著者らは、バイオインフォマティクス・ソリューションズ社のジョナサン・クリーガー博士が代表的な実験のために質量分析計を操作してくれたことに感謝し、Geddes-McAlisterグループのメンバーが実験的なセットアップと原稿フィードバックを支援してくれたことに感謝する。著者らは、バンティング研究財団-ジャリスロウスキー・フェローシップ・ディスカバリー賞、ニューフロンティア研究基金-探査(NFRFE-2019-00425)、.M カナダイノベーション財団(JELF 38798)、NsERCカナダ大学院奨学金奨学金、オンタリオ州卒業奨学金B..B

<ol>
	<li>Bongomin, F., Gago, S., Oladele, R., Denning, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+and+Multi-National+Prevalence+of+Fungal+Diseases-Estimate+Precision.">Global and Multi-National Prevalence of Fungal Diseases-Estimate Precision.</a> Journal of Fungi. , (2017).</li><li>Tugume, L., 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=HIV-Associated+cryptococcal+meningitis+occurring+at+relatively+higher+CD4+counts.">HIV-Associated cryptococcal meningitis occurring at relatively higher CD4 counts.</a> Journal of Infectious Diseases. 219 (6), 877-883 (2019).</li><li>Rajasingham, R., 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=Global+burden+of+disease+of+HIV-associated+cryptococcal+meningitis:+an+updated+analysis.">Global burden of disease of HIV-associated cryptococcal meningitis: an updated analysis.</a> The Lancet Infectious Diseases. , (2017).</li><li>Perfect, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+antifungal+pipeline:+A+reality+check.">The antifungal pipeline: A reality check.</a> Nature Reviews Drug Discovery. , (2017).</li><li>Bermas, A., Geddes-McAlister, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Combatting+the+evolution+of+anti-fungal+resistance+in+Cryptococcus+neoformans.">Combatting the evolution of anti-fungal resistance in Cryptococcus neoformans.</a> Molecular Microbiology. , 1-14 (2020).</li><li>Geddes-McAlister, J., Shapiro, R. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=New+pathogens,+new+tricks:+Emerging,+drug-resistant+fungal+pathogens+and+future+prospects+for+antifungal+therapeutics.">New pathogens, new tricks: Emerging, drug-resistant fungal pathogens and future prospects for antifungal therapeutics.</a> Annals of the New York Academy of Sciences. , (2018).</li><li>Ball, B., Bermas, A., Carruthers-Lay, D., Geddes-McAlister, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mass+Spectrometry-Based+Proteomics+of+Fungal+Pathogenesis,+Host-Fungal+Interactions,+and+Antifungal+Development.">Mass Spectrometry-Based Proteomics of Fungal Pathogenesis, Host-Fungal Interactions, and Antifungal Development.</a> Journal of Fungi. , (2019).</li><li>Sukumaran, A., 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=Decoding+communication+patterns+of+the+innate+immune+system+by+quantitative+proteomics.">Decoding communication patterns of the innate immune system by quantitative proteomics.</a> J Leukocyte Biol. , (2019).</li><li>Salas, D., Stacey, R. G., Akinlaja, M., Foster, L. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Next-generation+interactomics:+Considerations+for+the+use+of+co-elution+to+measure+protein+interaction+networks.">Next-generation interactomics: Considerations for the use of co-elution to measure protein interaction networks.</a> Molecular and Cellular Proteomics. , (2020).</li><li>Aebersold, R., Mann, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mass-spectrometric+exploration+of+proteome+structure+and+function.">Mass-spectrometric exploration of proteome structure and function.</a> Nature. 537 (7620), 347-355 (2016).</li><li>Mann, M., Kulak, N. A., Nagaraj, N., Cox, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Coming+Age+of+Complete,+Accurate,+and+Ubiquitous+Proteomes.">The Coming Age of Complete, Accurate, and Ubiquitous Proteomes.</a> Molecular Cell. 49 (4), 583-590 (2013).</li><li>Cox, J., 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=Accurate+Proteome-wide+Label-free+Quantification+by+Delayed+Normalization+and+Maximal+Peptide+Ratio+Extraction,+Termed+MaxLFQ.">Accurate Proteome-wide Label-free Quantification by Delayed Normalization and Maximal Peptide Ratio Extraction, Termed MaxLFQ.</a> Molecular &amp; Cellular Proteomics. 13 (9), 2513-2526 (2014).</li><li>Ankney, J. A., Muneer, A., Chen, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Relative+and+Absolute+Quantitation+in+Mass+Spectrometry-Based+Proteomics.">Relative and Absolute Quantitation in Mass Spectrometry-Based Proteomics.</a> Annual Review of Analytical Chemistry. , (2018).</li><li>Cox, J., Mann, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MaxQuant+enables+high+peptide+identification+rates,+individualized+p.p.b.-range+mass+accuracies+and+proteome-wide+protein+quantification.">MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.</a> Nature Biotechnology. 26 (12), 1367-1372 (2008).</li><li>Tyanova, S., 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=The+Perseus+computational+platform+for+comprehensive+analysis+of+(prote)omics+data.">The Perseus computational platform for comprehensive analysis of (prote)omics data.</a> Nature Methods. 13 (9), 731-740 (2016).</li><li>Cox, J., 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=Andromeda:+A+peptide+search+engine+integrated+into+the+MaxQuant+environment.">Andromeda: A peptide search engine integrated into the MaxQuant environment.</a> Journal of Proteome Research. , (2011).</li><li>Zhong, Z., Pirofski, L. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Opsonization+of+Cryptococcus+neoformans+by+human+anticryptococcal+glucuronoxylomannan+antibodies.">Opsonization of Cryptococcus neoformans by human anticryptococcal glucuronoxylomannan antibodies.</a> Infection and Immunity. , (1996).</li><li>Nicola, A. M., 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=Macrophage+autophagy+in+immunity+to+Cryptococcus+neoformans+and+Candida+albicans.">Macrophage autophagy in immunity to Cryptococcus neoformans and Candida albicans.</a> Infection and Immunity. , (2012).</li><li>Ball, B., Geddes-McAlister, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+Proteomic+Profiling+of+Cryptococcus+neoformans.">Quantitative Proteomic Profiling of Cryptococcus neoformans.</a> Current Protocols in Microbiology. , (2019).</li><li>Geddes-McAlister, J., Gadjeva, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mass+spectromerty-based+quantitative+proteomics+of+murine-derived+polymorphonuclear+neutrophils.">Mass spectromerty-based quantitative proteomics of murine-derived polymorphonuclear neutrophils.</a> Current Protocols in Immunology. , (2019).</li><li>Geddes, J. M. 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=Secretome+profiling+of+Cryptococcus+neoformans+reveals+regulation+of+a+subset+of+virulence-associated+proteins+and+potential+biomarkers+by+protein+kinase+A.">Secretome profiling of Cryptococcus neoformans reveals regulation of a subset of virulence-associated proteins and potential biomarkers by protein kinase A.</a> BMC Microbiology. , (2015).</li><li>Geddes, J. M. 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=Analysis+of+the+protein+kinase+a-regulated+proteome+of+Cryptococcus+neoformans+identifies+a+role+for+the+ubiquitin-proteasome+pathway+in+capsule+formation.">Analysis of the protein kinase a-regulated proteome of Cryptococcus neoformans identifies a role for the ubiquitin-proteasome pathway in capsule formation.</a> mBio. 7 (1), 1-15 (2016).</li><li>Al Shweiki, M. H. D. R., 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=Assessment+of+Label-Free+Quantification+in+Discovery+Proteomics+and+Impact+of+Technological+Factors+and+Natural+Variability+of+Protein+Abundance.">Assessment of Label-Free Quantification in Discovery Proteomics and Impact of Technological Factors and Natural Variability of Protein Abundance.</a> Journal of Proteome Research. , (2017).</li><li>Ong, S. 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H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Organellar+Maps+Through+Proteomic+Profiling+-+A+Conceptual+Guide.">Organellar Maps Through Proteomic Profiling - A Conceptual Guide.</a> Molecular &amp; Cellular Proteomics. , (2020).</li><li>Gingras, A. C., Abe, K. T., Raught, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Getting+to+know+the+neighborhood:+using+proximity-dependent+biotinylation+to+characterize+protein+complexes+and+map+organelles.">Getting to know the neighborhood: using proximity-dependent biotinylation to characterize protein complexes and map organelles.</a> Current Opinion in Chemical Biology. , (2019).</li><li>Kugadas, A., 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=Frontline+Science:+Employing+enzymatic+treatment+options+for+management+of+ocular+biofilm-based+infections.">Frontline Science: Employing enzymatic treatment options for management of ocular biofilm-based infections.</a> Journal of Leukocyte Biology. , (2019).</li><li>Yeung, J., Gadjeva, M., Geddes-McAlister, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Label-Free+Quantitative+Proteomics+Distinguishes+General+and+Site-Specific+Host+Responses+to+Pseudomonas+aeruginosa+Infection+at+the+Ocular+Surface.">Label-Free Quantitative Proteomics Distinguishes General and Site-Specific Host Responses to Pseudomonas aeruginosa Infection at the Ocular Surface.</a> Proteomics. , (2020).</li><li>Yeung, J., Lamb, J., Krieger, J. 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著者らは利益相反を宣言しない。

プロトコルの重要なステップには、マクロファージ細胞の調製および細胞への中断を最小限に抑えたタンパク質処理のための共培養サンプルの収集が含まれる。採取前に細胞の不必要なリシスを防ぐために、洗浄、接種、および接着性マクロファージ細胞を穏やかに慎重に除去する手順を実行することが重要です。実験に対して正しいMOIを確立することは、過度に高いMOIを接種すると、急速なマクロファージ細胞死を引き起こし、MS.のサンプルの収集と処理が困難になる可能性もあるので、MOI数が少ないと貪食細胞が少なくなり、生物学的系における真菌タンパク質の検出が制限される可能性があります。このような制限を克服するために、細胞死アッセイ(例えば、LDH定量)でサポートされる様々なMOIを用いて試験実験を行い、宿主応答を開始するが、収集前に宿主細胞を殺さない真菌細胞の数を定義することを推奨します。実験では、感染に関連する真菌タンパク質の同定を目指し、高いMOI(100:1)を必要とし、豊富な宿主タンパク質の中から真菌タンパク質を適切に検出することを目指しています。実験全体を行う前に、マクロファージ細胞傷害アッセイを日常的に行い、宿主細胞死に対するMOIの影響を評価する。C.ネオフォーマン細胞のマクロファージを有する培養のタイミングは、真菌細胞が大きな多糖カプセルを有する可能性があるため、マクロファージは巻き込みのためのより多くの時間を必要とするため、また重要である。我々は、この時点で真菌プロテオームの良好なカバレッジを発見し、ホスト応答の「スナップショット」を提供するために、概説された実験のために3時間の共培養時間を使用することを選択します。しかし、研究者は、より早く、後の時点を探索し、タイミングが真菌および宿主タンパク質産生にどのような影響を与えるかを観察したいと考えるかもしれません。あるいは、オプホ化のためのマクロファージのプライミングが行われ、貪食プロセス17,18を支援する。
サンプル採取のために、サンプルが液体窒素ですぐにフラッシュ凍結を処理されていない場合、サンプル中に存在するプロテアーゼによるタンパク質の望ましくない分解を防ぐのに役立ちます。また、MSベースのプロテオミクス解析では、実験を始める前にニトリル手袋、実験室用コート、70%エタノールで表面を洗浄することにより、ほこりやケラチン(皮膚細胞や毛細胞など)からの汚染の可能性を制限する必要があります。さらに、上記のプロトコルは、説明した共培養サンプルセットに固有であるが、タンパク質抽出ワークフローの最適化のために、必要に応じて19,20に変更することができる。特定の細胞タイプに対してワークフローを変更し最適化する機会としては、選択した機械的および化学的破壊技術、酵素消化の持続時間と温度、サンプルの分離などがあります。例えば、C.ネオフォーマン細胞のリシスは、典型的には機械的なビーズの鼓動によって行われる。しかし、我々は、プローブ超音波処理に続く増加したプロテオームのカバレッジを観察し、したがって、感染細胞19、21、22の機械的破壊のためにそれをお勧めします。例えば、高pH分画またはサイズ排除クロマトグラフィーによってペプチドサンプルをアリコートに分画すると、サンプルの複雑さが減少し、質量分析計9上の被覆深度が向上する可能性がある。さらに、約8,000個の宿主および真菌タンパク質を同定するための被覆深度を達成するためには、高解像度の質量分析システム(例えば、QExactive Exploris、フュージョンルモス、timsTOF Pro)が必要です。
感染時のタンパク質レベルの変化を定量化するためのLFQの使用は、MSベースのプロテオミクス12に対して信頼性が高く、費用対効果の高いアプローチである。これは、追加のサンプル処理ステップを必要とせずに、相対的な豊富さによってタンパク質の定量を可能にします。また、実験終了後に解析を行い、ユニバーサルアプリケーションや柔軟な学習設計に貸し出します。しかし、LFQの制限には、サンプルを順次実行する必要があり、組み合わせることができないため、計測時間の増加、サンプル間の比較性が困難であり、欠損値を置き換える代用の必要性が高い23が含まれます。タンパク質の豊富さを定量化するための代替アプローチとしては、代謝(例えば、細胞培養におけるアミノ酸の安定同位体標識)および化学的(タンデム質量タグ)標識技術が挙げられる。しかし、このようなアプローチは、追加のサンプル処理および処理ステップを必要とし、ウェットラボ実験の複雑さと時間を増加させ、また、固定実験計画を必要とする。提案された実験に最適な定量化方法を選択するには、サンプルの設計と比較可能性、ウェットラボの複雑さとデータ分析、および計測時間の可用性とコストを考慮する必要があります。
提示されるプロトコルの新しいことは、1回の実験で宿主と病原体の両方の視点からプロテオームの変化を定義する能力である。両方のプロテオームの範囲の深さは、病原体が感染を開始する方法とホストが防御にどのように反応するかについての新しい洞察を可能にします。特に、このアプローチは細胞全体の感染に焦点を当てています。しかしながら、サブプロテオームまたは感染に対する区画化応答を定義する機会は、空間的局在化技術(例えば、遠心分離、濃縮、標識)26,27との組み合わせを通じて存在する。ここでは、マクロファージと真菌病原体C.ネオフォーマンとの相互作用について詳しく説明します。しかし、このアプローチは普遍的であり、多様な生物学的システム間の相互作用に適用することができます。例えば、最近、同様のワークフローを使用して、眼角膜炎28、29、30のマウスモデルに由来する好中球の一般的および部位特異的な応答を明らかにした。さらに、本プロトコルから生成された感染性データセットは、生体外プロテオームおよび病原体の分泌物プロファイリングと統合して、宿主細胞の存在下で変化した存在量のタンパク質を検出することができる。このようなタンパク質は、感染関連タンパク質と呼ばれ、経時的な調節、局在化、および宿主との直接的なタンパク質タンパク質相互作用を含む、さらなる特徴付けのための既知および新しい毒性因子の多くを提供する。全体として、概説されたMSベースのプロテオミクスワークフローは、普遍性と理解が一般的に利用できない単一の実験で宿主と病原体の複雑な関係を調査する新しい機会を提供します。

侵襲性真菌感染症の有病率は非常に増加しており、容認できないほど高い死亡率と相関しており、免疫不全素因を有する個体で最も一般的に報告される1。クリプトコッカス・ネオフォーマンは、宿主マクロファージ細胞内で細胞内生存が可能な悪名高い日和見真菌病原体である。不十分な抗真菌介入は、真菌の播種およびクリプトコッカス髄膜炎および髄膜脳炎の生命を脅かす症状をもたらす 2,3.免疫不全状態の世界的な増加は、C.ネオフォーマンを含む多くの真菌種が4、5、6に対する耐性をますます進化させた抗真菌剤の使用の並行増加を要求している。したがって、宿主の防御応答と微生物の病因に関する重要な生物学的質問に答えるために、堅牢で効率的な技術を実装することが不可欠です。
強力な計算およびバイオインフォマティクスパイプラインの生成を含む質量分析(MS)の技術進歩の新時代は、宿主病原体研究7,8の大規模分析のための統合的ビジョンの基礎を提供する。従来の病原体主導のプロテオミクス解析は、タンパク質相関プロファイリング、プロテオミクスと結合したアフィニティークロマトグラフィー、およびインタークトミクス9などの包括的な方法論を含む、宿主または病原体の観点から感染の見解を一般的にプロファイルする。ホストシステムにおける危険病原体の病原性に関する調査は、非常に臨床的に重要である。しかし、単一の実験での二重遠近解析の適用は、以前は達成不可能と考えられていました。例えば、感染に対する病原体の視点は、多くの場合、低い豊富な真菌タンパク質の検出のための感度を低下させる豊富な宿主タンパク質によって圧倒される7。さらに、高いサンプルの複雑さは、単一の実験システムで調査するために多くのターゲットを招き、特定の病原体タンパク質の作用機序を解明する挑戦を証明する。
ボトムアッププロテオミクスは、管理しやすいサンプル調製を可能にする一般的なMS技術であり、その後に液体クロマトグラフィー分離、同定、定量をMS10、11で配列特異的酵素消化してペプチドを生成する。ここでは、感染に基づくプロテオームまたは「感染性」の公平なカバレッジを達成することを目的としたデータ依存的な取得戦略を示す方法を提示する。具体的には、ラベルフリー定量(LFQ)は、複数のプロテオーム間でタンパク質レベル変化を確実かつ正確に同定するための化学的または代謝ラベルへの依存性を低下させ、サンプル処理および処理ステップ12、13を低減する。この普遍的なアプリケーションは、任意の予想されるタンパク質産生とは無関係に細胞内の特定の瞬間に産生されたタンパク質を尋問します。したがって、感染に不可欠な新しい洞察が発見される可能性があります。
本明細書に記載されているワークフローは、宿主免疫細胞を用いた感染模倣状態の間の C.ネオフォーマン のタンパク質レベル変化を探索するために最適化される(図1)。このアプローチは、細胞型の分離と分離に頼るのではなく、宿主と病原体プロテオームを一緒に抽出し、生物特異的な2つのデータベースを用いた生物情報分離を利用して、種特異的タンパク質産生を区別する。この方法は、同位体ベースのラベリング研究または分画に必要な余分なコストのかかる準備ステップなしで処理されるサンプルの無制限の数のための利点を提供します。さらに、このワークフローは、宿主の免疫細胞を標的化し感染させることができる幅広い真菌および細菌病原体に伝達可能な最適化されたタンパク質抽出プロトコルをサポートします。全体として、このプロトコルは、高分解能MSのための公平なタンパク質抽出とサンプル処理を完了するための手順を概説し、続いて、感染に有意な真菌タンパク質に関する豊富な知識を提供し、宿主防御応答の包括的なプロファイリングを行うことができるデータおよび統計分析を行う。

<table>
	<tbody>
		<tr>
			<td>100 mM Tris-HCl, pH 8.5</td>
			<td>Fisher Scientific</td>
			<td>BP152-1</td>
			<td>Maintain at 4&#176;C</td>
		</tr>
		<tr>
			<td>60 x 15 mm Dish, Nunclon Delta</td>
			<td>ThermoFisher Scientific</td>
			<td>174888</td>
			<td></td>
		</tr>
		<tr>
			<td>6-well cell culture plate</td>
			<td>ThermoFisher Scientific</td>
			<td>140675</td>
			<td></td>
		</tr>
		<tr>
			<td>Acetonitrile, MS grade</td>
			<td>Pierce</td>
			<td>TS-51101</td>
			<td></td>
		</tr>
		<tr>
			<td>Acetic Acid</td>
			<td>Sigma Aldrich</td>
			<td>1099510001</td>
			<td></td>
		</tr>
		<tr>
			<td>Acetone</td>
			<td>Sigma Aldrich</td>
			<td>34850-1L</td>
			<td></td>
		</tr>
		<tr>
			<td>Ammonium bicarbonate (ABC)</td>
			<td>ThermoFisher Scientific</td>
			<td>A643-500</td>
			<td>Prepare a stock 50 mM ABC solution, stable at room temperature for up to one month.</td>
		</tr>
		<tr>
			<td>Bel-Art&#8482; HiFlow Vacuum Aspirator Collection System</td>
			<td>Fisher Scientific</td>
			<td>13-717-300</td>
			<td>Not essential, serological pipettes can be used to remove media.</td>
		</tr>
		<tr>
			<td>C18 resin</td>
			<td>3M Empore</td>
			<td>3M2215</td>
			<td></td>
		</tr>
		<tr>
			<td>Cell Scrapers</td>
			<td>VWR</td>
			<td>10062-906</td>
			<td>Not essential, other methods to release macrophage cells can be used.</td>
		</tr>
		<tr>
			<td>Centrifugal vaccuum concentrator</td>
			<td>Eppendorf</td>
			<td>07-748-15</td>
			<td></td>
		</tr>
		<tr>
			<td>Complete Filtration Unit</td>
			<td>VWR</td>
			<td>10040-436</td>
			<td></td>
		</tr>
		<tr>
			<td>Conical falcon tubes (15 mL)</td>
			<td>Fisher Scientific</td>
			<td>05-539-12</td>
			<td></td>
		</tr>
		<tr>
			<td>Countess II Automated Cell Counter</td>
			<td>ThermoFisher Scientific</td>
			<td>AMQAX1000</td>
			<td>Not essential, haemocytometer can be used as an alternative.</td>
		</tr>
		<tr>
			<td>CytoTox 96 Non-Radioactive Cytotoxicity Assay</td>
			<td>Promega</td>
			<td>G1780</td>
			<td></td>
		</tr>
		<tr>
			<td>Dithiothreitol (DTT)</td>
			<td>ThermoFisher Scientific</td>
			<td>R0861</td>
			<td>Prepare bulk stock solution of 1 M DTT, flash frozen and stored at -20 &#176;C until use. Discard after each use (do not freeze-thaw repeatedly).</td>
		</tr>
		<tr>
			<td>DMEM, high glucose, GlutaMAX Supplement</td>
			<td>ThermoFisher Scientific</td>
			<td>10566016</td>
			<td></td>
		</tr>
		<tr>
			<td>Fetal Bovine Serum (FBS)</td>
			<td>ThermoFisher Scientific</td>
			<td>12483020</td>
			<td>Heat inactivate by incubating at 60&#176;C for 30 minutes. Prepare 50 ml aliquots and flash freeze. Thaw prior to media preparation</td>
		</tr>
		<tr>
			<td>Haemocytometer</td>
			<td>VWR</td>
			<td>15170-208</td>
			<td></td>
		</tr>
		<tr>
			<td>HEPES</td>
			<td>Sigma Aldrich</td>
			<td>H3375</td>
			<td>Prepare 40 mM HEPES/8 M Urea in bulk stock solution, flash frozen, store at -20&#176;C until use. Discard after each use (do not freeze-thaw repeatedly).</td>
		</tr>
		<tr>
			<td>High-performance liquid chromatography system</td>
			<td>ThermoFisher Scientific</td>
			<td>LC140</td>
			<td>Gradient length is based on sample complexity, recommended 120 min gradient for infectome samples.</td>
		</tr>
		<tr>
			<td>High-resolution mass spectrometer</td>
			<td>ThermoFisher Scientific</td>
			<td>726042</td>
			<td></td>
		</tr>
		<tr>
			<td>Iodoacetamide (IAA)</td>
			<td>Sigma Aldrich</td>
			<td>I6125</td>
			<td>Prepare 0.55 M bulk stock solution, flash frozen, store at -20&#176;C until use. Discard after each use (do not freeze-thaw repeatedly).</td>
		</tr>
		<tr>
			<td>L-glutamine</td>
			<td>ThermoFisher Scientific</td>
			<td>25030081</td>
			<td>Can be aliquot and frozen for storage. Thaw prior to media preparation.</td>
		</tr>
		<tr>
			<td>LoBind Microcentrifuge tubes</td>
			<td>Eppendorf</td>
			<td>13-698-794</td>
			<td></td>
		</tr>
		<tr>
			<td>MaxQuant</td>
			<td>https://maxquant.org/</td>
			<td></td>
			<td>MaxQuant is a public platform that offers tutorials, such as the MaxQuant Summer School, outlining the computational analysis steps of large MS data sets</td>
		</tr>
		<tr>
			<td>Microcentrifuge</td>
			<td>Eppendorf</td>
			<td>13864457</td>
			<td></td>
		</tr>
		<tr>
			<td>Penicillin : Streptomycin 10k/10k</td>
			<td>VWR</td>
			<td>CA12001-692</td>
			<td>Can be aliquot and frozen for storage. Thaw prior to media preparation.</td>
		</tr>
		<tr>
			<td>Peptide separation columns</td>
			<td>ThermoFisher Scientific</td>
			<td>ES803</td>
			<td></td>
		</tr>
		<tr>
			<td>Perseus Software</td>
			<td>http://maxquant.net/perseus/</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Phosphate Buffered Saline</td>
			<td>VWR</td>
			<td>CA12001-676</td>
			<td>Puchase not required. PBS can also be prepared but sterile filteration must be performed before use.</td>
		</tr>
		<tr>
			<td>Pierce BCA Protein Assay</td>
			<td>ThermoFisher Scientific</td>
			<td>&#160;23225</td>
			<td></td>
		</tr>
		<tr>
			<td>Pipette, Disposable Serological (10 mL)</td>
			<td>Fisher Scientific</td>
			<td>13-678-11E</td>
			<td></td>
		</tr>
		<tr>
			<td>Pipette, Disposable Serological (25 mL) Basix</td>
			<td>Fisher Scientific</td>
			<td>14955235</td>
			<td></td>
		</tr>
		<tr>
			<td>Probe sonciator</td>
			<td>ThermoFisher Scientific</td>
			<td>100-132-894</td>
			<td></td>
		</tr>
		<tr>
			<td>Protease inhibitor cocktail tablet</td>
			<td>Roche</td>
			<td>4693159001</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium dodecyl sulfate</td>
			<td>ThermoFisher Scientific</td>
			<td>28364</td>
			<td>20% (w/v)</td>
		</tr>
		<tr>
			<td>Spectrophotometer (Nanodrop)</td>
			<td>ThermoFisher Scientific</td>
			<td>ND-2000</td>
			<td></td>
		</tr>
		<tr>
			<td>STAGE tipping centrifuge</td>
			<td>Sonation</td>
			<td>STC-V2</td>
			<td></td>
		</tr>
		<tr>
			<td>Thermal Shaker</td>
			<td>VWR</td>
			<td>NO89232-908</td>
			<td></td>
		</tr>
		<tr>
			<td>Trifluoroacetic acid</td>
			<td>ThermoFisher Scientific</td>
			<td>85183</td>
			<td></td>
		</tr>
		<tr>
			<td>Trypsin/Lys-C protease mix, MS grade</td>
			<td>Pierce</td>
			<td>A40007</td>
			<td>Maintain at -20 &#176;C.</td>
		</tr>
		<tr>
			<td>Ultrasonic bath</td>
			<td>Bransonic</td>
			<td>A89375-450</td>
			<td>Stored in cold room (4C)</td>
		</tr>
		<tr>
			<td>Urea</td>
			<td>Sigma Aldrich</td>
			<td>U1250-1KG</td>
			<td>Prepare 40 mM HEPES/8 M Urea in bulk stock solution, flash frozen, store at -20 &#176;C until use. Discard after each use (do not freeze-thaw repeatedly).</td>
		</tr>
		<tr>
			<td>Yeast-extract peptone dextrose broth</td>
			<td>BD Difco</td>
			<td>BM20</td>
			<td></td>
		</tr>
	</tbody>
</table>

label-free quantitative proteomics workflow for discovery-driven host-pathogen interactions

質量分析(MS)ベースの定量プロテオミクスの技術的成果は、様々な条件下で生物のグローバルプロテオームを分析するための多くの未知の道を開きます。この強力な戦略は、微生物病原体と所望の宿主との相互作用に適用され、感染に対する両方の視点を包括的に特徴付ける。本明細書において、ワークフローは、不滅のマクロファージ細胞の存在下で、致命的な疾患クリプトコッカス症の原因物質である真菌性の栄養性細胞内病原体である クリプトコッカス・ネオフォルマンの 感染者のラベルフリー定量(LFQ)について説明する。このプロトコルは、単一の実験で病原体細胞と哺乳類細胞の両方に適切なタンパク質調製技術を詳述し、液体クロマトグラフィー(LC)-MS/MS分析のための適切なペプチドサブミッションをもたらす。LFQの高スループットの一般的な性質は、タンパク質の同定と定量の広いダイナミックレンジだけでなく、任意の宿主病原体感染設定への転写性を可能にし、極度の感受性を維持する。この方法は、感染模倣条件内の病原体の広範で公平なタンパク質豊富プロファイルをカタログ化するために最適化されています。具体的には、ここで示す方法は、毒性に必要なタンパク質産生などの C.ネオフォルマン病 原発病に関する重要な情報を提供し、微生物の侵入に応答する重要な宿主タンパク質を特定する。

上記で概説したプロトコルは、真菌病原体、C.ネオフォーマンおよび宿主であるマクロファージ細胞の両方から得られたタンパク質を単一の実験で同定し、定量化する。共培養後、細胞は、各種に特異的なペプチドプロファイルに基づいて、一緒に収集および処理され、生物的に分離される。これは、感染時の宿主病原体関係の相互作用を定義するための強力なアプローチである。実験から同定されるタンパク質の数は、出発物質、サンプル調製、勾配長、MS計測、バイオインフォマティクスワークフローによって異なります。本明細書に記載されたプロトコルを用いて、我々は通常、1,500 C. ネオフォーマンタンパク質および6,500個の宿主タンパク質を用いて実験から約8,000個のタンパク質を同定する。データセットの処理に続いて、分析を推進する重要な要因を観察するための主成分分析(PCA)プロットを生成します(図2A)。ここでは、実験計画(成分1、79.8%)から予想されるように、データ間の分離の最大の成分が感染しているのと非感染サンプルが観測され、サンプルの第2の特徴は生物学的変動性(成分2、5.7%)である。次に、ユークリッド距離による階層クラスタリングと組み合わせたピアソン相関をサンプルをグループ化し、反復の変動性を定量化する(図2B)。我々の分析では、感染したサンプルと非感染サンプルの明確なクラスタリングを観察し、再現性を95~96%に再現し、複製物の再現性を表しています。最後に、Benjamini-Hochberg偽発見率(FDR)(p-value 0.05)を使用して複数仮説テストを修正した学生のt検定 ≤を実行します。FDR = 0.01;s0 = 1) 非感染コントロールと比較して感染中に存在量が有意に異なるタンパク質を同定する(図2C)。ここでは、感染時に有意な増加を示す86と31の86の宿主タンパク質を含む、豊富な有意な変化を有する760タンパク質を同定する。特に、感染時に予想されるように、真菌タンパク質の豊富さの有意な増加も観察する。これらのデータを用いて、ネットワークマッピング、インシリコ特性解析、フォローアップ実験を含む後続の分析が行われ、データを検証し、病原性に対する宿主応答を支える分子メカニズムを探求する。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61881/61881fig1v4.jpg"> 図1: C. ネオフォーマンに感染したマクロファージの分析のための質量分析法ベースのプロテオミクスワークフロー .ワークフローは、C.ネオフォーマンまたは非感染コントロールに感染したマクロファージのコレクションから始まります。タンパク質は機械的および化学的破壊によって抽出され、その後、還元およびアルキル化、アセトン沈殿、酵素消化が行なわれている。ペプチドは、C18 STAGEチップ上で精製され、高速液体クロマトグラフィーで分離され、エレクトロスプレーイオン化を行い、高解像度の質量分析計で測定されます。データは、一般に公開されているバイオインフォマティクスプラットフォーム、MaxQuant(アンドロメダ付き)およびペルセウス14、15、16で処理、分析、視覚化されます。生物学的四重で行われた実験。<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61881/61881fig1v3large.jpg" target="_blank">この図の大きなバージョンを表示するには、ここをクリックしてください。</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61881/61881fig2v3.jpg"> 図2:マクロファージ細胞のC.ネオフォーマン感染の代表的データ。(A)主成分分析は、感染したマクロファージと非感染マクロファージ(成分1、79.8%)と生物学的複製物(成分2、5.7%)のクラスタリングの区別を示す。(B) ユークリッド距離による階層クラスタリングによってプロットされたピアソン相関のヒートマップ(感染と非感染)のクラスタリングを示し、再現性(>95%)を再現する。(C) 同定されたタンパク質の火山プロット。青 = 豊富な有意な変化を有する真菌タンパク質;黒 = 豊富な大きな変化を有するマクロファージタンパク質。学生のt-test(p-値≤ 0.05), FDR = 0.01;s0 = 1.<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/61881/61881fig2v3large.jpg" target="_blank">この図の大きなバージョンを表示するには、ここをクリックしてください。</a>

Watch this Scientific Journal Video about Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions at JoVE.com

Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions

ここでは、質量分析ベースのプロテオミクスによる感染時の宿主と病原体の相互作用をプロファイルするプロトコルを提示する。このプロトコルは、標識のない定量を使用して、1回の実験で、宿主(例えばマクロファージ)と病原体(例えば クリプトコッカス・ネオフォルマン)の両方のタンパク質の存在量の変化を測定する。

発見駆動型宿主間相互作用のためのラベルフリー定量プロテオミクスワークフロー

The technological achievements of mass spectrometry (MS)-based quantitative proteomics opens many undiscovered avenues for analyzing an organism&#8217;s ...

label-free-quantitative-proteomics-workflow-for-discovery-driven-host

Nanyang Technological University

Research

JoVE Journal

Immunology and Infection

6.5K ビュー.  University of Guelph. ここでは、質量分析ベースのプロテオミクスによる感染時の宿主と病原体の相互作用をプロファイルするプロトコルを提示する。このプロトコルは、標識のない定量を使用して、1回の実験で、宿主(例えばマクロファージ)と病原体(例えば クリプトコッカス・ネオフォルマン)の両方のタンパク質の存在量の変化を測定する。

ビデオ: Label-Free Quantitative Proteomics Workflow for Discovery-Driven Host-Pathogen Interactions

Use of an Optical Trap for Study of Host-Pathogen Interactions for Dynamic Live Cell Imaging

Dynamic live cell imaging allows direct visualization of real-time interactions between cells of the immune system1, 2; however, the lack of spatial and temporal control between the phagocytic cell and microbe has rendered focused observations into the initial interactions of host response to pathogens difficult. Historically, intercellular contact events such as phagocytosis3 have been imaged by mixing two cell types, and then continuously scanning the field-of-view to find serendipitous intercellular contacts at the appropriate stage of interaction. The stochastic nature of these events renders this process tedious, and it is difficult to observe early or fleeting events in cell-cell contact by this approach. This method requires finding cell pairs that are on the verge of contact, and observing them until they consummate their contact, or do not. To address these limitations, we use optical trapping as a non-invasive, non-destructive, but fast and effective method to position cells in culture.
Optical traps, or optical tweezers, are increasingly utilized in biological research to capture and physically manipulate cells and other micron-sized particles in three dimensions4. Radiation pressure was first observed and applied to optical tweezer systems in 19705, 6, and was first used to control biological specimens in 19877. Since then, optical tweezers have matured into a technology to probe a variety of biological phenomena8-13.
We describe a method14 that advances live cell imaging by integrating an optical trap with spinning disk confocal microscopy with temperature and humidity control to provide exquisite spatial and temporal control of pathogenic organisms in a physiological environment to facilitate interactions with host cells, as determined by the operator. Live, pathogenic organisms like Candida albicans and Aspergillus fumigatus, which can cause potentially lethal, invasive infections in immunocompromised individuals15, 16 (e.g. AIDS, chemotherapy, and organ transplantation patients), were optically trapped using non-destructive laser intensities and moved adjacent to macrophages, which can phagocytose the pathogen. High resolution, transmitted light and fluorescence-based movies established the ability to observe early events of phagocytosis in living cells. To demonstrate the broad applicability in immunology, primary T-cells were also trapped and manipulated to form synapses with anti-CD3 coated microspheres in vivo, and time-lapse imaging of synapse formation was also obtained. By providing a method to exert fine spatial control of live pathogens with respect to immune cells, cellular interactions can be captured by fluorescence microscopy with minimal perturbation to cells and can yield powerful insight into early responses of innate and adaptive immunity.

A method is described to individually select, manipulate, and image live pathogens using an optical trap coupled to a spinning disk microscope. The optical trap provides spatial and temporal control of organisms and places them adjacent to host cells. Fluorescence microscopy captures dynamic intercellular interactions with minimal perturbation to cells.

動的な生細胞イメージングのための宿主 - 病原体相互作用の研究のための光トラップの使用

Dynamic live cell imaging allows direct visualization of real-time interactions between cells of the immune system1, 2; however, the lack of spatial and ...

免疫・感染学

Use of the EpiAirway Model for Characterizing Long-term Host-pathogen Interactions

Nontypeable Haemophilus influenzae (NTHi) are human-adapted Gram-negative bacteria that can cause recurrent and chronic infections of the respiratory mucosa 1; 2. To study the mechanisms by which these organisms survive on and inside respiratory tissues, a model in which successful long-term co-culture of bacteria and human cells can be performed is required. We use primary human respiratory epithelial tissues raised to the air-liquid interface, the EpiAirway model (MatTek, Ashland, MA). These are non-immortalized, well-differentiated, 3-dimensional tissues that contain tight junctions, ciliated and nonciliated cells, goblet cells that produce mucin, and retain the ability to produce cytokines in response to infection. 
This biologically relevant in vitro model of the human upper airway can be used in a number of ways; the overall goal of this method is to perform long-term co-culture of EpiAirway tissues with NTHi and quantitate cell-associated and internalized bacteria over time. As well, mucin production and the cytokine profile of the infected co-cultures can be determined. This approach improves upon existing methods in that many current protocols use submerged monolayer or Transwell cultures of human cells, which are not capable of supporting bacterial infections over extended periods3. For example, if an organism can replicate in the overlying media, this can result in unacceptable levels of cytotoxicity and loss of host cells, arresting the experiment. The EpiAirway model allows characterization of long-term host-pathogen interactions. Further, since the source for the EpiAirway is normal human tracheo-bronchial cells rather than an immortalized line, each is an excellent representation of actual human upper respiratory tract tissue, both in structure and in function4.
For this method, the EpiAirway tissues are weaned off of anti-microbial and anti-fungal compounds for 2 days prior to delivery, and all procedures are performed under antibiotic-free conditions. This necessitates special considerations, since both bacteria and primary human tissues are used in the same biosafety cabinet, and are co-cultured for extended periods.

This method allows characterization of extended bacterial co-culture with EpiAirways, primary human respiratory epithelial tissue grown at the air-liquid interface, a biologically relevant in vitro model. The approach can be used with any microbe that is amenable to long-term co-culture.

長期宿主 - 病原体相互作用を特徴付けるためのEpiAirwayモデルの利用

Nontypeable Haemophilus influenzae  (NTHi) are human-adapted Gram-negative bacteria that can cause recurrent and chronic infections of the respiratory ...

A Comparative Approach to Characterize the Landscape of Host-Pathogen Protein-Protein Interactions

Significant efforts were gathered to generate large-scale comprehensive protein-protein interaction network maps. This is instrumental to understand the pathogen-host relationships and was essentially performed by genetic screenings in yeast two-hybrid systems. The recent improvement of protein-protein interaction detection by a Gaussia luciferase-based fragment complementation assay now offers the opportunity to develop integrative comparative interactomic approaches necessary to rigorously compare interaction profiles of proteins from different pathogen strain variants against a common set of cellular factors.
This paper specifically focuses on the utility of combining two orthogonal methods to generate protein-protein interaction datasets: yeast two-hybrid (Y2H) and a new assay, high-throughput Gaussia princeps protein complementation assay (HT-GPCA) performed in mammalian cells.
A large-scale identification of cellular partners of a pathogen protein is performed by mating-based yeast two-hybrid screenings of cDNA libraries using multiple pathogen strain variants. A subset of interacting partners selected on a high-confidence statistical scoring is further validated in mammalian cells for pair-wise interactions with the whole set of pathogen variants proteins using HT-GPCA. This combination of two complementary methods improves the robustness of the interaction dataset, and allows the performance of a stringent comparative interaction analysis. Such comparative interactomics constitute a reliable and powerful strategy to decipher any pathogen-host interplays.

This article focuses on the identification of high-confident interaction datasets between host and pathogen proteins using a combination of two orthogonal methods: yeast two-hybrid followed by a high-throughput interaction assay in mammalian cells called HT-GPCA.

ホスト病原体のタンパク質 - タンパク質相互作用の風景を特徴づけるために比較的アプローチ

Significant efforts were gathered to generate  large-scale comprehensive protein-protein interaction network maps. This is  instrumental to understand the ...

Detection of Human Leukocyte Antigen Biomarkers in Breast Cancer Utilizing Label-free Biosensor Technology

According to the American Cancer Society, more than 200,000 women will be diagnosed with invasive breast cancer each year and approximately 40,000 will die from the disease. The human leukocyte antigen (HLA) class I samples peptides derived from proteasomal degradation of cellular proteins and presents these fragments on the cell surface for interrogation by circulating cytotoxic T lymphocytes (CTL). Generation of T-cell receptor mimic (TCRm) monoclonal antibodies (mAbs) which recognize breast cancer specific peptide/HLA-A*02:01 complexes such as those derived from macrophage migration inhibitory factor (MIF19-27) and NY-ESO-1157-165 enable detection and destruction of breast cancer cells in the absence of an effective anti-tumor CTL response. Intact class I HLA/peptide complexes are shed by breast cancer cells and represent potentially relevant cancer biomarkers. In this work, a breakthrough biomarker screening system for cancer diagnostics incorporating T-cell receptor mimic monoclonal antibodies combined with a novel, label-free biosensor utilizing guided-mode resonance (GMR) sensor technology is presented. Detection of shed MIF/HLA-A*02:01 complexes in MDA-MB-231 cell supernatants, spiked human serum, and patient plasma is demonstrated. The impact of this work could revolutionize personalized medicine through development of companion disease diagnostics for targeted immunotherapies.

Intact class I HLA/peptide complexes are shed by cancer cells, representing a potential relevant cancer biomarker. Utilizing label-free sensor technology and T-cell receptor mimicking monoclonal antibodies, detection of shed MIF/HLA-A*02:01 complexes in MDA-MB-231 cell supernatants, spiked human serum, and patient plasma is demonstrated, enabling development of a novel cancer diagnostic platform.

ラベルフリーバイオセンサー技術を活用乳癌におけるヒト白血球抗原バイオマーカーの検出

According to the American Cancer Society, more than 200,000 women will be diagnosed with invasive breast cancer each year and approximately 40,000 will ...

Electroporation of Functional Bacterial Effectors into Mammalian Cells

The study of protein interactions in the context of living cells can generate critical information about localization, dynamics, and interacting partners. This information is particularly valuable in the context of host-pathogen interactions. Many pathogen proteins function within host cells in a variety of way such as, enabling evasion of the host immune system and survival within the intracellular environment. To study these pathogen-protein host-cell interactions, several approaches are commonly used, including: in vivo infection with a strain expressing a tagged or mutant protein, or introduction of pathogen genes via transfection or transduction. Each of these approaches has advantages and disadvantages. We sought a means to directly introduce exogenous proteins into cells. Electroporation is commonly used to introduce nucleic acids into cells, but has been more rarely applied to proteins although the biophysical basis is exactly the same. A standard electroporator was used to introduce affinity-tagged bacterial effectors into mammalian cells. Human epithelial and mouse macrophage cells were cultured by traditional methods, detached, and placed in 0.4 cm gap electroporation cuvettes with an exogenous bacterial pathogen protein of interest (e.g. Salmonella Typhimurium GtgE). After electroporation (0.3 kV) and a short (4 hr) recovery period, intracellular protein was verified by fluorescently labeling the protein via its affinity tag and examining spatial and temporal distribution by confocal microscopy. The electroporated protein was also shown to be functional inside the cell and capable of correct subcellular trafficking and protein-protein interaction. While the exogenous proteins tended to accumulate on the surface of the cells, the electroporated samples had large increases in intracellular effector concentration relative to incubation alone. The protocol is simple and fast enough to be done in a parallel fashion, allowing for high-throughput characterization of pathogen proteins in host cells including subcellular targeting and function of virulence proteins.

Electroporation was used to insert purified bacterial virulence effector proteins directly into living eukaryotic cells. Protein localization was monitored by confocal immunofluorescence microscopy. This method allows for studies on trafficking, function, and protein-protein interactions using active exogenous proteins, avoiding the need for heterologous expression in eukaryotic cells.

哺乳動物細胞への機能性細菌エフェクターのエレクトロポレーション

The study of protein interactions in the context of living cells can generate critical information about localization, dynamics, and interacting partners. ...

Evaluation of the Efficacy And Toxicity of RNAs Targeting HIV-1 Production for Use in Gene or Drug Therapy

Small RNA therapies targeting post-integration steps in the HIV-1 replication cycle are among the top candidates for gene therapy and have the potential to be used as drug therapies for HIV-1 infection. Post-integration inhibitors include ribozymes, short hairpin (sh) RNAs, small interfering (si) RNAs, U1 interference (U1i) RNAs and RNA aptamers. Many of these have been identified using transient co-transfection assays with an HIV-1 expression plasmid and some have advanced to clinical trials. In addition to measures of efficacy, small RNAs have been evaluated for their potential to affect the expression of human RNAs, alter cell growth and/or differentiation, and elicit innate immune responses. In the protocols described here, a set of transient transfection assays designed to evaluate the efficacy and toxicity of RNA molecules targeting post-integration steps in the HIV-1 replication cycle are described. We have used these assays to identify new ribozymes and optimize the format of shRNAs and siRNAs targeting HIV-1 RNA. The methods provide a quick set of assays that are useful for screening new anti-HIV-1 RNAs and could be adapted to screen other post-integration inhibitors of HIV-1 replication.

Methods to evaluate the efficacy and toxicity of RNA molecules targeting post-integration steps of the HIV-1 replication cycle are described. These methods are useful for screening new molecules and optimizing the format of existing ones.

遺伝子または薬物療法で使用するためのHIV-1産生を標的RNAの有効性および毒性の評価

Small RNA therapies targeting post-integration steps in the HIV-1 replication cycle are among the top candidates for gene therapy and have the potential ...

A New Method for Qualitative Multi-scale Analysis of Bacterial Biofilms on Filamentous Fungal Colonies Using Confocal and Electron Microscopy

Bacterial biofilms frequently form on fungal surfaces and can be involved in numerous bacterial-fungal interaction processes, such as metabolic cooperation, competition, or predation. The study of biofilms is important in many biological fields, including environmental science, food production, and medicine. However, few studies have focused on such bacterial biofilms, partially due to the difficulty of investigating them. Most of the methods for qualitative and quantitative biofilm analyses described in the literature are only suitable for biofilms forming on abiotic surfaces or on homogeneous and thin biotic surfaces, such as a monolayer of epithelial cells.
While laser scanning confocal microscopy (LSCM) is often used to analyze in situ and in vivo biofilms, this technology becomes very challenging when applied to bacterial biofilms on fungal hyphae, due to the thickness and the three dimensions of the hyphal networks. To overcome this shortcoming, we developed a protocol combining microscopy with a method to limit the accumulation of hyphal layers in fungal colonies. Using this method, we were able to investigate the development of bacterial biofilms on fungal hyphae at multiple scales using both LSCM and scanning electron microscopy (SEM). This report describes the protocol, including microorganism cultures, bacterial biofilm formation conditions, biofilm staining, and LSCM and SEM visualizations.

This protocol describes a new method to grow and qualitatively analyze bacterial biofilms on fungal hyphae by confocal and electron microscopy.

共焦点顕微鏡および電子顕微鏡を用いて糸状菌コロニー上の細菌のバイオフィルムの定性的マルチスケール解析のための新しい方法

Bacterial biofilms frequently form on fungal surfaces and can be involved in numerous bacterial-fungal interaction processes, such as metabolic ...

Label-free Neutrophil Enrichment from Patient-derived Airway Secretion Using Closed-loop Inertial Microfluidics

Airway secretions contain a large number of immune-related cells, e.g., neutrophils, macrophages, and lymphocytes, which can be used as a major resource to evaluate a variety of pulmonary diseases, both for research and clinical purposes. However, due to the heterogeneous and viscous nature of patient mucus, there is currently no reliable dissociation method that does not damage the host immune cells in the patient airway secretion. In this research, we introduce a sample preparation method that uses inertial microfluidics for the patient&#39;s immune assessment. Regardless of the heterogeneous fluidic properties of the clinical samples, the proposed method recovers more than 95% of neutrophils from airway secretion samples that are diluted 1,000-fold with milliliters of clean saline. By recirculating the concentrated output stream to the initial sample reservoir, a high concentration, recovery, and purity of the immune cells are provided; recirculation is considered a trade-off to the single-run syringe-based operation of inertial microfluidics. The closed-loop operation of spiral microfluidics provides leukocytes without physical or chemical disturbance, as demonstrated by the phorbol 12-myristate 13-acetate (PMA)-induced elastase release of sorted neutrophils.

In this research, we demonstrate a label-free neutrophil separation method from clinical airway secretions using closed-loop operation of spiral inertial microfluidics. The proposed method would expand the clinical in vitro assays for various respiratory diseases.

閉ループ慣性マイクロ流路を用いた患者由来気道分泌からラベル無料好中球の濃縮

Airway secretions contain a large number of immune-related cells, e.g., neutrophils, macrophages, and lymphocytes, which can be used as a major resource ...

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

We describe here a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and machine learning. Identification of lymphocyte subtypes is important for the study of immunology as well as diagnosis and treatment of various diseases. Currently, standard methods for classifying lymphocyte types rely on labeling specific membrane proteins via antigen-antibody reactions. However, these labeling techniques carry the potential risks of altering cellular functions. The protocol described here overcomes these challenges by exploiting intrinsic optical contrasts measured by 3D quantitative phase imaging and a machine learning algorithm. Measurement of 3D refractive index (RI) tomograms of lymphocytes provides quantitative information about 3D morphology and phenotypes of individual cells. The biophysical parameters extracted from the measured 3D RI tomograms are then quantitatively analyzed with a machine learning algorithm, enabling label-free identification of lymphocyte types at a single-cell level. We measure the 3D RI tomograms of B, CD4+ T, and CD8+ T lymphocytes and identified their cell types with over 80% accuracy. In this protocol, we describe the detailed steps for lymphocyte isolation, 3D quantitative phase imaging, and machine learning for identifying lymphocyte types.

We describe a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and a machine learning algorithm. Measurements of 3D refractive index tomograms of lymphocytes present 3D morphological and biochemical information for individual cells, which is then analyzed with a machine-learning algorithm for identification of cell types.

ラベル無料三次元定量的な位相画像を用いたリンパ球サブタイプの同定と機械学習

We describe here a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and machine learning. Identification ...

Evaluation of Host-Pathogen Responses and Vaccine Efficacy in Mice

Vaccines are a 20th century medical marvel. They have dramatically reduced the morbidity and mortality caused by infectious diseases and contributed to a striking increase in life expectancy around the globe. Nonetheless, determining vaccine efficacy remains a challenge. Emerging evidence suggests that the current acellular vaccine (aPV) for Bordetella pertussis (B. pertussis) induces suboptimal immunity. Therefore, a major challenge is designing a next-generation vaccine that induces protective immunity without the adverse side effects of a whole-cell vaccine (wPV). Here we describe a protocol that we used to test the efficacy of a promising, novel adjuvant that skews immune responses to a protective Th1/Th17 phenotype and promotes a better clearance of a B. pertussis challenge from the murine respiratory tract. This article describes the protocol for mouse immunization, bacterial inoculation, tissue harvesting, and analysis of immune responses. Using this method, within our model, we have successfully elucidated crucial mechanisms elicited by a promising, next-generation acellular pertussis vaccine. This method can be applied to any infectious disease model in order to determine vaccine efficacy.

Here we present an elegant protocol for in vivo evaluation of vaccine effectiveness and host immune responses. This protocol can be adapted for vaccine models that study viral, bacterial, or parasitic pathogens.

宿主-病原体応答とマウスでワクチンの有効性の評価

Vaccines are a 20th century medical marvel. They have dramatically reduced the morbidity and mortality caused by infectious diseases and contributed to a ...