パンの実験室での酵母の作業は、味の素（株）日本衛生研修グラントT32GM007183 - 33の国立研究所からの助成金によって賄われていた。我々は、酵母のプロジェクトの長期的協力のための医学のインディアナ大学で博士ロナルドWekに感謝。我々はまた、マイクロアレイ法を充電tRNAを開発するための博士キンバリーディトマーに感謝。

パート1：総荷電tRNAの分離<ol><li> 5分2000 RFCで卓上遠心機で細胞をペレット化する。細胞のOD 600 1の等価ではトータルRNAの少なくとも1μgのを生成する必要がありますおよび30μgの最小値は、プロシージャをお勧めします。 </li><li> 0.3 Mから成る溶解緩衝液に再懸濁し、細胞は+ -酢酸（pH4.5）を、10 mMのEDTA Naはバッファ。 30秒に3回各液（pH4.5）のNa + -酢酸飽和フェノール/クロロホルム等量の持つ渦常にボルテックスの間に氷上でサンプルを保管してください。フェノール/クロロホルム飽和酢酸を1部5 Mを混合して調製することができますがバッファリングされた酢酸はNaOACとのHOAcから調製することができる9部フェノール/クロロホルムと酢酸（pHは4.5）バッファ。 注：トータルRNAの分離は、荷電tRNAの脱アシル化を避けるために穏やかな酸性条件下で、氷上で行われる。 </li><li> 4℃で15分間18600 RFC℃で遠心水層を取り外し、別の酢酸飽和フェノール/クロロホルム抽出を行います。 </li><li> 2回目の抽出後は4℃で30分間RFC 18600でエタノール及び遠心分離の2.7倍のボリュームを追加することによりRNAを沈殿℃を</li><li>溶解緩衝液にして、懸濁しますRNAペレットは、エタノールで2回目を沈殿させる。 </li><li>最後に、10mMのバッファ酢酸（pH4.5）を、その後の治療のために1mMのEDTAを含む溶液中でRNAを懸濁します。サンプルを安定的に約2週間-80℃で保存することができます。いくつかの荷電tRNAはアシル基を除去するために開始されるように長期保存が推奨されていません。 </li></ol>パート2：tRNAの標準の調製<ol><li>熱E. 10.5μM〜85 2分の45 mMのトリス- ClのpHが7.5 ° Cでの大腸菌のtRNA規格。チューブを取り出し、3分間室温でおきます。 </li><li> 37℃の20mMとインキュベートの最終濃度℃で5分間にMgCl 2を追加。 </li><li>最後の反応は5μMtRNAは、60mMのトリス- HCl（pH 7.5）、12.5 mMのMgCl 2、10mMのKClを、3 mMジチオスレイトール、1.5mMのATP、1mMのスペルミン、それぞれの1mMのが含まれているので、合成反応ミックスを追加アミノ酸、および4.2ユニット/μlE.大腸菌アミノアシルtRNA合成酵素ミックス。 37℃で15分間。 </li><li> 0.5 Mバッファ酢酸（pH4.5）を等量のを追加し、pHを4.5に酢酸飽和クロロホルム/フェノール3で抽出する。 </li><li> 4℃で30分間18600 RFC℃でエタノール及び遠心分離の2.7倍のボリュームを持つtRNAの基準を沈殿させる</li><li>最大1ヶ月間-80℃で1mMのEDTA及び店舗° Cと50 mMのバッファ酢酸（pH4.5）に規格を再懸濁します。 </li></ol>パート3：tRNAのCy3/Cy5ラベリング<ol><li>荷電tRNAのサンプルでは各E.は 0.066μMでは0.1μg/μlの濃度でトータルRNAをインキュベート室温で30分間、50mMのNaIO4の存在下で大腸菌 tRNAの基準（すなわち、0.67ピコモルμgのトータルRNAあたりの各標準）と100 mMのバッファ酢酸（pH4.5）を。コントロールトータルのtRNAサンプルについてNaIO4の代わりのNaCl 50mMのを使用してください。充電を保つためにのみ緩衝液でRNAを希釈することを忘れないでください。 </li><li>反応をクエンチするためには、100mMのためにグルコースを追加し、室温で5分間インキュベートする。 </li><li>サンプルから、残りのNaIO4を削除するG25スピンカラムを用いてバッファー交換を行うためには。最良の結果は、サンプルを適用する前に、それを介して200 mMのバッファ酢酸緩衝液（pH4.5）を実行して、最初のカラムを平衡化するため。 </li><li> 66 mMにエタノールの2.7倍のボリュームの最終濃度〜133 mMのNaClの最終濃度に緩衝酢酸液（pH4.5）を加えることでサンプルを沈殿させる。時折2回目の沈殿は、酸化試料のために必要になる場合があります。これは、ペレットは、同じサンプルの制御ペレットよりも大幅に違って見える場合にのみ必要です。たとえば、かなり大きなもの以上のびまん性ペレット。第二エタノール沈殿を50mM酢酸緩衝液（pH4.5）とエタノールの添加前にNaClを200mMのでペレットを再懸濁しますを必要としている場合。 </li><li>脱アシル化のためのtRNA試料を50mMトリス- HCl（pH 9）と37℃で30分間再懸濁する。反応は、50mMのバッファ酢酸液（pH4.5）と100mMのNaCl、等量の添加により中和される。エタノールの2.7倍のボリュームに沈殿する。沈殿後、RNAを〜1μg/μLの水に再懸濁されている。制御と酸化サンプルの両方がRNAの品質をチェックするためにアガロースゲル上で実行する必要があります。 </li><li> tRNAは0.1μgの上に蛍光オリゴタグを添付するには/μlを脱アシル化されたRNAは、1 ×リガーゼバッファー、15％DMSO、4μMのCy3 -またはCy5を含むオリゴヌクレオチドを​​、0.5単位/μlのT4 DNAリガーゼ、及び酵母エキソ - ホスファターゼ（5,000でインキュベートする16のユニット/μl）（H 16以上）℃で一晩。エキソ - ホスファターゼは、酵母から得られた試​​料のためだけに必要で、omitteすることができますDこのプロトコルは、E.のために使用されている場合大腸菌やヒトサンプル。 </li><li>ライゲーションサンプルを、50mM KOAc液（pH7）の4巻、200mMのKClで混合し、フェノール/クロロホルムの等量で抽出された後。水相の抽出に続いて、RNA調製物をエタノールで沈殿されており、約は0.1μg/μlに水に再懸濁した。 </li><li>ライゲーション効率は、7M尿素を含む12％ポリアクリルアミドゲル上で試料の5-10％を実行することによって評価し、蛍光ゲルスキャナーで視覚化することができます。このPAGE分析では、マイクロアレイハイブリダイゼーションに必要な量の酸化およびコントロールサンプルを決定するためにも有用です。良いライゲーションの結果は〜Cy3/Cy5含有オリゴヌクレオチドの10％以上がtRNAにライゲートされて表示されるはずです。良い標識効率を持つサンプルを、サンプルあたり0.1〜0.5μgのトータルRNAをアレイハイブリダイゼーションに使用されます。 </li></ol>パート4：マイクロアレイのハイブリダイゼーションおよび分析<ol><li>ハイブリダイゼーションマイクロアレイスライドの前には、未結合のオリゴヌクレオチドを​​除去するために1〜2分間蒸留水で煮沸されています。 </li><li> Cy3/Cy5標識サンプルは、ハイブリダイゼーション緩衝液および70μg/ mlの最終濃度は140μg/ mlのポリ（）の最終濃度にサケ精子DNAを140μlで結合されます。 </li><li>ハイブリダイゼーションのプログラム、（表1）は、自動配列のhybridizer上で実行されます。 tRNAの作業では、それは非常にマニュアルハイブリダイゼーションは、高いバックグラウンドを生成するので、自動ハイブリダイゼーションのマシンを使用することをお勧めします。 </li><li>プログラムが完了した後に手動で軽く3〜5分、50 mlコニカルチューブに振盪することによって、少なくとも二回洗浄3水溶液でスライドを洗浄する。 </li><li>スライドは5〜10分間、200未満RFC、低速で遠心分離することによって乾燥させることができる。それは、光への曝露が漂白剤のフルオロフォアを意志ので、光のスライドを保つことが重要です。 </li><li>スライドを乾燥された後、彼らは、最適な結果を得るためにすぐにスキャンする必要があります。必要に応じてそれらは室温で乾燥した暗い場所で数日間保存することができます。スライドは、画像品質の顕著な劣化なしで2〜3回スキャンすることができます。 </li></ol>パート5：代表的な結果 2の近傍でOD 280比率：適切に絶縁されたときにトータルRNAサンプルは、OD 260と非常にきれいなスペクトルを生成する必要があります。は、検出可能なタンパク質またはフェノール汚染はないはずです。 1％アガロースゲル上で実行すると、強力なtRNAのバンドは、生物間の様々な他の可能な核酸のバンドで観測する必要があります。酸化後に清浄なtRNAのバンドが観察されるべきである。サンプルが他のサンプルまたは不鮮明に比べ著しく弱いことが認められた場合、サンプルはマイクロアレイには使用しないでください。マイクロアレイは、最も弱いtRNAのプローブよりも低い一桁ですが非常に低いバックグラウンドを持つ必要があります。高いバックグラウンドは、通常の洗浄工程の間に問題があるためです。これは通常、新鮮な洗浄溶液を調製し、徹底的に残留し、沈殿したSDSを除去するためにすべての機器や配管を洗浄することにより固定することができます。 <table border="1"><tbody><tr><td>ステップ</td><td>詳細</td></tr><tr><td> O -リングの調整</td><td> 75 ° C、2分</td></tr><tr><td>サンプルを紹介</td><td> 60 ° C </td></tr><tr><td>変性のサンプル</td><td> 90 ° C、5分</td></tr><tr><td>交配</td><td> 60 ° C、16H </td></tr><tr><td> 1を洗う</td><td> 50 ° C、流量の10秒が20を保持する</td></tr><tr><td> 2を洗う</td><td> 42 ° C、流量の10秒が20を保持する</td></tr><tr><td> 3を洗う</td><td> 42 ° C、流量の10秒が20を保持する</td></tr></tbody></table> 表1：ハイブリダイゼーションのプロトコール

<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>

利害の衝突は宣言されません。

我々は、同時にゲノムスケールでのすべてのtRNAの相対的な充電レベルを決定するための方法を提示する。ここで紹介するプロトコルは、S.用に最適化されています出芽酵母 、そしてそれはまた、正常E.でtRNAの充電プロファイルを測定するために使用されています大腸菌やヒトの培養細胞。それは全体の荷電tRNAを得られる限り、知られているゲノム配列（全tRNA遺伝子の注釈を可能にする）と任意の生物を収容するように変更することができます。

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<td>Microspin G-25 columns</td>
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<td>GE Healthcare</td>
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<td>E. coli tRNAPhe</td>
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<td>Sigma-Aldrich</td>
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<td>Sigma-Aldrich</td>
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genome-wide analysis of aminoacylation (charging) levels of trna using microarrays

tRNAのアミノアシル化、または充電、レベルは急速に環境[1]に応答して細胞内で変更することができます。原核生物と真核細胞の両方におけるtRNAの充電レベルの変化は、ストレス応答の主要な細胞機構である翻訳調節につながる。身近な例では、緊縮応答になります<em&gt; E.大腸菌</em&gt;と酵母のGCN2ストレス応答経路（[2-6]）。の最近の仕事<em&gt; E.大腸菌</em&gt;と<em&gt; S.出芽酵母</em&gt; tRNAの充電パターンは非常に動的であり、細胞を[1、6、7]が経験するストレスの種類に依存していることを示している。 tRNAの充電の非常に動的、変数の性質は完全に環境変化に対する細胞応答を解明するために、ゲノムスケールでのtRNAの充電レベルの変化を決定することが不可欠です。このレビューでは、同時に内のすべてのtRNAの相対的な充電レベルを測定するための方法を提示<em&gt; S.出芽酵母</em&gt;。ここで紹介するプロトコールは酵母のためですが、このプロトコルが正常にを含む生物の様々な相対的な充電レベルを決定するために適用されています<em&gt; E.大腸菌</em&gt;と人間の細胞培養[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

私達はからすべてのtRNAの相対的アミノアシル化レベルを決定するためにマイクロアレイ解析のための方法を説明します。<em&gt; S. cerivisiae。</em

マイクロアレイを用いたtRNAのアミノアシル化（充電）レベルのゲノムワイド解析

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 ビュー.  University of Chicago. 私達はからすべてのtRNAの相対的アミノアシル化レベルを決定するためにマイクロアレイ解析のための方法を説明します。 S. cerivisiae。

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

Bacterial Gene Expression Analysis Using Microarrays

マイクロアレイを用いた細菌の遺伝子発現解析

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.

アイソフォーム特異的遺伝子標的を同定するためのチップを使用してゲノムワイド解析

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.

ミッションターゲットIDのライブラリを使用してmiRNAのターゲットのゲノムワイドスクリーン

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.

の定量と自動ハイスループットゲノムワイドRNAiスクリーニング C.エレガンス</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.

微小環境マイクロアレイの作製と利用（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.

マイクロアレイを用いた植物細胞壁ポリマーの糖鎖プロファイリング

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.

多様ChIPseqデータ型のゲノムワイドな解析のための新規ベイジアン変化点アルゴリズム

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.

ゲノムワイドな遺伝子欠失で<em&gt;ストレプトコッカス血統</em&gt;ハイスループットPCRによる

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.

ゲノム規模で哺乳類Translatomesのソーム分画および分析

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).

生細胞内のタンパク質断片相補アッセイ（PCA）によるゲノムワイドなタンパク質間相互作用のスクリーニング

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