酵母泛实验室工作是由一个从日本味之素公司，日本和美国国立卫生培训资助T32GM007183 - 33研究院授予。我们感谢酵母项目的长期合作在美国印第安纳大学医学院的罗纳德WEK博士。我们也感谢金佰利Dittmar博士充电芯片的方法开发的tRNA。

第1部分：总收取的tRNA分离<ol><li>在2000年的RFC台式离心机在5分钟沉淀细胞。相当于600 1细胞的OD应该产生至少1μg总RNA和程序建议至少30微克。 </li><li>悬浮细胞裂解缓冲液中，0.3米的缓冲 NA +醋酸（pH值4.5）和10 mM EDTA。 NA +醋酸饱和酚/氯仿（pH值4.5）的三倍为30秒同等体积的涡一定要始终保持在冰样本之间震荡。缓冲醋酸可准备从NaOAC和醋酸，而醋酸饱和酚/氯仿可以准备9份酚/氯仿混合1份5个M醋酸缓冲液（pH值4.5）。 注：总RNA的分离是轻度酸性条件下和在冰上进行，以避免收取的tRNA deacylation。 </li><li>离心15分钟，在18600的RFC 4 ° C。取出水层和执行另一个醋酸饱和酚/氯仿抽提。 </li><li>第二萃取沉淀后加入2.7倍体积的乙醇​​和30分钟，离心18600 RFC在4 ° C的RNA </li><li> RNA的悬浮颗粒在裂解缓冲液，然后用乙醇沉淀的第二次。 </li><li>最后，重悬的RNA在含有10 mM的醋酸缓冲液（pH 4.5）和1 mM EDTA的后续治疗解决方案。样本可稳定储存于-80 ° C，大约两个星期。更长的储存，不建议作为部分收取的tRNA将开始deacylate。 </li></ol>第2部分：tRNA的标准准备<ol><li>热E。大肠杆菌 tRNA的标准10.5微米至85 ° C在45毫米的Tris - CL pH值7.5 2分钟。以管为3分钟，在室温下离开。 </li><li> MgCl 2的一个终浓度为20毫米，并培育在37℃5分钟。 </li><li>所以最终的反应含有氯化钾10毫米，5微米的tRNA，60毫米的Tris - HCl（pH值7.5），12.5毫米氯化镁2，3 mM的二硫苏糖醇，1.5毫米ATP，精胺1毫米，1毫米各自添加合成反应混合物氨基酸，4.2单位/μL 大肠杆菌大肠杆菌氨酰- tRNA合成酶混合。 37 ° 15分钟。 </li><li>加入同等体积的0.5 M的醋酸（pH值4.5）缓冲和醋酸饱和酚/ 氯仿提取，在pH值4.5 。 </li><li> tRNA的标准2.7倍体积的乙醇​​，离心沉淀在18600 RFC 30分钟，在4 ° C </li><li>重悬在50毫米缓冲醋酸（pH值4.5）与1毫米EDTA和储存在-80 ° C至一个月标准。 </li></ol>第3部分：Cy3/Cy5 tRNA的标签<ol><li>对于收取的tRNA样品与0.066μM孵化总RNA E.浓度为0.1微克/μL 大肠杆菌 tRNA的标准（即0.67 pmole标准每人μg总RNA）和100毫米的缓冲醋酸（pH值4.5）在室温下30分钟的50毫米NaIO4存在。对于控制总tRNA的样品使用50毫米NaIO4地方氯化钠。请记住只稀释与缓冲的解决方案，以保持充电的RNA。 </li><li>解渴的反应，添加葡萄糖100毫米，在室温下孵育5分钟。 </li><li>为了从样本中删除任何剩余的NaIO4执行缓冲区交换使用G25离心柱。为了达到最佳效果平衡列运行通过它的200毫米的缓冲醋酸缓冲液（pH值4.5）之前申请您的样品。 </li><li>样品缓冲醋酸（pH值4.5）的133毫米和NaCl终浓度加入到终浓度为66毫米和2.7倍体积的乙醇​​沉淀。偶尔氧化样品可能需要第二次降水。这是必要的，只有当颗粒看起来明显高于同一样品的控制颗粒不同。例如，一个显着更大的或更分散的颗粒。如果第二个需要乙醇沉淀重新悬浮颗粒在50 mM的醋酸缓冲液pH值4.5，氯化钠和200毫米，前增加乙醇。 </li><li> deacylation tRNA的样品悬浮于50毫米的Tris - HCl（pH值）在37 ° C孵育30 min。反应是同等体积的50 mM的醋酸缓冲液（pH 4.5）和100 mM氯化钠此外瓦解。沉淀用2.7倍体积的乙醇​​。 RNA是经过沉淀，悬浮在水中〜1μg/μL的。琼脂糖凝胶上运行，在控制和氧化样品应检查RNA的质量。 </li><li>要连接到tRNA的0.1微克/μLdeacylated RNA荧光寡核苷酸标记孵育1X连接酶缓冲液，15％二甲基亚砜，4微米Cy3标记或Cy5的含寡核苷酸，0.5单位/μLT4 DNA连接酶和酵母外磷酸酶（5000单位/μL16）° C过夜（超过16小时）。 EXO -磷酸仅适用于从酵母中获得的样本是必要的和可omitted如果这个协议是用于大肠杆菌大肠杆菌或人体标本。 </li><li>结扎后的样品混合50 mM的KOAc（pH值7），200毫米氯化钾，并与等体积酚/氯仿提取，然后用4卷。水相萃取后，用乙醇沉淀RNA的准备工作和在水中的悬浮约0.1μg/μL的。 </li><li>连接效率，可以运行5-10％的样本含7M尿素的12％聚丙烯酰胺凝胶，并用荧光凝胶扫描仪观察评估。此页的分析也是有用的，确定氧化和控制芯片杂交所需的样本量。一个良好结扎结果表明，〜10％或更多的Cy3/Cy5含有寡核苷酸酶的tRNA。对于良好的标记效率的样品，每个样品的总RNA 0.1-0.5微克用于阵列杂交。 </li></ol>第4部分：杂交和基因芯片分析<ol><li>杂交芯片幻灯片之前在蒸馏水中煮沸1-2分钟，以去除未结合的寡核苷酸。 </li><li> Cy3/Cy5标记的样品相结合，与140μL的杂交缓冲液和鲑鱼的精子DNA的终浓度为140微克/毫升的poly（A）到终浓度为70微克/毫升。 </li><li>杂交方案（表1），运行自动数组hybridizer。 tRNA的工作，这是强烈建议使用，因为人工杂交产生高的背景自动杂交机。 </li><li>程序完成后手动用洗3解决方案的幻灯片至少两次轻轻摇晃3-5分钟，在50 ml锥形管。 </li><li>幻灯片可以干离心5-10分钟，在较低的速度，小于200的RFC。重要的是保持光的幻灯片，因为暴露在光线下会漂白荧光团。 </li><li>幻灯片干燥后，他们应该被扫描立即以达到最佳的效果，如果必要的话，他们可以几天在干燥的室温避光的地方保存。幻灯片可以扫描在图像质量没有明显退化的2-3倍。 </li></ol>第5部分：代表结果当孤立的总RNA样品应产生一个非常干净的频谱与外径 260： 外径 280接近2的比例。应该没有检测的蛋白质或酚污染。 1％琼脂糖凝胶上运行时，强大的tRNA的频段应与其他可能的核酸生物之间的不同波段观察。氧化后，一个干净的tRNA的频段应得到遵守。如果样品是明显比其他样品或涂抹观察弱，样品不应该被用来对芯片。微阵列应该有非常低的背景，这是一个幅度低于最弱的tRNA的探头。高的背景通常是由于在洗涤步骤中的问题。这通常是固定的准备新鲜的清洗解决方案，并彻底清洗所有设备和管道，以去除残留和SDS沉淀。 <table border="1"><tbody><tr><td>步骤</td><td>详情</td></tr><tr><td> O型环空调</td><td> 75℃，2分钟</td></tr><tr><td>介绍样本</td><td> 60℃ </td></tr><tr><td>变性样品</td><td> 90℃，5分钟</td></tr><tr><td>杂交</td><td> 60℃，16H </td></tr><tr><td>洗1 </td><td> 50℃，流量10秒按住20秒</td></tr><tr><td>洗2 </td><td> 42℃，流量10秒按住20秒</td></tr><tr><td>洗3 </td><td> 42℃，流量10秒按住20秒</td></tr></tbody></table> 表1：杂交协议

<ol>
	<li>Zaborske, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+analysis+of+tRNA+charging+and+activation+of+the+eIF2+kinase+Gcn2p.">Genome-wide analysis of tRNA charging and activation of the eIF2 kinase Gcn2p.</a> J Biol Chem. 284 (37), 25254-25267 (2009).</li><li>Wek, S. A., Zhu, S., Wek, R. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+histidyl-tRNA+synthetase-related+sequence+in+the+eIF-2+alpha+protein+kinase+GCN2+interacts+with+tRNA+and+is+required+for+activation+in+response+to+starvation+for+different+amino+acids.">The histidyl-tRNA synthetase-related sequence in the eIF-2 alpha protein kinase GCN2 interacts with tRNA and is required for activation in response to starvation for different amino acids.</a> Mol Cell Biol. , 4497-4506 (1995).</li><li>Hinnebusch, A. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Translational+Regulation+of+GCN4+and+the+General+Amino+Acid+Control+of+Yeast.">Translational Regulation of GCN4 and the General Amino Acid Control of Yeast.</a> Annual Review of Microbiology. 59 (1), 407-450 (2005).</li><li>Braeken, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=New+horizons+for+(p)ppGpp+in+bacterial+and+plant+physiology.">New horizons for (p)ppGpp in bacterial and plant physiology.</a> Trends in Microbiology. 14 (1), 45-54 (2006).</li><li>Dong, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Uncharged+tRNA+Activates+GCN2+by+Displacing+the+Protein+Kinase+Moiety+from+a+Bipartite+tRNA-Binding+Domain.">Uncharged tRNA Activates GCN2 by Displacing the Protein Kinase Moiety from a Bipartite tRNA-Binding Domain.</a> Molecular Cell. 6 (2), 269-279 (2000).</li><li>Cashel, M., Neidhardt, F. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Stringent+Response.">The Stringent Response.</a> Escherichia coli and Salmonella: cellular and molecular biology. , 1458-1496 (1996).</li><li>Dittmar, K. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Selective+charging+of+tRNA+isoacceptors+induced+by+amino-acid+starvation.">Selective charging of tRNA isoacceptors induced by amino-acid starvation.</a> EMBO Rep. 6 (2), 151-157 (2005).</li><li>Zhou, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High+levels+of+tRNA+abundance+and+alteration+of+tRNA+charging+by+bortezomib+in+multiple+myeloma.">High levels of tRNA abundance and alteration of tRNA charging by bortezomib in multiple myeloma.</a> Biochem Biophys Res Commun. 385 (2), 160-164 (2009).</li></ol>

我们提出了一个方法，同时确定在基因组范围内的所有tRNA的收费水平相对。这里介绍的协议进行了优化， 为 S. 酵母 ，它也被成功地用来衡量在大肠杆菌 tRNA的充电配置文件大肠杆菌和人类细胞培养。它可以修改，以适应任何生物体与已知的基因组序列（允许所有的tRNA基因的注释），只要可以得到总收取的tRNA。

<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">	
		
		<div>
		<table border="1">
		<thead>
		<tr>
		<th>Material Name</th>
		<th>Type</th>
		<th>Company</th>
		<th>Catalogue Number</th>
		<th>Comment</th>
		</tr>
		</thead>
		<tbody>
		
<tr>
<td>Microspin G-25 columns</td>
<td>&nbsp;</td>
<td>GE Healthcare</td>
<td>25-5325-01</td>
<td>&nbsp;</td>
<tr>
<td>E. coli tRNAPhe</td>
<td>&nbsp;</td>
<td>Sigma-Aldrich</td>
<td>R3143</td>
<td>&nbsp;</td>
<tr>
<td>E. coli tRNATyr</td>
<td>&nbsp;</td>
<td>Sigma-Aldrich</td>
<td>R0258</td>
<td>&nbsp;</td>
<tr>
<td>E. coli tRNALys</td>
<td>&nbsp;</td>
<td>Sigma-Aldrich</td>
<td>R6018</td>
<td>&nbsp;</td>
<tr>
<td>E. coli aminoacyl-tRNA synthetases</td>
<td>&nbsp;</td>
<td>Sigma-Aldrich</td>
<td>A3646</td>
<td>&nbsp;</td>
<tr>
<td>T4 DNA ligase</td>
<td>&nbsp;</td>
<td>USB</td>
<td>70042X</td>
<td>&nbsp;</td>
<tr>
<td>PerfectHyb Plus</td>
<td>&nbsp;</td>
<td>Sigma-Aldrich</td>
<td>H-7033</td>
<td>&nbsp;</td>
<tr>
<td>Hyb4 station</td>
<td>&nbsp;</td>
<td>Digilab</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<tr>
<td>GenePix 4000B scanner</td>
<td>&nbsp;</td>
<td>Axon instruments</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<tr>
<td>GenePix 6.0</td>
<td>&nbsp;</td>
<td>Axon instruments</td>
<td>&nbsp;</td>
<td>&nbsp;</td>		</tbody>
		</table>
		</div>

genome-wide analysis of aminoacylation (charging) levels of trna using microarrays

tRNA的氨酰化，或充电，水平可以迅速改变细胞内的环境[1]。 tRNA的变化，在原核和真核细胞收费水平，导致翻译调控，这是一个重大的应激反应的细胞机制。熟悉的例子是在严格的响应<em&gt; E。大肠杆菌</em&gt;和酵母中的GCN2压力反应途径（[2-6]）。最近的工作IN<em&gt; E。大肠杆菌</em&gt;<em&gt; S。酵母</em&gt;表明，tRNA的收费模式是高度动态的，取决于细胞[1，6，7]的压力。高度动态的变量的性质，tRNA的收费使得有必要在基因组的规模确定tRNA的收费水平的变化，以充分阐明细胞反应环境变化。在这次审查中，我们目前的方法同时测量所有的tRNA相对充电水平<em&gt; S。酵母</em&gt;。酵母虽然这里介绍的协议，这个协议已经成功地应用在各种各样的生物体，包括确定相对的收费水平<em&gt; E。大肠杆菌</em&gt;和人类细胞培养[7，8]。

Watch this Scientific Journal Video about Genome-wide Analysis of Aminoacylation Charging Levels of tRNA Using Microarrays at JoVE.com

Genome-wide Analysis of Aminoacylation Charging Levels of tRNA Using Microarrays

我们描述了一种用于微阵列分析的方法来确定所有的tRNA相对酰化水平<em&gt; S。 cerivisiae。</em

（充电）酰化tRNA的使用微阵列技术水平的全基因组分析

tRNA aminoacylation, or charging, levels can rapidly change within a cell in response to the environment[1]. Changes in tRNA charging levels in both ...

genome-wide-analysis-aminoacylation-charging-levels-trna-using

Research

JoVE Journal

Biology

Genome-wide Analysis of Aminoacylation (Charging) Levels of tRNA Using Microarrays

12.3K 次观看.  University of Chicago. 我们描述了一种用于微阵列分析的方法来确定所有的tRNA相对酰化水平 S。 cerivisiae。

视频: Genome-wide Analysis of Aminoacylation Charging Levels of tRNA Using Microarrays

Bacterial Gene Expression Analysis Using Microarrays

使用微阵列的细菌基因表达分析

科研

生物学

Genome-wide Analysis using ChIP to Identify Isoform-specific Gene Targets

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the protein domains that bind to specific histone modifications. One such domain is the plant homeodomain (PHD), found in several chromatin-binding proteins. The epigenetic factor RBP2 has multiple PHD domains, however, they have different functions (Figure 4). In particular, the C-terminal PHD domain, found in a RBP2 oncogenic fusion in human leukemia, binds to trimethylated lysine 4 in histone H3 (H3K4me3)1. The transcript corresponding to the RBP2 isoform containing the C-terminal PHD accumulates during differentiation of promonocytic, lymphoma-derived, U937 cells into monocytes2. Consistent with both sets of data, genome-wide analysis showed that in differentiated U937 cells, the RBP2 protein gets localized to genomic regions highly enriched for H3K4me33. Localization of RBP2 to its targets correlates with a decrease in H3K4me3 due to RBP2 histone demethylase activity and a decrease in transcriptional activity. In contrast, two other PHDs of RBP2 are unable to bind H3K4me3. Notably, the C-terminal domain PHD of RBP2 is absent in the smaller RBP2 isoform4. It is conceivable that the small isoform of RBP2, which lacks interaction with H3K4me3, differs from the larger isoform in genomic location. The difference in genomic location of RBP2 isoforms may account for the observed diversity in RBP2 function. Specifically, RBP2 is a critical player in cellular differentiation mediated by the retinoblastoma protein (pRB). Consistent with these data, previous genome-wide analysis, without distinction between isoforms, identified two distinct groups of RBP2 target genes: 1) genes bound by RBP2 in a manner that is independent of differentiation; 2) genes bound by RBP2 in a differentiation-dependent manner.
To identify differences in localization between the isoforms we performed genome-wide location analysis by ChIP-Seq. Using antibodies that detect both RBP2 isoforms we have located all RBP2 targets. Additionally we have antibodies that only bind large, and not small RBP2 isoform (Figure 4). After identifying the large isoform targets, one can then subtract them from all RBP2 targets to reveal the targets of small isoform. These data show the contribution of chromatin-interacting domain in protein recruitment to its binding sites in the genome.

Here we are presenting a chromatin immunoprecipitation (ChIP) procedure for genome-wide location analysis of protein isoforms that differ in a histone-binding domain. We are applying it to ChIP-Seq analysis to identify the targets of the KDM5A/JARID1A/RBP2 histone demethylase.

采用芯片识别亚型特定的靶基因的全基因组分析

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the ...

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library

The Target ID Library is designed to assist in discovery and identification of microRNA (miRNA) targets. The Target ID Library is a plasmid-based, genome-wide cDNA library cloned into the 3'UTR downstream from the dual-selection fusion protein, thymidine kinase-zeocin (TKzeo). The first round of selection is for stable transformants, followed with introduction of a miRNA of interest, and finally, selecting for cDNAs containing the miRNA's target. Selected cDNAs are identified by sequencing (see Figure 1-3 for Target ID Library Workflow and details).
To ensure broad coverage of the human transcriptome, Target ID Library cDNAs were generated via oligo-dT priming using a pool of total RNA prepared from multiple human tissues and cell lines. Resulting cDNA range from 0.5 to 4 kb, with an average size of 1.2 kb, and were cloned into the p3&#900;TKzeo dual-selection plasmid (see Figure 4 for plasmid map). The gene targets represented in the library can be found on the Sigma-Aldrich webpage. Results from Illumina sequencing (Table 3), show that the library includes 16,922 of the 21,518 unique genes in UCSC RefGene (79%), or 14,000 genes with 10 or more reads (66%).

The Target ID Library is a plasmid-based, genome-wide collection of cloned cDNA used to identify miRNA targets. Here we demonstrate its use and application.

miRNA的目标使用的任务目标ID库的全基因组屏幕

The Target ID Library is designed to assist in discovery and identification of microRNA (miRNA) targets. The Target ID Library is a plasmid-based, ...

Quantitative and Automated High-throughput Genome-wide RNAi Screens in C. elegans

RNA interference is a powerful method to understand gene function, especially when conducted at a whole-genome scale and in a quantitative context. In C. elegans, gene function can be knocked down simply and efficiently by feeding worms with bacteria expressing a dsRNA corresponding to a specific gene 1. While the creation of libraries of RNAi clones covering most of the C. elegans genome 2,3 opened the way for true functional genomic studies (see for example 4-7), most established methods are laborious. Moy and colleagues have developed semi-automated protocols that facilitate genome-wide screens 8. The approach relies on microscopic imaging and image analysis. 
	Here we describe an alternative protocol for a high-throughput genome-wide screen, based on robotic handling of bacterial RNAi clones, quantitative analysis using the COPAS Biosort (Union Biometrica (UBI)), and an integrated software: the MBioLIMS (Laboratory Information Management System from Modul-Bio) a technology that provides increased throughput for data management and sample tracking. The method allows screens to be conducted on solid medium plates. This is particularly important for some studies, such as those addressing host-pathogen interactions in C. elegans, since certain microbes do not efficiently infect worms in liquid culture.
	We show how the method can be used to quantify the importance of genes in anti-fungal innate immunity in C. elegans. In this case, the approach relies on the use of a transgenic strain carrying an epidermal infection-inducible fluorescent reporter gene, with GFP under the control of the promoter of the antimicrobial peptide gene nlp 29 and a red fluorescent reporter that is expressed constitutively in the epidermis. The latter provides an internal control for the functional integrity of the epidermis and nonspecific transgene silencing9. When control worms are infected by the fungus they fluoresce green. Knocking down by RNAi a gene required for nlp 29 expression results in diminished fluorescence after infection. Currently, this protocol allows more than 3,000 RNAi clones to be tested and analyzed per week, opening the possibility of screening the entire genome in less than 2 months.

Quantitative and Automated High-throughput Genome-wide RNAi Screens in C. elegans

We describe a protocol using C. elegans and RNAi feeding libraries that allows automated measurement of multiple parameters such as fluorescence, size and opacity of individual worms in a population. We give one example of a screen to identify genes involved in anti-fungal innate immunity in C. elegans.

定量和自动化高通量的全基因组RNAi屏幕 C。线虫</em

RNA interference is a powerful method to understand gene function, especially when conducted at a whole-genome scale and in a quantitative context. In C. ...

Fabrication and Use of MicroEnvironment microArrays (MEArrays)

The interactions between cells and their surrounding microenvironment have functional consequences for cellular behaviour. On the single cell level, distinct microenvironments can impose differentiation, migration, and proliferation phenotypes, and on the tissue level the microenvironment processes as complex as morphogenesis and tumorigenesis1. Not only do the cell and molecular contents of microenvironments impact the cells within, but so do the elasticity2 and geometry3 of the tissue. Defined as the sum total of cell-cell, -ECM, and -soluble factor interactions, in addition to physical characteristics, the microenvironment is complex. The phenotypes of cells within a tissue are partially due to their genomic content and partially due to the combinatorial interactions with the microenviroment. A major challenge is to link specific combinations of microenvironmental components with distinctive behaviours.
Here, we present the microenvironment microarray (MEArray) platform for cell-based functional screening of interactions with combinatorial microenvironments4. The method allows for simultaneous control of the molecular composition and the elastic modulus, and combines the use of widely available microarray and micropatterning technologies. MEArray screens require as few as 10,000 cells per array, which facilitates functional studies of rare cell types such as adult progenitor cells. A limitation of the technology is that entire tissue microenvironments cannot be completely recapitulated on MEArrays. However, comparison of responses in the same cell type to numerous related microenvironments, for instance pairwise combinations of ECM proteins that characterize a given tissue, will provide insights into how microenvironmental components elicit tissue-specific functional phenotypes.
MEArrays can be printed using a wide variety of recombinant growth factors, cytokines, and purified ECM proteins, and combinations thereof. The platform is limited only by the availability of specific reagents. MEArrays are amenable to time-lapsed analysis, but most often are used for end point analyses of cellular functions that are measureable with fluorescent probes. For instance, DNA synthesis, apoptosis, acquisition of differentiated states, or production of specific gene products are commonly measured. Briefly, the basic flow of an MEArray experiment is to prepare slides coated with printing substrata and to prepare the master plate of proteins that are to be printed. Then the arrays are printed with a microarray robot, cells are allowed to attach, grow in culture, and then are chemically fixed upon reaching the experimental endpoint. Fluorescent or colorimetric assays, imaged with traditional microscopes or microarray scanners, are used to reveal relevant molecular and cellular phenotypes (Figure 1).

Fabrication and Use of MicroEnvironment microArrays MEArrays

A combinatorial functional screening method for gaining insights into the impacts of the molecular composition of microenvironments on cellular functions is described. The method takes advantage of existing microarray-based technologies to generate arrays of defined combinatorial microenvironments that support cell adhesion and functional analysis.

制造和使用微环境芯片（MEArrays）

The interactions between cells and their surrounding microenvironment have functional consequences for cellular behaviour. On the single cell level, ...

Glycan Profiling of Plant Cell Wall Polymers using Microarrays

Plant cell walls are complex matrixes of heterogeneous glycans which play an important role in the physiology and development of plants and provide the raw materials for human societies (e.g. wood, paper, textile and biofuel industries)1,2. However, understanding the biosynthesis and function of these components remains challenging.
Cell wall glycans are chemically and conformationally diverse due to the complexity of their building blocks, the glycosyl residues. These form linkages at multiple positions and differ in ring structure, isomeric or anomeric configuration, and in addition, are substituted with an array of non-sugar residues. Glycan composition varies in different cell and/or tissue types or even sub-domains of a single cell wall3. Furthermore, their composition is also modified during development1, or in response to environmental cues4.
In excess of 2,000 genes have Plant cell walls are complex matrixes of heterogeneous glycans been predicted to be involved in cell wall glycan biosynthesis and modification in Arabidopsis5. However, relatively few of the biosynthetic genes have been functionally characterized 4,5. Reverse genetics approaches are difficult because the genes are often differentially expressed, often at low levels, between cell types6. Also, mutant studies are often hindered by gene redundancy or compensatory mechanisms to ensure appropriate cell wall function is maintained7. Thus novel approaches are needed to rapidly characterise the diverse range of glycan structures and to facilitate functional genomics approaches to understanding cell wall biosynthesis and modification.
Monoclonal antibodies (mAbs)8,9 have emerged as an important tool for determining glycan structure and distribution in plants. These recognise distinct epitopes present within major classes of plant cell wall glycans, including pectins, xyloglucans, xylans, mannans, glucans and arabinogalactans. Recently their use has been extended to large-scale screening experiments to determine the relative abundance of glycans in a broad range of plant and tissue types simultaneously9,10,11.
Here we present a microarray-based glycan screening method called Comprehensive Microarray Polymer Profiling (CoMPP) (Figures 1 &amp; 2)10,11 that enables multiple samples (100 sec) to be screened using a miniaturised microarray platform with reduced reagent and sample volumes. The spot signals on the microarray can be formally quantified to give semi-quantitative data about glycan epitope occurrence. This approach is well suited to tracking glycan changes in complex biological systems12 and providing a global overview of cell wall composition particularly when prior knowledge of this is unavailable.

A technique called Comprehensive Microarray Polymer Profiling (CoMPP) for the characterisation of plant cell wall glycans is described. This method combines the specificity of monoclonal antibodies directed to defined glycan-epitopes with a miniature microarray analytical platform allowing screening of glycan occurrence in a broad range of biological contexts.

糖基谱研究植物细胞壁聚合物，用微阵列

Plant cell walls are complex matrixes of heterogeneous glycans which play an important role in the physiology and development of plants and provide the ...

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of protein-bound DNA and aligning the short reads to a reference genome. Enriched regions are revealed as peaks, which often differ dramatically in shape, depending on the target protein1. For example, transcription factors often bind in a site- and sequence-specific manner and tend to produce punctate peaks, while histone modifications are more pervasive and are characterized by broad, diffuse islands of enrichment2. Reliably identifying these regions was the focus of our work.
Algorithms for analyzing ChIPseq data have employed various methodologies, from heuristics3-5 to more rigorous statistical models, e.g. Hidden Markov Models (HMMs)6-8. We sought a solution that minimized the necessity for difficult-to-define, ad hoc parameters that often compromise resolution and lessen the intuitive usability of the tool. With respect to HMM-based methods, we aimed to curtail parameter estimation procedures and simple, finite state classifications that are often utilized.
Additionally, conventional ChIPseq data analysis involves categorization of the expected read density profiles as either punctate or diffuse followed by subsequent application of the appropriate tool. We further aimed to replace the need for these two distinct models with a single, more versatile model, which can capably address the entire spectrum of data types.
To meet these objectives, we first constructed a statistical framework that naturally modeled ChIPseq data structures using a cutting edge advance in HMMs9, which utilizes only explicit formulas-an innovation crucial to its performance advantages. More sophisticated then heuristic models, our HMM accommodates infinite hidden states through a Bayesian model. We applied it to identifying reasonable change points in read density, which further define segments of enrichment. Our analysis revealed how our Bayesian Change Point (BCP) algorithm had a reduced computational complexity-evidenced by an abridged run time and memory footprint. The BCP algorithm was successfully applied to both punctate peak and diffuse island identification with robust accuracy and limited user-defined parameters. This illustrated both its versatility and ease of use. Consequently, we believe it can be implemented readily across broad ranges of data types and end users in a manner that is easily compared and contrasted, making it a great tool for ChIPseq data analysis that can aid in collaboration and corroboration between research groups. Here, we demonstrate the application of BCP to existing transcription factor10,11 and epigenetic data12 to illustrate its usefulness.

Our Bayesian Change Point (BCP) algorithm builds on state-of-the-art advances in modeling change-points via Hidden Markov Models and applies them to chromatin immunoprecipitation sequencing (ChIPseq) data analysis. BCP performs well in both broad and punctate data types, but excels in accurately identifying robust, reproducible islands of diffuse histone enrichment.

一种新型的全基因组分析的贝叶斯变点算法的多元ChIPseq数据类型

ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of ...

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

Transposon mutagenesis and single-gene deletion are two methods applied in genome-wide gene knockout in bacteria 1,2. Although transposon mutagenesis is less time consuming, less costly, and does not require completed genome information, there are two weaknesses in this method: (1) the possibility of a disparate mutants in the mixed mutant library that counter-selects mutants with decreased competition; and (2) the possibility of partial gene inactivation whereby genes do not entirely lose their function following the insertion of a transposon. Single-gene deletion analysis may compensate for the drawbacks associated with transposon mutagenesis. To improve the efficiency of genome-wide single gene deletion, we attempt to establish a high-throughput technique for genome-wide single gene deletion using Streptococcus sanguinis as a model organism. Each gene deletion construct in S. sanguinis genome is designed to comprise 1-kb upstream of the targeted gene, the aphA-3 gene, encoding kanamycin resistance protein, and 1-kb downstream of the targeted gene. Three sets of primers F1/R1, F2/R2, and F3/R3, respectively, are designed and synthesized in a 96-well plate format for PCR-amplifications of those three components of each deletion construct. Primers R1 and F3 contain 25-bp sequences that are complementary to regions of the aphA-3 gene at their 5' end. A large scale PCR amplification of the aphA-3 gene is performed once for creating all single-gene deletion constructs. The promoter of aphA-3 gene is initially excluded to minimize the potential polar effect of kanamycin cassette. To create the gene deletion constructs, high-throughput PCR amplification and purification are performed in a 96-well plate format. A linear recombinant PCR amplicon for each gene deletion will be made up through four PCR reactions using high-fidelity DNA polymerase. The initial exponential growth phase of S. sanguinis cultured in Todd Hewitt broth supplemented with 2.5% inactivated horse serum is used to increase competence for the transformation of PCR-recombinant constructs. Under this condition, up to 20% of S. sanguinis cells can be transformed using ~50 ng of DNA. Based on this approach, 2,048 mutants with single-gene deletion were ultimately obtained from the 2,270 genes in S. sanguinis excluding four gene ORFs contained entirely within other ORFs in S. sanguinis SK36 and 218 potential essential genes. The technique on creating gene deletion constructs is high throughput and could be easy to use in genome-wide single gene deletions for any transformable bacteria.

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

An efficient genome-wide single gene mutation method has been established using Streptococcus sanguinis as a model organism. This method has achieved via high throughput recombinant PCRs and transformations.

全基因组基因缺失<em&gt;链球菌血统</em通过高通量PCR

Transposon  mutagenesis and single-gene deletion are two methods applied in genome-wide  gene knockout in bacteria 1,2. Although transposon mutagenesis is ...

Polysome Fractionation and Analysis of Mammalian Translatomes on a Genome-wide Scale

mRNA translation plays a central role in the regulation of gene expression and represents the most energy consuming process in mammalian cells. Accordingly, dysregulation of mRNA translation is considered to play a major role in a variety of pathological states including cancer. Ribosomes also host chaperones, which facilitate folding of nascent polypeptides, thereby modulating function and stability of newly synthesized polypeptides. In addition, emerging data indicate that ribosomes serve as a platform for a repertoire of signaling molecules, which are implicated in a variety of post-translational modifications of newly synthesized polypeptides as they emerge from the ribosome, and/or components of translational machinery. Herein, a well-established method of ribosome fractionation using sucrose density gradient centrifugation is described. In conjunction with the in-house developed &#8220;anota&#8221; algorithm this method allows direct determination of differential translation of individual mRNAs on a genome-wide scale. Moreover, this versatile protocol can be used for a variety of biochemical studies aiming to dissect the function of ribosome-associated protein complexes, including those that play a central role in folding and degradation of newly synthesized polypeptides.

Ribosomes play a central role in protein synthesis. Polyribosome (polysome) fractionation by sucrose density gradient centrifugation allows direct determination of translation efficiencies of individual mRNAs on a genome-wide scale. In addition, this method can be used for biochemical analysis of ribosome- and polysome-associated factors such as chaperones and signaling molecules.

多聚核糖体分离和哺乳动物Translatomes分析在基因组范围内

mRNA translation plays a central role in the regulation of gene expression and represents the most energy consuming process in mammalian cells. ...

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

Proteins are the building blocks, effectors and signal mediators of cellular processes. A protein&#8217;s function, regulation and localization often depend on its interactions with other proteins. Here, we describe a protocol for the yeast protein-fragment complementation assay (PCA), a powerful method to detect direct and proximal associations between proteins in living cells. The interaction between two proteins, each fused to a dihydrofolate reductase (DHFR) protein fragment, translates into growth of yeast strains in presence of the drug methotrexate (MTX). Differential fitness, resulting from different amounts of reconstituted DHFR enzyme, can be quantified on high-density colony arrays, allowing to differentiate interacting from non-interacting bait-prey pairs. The high-throughput protocol presented here is performed using a robotic platform that parallelizes mating of bait and prey strains carrying complementary DHFR-fragment fusion proteins and the survival assay on MTX. This protocol allows to systematically test for thousands of protein-protein interactions (PPIs) involving bait proteins of interest and offers several advantages over other PPI detection assays, including the study of proteins expressed from their endogenous promoters without the need for modifying protein localization and for the assembly of complex reporter constructs.

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells

Proteins interact with each other and these interactions determine in a large part their functions. Protein interaction partners can be identified at high-throughput in vivo using a yeast fitness assay based on the dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA).

全基因组蛋白质相互作用通过蛋白质片段互补法（PCA）的活细胞筛选

Proteins are the building blocks, effectors and signal mediators of cellular processes. A protein&#8217;s function, regulation and localization often ...