. cerevisiae cultures which are automatically handled by robots during inoculation, incubation and imaging. Any of these automated steps can be replaced by an equivalent, manual procedure, with an associated reduction in throughput, and we also present a lower throughput manual protocol. The same QFA software tools can be applied to images captured in either workflow.
We have extensive experience applying QFA to cultures of the budding yeast S. cerevisiae but we expect that QFA will prove equally useful for examining cultures of the fission yeast S. pombe and bacterial cultures.

Quantitative Fitness Analysis (QFA) is a complementary series of experimental and computational methods for estimating microbial culture fitnesses. QFA estimates the effect of genetic mutations, drugs or other applied treatments on microbe growth. Experiments scaling from focussed analysis of single cultures to thousands of parallel cultures can be designed.

定量健身分析工作流程

Quantitative Fitness Analysis (QFA) is an experimental and computational workflow for comparing fitnesses of microbial cultures grown in parallel1,2,3,4. ...

High-throughput Functional Screening using a Homemade Dual-glow Luciferase Assay

We present a rapid and inexpensive high-throughput screening protocol to identify transcriptional regulators of alpha-synuclein, a gene associated with Parkinson&#39;s disease. 293T cells are transiently transfected with plasmids from an arrayed ORF expression library, together with luciferase reporter plasmids, in a one-gene-per-well microplate format. Firefly luciferase activity is assayed after 48 hr to determine the effects of each library gene upon alpha-synuclein transcription, normalized to expression from an internal control construct (a hCMV promoter directing Renilla luciferase). This protocol is facilitated by a bench-top robot enclosed in a biosafety cabinet, which performs aseptic liquid handling in 96-well format. Our automated transfection protocol is readily adaptable to high-throughput lentiviral library production or other functional screening protocols requiring triple-transfections of large numbers of unique library plasmids in conjunction with a common set of helper plasmids. We also present an inexpensive and validated alternative to commercially-available, dual luciferase reagents which employs PTC124, EDTA, and pyrophosphate to suppress firefly luciferase activity prior to measurement of Renilla luciferase. Using these methods, we screened 7,670 human genes and identified 68 regulators of alpha-synuclein. This protocol is easily modifiable to target other genes of interest.

We present a rapid and inexpensive screening method for identifying transcriptional regulators using high-throughput robotic transfections and a homemade dual-glow luciferase assay. This protocol rapidly generates direct side-by-side functional data for thousands of genes and is easily modifiable to target any gene of interest.

使用自制双辉光萤光素酶检测的高通量筛选功能

We present a rapid and inexpensive high-throughput screening protocol to identify transcriptional regulators of alpha-synuclein, a gene associated with ...

Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction

Among methods to study protein-protein interaction inside cells, Bimolecular Fluorescence Complementation (BiFC) is relatively simple and sensitive. BiFC is based on the production of fluorescence using two non-fluorescent fragments of a fluorescent protein (Venus, a Yellow Fluorescent Protein variant, is used here). Non-fluorescent Venus fragments (VN and VC) are fused to two interacting proteins (in this case, AKAP-Lbc and PDE4D3), yielding fluorescence due to VN-AKAP-Lbc-VC-PDE4D3 interaction and the formation of a functional fluorescent protein inside cells.
BiFC provides information on the subcellular localization of protein complexes and the strength of protein interactions based on fluorescence intensity. However, BiFC analysis using microscopy to quantify the strength of protein-protein interaction is time-consuming and somewhat subjective due to heterogeneity in protein expression and interaction. By coupling flow cytometric analysis with BiFC methodology, the fluorescent BiFC protein-protein interaction signal can be accurately measured for a large quantity of cells in a short time. Here, we demonstrate an application of this methodology to map regions in PDE4D3 that are required for the interaction with AKAP-Lbc. This high throughput methodology can be applied to screening factors that regulate protein-protein interaction.

Flow cytometric analysis of Bimolecular Fluorescence Complementation provides a high throughput quantitative method to study protein-protein interaction. This methodology can be applied to mapping protein binding sites and for screening factors that regulate protein-protein interaction.

双分子荧光互补流式细胞仪分析：高通量的定量方法研究蛋白质 - 蛋白质相互作用

Among methods to study protein-protein interaction inside cells, Bimolecular Fluorescence Complementation (BiFC) is relatively simple and sensitive. BiFC ...

High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes

Both transcriptional and post-transcriptional regulation have a profound impact on genes expression. However, commonly adopted cell-based screening assays focus on transcriptional regulation, being essentially aimed at the identification of promoter-targeting molecules. As a result, post-transcriptional mechanisms are largely uncovered by gene expression targeted drug development. Here we describe a cell-based assay aimed at investigating the role of the 3&#39; untranslated region (3&#8217; UTR) in the modulation of the fate of its mRNA, and at identifying compounds able to modify it. The assay is based on the use of a luciferase reporter construct containing the 3&#8217; UTR of a gene of interest stably integrated into a disease-relevant cell line. The protocol is divided into two parts, with the initial focus on the primary screening aimed at the identification of molecules affecting luciferase activity after 24 hr of treatment. The second part of the protocol describes the counter-screening necessary to discriminate compounds modulating luciferase activity specifically through the 3&#8217; UTR. In addition to the detailed protocol and representative results, we provide important considerations about the assay development and the validation of the hit(s) on the endogenous target. The described cell-based reporter gene assay will allow scientists to identify molecules modulating protein levels via post-transcriptional mechanisms dependent on a 3&#8217; UTR.

Here we describe a cell-based reporter gene assay as a valuable tool to screen chemical libraries for compounds modulating post-transcriptional control mechanisms exerted through 3&#8217; UTR.

高通量筛选转录后调控基因的化学调节剂

Both transcriptional and post-transcriptional regulation have a profound impact on genes expression. However, commonly adopted cell-based screening assays ...

qPCRTag Analysis - A High Throughput, Real Time PCR Assay for Sc2.0 Genotyping

The Synthetic Yeast Genome Project (Sc2.0) aims to build 16 designer yeast chromosomes and combine them into a single yeast cell. To date one synthetic chromosome, synIII1, and one synthetic chromosome arm, synIXR2, have been constructed and their in vivo function validated in the absence of the corresponding wild type chromosomes. An important design feature of Sc2.0 chromosomes is the introduction of PCRTags, which are short, re-coded sequences within open reading frames (ORFs) that enable differentiation of synthetic chromosomes from their wild type counterparts. PCRTag primers anneal selectively to either synthetic or wild type chromosomes and the presence/absence of each type of DNA can be tested using a simple PCR assay. The standard readout of the PCRTag assay is to assess presence/absence of amplicons by agarose gel electrophoresis. However, with an average PCRTag amplicon density of one per 1.5 kb and a genome size of ~12 Mb, the completed Sc2.0 genome will encode roughly 8,000 PCRTags. To improve throughput, we have developed a real time PCR-based detection assay for PCRTag genotyping that we call qPCRTag analysis. The workflow specifies 500 nl reactions in a 1,536 multiwell plate, allowing us to test up to 768 PCRTags with both synthetic and wild type primer pairs in a single experiment.

Designer chromosomes of the Synthetic Yeast Genome project, Sc2.0, can be distinguished from their native counterparts using a PCR-based genotyping assay called PCRTagging, which has a presence/absence endpoint. Here we describe a high-throughput real time PCR detection method for PCRTag genotyping.

qPCRTag分析 - 高通量，实时定量PCR检测方法的基因分型Sc2.0

The Synthetic Yeast Genome Project (Sc2.0) aims to build 16 designer yeast chromosomes and combine them into a single yeast cell. To date one synthetic ...

High Throughput Danio Rerio Energy Expenditure Assay

Zebrafish are an important model organism with inherent advantages that have the potential to make zebrafish a widely applied model for the study of energy homeostasis and obesity. The small size of zebrafish allows for assays on embryos to be conducted in a 96- or 384-well plate format, Morpholino and CRISPR based technologies promote ease of genetic manipulation, and drug treatment by bath application is viable. Moreover, zebrafish are ideal for forward genetic screens allowing for novel gene discovery. Given the relative novelty of zebrafish as a model for obesity, it is necessary to develop tools that fully exploit these benefits. Herein, we describe a method to measure energy expenditure in thousands of embryonic zebrafish simultaneously. We have developed a whole animal microplate platform in which we use 96-well plates to isolate individual fish and we assess cumulative NADH2 production using the commercially available cell culture viability reagent alamarBlue. In poikilotherms the relationship between NADH2 production and energy expenditure is tightly linked. This energy expenditure assay creates the potential to rapidly screen pharmacological or genetic manipulations that directly alter energy expenditure or alter the response to an applied drug (e.g. insulin sensitizers).

High Throughput Danio Rerio Energy Expenditure Assay

Zebrafish are an important model organism for the study of energy homeostasis. By utilizing a NADH2 sensitive redox indicator, alamar Blue, we have developed an assay that measures the metabolic rate of zebrafish larvae in a 96 well plate format and can be applied to drug or gene discovery.

高通量<em&gt;斑马鱼</em&gt;能量消耗分析

Zebrafish are an important model organism with inherent advantages that have the potential to make zebrafish a widely applied model for the study of ...